Venus Express: Results of the Nominal Mission

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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, E00B19, doi:10.1029/2008JE003202, 2008

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Introduction to the special section on Venus Express: Results of the Nominal Mission D. V. Titov,1,2 F. W. Taylor,3 and H. Svedhem4 Received 21 May 2008; accepted 7 August 2008; published 17 December 2008.

Citation: Titov, D. V., F. W. Taylor, and H. Svedhem (2008), Introduction to the special section on Venus Express: Results of the Nominal Mission, J. Geophys. Res., 113, E00B19, doi:10.1029/2008JE003202.

[1] More than 25 spacecraft from the United States and the Soviet Union visited Venus in the 20th century, but in spite of the many successful measurements they made, a great number of fundamental problems in the physics of the planet remained unsolved [Taylor, 2006; Titov et al., 2006]. In particular, a systematic and long-term survey of the atmosphere was missing, and most aspects of atmospheric behavior remained puzzling. After the Magellan radar mapping mission ended in 1994, there followed a hiatus of more than a decade in Venus research, until the European Space Agency took up the challenge and sent its own spacecraft to our planetary neighbor. The goal of this mission, Venus Express, is to carry out a global, long-term remote and in situ investigation of the atmosphere, the plasma environment, and some aspects of the surface of Venus from orbit [Titov et al., 2001; Svedhem et al., 2007]. [2] Venus Express continues and extends the investigations of earlier missions by providing detailed monitoring of processes and phenomena in the atmosphere and near-space environment of Venus. Radio, solar, and stellar occultation, together with thermal emission spectroscopy, sound the atmospheric structure in the altitude range from 150 to 40 km with vertical resolution of few hundred meters, revealing strong temperature variations driven by radiation and dynamical processes. In particular, by stellar occultation, Spectroscopy for Investigation of Characteristics of the Atmosphere of Venus (SPICAV) discovered a warm layer at the mesopause (100 km) apparently caused by adiabatic heating of descending air. Observations by the Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) and radio occultation sounding by Venus Radio Science Experiment (VeRa) indicate remarkable latitudinal temperature variations at the cloud tops, as well as the presence of a convective region within the cloud deck all over the planet. The temperature field in the mesosphere of the southern hemisphere is similar to that observed in the northern hemisphere by earlier missions, thus indicating global north/south symmetry. 1 Max-Planck Institute for Solar System Research, Katlenburg-Lindau, Germany. 2 On leave from the Space Research Institute, Moscow, Russia. 3 Department of Atmospheric, Oceanic, and Planetary Physics, Oxford University, UK. 4 ESA/ESTEC, Noordwijk, Netherlands.

Copyright 2008 by the American Geophysical Union. 0148-0227/08/2008JE003202$09.00

[3] Multispectral imaging is performed to investigate the cloud morphology at different levels and its latitudinal and temporal changes. Venus Monitoring Camera shows evidence of convection in the cloud layer at low latitudes. Streaky cloud features indicate that the convection ceases in the middle and high latitudes. Sequences of images are used to track motions of the cloud features and to derive wind speeds in the 50– 70 km altitude range. The polar orbit of Venus Express provides near-nadir viewing of middle and high latitudes in the southern hemisphere, enabling the first detailed observations of the cloud morphology and dynamics, leading to the discovery and characterization of the complex ‘‘eye’’ of the southern polar vortex by VIRTIS. The imaging instruments also perform thermal mapping of the surface in the near-IR spectral windows on the nightside, searching for surface emissivity anomalies and signs of active volcanism. [4] The chemistry and dynamics of the mesopause region (100 km altitude) is being studied by observing nonlocal thermodynamic equilibrium (non-LTE) emission from O2 and NO molecules in the UV and near-IR. This airglow peaks at the equator, close to midnight and approximately at the mesopause level, indicating a thermospheric solarantisolar component in the atmospheric circulation. Composition measurements over a wide range of altitudes are providing vertical profiles of CO, H2O, HDO, HCl, HF, and SO2 in the mesosphere (70 – 100 km), and global mapping of CO, COS, H2O, and SO2 in the lower atmosphere at heights around 35 km. These results provide powerful tests of dynamical and chemical models of the Venusian atmosphere. [5] The magnetometer (MAG) and Analyzer of Space Plasma and Energetic Atoms (ASPERA) measure the magnetic field and densities of neutral atoms, ions, and electrons in situ. These observations determine the structure and properties of the circumplanetary plasma and characterize escape processes at Venus. They cover the time of solar minimum and thus complement Pioneer Venus investigations at solar maximum. Interestingly, the H/O ratio was found to be approximately two thus suggesting that these ions are escaping in the stoichiometric ratio of water. The magnetometer is detecting whistler signals during 70% of the pericenter passes. This is interpreted as evidence of lightning and the rate is estimated to be at least as frequent as on Earth. [6] The first results from the Venus Express mission were published in nine papers in the special section ‘‘Venus

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Express’’ in Nature, 450, 629 – 662, 2007. This special section contains original papers describing results and analysis of the observations during the whole nominal mission (4 June 2006 to 2 October 2007), along with supporting modeling efforts. The papers are grouped in topical blocks and are published in two groups. This first group of papers contains a mission overview and papers on atmospheric composition, atmospheric dynamics, non-LTE emissions, and surface investigations. A second group of papers, to appear about 3 months later, will contain papers on atmospheric structure, clouds and hazes, and plasma environment.

References Svedhem, H., et al. (2007), Venus Express: The first European mission to Ve n u s , P l a n e t . S p a c e S c i . , 5 5 , 1 6 3 6 – 1 6 5 2 , d o i : 1 0 . 1 0 1 6 / j.pss.2007.01.013.

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Taylor, F. W. (2006), Venus before Venus Express, Planet. Space Sci., 54, 1249 – 1262, doi:10.1016/j.pss.2006.04.031. Titov, D. V., E. Lellouch, F. W. Taylor, and L. Marinangeli, H. Opgenoorth, and the Venus Express Team (2001), Venus Express: An orbiter for the study of the atmosphere, the plasma environment, and the surface of Venus, Mission Definition Rep. ESA-SCI (2001)6, Eur. Space Agency, Paris. Titov, D. V., H. Svedhem, and F. W. Taylor (2006), The atmosphere of Venus: Current knowledge and future investigations, in Solar System Update, edited by P. Blondel and J. W. Mason, pp. 87 – 110, Springer, Berlin. H. Svedhem, ESA/ESTEC, Keplerlaan 1, Postbus 299, NL-2200 AG Noordwijk, Netherlands. F. W. Taylor, Department of Atmospheric, Oceanic, and Planetary Physics, Oxford University, Parks Road, Oxford OX1 3PU, UK. D. V. Titov, Max-Planck Institute for Solar System Research, Max Planck Strasse 2, D-37191 Katlenburg-Lindau, Germany. ([email protected])

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Venus Express mission H. Svedhem,1 D. Titov,2 F. Taylor,3 and O. Witasse1 Received 30 October 2008; revised 18 December 2008; accepted 5 January 2009; published 19 March 2009.

[1] Venus Express is well and healthy and has now been providing exciting new data from

Venus, our nearby twin planet, for over 2 years. Many of the new results are presented and discussed in the subsequent papers in this special section. The overall scientific objective of Venus Express is to carry out a detailed study of the atmosphere of Venus, including the interaction of the upper atmosphere with the solar wind and the interaction of the lowest part of the atmosphere with the surface of the planet. In addition, the plasma environment and magnetic fields as well as some aspects of the surface of the planet are addressed. For the first time, investigations make systematic use of the transparent infrared spectral windows in order to probe the atmosphere in four dimensions: three spatial dimensions plus time. The spacecraft design is taken from Mars Express with some modifications necessary owing to the specific environment around Venus. The payload is composed of three spectrometers, a camera, a magnetometer, an instrument for detecting energetic particles, and a radio science package. The orbit is polar and highly elliptic, with a pericenter altitude of about 200 km over the northern polar region and an apocenter altitude of 66,000 km. Presently, the coverage of the southern hemisphere is very good, but important gaps still do exist. The coverage of the northern hemisphere is much less dense. Venus Express is a part of the European Space Agency’s program for the exploration of the inner solar system, which includes missions to study the Sun, Mercury, Venus, the Moon, Mars, and comets and asteroids. Citation: Svedhem, H., D. Titov, F. Taylor, and O. Witasse (2009), Venus Express mission, J. Geophys. Res., 114, E00B33, doi:10.1029/2008JE003290.

1. Introduction [2] Venus was a forgotten planet for more than a decade after the emphasis for investigations of the terrestrial planets shifted from Venus toward Mars during the late 1980s. There were, however, still a large number of fundamental questions to be answered about the past, present and future of Earth’s sister planet, and for an improved understanding of the general evolution of the terrestrial planets better knowledge of Venus is essential [Taylor, 2006; Titov et al., 2006a; Moroz, 2002]. The European Space Agency (ESA) launched Venus Express to open up opportunities for new investigations with a combination of instruments employing completely new techniques and improved versions of conventional instruments. The response to the initial results and findings has been very enthusiastic, generating renewed interest both in the scientific community and in the major space agencies, with new missions being developed or planned in Japan, the United States, and Russia. Venus has come back into the forefront in planetary science.

1

ESA, ESTEC, Noordwijk, Netherlands. Max-Planck Institute for Solar System Research, Katlenburg-Lindau, Germany. 3 Department of Atmospheric, Oceanic, and Planetary Physics, Oxford University, Oxford, UK. 2


[3] Venus Express is an independent follow-up to the successful Mars Express mission, launched a few years earlier, and is reusing much of the basic design for spacecraft, launcher and ground system in order to keep costs down [Titov et al., 2001; Svedhem et al., 2007a, 2009]. It is the third spacecraft in the family that started with the (much larger) Rosetta spacecraft. Both Venus Express and Mars Express benefit greatly from the generic developments carried out for that mission. With only 3 years between mission approval and the launch date, plus about a year of preparatory work preapproval, Venus Express is by far the most rapidly developed scientific project of ESA. The short developments time and the significant design heritage have enabled a powerful mission to materialize at a substantially lower cost compared to a single mission developed in isolation. [4] With the new data from Venus Express, there is a picture emerging of Venus where the existing conditions and the ongoing processes are becoming clearer. Comparisons with the Earth will continue and intensify. Venus and Earth are very different twin planets, and while they will remain different, we are moving closer toward understanding how and why the two planets have their distinct characteristics, particularly with regard to climate [Svedhem et al., 2007b]. [5] This paper describes the mission objectives of the Venus Express mission and gives an overview of the main features of the spacecraft, with emphasis on the differences compared to Mars Express, and the scientific instruments on board. It discusses the mission scenario and the operational

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orbit and the specific questions related to the choice of orbit and it gives an introduction to the various activities of the scientific operations. It discusses the coverage achieved to date by the different instruments with respect to longitude, latitude and local solar time, for the atmospheric and surface observations as well as the coverage of the different regions in and around the induced magnetosphere. Temporal coverage is discussed for a few special cases but the principles are applicable to most of the measured parameters. It outlines the place of Venus Express in the scientific program of the European Space Agency and finally it discusses topics to be studied in an extended mission, beyond May 2009, that was requested during the latter part of 2008.

2. Mission Objectives and Investigations [6] The overall scientific objective of Venus Express is to carry out a detailed and systematic study of a large number of aspects of the atmosphere of Venus. This includes the interaction of the upper atmosphere with the solar wind and the interaction of the lowest part of the atmosphere with the surface of the planet. In addition, dedicated instruments are focusing on the study of the plasma environment and magnetic fields. Some aspects of the surface are addressed. The objectives have been structured and organized under the following seven themes: (1) atmospheric structure, (2) atmospheric dynamics, (3) atmospheric composition and chemistry, (4) cloud layer and hazes, (5) energy balance and greenhouse effect, (6) plasma environment and escape processes, and (7) surface properties and geology. [7] The evolution of the planet and in particular the atmosphere and the climate are topics of great interest that span over all the themes. Most themes include aspects of comparative planetology and comparison with the other terrestrial planets, the Earth in particular, is an important objective. Direct comparison of data from Mars Express and Venus Express are enabled by the fact that several instruments are identical or similar on the two spacecraft. Also, several of the Venus Express scientists are involved in both missions. This opens up possibilities not found before on interplanetary spacecraft. 2.1. Atmospheric Structure [8] Knowledge about the atmospheric structure is of great importance for the understanding of the state of the atmosphere and the processes active therein. Previous missions provided a basic understanding of the thermal structure, but many questions remained to be answered [Taylor et al., 1980; Seiff et al., 1985]. The atmosphere can be roughly divided into three main layers based on the temperature distribution and the various processes that govern the state of the layers: the troposphere (0 –60 km), the mesosphere (60 – 100 km), and the thermosphere (above 100 km). The isothermal region between the troposphere (where temperature falls with height) and the mesosphere (where temperature increases with height) that would correspond to the stratosphere on the Earth is of very limited vertical extent on Venus and is not usually described as a separate layer. [9] Below 30 km the temperature is believed to be fairly constant all over the planet [Seiff et al., 1985] but the latitudinal coverage of the data is fairly poor. The mesosphere shows a much more variable temperature, especially in

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latitude, but what the dominating processes are is not clear [Lellouch et al., 1997]. [10] The thermosphere turned out to be surprisingly cool, especially at night (leading to the coining of the term ‘‘cryosphere’’ for the nighttime thermosphere). While it is clear that radiative processes play a major role, additional, and more densely sampled data covering all latitudes and local times are needed to achieve a good understanding [Fox and Bougher, 1991]. [11] Venus Express studies the thermal structure of the atmosphere by means of global, simultaneous, and spatially resolved spectroscopic observations in the wavelength range from UV to thermal IR. Specifically, it (1) investigates the upper atmosphere (140– 90 km) at high vertical resolution through solar and stellar occultation measurements [Bertaux et al., 2007b], (2) sounds the temperature of the middle atmosphere (100– 60 km) using spectroscopic observations of the 4.3 mm CO2 bands, with a coverage in latitude and local solar time corresponding to a spatial resolution of a few tens of kilometers or better [Formisano et al., 2006; Drossart et al., 2007a], (3) sounds the temperature in the altitude range 80– 40 km by radio occultation, providing vertical resolution of a few hundred meters [Haüsler et al., 2006; Pa¨tzold et al., 2007], and (4) maps the surface temperature on the nightside as a function of surface elevation [Drossart et al., 2007a]. [12] Together, these different techniques cover the range from 140 km down to 35 km. The optical measurements can be made only during nighttime since it would be very difficult to discriminate between thermal radiation and scattered sunlight. Unfortunately the PFS instrument, which would have used the 15 mm CO2 band for daytime profiling, is not operating. The use of other instruments has been rescheduled in order to as much as possible minimize the impact of this loss. 2.2. Atmospheric Dynamics [13] Data from previous missions and ground-based observations have shown that the general circulation of the atmosphere can be divided into two regimes: A retrograde zonal superrotation in the troposphere and mesosphere [Gierasch et al., 1997] and a solar to antisolar component across the terminator in the thermosphere [Bougher et al., 1997]. The zonal superrotation has a maximum wind velocity of about 100 m/s at the cloud top level (70 km), decreasing to almost zero at the surface. At the same time, there is a ‘‘Hadley-like’’ slower (about 10 m/s) overturning of the atmosphere from the equator to the high latitudes, with giant vortices at each pole recycling the air downward. No attempt to model the superrotation has been completely successful so far, indicating that the basic mechanisms of the phenomenon are unclear. More and better data, with improved spatial and temporal coverage, are needed in order to understand the detailed physical condition in this region and to modify the models to better reflect the reality. [14] Venus Express investigates the atmospheric dynamics by observing clouds at different levels [Drossart et al., 2007a; Markiewicz et al., 2007a], by deriving thermal winds from thermal profiles, and by monitoring airglow of different species. Specifically, it (1) measures the global wind fields in three dimensions and investigates whether the meridional circulation is one large basic ‘‘Hadley’’ cell extending from the surface to the upper atmosphere, or a variation of such a

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Figure 1. The abundance of the most important minor gases in the atmosphere of Venus as determined by missions before Venus Express. The individual gases are color coded as shown in the bottom left corner. The sizes of the blocks indicate the spread in several measurements rather than measurement errors. The bars with arrows indicate the limits of detection for the Venus Express instruments.

cell, or something completely different; (2) studies the temporal behavior of the polar vortices: how they evolve and how they couple to the two main components of the global circulation [Piccioni et al., 2007]; and (3) studies how the transition between the retrograde superrotation and the solar to antisolar circulation takes place in the interface between the mesosphere and the thermosphere. [Drossart et al., 2007b]. 2.3. Atmospheric Composition and Chemistry [15] The main components of the Venus atmosphere are CO2 (96.5%) and N2 (3.5%). Sulfur-bearing trace gases, carbon and chlorine compounds, and water vapor are present in the atmosphere in amounts from few to few hundred parts per million (ppm) [de Bergh et al., 2006]. Figure 1 shows the mean abundance of the main trace gases and their vertical profiles measured in the Venus atmosphere by earlier missions. [16] Although present in small amounts, the trace gases are involved in important complex chemical cycles. In the region around the cloud tops photochemical reactions between CO2, SO2, H2O, and chlorine compounds lead to the formation of sulfuric acid, which is the main component of the cloud particles. The chemistry of the lower atmosphere is dominated by thermal decomposition of sulfuric acid, and thermochemical cycles that include sulfur and carbon species and water vapor. Surface minerals can also play a significant role in buffering the abundance of certain gases in the lower atmosphere [Fegley et al., 1997]. The main sulfur-bearing gas, SO2, is present in the Venus atmosphere in amounts of a few hundred ppm, which is much more than expected from

thermal equilibrium with the surface minerals. Pioneer Venus measured a strong continuous decline of the SO2 abundance at the cloud tops during its 14 years of operation, indicating possible recent volcanic activity. [17] Venus Express measurements of the chemical composition of the atmosphere address the following: (1) The abundance and spatial and temporal variation of SO2, SO, H2O, HCl, and CO at the cloud tops, to improve the understanding of the physical and chemical processes in this region, including the production of sulfuric acid aerosols [Bertaux et al., 2007a], (2) vertical profiles of SO, SO2, H2O and HDO, HCL, and HF between 80 km and the cloud tops, and vertical profiles of CO from the cloud tops up to about 120 km, by stellar and solar occultation [Bertaux et al., 2007b], and (3) the abundance and spatial variation of H2O, SO2, COS, CO, H2O, HCl, and HF in the lower atmosphere, to improve the understanding of chemistry, dynamics, and radiative balance of the lower atmosphere, and to search for local volcanic activity [Drossart et al., 2007a; Svedhem et al., 2007b]. 2.4. Cloud Layer and Hazes [18] Venus appears completely featureless in visible light owing to a thick cloud layer located between 50 km and 70 km altitude. However, images made in the UV blue spectral range show much structure, both at large scale and at small scale (Figure 2). It has important implications for the energy balance since about half of the solar energy entering into the atmosphere is absorbed in this region. The nature of the UV absorbing matter remains one of the mysteries of Venus. Earlier observations have shown that the upper cloud layer consists of micron-sized droplets of 75% sulfuric acid.

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radiation penetrates through the atmosphere and heats the surface [Taylor et al., 2007]. The radiation balance, however, is not well constrained and in particular the spectral characteristics and the spatial and temporal variations of the outgoing radiation are not well known. [21] Venus Express measures the outgoing radiative flux (reflected solar light and thermal radiation from the atmosphere and the surface) over the near-IR spectral range and maps the temperature field and the cloud layer in three dimensions. This will constrain the optical models of the atmosphere and give an insight into the radiative and dynamical heat transport, and the role of the different species in the greenhouse mechanism and the planetary heat balance.

Figure 2. An image of Venus made by the Venus Monitoring Camera (VMC) instrument in ultraviolet light at 365 nm from a distance of about 35,000 km. The south pole is at the bottom right in the image and the equator at the top left. The cloud structure is shown to be very different in the polar region compared to the equatorial region. A bright elongated cloud is shown in the middle southern latitudes.

These droplets make up a major portion of the cloud mass but the processes and conditions for their formation are poorly understood, while the physical and chemical processes forming the rest of the cloud population are virtually unknown. [19] Venus Express sounds the structure, composition, dynamics, and variability of the cloud layer. Specifically, it (1) images the spatial distribution and temporal variability of the unknown UVabsorber [Markiewicz et al., 2007b; Drossart et al., 2007a]; (2) measures the vertical structure and microphysical properties of the upper cloud layer and the hazes above it, by stellar and solar occultation, limb observations, and nadir sounding [Bertaux et al., 2007a]; (3) measures the spatial variations of the opacity and particle sizes on the nightside (through the IR ‘‘spectral windows’’); this data together with information on the composition will help to constrain models of the cloud formation and evolution [Titov et al., 2006b]; and (4) searches for correlation in the spatial distribution of the unknown UV absorber with the abundance of SO2 and other trace gases. 2.5. Energy Balance and Greenhouse Effect [20] With a surface temperature of more than 730 K Venus has the strongest greenhouse effect in the solar system. The dense CO2 atmosphere is responsible for this extreme behavior, but water vapor and sulfur dioxide and the cloud layer also play a role [Crisp and Titov, 1997; Titov et al., 2007]. It is even more impressive when considering that Venus, in fact, absorbs less energy from the Sun than the Earth does, owing to the high albedo (76%), which is caused by the thick cloud cover of the planet. Less than 10% of the incoming solar

2.6. Plasma Environment and Escape Processes [22] Today Venus has only very little water, mostly in the form of water vapor. If it all were condensed it would form a global layer of about 3 cm, compared with 3 km on the Earth. There is no good reason why initially the two planets should have been significantly different and it is expected that Venus indeed has had large quantities of water, and possibly other volatile species, perhaps as much as the Earth. If so, how, when and why did this disappear? Pioneer Venus found that deuterium is enhanced relative to hydrogen about a factor 150 compared to the Earth’s value [Donahue et al., 1997]. This is an indication that hydrogen has been lost, while deuterium, having twice the mass, does not escape as easily as hydrogen. Therefore information on the present abundance of deuterium, hydrogen and oxygen and their escape rates are essential to the understanding of the history of water on Venus. [23] The lack of an internal magnetic field causes the solar wind to act on the upper atmosphere in a much more violent way than it does on the Earth. The solar wind interacts with the top of the ionosphere to form a complex system of plasma clouds, tail rays, filaments, and ionospheric holes on the nightside through which a substantial amount of material can leave the planet [Brace and Kliore, 1991]. The escape mechanisms induced by the solar wind are the dominant ones for the loss of heavy atmospheric gases such as oxygen because the gravitational force inhibits both Jeans escape and nonthermal escape. [24] To address the problems of atmospheric escape and investigate the plasma environment, Venus Express (1) determines the positions of the plasma boundaries for the different domains in the planetary environment, and their dependence on the solar activity [Zhang et al., 2008a, 2008b; Martinecz et al., 2008], (2) measures in situ, for the different domains, directional flux and energy of energetic neutral atoms, ions and electrons; of particular importance are the measurements of the escape rate of key species like hydrogen, deuterium and oxygen [Barabash et al., 2007a, 2007b], (3) measures the magnetic field in the different domains in the planetary environment [Zhang et al., 2006, 2007], (4) determines the vertical structure of the ionosphere, by radio occultation measurements [Haüsler et al., 2006; Pa¨tzold et al., 2007], and (5) measures the composition and energies of the undisturbed solar wind, as a reference for the Venus measurements and for comparison with similar measurements around the other planets of the solar system. The Venus Express measurements are taken at solar minimum activity, thus complementing the Pioneer Venus plasma studies that were acquired during solar maximum conditions.

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2.7. Surface Properties and Geology [25] Venus has a geologically young surface at an age of 700 Ma ± 200 Ma, as estimated on the basis of impact crater statistics. In addition there are indications that the age of the different regions do not differ much from each other. Volcanic and tectonic activities have strongly affected the surface [Solomon et al., 1992] forming highly deformed plateaus, tesserae, and extensive lowlands, planitiae. Many scientists favor a catastrophic global resurfacing of the crust, a mechanism unique among the terrestrial planets [Strom et al., 1994]. Important open questions are related to the surface mineralogy, the existence of present volcanic activity and the role of chemical interaction at the surface-atmosphere interface. Despite the fact that Venus Express is not carrying any instruments specifically devoted to surface science, it contributes to these studies in several ways [Titov et al., 2006b], by [26] 1. Investigating regions of high radar reflectivity, by bistatic measurements, focusing on highland regions like Aphrodite Terra, Beta and Atla Regio, and Maxwell Montes [Haüsler et al., 2006]. These areas have shown anomalously high reflectivity in the Magellan radar images. Unfortunately, these measurements have had to be discontinued since mid2007 owing to a still unexplained loss of power in the S band transmitter chain. [27] 2. Investigating the mass distribution in and around Atalanta Planitia by radio science gravity studies, through orbital trajectory analysis. [28] 3. Mapping the surface temperature and estimate the surface emissivity by observations in the 1 mm spectral window. [29] 4. Searching for surface hot spots and local deviations in chemical abundance in the lower atmosphere, in particular SO2, indicating possible volcanic activity. [30] 5. Searching for atmospheric waves generated by seismic activity and coupled to the atmosphere owing to the high atmospheric density at the surface [Drossart et al., 2007a].

3. Spacecraft [31] Venus Express uses the basic design of the Mars Express spacecraft, which was launched in 2003, adapted for the specific conditions at Venus [Sivac and Schirmann, 2009]. The main modifications to the spacecraft are related to the thermal control system and the power system. The most important characteristics are summarized in the following paragraphs. 3.1. Structure and Propulsion [32] The Venus Express spacecraft is based on a box-like structure with the dimensions 1.7 m 1.7 m 1.4 m. The distance from tip to tip of the deployed solar panels is about 8 m. The principal mechanical structures inside the spacecraft are the two fuel tanks, with MonoMethyl Hydrazine (MMH) as the fuel and Nitrogen TetrOxide (NTO) as the oxidizer, and a smaller helium tank for main tank pressurization. On both sides of these tanks two internal shear walls are built. The spacecraft, without solar panels, can be seen during the solar illumination testing in Figure 3 and the fully integrated spacecraft during its mating to the Fregat upper stage can

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be seen in Figure 4. The mass of the dry spacecraft is 633 kg, including 94 kg for the payload. The tanks were filled at launch with 570 kg of propellant, most of which was consumed during the orbit insertion maneuvers. Together with the launch vehicle adapter this made up the total launch mass of 1241 kg, leaving a comfortable margin to the maximum allowed mass of 1270 kg for the Soyuz Fregat launcher combination. Venus Express as well as Mars Express has been designed to sustain soft aerobraking, at a maximum dynamic pressure of 0.3 N/m2. 3.2. Thermal Control [33] The closer distance to the Sun, and the higher albedo of Venus compared to Mars, made it necessary to completely redesign the thermal control system. A new thermal blanket is used, giving the spacecraft a golden finish, in contrast to the black appearance of Mars Express. Several instruments and subsystems are connected to radiators mounted on different faces on the outside of the spacecraft. One side of the spacecraft, the -X side, carries the largest radiators which act as heat sinks for the coolers for the instruments requiring cryogenic temperatures. This side must never be exposed to the sun at any angle. The -Z side, where the main engine nozzle and the launch vehicle adapter interface reside, also must avoid illumination by the sun since these will absorb the solar heat very quickly. This design, together with specific operational constraints, ensures a low maximum temperature even in Venus orbit. Under some conditions the temperature would be too low, and therefore a set of electrical heaters are fitted to the most critical units. These heaters are either switched by mechanical thermostats or by the on board computer. 3.3. Power System [34] As the solar input in Venus orbit is about 2.6 kW/m2, which is about four times that of Mars or twice that of the Earth, the size of the solar panels could be significantly reduced. The original silicon-based solar cells have been replaced by gallium arsenide, optimized for operation at higher temperature. To keep the temperature in the proper range these cells are mounted in rows interleaved with optical solar reflectors, giving the solar panels a striped appearance. The electrical output in Venus orbit is approximately 1400 W. The remaining part of the power system, including the batteries, required only minor modification from the Mars Express version. 3.4. Telecommunication System [35] The spacecraft uses X band communications for both the telecommand uplink and the telemetry downlink. Fully redundant cross-strapped communication chains, including two 65 W traveling wave tube power amplifiers, feed the signal to the two high-gain antennas of 1.3 m and 0.3 m diameter. S band communications, with dual 5 W solid state power amplifiers, are included as a backup system and for use near the Earth during the first weeks following the launch. The data rate varies between 15 kbps to 228 kbps, corresponding to a downlink capability of between 400 Mbit to 6.5 Gbit per day, depending on the actual distance between Venus and the Earth. The antennas are body fixed and thus the spacecraft will turn toward the Earth for dumping the data

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Figure 3. The compete spacecraft excluding solar panels during the solar illumination test at Intespace, Toulouse, France. The facility was upgraded for this test in order to be able to produce the required beam with an intensity 2.6 kW/m2 over the full surface of the spacecraft. The front side shows the 1.3 m diameter high-gain antenna covered with its solar shield, a thin foil with germanium deposit to keep the antenna within its operational temperature during all spacecraft attitudes. The back side of the smaller high-gain antenna is the white dish seen on the top platform where all remote sensing instruments are also located. The top and front sides are allowed to be continuously illuminated by the Sun at Venus, while all other sides have strict limitations on the duration of solar illumination. The height of the structure is about 1.4 m.

each telemetry session, usually 8 – 10 h per day. The data are stored in a 12 Gbit solid state data recorder during the observation passes. [36] The radio science investigation makes full use of the telecommunication system and an ultrastable oscillator has been added to one chain of the system to enhance the performance for this purpose. The S band system experienced a drop in signal level during the solar conjunction in July 2006 and has therefore only been used occasionally since then. The reason for this anomaly is still not known. [37] The main ground station is the Cebreros station west of Madrid, Spain, which is used for all uplink and most data downlink activity. The New Norcia station in Australia is used for radio science activities. Occasionally the NASA DSN assists by giving extra coverage, for radio science activities in particular, but also in connection with periods of high data rate requirements, for example, when measurements at high temporal resolution are made, such as highresolution movies of the cloud motions. 3.5. Operational Constraints [38] While pointing to the Earth from Mars during communications the Sun is always within a cone of 40° from the Earth, making it easy to avoid illumination of protected surfaces. In addition the high-gain antenna acts a thermal shield of the spacecraft. From Venus, however, the Sun can appear anywhere in the sky, making it impossible to avoid

illumination of specific surfaces. To deal with this a second, smaller, high-gain antenna has been mounted on top of the +Z face of the spacecraft, pointing in the opposite direction from the main antenna. Then it is possible always to find an attitude where illumination of the forbidden faces can be avoided. Figure 5 shows that inside the quadrature, where the Venus-Sun-Earth angle is less than 45°, the small antenna is used; that is, the spacecraft needs to rotate 180° about the z axis at entry and exit of the quadrature. In addition the spacecraft will need to rotate 180° about the x axis at inferior and superior solar conjunction.

4. Payload [39] The payload is composed of seven instruments in three different categories; spectrometers and (spectral) imagers for remote sensing, plasma and magnetic field instruments for in situ measurements, and the ultrastable oscillator used for radio science. [40] Great care was taken in selecting the instrument complement in order to make optimum use of the limited resources on board. A complication was the short time available for development since the project schedule was very compressed. As it turned out, a very well balanced set of instruments could be assembled from existing designs and even from spare parts and units from other recent missions, mainly Mars Express and Rosetta. In addition, two completely new instruments

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Figure 4. The fully integrated spacecraft is being mounted on top of the Fregat upper stage. The black conical structure is the launch adapter, which remains on the Fregat stage after separation. Protective covers are still mounted on the folded solar panels and on the instrument apertures and the two cylindrical baffles of the star trackers. The white recessed surface on the front is the VIRTIS thermal radiator. were designed, developed and built in this short time, namely the VMC and the SOIR. [41] Figures 6 and 7 show how the different instruments complement each other with respect to their fields of view and their spectral range and resolution. Venus Express is the first dedicated atmospheric mission to Venus since the discovery of the near infrared spectral windows [Allen and Crawford, 1984; Baines et al., 2006]. The payload was composed in order to maximize the benefit from this new opportunity to study the atmosphere in three dimensions. Table 1 lists the individual instruments and their main functions and Figure 8 shows the locations of the instruments on the spacecraft. 4.1. Remote Sensing Instruments [42] Spectroscopy for the Investigation of the Characteristics of the Atmosphere of Venus/Solar Occultation in the Infrared (SPICAV/SOIR) [Bertaux et al., 2007a, 2009] is a set of three spectrometers optimized for providing thermal profiles and composition in the upper atmosphere by observations in stellar and solar occultation mode. It is also used in nadir mode, together with other instruments. The solar and stellar occultation technique provides very good vertical

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resolution and high sensitivity for species of low abundance (like SO2, COS, CO, HCL, HF, and others). Particularly important molecules to measure are H2O and HDO, since the ratio between the two carries information on the history of the total amount of water on the planet. Temperature and density profiles are retrieved by analyzing the absorption in various CO2 bands. The instrument covers three wavelength bands: 110 –310 nm, 0.7 –1.7 mm and 2.2 – 4.4 mm (SOIR). The SOIR unit utilizes a miniaturized Stirling-cycle engine to cool the detector to 90 K, and an acoustooptic tunable filter, together with a grating used at a high order of diffraction, to achieve a spectral resolution (l/dl) of more than 20,000. [43] The basic SPICAV unit has an important heritage from the SPICAM instrument on Mars Express, while the SOIR channel is a completely new development. Important recent findings include the determination of the D/H ratio as a function of altitude, the discovery of a warm layer on the nightside at the base of the thermosphere (90 km) and the discovery of a set of previously unobserved absorption lines of the CO2 isotope 16O12C18O [Bertaux et al., 2008; Wilquet et al., 2008]. [44] Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) is a combination of a visible (0.27– 1.1 mm) and an infrared (1.05– 5.2 mm) medium resolution imaging spectrometer (VIRTIS-M) with a high-resolution infrared (1.8 – 5.0 mm) spectrometer (VIRTIS-H) [Piccioni et al., 2009; Drossart et al., 2007a]. The infrared detectors are actively cooled to 80 K, while the visible sensor is passively cooled to below 200 K. The instrument is almost identical to the VIRTIS instrument presently flying on the Rosetta spacecraft. The imaging spectrometer has a field of view of 64 mrad 64 mrad, with a pixel size of 0.25 mrad 0.25 mrad. The images have a spatial dimension of 256 256 pixels and a spectral dimension of 432 lines. From near apocenter, the field of view covers about 1/3 of the diameter of Venus. Mosaics of 3 3 frames are constructed by repointing of the spacecraft in order to generate global images of the southern hemisphere. VIRTIS is addressing a large number of scientific questions and has generated dramatic images and video sequences of the south polar vortex, nonLTE emission patterns, profiles of abundance of several atmospheric gases, wind field maps and temperature profiles over the southern hemisphere, images of cloud structure at several altitudes and surface temperature and emissivity maps, to mention just a few. [45] Venus Monitoring Camera (VMC) is a small but efficient camera operating simultaneously in four narrow spectral bands at 365, 513, 965, and 1000 nm [Markiewicz et al., 2007a, 2009]. The UV band is mapping the cloud tops in reflected sunlight during daytime, where structure is visible owing to the still unknown UV absorber. These images are used for deriving global wind fields at this altitude and for studying the cloud morphology. The near-IR 1mm band is used for mapping the surface brightness during nighttime. The remaining two bands include O2 airglow emission and possible water vapor absorption. VMC has an unusual design with four optical chains, each one with its own permanent filter, sharing one CCD, with each optical system having its own dedicated quadrant on the CCD. When the CCD is read out in normal mode each image frame actually contains four images. The field of view is 17.5° (0.3 rad), resulting in a pixel footprint size on the surface ranging from 200 m at

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Figure 5. The orbit of Venus in a Sun-Earth fixed frame, with the Sun at the center and the Earth at the left. The attitude of the spacecraft while Earth pointing will change depending on the relative position in this frame in a way such that the Sun is illuminating only two of the sides of the spacecraft (+X and +Z). To achieve this, the spacecraft is rotated 180° around the z axis at the points marked quadrature and 180° around the x axis at inferior and superior conjunction.

pericenter to 45 km at apocenter. VMC has sampled a large number of images, and important findings include the determination of global wind fields at different times, the characterization of the cloud morphology in different regions and synthesis of surface maps. [46] Planetary Fourier Spectrometer (PFS) is a high spectral resolution double pendulum spectrometer with two channels, a short wave channel (0.9 – 5.5 mm) and a long wave channel (5.5 – 45 mm) [Formisano et al., 2006, 2009]. The history of this instrument goes back to the Russian Mars-96 mission that unfortunately was lost in a launch failure in 1996. It flew again on Mars Express where it performs very well and has made important discoveries. Some of the main objectives for Venus were determination of the temperature field in the altitude range 55– 100 km, both on the nightside and on the dayside, mapping of the surface temperature, and making profiles of the abundance of a large number of gases in the middle and lower atmosphere. It was also to measure the outgoing thermal flux to determine the global radiation budget. Unfortunately, a mechanism that controls the mirror that switches the beam between the different calibration targets and the view toward the planet is stuck in its launch position, pointing toward a blackbody target. In spite of many attempts it has not been possible to move the scanner since the launch. 4.2. In Situ Instruments [47] Analyzer of Space Plasma and Energetic Atoms (ASPERA-4) is the fourth of this series of instruments to

fly in space, but the first of its kind around Venus [Barabash et al., 2007a, 2009]. ASPERA-4 is identical to ASPERA-3 presently in orbit around Mars on Mars Express. The instrument has four different sensors, housed in two separate units. The ion mass analyzer, IMA, measures ions separated by mass up to m/q = 40 for energies from 10 eV/q to 36 keV/q. The ions are mapped at 22.5° resolution over 360° in azimuth and at 4.5° resolution over ±45° in elevation with respect to the instrument orientation. The Neutral Particle Imager (NPI) measures the integral flux of neutral particles between 100 eV and 60 keV with an instantaneous field of view of 9° by 344° at an angular resolution of 4.6° by 11.5°. The Neutral Particle Detector (NPD) consists of two identical units that are basically pinhole cameras for the energy range 0.1 – 10 keV, each with a field of view of 9° by 90° and a resolution of 5° by 30° with separation of hydrogen and oxygen. The Electron Spectrometer (ELS) is a compact electrostatic analyzer for the energy range 1 eV to 15 keV with an energy resolution of 7%. The field of view is 10° in elevation and 360° in azimuth with a resolution of 22.5° in azimuth. ASPERA has made a large number of measurements in all accessible domains around Venus and in the solar wind and has characterized the different regions and their boundaries with respect to flux, energy and composition of the detected particles. A major result is the absolute determination of the escape rates of H+, O+ and He+ which is important for estimating the historic water content on the planet. [48] Magnetometer (MAG) is a dual sensor fluxgate instrument with the main sensor mounted on the tip of a 1 m

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Figure 6. A comparison of the fields of view (FOV) of the remote sensing instruments onboard and the size of Venus corresponding to a distance of 20,000 km. The background image was taken by VMC at ultraviolet wavelengths from about 35,000 km. The south pole is at the bottom of the image. At apocenter the planetary disk is smaller than the VMC FOV and about three times the VIRTIS-M FOV.

long boom and an auxiliary sensor mounted at the foot of the boom on the outer surface of the spacecraft [Zhang et al., 2006, 2009]. This dual sensor configuration has proven very useful since the spacecraft was developed without a specific magnetic cleanliness program and therefore disturbances from the spacecraft itself are unavoidable. The data from the auxiliary sensor are used to clean the data of the main sensor, and a performance similar to that of a magnetically clean spacecraft is achieved. Venus Express is the first spacecraft where this concept has been used. MAG is the only instrument on board that is making continuous measurements. Samples are taken at 1 Hz frequency when the spacecraft is in the solar wind and at up to 128 Hz around the pericenter. The results include detailed models of the bow shock and the induced magnetopause, and detection of foreshock activities and upstream waves. Particularly interesting is the detection of whistler waves that are interpreted as evidence of lightning in the atmosphere. 4.3. Radio Science [49] Venus Radio science (VeRa) is the radio science investigation [Haüsler et al., 2006, 2009]. The main objective is

to determine the temperature and the density of the lower atmosphere in the altitude range 40– 90 km and to determine the electron density of the ionosphere up to the ionopause by means of radio occultation of the telemetry signal. Additional objectives are to carry out bistatic radar measurements over selected areas of the surface, including those highly reflective (at radar wavelength) areas discovered by the Magellan radar, in particular in areas of high elevation, like Maxwell Montes and Thetis Regio. Measurements are made by directing the main S/C antenna toward the area of interest on the surface, and receiving the reflected signal on a large antenna back on Earth. This was attempted successfully several times until the power in the S band channel was lost, as described above. The absorption in the atmosphere at S band is manageable but at X band it is too high for reliable operation. [50] VeRa occultation profiles have been used to map the thermal structure in the lower atmosphere at a high vertical resolution and medium horizontal resolution, separating the daytime and the nighttime regions. The lower parts show little difference between day and night but in the higher regions variations are clearly visible.

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Figure 7. A comparison between the wavelength ranges and spectral resolutions of the Venus Express remote sensing instruments.

Figure 8. The Venus Express spacecraft in a semitransparent view, showing the positions of the seven scientific instruments. The top face, the +Z platform, accommodates all the apertures of the remote sensing instruments. This side is turned toward Venus during observations. The two ASPERA units are mounted on the bottom side ( Z platform). 10 of 19

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Table 1. The Seven Instruments of the Venus Express Payloada Name

Function

SPICAV/SOIR

UV-IR spectrometer

VIRTIS VMC PFS ASPERA-4 MAG VeRa

Measured Parameters

Major Topics of Interest

Wavelength 110 – 310 nm, Solar and stellar occultation. Upper atmosphere: 0.7 – 1.65 mm, 2.2 – 4.4 mm Composition, D/H ratio, thermal profiles. Resolving power up to 20,000 UV-Vis-IR Virtis-M, 0.27 – 5.2 mm imaging Atmospheric dynamics, structure and composition. Imaging spectrometer Virtis-H 1.8 –5.0 mm single point at resolving power 2000 Surface temperature, emissivity. 4 band camera Wavelength 365 nm, 513 nm, 965 nm, 1000 nm Wind speed, clouds, surface, global context Fourier spectrometer Wavelength 0.9 – 45mm, resolving power 1200 Thermal profiles, composition. Presently non operational Energetic particles Electrons 1eV – 15 keV, ions 0.01 – 36 keV/q, Atmosphere solar wind interaction, neutral particles 0.1 – 60 keV atmospheric escape. Magnetic fields B field, 8 pT – 262 nT, at 128 Hz Induced magnetosphere. Plasma boundaries. Lightning Occultation of telecom link X- and S-band, Doppler shift, Lower atmosphere density and thermal structure. polarization, amplitude Ionosphere. Surface radar reflectivity.

a The total mass is 92 kg. The instruments together produce between 1 Gbit and 6 Gbit data per day, adapted to the telemetry transfer rate which depends on the actual Venus-Earth distance.

[51] The radio measurements also contribute to surface science by precise tracking of the spacecraft trajectory, from which data the gravity field is determined and gravity anomalies are derived. The gravity field of Venus is in general well known from previous missions, but a few anomalies need to be further investigated, for example in the region of Atalanta Planitia. A secondary objective is to study the solar corona at times near Venus superior conjunction, when the signal from Venus passes close to the disk of the Sun, revealing the structure, density and dynamics of the solar corona.

[54] Many scientific and technical constraints were considered when the operational orbit was selected. To achieve good global coverage, a polar obit was deemed to be essential. The need for a global view and long duration measure-

5. Mission Scenario and Operational Orbit [52] Venus Express was launched by a Soyuz-Fregat combination from Baikonur, Kazakhstan, on 9 November 2005 and arrived at Venus on 11 April 2006 (Figure 9). The injection into the heliocentric transfer orbit by the launcher was very precise and very little fuel had to be spent for trajectory corrections during the cruise to Venus. During the first part of the 153 days cruise all spacecraft and payload elements were checked out for proper operation. [53] The Venus orbit insertion was divided into several steps, starting with a capture maneuver which included a 52 min long burn by the 400 N main engine, to achieve the required delta-v of 1251 m/s [Warhaut and Accomazzo, 2009]. This delta-v is significantly higher than that required for the capture of Mars Express into Mars orbit. The reason is twofold; the differential velocity between the spacecraft and the planet is larger at Venus, and the larger mass of Venus requires more energy to reduce the apocenter height once captured. The insertion burn placed the spacecraft in a 9-day orbit with an apocenter height of 330,000 km. During this initial ‘‘capture’’ orbit, six blocks of scientific observations were included to benefit from the unique opportunity of having a very high apocenter distance that allowed global observations and dynamic studies on a large scale. After finishing the capture orbit, a sequence of smaller engine burns took place to reduce the apocenter height to the operational value. During the capture maneuver and the subsequent orbit reduction burns a total of 482 kg of fuel was consumed. The final operational orbit was reached on 6 May 2006.

Figure 9. Venus Express liftoff on a Soyuz-Fregat launcher from Baikonur Cosmodrome, Kazakhstan, 0333 UT, 9 November 2005. The spacecraft will subsequently be inserted into a perfect heliocentric trajectory toward Venus.

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ments for dynamic studies, together with close-up studies and near-planet in situ measurements, drove the requirement toward a highly elliptical orbit. A low circular orbit was not desired but would also not be feasible owing to the high fuel demands. An orbit with a pericenter height of 250 km and an apocenter height of 66,000 km, resulting in a period of 24 h, was selected. Such an orbit also had the advantage of allowing operators work at the same time each day and, since the main ground station is located in Spain, this turns out to be normal daytime working hours, saving a significant amount in the operational costs. [55] The remaining parameter to decide was the latitude of the pericenter. Since Venus rotates very slowly, only one revolution in 243 Earth days, there is very little polar flattening and consequently very little drift in the pericenter latitude, only about 3° per year. This makes the decision more critical for Venus than for Mars or the Earth. The orbital trajectory at arrival left basically only two options free for the choice of pericenter latitude, either 15° or +78°. In order to study one hemisphere at large distance and the other at short distance +78° was selected. This choice also nicely complements Pioneer Venus, which had its pericenter around the equator. The final orbit is shown in Figure 10. [56] A specific feature with a highly elliptical, polar orbit around Venus is the drift in pericenter altitude, due to perturbations by the solar gravity, which causes the pericenter altitude to drift upward or downward at a rate of 1 – 3 km per day. For the case of Venus Express the drift is upward until May 2009, then downward. This drift is compensated for by regular thrusting by four 10 N thrusters, to maintain the pericenter height between 250 km and 400 km and, after August 2008, between 175 km and 275 km. This will ultimately lead to the depletion of the fuel and an end of the mission in late 2013 if no countermeasures are taken. A possible alternative is to use aerobraking in the upper atmosphere of Venus to reduce the apocenter height (and the orbital period), as well as the rate of the drift of the pericenter height. A 12 h (37,000 km apocenter height) or 8 h (27,000 km apocenter height) orbit would dramatically reduce the drift rate and allow operation of the mission for another decade if required.

6. Science Planning and Operations [57] The planning of the scientific operations is a complex process that is carried out at different levels in a sequential manner. At the highest level, governed by the Science Requirements Documents, the Venus Express Science Working Team develops a long-term Science Activity Plan (SAP) typically covering a year or more of observations [Titov et al., 2006b]. This plan identifies specific orbital, and other, characteristics like solar illumination, accessible surface targets, solar eclipse periods, and Earth occultation periods. A scheme for the operations is then designed, on the basis of 10 different building blocks, called Science Cases, which can be put together in different combinations. [58] A medium-term plan (MTP) covering a period of 4 weeks, is agreed between representatives of the instrument teams on the basis of the guidelines in the SAP, approximately 3 months before its execution. [59] The lowest level in the planning cycle is the short-term plan (STP) which is prepared on a weekly basis. Here the

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Figure 10. The operational 24 h orbit around Venus. There are four major operational phases: (1) Around apocenter, at 10 h to 14 h, full disk imaging is made by VIRTIS and VMC, (2) along the ascending branch, between 14 h and 22 h, longduration observations are made for studies of the dynamics and cloud evolution, and (3) around pericenter, 23 h to 1 h, high spatial resolution observations and occultation measurements are performed. (4) Data transmission to the Earth takes place with the spacecraft Earth pointing from 2 h to 10 h orbital time. detailed command files with all settings for the instruments are included. The whole process is based on an exchange of a large number of files of different complexity between the PI teams, the science operations center (VSOC) and the mission operations center (VMOC), following a well-defined scheme [Koschny et al., 2009]. [60] The orbit is normally split into four parts where different activities take place (Figure 10). The region around apocenter is used for global mapping by VMC and synthesis of 3 3 frame mosaics for full disk coverage by VIRTIS. The ascending branch, from 10 h before pericenter to about 2 h before pericenter, has a long duration view of the same area of the planet and is used for studies of atmospheric dynamics. At times of high data rate, time lapse movies are composed here. The pericenter region, between about 1 h before and 1 h after pericenter, is shared between high spatial resolution

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Figure 11. The present coverage of the VIRTIS-M images of the southern hemisphere as a function of latitude and local solar time is shown as a color-coded image. The coverage of the latitudes between 50°S and the south pole is obviously very good, while the coverage at the equator and northward of the equator is very limited.

nadir measurements and stellar occultation measurements. If in a solar eclipse period or an Earth radio occultation period, solar and radio occultation measurements also take place here. The descending branch of the orbit, from 2 h after pericenter to apocenter, is normally reserved for telecommunication and data downlink.

7. Coverage of Observations [61] A global and systematic survey of the atmospheric and plasma phenomena is a major goal of the Venus Express mission. To separate spatial variations from temporal variations, good coverage in a multiparameter space is essential for most measurements. Therefore priority is given to areas with poor coverage as far as possible. In general the coverage of the southern hemisphere is good and the coverage of the northern hemisphere is limited. This is inherent in the present orbit that has a larger distance to the planet, allowing more time for measurements over the southern hemisphere. [62] The coverage of VIRTIS spectral images over the southern hemisphere is shown in Figure 11. It can be seen that the polar region is well covered and the nightside of the southern hemisphere is reasonable well covered south of 50° southern latitude. On the day side, the equatorial region and the midlatitudes have very poor coverage. In addition the coverage of the northern hemisphere is very poor. Figure 12 shows the VIRTIS surface coverage from measurements dur-

ing nighttime. These data are essential for making surface maps. It can be seen that the coverage at midlatitudes between longitudes 150° and 30° is fairly good. The rest of the southern hemisphere is less well covered, in particular the longitudes between +45° to +150°. Also the surface coverage in the northern hemisphere is very poor. [63] The VMC dayside images provide uniform coverage of the southern hemisphere. These observations are used for cloud morphology and wind tracking studies. In the north only small-scale imaging is possible owing to the close proximity of the planet and the fast motion of the spacecraft. The VMC surface observations are limited to low latitudes (±40°). These images are made during nighttime, but owing to stray light only images taken during eclipse are useful. The observations complement well VIRTIS thermal mapping of the southern hemisphere. However, full coverage of all longitudes at low latitudes would require about 7 years since the part of the surface seen in eclipse drifts slowly because the rotation period of the planet is close to a Venus year. [64] The coverage of the SPICAV stellar occultation profiles depends on the availability of sufficiently bright UV stars. Presently, about 30 stars are used by SPICAV UV for occultation studies of the upper atmosphere, covering latitude ranging from 50°S to 40°N. A given star will always occult Venus at the same latitude but at varying local time, resulting in a coverage appearing as horizontal lines (Figure 13). The SPICAV and SOIR solar occultations by definition always

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Figure 12. The present coverage of the VIRTIS-M nighttime images of the southern hemisphere as a function of latitude and longitude is shown as a color-coded image. The coverage of the latitudes between 40°S and 80°S for longitudes between 150° and 30° is fairly good, while the coverage of other locations is limited. These data are required for seeing the lower atmosphere and the surface. occur at the terminator, i.e., 0600 or 1800 local solar time (Figure 14b), except very close to the poles owing to the slight tilt of the axis of rotation of the planet. Full coverage in local solar time is thus not possible, although improved temporal coverage, to study both short-term and long-term variations, can be achieved by more measurements. [65] Figure 14a shows the coverage of the VeRa Earth radio occultations. It can be seen that even after three full occultation seasons the coverage is not very dense, in particular in the northern hemisphere, from the equator up to about 60° northern latitude. [66] ASPERA has a very good coverage inside the bow shock, except for in a small corridor straight south of the planet. It also has a good set of reference data taken in the undisturbed solar wind, well outside the bow shock (Figure 15). [67] MAG is the only instrument that is almost constantly on. In the solar wind it samples at a fixed frequency of 1Hz and close to pericenter it samples at 128Hz for a few minutes. This thorough coverage provides data for a multitude of processes within and outside the induced magnetosphere.

8. Mission Extension Topics [68] Presently, the Venus Express mission is funded for operations until May 2009. Most of the original primary objectives will have been met by then, but a number of old and new questions that have arisen would benefit from more data and longer total mission duration. As described above, the coverage for many parameters both in local solar time and

in latitude and longitude is not yet sufficient for detailed analysis. The present solar minimum has surprised us by being longer than normal, and the solar activity is expected to increase soon. It is very valuable to monitor the response by the atmosphere to this increase and to compare it to the situation with the low solar activity we have had over the past 2 years, and to the Pioneer Venus measurements at high solar activity in the years around 1980. An improved knowledge on the influence of the solar activity on the escape rate of various species may have important implications for the understanding of the evolution of the atmosphere. [69] Long-duration monitoring of key species, for example SO2, and of cloud properties and wind fields is essential to make conclusions on secular variation of composition and dynamics. Likewise, long-duration observations of the surface increase the likelihood of finding local hot spots and/or volcanic activity. An extended operational life will also allow time for a further reduction of the pericenter altitude, perhaps to 170 km in order to improve the basis for the in situ measurements of energetic particles and magnetic fields. The thermal structure and density in the otherwise inaccessible region between 170 and 190 km can be studied by atmospheric drag measurements. [70] An extension beyond 2010 would open up the possibility for joint observations with the Japanese Planet-C spacecraft. Planet-C is focusing on small-scale atmospheric dynamics and weather phenomena [Nakamura et al., 2007], while Venus Express aims at a more general study of the atmosphere. Planet-C will be placed in a highly elliptical

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Figure 13. Coverage of the SPICAV stellar occultations in the ultraviolet channel; latitude versus local time. From these measurements, thermal profiles in the altitude range 90 km to 140 km are derived together with and abundances of several atmospheric gases. The coverage is poor at high latitudes, and more data will be needed for a full coverage. These measurements are only effective over the dark hemisphere.

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equatorial orbit, complementing well the polar orbit of Venus Express. Joint observations would allow simultaneous highresolution imaging and global context imaging, and detailed 3-D imaging of the morphology of the upper clouds. This would be of particular interest for the studies of cloud dynamics and cloud evolution. [71] As discussed above an attractive way to extend the lifetime of the mission is to significantly lower the apocenter altitude by aerobraking. This would allow both an extended duration of the present observations and allow new investigations from the vantage of a different orbit. In addition density and temperature of the upper atmosphere can be retrieved from the drag exerted on the spacecraft by the atmosphere, parameters that in this altitude (140– 170 km) range cannot easily be measured remotely. Aerobraking in 2012 is under consideration and will be studied in detail in the near future.

9. Venus Express and the ESA Solar System Science Program

Figure 14. (a) Coverage of VeRa radio occultation data; latitude versus local time. From these measurements, profiles of atmospheric density and temperature are derived in the altitude range 35 km to 90 km. Additional data will be needed for a full coverage. (b) Coverage of the SOIR solar occultation measurements. By principle, only data along the terminator are available. More data will be needed for coverage at high southern latitudes and between 40°N and 80°N.

[72] Since Venus Express is very similar to Mars Express, with several instruments in common and simultaneous operations, interesting comparisons can be made. Together with data from Earth orbiting science satellites like Envisat and the Cluster spacecraft, an important basis for the comparative planetology of the terrestrial planets with atmospheres is laid. Possibilities also open up for simultaneous investigations of the solar wind and atmosphere interactions, and ‘‘space weather’’ studies on the three planets. The Solar System Science program of ESA is addressing all major bodies of the inner solar system from the Sun to Mars, and asteroids and comets. The three planets mentioned above all have ESA spacecraft presently orbiting them and the Sun is being observed, jointly with NASA, by the SOHO spacecraft from L1. [73] The last of the terrestrial planets, Mercury, will be studied in depth, jointly with Japan, by the dual spacecraft mission BepiColombo, which will be launched in 2014. The Rosetta spacecraft is already on its way to a comet and is due to arrive at its target, comet 67P/Churyumov-Gerasimenko, in 2015. During the cruise two asteroid flybys are scheduled. Thus, a fairly complete portion of the inner solar system is being researched, and synergistic effects can also be expected even if not predictable in advance. The information gathered will form a basis for the definition of other missions still to come. [74] ESA’s science program for the future, ‘‘Cosmic Vision,’’ with launches foreseen in the timeframe 2015 – 2025, is addressing a number of basic scientific themes with questions of a fundamental nature, like ‘What are the conditions for life and planetary formation?’ and ‘How does the Solar System work?’. A large number of missions were proposed for Cosmic Vision and several of them are now being studied in a competitive phase for a final selection in 2009. As discussed above, Venus Express already contributes to parts of these objectives, in particular in the field of comparative planetology and planetary evolution. [75] The ESA Planetary Science Archive, PSA, stores the data from all missions and makes them available to the world wide community. The first data from Venus Express, as well

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Figure 15. The ASPERA coverage shown in Venus solar cylindrical coordinates (positive x axis pointing toward the Sun and the y axis showing the distance toward the Venus-Sun line). The coverage is excellent in most of the volume inside the bow shock, with a slightly weak coverage only straight south of the southern hemisphere. A very good set of solar wind reference data are available from measurements around the apocenter. as the other missions, are available for immediate electronic download at www.rssd.esa.int/psa.

10. Conclusions [76] Originally born as a ‘‘mission of opportunity’’ reusing the Mars Express design and available instruments, Venus Express has proven to be the most powerful mission ever in Venus orbit. A fortunate combination of a versatile spacecraft, a state of the art payload, and an efficient ground segment allows scientists to carry out a complex and systematic survey of the planet from the surface to the thermosphere and above. The observations include nadir and limb geometry, stellar, solar and Earth occultation measurements, and in situ plasma investigations. These studies have unveiled details of the atmospheric structure, composition, cloud morphology, dynamics and escape processes never observed before. [77] The papers following this one in this special section of JGR report on the most important findings to date. Many of the original objectives have been addressed to a significant

depth, while additional objectives are being formulated for the extended mission presently being planned. The analysis of the large amounts of data collected is a formidable task, and the science teams are engaged with both operations and processing the data. There is much more to be done with the data sets already acquired and new types of observations still to be carried out, such as atmospheric drag measurements and coordinated measurements with Planet-C, are promising new exciting results. [78] Everyone involved in the mission is appreciating the worldwide revival of the interest in Venus science that Venus Express appears to have triggered. The spacecraft and the instruments are in a good condition, and should continue to provide new data for scientists worldwide for several years to come. [79] Acknowledgments. The authors are grateful for the professionalism and enthusiastic commitment shown by all colleagues in the experimental teams, at EADS-Astrium, ESTEC, ESOC, and ESAC, which contributed greatly to the positive spirit and the success of the Venus Express mission.

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Lellouch, E., T. Clancy, D. Crisp, A. J. Kliore, D. Titov, and S. W. Bougher (1997), Monitoring of mesospheric structure and dynamics, in Venus-II: Geology, Geophysics, Atmosphere, and Solar Wind Environment, edited by S. W. Bougher et al., pp. 295 – 324, Univ. of Ariz. Press, Tucson. Markiewicz, W. J., et al. (2007a), Venus Monitoring Camera for Venus Express, Planet. Space Sci., 55, 1701 – 1711, doi:10.1016/j.pss.2007. 01.004. Markiewicz, W. J., et al. (2007b), Morphology and dynamics of the upper cloud layer of Venus, Nature, 450, 633 – 636, doi:10.1038/nature06320. Markiewicz, W. J., et al. (2009), VMC: The Venus Monitoring Camera, in Venus Express, edited by A. Wilson, Eur. Space Agency Spec. Publ., ESA SP-1295, in press. Martinecz, C., et al. (2008), Location of the bow shock and ion composition boundaries at Venus: Initial determinations from Venus Express ASPERA-4, Planet. Space Sci., 56, 780 – 784, doi:10.1016/j.pss.2007.07.007. Moroz, V. I. (2002), Studies of the atmosphere of Venus by means of spacecraft: Solved and unsolved problems, Adv. Space Res., 29, 215 – 225, doi:10.1016/S0273-1177(01)00571-3. Nakamura, M., et al. (2007), Planet-C: Venus climate orbiter mission of Japan, Planet. Space Sci., 55, 1831 – 1842, doi:10.1016/j.pss.2007.01.009. Pa¨tzold, M., et al. (2007), The structure of Venus’ middle atmosphere and ionosphere, Nature, 450, 657 – 660, doi:10.1038/nature06239. Piccioni, G., et al. (2007), South polar features on Venus similar to those near the north pole, Nature, 450, 637 – 640, doi:10.1038/nature06209. Piccioni, G., et al. (2009), VIRTIS: The Visible and Infrared Thermal Imaging Spectrometer, in Venus Express, edited by A. Wilson, Eur. Space Agency Spec. Publ., ESA SP-1295, in press. Seiff, A., J. T. Schofield, A. J. Kliore, F. W. Taylor, S. S. Limaye, H. E. Revercomb, L. A. Sromovsky, V. V. Kerzhanovich, V. I. Moroz, and M. Y. Marov (1985), Models of the structure of the atmosphere of Venus from the surface to 100 km altitude, Adv. Space Res., 5, 3 – 58, doi:10.1016/ 0273-1177(85)90197-8. Sivac, P., and T. Schirmann (2009), The Venus Express spacecraft system design, in Venus Express, edited by A. Wilson, Eur. Space Agency Spec. Publ., ESA SP-1295, in press. Solomon, S., et al. (1992), Venus tectonics: An overview of Magellan observations, J. Geophys. Res., 97, 13,199 – 13,255. Strom, R. G., G. G. Schaber, and D. D. Dawson (1994), The global resurfacing of Venus, J. Geophys. Res., 99, 10,899 – 10,926, doi:10.1029/ 94JE00388. Svedhem, H., et al. (2007a), Venus Express - The first European mission to Venus, Planet. Space Sci., 55, 1636 – 1652, doi:10.1016/j.pss.2007. 01.013. Svedhem, H., D. V. Titov, F. W. Taylor, and O. Witasse (2007b), Venus as a more Earth-like planet, Nature, 450, 629 – 632, doi:10.1038/nature06432. Svedhem, H., et al. (2009), Venus Express: Mission overview, in Venus Express, edited by A. Wilson, Eur. Space Agency Spec. Publ., ESA SP1295, in press. Taylor, F. W. (2006), Venus before Venus Express, Planet. Space Sci., 54, 1249 – 1262, doi:10.1016/j.pss.2006.04.031. Taylor, F. W., et al. (1980), Structure and meteorology of the middle atmosphere of Venus: Infrared remote sounding from the Pioneer Orbiter, J. Geophys. Res., 85, 7963 – 8006, doi:10.1029/JA085iA13p07963. Taylor, F. W., H. Svedhem, and D. Titov (2007), Venus Express and terrestrial planet climatology, in Exploring Venus as Terrestrial Planet, edited by L. W. Esposito, E. R. Stofan, and Th. E. Cravens, Geophys. Monogr. Ser., vol. 176, pp. 157 – 170, AGU, Washington D. C. Titov, D. V., E. Lellouch, F.W. Taylor, L. Marinangeli, H. Opgenoorth, and Venus Express Team (2001), Venus Express: An orbiter for the study of the atmosphere, the plasma environment, and the surface of Venus, Mission Definition Rep. ESA-SCI (2001)6, Eur. Space Agency, Paris. Titov, D. V., H. Svedhem, and F. W. Taylor (2006a), The atmosphere of Venus: Current knowledge and future investigations, in Solar System Update, edited by P. Blondel and J. W. Mason, pp. 87 – 110, Springer, Berlin. Titov, D. V., et al. (2006b), Venus Express science planning, Planet. Space Sci., 54, 1279 – 1297, doi:10.1016/j.pss.2006.04.017. Titov, D. V., M. Bullock, D. Crisp, N. Renno, F. W. Taylor, and L. V. Zasova (2007), Radiation in the atmosphere of Venus, in Exploring Venus as Terrestrial Planet, edited by L. W. Esposito, E. R. Stofan, and Th. E. Cravens, Geophys. Monogr. Ser., vol. 176, pp. 121 – 138, AGU, Washington D. C. Warhaut, M., and A. Accomazzo (2009), The ground segment and mission operations, in Venus Express, edited by A. Wilson, Eur. Space Agency Spec. Publ., ESA SP-1295, in press. Wilquet, V., et al. (2008), Line parameters for the 01111-00001 band of C12O16O18 from SOIR measurements of the Venus atmosphere, J. Quant. Spectrosc. Radiat. Transfer, 109, 895 – 905, doi:10.1016/j.jqsrt. 2007.12.021.

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Zhang, T. L., et al. (2006), Magnetic field investigation of the Venus plasma environment: Expected new results from Venus Express, Planet. Space Sci., 54, 1336 – 1343, doi:10.1016/j.pss.2006.04.018. Zhang, T., et al. (2007), Little or no solar wind enters Venus’ atmosphere at solar minimum, Nature, 450, 654 – 656, doi:10.1038/nature06026. Zhang, T. L., et al. (2008a), Initial Venus Express magnetic field observations of the magnetic barrier at solar minimum, Planet. Space Sci., 56, 790 – 795, doi:10.1016/j.pss.2007.10.013. Zhang, T. L., et al. (2008b), Initial Venus Express magnetic field observations of the Venus bow shock location at solar minimum, Planet. Space Sci., 56, 785 – 789, doi:10.1016/j.pss.2007.09.012.

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Zhang, T. L., et al. (2009), MAG: The fluxgate magnetometer of Venus Express, in Venus Express, edited by A. Wilson, Eur. Space Agency Spec. Publ., ESA SP-1295, in press. H. Svedhem and O. Witasse, ESA, ESTEC, Keplerlaan 1, NL-2200 AG Noordwijk, Netherlands. ([email protected]) F. Taylor, Department of Atmospheric, Oceanic, and Planetary Physics, Oxford University, Oxford OX1 3PU, UK. D. Titov, Max-Planck Institute for Solar System Research, Max-PlanckStrasse 2, D-37191 Katlenburg-Lindau, Germany.

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A latitudinal survey of CO, OCS, H2O, and SO2 in the lower atmosphere of Venus: Spectroscopic studies using VIRTIS-H E. Marcq,1,2 B. Be´zard,1 P. Drossart,1 G. Piccioni,3 J. M. Reess,1 and F. Henry1 Received 14 January 2008; revised 8 April 2008; accepted 10 July 2008; published 23 September 2008.

[1] The high-resolution channel (R ’ 2000) of the Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) instrument (VIRTIS-H) aboard Venus Express has provided numerous spectra of the nightside infrared thermal emission in the 2.3-mm window. Mixing ratios of various minor species in the 30–40 km range could therefore be inferred using this spectral window at higher latitudes accessible to the spacecraft but which cannot be observed from Earth. The previously known enhancement in carbon monoxide (CO) toward high latitudes is confirmed and extended up to 60° with a mixing ratio varying from 24 ± 3 to 31 ± 2 ppmv at 36 km. Measurements of carbonyl sulfide (OCS) also agree with the previously suspected latitudinal variations that are anticorrelated with those of CO, ranging between 2.5 ± 1 and 4 ± 1 ppmv at 33 km. New constraints were also derived on the mean abundance of water vapor (H2O, 31 ± 2 ppmv) and sulfur dioxide (SO2, 130 ± 50 ppmv) in the probed altitude range. CO and OCS variations are interpreted as caused by large-scale vertical motions, an explanation under current testing by various chemical and dynamical modeling. In such a case, these variations may help constrain the chemical time scale of those species in the lower troposphere. Citation: Marcq, E., B. Be´zard, P. Drossart, G. Piccioni, J. M. Reess, and F. Henry (2008), A latitudinal survey of CO, OCS, H2O, and SO2 in the lower atmosphere of Venus: Spectroscopic studies using VIRTIS-H, J. Geophys. Res., 113, E00B07, doi:10.1029/2008JE003074.

1. Introduction [2] The discovery of Venus’ nightside near infrared emission in 1983 by Allen and Crawford [1984] provided a new, valuable method to investigate the deep atmosphere of the planet. The physical origin of this emission was rapidly understood in the 1980s, on the basis of its physical properties and successful modeling [Crisp et al., 1989; Kamp et al., 1988]. The hot and deep layers of the lower atmosphere emit strong thermal radiation that can partly escape the thick atmosphere in a few narrow spectral windows thanks to the sub-Lorentzian behavior of CO2 line shape and the weakly absorbing scattering by the H2SO4 particles within the overlying cloud layers. In the 2.3-mm window considered hereafter, the emission originates from the 30– 40 km altitude range (average temperature of about 500 K). [3] Besides the imaging of the lower cloud deck whose opacity modulates the intensity of these emissions [Carlson 1 Laboratoire d’E´tudes Spatiales et d’Instrumentation en Astrophysique, UMR8109, CNRS, Observatoire de Paris, Meudon, France. 2 Laboratoire de Me´teórologie Dynamique, UMR8539, CNRS, Universite´ Paris 6, Paris, France. 3 Istituto di Astrofisica Spaziale e Fisica Cosmica, Istituto Nazionale di Astrofisica, Rome, Italy.


et al., 1991], the main scientific interest comes from its spectroscopic analysis. The comparison with radiative transfer synthetic models [Be´zard et al., 1990; Kamp and Taylor, 1990; Pollack et al., 1993; de Bergh et al., 1995] provided numerous constraints on the abundance of minor gaseous constituents. In the 2.3-mm window, carbon monoxide (CO), carbonyl sulfide (OCS), water vapor (H2O, HDO), sulfur dioxide (SO2) and hydrofluoric acid (HF) were detected and measured thanks to their spectral signatures. Furthermore, the spectral behaviors of CO and OCS are rich enough so that not only their mean abundance level, but also their respective vertical gradients could be derived, and were shown to be opposite by Pollack et al. [1993], with CO increasing with altitude as it is produced by the photochemistry of CO2 above 60 km. [4] The study of local compositional variations began later, in the 1990s, when Collard et al. [1993] noticed in the NIMS/Galileo spectra recorded during the flyby that the spatial variations of the CO band shape indicated a northern enrichment at higher latitudes of about 35%. More recently, spectra have been acquired at the NASA IRTF using the spectrometer SpeX during the quadratures that occurred since 2003. Their better spectral resolution compared to NIMS allowed us to confirm the CO northern enrichment and report a similar enrichment in the southern hemisphere, as well as a correlated depletion of OCS [Marcq et al., 2005, 2006] in the portions of Venus’ nightside that can be

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MARCQ ET AL.: VIRTIS-H STUDY OF TRACE GASES VARIATIONS

Table 1. Summary of Mentioned Science Cases Science Case

Description

1 2 3 5 7

pericenter nadir observations off-pericenter nadir observations apocenter mosaic observations stellar occultations limb observations

observed from Earth (below 40° in latitude). A latitudinal variability of the vertical gradient of these two species in the probed layers could also be measured thanks to the high signal-to-noise ratio in the best data sample, and it appears that both gradients were steeper at higher latitudes than in the equatorial region [Marcq et al., 2006]. Spatial variations in water vapor have also been sought by several observers [Drossart et al., 1993] since the localized enrichment reported by Bell et al. [1991], which was hypothesized to be due to cloud subsidence and evaporation. [ 5 ] Keeping in mind that CO increases and OCS decreases with increasing altitude, the latitudinal variability of CO and OCS can be qualitatively understood as being primarily caused by the global meridional dynamics. At high latitudes, downward fluxes from the Hadley cell circulation bring air depleted in OCS and enriched in CO, whereas the ascending branch of these cells has the opposite effect close to the equator. Quantitative modeling is currently progressing (Y. Yung et al., Modeling the distribution of OCS in the lower atmosphere of Venus, submitted to Journal of Geophysical Research, 2008; E. Marcq et al., Simulations of the latitudinal variations of CO and OCS below the clouds of Venus using a general circulation model, submitted to Journal of Geophysical Research, 2008). Besides the use of minor species as tracers of the deep circulation, the study of the compositional variability can also be related to the geological activity of the surface; a local enrichment in water vapor and SO2 and a correlated modification of the D/H ratio of water may indicate an ongoing volcanic eruption [Donahue et al., 1997; Grinspoon, 1993; Donahue, 1999]. [6] After more than a year of activity, the VIRTIS instrument aboard ESA’s Venus Express spacecraft has provided numerous spectra from the nightside of Venus very similar to the SpeX/IRTF ones. Using very similar methods to our previous studies, we present in this paper the new constraints we have derived from these data about the spatial variations of CO, OCS and water vapor, as well as a semiquantitative interpretation of the aforementioned variations.

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probe, there is an asymmetry in the observation times for both hemispheres: the northern one is seen from a nearer vantage point, but for shorter durations. [8] Within each 24-hour orbit, 8 hours are dedicated to communication with the Earth. The remaining 16 hours are used by the six functioning instruments (ASPERA, MAG, SPICAV-SOIR, VeRa, VIRTIS, VMC) for the various Venus-related observations (such as limb or nadir sounding, solar or stellar occultations) according to preplanned schedules named ‘‘science cases.’’ A brief summary of the science cases mentioned in this article is available in Table 1 [from Titov et al., 2006b]. 2.1.2. VIRTIS-H [9] The VIRTIS spectral imager is described in detail by Coradini et al. [1998]. Here we describe the characteristics of the H-channel that are relevant to our analysis. The specific optical device of the H-channel consists of an echelle grating spectrometer, and dispersion orders from 8 to 15 are focused on the detector plane. The 2.3-mm emission can be seen on three among these orders, designated as orders 5, 6 and 7. The spectral resolution of each of these spectra varies with the wavelengths, as shown on Figure 1. The spectra are then sampled on 432 discrete wavelengths for each dispersion order, although the first tens of pixels on the right side (short wavelengths) of the focal plane array usually yield smaller radiances than expected because of the low grating efficiency on the edges of the orders, thus reducing their effective spectral ranges. Another peculiarity of the array reading system that we had to take into account in our processing methods is the difference in fluxes between odd and even pixels along a given spectrum. Nevertheless, this peculiarity is partially corrected by the calibration process, yielding a few percent difference in the absolute intensities between even and odd pixels. 2.2. Quality of the Observations 2.2.1. Orbits [10] We have currently reviewed all VIRTIS-H spectra between orbits 7 (27 April 2006) and 298 (13 February 2007). The proportion of orbits during which nightside spectra could be successfully acquired is close to 40%. This

2. Observations 2.1. VIRTIS and the Venus Express Mission 2.1.1. Brief Overview of the Mission [7] A detailed description of the Venus Express mission is provided by Titov et al. [2006a]. Here we summarize the mission characteristics relevant to this study. The orbit of the spacecraft is quasi-polar, with a periapsis located at 78°N, and highly elliptical (the distance to the surface ranging from 250 km up to 66000 km) with a 24-hour period. As a consequence of the Keplerian motion of the

Figure 1. Spectral resolution versus wavelength for orders 5 (red), 6 (green), and 7 (blue).

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Table 2. Processed Orbits Orbit

Number of Processed Spectra

Science Case

Latitude on Venus

Single Spectrum S/N Ratio

007 024 027 033 040 042 060 061 073 074 076 077 078 079 082 088 095 096 097 098 100 102 103 104 110 110 111 113 116 125 128 129 134 136 139 143 148 243 248 249 258 277 284 298

128 100 35 32 50 40 160 140 230 230 128 192 300 200 80 80 50 1728 40 1664 30 30 30 50 2880 150 640 50 50 32 1400 320 1200 200 40 96 50 160 1100 150 250 96 51 200

N/Aa 1 1 7 7 7 7 2 1 5 1 2 1 1 1 7 2 2 1 2 2 2 5 2 2 2 2 2 2 5 2 2 2 2 2 2 2 1 3 1 5 1 2 1

29°S – 26°S 20°S – 55°N 20°N – 60°N 20°N – 50°N 0° – 60°N 40°N – 60°N 10°S – 70°N 30°S – 50°N 30°S – 20°N 30°S – 20°N 35°N – 55°N 44°S – 40°S 10°S – 70°N 0° – 70°N 25°N – 60°N 30°N – 70°N 40°N – 70°N 58°S – 44°S 40°N – 60°N 55°S – 28°S 45°N – 55°N 30°N – 55°N 30°N – 50°N 10°N – 55°N 50°S – 15°S 10°N – 60°N 49°S – 24°S 35°N – 60°N 35°N – 60°N 35°N – 60°N 60°S – 45°S 11°S – 50°N 60°S – 45°S 56°S – 54°S 20°N – 70°N 10°S – 5°N 11°N – 25°N 25°S – 40°N 43°S – 40°S 0° – 60°N 30°S – 0° 20°S – 5°S 20°S – 50°N 10°S – 40°N

10 3 – 10 8 – 12 4–8 4–6 6 3–8 4–8 4 – 12 10 – 30 4–8 5 2–4 4 – 25 2–6 4 – 18 4–8 3–4 3 3–4 4 5 5 2.5 4 4 2–8 3 3 3 4 4 4 10 3–4 3 5 12 10 20 6 20 4 – 10 4 – 14

a

During insertion orbit.

proportion and the temporal distribution of these orbits are consistent with the expected requirements (VIRTIS operational and pointing to a location on the nightside). Nevertheless, a subset of these orbits (14% of the total number) show a S/N ratio significantly better than those of the typical spectra taken at similar latitudes. They are summarized in Table 2. The retrievals shown in section 3.2 have therefore been conducted on this subset. 2.2.2. Influence of the Observing Mode (‘‘Science Case’’) [11] Quite surprisingly, the periapsis observations (case 1) were not the ones which consistently yielded the best spectra. It can be understood when we consider that the limiting factor in the processing of the VIRTIS-H spectra is the signal-to-noise ratio. In order to improve it, we have to integrate signal for as long as possible: at least a couple of minutes (around 20 frames in case 1) and up to half an hour, and a few hundred spectra for off-pericenter (case 2) or apoapsis mosaic observations (case 3), when the orbital velocity is slower than for periapsis observations. Longer

integration times are not suitable for a study of spatial variations, since the location on Venus of the various spectra would be blurred owing to the motion of the spacecraft. As a comment, it should be noted that even if it could theoretically be done, it would be useless to shorten the integration time to enhance the spatial resolution below roughly 30 km, since multiple scattering of the thermal emission by the cloud particles causes an intrinsic blurring of the signal over such a horizontal scale. Actually, our integration signal causes a latitudinal extension of the spectra’s location over a few degrees at most (longitudinal extension can be neglected owing to the quasi-polar orbital motion), thus enabling an integrated S/N ratio around 100 (individual spectra’s S/N ratios are given in Table 2). [12] Considering this trade-off between spatial resolution and signal-to-noise ratio, it appears that case 1 observations are well suited to low-latitude regions, where the signal is strong enough to cope with the moderate integration durations available in this science case owing to the high orbital velocity. On the other hand, midlatitudinal to high-latitudinal

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Figure 2. Superimposition of averaged nightside spectra of the same location on orders 5 (red), 6 (green), and 7 (blue) and their respective zero level. Note also the difference of spectral resolution between the various orders, in agreement with Figure 1. regions require a slower orbital velocity which can ensure a longer integration in spite of an increased distance. This explains why high-latitude regions have been mostly successfully observed during science cases 2, 5 or even 3. 2.3. Comparison with Similar Earth-Based Spectra [13] Sets of very similar spectra in terms of spectral range (1.92 – 2.52 mm) and resolution (R = 2000) have been acquired since 2003 using SpeX/IRTF by Marcq et al. [2005]. These observation campaigns were intended as a benchmark for the methods used hereafter, so that a comparison between these previous studies [Marcq et al., 2005, 2006] and the present one is very interesting. However there are some differences between both data sets that should be highlighted. 2.3.1. Spatial and Temporal Coverage [14] First of all, the spatial and temporal coverage is of course much better for VIRTIS, a dedicated instrument aboard a space probe orbiting Venus, than for the groundbased observations. From Earth, the nightside of Venus can be successfully observed only for a few weeks between inferior conjunction and maximal elongation. This period of favorable observing geometry occurs only twice in every 19.2 months. In contrast, VIRTIS has been providing spectra of the nightside of Venus on a regular basis (40% of the orbits on average) since April 2004 and will continue until at least the end of the extended mission in 2009. Another interesting feature of the Venus Express observations is that the quasi-polar orbit enables the observation of northern and southern circumpolar regions which are otherwise unreachable; the farthest observations with respect to the Venusian equator using SpeX could only reach 40 degrees of latitude. 2.3.2. Preliminary Processing [15] Another major advantage of space-based observations is the absence of any sunlight reflected from the dayside in virtually all nightside spectra, thanks to the apparent size of Venus as seen from the spacecraft. For SpeX observations, the much smaller angular diameter of

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Venus and the fact that a significant portion of the visible Venusian disk is illuminated near maximal elongation can lead to a strong scattering of the much brighter light from the solar illuminated portion of the planet onto the nightside, reducing the attainable S/N. The removal of the diffused component is a significant step in the processing and a major source of uncertainty for the retrievals of Marcq et al. [2005] and Marcq et al. [2006]: it prevented the observation of the nightside hemisphere close to the terminator, thus contributing to the narrowness of the latitudinal range of Earth-based observations. [16] Nevertheless, some processing is also needed for VIRTIS-H spectra, since the 2.3 mm window is covered by 3 different orders of dispersion that should agree on the actual intensity observed within their common spectral ranges. A typical superimposition is shown on Figure 2 for orders 5, 6 and 7. The differences on the actual value of the zero-intensity level are clearly visible between order 6 on one hand and orders 5 and 7 on the other hand. In the subsequent processing, the actual zero level for order 6 is computed from the zero levels measured on the orders 5 and 7, whose mutual agreement despite being on opposite sides of the 2.3-mm window make more trustworthy than order 6’s short-wavelength zero level. [17] As a last comment, Earth-based observations must also be corrected for the spectral signature of the terrestrial atmosphere, especially for water vapor which is much more abundant on Earth than on Venus. Such a concern is absent for VIRTIS observations, which makes them more reliable, especially for water vapor retrievals. 2.3.3. Signal-to-Noise Ratio [18] One major asset of Earth-based observations compared to VIRTIS-H ones is their quality. The longer integration time and especially the much greater light collecting area available more than compensate for the much greater distance between the instrument and Venus. A typical individual VIRTIS-H spectrum in a low-latitude to midlatitude region has a S/N ratio of between 5 and 10. Averaging over a few tens of spectra improves this ratio to about 50, at the expense of a having a spatial precision roughly equivalent to that of SpeX observations with a seeing of about 1 arcsec. SpeX spectra which are comparable in terms of integration time, location on Venus and latitudinal extension show an S/N ratio of 100 or higher. Thus, rather than supplementing Earth-based observations, VIRTIS provides a long-needed complementary spatial and temporal coverage of nightside deep atmosphere.

3. Retrievals 3.1. Algorithms 3.1.1. Synthetic Model [19] The forward synthetic model that we use is exactly the same as described by Marcq et al. [2006]. A typical fit is shown on Figure 3. [20] A major flaw of all radiative transfer models used to investigate the 2.3-mm window is the poor fit in the CO2 dominated spectral domain between 2.2 and 2.3 mm. Therefore, we have tried the new CDSD-750 database for CO2, suited for high-temperature calculations as encountered in the probed altitude region. Unfortunately, the fit we obtained was worse compared to the same model using the

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Figure 3. Comparison in the 2.2– 2.5 mm interval of a good quality order 6 spectrum (from orbit 277, in black) with the synthetic model. The relatively poor agreement at wavelengths shortward of 2.3 mm is due to the poor knowledge of CO2 opacity. older HITEMP database, whether a continuum absorption for CO2 was considered or not. So we discarded the CDSD-750 database, reverted to the HITEMP database and kept the continuum absorption of CO2 to the usual constant value of 3.5 108 cm1.amagat2 throughout the window [Pollack et al., 1993; Marcq et al., 2006], owing to the lack of experimental measurements at the relevant pressures and temperatures. [21] An update of the temperature profile that we consider in all of our simulations has also been tried from the older mean VIRA profile [Seiff, 1983] to the newer VIRA-2 [Moroz and Zasova, 1997]. A comparison between the two profiles in the altitude range where the emission originates from (30 to 40 km) showed very little difference, especially for the vertical gradient dT/dz. This latter could have been a concern since altering dT/dz distorts the simulated spectra in almost the same way as a change in CO abundance; it affects the relative brightness temperature of neighboring atmospheric layers where CO mixing ratio differs. On the contrary, changing the mean temperature of the 30– 40 km region while maintaining the temperature gradient produces almost no shape distortion of the spectra, but merely alters the integrated flux in the whole window, as expected. As far as we are concerned, the differences were too weak to justify a change of our temperature profile. [22] Still, the assumption of a horizontally uniform temperature profile could be invalid in the polar regions (above 60° in latitude), where some variability of T(z) is expected even in the lower atmosphere [Pa¨tzold et al., 2007]. But these regions yield a too weak signal in VIRTIS-H to be studied, so that we maintained this assumption. It is also worth noting that the weaker fluxes of the polar region are caused by a thicker lower cloud cover, whose modal distribution of aerosols is poorly known. A proper modeling of nightside polar spectra would therefore require a good understanding of both temperature profile and vertical

and modal distribution of cloud particles, which are both difficult to assess yet. [23] Finally, the synthetic spectra were sampled and convolved according to the instrumental function to allow straightforward comparison between synthetic and observed spectra. The instrumental functions differ for orders 5 and 6, and their width and shape vary with wavelength in a complex way. 3.1.2. One-Parameter Retrieval [24] The algorithm is based on a simple calculation of the residuals between an observed spectrum and various sets of precomputed synthetic spectra of various prescribed atmospheric parameters (e.g., vertical profiles of minor constituents, cloud opacity) within a few specific spectral intervals. This algorithm is almost exactly similar to the one described in section 4.1.1. by Marcq et al. [2006], except for some minor adaptations due to the differences between VIRTIS-H and SpeX. [25] Actually, besides the different determination of the true zero level (see 2.3.2) and, more generally, the completely different preprocessing, especially regarding diffused sunlight removal for SpeX, the key differences come from the fact that VIRTIS-H nightside spectra are available on two dispersion orders (5 and 6; order 7 was only used for zero-level determination since it does not cover the minor species absorption bands). Furthermore, the totality of the 2.3-mm window is only available on order 6, as can be seen on Figure 2: the 2.2-2.3 mm interval is not accurately rendered in order 5. From these considerations, the following adaptations were required: [26] 1. The former version of the algorithm used the same spectral interval to compute the free-scaling parameter ai (designed to have the best possible agreement between a given synthetic spectrum Si and the observed spectrum E) and to compute the residuals between E and Si. Owing to the lack of coverage for order 5, the algorithm is now able to use spectral intervals that are distinct for these two uses, the

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Figure 4. Spectral intervals used by our algorithm for the various species. The thick lines correspond to the residual computation intervals, whereas the whole lines (thin and thick) correspond to the scaling parameter determination intervals.

scaling interval typically being a superset of the residual estimation interval. The actual values, which can differ from order to order, are shown on Figure 4. [27] 2. Orders 5 and 6 of VIRTIS-H overlap between 2.3 and 2.5 mm. Although the analysis on both orders is done separately since they yield two distinct spectra of the same location, the estimates are considered as two independent measurements of the same spectrum: the resulting estimation e and its 2 uncertainty s are given by the following s6 e5 þs25 e6 5 s6 ffi , where the subscripts formulæ: e = s2 þs2 and s = psffiffiffiffiffiffiffiffiffi 2 2 6

5

s5 þs6

5 and 6 refer to the considered order. To ensure the consistency of such a combination, the retrievals were dismissed if their maximal normalized probability density, 2 5Þ ], was inferior to a threshold depending given by exp[ðes62e þs25 6 on the species, usually between 0.8 and 0.9. Note that coadding spectra from orders 5 and 6 would not make sense since their spectral resolution differ on their common wavelength range. 3.1.3. Strategy for Retrieving Gaseous Profiles [28] The global retrieval strategy is mostly unchanged since Marcq et al. [2006], where the 2.3-mm window was studied sequentially from the shorter wavelengths to the longer ones. The spectral intervals used for the retrievals of these three species are shown on Figure 4. Note that wavelengths less than 2.35 mm could not be used for order 5-based retrievals, since the instrument underestimates the intensity at these wavelengths for this order. 3.1.3.1. Carbon Monoxide Retrieval [29] The CO absorption band centered at 2.33 mm cannot be properly observed on order 5, so that only order 6 can be used for the retrievals. Spectral distortions due to the variation of the lower cloud opacity between the various spectra are taken into account as by Marcq et al. [2006] by first measuring the cloud opacity using integrated spectral radiance between 2.2 and 2.5 mm and then accordingly interpolating the synthetic spectra to the measured opacity. The considered set of CO vertical profiles are all proportional to the standard VIRA-2 profile. 3.1.3.2. Water Vapor Retrieval [30] Spectra from both orders 5 and 6 can be used for this species. Variations of lower cloud opacity, which affect spectral radiance in the whole window, and carbon monoxide, measured at the previous step and whose absorption band overlaps the water absorption band, are taken into account in the same way as previously stated. The set of synthetic profiles are here also proportional to the standard VIRA-2

profile, with no attempt to explore various D/H ratios at this stage. 3.1.3.3. Carbonyl Sulfide Retrieval [31] Spectra from orders 5 and 6 were also processed for the retrievals. Spectral overlapping with water vapor and carbon monoxide requires that the variations of these two species are taken into account, not to mention the consideration of cloud opacity variations here also. The set of considered synthetic profiles are various vertical translations of the standard VIRA-2 profile. 3.1.3.4. Sulfur Dioxide Retrieval [32] Its spectral signature being very narrow, sulfur dioxide retrievals are performed tentatively with a very low accuracy, using orders 5 and 6. Variations of carbonyl sulfide, water vapor and lower cloud opacity are taken into account, with the SO2 profiles assumed to be vertically uniform. 3.2. Results 3.2.1. CO [33] The mixing ratio of carbon monoxide at 36 km inferred at various locations is plotted on Figure 5. The minimal value of 24 ± 3 ppmv appears to be slightly off the equator, between 0° and 20°S. Its abundance then rises symmetrically with respect to this minimum toward higher latitudes, reaching 31 ± 2 ppmv at 60°S and between 30 and

Figure 5. Variations of the CO abundance at 36 km according to various VIRTIS-H spectra (orbits shown in various colors). Error bars stand for 1-s uncertainty levels.

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3.2.3. H2O [35] As concerns water vapor, no similar latitudinal trend could be observed on our results shown on Figure 7. Nevertheless, the combination of order 5 and 6 retrievals has enabled us to enhance the accuracy on its mean value significantly: 31 ± 2 ppmv between 30 and 40 km. Some temporal and/or longitudinal variability may also be possible with slightly drier conditions (29 ± 1 ppm) past 20°N, but this departure with the mean value is not significant: we have no evidence for any variations within this data set. 3.2.4. SO2 [36] Sulfur dioxide measurements could not reach a high accuracy considering the narrowness of its spectral band on the long-wavelength edge of the 2.3-mm spectral window, so that no latitudinal variation could be detected (see Figure 8). The assumption that the mixing ratio is uniform yields a mean mixing ratio of 130 ± 50 ppmv. Figure 6. Variations of the OCS abundance at 33 km according to various VIRTIS-H spectra (orbits shown in various colors). Error bars stand for 1-s uncertainty levels. 40 ppm at 60°N, where the coverage is not dense enough to ensure as much accuracy as in its southern counterpart. The dispersion of the measurements suggests some longitudinal and/or temporal variability within less than the 300-day interval during which the processed spectra have been acquired, although never exceeding 20% of the zonal mean value nor the pole-to-equator gradient. 3.2.2. OCS [34] Carbonyl sulfide latitudinal variations are shown on Figure 6. These variations appear to be anticorrelated to those of carbon monoxide, showing a maximal value of 4 ± 1 ppmv between 20°S and the equator, which was also the latitude where CO was minimal; the asymmetry of CO and OCS variations with respect to the equator could be related to the presence of high-elevation landmasses (namely, Aphrodite Terra, which is centered around 10° S) through the perturbations they cause to the general circulation. The minimum value of 2.5 ± 1 ppmv is reached near 60°S. The relative uncertainty on these retrievals is quite high, higher than what the S/N value or the accuracy of the fitting could account for, and such that we cannot dismiss a latitudeindependent mixing ratio between 3 and 3.5 ppm. Our current interpretation of this poor precision is related to the choice of the 1-D vertical synthetic profile set mentioned in section 3.1.3, which implies a univocal relation between mean abundance and vertical gradient. This relation prevents the exploration of the whole 2-D parameter space to which spectra are sensitive (i.e., vertical gradient and, independently, mean abundance in the 30– 36 km range). The explored subset is probably out of the optimal fit region since the dispersion of measurements is consistently inferior to the 1-s error bars, which points out that statistical noise is not the only source of discrepancy between synthetic and observed spectra. The same concern was raised by Marcq et al. [2006], and was solved by enabling the vertical gradient and the abundance to be retrieved separately. Such a study requires very high S/N ratio, and is therefore still in progress for the best VIRTIS-H spectra currently available.

3.3. Comparison with other Spectroscopic Studies 3.3.1. Lower-Resolution Spectra [37] Observations at lower spectral resolution could constrain with a similar accuracy the species with the most prominent band in the whole 2.3-mm window, namely, CO. H2O and OCS retrievals are currently in progress at a lesser precision compared to VIRTIS-H retrievals. Nevertheless, these studies require less signal to work on, so their horizontal coverage is substantially larger. The observations of Collard et al. [1993] using NIMS/Galileo were the first of this kind, and pointed to a northern enrichment in CO of 35 ± 15% from the equator to 47°N, in a good agreement with ours (30 ± 15% in the same latitudinal range). More recently, Tsang et al. [2008] has used VIRTIS-M spectra to map the CO abundance over large portions of the night hemisphere, and their conclusions about the latitudinal trends for CO corroborate ours in both hemispheres: from 23 ± 2 ppm near the equator to 32 ± 2 ppm at 35 km around 60° –70°. 3.3.2. Similar Resolution or Higher-Resolution Spectra [38] Spectra at a resolution similar or higher than ours in the 2.3-mm spectral window have been acquired and pro-

Figure 7. Variations of the water vapor at 33 km according to various VIRTIS-H spectra (orbits shown in various colors). Error bars stand for 1-s uncertainty levels.

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Figure 8. Variations of the sulfur dioxide at 35 km according to various VIRTIS-H spectra (orbits shown in various colors). Error bars stand for 1-s uncertainty levels. cessed before, but focused first on obtaining horizontally averaged spectra rather than studying their spatial variations. Comparisons are nevertheless useful, and agree with our results. The mean values of the CO mixing ratio at 36 km given by Pollack et al. [1993], Be´zard et al. [1990] and Taylor et al. [1997] range between 20 and 30 ppm, consistent with our own measurements. Using the same spectra, OCS and SO2 were also constrained, and respectively found to be 4.4 ± 1 ppm at 33 km (OCS [Pollack et al., 1993]) and 130 ± 40 ppm between 30 and 40 km (SO2 [Be´zard et al., 1993]). These determinations are also in full agreement with our own averages. The mean H2O mixing ratio between 30 and 40 km was found using high-resolution (R 20000) CFHT spectra in the 1.74 and 2.3-mm windows by de Bergh et al. [1995] to be 30 ± 10 ppm, which is also fully compatible with ours. In contrast, the lower estimate of 26 ± 4 ppm found by Marcq et al. [2006] is possibly due to contamination by terrestrial water vapor: the mean abundance is probably closer to 30 ppmv than to 26. [39] More recently, latitudinal studies in this spectral window were conducted using SpeX/IRTF spectra [Marcq et al., 2005, 2006] in order to prepare the processing of VIRTIS-H spectra. These results also agree with ours, at least in the latitude interval that can be observed from Earth: CO was found to vary between 26.5 ± 0.5 ppm (36 km) near 40°S and 23.75 ± 0.75 ppm near the equator, which matches our results. OCS variations were similarly anticorrelated with CO, although quantitative comparisons are harder considering the large vertical gradient of OCS in this region since their results are given at a different altitude of reference (36 km instead of 33). 3.4. Dynamical Interpretations 3.4.1. Qualitative Interpretation [40] The current interpretation of the anticorrelated variations of CO and OCS relies upon our knowledge of the global-scale circulation, especially vertical motions. It is known [Pollack et al., 1993; Marcq et al., 2006] that in the 30– 40 km altitude range, CO mixing ratio increases with height whereas OCS decreases. These observed vertical gradients are also predicted by most chemical models

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[Krasnopolsky, 2007; Krasnopolsky and Pollack, 1994]: CO is produced by photo-dissociation of CO2 by UV radiation above the clouds and consumed in the lower atmosphere as a reducing agent, whereas OCS is produced at ground level by reactions involving pyrite, CO and CO2 whereas it is strongly oxidized by SO3 (coming from the evaporation of H2SO4 at the lower cloud floor). The Hadley-cell vertical circulation of Venus causes upward motion in the equatorial region (due to solar heating) and downward motion at higher latitudes; most global circulation models (GCMs) predict the peak of downward velocity around 60° (S. Lebonnois et al., Superrotation of Venus’ atmosphere analysed with a full General Circulation Model, manuscript in preparation, 2008). Therefore, there is a tendency to deplete the CO mixing ratio around the equator (and increase that of OCS, which is more abundant at lower levels) whereas the trends are opposite at higher latitudes, which is precisely the observed pattern for CO and OCS. We can also note that the other measured species (H2O and SO2), which do not exhibit latitudinal variations, have no detectable vertical gradient either. 3.4.2. Estimate of Typical Relaxation Times [41] If the explanation given above is true, we can estimate the order of magnitude of the chemical relaxation time toward thermochemical equilibrium profiles for CO and OCS, assuming that the latitudinal contrasts we observe are caused only by the large-scale vertical motions. This coarse approach neglects other phenomena such as diffusion, and is very simplistic as for the chemical modeling, so it cannot yield more than order-of-magnitude estimates of the chemical time scale. [42] The advected net flux of a tracer whose mixing ratio ~ (q~ is q is given in a 1-D vertical model by r v) = w dq dz dw v stands for the wind speed vector, w for the dz q, where ~ vertical wind velocity and z for the altitude. Neglecting the variation of w between 30 and 40 km, this flux is in balance 0 ð zÞ where t with the chemical relaxation @q@tðzÞjchem = qðzÞq t stands for the characteristic time and q0(z) the expected vertical profile in the absence of any dynamical perturbation. In a stationary regime, this yields t¼

q0 q w dq dz

q q0 is typically around a few ppm, jdq/dzj about 1 ppm/ km for CO and OCS [Pollack et al., 1993; Taylor et al., 1997; Marcq et al., 2006] and GCMs predict typical velocities below 1 mm/s. This gives for both species characteristic times t 106 – 107, s in order to allow the formation of the equator-to-pole gradients in CO and OCS seen here. We would like to stress that this value is not related to a definite chemical process, but should merely be seen as a threshold: a faster chemistry (with a smaller t) would not allow enough time for the vertical circulation to establish the latitudinal contrasts described in section 3.2. Other characteristic times could be expressed regarding other processes (e.g., horizontal circulation, eddy diffusion), but a more detailed description is beyond the scope of this paper. 3.4.3. Quantitative Studies in Progress [43] To progress further in the interpretation of CO and OCS variations, a number of modeling efforts are currently

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in progress. The one from Yung et al. (submitted manuscript, 2008) focuses on OCS and adopts an accurate chemical scheme embedded in a 2-D circulation model, whereas Marcq et al. (manuscript in preparation, 2008) models both CO and OCS-type tracers with a simple relaxation scheme within a full 3-D radiatively consistent GCM. The results of these two models can be found in the above-quoted papers.

4. Conclusion [44] First of all, these results stress the complementary role of VIRTIS-M and VIRTIS-H channels of Venus Express, the precision of abundance retrievals and the number of species being the main assets of VIRTIS-H in this field whereas the CO mapping capabilities of VIRTIS-M over large extents on the Venus nightside are not accessible here. We have also significantly improved the accuracy of water vapor retrievals thanks to the availability of two spectral orders over the 2.3-mm spectral window. Even using a single order, CO variations were confirmed and validated up to 60° in latitude for both hemispheres, still fully compatible with previous estimates by Collard et al. [1993] and Marcq et al. [2005] (30 ± 15%). Anticorrelated OCS variations first noticed from Earth were also tentatively confirmed and extended in the same latitudinal range, though the relatively poor accuracy of the retrievals cannot rule out a constant value; the accuracy of the OCS retrievals should greatly benefit from a more detailed study with OCS abundance and vertical gradient being derived separately as it was the case for Marcq et al. [2006]. [45] With the oncoming extended phase of Venus Express, the prospective of future observations are very good. Exploration of high-latitude region (beyond 60°) could be possible thanks to the implementation of new science cases (enabling the tracking of a specific location during longer times), although such a study would probably require new additional information (better knowledge of possible horizontal variations of the T(p) profile, modal and vertical distribution of aerosol particles). Supportive studies from space and/or Earth in the other spectral windows could also help in constraining the vertical profiles of several minor species at different altitudes, especially H2O. [46] The first results of coupled dynamical-chemical simulations are also expected to improve significantly our understanding of the chemistry below the clouds of Venus, especially regarding the relative time scales of various dynamical and chemical phenomena. Observational constraints provided by the present paper are also of paramount interest for developing and improving such complex coupled simulations. [47] Acknowledgments. This work has been supported and funded by the Centre National d’E´tudes Spatiales (CNES).

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B. Be´zard, P. Drossart, F. Henry, E. Marcq, and J. M. Reess, Laboratoire d’E´tudes Spatiales et d’Instrumentation en Astrophysique, UMR8109, CNRS, Observatoire de Paris, 5 place Juleps Janssen, F-92195 Meudon, France. ([email protected]) G. Piccioni, Istituto di Astrofisica Spaziale e Fisica Cosmica, Istituto Nazionale di Astrofisica, Via Fosso del Cavaliere 100, I-00133 Roma, Italy.

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Tropospheric carbon monoxide concentrations and variability on Venus from Venus Express/VIRTIS-M observations Constantine C. C. Tsang,1 Patrick G. J. Irwin,1 Colin F. Wilson,1 Fredric W. Taylor,1 Chris Lee,2 Remco de Kok,1 Pierre Drossart,3 Giuseppe Piccioni,4 Bruno Bezard,3 and Simon Calcutt1 Received 26 January 2008; revised 10 June 2008; accepted 25 June 2008; published 1 October 2008.

[1] We present nightside observations of tropospheric carbon monoxide in

the southern hemisphere near the 35 km height level, the first from Venus Express/Visible and Infrared Thermal Imaging Spectrometer (VIRTIS)-M-IR. VIRTIS-M data from 2.18 to 2.50 mm, with a spectral resolution of 10 nm, were used in the analysis. Spectra were binned, with widths ranging from 5 to 30 spatial pixels, to increase the signal-to-noise ratio, while at the same time reducing the total number of retrievals required for complete spatial coverage. We calculate the mean abundance for carbon monoxide at the equator to be 23 ± 2 ppm. The CO concentration increases toward the poles, peaking at a latitude of approximately 60°S, with a mean value of 32 ± 2 ppm. This 40% equator-to-pole increase is consistent with the values found by Collard et al. (1993) from Galileo/NIMS observations. Observations suggest an overturning in this CO gradient past 60°S, declining to abundances seen in the midlatitudes. Zonal variability in this peak value has also been measured, varying on the order of 10% (3 ppm) at different longitudes on a latitude circle. The zonal variability of the CO abundance has possible implications for the lifetime of CO and its dynamics in the troposphere. This work has definitively established a distribution of tropospheric CO, which is consistent with a Hadley cell circulation, and placed limits on the latitudinal extent of the cell. Citation: Tsang, C. C. C., P. G. J. Irwin, C. F. Wilson, F. W. Taylor, C. Lee, R. de Kok, P. Drossart, G. Piccioni, B. Bezard, and S. Calcutt (2008), Tropospheric carbon monoxide concentrations and variability on Venus from Venus Express/VIRTIS-M observations, J. Geophys. Res., 113, E00B08, doi:10.1029/2008JE003089.

1. Introduction 1.1. Past Observations of Tropospheric CO [2] The near-infrared thermal emissions were discovered by Allen and Crawford [1984] and were shown to be emanating from the deep troposphere of Venus by radiative transfer modeling by Kamp et al. [1988] and Bezard et al. [1990]. These emissions, spanning spectral wavelengths from 1.01 to 2.50 mm, are attenuated as they pass through the cloud layer, so the radiance observed from space is sensitive to changes in the total cloud optical depths. It is also sensitive to abundances of minor species, namely CO, H2O, HDO, OCS, SO2, HF, and HCl, at altitudes ranging from the surface to the base of the cloud layer at 45 km. A compre-

1 Atmospheric, Oceanic and Planetary Physics, Clarendon Laboratory, Department of Physics, University of Oxford, Oxford, UK. 2 Geophysics and Planetary Sciences, California Institute of Technology, Pasadena, California, USA. 3 LESIA, Observatoire de Paris, Meudon, France. 4 INAF-IASF, Rome, Italy.

hensive review of the near-infrared windows can be found in the work of Taylor et al. [1997] and Baines et al. [2006]. [3] While Kamp et al. [1988] were the first to propose retrieving abundances of these minor species, it was the subsequent observations by Bezard et al. [1990] and Pollack et al. [1993] using the Canada France Hawaii Telescope, who first measured the CO abundance in the troposphere, estimating mean values at 36 km of 40 ppm and 23 ± 5 ppm, respectively. Pollack et al. [1993] also retrieved a CO vertical profile, with increases of 1.20 ± 0.45 ppm km1 increasing with height. At approximately the same time, the Galileo spacecraft flew past Venus on its trajectory to Jupiter. Onboard, the NIMS imaging spectrometer took the first images from space of the nightside of Venus, most notably at the wavelengths of 1.74 and 2.30 mm. [4] Collard et al. [1993] measured the latitude distribution of CO from the Galileo/NIMS VJBAR data set. Rather than using a spectral fitting technique, Collard et al. [1993] positively detected a latitude CO gradient using a method of ratios of spectra at two different wavelengths. It was shown that a CO meridional gradient exists in the lower atmosphere, with an enhancement mainly poleward of 47°N. This work


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remained unverified until only relatively recently, when Marcq et al. [2005, 2006] using the NASA/IRTF/SpeX, observed the same CO gradient from ground-based highresolution nightside spectra. From this analysis, a lower limit for the equator-to-pole CO gradient was found to increase by approximately 15% from the equator to 40°N/S, less than the 35% increase found by Collard et al. [1993]. While this is a lower gradient, it does not cover the high latitudes covered by Galileo/NIMS. In addition, neither Collard et al. [1993] nor Marcq et al. [2006] were able to measure any zonal distribution or variability of CO because of the limited horizontal and temporal sampling of their observations. 1.2. CO as a Dynamical Tracer [5] Gierasch [1975] postulated that a mean meridional circulation would exist in the Venus atmosphere, transporting momentum from the equator to the poles. This was verified by the in situ measurements of meridional winds by the Pioneer large and small probes [Counselman et al., 1980]. Images of Venus’ dayside in the UV reveal cloud features at 65–75 km altitude; tracking these features with potential vorticity data revealed meridional wind speeds of >10 m s1 [Limaye, 2007] at this altitude. Nightside images of Venus at 1.7 and 2.3 mm exhibit contrast because of variability in the lower cloud layer at 50 km. Analysis of Galileo/NIMS [Carlson et al., 1991] found that the motion of the features indicated that meridional flow is still poleward at this altitude. This would indicate the Hadley cell extended to a depth of at least this altitude. It is this Hadley cell circulation which maintains the midlatitude jets [Lee et al., 2007], which in turn support the super-rotation, with high zonal wind speeds. In addition, because the Hadley cell is transporting angular momentum toward the poles, it may also be responsible for maintaining the polar vortex seen from a latitude of 70° poleward. There are strong indications, from both observations and general circulation models (GCM), that this equator-to-pole circulation exists in both hemispheres. However, both the vertical and latitudinal extent of the Hadley cells are yet to be fully understood. Outstanding questions include how far in latitude do the Hadley cells extend, how deep in altitude they go, are the southern and northern hemisphere cells symmetric about the equator, and are there any timedependent horizontal variations of these cells. [6] The tropospheric CO enhancement was interpreted by Taylor [1995] to be caused by the descending arm of the Hadley cell, bringing down CO from above the cloud tops where the photolysis of CO2 by UV, with an energetic threshold for photo-dissociation near 224 nm [Von Zahn et al., 1983], creates CO (noting that the CO2 cross section is dominated by Rayleigh scattering longward of 205 nm [Shemansky, 1972; Karaiskou et al., 2004]). As CO descends, it is believed to be converted to OCS and CO2 [Krasnopolsky, 2007; Fegley et al., 1997; Hong and Fegley, 1997; Y. Yung et al., Modeling the distribution of OCS in the lower atmosphere of Venus, submitted to Journal of Geophysical Research, 2008]. It is therefore possible to use CO as a dynamical tracer, which is entrained by the circulation of the lower atmosphere. This would not only provide mean abundances of CO in the lower atmosphere,

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but also provide information on the size and variability of the Hadley cell in the deep atmosphere.

2. Venus Express/VIRTIS-M Observations [7] Venus Express entered Venus orbit in April 2006. Onboard are seven science instruments devoted to observing the atmosphere and surface of Venus from UV to infrared and radio wavelengths. Visible and Infrared Thermal Imaging Spectrometer (VIRTIS), which is also flying on the Rosetta mission [Coradini et al., 1998], is one of these instruments. Covering wavelengths from 0.27 to 5.19 mm, VIRTIS is divided into two subsystems; the nonimaging H channel covering the spectral ranges from 1.84 to 4.99 mm, with a spectral resolution of 1nm, and the imaging M channel, which is further divided into two channels. One channel, viewing in the UV and visible wavelengths, is named M-Vis, while the 1.05 to 5.19 mm spectral region is sounded by the M-IR channel. The spectral resolution of VIRTIS-M is approximately 2 nm and 10 nm for the VIS and IR sub-channels, respectively. In this analysis, we will be using data from the VIRTIS-M-IR subsystem only. A comprehensive review of VIRTIS is given by Drossart et al. [2007] and Piccioni et al. [2007]. [8] While the ability of VIRTIS-H to obtain higher resolution spectra has obvious advantages, such as better retrieval of the absolute abundances of minor species as well as their vertical distribution, the multispectral imaging capabilities of VIRTIS-M makes it uniquely able to map global-scale properties such as cloud opacity and wind field measurements. It is this capability not only to measure the global-scale values of minor species, but also to do so as a function of time, which we intend to take advantage of. Indeed, this has always been one of the key measurements to be made with VIRTIS [Baines et al., 2006]. [9] The single linear array of VIRTIS-M has an instantaneous field of view of 0.25 64 mrad, divided into 256 spatial pixels. At an apogee of 66,000 km, this instantaneous field of view spans one third of the diameter of Venus. Using a scan mirror, with 256 step positions, the field of view expands to 64 64 mrad. Therefore, at apogee, a full image of the Venus disc is created when nine images are taken sequentially to create a 3 3 mosaic image. [10] Table 1 shows the list of observations used in this analysis; two orbit insertion observations, CIOB00 taken on 12 April 2006, and CIOB03 on 16 April 2006, and two nominal orbit observations in orbit number 99 and 121. The orbit insertion observations were used because the whole disc of Venus was captured in the field of view, as the initial orbit of Venus Express in April 2006 was extremely large. The distance from Venus at the time of these two observations was approximately 214,000 km and 315,000 km, respectively. Nominal orbit observations were MTP03-99-2, taken on 28 July 2006 from a distance of 61,000 km, and MTP04121-0 taken on 12 April 2006 at a distance of 65,000 km. [11] The raw observations were spectrally and spatially binned to increase the signal-to-noise ratio of the data, as well as to reduce the number of spectra needed to achieve complete spatial coverage over the observation image. As the spatial scale of each observation varied, a bin of various widths was used for different observations. A typical bin width of 20 20 pixels would be used, which is then shifted

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Table 1. Summary of the Observations Used in This Analysis and Their Important Parametersa Observation Name

Integration Time (s)

Number of Spectra

Longitude Ranges (°)

Latitude Ranges (°)

Subsolar Longitude (°)

CIOB00 – 0 CIOB03 – 7 MTP03-99-2 MTP04-121-0

8.0 8.0 8.0 3.3

300 375 300 289

0.0: 359.9 0.0: 359.9 196.71: 286.17 0.00: 360.0

0.77: 90 13.16: 89.65 22.6: 76.07 13.13: 82.69

70.74 83.69 39.71 107.69

a

To compare the local time aspect of each observation, subtracting the longitudes by the subsolar longitude will yield the local time in units of longitude.

by 10 pixels across the image for adequate Nyquist sampling. An average of some 300 spectra per observation would be generated in this manner. The associated latitudes, longitudes, and geometries is also averaged in this way. The result can be seen in an example (Figure 1) from observation CIOB00.

3. Radiative Transfer Modeling 3.1. Spectral Data [12] We employ a radiative transfer model to generate synthetic spectra, initially developed for the analysis of Galileo/NIMS data [Irwin et al., 2004], which uses the method of correlated-k [Lacis and Oinas, 1991] to calculate

the absorption coefficients of the gases in the atmosphere. The k tables are precalculated from line-by-line spectral databases. The tables are calculated on a grid of 20 evenly spaced log-pressure levels (e17 to e5 bars) and 20 evenly spaced temperature levels (150 K to 760 K). These values correspond to approximately the 120 km and surface height range. The spectral resolutions are calculated at 10 nm and 1 nm which correspond to the resolutions of the VIRTIS-M and H subsystems. For gaseous opacity, the HITEMP database is used for CO2, while the remaining trace gases use HITRAN2K. A CO2 sub-Lorenztian lineshape from Tonkov et al. [1996] was implemented, with a cutoff of 150 cm1 from the line center. CO2-CO2 self-broadening

Figure 1. A typical binning scheme used to reduce the data to manageable sizes, as well as increasing the signal-to-noise ratio. This example is taken from orbit insertion observation CIOB00. Only nightside data can be used for this analysis, hence we only use data from half of the hemisphere. 3 of 13

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Figure 2. The Venus a priori temperature profile used in the retrieval processes taken from Seiff [1983]. half-widths were taken from the HITEMP database, while the CO2-H2O foreign half-widths were taken from Delaye et al. [1989]. The other remaining gases use the air-broadened half-widths given in HITRAN2K. CO2 collision induced

absorption is parameterized according to Bezard et al. [1990], with a value of 3.5 108 cm1 amagat2 used as the opacity factor.

Figure 3. A priori concentrations of minor constituents as a function of height. 4 of 13

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3.2. Atmospheric Profiles [13] The temperature profile used as the a priori was taken from Seiff [1983] and is plotted in Figure 2. The a priori vertical distribution of gases is given in Figure 3. The amount of CO2 is 0.965 fractional abundance, which is assumed to be vertically uniform. The abundance of CO and HF has been taken from Collard [1993], where HF is uniform, and kept uniform during the retrieval process, at 0.005 ppm. The remaining vertical profiles have been taken from Marcq et al. [2005], who have taken their profiles from a mixture of Oyama et al. [1980], Von Zahn et al. [1983], and Taylor et al. [1997]. 3.3. Cloud Profile [14] We use an a priori cloud model which follows the vertical profiles of Pollack et al. [1993]. The clouds in the model extend from 50 km to some 80 km. There are four modes in the model, which are distinguished by their size parameter. The refractive indices used in the model have been taken from Palmer and Williams [1975], for a mixture of 75% sulphuric acid and 25% water vapor. This is in line with other models of the near-infrared windows such as Pollack et al. [1993] and Marcq et al. [2006]. These refractive indices are used to calculate the single scattering albedo and absorption cross sections as look-up tables assuming Mie theory. Limited tests have been conducted for different mixtures of sulphuric acid/water vapor and their impact on the 2.3 mm spectrum. Tests show, for concentrations of 75%, 85%, and 96% H2SO4, the change to the ratio value at 2.30/2.33 mm (radiance outside the CO absorption band to radiance inside the band) is on the order of much less than 1%. This shows that we are really not that sensitive to potential changes in concentrations of H2SO4/ H2O in the Venus atmosphere. [15] To account for the multiple-scattering atmosphere and aerosols, as well as to accurately model high emissionangle observations, we use a matrix operator method to calculate for multiple scattering of the radiation [Plass et al., 1973]. We calculate this using ten streams (five up, five down).

4. Analysis [16] The retrieval model used in this work, named Nonlinear Optimal Estimator for Multivariate Spectral Analysis (NEMESIS), follows the formulation of Rodgers [1976]. The retrieval scheme starts with the prescribed forward model synthetic spectrum, generated with an a priori atmosphere, described above. The convergence limit f (denoted flimit) is also required by the user to start the iterative process. This produces a predicted spectrum yn with an initial cost function fi, given by T 1 f ¼ ðym yn ÞT S1 m ðym yn Þ þ ðx xa Þ Sx ðx xa Þ

ð1Þ

where ym is the measured radiance, yn is the predicted radiance, Sm is the measurement covariance matrix, x is the state vector, Sx is the state vector covariance matrix, and xa is the a priori state vector. The measurement covariance matrix Sm contains both the measurement errors and the forward modeling errors. The forward modeling error is

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included to compensate for any lack of physics within the forward model, as well as any inaccuracies incurred when using the correlated-k approximation. The calculation and estimates of the forward modeling and a priori errors can be found in the work of Irwin et al. [2008a] and Tsang et al. [2008], respectively, and are negligible overall. [17] The first term on the right-hand side of the cost function is known as c2 and is a measure of how well the synthetic spectrum is fitting to the observed spectrum. It is f which is minimized to arrive at a possible solution. Ultimately, the optimized solution to the nonlinear problem can be found for the unknown state vector by solving xiþ1 ¼ xa þ Sx KTi Ki Sx KTi þ Sm

1

ðym yi Ki ðxa xi ÞÞ ð2Þ

where Ki is the K matrix associated with the ith iteration state vector xi. A modification is added to this GaussNewton iteration with the addition of a ‘‘braking parameter,’’ l, to keep the iteration stable for cases where the problem is nonlinear and is given by a LevenbergMarquardt constraint of the form x0iþ1 ¼ xi þ

ðxiþ1 xi Þ 1þl

ð3Þ

where x0i+1 is the modified state vector. If x0i+1 makes the fit worse, l is increased and x0i+1 is calculated again. If x0i+1 239 yields a better fit, l is decreased and the iteration continues. [18] The iterative process then proceeds to take a predefined a priori covariance error Sa, the measured spectrum ym, and the accompanied measurement covariance (error) Sm. This produces an estimate of the state vector x, with which a new spectrum yn is calculated using the radiative transfer equations. This yn has an accompanied fn. If this fn is greater than the previous f(n1), the braking parameter (equation (3)) is increased by tenfold and the iteration begins again. If fn is less than the previous f value, braking parameter l is decreased to 0.3l and the next iteration begins. This iteration scheme terminates when fn1 fn < flim it fn

ð4Þ

where flimit is a percentage change. This yields a possible state of the atmosphere xi, given the initial measured spectrum ym. In this work, the retrieval model terminates when the number of runs reaches a maximum of 60 iterations, or if the change in cost function f between iterations drops below 0.1%, whichever is reached first (these values in practice change depending on the level of fit specified and time allotted). In practice, a solution is reached usually after approximately 10– 20 iterations, when the cost function drops below the allotted 0.1% minimum. A full review of the radiative transfer and retrieval model used in this work can be found in the work of Irwin et al. [2008a].

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Figure 4. (left) The contribution functions at 2.35 mm for the background temperature, CO at nadir, and CO at an emission angle of 80°. (right) The absorbing line strengths of CO in this spectral region. [19] Figure 4, left, shows the nadir contribution functions for 2.35 mm, produced by the radiative transfer model, which shows the background emissions coming from 30 km, with a sensitivity to CO at 35 km. Between 2.30 and 2.5 mm, the rotational-vibrational lines of the 2-0 band of carbon monoxide are active [Bezard et al., 1990], the spectral lines of which are also shown in Figure 4, right. It is these lines which provide us with the ability to retrieve the concentration of CO in the 35 km height range. [20] We take the entire spectral range, from 2.20 to 2.50 mm, to retrieve the tropospheric carbon monoxide concentrations. A sample spectrum can be seen in Figure 5. From wavelengths of 2.18 to 2.30 mm, the absorption is dominated by the presence of CO2. The radiation is also attenuated by the intervening cloud layer. This attenuation has a predominantly gray effect over this entire spectral window [Bezard et al., 1990; Marcq et al., 2006; Pollack et al., 1993]. Thus, the spectra covering these wavelengths are used to fit to the varying optical depths of the clouds across the planet. Once this is done, the rest of the spectra, from 2.30 to 2.50 mm, are assumed to have the same absorption as that caused by the clouds from 2.18 to 2.30 mm. [21] The remaining absorption in this region is due to the presence of trace gases which are active absorbers in this spectral region. The sensitivity of this spectrum in wavelength and height of trace gases can be found in the work of

Tsang et al. [2008] and Baines et al. [2006]. While we fit the 2-0 band of CO from 2.30 to 2.35 mm, we also simultaneously fit the rest of the spectrum to retrieve abundances for H2O and OCS, the results of which will not be shown in this work as it has no impact on the retrieved CO abundances. An example of our ability to fit our synthetic spectra to the data is shown in Figure 5. The residual, a simple difference between the measured and the synthetic spectrum, is also shown in Figure 5. Once f has been minimized, a best estimate for the CO abundance is found. In the case of CO, we retrieve a scaling factor which is multiplied by the a priori profile to yield a new abundance. Since the peak of the contribution function for CO is at approximately 35 km, we will show the abundances for that altitude.

5. Results [22] Figure 6 shows the observation and the results from the first orbit insertion observation, CIOB00. The 2.3 mm radiances resulting from the variations in the optical depths of the clouds that are attenuating the thermal radiation from above 35 km are shown at the spatial resolution of the binned spectra. After spectral fitting using the method described above, we can also display the c2/n values for different areas of the map, showing our ability to fit spectra

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Figure 5. An example Visible and Infrared Thermal Imaging Spectrometer (VIRTIS)-M spectrum between 2.18 and 2.50 mm used in this analysis. (top) Solid line (no symbol) is the measured spectrum, with dotted lines showing the limits of uncertainty in the radiances which arise from forward modeling and the measurement of the radiances. Solid, star symbol line is the model fit when fitting to cloud opacity. Solid, diamond symbol line is the model fit when fitting to CO, H2O, and OCS, assuming a cloud opacity derived from the solid star line. (bottom) The radiance residual between the measured and fitted spectrum. at those precise locations. The c2/n map is quite homogeneous, with values of between 1 and 3. Much higher c2/n values appear near the day-night terminators and at the poles because of bad spectra and poor signal-to-noise. It should be noted here that the c2/n fitting is still in an acceptable range, even though the optical depth has increased because of the presence of the polar cloud and vortex. The primary driver of the retrieved errors is the cloud opacity. If the cloud opacity is extremely high, like at the polar vortex, the signal-to-noise reduces dramatically, and thus the retrieved errors increase as well. [23] The resulting CO abundances and related errors are plotted on Figure 6. The mean retrieved errors in the abundances are of the order of 2 to 3 ppm (approximately 10% of the retrieved variance to mean ratio abundances) in the midlatitudes. The errors then increase toward the poles and the equator. The large errors at the equator are due to the geometry of the observation during this orbit insertion phase, where the subspacecraft point was above the poles. This results in radiances with high emission angles at the equator, with reduced signal-to-noise ratios. Correspondingly, the polar regions beyond 70°S are also subject to low signal-to-noise because of the increase in optical depths of the polar clouds. [24] We see that the mean equatorial values of 23 ± 3 ppm increase toward the poles, reaching a maximum near 60°S, of 32 ± 3 ppm. The distribution of CO poleward of 60°S

also indicates a possible decrease in abundance. This general first-order trend of increasing from equator, reaching maximum near/at 60°S, and inversion toward the poles is best seen in Figure 6. This map also shows a degree of variability of CO in solar longitude. We can best see this in Figure 7, where we plot the retrieved CO abundances as a function of the solar longitude. Solar longitude of 100° corresponds to the dusk terminator, while 260° corresponds to the dawn terminator. The first-order trends are apparent, but variability seen in Figure 7 as a function of solar longitude indicates a number of general features which are of note: (1) The mean abundance of CO on longitudes near the dusk terminator has greater absolute value than post local midnight and near dawn. (2) The maximum of the CO abundance near dusk is at 60°S, while the CO maximum near dawn is closer to 70°S. (3) Midlatitude (approximately 40°S) abundances of CO at approximately 26 ± 2 ppm have less variation than at other latitudes. [ 25 ] The same picture is painted with observation CIOB03, taken some 96 h later, again from orbit insertion (Figure 8). The same evening/morning asymmetry in the CO abundance can be seen, although it is less pronounced in this observation than CIOB00. Less of the equator is seen as CIOB03 was taken in the ascending branch of the Venus Express orbit. We also note that the midlatitude values are very constant across solar longitude, while variability can be seen in the dusk and dawn part of the nightside disc. It

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Figure 6. Orbit Insertion Observation CIOB00: (top left) 2.30 1 m radiance, a proxy for cloud opacity, plotted after binning; (top right) c2/n values when fitting for CO between 2.30 and 2.35 mm; (bottom left) the retrieved CO abundance at 35 km; and (bottom right) the error in the retrieved CO abundance. Areas of the observation where the retrieved error exceeds 8 ppm are colored white. Regions of high c2/n are reflected in the map of the retrieved errors (in white).

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Figure 7. The retrieved CO gradient versus latitude from CIOB00 observation, plotted as a function of solar longitude, interpolated in spacings of 5°. These retrieved CO abundances are referenced to 35 km. Dusk terminator at 100° and dawn terminator at 260°. should also be noted that the mean abundances at 60°S for CIOB00 and CIOB03 are 32 and 34 ± 2 ppm, respectively. [26] To investigate some of these features further, to enhance the sampling and to see the if the degree of variability of CO concentrations changes over time, we take observations from the nominal orbit during ‘‘case 2’’ observations, MTP03-99-2 and MTP04-121-0. These are shown in Figures 9 and 10, respectively. MTP03-99-2 shows a constant band of maximum CO abundance of between 35 to 36 ± 2 ppm at a latitude of 60°S. Zonal variability at this latitude can just be made out, with local maxima of CO at 90 and 135°W and a minimum at approximately 112°W, with a value of 34 ppm. However, it should be noted that the retrieved error is of the order of these fluctuations and therefore is not entirely without doubt. In addition, the maximum in CO commencing at 60°S continues at a steady value until as high as 70°, where a slight decrease in the abundance past 70°S can be made out. The retrieved errors in this region have not increased significantly. Observation MTP04-121-0 shows a similar trend. Again, the CO maximum is located at 60°S, with mean upper limit of 36 ± 3 ppm, but in this instance seems more localized around a longitude of 45°W, decreasing by 3 ppm toward 68°W. However, caution should be taken again as this decrease is

of the order of the retrieved error. The decrease from 65°S in the CO abundance to 30 ppm toward the poles is matched by increased error caused by the increase in the cloud optical depth of the polar vortex, decreasing the signal-tonoise ratio. Therefore, we cannot fully confirm the validity of the overturning from this image alone.

6. Discussion [27] We first confirm the equator-to-pole increase of CO in the troposphere first seen by Collard et al. [1993] from Galileo/NIMS data, validated by Marcq et al. [2006] from IRTF/SpeX data. It is also consistent with observations made by Marcq et al. [2008], using much higher spectral resolution subsystem of VIRTIS-H, who have shown very similar results for both equatorial and polar values of CO. The authors of the above work found a 30 ± 15% increase from 0° to 60°, corresponding to 24 ± 3 to 31 ± 2 ppm at 36 km. This is very consistent with the results of this paper, which show on first order, an increase in the same latitude range of 23 ± 2 to 32 ± 2 ppm at the same altitude. [28] In addition, because of the unique imaging capabilities of the VIRTIS-M subsystem, this analysis has also possibly revealed two new features in the tropospheric CO

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Figure 8. Orbit Insertion Observation CIOB03: (left) Retrieved CO abundance at 35 km with (right) the associated retrieval errors. Areas of the observation where the retrieved error exceeds 8 ppm are colored white.

Figure 9. (left) The retrieved CO abundance at 35 km and (right) the errors for observation MTP03-99-2. Variability in the CO abundance at 60°S across different longitudes can be seen. 10 of 13

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Figure 10. (left) Retrieved CO from observation MTP04-121-0, with (right) the retrieved errors. Areas of the observation where the retrieved error exceeds 8 ppm are colored white. distribution which have not been previously observed and warrant further discussion: the evening/morning asymmetry in the CO zonal profiles and the reversal in CO concentration poleward of 60°. Both these features can be explained by a combination of the Hadley-type circulation, as depicted in Figure 11, and photochemistry from the cloud tops.

[29] In regard to the overturning of CO past 60°S, the mechanism for this is likely to be purely dynamical in nature. The general idea is that the descending branch of the Hadley cell occurs at 60°. This is consistent with GCM models such as Lee et al. [2007] where the maximum downwelling occurs at 60°N/S. It is also consistent with measurements of the dayside meridional winds from Pioneer

Figure 11. A pictorial representation of the Hadley cell type circulation which exists in the Venus atmosphere. A complex interplay between dynamical and chemical cycles combines to yield the features seen in the retrieved CO maps. Photo-disassociation reaction taken from Huebner et al. [1992]. 11 of 13

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Venus which indicate the horizontal convergence of the meridional flow is greatest between 50 and 60° [Limaye, 2007]. We expect the maximum downwelling of the Hadley cell to be associated with regions of maximum vertical winds, and therefore would bring the most CO rich air from mesosphere (>65 km) to the troposphere. If this downwelling is greatest at 60°, it implies a poleward decrease in CO concentration past 60°S, and therefore would be consistent with our observations. [30] The situation with respect to the east-west asymmetry in the CO abundances is probably a mixture of photochemistry and dynamics processes, which is responsible for the concentration of CO at this altitude since the dominant chemistry occurs way above or way below this 35 km region. The supply of CO is governed by the upper atmosphere source. The two options to create this east-west asymmetry in CO at 35 km is either an east-west gradient in the source of CO in the source region and constant downwelling, or a constant source of CO in the source region and an east-west gradient in the downwelling across longitudes. It is indeed likely to be a combination of both processes. [31] The evening/morning asymmetry seen in Figures 6 and 8 can be, on first order, simply a function of the photochemistry which is occurring at the cloud tops at 65 km. If we consider what is occurring at the cloud tops, during the early morning, we would expect a minimum in the early morning with a ramp up of CO production in the morning sunshine due to the photolysis by solar UV, and would depend on the photolysis rate of CO above the clouds. This would peak in the early evening (before sunset probably). Maps of CO on the nightside of Venus above the clouds [Irwin et al., 2008b] indicate enhanced CO at the evening terminator and are consistent with these observations of the tropospheric CO and photochemical mechanisms above the clouds. As the photolysis stops because of the lack of sunlight, no more CO is produced on the nightside. The meridional circulation, however, will continue to pump mesospheric air into the lower troposphere. At the start of the evening you would therefore expect high abundances of CO in the lower atmosphere at the location of the downwelling of the Hadley cell and for this concentration to decay as the night progresses, assuming the vertical velocity of this downwelling was constant across the nightside (which is most likely not the case). However, CO is constantly being destroyed by sink reactions, most notably by OCS [Pollack et al., 1993] and S2 [Hong and Fegley, 1997], and reconverted back to CO2. Therefore, we also need to know something about the creation and destruction rates for CO in the lower atmosphere, as well as the meridional overturning rates (10 m s1 poleward winds at 100 mbar). From the observations of Figures 6 and 8, a change in the concentration of CO of 2 ppm from the evening to morning terminator can be seen. There may be some problem accounting for this given what is known about the reaction rates and atmospheric dynamics.

7. Conclusions [32] Observations of the nightside of Venus taken from Venus Express/VIRTIS-M instrument at wavelengths between 2.20 to 2.50 mm have been used to measure and map the distribution of CO in the troposphere of Venus. We have

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taken spectra from four different observations, two from orbit insertion, and two from the nominal orbit, which are then coadded and binned for increased signal-to-noise and homogeneous spatial sampling. We use a spectral fitting algorithm to first account for variations in the cloud optical depth by fitting spectra from 2.20 to 2.30 mm. This is then followed by fitting the remaining spectra from 2.30 to 2.35 mm, which is sensitive to the 2-0 CO band, by scaling a known a priori profile. [33] We first confirm the presence of the CO enhancement from equator to pole, with a mean increase of between 40% and 45% from 23 ± 2 ppm to 32 ± 2 ppm, which is consistent with observations made by the Galileo/NIMS flyby of Venus in 1990. We find the peak value of CO to occur between 55 and 65°S. We also find evidence of two possibly new features in this CO gradient; decreases of CO poleward of 60°S and variability in longitude. [34] A mixture of photochemistry and dynamics will be responsible for the concentration of CO at this altitude because the dominant chemistry does not occur near 35 km. Advection of CO from below is unlikely because of a lack of a continuous source, leaving photolysis of CO2 above 60 km as the most likely source of CO in the lower atmosphere. The CO is then advected into the lower atmosphere, particularly in the downwelling region near 60°, where it is destroyed. Given the need for sunlight to photolyse the CO2, the distribution of CO at the source altitude probably follows the solar zenith angle, peaking in the afternoon hemisphere because of the background flow. Poleward advection in the source region and downwelling at the poles then transports the CO into the lower atmosphere, producing the observed latitudinal gradient at 35 km. These observations would then place the downwelling branch of the Hadley cell near 60°, consistent with observations of peak westward flow at 60° and GCM simulations such as Lee et al. [2007]. It is also consistent with measurements of the dayside meridional winds from Pioneer Venus which indicate the horizontal divergence (converging air mass) of the meridional flow is greatest between 50 and 60° [Limaye, 2007]. Poleward of 60°, the vortex ‘‘barrier’’ and decreasing sunlight would contribute to a negative latitudinal gradient in CO, as observed. In addition, the fact that we observe the CO gradient at 35 km, which is a proxy for the return branch of the Hadley cell, implies the vertical extent of the cell(s) is at least 40 km. [35] Acknowledgments. I would like to thank Emmanuel Marcq for his discussions of CO and his work with VIRTIS-H spectra and its findings. We would also like to thank Frank Mills and Robert Carlson for their discussions on this subject. This work was made possible from funding given by the United Kingdom Science Technology Facilities Council, as well as the continued support of CNES and ASI.

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spectroscopy measurements at Venus, Science, 253, 1541 – 1548, doi:10.1126/science.253.5027.1541. Collard, A. (1993), Thermal emission from the nightside of Venus, D.Phil. thesis, Univ. of Oxford, UK. Collard, A., F. Taylor, S. Calcutt, R. Carlson, L. Kamp, and K. Baines (1993), Latitudinal distribution of carbon monoxide in the deep atmosphere of Venus, Planet. Space Sci., 41(7), 487 – 494. Coradini, A., et al. (1998), VIRTIS: An imaging spectrometer for the Rosetta mission, Planet. Space Sci., 46(9 – 10), 1291 – 1304. Counselman, C., S. Gourevitch, R. King, G. Loriot, and E. Ginsberg (1980), Zonal and meridional circulation of the lower atmosphere of Venus determined by radio interferometry, J. Geophys. Res., 85, 8026 – 8030, doi:10.1029/JA085iA13p08026. Delaye, C., J. Hartmann, and J. Taine (1989), Calculated tabulations of H2O line broadening by H2O, N2, O2, and CO2 at high temperature, Appl. Opt., 28(23), 5080 – 5087. Drossart, P., et al. (2007), Scientific goals for the observation of Venus by VIRTIS on ESA/Venus Express mission, Planet. Space Sci., 55, 1653 – 1672, doi:10.1016/j.pss.2007.01.003. Fegley, B. J., M. Y. Zolotov, and K. Lodders (1997), The oxidation state of the lower atmosphere and surface of Venus, Planet. Space Sci., 125, 416 – 439. Gierasch, P. (1975), Meridional circulation and the maintenance of the Venus atmospheric rotation, J. Atmos. Sci., 32, 1038 – 1044, doi:10.1175/ 1520-0469(1975)0322.0.CO;2. Hong, Y., and B. Fegley (1997), Formation of carbonyl sulphide (OCS) from carbon monoxide and sulphur vapour and applications to Venus, Icarus, 130, 495 – 504, doi:10.1006/icar.1997.5824. Huebner, W. F., J. J. Keady, and S. P. Lyon (1992), Solar photon rates for planetary atmospheres and atmospheric pollutants, Astrophys. Space Sci., 195, 1 – 294, doi:10.1007/BF00644558. Irwin, P., P. Parrish, T. Fouchet, S. Calcutt, F. Taylor, A. Simon-Miller, and A. Nixon (2004), Retrievals of Jovian tropospheric phosphine from Cassini/CIRS, Icarus, 172, 37 – 49, doi:10.1016/j.icarus.2003.09.027. Irwin, P. G. J., N. Teanby, R. de Kok, L. Fletcher, C. Howett, C. C. C. Tsang, C. F. Wilson, S. B. Calcutt, C. Nixon, and P. Parrish (2008a), The NEMESIS planetary atmosphere radiative transfer and retrieval tool, J. Quant. Spectrosc. Radiat. Transf., 109, 1136 – 1150, doi:10.1016/ j.jqsrt.2007.11.006. Irwin, P. G. J., R. de Kok, A. Negraõ, C. C. C. Tsang, C. F. Wilson, P. Drossart, G. Piccioni, D. Grassi, and F. W. Taylor (2008b), Spatial variability of carbon monoxide in Venus’ mesosphere from Venus Express/VIRTIS Visible and Infrared Thermal Imaging Spectrometer measurements, J. Geophys. Res., 113, E00B01, doi:10.1029/ 2008JE003093. Kamp, L., F. Taylor, and S. Calcutt (1988), Structure of Venus’s atmosphere from modelling of night-side infrared spectra, Nature, 336, 360 – 362, doi:10.1038/336360a0. Karaiskou, A. C., C. Vallance, V. Papadakis, I. M. Vardavas, and T. P. Rakitzis (2004), Absolute absorption cross-section measurements of CO2 in the ultraviolet from 200 to 206 nm at 295 and 373 K, Chem. Phys. Lett., 400, 30 – 34. Krasnopolsky, V. A. (2007), Chemical kinetic model for the lower atmosphere of Venus, Icarus, 191, 25 – 37, doi:10.1016/j.icarus.2007.04.028. Lacis, A., and V. Oinas (1991), A description of the correlated k-distribution method for modeling non-grey gaseous absorption, thermal emission and multiple scattering in vertically inhomogeneous atmospheres, J. Geophys. Res., 96, 9027 – 9063, doi:10.1029/90JD01945. Lee, C., S. Lewis, and P. Read (2007), Superrotation in a Venus general circulation model, J. Geophys. Res., 112, E04S11, doi:10.1029/ 2006JE002874. Limaye, S. (2007), Venus atmospheric circulation: Known and unknown, J. Geophys. Res., 112, E04S09, doi:10.1029/2006JE002814.

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Marcq, E., B. Bezard, T. Encrenaz, and M. Birlan (2005), Latitudinal variations of CO and OCS in the lower atmosphere of Venus from nearinfrared nightside spectro-imaging, Icarus, 179, 375 – 386, doi:10.1016/ j.icarus.2005.06.018. Marcq, E., T. Encrenaz, B. Bezard, and M. Birlan (2006), Remote sensing of Venus’ lower atmosphere from ground-based spectroscopy: Latitudinal and vertical distribution of minor species, Planet. Space Sci., 54, 1360 – 1370, doi:10.1016/j.pss.2006.04.024. Marcq, E., B. Bezard, P. Drossart, and G. Piccioni (2008), A latitudinal survey of CO, OCS, H2O and SO2 in the lower atmosphere of Venus: Spectroscopic studies using VIRTIS-H, J. Geophys. Res., doi:10.1029/ 2008JE003074, in press. Oyama, V., G. Carle, F. Woeller, J. Pollack, R. Reynolds, and R. Craig (1980), Pioneer Venus gas chromatography of the lower atmosphere of Venus, J. Geophys. Res., 85, 7891 – 7902, doi:10.1029/JA085iA13p07891. Palmer, K., and D. Williams (1975), Optical constants of sulfuric acid; Application to the clouds of Venus?, Appl. Opt., 14(1), 208 – 219. Piccioni, G., et al. (2007), VIRTIS: The Visible and Infrared Thermal Imaging Spectrometer, ESA Spec. Publ. SP-1295, pp. 1 – 27, Eur. Space Agency, Paris. Plass, G. N., G. W. Kattawar, and F. E. Catchings (1973), Matrix operator theory of radiative transfer: 1. Rayleigh scattering, Appl. Opt., 12, 314 – 329. Pollack, J., J. Dalton, D. Grinspoon, R. Wattson, R. Freedman, D. Crisp, D. Allen, B. Bezard, C. de Bergh, and L. Giver (1993), Near-infrared light from Venus’ nightside: A spectroscopic analysis, Icarus, 103, 1 – 42, doi:10.1006/icar.1993.1055. Rodgers, C. (1976), Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation, Rev. Geophys., 14(4), 609 – 624, doi:10.1029/RG014i004p00609. Seiff, A. (1983), Thermal structure of the atmosphere of Venus, in Venus, pp. 215 – 279, Univ. of Ariz., Tucson. ˚, Shemansky, D. E. (1972), CO2 extinction coefficient 1700 – 3000 A J. Chem. Phys., 56, 1582 – 1587, doi:10.1063/1.1677408. Taylor, F. (1995), Carbon monoxide in the deep atmosphere of Venus, Adv. Space Res., 16(6), 81 – 88, doi:10.1016/0273-1177(95)00253-B. Taylor, F., D. Crisp, and B. Bezard (1997), Near-infrared sounding of the lower atmosphere of Venus, in Venus II: Geology, Geophysics, Atmosphere, and Solar Wind Environment, pp. 325 – 351, Univ. of Ariz. Press, Tucson. Tonkov, M., N. Filippov, V. Bertsev, J. Bouanich, V.-T. Nguyen, C. Brodbeck, J. Hartmann, C. Boulet, and F. Thibault (1996), Measurements and empirical modeling of pure CO2 absorption in the 2.3 mm region at room temperature: Far wings, allowed and collision- induced bands, Appl. Opt., 35(24), 4863 – 4870. Tsang, C. C. C., P. G. J. Irwin, F. W. Taylor, and C. F. Wilson (2008), A correlated-k model of radiative transfer in the near infrared windows of Venus, J. Quant. Spectrosc. Radiat. Transf., 109, 1118 – 1135, doi:10.1016/ j.jqsrt.2007.12.008. Von Zahn, U., S. Kumar, H. Niemann, and R. Prinn (1983), Composition of the Venus atmosphere, in Venus, pp. 299 – 430, Univ. of Ariz., Tucson.

B. Bezard and P. Drossart, LESIA, Observatoire de Paris, 5 place Jules Janssen, F-92195 Meudon, France. S. Calcutt, R. de Kok, P. G. J. Irwin, F. W. Taylor, C. C. C. Tsang, and C. F. Wilson, Atmospheric, Oceanic and Planetary Physics, Clarendon Laboratory, Department of Physics, University of Oxford, Parks Road, Oxford OX1 3BH, UK. ([email protected]) C. Lee, Geophysics and Planetary Sciences, California Institute of Technology, MC 150-21, Pasadena, CA 91125, USA. G. Piccioni, INAF-IASF, Via del Fosso del Cavaliere 100, I-00133 Rome, Italy.

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Spatial variability of carbon monoxide in Venus’ mesosphere from Venus Express/Visible and Infrared Thermal Imaging Spectrometer measurements P. G. J. Irwin,1 R. de Kok,1 A. Negraõ,2 C. C. C. Tsang,1 C. F. Wilson,1 P. Drossart,3 G. Piccioni,4 D. Grassi,2 and F. W. Taylor1 Received 1 February 2008; revised 16 April 2008; accepted 5 May 2008; published 23 July 2008.

[1] Observations of Venus’ mesosphere by the Visible and Infrared Thermal Imaging

Spectrometer (VIRTIS)-M instrument of Venus Express have been used to investigate the spatial distribution of CO above Venus’ nightside cloud tops by fitting the CO absorption in the (1–0) CO band around 4.7 mm. We find little spatial variation in the abundance of CO at midlatitudes, with a retrieved abundance of approximately 40 ± 10 ppm just above the cloud tops between 65 and 70 km altitude. Unfortunately, we find it very difficult to constrain the abundance of CO in the cold polar collar, centered at about 70°S, as the retrieved temperature structure in the CO line-forming region masks the absorption lines. However, there is a possibility that CO increases toward the poles, as we detect a significant signature of high levels of CO over Venus’ south polar dipole feature in all the observations analyzed so far. To constrain the abundance of CO more closely will require the analysis of higher-resolution VIRTIS-H observations. In addition, limb observations would greatly help to resolve any possible temperature/cloud ambiguities and allow us to assess vertical variations in the abundance of CO. Citation: Irwin, P. G. J., R. de Kok, A. Negraõ, C. C. C. Tsang, C. F. Wilson, P. Drossart, G. Piccioni, D. Grassi, and F. W. Taylor (2008), Spatial variability of carbon monoxide in Venus’ mesosphere from Venus Express/Visible and Infrared Thermal Imaging Spectrometer measurements, J. Geophys. Res., 113, E00B01, doi:10.1029/2008JE003093.

1. Introduction [2] Carbon monoxide is produced at equatorial and midlatitudes in the dayside upper atmosphere of Venus by UV photolysis (l < 224 nm) of CO2 [von Zahn et al., 1983; Huebner et al., 1992]. It is thought to be transported toward the poles by the planet’s Hadley cell circulation, where it is then subducted and recombined with O near Venus’ hot surface, perhaps via sink reactions with either carbonyl sulfide (OCS) [Pollack et al., 1993], or S2 [Hong and Fegley, 1997]. It is thus expected that the volume mixing ratio of CO decreases as we move deeper into the atmosphere, both owing to such recombination reactions and also owing to the CO-rich air from high altitudes being steadily diluted by mixing with CO-poor air at lower altitudes. Superimposed on this meridional circulation, CO is also rapidly transported longitudinally in Venus’ upper atmosphere by Venus’ strong zonal winds at these pressure levels. Thus, even though it is produced on Venus’ dayside, CO is expected to be quickly transported to Venus’ night-

1 Atmospheric, Oceanic, and Planetary Physics, Clarendon Laboratory, University of Oxford, Oxford, UK. 2 INAF-IFSI, Rome, Italy. 3 LESIA, Observatoire de Paris, Meudon, France. 4 INAF-IASF, Rome, Italy.


side where it can be detected at thermal-IR wavelengths in the (1 –0) CO band around 4.7 mm. [3] Existing observations of CO prior to the arrival of the Venus Express mission at Venus in 2006 are reviewed by de Bergh et al. [2006] and issues concerning the modeling of the distribution of CO are reviewed by Mills and Allen [2007]. The abundance of CO at high altitudes (around 100 km altitude) is estimated from observations at millimeter [e.g., Clancy and Muhleman, 1991; Gurwell et al., 1995] and submillimeter [e.g., Clancy et al., 2003] wavelengths to be highly variable and of the order of 100 – 1000 ppm, while deeper in the atmosphere, the CO abundance has been inferred from measurements of the near-infrared emission from Venus’ night side. Be´zard et al. [1990] and Pollack et al. [1993] used data from the Canada France Hawaii Telescope to infer mean values of 40 ppm and 23 ± 5 ppm at 36 km respectively, with Pollack et al. [1993] also estimating that the abundance of CO increased with height at a rate of 1.20 ± 0.45 ppm/km. These observations roughly coincided with the flyby of Venus of NASA’s Galileo spacecraft, on its way to Jupiter. During the encounter, the NIMS (Near-Infrared Mapping Spectrometer) instrument made the first space-based observations of Venus’ night side near-infrared emission and Collard et al. [1993] used these data to determine the latitudinal distribution of CO in the deep atmosphere, finding that it increased toward the poles with a maximum abundance at approximately 60°N.

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IRWIN ET AL.: VENUS EXPRESS/VIRTIS MESOSPHERE CO

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Table 1. Summary of Observations Used in This Studya Name

Orbit

VI0029_06 VI0029_07 VI0029_08 VI0038_00 VI0067_00 VI0067_01 VI0067_02 VI0067_03 VI0067_04 VI0067_05

29 29 29 38 67 67 67 67 67 67

Date 19 19 19 28 26 26 26 26 26 26

May May May May June June June June June June

2006 2006 2006 2006 2006 2006 2006 2006 2006 2006

Start Time, UT

End Time, UT

nl

ns

nl

texp (s)

1658 1713 1729 1641 1449 1503 1517 1534 1542 1553

1708 1724 1740 1651 1454 1509 1523 1540 1548 1559

432 432 432 432 432 432 432 432 432 432

256 256 256 256 256 256 256 256 256 256

253 254 254 228 126 126 126 126 126 126

0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36

a

Here nl is the number of wavelengths, ns is the number of spatial samples, and nl is the number of spatial lines; texp is the exposure time.

[4] New observations of Venus’ near-infrared emission have been made by the VIRTIS instrument on Venus Express and these data have been used to investigate further the spatial variation of CO in the deep atmosphere by C. C. C. Tsang et al. (Tropospheric Carbon Monoxide Concentrations and Variability on Venus from Venus Express/VIRTIS-M Observations, submitted to Journal of Geophysical Research, 2008) and E. Marcq et al. (A latitudinal survey of CO, OCS, H2O and SO2 in the lower atmosphere of Venus: Spectroscopic studies using VIRTIS-H, submitted to Journal of Geophysical Research, 2008). Higher in the atmosphere, SPICAV/SOIR UV data have been used to determine the vertical distribution of CO above 60 km, finding values of 10 –20 ppm at midlatitudes at an altitude of 70 km, decreasing to 4 ppm at 90 km, and then increasing rapidly at higher altitudes [Svedhem et al., 2007; Bertaux et al., 2007].

2. Measurements [5] The Venus Express spacecraft was launched on a Soyuz-Fregat rocket from Baikonur, Kazakhstan on 9 November 2005 and went into a near-polar 24 h orbit about Venus on 11 April 2006, where it will remain until at least May 2009. [6] The spacecraft carries a number of remote sensing instruments including VIRTIS (Visible and Infrared Thermal Imaging Spectrometer), which covers the wavelength range 0.27 to 5.19 mm. The VIRTIS instrument is split into two main subsystems, the high-resolution subsystem, VIRTIS-H, and the mapper subsystem, VIRTIS-M. The VIRTIS-H subsystem is an Echelle spectrometer covering the 1.84 to 4.99 mm range at a spectral resolution of 0.001 mm, with an instantaneous field of view (IFOV) of 0.45 2.25 mrad. The VIRTIS-M subsystem, has an IFOV of 0.25 64 mrad sampled by 256 rows of a CCD array, which can be scanned to generate an image covering 64 64 mrad with an angular resolution of 0.25 0.25 mrad. At an apogee of 66,000 km, Venus’ disk extends approximately 180 mrad and thus a complete image of Venus’ disk may be generated by mosaicking 3 3 VIRTIS-M observations. The VIRTIS-M subsystem is itself split into two components: one component covers the 0.27 to 1.0 mm wavelength range with a spectral resolution of 0.002 mm, while an infrared component covers the 1.05 to 5.19 mm range at a lower spectral resolution of 0.01 mm. [7] To investigate the distribution of CO in Venus’ mesosphere we analyzed VIRTIS spectra from 4– 5 mm.

In this spectral range, there is a strong CO2 absorption band at 4.3 mm and a weaker one at 4.8 mm, with the (1 – 0) absorption band of CO observable at around 4.7 mm. No other gases have a significant contribution in this range. At other wavelengths where gas absorption becomes negligible, the absorption of the upper cloud decks becomes important. Day side observations at these wavelengths are considerably complicated by reflected sunlight from the cloud tops and so we chose to concentrate upon VIRTIS-M night side observations where only thermal emission is important, and for which there exist several wide area maps from the south pole to the equator. We chose two sets of observations in particular, VI0029 and VI0067, whose characteristics are summarized in Table 1. These mapping observations were chosen because, at the time of writing, they covered the largest area and had the longest integration time, and consequently best signalto-noise ratio. The signal-to-noise ratio of these spectra typically varies from about 1.0 in the center of the strong CO2 band at 4.3 mm to roughly 40 where the radiance is greatest, near 4.9 mm. To improve the signal-to-noise ratio, the 4.0– 4.95 mm spectra from the data ‘cubes’ sampling Venus’ night side for these orbits (six cubes for VI0067 and three for VI0029) were averaged into 20 20 pixel boxes and stepped by 10 pixels in each direction to achieve Nyquist sampling. The areas covered by both sets of observations are shown in Figure 1. pThe ffiffiffi error on the spectral points was set to either the NESR/ n or the variance of the measured points pffiffiffi divided by n, whichever was larger, where n is the number of points averaged in a single bin and NESR is the noise equivalent spectral radiance. [8] We also searched the measured data set for VIRTIS-M limb-sounding observations of Venus’ night side, but at the time of writing could only identify one suitable observation, which was of the equatorial region and had insufficient vertical sampling to improve upon what could be extracted from the VIRTIS-M nadir observations.

3. Retrieval Model [9] To analyze the recorded spectra, we used the NEMESIS correlated-k radiative transfer and retrieval model [Irwin et al., 2008], which has been successfully applied to numerous planetary remote sensing missions, and has also been used by Tsang et al. (submitted manuscript, 2008) to determine the variability of CO in Venus’ deep atmosphere. The NEMESIS retrieval code is based upon the optimal estimation formalism of Rodgers [2000], which assumes that a good knowledge exists of the expected

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Figure 1. Sampling of Venus’ southern hemisphere by the Venus Express/VIRTIS (left) VI0067 observations and (right) VI0029 observations. variation in the retrieved parameters from other sources. Such a situation does not exist in this case and hence the a priori guessed profiles and covariance matrices are instead tuned to provide a reasonable balance between precision and vertical smoothing, in the manner of the constrained linear inversion technique [Conrath et al., 1998; Hanel et al., 2003], as described by Irwin et al. [2008]. [10] To use the model, k-distribution tables were first generated with VIRTIS-M resolution from line data for the different spectrally active gases in this spectral and pressure region. We used line data from the HITRAN 2004 database [Rothman et al., 2005] with a Voigt line shape for all gases except CO2, for which we used a sub-Lorentzian line shape of Tonkov et al. [1996] with a cutoff at 150 cm1 from the line centers. Although this line shape was originally developed for analyzing spectra at 2.3 mm, we find that it allows us to reproduce the entire 4 – 5 mm spectrum very well, in particular the section from 4 to 4.3 mm, which is not wellproduced by other line shapes. Our final retrieved temperature maps match closely those retrieved by Grassi et al. [2008] from the 4.3 to 5.1 mm range (excluding the CO band at 4.7 mm) who use a different sub-Lorentzian correction. Hence, the use of the Tonkov et al. [1996] line shape does not significantly effect our temperature retrievals, and allows us to better model the weak CO absorption feature. The k-tables were calculated on a grid with 20 temperatures spread equally between 100 and 350 K, and 20 pressure levels spread logarithmically between 4 108 and 2.7 bar, with a square spectral resolution of width 10 nm. When calculating spectra, NEMESIS convolved the output with a further square bin of width 10 nm, to achieve an effective instrument function close to that experimentally determined for VIRTIS-M. [11] For these tests we assumed that the upper cloud optical depth was dominated by a lognormal distribution of H2SO4 droplets of mean radius of 1.0 mm and a variance of 1.29 (i.e., the mode 2 particles of Grinspoon et al. [1993]) and computed the extinction cross-section spectrum from Mie theory. Scattering effects were found to be negligible and so synthetic spectra were calculated assuming thermal emission only. [12] Our first-guess, or a priori, temperature profile was taken from Seiff [1983], while for clouds we assumed the

model of Roos et al. [1993], who used limb-darkening observations from Galileo/NIMS observations to determine that the mean cloud structure at equatorial latitudes was best matched by a cloud with a scale height of approximately 5.2 km. The a priori cloud profile density was thus set to decrease with height at this rate and the profile scaled to give a visible optical depth, at 630 nm, of 1.0 at an altitude of 65 km. For gaseous profiles the mole fraction of CO2 was set to 0.965 [von Zahn et al., 1983] and the remaining gases included were H2O and SO2, whose a priori profiles were set as recommended by von Zahn et al. [1983] and Krasnopolskii and Parshev [1983], although these gases were found to contribute negligibly to this spectral region. For CO we assumed a constant mole fraction with an a priori abundance of 100 ppm, based on determinations of the abundance in the lower atmosphere of 30 ppm (Tsang et al., submitted manuscript, 2008) and assuming the abundance increases with height. [13] NEMESIS can simultaneously retrieve multiple variables and can model atmospheric constituents either as continuous or parameterized profiles. To assess the retrieval model and the validity of the correlated-k approximation [Lacis and Oinas, 1991], NEMESIS was first used to retrieve the vertical temperature profile alone from VIRTIS-M observations and the results compared with the temperature retrievals of Grassi et al. [2008]. Very good agreement was achieved, adding confidence to both retrieval models. In addition, we found that the effects of variable cloud opacity and temperature at altitudes less than 70 km were indistinguishable since the calculated extinction cross section of the assumed cloud particles has very little variation with wavelength across the 4 – 5 mm window. [14] To investigate whether the CO abundance in Venus’ mesosphere could indeed be retrieved from VIRTIS-M observations of Venus’ night side from 4 to 5 mm, a series of retrieval tests was first undertaken whereby a set of synthetic spectra were generated from a set of randomly varied atmospheres. Synthetic Gaussian noise was added to these spectra, whose amplitude was set by the expected NESR of VIRTIS for typical observations. These synthetic ‘measured’ spectra were then fed into NEMESIS to retrieve some or all of the quantities varied and the retrieved properties compared with the values originally used to

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Figure 2. Correlation of retrieved CO with synthetic (or ‘true’) CO for the tests where temperature and mean CO abundance were retrieved with NEMESIS from synthetic spectra generated by randomly varying the temperature, mean CO abundance, and cloud opacity and scale height about their a priori values. The agreement between retrieved and ‘true’ CO can be seen to be very good. Cases where the retrieved CO is significantly greater than the true CO are cases where the cloud scale height exceeded 7 km. generate the synthetic spectra. The error on the ‘measured’ spectra was taken to be the same NESR used to generate the random noise together with a degree of forward modeling error, which accounts for uncertainties in the line data and correlated-k approximation. [15] Analysis of the spectrum generated from our a priori atmosphere showed that we are sensitive to the abundance of CO just about the cloud tops, whose lines usually appear in absorption as temperature falls with height at this altitude for most regions of the planet. Unfortunately, we found that these observations could reveal very little about the vertical distribution of CO and were thus forced to assume a vertical profile and decided to assume that the mole fraction varies little with height in this region, retrieving a single scaling factor. [16] In the first test, the cloud profile was kept constant and a range of spectra generated by varying the temperature profile and CO mean mixing ratio. For the temperature profile, sine waves of temperature (varying with respect to height) were added to the a priori temperature profile with a random variation of phase, wavelength and amplitude. Similarly, the CO mean mixing ratio was varied randomly about the a priori value. NEMESIS then used the resulting synthetic spectra (to which Gaussian noise had been added) to retrieve the temperature profile and mean CO abundance, and the retrieved CO values were compared to the true values. In all cases the retrieved CO closely matched the ‘true’ CO values. However, we were concerned that variation in the cloud profile might be aliased as CO variations and so an additional set of test spectra was generated where

temperature, CO and the cloud profile (represented by two parameters: (1) the integrated visible optical depth from space to an altitude of 65 km and (2) a scale height above and below that altitude) were randomly varied. These test spectra were then used (1) to again retrieve the temperature profile and mean CO mole fraction and (2) to retrieve the temperature profile, mean CO mole fraction and the two cloud parameters. In both cases the correlation between the retrieved and true CO was found to be very good (Figure 2) and can be explained by the fact that variations in the cloud profile produced spectral effects that are indistinguishable from thermal variations and also that we are most sensitive to CO just above the clouds. The cases in Figure 2 where the fitted CO greatly exceeded the true value were where the cloud scale height exceeded 7 km, which is a value far greater than anticipated for Venus, especially near the poles, where values of less than 5 km are expected. Hence, when analyzing the real observations, discussed in the next section, the cloud profile was kept fixed at its a priori profile and only the vertical temperature profile and mean CO mole fraction retrieved.

4. Retrievals [17] Before analyzing the extended maps, the measurements in the VI0029 and VI0067 data sets, which had already been averaged into 20 20 pixel bins as described earlier, were further averaged by latitude into bins of width 10° and spaced every 5°, again to achieve Nyquist sampling. Since the Venus Express spacecraft was virtually over the south pole for both sets of observations, the emission

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Figure 3. Variation of the retrieved CO volume mixing ratio scaling factor (of an assumed profile with 100 ppm CO at all altitudes) with latitude for the VI0067 and VI0029 observations. The horizontal dashed line indicates the a priori scaling factor and the dash-dotted lines indicate the a priori error limits. The retrieved CO abundance is indicated by the solid line, with retrieved error limits indicated by the dotted lines. angle did not vary much with longitude and so the measured spectra could simply be averaged and a mean value of the emission angle assigned to each latitude bin. The a priori temperature profile was set as described earlier with an assumed temperature error of 10 K at all altitudes and a correlation length of 1.5 scale heights. For CO we initially assumed a scaling factor of 0.1 ± 0.2 of the a priori profile where the mole fraction was set to 100 ppm at all altitudes. To prevent the retrieved mole fraction ever going negative

NEMESIS was set to retrieve the logarithm of the CO scaling factor. [18] Figure 3 shows the variation of retrieved CO factor with latitude in the southern hemisphere for the VI0029 and VI0067 observations. It can be seen that in both cases at low latitudes the mean retrieved CO abundance is reasonably well constrained at a level of 40– 50 ppm with an error of about 15 ppm. However, poleward of 50°S, the retrieved abundance drops rapidly to around 10 ppm, although the

Figure 4. (left) Fitted temperature profile contour map, (middle) CO functional derivative (rate of change of mean radiance in the range 4 – 4.95 mm (mW cm2 sr1 mm1) with CO mole fraction) contour map, and (right) sections of the CO functional derivatives at 20°S (dotted line) and 70°S (solid line) for the combination of the VI0067 and VI0029 observations. 5 of 11

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Figure 5. Quality of fit to the VI0067 observations at 20°S and 70°S. Measured spectra are shown as points and the units of radiance are mW cm2 sr1 mm1. Fitted spectra are shown by the solid lines. The spectrum calculated for the same temperature profile, but with 0 ppm of CO is shown as the dotted line, while the spectrum calculated for the same temperature profile, but with 100 ppm of CO is shown as the dash-dotted line. The top row compares the spectra, while the bottom row shows the residual difference between the measured and fitted spectra. The small absorption spike at 4.69 mm, which remains even for the 0 ppm CO case, is due to a weak Q-branch absorption of CO2. retrieved error increases markedly, approaching its a priori value, indicating the solution is poorly constrained by the measurements. Intriguingly, while Figure 3 shows the low value of CO extending to the pole for the VI0067 observations, the retrieved abundance for the VI0029 observations seems to increase toward the pole. [19] To understand why the model has difficulty in retrieving a CO scaling factor in the polar collar around 70°S we need to look at the retrieved temperature profile, shown in Figure 4. From Figure 4 we can see that there is a marked temperature inversion in the 65– 70 km altitude region at these latitudes associated with Venus’ polar collar. Figure 4 also shows the CO functional derivative, that is to say the sensitivity of the calculated radiance (integrated over 4 – 4.95 mm) with respect to the CO abundance at all latitudes and altitudes, and sections are also shown at 20°S (typical midlatitude) and 70°S (in the middle of the polar collar). At 20°S we can see that the sensitivity to CO abundance is greatest at 68 km and that increasing the CO fraction at all altitudes reduces the radiance, indicating the

CO spectral lines to be in absorption. In contrast, at 70°S we can see that while the derivative of radiance with CO abundance is negative at some altitudes, it becomes positive in the temperature inversion region. Given that we can only retrieve a mean CO fraction from these measurements, having a situation where CO increases the mean radiance at some altitudes and reduces it at others leads to limited and ambiguous sensitivity to this species. As a test, we took the retrieved temperature profile and then recalculated the spectra assuming a CO abundance of (1) 0 ppm and (2) 100 ppm, shown in Figure 5, together with the spectrum calculated with the fitted CO value. In the region of the polar collar (70°S), very little difference between the spectra calculated with the retrieved CO abundance and the two trial abundances could be seen, whereas at midlatitudes (20°S) varying the mean CO abundance had a very clear effect on the spectra. Hence, while we can be reasonably confident of the retrieved CO abundance at low to mid latitudes, the abundance in the polar collar is effectively

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Figure 6. Measured radiance (in units of mW cm2 sr1 mm1) averaged over the range 4.8– 5.0 mm for the (left) VI0067 and (right) VI0029 observations (binned into 20 20 pixel bins and stepped every 10 pixels in both directions). unconstrained as is indicated by the retrieved errors differing little from the assumed a priori limits. [20] As a final test, we also attempted to fit the observations with a temperature profile alone, keeping the CO value fixed at 100 ppm. We found that allowing the CO abundance to vary led to significantly improved fits to the observations in regions where we are sensitive to CO, showing that CO indeed has a significant and measurable effect on the observed spectrum. [21] Having investigated the fitting of temperature and CO to the longitudinally averaged spectra, and determined at

which latitudes CO might be retrieved, we then took the mapped observations and attempted to retrieve maps of the mean CO abundance in the southern hemisphere. Figure 6 shows the measured radiance (integrated from 4.8 to 5.0 mm) for the two sets of observations, indicating the position of the southern polar vortex and Figure 7 shows the precision to which the spectra could be fitted with our model expressed n c2 1 X ððmi si Þ=ei Þ2 , where mi are the measured ¼ as n 1 n radiances of estimated error ei, si are the calculated synthetic radiances, and n is the number of observations.

Figure 7. Fitted c2/n for the (left) VI0067 and (right) VI0029 observations. 7 of 11

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Figure 8. Retrieved CO scaling factor for the (left) VI0067 and (right) VI0029 observations, where areas with c2/n > 3 have been masked (black areas). Note that a scaling factor of 1.0 corresponds to 100 ppm of CO. Areas where c2/n is less than 1 are regions where our model fits the observed spectra well and thus where we can have some confidence in our retrieved temperatures and CO abundances, if CO is detectable. Tests were conducted with a priori CO scaling factors of 1.0 ± 2.0, 0.1 ± 2.0 and 0.1 ± 0.2. In all cases very similar results were achieved since there is no trade-off to be made between vertical resolution and precision in these retrievals as the shape of the CO profile is fixed, resulting in a simple least squares solution for CO abundance that does

not depend on the initial guess. Here we will show the results for the case where the CO a priori scaling factor was set to 0.1 ± 0.2. Since we fit the log of the scaling factor, it is important to remember that the a priori error assumed by NEMESIS was actually the fractional error in the abundance, in this case 2.0. In regions where there is little CO information, the retrieved fractional error remains close to 2.0, while in regions where CO is better constrained the fractional error reduces to less than 0.5. Figure 8 shows the retrieved CO factor regardless of retrieved error, while

Figure 9. Fitted CO scaling factor for the (left) VI0067 and (right) VI0029 observations, where regions with a fractional error exceeding 0.4 and c2/n > 3 have been masked. 8 of 11

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Figure 10. Retrieved values of T(70 km) T(65 km) for the (left) VI0067 and (right) VI0029 observations, showing the inverted temperature profile at these altitudes over the polar collar. Figure 9 shows the retrieved CO factor for cases where the fractional error has been reduced to less than 0.4 and thus where we can be confident that the CO abundance really is being constrained by the observations. The large black areas in Figure 9, mostly coincide with regions of the polar collar temperature inversion around 70°S, as shown in Figure 10. [22] Comparing the retrieved CO maps of Figures 8 and 9 with the mean latitudinal variation for the two sets of observations shown earlier in Figure 3 we can see many

of the same features: roughly constant CO abundance at low to mid latitudes, decreasing in the polar collar (but with much less constraint) and then increasing again toward the pole in the case of the VI0029 observations. Both maps seem to indicate a higher abundance of CO near the evening terminator at 50°S – 70°S, although since the abundance of CO at similar latitudes on the morning terminator is not well constrained (as can be seen from Figure 9) owing to the vertical temperature structure there, it is not possible to determine if this is a real increase; it is possible that the CO

Figure 11. (left) Measured radiance (in units of mW cm2 sr1 mm1) averaged over the range 4.8 – 5.0 mm for the VI0038_00 observation, together with (right) the fitted CO abundance, again showing high CO over one of the two bright dipoles at 75°N, 0°E, indicative of rapid downwelling. 9 of 11

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abundance is higher at all latitudes between 50°S and 70°S, but only observable near the evening terminator. [23] The retrieved CO abundance near the pole for the VI0029 observation is particularly interesting as it seems to pick out high CO values over the polar dipole feature at 80°S, 45°E and 85°S, 45°W. This feature would appear to be absent in the VI0067 observation owing to the polar dipole being mostly on the day side in this case as indicated in Figure 6. To check if this was a repeatable feature, an additional observation, VI0038_00, which covers the south polar dipole, was analyzed in the same way to yield the fitted CO map shown in Figure 11. Again, we see high CO over one of the dipole hot spots at 75°N, 0°E, which would be consistent with this being a region of rapid downwelling dragging CO-rich air from higher altitudes to just above the cloud tops where it can be observed.

5. Discussion and Conclusions [24] Observations of the (1 – 0) absorption band of CO at 4.7 mm on Venus’ night side by the VIRTIS-M instrument of Venus Express have been used to investigate the spatial distribution of this gas above Venus’ cloud tops. Retrieval tests showed the effects of cloud top temperature and cloud opacity to be indistinguishable in the 4 – 5 mm range and that the retrieved CO abundance differed little if clouds were included or omitted. Hence, the cloud opacity was fixed and a vertical temperature profile fitted simultaneously with the CO abundance. [25] We find little spatial variation in the abundance of CO at midlatitudes, with a retrieved abundance of approximately 40 ± 10 ppm, a figure slightly higher than the levels observed by SPICAV/SOIR [Svedhem et al., 2007] of 10– 20 ppm at an altitude of 70 km. However, SPICAV/SOIR estimated the CO abundance at this altitude to decrease with height and as we are most sensitive to the abundance just above the cloud tops at 65 km, our results are broadly consistent. [26] Unfortunately, we find it very difficult to constrain the abundance of CO in the cold polar collar at 70°S as the retrieved temperature structure in the CO line-forming region masks the lines. However, the observations would suggest that CO increases toward the poles as we detect a significant signature of CO over Venus’ south pole, coinciding with the areas of the bright dipole feature, which would be consistent with rapid downwelling in these features bringing CO-rich air from high altitudes to just above the clouds where it can be detected. In addition, both observations suggest the possibility of higher abundances of CO between 70°S and 50°S near Venus’ evening terminator, although it is possible that this enhancement exists at all other longitudes, but cannot be detected owing to the CO lines being masked by the vertical temperature profile. [27] A caveat to the conclusion of high CO over the polar dipole is that the shape of the vertical profile of CO had to be assumed in these retrievals, owing to the observations having effectively no vertical resolution assumption in the line forming region (65 – 70 km). It was assumed that the abundance of CO did not vary with altitude, but if the CO abundance actually decreases rapidly with altitude and if cloud tops in the polar dipole are much deeper than elsewhere, as is thought to be the case, then we would see

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a longer path length of CO in this region due simply to CO’s vertical profile. However, since the polar dipole is thought to be a region of rapid downwelling and since we know the abundance of CO increases with height in the high atmosphere, where CO is formed by UV photolysis, it could be argued that this possibility is unlikely. To answer some of these questions and constrain the abundance of CO more closely will require more work and the analysis of many more observations than has been possible in this paper. One approach will be to analyze also the VIRTIS-H observations, which have much better spectral resolution, but poorer spatial sampling. This work is currently in progress, but the results are not yet mature enough to be presented here. In addition, limb observations would allow us to directly determine the vertical profile of CO above the cloud tops around the planet and help greatly to resolve any possible temperature/cloud ambiguities and we hope that such measurements can be conducted by VIRTIS during the remainder of its mission. [28] Acknowledgments. The Venus Express/VIRTIS team is acknowledged for their work. This research was made possible by funding from the United Kingdom Science and Technology Facilities Council.

References Bertaux, J.-L., et al. (2007), A warm layer in Venus’ cryosphere and high altitude measurements of HF, HCl, H2O and HDO, Nature, 450, 646 – 649, doi:10.1038/nature05974. Be´zard, B., C. de Bergh, D. Crisp, and J. Maillard (1990), The deep atmosphere of Venus revealed by high-resolution night side spectra, Nature, 345, 508 – 511, doi:10.1038/345508a0. Clancy, R. T., and D. O. Muhleman (1991), Long-term (1979 – 1990) changes in the thermal, dynamical and compositional structure of the Venus mesosphere as inferred from microwave spectral line observations of 12CO, 13CO, and C18O, Icarus, 89, 129 – 146, doi:10.1016/ 0019-1035(91)90093-9. Clancy, R. T., B. J. Sandor, and G. H. Moriarty-Schieven (2003), Observational definition of the Venus mesopause: Vertical structure, diurnal variation, and temporal instability, Icarus, 161, 1 – 16, doi:10.1016/ S0019-1035(02)00022-2. Collard, A., F. Taylor, S. Calcutt, R. Carlson, L. Kamp, and K. Baines (1993), Latitudinal distribution of carbon monoxide in the deep atmosphere of Venus, Planet. Space Sci., 41, 487 – 494, doi:10.1016/ 0032-0633(93)90031-V. Conrath, B. J., P. J. Gierasch, and E. A. Ustinov (1998), Thermal structure and para hydrogen fraction in the outer planets from Voyager IRIS measurements, Icarus, 135, 501 – 517, doi:10.1006/icar.1998.6000. de Bergh, C., V. I. Moroz, F. W. Taylor, D. Crisp, B. Bezard, and L. V. Zasova (2006), The composition of the atmosphere of Venus below 100 km altitude: An overview, Planet. Space Sci., 54, 1389 – 1397, doi:10.1016/j.pss.2006.04.020. Grassi, D., P. Drossart, G. Piccioni, N. I. Ignatiev, L. V. Zasova, A. Adriani, M. L. Moriconi, P. G. J. Irwin, A. Negrao, and A. Migliorini (2008), Retrieval of air temperature profiles in the Venusian Mesosphere from VIRTIS-M data: Description and validation of algorithms, J. Geophys. Res., doi:10.1029/2008JE003075, in press. Grinspoon, D. H., J. B. Pollack, B. R. Sitton, R. W. Carlson, L. W. Kamp, K. H. Baines, T. Encrenaz, and F. W. Taylor (1993), Probing Venus’s cloud structure with Galileo NIMS, Planet. Space Sci., 41, 515 – 542, doi:10.1016/0032-0633(93)90034-Y. Gurwell, M. A., D. O. Muhleman, K. P. Shah, G. L. Berge, D. J. Rudy, and A. W. Grossman (1995), Observations of the CO bulge on Venus and implications for mesospheric winds, Icarus, 115, 141 – 158, doi:10.1006/ icar.1995.1085. Hanel, R. A., B. J. Conrath, D. E. Jennings, and R. E. Samuelson (2003), Exploration of the Solar System by Infrared Remote Sensing, 2nd ed., Cambridge Univ. Press, Cambridge, U. K. Hong, Y., and B. Fegley (1997), Formation of carbonyl sulfide (OCS) from carbon monoxide and sulfur vapor and applications to Venus, Icarus, 130, 495 – 504, doi:10.1006/icar.1997.5824. Huebner, W. F., J. J. Keady, and S. P. Lyon (1992), Solar photo rates for planetary atmospheres and atmospheric pollutants, Astrophys. Space Sci., 195, 1 – 289, 291 – 294.

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Irwin, P. G. J., N. A. Teanby, R. de Kok, L. N. Fletcher, C. J. A. Howett, C. C. C. Tsang, C. F. Wilson, S. B. Calcutt, C. A. Nixon, and P. D. Parrish (2008), The NEMESIS planetary atmosphere radiative transfer and retrieval tool, J. Quant. Spectrosc. Radiat. Transf., 109, 1136 – 1150, doi:10.1016/j.jqsrt.2007.11.006. Krasnopolskii, V. A., and V. A. Parshev (1983), Photochemistry of the Venus atmosphere, in Venus, pp. 431 – 458, Univ. of Ariz. Press, Tucson. Lacis, A. A., and V. Oinas (1991), A description of the correlated-k distribution method for modeling nongray gaseous absorption, thermal emission, and multiple scattering in vertically inhomogeneous atmospheres, J. Geophys. Res., 96, 9027 – 9063, doi:10.1029/90JD01945. Mills, F. P., and M. Allen (2007), A review of selected issues concerning the chemistry in Venus’ middle atmosphere, Planet. Space Sci., 55, 1729 – 1740, doi:10.1016/j.pss.2007.01.012. Pollack, J., J. Dalton, D. Grinspoon, R. Wattson, R. Freedman, D. Crisp, D. Allen, B. Bezard, C. de Bergh, and L. Giver (1993), Near-infrared light from Venus’ night side: A spectroscopic analysis, Icarus, 103, 1 – 42, doi:10.1006/icar.1993.1055. Rodgers, C. D. (2000), Inverse Methods for Atmospheric Sounding: Theory and Practice, World Sci., Hackensack, N. J. Roos, M., P. Drossart, T. Encrenaz, E. Lellouch, B. Bezard, R. W. Carlson, K. H. Baines, L. W. Kamp, F. W. Taylor, and A. D. Collard (1993), The upper clouds of Venus: Determination of the scale height from NIMSGalileo infrared data, Planet. Space Sci., 41, 505 – 514, doi:10.1016/ 0032-0633(93)90033-X. Rothman, L. S., et al. (2005), The HITRAN 2004 molecular spectroscopic database, J. Quant. Spectrosc. Radiat. Transf., 96, 139 – 204, doi:10.1016/j.jqsrt.2004.10.008.

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Seiff, A. (1983), Thermal structure of the atmosphere of Venus, in Venus, pp. 215 – 279, Univ. of Ariz. Press, Tucson. Svedhem, H., D. V. Titov, F. W. Taylor, and O. Witasse (2007), Venus as a more Earth-like planet, Nature, 450, 629 – 632, doi:10.1038/nature06432. Tonkov, M., N. Filippov, V. Bertsev, J. Bouanich, V.-T. Nguyen, C. Brodbeck, J. Hartmann, C. Boulet, and F. Thibault (1996), Measurements and empirical modeling of pure CO2 absorption in the 2.3 mm region at room temperature: Far wings, allowed and collision-induced bands, Appl. Opt., 35, 4863 – 4870. von Zahn, U., S. Kumar, H. Niemann, and R. Prinn (1983), Composition of the Venus Atmosphere, in Venus, pp. 299 – 430, Univ. of Ariz. Press, Tucson.

R. de Kok, P. G. J. Irwin, F. W. Taylor, C. C. C. Tsang, and C. F. Wilson, Atmospheric, Oceanic, and Planetary Physics, Clarendon Laboratory, University of Oxford, Parks Road, Oxford OX1 3PU, UK. ([email protected]) P. Drossart, LESIA, Observatoire de Paris, 5 place Jules Janssen, F-92195 Meudon, France. D. Grassi and A. Negraõ, INAF-IFSI, Via del Fosso del Cavaliere, 100, I-00133 Rome, Italy. G. Piccioni, INAF-IASF, Via del Fosso del Cavaliere, 100, I-00133 Rome, Italy.

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Composition of the Venus mesosphere measured by Solar Occultation at Infrared on board Venus Express A. C. Vandaele,1 M. De Mazie`re,1 R. Drummond,1 A. Mahieux,1 E. Neefs,1 V. Wilquet,1 O. Korablev,2 A. Fedorova,2 D. Belyaev,2 F. Montmessin,3,4 and J.-L. Bertaux3,4 Received 14 March 2008; revised 20 June 2008; accepted 4 September 2008; published 27 December 2008.

[1] Solar Occultation at Infrared (SOIR), which is a part of the Spectroscopy for

Investigation of Characteristics of the Atmosphere of Venus (SPICAV) instrument on board Venus Express, combines an echelle-grating spectrometer with an acoustooptical tunable filter. It performs solar occultation measurements in the IR region at a high spectral resolution better than all previously flown planetary spectrometers. The wavelength range probed allows for a detailed chemical inventory of the Venus atmosphere above the cloud layer, with an emphasis on the vertical distribution of the gases. A general description of the retrieval technique is given and is illustrated by some results obtained for CO2 and for a series of minor constituents, such as H2O, HDO, CO, HCl, and HF. Detection limits for previously undetected species will also be discussed. Citation: Vandaele, A. C., et al. (2008), Composition of the Venus mesosphere measured by Solar Occultation at Infrared on board Venus Express, J. Geophys. Res., 113, E00B23, doi:10.1029/2008JE003140.

1. Introduction [2] Venus is a very warm and dry planet with a dense atmosphere composed mainly of carbon dioxide (CO2, 96.5%) and Nitrogen (N2, 3.5%). Chemically active species, such as sulfuric bearing gases (OCS and SO2) and halides (HCl and HF) have already been reported (see de Bergh et al. [2006] for a general review on the composition of the atmosphere of Venus below 100 km altitude). Measurements have been performed essentially in the mesosphere below 100 km and below the clouds. Information about minor atmospheric constituents, their concentration, reactions, sources and sinks is incomplete, as for example only scarce measurements have been performed above 100 km altitude. In particular, photochemical models of the middle atmosphere would benefit from abundance measurements of Cl-bearing gases. [3] The Solar Occultation at Infrared (SOIR) spectrometer is an extension mounted on top of the Spectroscopy for Investigation of Characteristics of the Atmosphere of Venus (SPICAV) instrument [Bertaux et al., 2007a]. SPICAV/ SOIR is one of the seven instruments on board Venus Express, a planetary mission of the European Space Agency (ESA) that was launched in November 2005 and inserted into orbit around Venus in April 2006 [Titov et al., 2006]. 1

Belgian Institute for Space Aeronomy, Brussels, Belgium. Space Research Institute, Moscow, Russia. 3 Service d’Ae´ronomie du CNRS, Verrie`res-le-Buisson, France. 4 Institut Pierre Simon Laplace, Universite´ de Versailles-Saint-Quentin, Saint Quentin en Yvelines, France. 2


[4] SOIR [Nevejans et al., 2006] is designed to measure at high resolution (0.15 cm1) the atmospheric transmission in the IR (2.2 – 4.3 mm) using solar occultations. This technique allows for the derivation of unique information about the vertical structure and composition of the Venus mesosphere. SOIR is the first high-resolution NIR spectrometer on board a spacecraft investigating the Venusian atmosphere and it enables a sensitive search for new minor species from the top of the clouds up to about 125 km of altitude.

2. Description of the Instrument [5] The instrument has already been extensively described elsewhere [Bertaux et al., 2007a; Mahieux et al., 2008; Nevejans et al., 2006] and will only be briefly described here. SOIR is an echelle-grating spectrometer operating in the IR, combined with an acoustooptic tunable filter (AOTF) for the selection of the diffraction-grating orders. The free spectral range (FSR) of the echelle spectrometer, i.e., the spectral interval in which there is no interference or superposition of light from adjacent orders equals 22.38 cm1, whereas the bandwidth of the AOTF was originally designed to be 20 cm1, as measured on ground before launch [Nevejans et al., 2006]. The real measured bandwidth of SOIR is 24 cm1 [Mahieux et al., 2008], creating some order leakage on the detector. The wave number domain that can be investigated by the SOIR instrument extends from 2256 to 4369 cm1, and is divided into 94 smaller ranges corresponding to the different orders (from 101 to 194). The detector width for orders 101 to 122 is smaller than the FSR of 22.38 cm1 and hence the detector will miss part of the spectrum. For orders 123 to

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Figure 1. Example of spectra obtained during one occultation (sunset 15 April 2007). Each transmittance is obtained by making the ratio of the solar spectrum seen through the Venus atmosphere to the unattenuated solar spectrum measured above the atmosphere. The selection of a spectral interval is achieved through the acoustooptic tunable (AOT) filter, tuned in this case to (a) 15809 kHz for diffraction order 121, (b) 19869 kHz for diffraction order 149, (c) 23031 kHz for diffraction order 171, and (d) 25742 kHz for diffraction order 190. In these particular ranges of wave number, the main absorption lines are from HDO (Figure 1a), CO2 (Figure 1b), H2O (Figure 1c), and CO (Figure 1d). 194 the inverse happens: the detector width is equal to or larger than the FSR and the detector will not be completely covered by the selected order. [6] The SOIR detector has 320 columns along the wave number axis and 256 rows along the spatial axis. The slit is projected on 32 fixed rows only. Since, owing to imposed telemetry limitations, only a data volume equivalent to 8 rows of 320 pixels can be retrieved per second, one is forced to bin the rows in eight groups of four rows, provided only one order (or AOTF frequency setting) is used during a given second. It is however possible to select up to four different orders (not necessarily sequential) per second, allowing us to gather a more versatile set of absorption lines. This reduces the maximum measurement time per order to 250 ms and implies that only 2 larger bins of 16 rows will be used if the complete slit height has to be covered. Later, the binning was changed to 2 bins of 12 rows because the outside rows of the illuminated part of the detector received a lower signal, because that part of the slit was too close to the edge of the Sun [Mahieux et al., 2008]. Background measurements are subtracted onboard from the measurements themselves. [7] Raw spectra, registered by SOIR and transmitted to Earth, need dedicated processing in order to upgrade them to a calibrated data set. This involves detector nonlinearity correction, spectral calibration and division by a reference solar spectrum. Ideally, the reference spectrum that is taken

outside the atmosphere would be measured with an identical relative slit position with respect to the solar disk. Attitude drift of the spacecraft, however, makes the slit float which results in a gradual linear change of the intensity. This effect is also corrected for Mahieux et al. [2008]. [8] A SOIR occultation observation can be taken either at sunset or sunrise. In the case of a sunset, the measurement cycle is started well before the instrument’s line of sight to the Sun intersects with the top layers of the atmosphere, and reference spectra are recorded (at a rate of 1 spectrum s – 1). Once the top of the atmosphere is reached, solar light is absorbed and the intensity of the recorded signal starts to decrease until the Sun gets so flattened by refraction that the spectrometer slit moves out of the refracted solar disk. One of the main advantages of solar occultations is that it is a self-calibrated technique in terms of transmission: dividing a spectrum obtained during the occultation by a reference solar spectrum recorded outside the atmosphere removes the solar signature and leaves a transmittance containing only information about the composition of the Venus atmosphere. The reference spectrum is in fact defined by selecting spectra recorded within the 40 s before the level at 220 km is reached. [9] Figure 1 gives an example of the evolution of the spectra through one occultation (sunset 15April 2007) in the orders 121, 149, 171, and 190 corresponding to the 2725– 2750, 3330 – 3357, 3820 – 3855, and 4245 – 4283 cm1

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Figure 2. Latitudinal distribution of measurements of (a) CO2, (b) CO, (c) HF, and (d) HCl. Dots represent measurements performed when Venus Express is separated from limb at 65 km tangent height by less than 5000 km and crosses when this distance is larger. In the latter case, the vertical resolution is coarser. Only measurements corresponding to the smallest distances have been considered in this study. Most of those correspond to north polar air masses. ranges, respectively. These transmittances show the characteristic behavior observed on all occultation series measured by SOIR. At the beginning of the series, the light path does not cross the atmosphere. No absorption signatures are present and transmittances are equal to unity. As the Sun

sets, the light path goes deeper and deeper into the atmosphere, and two absorption processes take place: the overall signal decreases owing to extinction by aerosols and absorption signatures appear. At the end of the observation, no light is captured anymore when the Sun disappears behind

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Figure 3. Typical examples of the vertical resolution for different types of orbits. The geometry of the orbit defines the field of view (instantaneous range of altitude encompassed by the instrument entrance slit taken at the beginning of the measurement, solid line) and the vertical resolution (dashed line). This corresponds to the portion of atmosphere sounded during one measurement, thus 250 ms. It highly depends on the distance from the planet and the velocity of Venus Express (VEX). Figures 3a and 3b show the vertical resolution and the field of view during orbits 223 (30 November 2006; distance to the limb at 65 km tangent height is 2000 km; 81°N) and 332 (19 March 2007; distance to the limb is 11198 km; 4°N) as a function of time. the cloud deck or moves out of SOIR’s field of view owing to diffraction. The structures seen in the spectra of Figure 1 are mainly attributed to HDO (Figure 1a), CO2 (Figure 1b), H2O (Figure 1c), and CO (Figure 1d). From Figure 1, it can also be seen that in general, the SOIR spectra contain information on the Venus atmosphere between 65 and 110 km for molecules such as HDO or HCl. For H2O and CO signatures are still observable up to 130 km altitude and CO2 features are seen up to 125 –130 km [Bertaux et al., 2007b; Wilquet et al., 2007]. [10] Most of the measurements of SOIR occur at high northern latitude because of the shape of the orbit with its pericenter located at about 250 km above the northern pole and its apocenter at about 65,000 km. When solar occultation occurs, a sunset or a sunrise can be observed. When the satellite is close to the planet the vertical resolution is less than 1 km. Measurements correspond to latitudes ranging from 60° to 90°N. When the satellite is located far from Venus, measurements have a poorer vertical resolution and occur at lower latitudes, typically from 70°S to 60°N. Figure 2 illustrates the latitudes and longitudes, corresponding to the tangent altitude of 65 km, of the measurements yielding information on CO2, CO, HCl, and HF. We have distinguished two types of geometries, when the distance between

Venus Express (VEX) to the limb (at 65 km tangent height) is less than 5000 km (dots), and when it is larger (crosses). Figure 2 will be discussed in more detail in section 4, where results for each species will be described individually. [11] We define the vertical resolution at the tangent point as the total altitude range scanned during one measurement. Because the slit is not always parallel to the limb, but rotating slightly, and because the measurement lasts for 250 ms, this vertical resolution may vary between 100 m and several kilometers in the worst cases. Another variable is the field of view, which is defined as the instantaneous height encompassed by the instrument slit at the beginning of the measurement. This parameter always gives a lower limit to the vertical resolution. The vertical resolution is mainly a function of the distance of the satellite to the planet. The distance of the spacecraft to the planet and the velocity of the spacecraft influence the vertical resolution in the following ways: (1) the further the spacecraft is from the limb, the larger the size of the instantaneous height measured in the atmosphere; (2) the velocity of the spacecraft projected at the limb on the atmospheric local vertical depends on the position of the spacecraft on its orbit around Venus at the moment of the occultation.

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Figure 4. Geometry of solar occultation measurements and definition of the onion- peeling method. [12] Figure 3 shows the variation of the field of view and vertical resolution as a function of time of orbit 223 (30 November 2006; distance to the limb at 65 km tangent height is 2000 km; 81°N) and 332 (19 March 2007; distance to the limb is 11,198 km; 4°N). During orbit 332, the spacecraft was far away from the planet which implies a large atmospheric height seen by the instrument slit. The field of view and vertical resolution are very similar because the Sun is rising almost vertically in the atmosphere and the velocity of the spacecraft is not too high (the spacecraft is almost at the equator). During orbit 223, spacecraft is close to the planet and the instantaneous height of the sounded atmosphere is much smaller. The velocity is higher, because the spacecraft is almost at pericenter (87°N). For the reasons explained just above, we will limit the retrieval to spectra obtained under favorable conditions, i.e., corresponding to low distance between VEX and the limb at 65 km tangent height (5000 km) distance to the limb. Typically the vertical profiles are obtained from 70 to 125 km altitude, as can be seen from the examples reproduced in Figure 10. Those profiles correspond to the orbits 341 (28 March 2007; distance to the limb is3346 km; 82°N), 356 (12 April 2007; distance to the limb is 3298 km; 84°N), and 366 (22 April 2007; distance to the limb is 3898 km;

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Figure 8. Investigation of the temperature effect on the detection of HCl. In the left, the corresponding density profiles are shown. For comparison, the line with dots corresponds to a profile of constant vmr of 0.5 ppm. The feature around 85 km does not disappear. In the right, the relative difference in the retrieved HCl densities is shown for the different test cases. Four different temperature profiles have been considered: VENUSREF, VENUSREF with 20% and +20% excursions, and a vertical temperature profile obtained by the Vera instrument on board Venus Express [Pa¨tzold et al., 2007]. The effect of displacing the AOTF function maximum by 1.0 cm1 is also indicated. 73°N). CO vmr have been obtained from the retrieved CO densities using the air densities calculated from temperature and pressure from the VENUSREF model at the considered level. All three profiles show values for the CO vmr between 10 and 50 ppm below 90 km, and then increasing up to 4 – 8 104 ppm at 125 km, consistent with the existence of a source of CO, the photodissociation of CO2, at high altitudes. These profiles were obtained at the northern polar region and seem to be consistent with the observations performed by VIRTIS-M [Irwin et al., 2008] which indicate high values of the CO abundance at the south pole for altitudes between 65 and 70 km. They found values ranging from 100 to 200 ppm, these values being mostly influenced by the position relative to the brightest features of the polar vortex dipole. [36] Our value at 75 km (30 to 50 ppmv) is slightly lower than that derived from ground-based microwave (55 ppmv) by Clancy et al. [2003]. However, there is a major discrepancy above 75 km: our measurements show a decreasing vmr with increasing altitude, while microwave measurements indicate a steady increase. We find a minimum of CO (at 10 ppmv around 85 km). At higher altitudes, we find a steady increase of the vmr with altitude up to the limit of our measurements (4,000 – 8000 ppmv at 125 km). This steady exponential increase, when extrapolated upward to 140 km, would give a mixing ratio of 1, while in situ measurements with the Pioneer Venus Bus Mass Spectrometer yielded 0.4

[von Zahn et al., 1980]. The increase of vmr from 90 km upward is not due to diffusive separation of CO from CO2, because the homopause as determined by N2 measurements is at an altitude of 136 km. Rather, the CO gradient is dominated by the production rate of CO from CO2 which increases with altitude. More puzzling is the strong minimum region (80 – 90 km) found by SOIR, which had escaped detection up to now. It should be recognized that the technique of solar occultation provides an unprecedented vertical resolution. It could be of dynamic origin or it could be the result of a strong chemical sink. 4.4. H2O and HDO [37] Water is scarcely present today and it is not yet known if Venus was already dry at its formation or evolved slowly to its present state. In order to refine theories and models describing the formation and evolution of Venus and its atmosphere, to characterize the escape of D atoms from the upper atmosphere and to provide a robust explanation to the problem of the origin of water on Venus, high-resolution vertically resolved measurements of H2O and HDO and their temporal variations are needed. As illustrated in Figure 1, SOIR is able to measure both isotopologues of water simultaneously during the same occultation. Three absorption lines owing to HDO are clearly seen in the spectra recorded in the 2720 – 2725 cm1 region (order 121), whereas most of the features in the 3825 – 3855 cm1 region

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Figure 9. Vertical profiles of the HF density (left) and volume mixing ratio in ppb (right) obtained during the occultations 357 (solid line, 13 April 2007; distance to the limb at 65 km tangent height is 3325 km; 83°N), 462 (dotted line, 27 July 2007; distance to the limb is 1864 km; 88°N), and 484 (dashed line, 18 August 2007; distance to the limb is 2670 km; 70°N). For comparison, the solid line with dots corresponds to a profile of constant vmr (5 ppb). Error bars correspond to the fitting error on the single fitted parameter, i.e., the density (vmr) in the corresponding layer, and does not take into account the errors on the densities (vmr) in the upper layers. The following values from the literature are also indicated: Connes et al. [1967] (circles) and Bjoraker et al. [1992] (inverted triangles). (order 171) originate from the main isotopologue of water H2O and CO2. Such simultaneous measurements have been performed on a regular basis and are described in more detail by Fedorova et al. [2008]. 4.5. Other Minor Constituents [38] Spectra were recorded in different spectral intervals where some key constituents for the chemistry of the Venus atmosphere have prominent absorption features. However, some of those gases are only present as traces in the atmosphere and only upper limits of detection could be derived. As an example, OCS absorbs in the 2900–2950 cm1 range, corresponding to the order 130, but spectra recorded with SOIR contain no signature of this species, as illustrated in Figure 11. The upper limit of detection for OCS has been estimated to be 1.6 ± 2 ppb between 70 and 90 km and 0.02 ± 0.01 ppm above 90 km. OCS has up to now never been observed above the cloud deck. Measurements performed at lower altitudes [Marcq et al., 2005; Marcq et al., 2006] indicate that the OCS shows latitudinal variations with abundances between 5 and 20 ppm at 30 km decreasing with altitude. At 36 km, OCS amounts to 0.55 ± 0.15 ppm. The same spectral interval could also be used to derive the upper limit of detection of H2CO leading to the values of 3 ± 2 ppb below 90 km and 0.015 ± 0.01 ppm above 90 km.

[39] Similarly, it is also possible to determine the upper limit of other trace gases. In the case of SO2, the procedure is somewhat more complex since the SO2 signature is overlapped by a band of CO2, as explained and discussed by Belayev et al. [2008], who found a positive detection of 0.3 to 3 ppmv in some occultation profiles. 4.6. Error Analysis [40] The most critical parameters influencing the retrieval are related to the definition of the AOTF function: its formulation, the position of its maximum, and its extension in wave numbers (number of orders taken into account in the forward model). To test the sensitivity of our retrieval toward these parameters, several test cases have been investigated: (1) the function has been approximated by a sinc2 function or by a more complex function resulting of the sum of 7 sinc2, with different positions of their maxima and widths; (2) the number of adjacent orders has been varied between 2 and 4; (3) the position of the function maximum has been shifted by 1.0 cm1, a slightly higher value than the estimated accuracy on the determination of this position (0.83 cm1) [Mahieux et al., 2008]. The reference test case corresponds to the complex AOTF function using 3 adjacent orders (simulation on 7 orders in total), which corresponds to the normal settings used for retrieval. The results of this sensitivity study are the

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Figure 10. Vertical profiles of the CO density (left) and volume mixing ratio in ppm (right) obtained during the three occultations 341 (solid line, 28 March 2007; distance to the limb at 65 km tangent height is 3346 km; 82°N), 356 (dotted line, 12 April 2007; distance to the limb is 3298 km; 84°N), and 366 (dashed line 22 April 2007; distance to the limb is 3898 km; 73°N). For comparison, the solid line with dots corresponds to the CO in the VENUSREF model. Error bars correspond to the fitting error on the single fitted parameter, i.e., the density (vmr) in the corresponding layer, and does not take into account the errors on the densities (vmr) in the upper layers. following: (1) choosing a sinc2 function with 2 or 3 adjacent orders give rise to an underestimation of the CO2 density of 20 to 50% depending on the altitude, the maximum value

being obtained at around 90 km; (2) using the complex AOTF function but on only 2 adjacent orders implies an underestimation of 5 to 40%; (3) including more adjacent

Figure 11. SOIR spectra recorded around 2915 cm1 (order 130) where OCS and H2CO signatures should be observed. The main absorption structures observed in this region are due to HCl. Absorption features due to OCS and H2CO are also shown. From such spectra, only upper limits of detection can be derived. 14 of 16

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orders leads to higher densities by 1 – 2%, but lengthens the time required for the forward modeling; (4) changing the position of the AOTF maximum implies a difference of 5 to 20%. From this discussion, it is clear that the most critical factor in the simulation or retrieval of SOIR spectra is the accurate definition of the AOTF function, in particular its width and the positions and intensities of its sidelobes. In the near future, significant efforts will be devoted to a better yet determination of these parameters from specifically dedicated in-flight calibration measurements.

5. Conclusions [41] The SOIR spectrometer, which is part of the SPICAV/ SOIR instrument on board Venus Express has proven its high potential for the detection of minor key species for the understanding of the chemical and dynamical processes occurring in the Venus mesosphere. Detection of CO, HCl, HF, H2O, and HDO has been confirmed at altitudes ranging from 65 to 105 km, even 125 km depending on the species. Measurements have shown that the instrument was also sensitive to temperature through its observations of CO2 absorption lines, although this will require future development of the retrieval algorithm. SOIR is also able to differentiate between three isotopologues of CO2, namely 12 16 16 C O O, 12C16O18O, and 12C16O17O. This will in fine provide the vertical distribution of the 17O/16O and 18O/16O isotopic ratios. Vertical profiles of HCl, HF, and CO in the mesosphere above the northern pole show low variability in time. The CO vertical profiles measured by SOIR indicate values of CO abundances from 10 to 50 ppm at 90 km with a pronounced minimum of 10 ppm at altitudes of the order of 85 km, followed by an increase toward the higher altitudes. HCl volume mixing ratio is about 0.1 – 0.2 ppm and that of HF decreases from 1 to 7 ppb showing some variability from one orbit to the other. Because of its high sensitivity and its wide spectral coverage, SOIR is also a good tool to determine upper limit of detection for a series of trace gases: for example, OCS upper limit of detection has been estimated to be 1.6 ± 2 ppb below 90 km and 0.02 ± 0.01 ppm above 90 km. [42] Acknowledgments. The research program was supported by the Belgian Federal Science Policy Office and the European Space Agency (ESA, PRODEX program, contracts C 90268, 90113, and 17645). Procurement of AOTF was funded by CNES. Russian team also acknowledges RFBR grant 06-02-72563.

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D. Belyaev, A. Fedorova, and O. Korablev, Space Research Institute, 84/32 Profsoyuznaya Street., 117997, Moscow, Russia. J.-L. Bertaux and F. Montmessin, Service d’Ae´ronomie du CNRS, BP3, 91371, Verrie`res-le-Buisson, France. R. Drummond, A. Mahieux, M. De Mazie`re, E. Neefs, A. C. Vandaele, and V. Wilquet, Belgian Institute for Space Aeronomy, 3 avenue Circulaire, B-1180 Brussels, Belgium. ([email protected])

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HDO and H2O vertical distributions and isotopic ratio in the Venus mesosphere by Solar Occultation at Infrared spectrometer on board Venus Express A. Fedorova,1 O. Korablev,1 A.-C. Vandaele,2 J.-L. Bertaux,3,4 D. Belyaev,1 A. Mahieux,2 E. Neefs,2 W. V. Wilquet,2 R. Drummond,2 F. Montmessin,3,4 and E. Villard3,4 Received 18 March 2008; revised 13 June 2008; accepted 26 August 2008; published 25 December 2008.

[1] Vertical distributions of the molecular density and mixing ratios of H2O and HDO

in the Venus mesosphere have been obtained using Solar Occultation at Infrared (SOIR), a high-resolution (with l/dl 20,000) echelle spectrometer on Venus Express. The atmosphere is sounded in solar occultation in the range of altitudes from 65 to 130 km. Simultaneous measurements of water vapor lines in the spectral range around 2.61 mm (3830 cm1) at altitudes between 70 and 110 km and HDO lines around 3.58 mm (2715cm1) at altitudes 70–95 km have been performed. During 1 1/2 years, from April 2006 to August 2007, 54 such measurements have been carried out at different locations of Venus from the north pole to middle south latitudes. Most of the observations at morning and evening terminator correspond to high northern latitudes. We report values of mixing ratio and isotopic ratio obtained for 22 of those measurements occurring in the northern polar area. The average value of the volume mixing ratio of H2O is 1.16 ± 0.24 ppm and that of HDO is 0.086 ± 0.020 ppm. A depletion in the mixing ratio for both H2O and HDO is observed at 85 km, which can be related to a depletion of CO2 density above (95 km) and a possible temperature inversion at these altitudes. The vertical variation of HDO and H2O mixing ratio is within a factor of 2–3 for the analyzed set of observations. The temporal variations have been investigated, and no noticeable variability of H2O is reported at high northern altitudes. The average ratio of HDO/H2O obtained in this work, 240 ± 25 times the terrestrial ratio, is higher (1.5 times) than the value of 157 ± 30 times terrestrial reported for the lower atmosphere. This could be explained by a lower photodissociation of HDO and/or a lower escape rate of D atoms versus H atoms. Citation: Fedorova, A., et al. (2008), HDO and H2O vertical distributions and isotopic ratio in the Venus mesosphere by Solar Occultation at Infrared spectrometer on board Venus Express, J. Geophys. Res., 113, E00B22, doi:10.1029/2008JE003146.

1. Introduction [2] Venus’ atmosphere is significantly drier than the atmosphere of the Earth: it contains from 1 to 100 ppm of water depending on altitude and location. As reviewed by de Bergh et al. [2006], the water vapor content measured below the cloud level is within 20– 30 ppm, with much larger controversy at the cloud tops. [3] Most of the H2O mesospheric observations were obtained from ground-based millimeter heterodyne spectroscopy [Encrenaz et al., 1991, 1995; Sandor and Clancy, 2005]. Using the HDO line at 225.9 GHz and assuming a 1

Space Research Institute, Moscow, Russia. Belgian Institute for Space Aeronomy, Brussels, Belgium. 3 Service d’Ae´ronomie du CNRS, Verrie`res-le-Buisson, France. 4 Also at Institut Pierre Simon Laplace, Universite´ de Versailles Saint Quentin en Yvelines, Guyancourt, France. 2


D/H enrichment of 120 with respect to the terrestrial value, Encrenaz et al. [1991] obtained a mixing ratio of 3.5 ± 2.0 ppm at 60– 95 km. Later, observing the 187.31 GHz transition of H2O, Encrenaz et al. [1995] obtained 1 ppm above the clouds and 7 ppm (+5.0, 4.0) from the HDO line at 225.9 GHz, also assuming D/H = 120 terrestrial value. From a series of microwave observations, Sandor and Clancy [2005] reported a strong global variability of H2O on a 1 – 2 month time scale: at 65– 100 km the mixing ratio ranged from 0 ± 0.06 to 3.5 ± 0.3 ppm. Gurwell et al. [2007] also reported an extreme variability of the Venus mesosphere dramatically demonstrated by submillimeter wave astronomy satellite (SWAS) observations from December 2002. Over the course of 5 days, a deep ground-state water absorption feature consistent with a water abundance of 4.5 ± 1.5 ppm suddenly has transformed into a significantly shallower absorption, implying a decrease in the water abundance by a factor of 50 in less than 48 h.

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FEDOROVA ET AL.: HDO AND H2O IN VENUS’ MESOSPHERE BY SOIR

[4] The isotopic ratio of HDO to H2O in standard mean ocean water (SMOW) equals 3.1153 104 and corresponds to ([D]/[H])smow = 1.5576 104 [Hagemann et al., 1970]. On Venus, first measurements of the D/H ratio were performed by the Pioneer Venus mass spectrometer LNMS in the low atmosphere (100 (±12.5) times terrestrial, corrected in 1997 to 157 (±30) times terrestrial) [Donahue et al., 1982, 1997] and the ion mass spectrometer on the Pioneer Venus Orbiter at 155– 160 km ([D+]/[H+] = (1.7 ± 0.6) 102) or [D]/[H] = (2.2 ± 0.6) 102) at the turbopause level of about 132 km) [Hartle and Taylor, 1983]. These values have been confirmed later by ground-based observations in the 2.3 mm nightside windows [de Bergh C. et al., 1991]. Both in situ and remote-sensing determinations agree with one other and point to a strong deuterium enrichment in the Venus’ atmosphere. [5] In the near IR range above the clouds only one measurement of H2O and HDO has been reported [Bjoraker et al., 1992]. High-resolution spectra were obtained between 2.59 and 2.65 mm (3860 – 3770 cm1). A volume mixing ratio of H2O of 2.09 ± 0.15 ppm at 72 km was reported and a D/H ratio of 157 ± 15 times the value in the terrestrial ocean. Unfortunately, these results have never been published in any refereed literature. [6] The enrichment of 150 of deuterium to hydrogen supports the idea of dramatic escape of water from Venus. Two scenarios are possible: either Venus has had at least 0.3% of the terrestrial ocean and lost its water during a catastrophic process, or Venus was dry from the beginning, and the present isotopic ratio is explained by cometary impacts, degassing, and escape processes. The unknown vertical distribution of H2O in the middle and upper atmosphere precludes accurate theoretical descriptions of the escape of D and H atoms. New measurements of HDO and H2O are necessary to understand the evolution of the Venusian climate. [7] SOIR is a part of the SPICAV/SOIR experiment on the Venus Express spacecraft, operating on the orbit around Venus from April 2006 [Titov et al., 2006]. It is a high-resolution IR spectrometer working in the range of 2.2– 4.3 mm [Bertaux et al., 2007a; Nevejans et al., 2006]. The experiment is dedicated to vertical sounding of the Venus’ mesosphere in the range of 60– 120 km by means of solar occultation. One of the main scientific goals of SOIR is a simultaneous measurement of H2O and HDO vertical profiles to retrieve the isotopic ratio in the mesosphere. The first results on water vapor measurements with SOIR have been reported by Bertaux et al. [2007b]. In this paper we present a new analysis of the observed H2O content, employing a better instrument calibration and using longer series of observations.

2. Measurements 2.1. Instrument Description [8] Echelle spectrometer SOIR with an acoustooptic filtration of light is the first instrument with a spectral resolution above 20,000 orbiting another planet. It was first proposed for Venus Express mission by Korablev and Bertaux [2002], and built at the Belgian Institute for Space Aeronomy in collaboration with a Belgian industry in a very short time.

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[9] SOIR is designed to operate during solar occultations, when the instrument entrance optics is pointed toward the Sun as the latter goes down or up, allowing measurement of the atmospheric transmission of different layers of the atmosphere. The spectral range of SOIR from 2.32 to 4.25 mm (2353– 4310 cm1) allows measuring a number of atmospheric constituents showing up as absorbers in the transmission spectrum. High dispersion is provided by a 4 grooves mm1, arctan(2) incidence angle, echelle grating operating in diffraction orders from 101 (corresponding to the wavelengths of 4.4 mm) to 194 (2.3 mm). Each diffraction order covers a spectral interval from 20 to 40 cm1. The diffraction orders are separated by an acoustooptic tunable filter (AOTF). In the AOTF the radiation is filtered owing to volume diffraction on the acoustooptic wave excited within a birefringent crystal by a piezoelectric transducer at high frequency (RF = 14– 26MHz). The diffracted light is then analyzed by the echelle spectrometer. The central wavelength of the AOTF band-pass function is determined for the RF applied from the frequency-wavelength calibration as described by Mahieux et al. [2008]. The full width at half maximum FWHM of the AOTF band pass is around 24 cm1, and the profile of the AOTF band-pass function is close to a (sinx/x)2 function with a number of significant side lobes. As a result, spectral features leaking from several adjacent diffraction orders contribute to the spectrum observed on the detector with different weights that will be discussed in section 3. [10] The SOIR detector (from SOFRADIR) has 320 columns oriented along the spectral dispersion (wavelengths) and 256 rows along the spectrometer’s slit (spatial dimension). To avoid saturation, short integration times are used (20 to 30 ms), depending on the wavelength at which the measurement is taken. The background signal (Dark Current + Thermal emission of optics) is measured and subtracted onboard. In order to improve the signal-to-noise ratio (SNR), a number of measurements can be accumulated as long as the total measuring time remains below 250 ms. [11] The slit height is 30 arc min, and it is projected onto 32 rows of the detector. The slit width is equivalent to two detector pixels in the spectral direction. The spectral resolution of the spectrometer is very high and equals 0.13 cm1 at 2500 cm1 (order 111) and 0.27 cm1 at 4300 cm1 (order 192). The corresponding resolving power [l/Dl = n/Dn] is 20,000 [Mahieux et al., 2008]. [12] Owing to telemetry limitations, only eight spectra, each of 320 pixels long can be downloaded per second. During most of observations on Venus orbit, these eight spectra are taken in four different diffraction orders (different tunings of the AOTF), each corresponding to two large bins of 16 or 12 rows on the detector. [13] The detailed description of the instrument can be found by Nevejans et al. [2006] and Bertaux et al. [2007a]. Calibrations and in-flight performances of the instrument, including data handling, on board background subtraction, calibrations of the AOTF and the echelle spectrometer are described in detail by Mahieux et al. [2008]. 2.2. Observations [14] For accurate measurements of the isotopic ratio, simultaneous observations of H2O and HDO absorption lines are required. As discussed above, only four 20– 30 cm1

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Figure 1. Synthetic spectra of H2O (green), HDO (blue), and CO2 (red) at the target altitude of 85 km in solar occultation geometry. The top corresponds to the spectral interval 3760– 3940 cm1 (2.6 mm) dedicated to H2O measurements; the bottom shows the spectral interval 2660 –2840 cm1 (3.6 mm) dedicated to HDO measurements. Order boundaries are shown as solid (beginning) and dashed (end) black lines. The numbers above each plot show the order number and are located at the center of the order. Orders 171 and 121 are used mainly for retrieval of H2O and HDO abundance, respectively. portions of the spectrum within the spectral range of SOIR (four diffraction orders) can be acquired during a single occultation. We therefore carefully chose the spectral range for H2O and HDO detection attributing at least one order for HDO and another one for H2O. The priority was given to spectral ranges with strong lines of the gases of interest, and minimal contamination from other gases. There are several strong transitions of H2O within the spectral range of SOIR. The most preferable for the H2O retrieval is the strongest 3600 – 3900 cm1 band, which includes lines of n 1 and n 3 fundamental transitions. Below 3750 cm1 a strong CO2 2.7 mm band prevents the accurate determination of H2O, being completely saturated below 85 km of tangent altitude in solar occultation geometry. [15] For HDO we can consider the two strongest transitions within SOIR spectral range: the fundamental bands n 3 (001– 000) near 3707 cm1 (2.69 mm) and n 1 (100– 000) near 2723 cm1 (3.67 mm). The first one is blended by the 2.56 mm band of the main H2O isotope, and the HDO transitions are weak. The best candidate is the strong fundamental HDO band n 1 (100 – 000), completely isolated from H2O transitions. Moreover, this spectral range is free from detectable CO2 lines. Considering the other molecules, HCl lines are also located in the range of 3.67 mm. But these lines are isolated and do not contaminate HDO lines. [16] Synthetic spectra of Venus atmosphere limb transmission expected at the tangent altitude of 90 km, showing separately H2O and HDO lines, and all isotopologues of CO2 are presented in Figure 1 for the selected ranges

discussed above. The diffraction orders of SOIR are indicated. The orders numbers from 170 to 172 and 174 have been used for H2O measurements. Most of the observations so far have been performed in the order 171. The strongest lines of H2O in this range correspond to the rotational structures of (001) – (000) transition. For the retrieval of the HDO density, the orders 121 and 125 have been chosen, most of the observations having been performed in the order 121. 2.3. Observation Coverage [17] We have considered the operations from April 2006 to August 2007. During this period 54 orbits have been dedicated specifically to simultaneous measurements of H2O and HDO in the Venus’ mesosphere. Among them, on 47 orbits an additional spectral range dedicated to CO2 was scanned simultaneously in order to measure independently the atmospheric density profile and to reduce spacecraft pointing uncertainties. We address an interested reader to the paper by Vandaele et al. [2008, Figure 1] for the illustration of transmission evolution during an occultation in these diffraction orders. Vandaele et al.’s Figure 1 gives an example of the evolution of one occultation (sunset 15 April 2007) in the order 121, 171, 149, and 190. It is a typical observation dedicated to H2O, HDO, and CO measurements. Order 149 has been chosen for CO2 retrieval in the range of 3330 – 3357 cm1 where strong P and Q branches of 21102– 00001 transition allow the measurement of the atmospheric density at the altitudes of 70– 120 km.

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Figure 2. Spatial coverage for the simultaneous measurements of H2O and HDO. The maps of longitude versus latitude and local time versus latitude are presented. Most of the observations are located at high northern latitudes, corresponding to low distances from spacecraft to the limb. It reflects the peculiarity of the Venus Express polar orbit with the pericenter near the north pole. [18] The list of the main CO2 bands used for retrieval of atmospheric density is presented by Vandaele et al. [2008, Table 1]. In case of H2O and HDO, for the retrieval of the CO2 density several orders (depending on the orbit) have been chosen: 111, 112 (isotope O16C12O18, spectral range 2470 – 2520 cm1), 123 (spectral range 2748 – 2773 cm1) and 148, and 149 (a transition 21102 – 00001 at 3285 – 3360 cm1). [19] The geographical distribution of observations for which H2O and HDO were measured simultaneously, and the dependence on local time are presented in Figure 2. [20] Venus Express spacecraft is on highly elongated orbit with a pericenter located at about 250 km near the north pole, and an apocenter at 65000 km above the southern hemisphere of Venus [Titov et al., 2006]. From such an orbit, most of the solar occultations are observed at high northern latitudes with smaller distance to limb (near the pericenter), another part corresponding to relatively large distances to the limb and low latitudes. The vertical resolution of the occultation depends directly on the distance to the limb. The attitude of the spacecraft is maintained so that the slit of SOIR would be nearly parallel to the limb at 65 km. Near the pericenter the vertical resolution is generally better than 1.5 km. But because of the rotation of the slit with respect to the limb during one occultation and as the distance to limb is changing with the spacecraft motion, the vertical resolution during one observation usually varies from several hundred meters to several kilometers. This effect is described in detail in the paper of Vandaele et al. [2008, section 3]. In the present study we have considered only the measurements taken near the pericenter with small distances to the limb in order to minimize uncertainties. About 35 H2O measurements have been performed near the pericenter with the distance to the limb below 6000 km. All of them are located close to the north pole. We have further constrained the analyzed set to identical spectral ranges. We concentrated on orbits where the orders numbers 171 and 121 have been used for H2O and HDO respectively. In all, 22 orbits have been analyzed. The list of reported observations including universal time, latitude, longitude, local time, and distance to the limb at the altitude of 80 km is presented in Table 1. Within the considered set of observations which includes both sunrises

and sunsets, latitudes vary from 60° to 86° N, and the local time is 5 –6 h and 17– 19 h.

3. Retrieval Process [21] In solar occultation the transmission spectrum is directly obtained from relative measurements, and the photometric calibration of the instrument is generally not required. The processing of raw SOIR data, including nonlinearity and other corrections up to the construction of transmission spectra, and spectral calibration is described by Mahieux et al. [2008]. The reference spectrum is obtained as a weighted average of solar spectra recorded outside of the atmosphere (at the altitudes from 160 to 200 km) [Vandaele et al., 2008]. A forward model of gas absorption and the retrieval of vertical profiles of atmospheric constituents based on ‘‘onion-peeling’’ method are described in detail by Vandaele et al. [2008]. In the present paper we discuss only the details related specifically to H2O and HDO spectroscopy. 3.1. Spectroscopic Data [22] As a basis, the HITRAN 2004 database [Rothman et al., 2005] with the update of 2006 for H2O has been used. Nevertheless, no significant modification in the spectral range of interest was found in the 2006 update. Among the spectroscopic data like line strengths and positions, temperature coefficients and pressure shifts, HITRAN database contains line widths for air and self broadening. The widths of spectral lines broadened by CO2 which is the main component of the Venus’ atmosphere (96.3%) are not well known. The first measurements of CO2 broadening for H2O lines have been done by Howard et al. [1956a, 1956b, 1956c, 1956d]. This work recommended a constant factor of 1.3 to translate from the air to CO2 broadening. This factor has been then widely used in the 1990s for investigation of the Venus’ atmosphere [Pollack et al., 1993]. Later accurate laboratory measurements of CO2 broadening in the near-infrared range were made by Gamache et al. [1995]. The authors compared their laboratory measurements of CO2-broadened half widths with some recent experimental work and theoretical calcu-

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Table 1. Venus Express Orbits During Which Simultaneous Observations of H2O, HDO, and CO2 Have Been Performed With Low Distances to the Limb, Near the Pericenter of an Orbita Orbit

Date

Obs

Longitude

Latitude

LT

Ls

DTL

O1

O2

O3

O4

244 247 251 255 262 345 347 349 358 434 435 438 440 442 443 445 447 456 462 471 486 487

21 Dec 2006 24 Dec 2006 28 Dec 2006 1 Jan 2007 8 Jan 2007 1 Apr 2007 3 Apr 2007 5 Apr 2007 14 Apr 2007 29 Jun 2007 30 Jun 2007 3 Jul 2007 5 Jul 2007 7 Jul 2007 8 Jul 2007 10 Jul 2007 12 Jul 2007 21 Jul 2007 27 Jul 2007 5 Aug 2007 20 Aug 2007 21 Aug 2007

IN IN IN IN IN E E E E IN IN IN IN IN IN IN IN IN IN IN IN IN

248.91 256.82 261.13 268.06 282.80 13.69 36.86 81.05 194.10 264.07 266.74 274.52 279.49 284.24 286.51 290.81 294.66 298.02 254.94 232.25 251.33 253.47

84.80 84.39 82.09 79.19 70.76 85.93 87.34 88.13 82.30 70.83 72.54 76.11 77.84 79.30 79.97 81.18 82.31 86.52 87.68 79.49 66.41 63.01

19.96 19.88 19.34 18.99 18.54 7.56 8.69 11.23 16.90 5.88 5.85 5.76 5.68 5.59 5.53 5.41 5.26 3.64 23.55 18.98 18.42 18.36

63.08 67.82 74.14 80.46 91.52 224.28 227.52 230.76 245.36 7.99 9.59 14.36 17.54 20.72 22.31 25.49 28.67 42.93 52.39 76.02 90.16 91.78

2160.52 1558.68 1627.02 1729.67 2111.65 3315.49 3293.40 3281.31 3434.61 2060.91 1980.57 1848.39 1805.21 1777.70 1768.19 1758.40 1751.90 1780.27 1833.11 2088.11 2766.10 3001.76

121 121 121 121 121 121 121 121 121 121 121 121 121 121 121 121 121 121 121 121 121 121

171 171 171 171 171 171 171 171 171 171 171 171 171 171 171 171 171 171 171 172 171 171

111 149 112 148 111 149 149 149 149 111 112 149 118 149 111 149 111 112 149 123 149 149

112 192 181 191 133 133 133 190 133 125 190 132 151 184 188 130 136 145 180 180 190 190

H2O-1 (ppm) 1.08 ± 1.38 ± 1.00 ± 0.89 ± 1.02 ± 0.84 ± 0.84 ± 0.80 ± 0.75 ± 1.28 ± 1.14 ± 1.54 ± 0.96 ± 1.47 ± 1.22 ± 1.66 ± 1.22 ± 1.24 ± 1.60 ± 0.94 ± 1.54 ± 1.11 ±

0.10 0.13 0.10 0.05 0.06 0.06 0.04 0.05 0.05 0.11 0.03 0.10 0.09 0.05 0.07 0.10 0.05 0.04 0.09 0.12 0.10 0.05

H2O-2 (ppm) 1.24 1.36 1.28 1.03 1.08 0.68 0.74 0.63 0.64 1.15 1.21 1.50 1.25 1.35 1.20 1.53 1.22 1.26 1.59 1.36 1.31 0.99

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.13 0.17 0.05 0.05 0.07 0.06 0.04 0.04 0.05 0.12 0.03 0.15 0.05 0.07 0.07 0.14 0.04 0.02 0.13 0.04 0.18 0.05

HDOb (ppm) 0.097 ± 0.010 0.098 ± 0.012 0.092 ± 0.004 0.081 ±0.003 0.086 ± 0.004 0.056 ± 0.007 0.070 ± 0.014 0.054 ± 0.006 0.057 ± 0.011 0.075 ± 0.011 0.084 ± 0.002 0.106 ± 0.010 0.084 ± 0.003 0.091 ± 0.005 0.087 ± 0.004 0.106 ± 0.010 0.088 ± 0.006 0.086 ± 0.003 0.106 ± 0.011 0.118±0.004 0.081 ± 0.006 0.079 ± 0.005

a Latitude, longitude, and local time are given for the altitude of 80 km. Obs can be sunrise as E or sunset as IN; LT is the local time on Venus in Venusian hours; Ls is the solar longitude; DTL is the distance to the limb in km; O1, O2, O3, and O4 are the number of orders observed during the orbit; H2O-1 is the H2O mixing ratio in ppm averaged for altitudes between 75 and 112 km; H2O-2 is the H2O mixing ratio in ppm averaged for altitudes between 75 and 95 km; and HDO is the HDO mixing ratio in ppm averaged for altitudes between 75 and 95 km.

lations, and investigated the resulting temperature dependence of the half widths. The scaling factor from air broadening varies from 1.3 to 2.0, depending on a particular transition. Recently, Brown et al. [2007] published the CO2-broadened water parameters (half width, line shift, and temperature dependence of the widths) for the transitions between 200 and 900 cm1. They obtained ratios of CO2broadened to N2-broadened widths which varied widely from 0.95 to 3.07 with an average ratio of 1.67. To account for the CO2 broadening of lines in the Venusian atmosphere, we have multiplied the air-broadened half widths from the HITRAN 2004 database by 1.7 [Gamache et al., 1995; Brown et al., 2007]. The HDO line strengths in the HITRAN are scaled to the isotopic ratio of HDO to H2O in standard mean ocean water (SMOW) (3.1115 104). 3.2. Forward Modeling of H2O and HDO Transmittance Spectra [23] Using the forward model [Vandaele et al., 2008], the synthetic spectra of water vapor and HDO are calculated for the geometry of the solar occultation. Figures 3 and 4 illustrate the synthetic models for HDO in the order 121 (2703 – 2727 cm1) and for H2O in the order 171 (3823 – 3853 cm1) at a tangential altitude of 90 km. CO2 absorption lines have also been considered in those simulations. Atmospheric temperature and pressure profiles were taken from the Venus International Reference Atmosphere (VIRA) [Keating et al., 1985] for the dayside. H2O and HDO are assumed uniformly mixed with the volume mixing ratio of 1 ppm and 100 ppb, respectively. Monochromatic spectra have been converted to SOIR spectral resolution. Figures 3 and 4 (top) show modeling of gaseous absorption in ‘‘clear’’ case assuming there is no order mixing on detector. [24] The contribution of adjacent diffraction orders complicates the spectra measured by SOIR with respect to the

synthetic model shown in Figures 3 and 4 (top). The mixing of diffraction orders occurs owing to a wider than specified AOTF band-pass function (FWHM of 25 cm1), and the side lobes of this function (see discussion in the section 3.1 of Vandaele et al. [2008]). Figure 5 clearly demonstrates the contribution of different orders (actually ±3 orders) around the main order 121. Moreover, as described by Mahieux et al. [2008], the shape of the AOTF band-pass function is different for the beams coming from different parts of the spectrometer’s slit. As a result the contribution of adjacent orders is different for bins 1 and 2 recorded simultaneously during an observation (see section 2.1), where bin1 and bin 2 correspond to the top and bottom parts of the slit. [25] We reconstruct SOIR spectra in the 121 and 171 diffraction orders for bins 1 and 2 taking into account the overlapping of three orders from each side in Figures 3 and 4 (middle and bottom). In all, the seven diffraction orders contribute into the shown transmission spectra (from 3 to +3). [26] With the mixing of orders the 121 diffraction order, which is originally free from CO2 lines, becomes contaminated by CO2 lines coming from adjacent orders 122– 123 (compare Figure 1). This emphasizes the need of high accurate AOTF function taken into account more orders. [27] The mixing of diffraction orders becomes even more important in the order 171 (2.6 mm) located close to a strong 2.7 mm CO2 band. In Figure 5 we compare the synthetic model assuming the contribution of three diffraction orders from each side (Figure 5a) with a model assuming contribution of six orders (Figure 5b) with SOIR spectrum from the orbit 462 recorded at the altitude of 107 km. At this altitude the signal is not decreased by aerosol extinction, and the signal-to-noise of SOIR for order 171 between 100 and 290 pixels is better than 1000. It allows detecting very weak absorption features with a relative depth below 0.1–

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Figure 3. Synthetic models of HDO and CO2 in the order 121 (2703 – 2727 cm1). Frequency preset on the acoustooptic tunable filter (AOTF) is 15822.62 kHz. Solar occultation geometry is considered with the tangential altitude of 90 km. The VIRA atmospheric model was used. Volume mixing ration of HDO was assumed to be equal to 100 ppb. Monochromatic spectra have been converted to SOIR resolution. The thick solid line corresponds to HDO, and the thin solid line corresponds to CO2. H2O lines are also taken into account (dashed thin line) but are weak in this range. Top shows a ‘‘clear’’ spectrum of gaseous absorption in the range of 2703 – 2727 cm1 without any order mixing. Middle shows a synthetic spectrum taking into account the mixing of 7 orders (from 3 to +3) for bin 1 (spectra obtained in the bottom part of the slit). Bottom shows a synthetic spectrum taking into account the mixing of 7 orders (from 3 to +3) for bin 2 (spectra obtained in the top part of the slit). The different calibration of frequency-wavelength functions for different bins (or rows on the matrix) introduces differences in the spectra which correspond to different rows of the detector. 0.2%. Figure 5 demonstrates that the structures in order 171 are not a noise but CO2 lines coming from the orders 165 to 167. These orders correspond to the third side lobe of the AOTF function which has an amplitude of only 1% of the central lobe. However, the strong absorption in the CO2 2.7 mm band (up to 50%) results in a quasi-chaotic structure with relative depths of 0.1 – 0.3% inside the order 171, contaminating the measured H2O spectrum. [28] Although the AOTF band pass function is not well constrained for such distant side lobes, taking into account six adjacent orders allows to reduce the residual error. We therefore take into consideration the contribution from ±6 orders in all further modeling of H2O and HDO spectra in orders 121 and 171.

3.3. Fitting [29] The local densities of considered molecules (CO2, HDO, and H2O) are retrieved using the traditional onionpeeling method [e.g., Rodgers, 2000]. The main idea of this method in application to SOIR data is described by Vandaele et al. [2008] (see section 3.3 and Figure 4). The local densities of molecules are retrieved from the top of the atmosphere downward. The retrieved quantities are used for simulating the spectra corresponding to the lower layers. To calculate the absorption cross sections of the selected molecules, the atmospheric temperature-pressure profile was taken from VIRA [Keating et al., 1985]. [30] The measured spectra are fitted to the model using minimization of c2 and the simplex algorithm [Press et al., 1992] with two variables: local density of molecules (H2O,

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Figure 4. Synthetic models of H2O and CO2 in the order 171 (3823– 3853 cm1). The frequency preset on the AOTF is 22946.44 kHz. Solar occultation geometry is considered with the tangential altitude of 90 km. VIRA atmospheric model was used. Volume mixing ratios of H2O were assumed to be equal to 1 ppm. Monochromatic spectra have been converted to the SOIR resolution. The thick solid line corresponds to H2O, and the thin solid line corresponds to CO2. HDO lines are also taken into account (dashed thin line) but are weak in this range. The presentation of different top, middle, and bottom is the same as for Figure 3.

Figure 5. Contamination of adjacent orders in the main order 171 used for H2O retrieval. (a) ±3 orders around the main order have been taken into account. (b) ±6 orders around the main order have been taken into account. Those data were obtained during orbit 462 (27.07.2007) at the location latitude 87°N, longitude 245°E, 23.55 h Venusian local time (LT), distance to the limb 1830 km. 7 of 16

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Figure 6. Examples of data fitting for three orders 121 (HDO), 149 (CO2), and 171 (H2O) at the altitudes 86– 89 km. Those data were obtained during orbit 462 (27.07.2007) at the location latitude 87°N, longitude 245°E, 23.55 h LT, distance to the limb 1800 km. The light boxes relate to the spectral range chosen for HDO and H2O retrieval.

HDO, or CO2) and aerosol extinction that determines the continuum level of the transmittance spectrum. The best density value for every measurement is retrieved automatically, minimizing c2 statistically weighted according to the uncertainties of the measured quantity: c2 ¼

p320 X

ðTdata ðiÞ Tmodel ðiÞÞ2 =sðiÞ2

p1

where Tdata(i) and Tmodel(i) are the measured and modeled spectrum of transmittance for pixel i. s(i) is the statistical error determined from the signal-to-noise of the measured spectra which vary depending on diffraction order and detector pixel. Examples of the best fit spectra for H2O, HDO, and CO2 are presented in Figure 6. The light gray boxes in the top and bottom indicate the spectral range chosen for the retrieval of H2O and HDO density. The preference has been given to the lines with higher intensity, lower sensitivity to temperature, which are less contaminated with CO2 lines. Some lines appear broader in the model than in the observed spectrum that could be explained by the uncertainty of the assumed temperature profile (as partially described by Vandaele et al. [2008]), and possibly by sampling on the detector’s pixels. For the retrieval we used several lines that minimize the discrepancy in the line widths, as described in more detail in section 3.4. The curvature of measured spectrum in Figure 6 (top) is due to undercorrection of detector’s nonlinearity apparent at the edges of order’s spectral range. Figure 7 demonstrates a typical result of the retrieval of the gaseous densities for one

selected orbit, showing the simultaneous vertical distributions of the local densities for HDO, H2O, and CO2. 3.4. Uncertainty of Results [31] There are several sources of uncertainties in our results: spectroscopic data set (in particular the broadening factor for H2O in the CO2 atmosphere), atmospheric data set and assumed temperature-pressure profiles, and instrumental calibration uncertainties. The calibration uncertainties include AOTF frequency-wavelength calibration that determines the position of the maximum of the AOTF function versus the preset frequency for bin 1 and bin 2. This has been obtained with a precision of 0.83 cm1 [Mahieux et al., 2008]. Uncertainties on the shape of the AOTF function, including the contribution of the side lobes are still poorly estimated. Detailed sensitivity analysis on this parameter will be performed in the near future. [32] The uncertainties due to a shift of the maximum of the AOTF function of 1 cm1 give an error of 8% on the retrieved values at altitude from 70 to 95 km for HDO and 7% for H2O. The uncertainties due to the shape of the AOTF function (the cutoff of the far wing in ±3 orders during the retrieval that corresponds to removal of the contribution of the far side lobes) is the most important for H2O retrieval and put the error to 4% at the altitude from 70 to 100 km, which increases up to 40% at the altitude of 110 km. For HDO uncertainties from order mixing are below than 4%. [33] The uncertainties coming from the assumed spectral instrument function of the echelle spectrometer also give

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Figure 7. Example of retrieved densities of HDO, H2O, and CO2 in molecules cm3 for orbit 255 corresponding to the northern latitude 79.2°, longitude 261°E, and 19 h LT and distance to the limb 1730 km at altitude 80 km. rise to some systematic bias in the result. The sensitivity to variation of the instrument spectral function half width was investigated for each molecule. A variation of the half width by 20% results on average 8.5% of error in gaseous density, slightly varying with altitude and diffraction order. For different orders the widths of this function varies from 0.16 cm1 for the order 121 to 0.22 cm1 for the order 171 that may also bias the relation between different species detected in distant orders. [34] The comparison of results obtained for bin 1 and bin 2 also can provide a good estimation of uncertainties. For HDO (order 121) the total uncertainty on the average amounts to 8 – 14% and varies a little with altitude and from orbit to orbit that could be explained by different calibrations for these spectra and rotation of slit with respect to the limb. [35] Uncertainties on spacecraft pointing and in fact the unknown real pressure and temperature at the location of measurements add systematic biases to the retrieval. To account for this, we should use CO2 density retrieved from CO2 lines during the same observation as presented in Figure 7. The absorption cross sections of the molecules are sensitive to temperature and pressure. In particular, the line strengths depend on the temperature and the power of dependence is determined by the energy of transition (see equation 6 by Vandaele et al. [2008]). As already mentioned, in the case of H2O and HDO retrieval, CO2 density is retrieved from several different orders: 111, 112, 123, 148, and 149. The line strengths inside these ranges are not very sensitive to temperature variations. The intensity of the H2O and HDO lines are also not very sensitive to temperature, no more than 50% increase for a variation of temperature of 100 K. The uncertainties on temperature can be estimated by comparing the retrievals obtained with two different temperature profiles (Figure 8). We have chosen an extreme case that is assuming a deep inversion of temperature at the altitudes between 85 and 95 km. Retrievals of CO2 (order

149, top) and H2O (order 171, bottom) obtained for orbit 462 are presented in Figure 8. Left shows the temperature profiles used for the retrievals, middle shows the retrieved densities, and right shows the relative difference in % calculated as N1 ð zÞ N2 ð zÞ N1 ð zÞ

where N1(z) and N2(z) are the local densities for the first and second temperature profiles. For H2O, variations due to temperature are not very high and do not exceed 10%. For CO2, depending on the order, the error can reach 20% at the altitudes of the inversion (85 – 100 km). [36] The importance of the retrieved density profiles for the determination of the volume mixing ratios of the minor species is shown in Figure 9. Mixing ratios of H2O from orbit 442 obtained by division by the model density from VIRA or by the CO2 density retrieved in this work are compared.

4. Results [37] We report here results for 22 orbits obtained from January 2006 to August 2007. All the orbits have been performed at the latitudes of 63°– 88°N on morning and evening terminators with the distance to limb below 3500 km. [38] To detect variability of HDO and H2O abundance with time, several sets of observations have been done. In the present analysis the following sets are considered: 5 orbits from 244 to 262 at December 2006 to January 2007, 4 orbits from 345 to 358 at April 2007, and 15 orbits from 434 to 487 at June– August 2007. Figure 10 demonstrates the density retrieved for 5 consecutive orbits from 244 to 262 and 3 orbits from 442 to 445 obtained close to the north pole. No strong variations of the H2O and HDO

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Figure 8. Sensitivity of the retrieved parameters to the temperature profile. The results are presented for retrieval of CO2 (order 149, top) and H2O (order 171, bottom). They were obtained during orbit 462 (27 July 2007 at latitude 87.7°N, longitude 255°E, and 23.6 h LT, distance to the limb at altitude 80 km is around 1830 km). Left shows the temperature profiles used for the retrievals; middle shows the retrieved densities; right shows the residual error in % calculated as (N1(z)-N2(z))/N1(z), where N1(z) and N2(z) are the densities obtained for two different temperature profiles.

Figure 9. Comparison of the volume mixing ratios (vmr) of H2O using VIRA densities and CO2 densities derived from measurements recorded on the same orbit. The example shows the results obtained for orbit 442 (7 July 2007, at latitude 79°N, longitude 284°E, and 20.7 h LT, distance to the limb at altitude 80 km is around 1780 km). 10 of 16

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Figure 10. Vertical distributions of the H2O and HDO local density for eight orbits: five consecutive orbits from MTP09 (from 19 December 2006 to 10 January 2007) and three consecutive orbits from MTP016 (from 1 July 2007 to 27 July 2007), where MTP is the medium term plan. The conditions of observations are listed in Table 1. profiles have been detected for the altitude range of 75– 110 km. The latitude of observations varies for these orbits from 70° to 88°N. The mixing ratios of H2O and HDO for the same orbits are presented in Figure 11. [39] A marked depletion of H2O is observed in the range 80– 90 km, for which we have no explanation yet, other than noting that this altitude range coincides with the mesospheric minimum temperature and the top of the haze layer. The bump of H2O and HDO at 90– 100 km relates to depletion of CO2 density retrieved from CO2 order. Figure 9 demonstrates the difference between mixing ratios obtained assuming CO2 density from observations and from VIRA models. This depletion could be related to a strong temperature inversion with minimum at altitude 85– 90 km and we keep this study for a separate paper dedicated to the retrieval temperature and pressure profiles from CO2 lines. The curves of both isotopes show little variability. [40] The mean mixing ratio of H2O has been calculated for each orbit by averaging at altitudes from 75 to 112 km. The values are varying from 0.8 to 1.5 ppm. The temporal variation from orbit to orbit and the latitude variation of mixing ratios are presented in Figure 12. There is no systematic behavior in the distribution detected and the variations could reflect uncertainties of the SOIR observations. For HDO, mixing ratios were averaged from 75 to 95 km, the values varying from 50 to 130 ppb. Table 1 summarizes the averaged values of H2O and HDO volume mixing ratios (vmr). To make the comparison easier, the averaging of H2O for the same altitude range as HDO is

also presented in Table 1. The error bar corresponds to the error of the average in this case. [41] The vertical distribution of D/H ratio can be easily obtained from H2O and HDO density profiles. The complete set of results is presented in Figure 13. There are some vertical variations of the isotopic ratio that have been discussed by Bertaux et al. [2007b]. The average isotopic ratios scaled to the standard mean ocean water (SMOW) ratio of 3.10693 104 and averaged at the range of altitudes from 75 to 90 km are shown in Figure 14 as a function of orbit number and latitude. The obtained values range between 200 and 300 times terrestrial with the average of 240 ± 25.

5. Discussion [42] There were no continuous sets of H2O observations in the Venusian mesosphere before Venus Express. Sandor and Clancy [2005] and Gurwell et al. [2007] have made systematic observations of the mesosphere from groundbased and orbital telescopes. The summary of previous measurements is presented in Table 2. Most of the groundbased observations correspond to measurements of the Venusian disc and the direct comparison with the local observations of SOIR is not possible. Ground-based observations cannot directly resolve the vertical distribution of the species and inversion techniques are required to derive the profile from a single observation. The advantage of SOIR is that direct measurements of vertical profiles from the orbit are made possible. Moreover, simultaneous obser-

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Figure 11. Vertical distributions of the H2O and HDO volume mixing ratios for eight orbits: four consecutive orbits from MTP09 and four consecutive orbits from MTP016. The conditions of observations are listed in Table 1. vations in spectral ranges corresponding to both H2O and HDO are sometimes difficult to realize from the Earth. Several observations of H2O [Encrenaz et al., 1991; Sandor and Clancy, 2005] are, in fact, measurements of HDO lines and rely on the HDO/H2O ratio obtained from other measurements. The volume mixing ratio of H2O (1 ppm)

measured by SOIR is well consistent or lower than obtained from the Earth. The HDO mixing ratio of 0.08 ppm from SOIR is also lower than values obtained by Encrenaz et al. [1991, 1995] and by Sandor and Clancy [2005], but all values are in agreement within the error bars.

Figure 12. Temporal and latitudinal evolution of the H2O volume mixing ratios averaged as described in the text for orbits listed in Table 1. The H2O mixing ratio has been obtained by division by CO2 densities from the same observations. 12 of 16

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Figure 13. Vertical distributions of the HDO/H2O ratio for several orbits. [43] High variability of H2O in the Venus mesosphere was recently detected by Sandor and Clancy [2005] and Gurwell et al. [2007]. This variability is not supported by SOIR observations at least near the north pole region. The

SOIR values show variations, but no more than a factor of 2 around the mean value (1 ppm), staying inside the SOIR uncertainties (except perhaps in the lowermost range 70– 75 km, where errors are greater). But the direct compar-

Figure 14. Temporal and latitudinal evolution of the HDO/H2O ratio scaled to Earth’s value. 13 of 16


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Table 2. Comparison of Averaged Values of the H2O and HDO Mixing Ratios With Previous Observations Altitude (km)

Spectral Range

H2O (ppm)

HDO (ppm)

Reference

1990

60 – 95

225.9GHz (HDO)

0.13

Encrenaz et al. [1991]

1990 – 1993

60 – 95

187.31GHz (H2O) 225.9GHz (HDO) 2.59 – 2.65 mm (HDO and H2O) 226 GHz (HDO) and 335 GHz 547.676GHz (H2O) and 556.936GHz 2.61 mm (H2O) 3.58 mm (HDO)

3.5 ± 2 (D/H = 120 terrestrial value) 1 ppm

0.26

Encrenaz et al. [1995]

2.09 ± 0.15

0.102

Bjoraker et al. [1992]

0 ± 0.06 3.5 ± 0.3

0 0.17

Sandor and Clancy [2005]

4.5 ± 1.5

-

Gurwell et al. [2007]

0.8 – 1.5

0.05 – 0.13

This research

Years

1992?

72

1998 – 2004

65 – 100

2002 – 2004

65 – 100

2006 – 2007

75 – 95

ison with the ground-based observations is not possible. SOIR measures only morning or evening profiles at terminator, whereas values of H2O from millimeter observation may reflect disc averaged variability. It is equally difficult to compare our results to the H2O afternoon bulge observed by Pioneer Venus orbiter at the altitudes of 62– 70 km in low and middle latitudes from 10°S to 60°N [Koukouli et al., 2005] Therefore, more SOIR occultations in wider latitude range are required to understand spatial variations of water vapor in the mesosphere. [44] Several measurements of the D/H ratio in the Venus’ mesosphere have already been performed before. The 2.3 mm window has been used to observe lines from both H2O and the deuterated water HDO and to measure the D/H ratio below the clouds from ground-based observations. de Bergh et al. [1991] derived a value of 120 ± 40 times the terrestrial SMOW value from high-resolution CFHT spectra on the nightside. Donahue et al. [1982, 1997] has reported the first determinations of the D/H ratio performed in situ by the neutral mass spectrometer of Pioneer Venus, the analysis of their data yields a value of D/H = 100 ±12.5 corrected later to 157 ± 30 times the ratio in Earth’s oceans. Bjoraker et al. [1992] have obtained the value of 157 ± 15 times terrestrial using near-IR dayside ground-based observations. The isotopic ratio retrieved from SOIR measurements (240 ± 25) times terrestrial is higher (factor 1.5) than the results of all previous measurements in the lower atmosphere 157 ± 30 [Donahue et al., 1997] and the measurements by Bjoraker et al. [1992] at the effective altitude of 72 km but they support the higher abundance of HDO to H2O compared to Earth. [45] One motivation to HDO and H2O measurements in the upper atmosphere of Venus was to determine if HDO was present up to the altitude of photodissociation, providing D atoms for further escape. Clearly this D escape needs to be quantified, if we want to extrapolate back in time what was the original content of water on Venus. In the Earth’s stratosphere, there is a cold trap at the tropopause, and preferential condensation of HDO versus H2O is an important factor of fractionation, which is circumvented by deuterated methane (CH3D) passing through the tropopause without condensing [Moyer et al., 1996]. On Mars, the observed depletion of atomic D in the upper atmosphere [Krasnopolsky et al., 1998] was explained by the lower HDO photolysis rate [Cheng et al., 1999],

preferential condensation of HDO [Fouchet and Lellouch, 2000; Bertaux and Montmessin, 2001], and the smaller observed abundance of H2 [Krasnopolsky and Feldman, 2001] than expected from models. Thermal and nonthermal escapes of D are weaker than those of H [Krasnopolsky, 2002] and tend to increase D/H. [46] Our present results show that on Venus, there is no condensation of H2O nor HDO, and no cold trap preventing HDO to be photodissociated in the region above 80 km. On the contrary, one has to explain why the ratio HDO/H2O is found 1.5 times higher than lower in the atmosphere, with a possible trend of increasing with altitude (Figure 13). Bertaux et al. [2007b] proposed two explanations: the higher photodissociation rate of H2O [Cheng et al., 1999] will preserve more HDO; also, if D atoms are not at all escaping at the top of the atmosphere, they may eventually recombine with OH radicals, generating a downward HDO flow, and further decrease the importance of HDO photodissociation. In any case, detailed modeling of these mechanisms is necessary, but it is beyond the scope of the present paper.

6. Conclusions [47] We report vertical distributions of the molecular density and mixing ratios of H2O and HDO in the Venus mesosphere by SOIR, a high-resolution (with R20,000) echelle spectrometer on board Venus Express. The spectrometer operates in solar occultation mode and sounds the atmosphere at the range of altitudes from 65 to 130 km. Simultaneous measurements of water vapor lines in the 2.61 mm range (3830 cm1) at altitudes of 70– 110 km and HDO lines in the 3.58 mm range (2715cm1) at altitudes of 70– 95 km have been performed. For a year and a half, from April 2006 to August 2007, 54 such measurements have been carried out at different locations on Venus from the north pole to middle south latitudes mainly in the high northern latitudes at the morning and evening terminator. After in-flight recalibration of the spectrometer during summer 2007, a new analysis of the observed H2O has been undertaken. We analyzed the mixing ratio and isotopic values obtained from 22 orbits corresponding to high northern latitudes. The temporal variations have been investigated. The averaged volume mixing ratios of H2O = 1.16 ± 0.24 ppm and HDO = 0.086 ± 0.020 ppm have been obtained for this set of orbits. The depletion in the mixing ratios of H2O and HDO near 85 km is related to the

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depletion in the CO2 density near 95 km and a possible temperature inversion at these altitudes. No large variability of H2O has been detected for the high northern latitudes observations. The time variation of the mixing ratios of HDO and H2O do not exceed 2 –3 times the mean value. The obtained HDO/H2O ratio equals 240 ± 25 times the ratio in the Earth’ ocean on the average, which is higher than previously reported values by a factor 1.5. [48] Future analysis of SOIR data corresponding to middle and lower latitudes of Venus and to a wider set of observations will allow understanding the possible long-term variations. We also count on more accurate instrumental calibration and improved simultaneously retrieval of temperature from SOIR data. [49] The resulting vertical profiles of water vapor and its isotopologue could give an exciting challenge to the development of new photochemical models of the upper atmosphere of Venus. Such investigations have already started with models such the one developed by M.-C. Liang and Y. L. Yung (Modeling the distribution of H2O and HDO in the upper atmosphere of Venus, submitted to Journal of Geophysical Research, 2008). Moreover, combination of VIRTIS and SPICAV nadir measurements of H2O below and above the clouds could reconstruct a global distribution of water vapor in the Venus atmosphere from the lower atmosphere to the upper mesosphere [Drossart et al., 2007; Bertaux et al., 2007a; Marcq et al., 2008]. [50] These profiles give also hints to estimation of past and present escape from the planet. If there were no escape of D atoms, now and in the past, the present 1 cm (equivalent liquid) and D/H ratio 0.025 (enrichment 150) would imply in the past only 1.5 m, compared to 2.8 km on Earth. With the present observation of plenty of HDO in the photodissociation region, D atoms are certainly present in the thermosphere. It would be important to quantify the escape of D atoms, possibly by measuring mass 2 ions escape as could do ASPERA on board Venus Express [Barabash et al., 2007]. In spite of the possible confusion with H+2 ions, it would provide a useful upper limit to D+ ion escape [McElroy et al., 1982; Hartle and Taylor, 1983]. [51] Acknowledgments. We would like to thank our reviewers for helpful comments that improved the manuscript. We thank our collaborators at the three institutes for the design and fabrication of the SOIR instrument, which was mainly built in Belgium by OIP company, under the direction of IASB-BIRA. Russian team acknowledges RFBR grant 06-0272563. Belgium team was supported by the Belgian Federal Science Policy Office and the European Space Agency (ESA, PRODEX program, contracts C 90268, 90113, and 17645). Procurement of AOTF was funded by CNES and the French authors are sponsored by CNRS and CNES. We thank A. Bensoussan (COB) for procuring in due time the SOFRADIR detector.

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D. Belyaev, A. Fedorova, and O. Korablev, Space Research Institute, 117997, 84/32 Profsoyuznaya Street, Moscow, Russia. ([email protected]. rssi.ru) J.-L. Bertaux, F. Montmessin, and E. Villard, Service d’Ae´ronomie du CNRS, BP 3, F-91371, Verrie`res-le-Buisson, France. R. Drummond, A. Mahieux, E. Neefs, A.-C. Vandaele, and W. V. Wilquet, Belgian Institute for Space Aeronomy, 3 avenue Circulaire, B-1180 Brussels, Belgium.

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First observations of SO2 above Venus’ clouds by means of Solar Occultation in the Infrared D. Belyaev,1 O. Korablev,1 A. Fedorova,1 J.-L. Bertaux,2,3 A.-C. Vandaele,4 F. Montmessin,2,3 A. Mahieux,4 V. Wilquet,4 and R. Drummond4 Received 18 March 2008; revised 29 July 2008; accepted 2 September 2008; published 31 December 2008.

[1] Solar Occultation in the Infrared (SOIR) is a part of the Spectroscopy for Investigation

of Characteristics of the Atmosphere of Venus (SPICAV)/SOIR occultation experiment on board Venus Express dedicated to the study of gaseous and aerosol vertical structure of Venus’ mesosphere. SOIR is an echelle spectrometer with acoustooptic selection of diffraction orders operating in the wavelengths range of 2.2–4.3 mm at high spectral resolution (l/Dl 20,000). Detection of minor constituents such as CO, H2O, HDO, HCl, HF, and SO2, at altitudes between 65 and 130 km has been demonstrated. We report results from a series of six occultations with observations of the 4-mm SO2 band at latitudes 69°–88°N and 23°–30°N. It is the first time when the vertical distribution of SO2 is retrieved above the clouds with the help of solar occultation direct method. The sulfur dioxide transmission spectrum is measured on a background of strong CO2 absorption. Each retrieved vertical profile of SO2 is characterized by few points; the mixing ratio of SO2 being 0.1 ppm with scale height 1 ± 0.4 km for polar measurements (evening observations) and 1 ppm with scale height 3 ± 1 km at low latitudes (morning observations) at the altitude of about 70 km. Upper limits of 5000 km with vertical resolution of >3 km). During an occultation two spectra are recorded simultaneously by two bins of the detector corresponding to

Table 1. Tabulation of Analyzed Occultations With Main Parameters of Observationa Orbit (in/eg) 235 244 251 262 434 435

(in) (in) (in) (in) (eg) (eg)

Date

Local Time

Lat

Lon

Dist, km

12 Dec 2006 21 Dec 2006 28 Dec 2006 8 Jan 2007 29 Jun 2007 30 Jun 2007

0149 1953 1916 1830 0558 0558

88° 85° 82° 69° 30° 23°

309° 246° 260° 282° 265° 267°

2130 2242 1741 2274 5502 6179

Spectral Range, cm1 (Order) 2483 – 2500 2483 – 2500 2505 – 2523 2483 – 2500 2483 – 2500 2505 – 2523

(111) (111) (112) (111) (111) (112)

Altitude Range, km

Altitude of t = 1, km

fSO2, ppm

HSO2, km

68 68 68 – 71 69 – 72 70 – 76 72 – 78

76 75 76 75 83 83

0.5 0.7 0.3 0.7 1 3

— — 1.1 0.8 2.8 3.3

a Orbit (in/eg), number of orbit (ingress/egress); Date, date of observation; Local time, local time on Venus; Lat, latitude; Lon, longitude; Dist, distance to limb; Spectral range (order), spectral range (cm1) with corresponded echelle order; Altitude range, altitude range of SO2 detection (km); Altitude at t = 1, altitude at which optical thickness is one; fSO2, mixing ratio of SO2 (ppm) at level of 69 km; HSO2, scale height of SO2 at 70 km.

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Figure 3. An example of transmission spectrum measured during an occultation on the orbit 244 at the altitude of 68 km is presented for the echelle order 111 (blue curve) with level of noise (red curve). ord (n, z) Tt (n, z) (see step 5 of the Simulated spectrum of combined transmission Tmod (n, z) = TCO2SO2 fitting algorithm) corresponds to the best fit of 0.7 ppm SO2 mixing ratio (black curve). Also, a fit in CO2 (n, z)Tt (n, z) is which SO2 absorption is forced to be zero and only the CO2 absorption remains Tmod shown by the green curve for the reference.

both parts of the spectrometer slit (see above). For the six occultations selected in present study the spectra measured in the two bins coincide within 10%. It means that for these occultations the slit is kept nearly parallel to the limb, and we have averaged spectra from the two bins. For the remaining observations the slit cannot be considered parallel to the limb; more sophisticated geometry calculations and appropriate data processing for this case will be addressed to a future paper about SOIR performance. 4.2. Data Analysis [19] Figure 3 displays a transmission spectrum measured at the altitude of 68 km during the occultation at orbit 244 (blue line). The spectrum is compared with a synthetic CO2 (n, z)Tt (n, z)) and of combined spectra of CO2 (Tmod CO2*SO2 (Tmod(n, z)) with retrieved optimal values of nCO2 and fSO2 as described above. The observed transmission is of the order of 0.3% only: the solar flux is highly attenuated, mainly by aerosols, with a horizontal optical thickness t a 5.8. The two synthetic spectra (for CO2 and CO2*SO2), which are built to mimic the observations, contain an aerosol attenuation, and the contribution of the adjacent orders, not completely eliminated by the AOTF function. This contribution amounts to 50% of the transmission in the main order observed. [20] The signature of SO2 in the measured spectrum may be extracted by dividing the observed transmission by the CO2 simulation using: SO2 Tdata ðn; zÞ ¼

Tdata ðn; zÞ CO2 ðn; zÞTt ðn; zÞ Tmod

ð5Þ

and comparing the result with what is obtained using the simulated transmission: SO2 Tmod ðn; zÞ ¼

Tmod ðn; zÞ CO2 ðn; zÞT ðn; zÞ Tmod t

ð6Þ

This is illustrated in Figure 4. The resulting model transmission of SO2 is slightly different from the one displayed on Figure 1, because here the contribution of adjacent orders leaking through the AOTF orders is taken into account. [21] An alternate estimation of SO2 contents was performed by comparing the measured SO2 transmission spectrum (equation 5) with the simulated one (equation 6) using SO2 a ‘‘scatter plot’’ where each point has for x coordinate Tmod SO2 (n, z), and for y coordinate the Tdata (n, z) (Figure 5a and equation (4)). The same exercise was performed with differential transmission dT, where dT = T – average(T(n)) (Figure 5b), where the average value is a sliding average over 1 cm1 along the whole 2484 – 2500 cm1 range. When the slope on the plot is one at some variable parameter fSO2, it means that the SO2 mixing ratio during the measurement is equal (or very near) to this fSO2. The slope is found using least squares method. In the example shown, the slope is k = 0.94 ± 0.20 for T, and k = 0.90 ± 0.16 for d(T). 4.3. Results [22] Vertical profiles of the sulfur dioxide mixing ratio are presented in Figure 6, together with aerosol slant optical thickness retrieved from the same measurement sequences as described above. Sometimes the fitting process does not allow finding a distinct minimum; in such cases only upper

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SO2 Figure 4. SO2 spectrum Tdata (n, z) (thin curve) retrieved from the same measured spectrum as in SO2 (n, z) (thick curve) following Figure 3 following equation (5) is compared with SO2 synthetic model Tmod equation (6). Arrows mark the most prominent absorption features of sulfur dioxide. Only the gaseous transmission without contribution of aerosol extinction is presented.

limits of the SO2 abundance can be found (indicated by triangles). All the remaining points signify a positive detection with error bars. For all four high-latitude occultations available (69° – 88°N) about 0.1 ppm of SO2 is detected at the level of 70 km, rapidly decreasing with a scale height of HSO2 = 1 ± 0.4 km above. At low latitudes (23° –30°N) we detect significantly higher contents of about 1– 3 ppm and HSO2 = 3 ± 1 km. We used only the detected SO2 values to estimate the scale height; upper limits were not taken into account. [23] The clouds top level seems to be a bit higher at low latitudes where mixing ratio of SO2 is found larger. The

altitude where t a = 1, a possible definition of clouds top level, is found to be of the order 75– 76 km for latitudes >69° and 83 km for latitudes 23° and 30°. Usually the clouds top is known to be at a lower altitude for high latitudes [Crisp, 1986]. It must be recognized that the clouds top is usually defined with a vertical depth of unity, at a much lower level than where the horizontal optical thickness is t a = 1. In fact, at this level we probe the upper haze, which stands above the main clouds. [24] In transmission we are sensitive to the total slant density of SO2 molecules, and not to the mixing ratio. This is why we would not be able to see SO2 at higher

Figure 5. ‘‘Scattered plots’’ illustrating the retrieval of SO2 transmission are presented for the same SO2 plotted versus the modeled one measurements as in Figures 3 and 4. (a) The measured transmission Tdata SO2 Tmod , with a linear regression of this dependence (solid line). (b) The transmission dT, where dT = T – average(T) is plotted in the same manner. When the slope of the linear approximation k is close to unity, the measured and theoretical SO2 contents are equal to each other (or very near). 6 of 10


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Figure 6. Vertical profiles of SO2 mixing ratio retrieved from all analyzed occultations (points with error bars) together with aerosol profiles (solid curves). Triangles denote the point where only the upper limits of the SO2 detection. The conditions of observations are indicted on the plots: d, distance to limb; Lat, latitude; Lon, longitude; LT, local time (see Table 1). altitudes, even if the SO2 mixing ratio would be >1 ppmv above 75 km.

5. Uncertainties [25] There are several aspects that impact on the accuracy of retrieved results. Some of them deal with specific features of the spectrometer and some others with uncertainties of the observation geometry. [26] The signal/noise ratio (SNR) of the instrument is about 103 for solar spectrum outside the atmosphere in the center of diffraction orders 111 – 112. On the edges of the detector SNR decreases by a factor of 4 – 5 due to combined effect of AOTF passband and echelle blaze functions. The extinction by aerosol and CO2 at the altitude of 68– 70 km (where SO2 is detected) decreases the signal by a factor of 5 103. The resulting SNR effective for the altitude range of SO2 detection varies from 2 to 3 in the center of the detector to 10– 15 at the edges. These uncertainties for each pixel are taken into account as si in equation (4), and they are translated into 40% of random error of retrieved SO2 abundance. [27] Systematic uncertainties considered are AOTF calibrations (different for the two parts of the slit), and altitude determination. The different AOTF calibrations [Mahieux et al., 2008] are minimized by averaging spectra from the two bins and can be estimated as 10% of relative error in the final gaseous content. The altitude uncertainties depend on the distance of the spacecraft to the limb (±0.5 km at

2000 km and ±2 km at 6000 km); also we have neglected the atmospheric refraction (see above). However, for the retrieval of the mixing ratio the altitude uncertainty results in extraction of CO2 local density with errors 5%. Summarizing all mentioned uncertainties we obtain errors of sulfur dioxide mixing ratio of about 50 –70%.

6. Discussions [28] The measurements of sulfur dioxide above clouds were performed during almost 40 years (Figure 7 and Table 2) with some gaps. Summarizing the history of SO2 observations on Venus clouds top, an immediate conclusion is that the SOIR measurements indicate higher mixing ratios by a factor 5 to 25 with respect to all previous measurements. The solar occultation measurements are made at the terminator of the planet; that is, around one half of the optical path is in the shade, and another is sunlit. Most of previous nadir and limb observations, with the exception Venera 15 IR measurements were performed in the UV range on the dayside of the planet. The increased contents of SO2 measured on the terminator may be explained by its photochemical behavior. Indeed, the photochemical lifetime of SO2 exposed to sun light at 68– 70 km altitude is 3 103 s (i.e., 1 h) [Mills et al., 2007], and its contents should depend on local time. However, other photochemical cycles in which the SO2 is involved are more complicated: with UV solar light SO2 is rapidly produced again from SO, and the timescale for net loss of SO2 via oxidation to SO3

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Figure 7. Measurements of SO2 on Venus above clouds, available from 1969 up to now (see Table 2). The SO2 content in ppb volume is considered at the level of 40 mbar (69 km of altitude). SOIR results indicate values for 68 and 70 km (only at high latitudes) and SPICAV UV results were derived from 100 to 110 km. is much longer, 106 – 107 s [Winick and Stewart, 1980; Krasnopolsky and Pollack, 1994; Pernice et al., 2004]. These estimations for the lifetime depends on the column abundance of SO2 and the production rate for SO3, both of which are not well constrained. [29] Comparing SOIR results on morning and evening terminators (see Table 1 and Figure 6), for morning occultations (at low latitudes) we measured 1 ppm of SO2 mixing ratio while in the evening (high latitudes) 0.1 ppm is detected at 70 km of altitude. Unfortunately, at present we do not have considerable statistics of observations to compare morning and evening occultations at similar latitudes. An extensive set of daytime and nighttime SO2 measurements was obtained by Fourier spectrometer (FS) on Venera 15. A strong latitude dependence of SO2 was retrieved from 20 ppb at the equator up to 0.5 ppm in polar regions [Zasova et al., 1993], but diurnal cycle dependencies were not analyzed. [30] Regarding the altitude distribution of SO2, on the average SOIR measurements indicate a scale height of 1 ±

0.4 km for polar measurements and 3 ± 1 km at low latitudes at the altitude about 70 km. The latter is comparable with the scale height retrieved by Venera 15 FS at 69 km [Zasova et al., 1993]. For polar regions the scatter of Venera 15 scale heights is large, data being clustered around 1 km and 4 – 6 km. The estimations of scale height from PV ultraviolet measurements [Esposito et al., 1984] ranges from 1.5 to 4 –5 km; the IUE measurements [Na et al., 1990] are consistent with a scale height of 3 ± 1 km. Our data are not in conflict with these previous measurements. The peculiarities of the solar occultation technique are operations at the planet terminator, hence, at local morning or evening, and a significant horizontal optical path, reaching 450 km at 70 km. Therefore, SOIR measurements might reflect evening/morning, and also horizontal inhomogeneities, the latter may be associated with small-scale haze structures. VIRTIS has observed such structures at a scale down to few kilometers [Piccioni, 2008]. [31] Several hypothesis have been put forward in order to explain the amazing evolution of sulfur dioxide content

Table 2. List of Previous Detections of Sulfur Dioxide Above Venus’ Clouds by Several Methods and in Several Spectral Rangesa Reference

Instrument

Spectral Range (nm)

SO2 Content (ppb)

Anderson et al. [1969] Owen and Sagan [1972] Esposito et al. [1988] Na et al. [1990] Parisot et al. [1986] Moroz et al. [1985]; Zasova et al. [1993] McClintock et al. [1994] Na and Esposito [1995] Bertaux et al. [2008] This paper

Rocket OAO UV spectrometer (PV) UV spectrometer (IUE) Balloon Fourier Spectrometer (Venera 15) Rocket HST SPICAV UV (VEX) SOIR (VEX)

200 – 300 200 – 360 200 – 320 200 – 220 200 – 320 7350, 8700, 19270

70°) and low

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PICCIALLI ET AL.: CYCLOSTROPHIC WINDS IN VENUS MESOSPHERE

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Figure 1. Contours of temperature field averaged over the range 2000 – 2200 LT for the VIRTIS-M orbit VI0072. Contour interval is 5 K. In gray the cloud layer is represented schematically. ( Ch0 = 30 m/s, by using equation (2) we deduce an upper bound on the meridional phase velocity U < 58 m/s. This analysis suggests that the meridional wind is opposite to the wave propagation direction with an amplitude greater than about 60 m/s. This conclusion is in favor of a subsolar to antisolar thermospheric meridional circulation consistent in direction and amplitude with previous results [Zhang et al., 1996; Bougher et al., 1997]. [33] In addition to the gravity wave dispersion analysis, the previous conclusion is also supported by the geographical distribution of gravity wavefronts. Sun et al. [2007]

demonstrated recently that gravity waves propagating against the wind direction are amplified relative to other propagation directions. Assuming a wave source close to the south pole and a strong subsolar to antisolar wind, this prediction is consistent with our observation of gravity wave features above the noise level only in the 0900– 1500 LT range. The wave feature distribution therefore also supports the presence of a strong subsolar to antisolar wind at the south pole.

5. Conclusion [34] The nadir images of non-LTE CO2 emissions present wave-like perturbations of about 0.5% RMS amplitude relative to background signal. These features are mainly observed in the 0900 – 1500 LT range, and their horizontal wavelengths are in the 90 – 400 km range. The dynamics of these wave features are similar for all the images processed along different orbits. Average zonal and meridional components of the horizontal phase velocities are respectively 70 m/s westward and 30 m/s northward. The horizontal phase velocities and horizontal wavelengths are compatible with gravity waves propagating in the 110 – 140 km altitude range which is sampled by non-LTE CO2 emissions at the 4.3 mm wavelength. The considerable uncertainty on the altitude of these perturbations restricts the acquisition of information on the vertical structure of the waves. However, the geographical distribution and the orientation of wavefronts clearly identify the polar vortex at work in the cloud layer as the source of gravity waves. Finally, the characteristics of gravity wave propagation can be used to infer the background atmospheric winds. The strong zonal component of gravity wave phase velocities is not due to their propagation from a source close to the south pole, and can consequently be attributed to the background atmospheric circulation in the 110 – 140 km altitude range. The gravity wave dispersion relation and the geographical distribution of wavefronts strongly argue in favor of the presence of a subsolar to antisolar wind of amplitude larger than 60 m/s close to the south pole.

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[35] This first direct mapping of thermospheric gravity waves demonstrates the strong influence of the polar vortex on the atmospheric polar circulation of Venus [Piccioni et al., 2007], and illustrates an upward energy transfer from the cloud layer to the thermosphere. Moreover, this study seems to demonstrate the persistence of superrotation dynamics at least up to 115 km in polar regions (up to 30° from the south pole). Furthermore, our data support the existence of the subsolar to antisolar circulation predicted by thermosphere circulation models [Zhang et al., 1996; Bougher et al., 1997], with meridional wind speeds greater than 60 m/s in this polar region. [36] The increase of the VIRTIS database will improve the statistics of wave features, and its combination with VMC data [Markiewicz et al., 2007] will make it possible to follow gravity wave propagation from the top of the cloud layer up to the thermosphere. In order to improve our description and understanding of these wave phenomena, a large-scale threedimensional model of CO2 volumetric emission rates should be developed by using the limb and nadir VIRTIS observations, the sensitivity of these emissions to gravity wave perturbations should be modeled, and the gravity wave propagation within the background circulation should be computed. Finally, these high-altitude emissions can also be used to investigate transient infrasonic signals generated by quakes and volcanic events [Garcia et al., 2005]. [37] Acknowledgments. Two anonymous reviewers improved this paper considerably by their constructive comments. We thank Ste´phane Erard and Alejandro Cardesin for computation and validation of VIRTIS-M data and corresponding observation geometry, Ricardo Hueso Alonso for his help with IDL data projection routines, F. Ayoub for his help with COSI-CORR software, Se´bastien Lebonnois for helpful discussions on the atmosphere dynamics, and P.E. Mallet for validating the correlation software. This study was funded by CNES through space research scientific projects.

References Alexander, M. (1992), A mechanism for the Venus thermospheric superrotation, Geophys. Res. Lett., 19, 2207 – 2210. Baker, R., G. Schubert, and P. Jones (1999), High rayleigh number compressible convection in Venus’ atmosphere: Penetration, entrainment, and turbulence, J. Geophys. Res., 104, 3815 – 3832. Bougher, S., and W. Borucki (1994), Venus O2 visible and IR nightglow: Implications for lower thermosphere dynamics and chemistry, J. Geophys. Res., 99, 3759 – 3776. Bougher, S., M. Alexander, and H. Mayr (1997), Upper atmosphere dynamics: Global circulation and gravity waves, in Venus II: Geology, Geophysics, Atmosphere, and Solar Wind Environment, edited by S. W. Bougher et al., pp. 259 – 291, Univ. of Ariz. Press, Tucson. Drossart, P., et al. (2004), VIRTIS imaging spectrometer for the ESA/Venus Express mission, in Earth Observing Systems IX, vol. 5583, pp. 175 – 185, Int. Soc. for Opt. Eng., Bellingham, Wash. Drossart, P., et al. (2007), A dynamic upper atmosphere of Venus as revealed by VIRTIS on Venus Express, Nature, 450, 641 – 645, doi:10.1038/nature06140. Garcia, R., P. Lognonne´, and X. Bonnin (2005), Detecting atmospheric perturbations produced by Venus quakes, Geophys. Res. Lett., 32, L16205, doi:10.1029/2005GL023558. Gierasch, P. (1987), Waves in the atmosphere of Venus, Nature, 328, 510 – 512. Gierasch, P., et al. (1997), The general circulation of the Venus atmosphere: An assessment, in Venus II: Geology, Geophysics, Atmosphere, and Solar Wind Environment, edited by S. W. Bougher et al., pp. 459 – 500, Univ. of Ariz. Press, Tucson. Gilli, G., M. Lo´pez-Valverde, P. Drossart, G. Piccioni, S. Erard, and A. Cardesıń-Moinelo (2009), Limb observations of CO2 and CO non-LTE emissions in the Venus atmosphere by VIRTIS/Venus Express, J. Geophys. Res., doi:10.1029/2008JE003112, in press. Hinson, D., and J. Jenkins (1995), Magellan radio occultations measurements of atmospheric waves on Venus, Icarus, 114, 310 – 327. Hunten, D., L. Colin, T. Donahue, and V. Moroz (Eds.) (1983), Venus, 1143 pp., Univ. of Ariz. Press, Tucson.

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Kasprzak, W. T., H. B. Niemann, A. E. Hedin, and S. W. Bougher (1993), Wave-like perturbations observed at low altitudes by the Pioneer Venus Orbiter Neutral Mass Spectrometer during orbiter entry, Geophys. Res. Lett., 20, 2755 – 2758. Leprince, S., S. Barbot, F. Ayoub, and J. Avouac (2007), Automatic and precise ortho-rectification, coregistration, and subpixel correlation of satellite images: Application to ground deformation measurements, IEEE Trans. Geosci. Remote Sens., 45, 1529 – 1558. Li, F., G. Swenson, A. Liu, M. Taylor, and Y. Zhao (2007), Investigation of a wall wave event, J. Geophys. Res., 112, D04104, doi:10.1029/ 2006JD007213. Lighthill, J. (2001), Waves in Fluids, 520 pp., Cambridge Univ. Press, Cambridge, U.K. Lopez-Puertas, M., and M. A. Lopez-Valverde (1995), Radiative energy balance of CO2 non-LTE infrared emissions in the Martian atmosphere, Icarus, 114, 113 – 129, doi:10.1006/icar.1995.1047. Lopez-Puertas, M., M. Lopez-Valverde, R. Garcia, and R. G. Roble (2000), A review of CO2 and CO abundances in the middle atmosphere, in Atmospheric Science Across the Stratopause, Geophys. Monogr. Ser., vol. 123, edited by D. E. Siskind, S. D. Eckermann, and M. E. Summers, pp. 83 – 100, AGU, Washington D.C. Lo´pez-Valverde, M.A., P. Drossart, R. Carlson, R. Mehlman, and M. RoosSerote (2007), Non-LTE infrared observations at Venus: From NIMS/ Galileo to VIRTIS/Venus Express, Planet. Space Sci., 55, 1757 – 1771, doi:10.1016/j.pss.2007.01.008. Markiewicz, W., et al. (2007), Morphology and dynamics of the upper cloud layer of Venus, Nature, 450, 633 – 636, doi:10.1038/nature06320. Mayr, H. G., I. Harris, W. T. Kasprzak, M. Dube, and F. Varosi (1988), Gravity waves in the upper atmosphere of Venus, J. Geophys. Res., 93, 11,247 – 11,262. Nappo, C. (2002), An Introduction to Atmospheric Gravity Waves, 276 pp., Academic, San Diego. Piccioni, G., et al. (2007), South-polar features on Venus similar to those near the north pole, Nature, 450, 637 – 640, doi:10.1038/nature06209. Roldań, C., M. Lo´pez-Valverde, and M. Lo´pez-Puertas (2000), Non-LTE infrared emissions of CO2 in the atmosphere of Venus, Icarus, 147, 11 – 25. Schmetz, J., K. Holmlund, J. Hoffman, B. Strauss, B. Mason, V. Gaertner, A. Koch, and L. Van De Berg (1993), Operational cloud-motion winds from Metosat infrared images, J. Appl. Meteorol., 32, 1206 – 1225. Snively, J. B., V. P. Pasko, M. J. Taylor, and W. K. Hocking (2007), Doppler ducting of short-period gravity waves by midlatitude tidal wind structure, J. Geophys. Res., 112, A03304, doi:10.1029/2006JA011895. Sun, L., W. Wan, F. Ding, and T. Mao (2007), Gravity wave propagation in the realistic atmosphere based on a three-dimensional transfer function model, Ann. Geophys., 25, 1979 – 1986. Vargas, F., G. Swenson, A. Liu, and D. Gobbi (2007), O(1S), OH, and O2(b) airglow layer perturbations due to AGWs and their implied effects on the atmosphere, J. Geophys. Res., 112, D14102, doi:10.1029/ 2006JD007642. Woo, R., J. W. Armstrong, and A. Ishimaru (1980), Radio occultation measurements of turbulence in the Venus atmosphere by Pioneer Venus, J. Geophys. Res., 85, 8031 – 8038. Woo, R., J. W. Armstrong, and A. J. Kliore (1982), Small-scale turbulence in the atmosphere of Venus, Icarus, 52, 335 – 345, doi:10.1016/00191035(82)90116-6. Young, R. E., R. L. Walterscheid, G. Schubert, A. Seiff, V. M. Linkin, and A. N. Lipatov (1987), Characteristics of gravity waves generated by surface topography on Venus: Comparison with the VEGA balloon results, J. Atmos. Sci., 44, 2628 – 2639. Zhang, S., S. Bougher, and M. Alexander (1996), The impact of gravity waves on the Venus thermosphere and O2 IR nightglow, J. Geophys. Res., 101, 23,195 – 23,205. Zhou, Q., and Y. Morton (2007), Gravity wave propagation in a nonisothermal atmosphere with height varying background wind, Geophys. Res. Lett., 34, L23803, doi:10.1029/2007GL031061.

P. Drossart, LESIA, Observatoire de Paris, UPMC, Universite´ ParisDiderot, CNRS, 5 place Jules Janssen, F-92195 Meudon, France. R. F. Garcia, Laboratoire de Dynamique Terrestre et Plane´taire, UMR5562, Observatoire Midi-Pyreńeés, Universite´ de Toulouse, 14 avenue Edouard Belin, F-31400 Toulouse, France. ([email protected]) M. Lo´pez-Valverde, Instituto de Astrofı´sica de Andalucıá, CISC, 50 Camino Bajo de Hue´tor, ES-18080 Granada, Spain. G. Occhipinti, Institut de Physique du Globe de Paris, Universite´ Denis Diderot Paris 7, 4 place Jussieu, F-75252 Paris CEDEX 05, France. G. Piccioni, INAF, IASF, via del fosso del cavaliere 100, I-00133 Rome, Italy.

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Characterization of mesoscale gravity waves in the upper and lower clouds of Venus from VEX-VIRTIS images J. Peralta,1 R. Hueso,1 A. Sańchez-Lavega,1 G. Piccioni,2 O. Lanciano,2 and P. Drossart3 Received 9 May 2008; revised 11 August 2008; accepted 22 August 2008; published 11 December 2008.

[1] Images obtained from the Visible and InfraRed Thermal Imaging Spectrometer

(VIRTIS)-M instrument onboard Venus Express present visible trains of alternating bands of cloud brightness in two different layers: at the upper cloud tops (66 km altitude) observed in the dayside hemisphere using reflected ultraviolet light (380 nm) and in the lower cloud (47 km altitude) observed in the nightside hemisphere using thermal radiation (1.74 mm). The waves are nearly zonal (with the bands perpendicular to latitude circles), have wavelengths of 60–150 km, propagate westward with low phase velocities relative to the zonal flow, and are confined in wave packets of 400 to 1800 km in length. The waves in the lower cloud observed in the infrared are widely distributed around the planet, and their appearance varies widely throughout the VIRTIS data set. The locations of both types of waves seem not correlated with latitude, local times, surface topography, or the structure of the wind. In both cases the characteristics of the waves correspond to gravity waves propagating in confined stable layers of the atmosphere. We examine the properties of these waves in terms of a linear model and perform a simple analysis to discuss the vertical stability of the atmosphere within Venus clouds. Citation: Peralta, J., R. Hueso, A. Sańchez-Lavega, G. Piccioni, O. Lanciano, and P. Drossart (2008), Characterization of mesoscale gravity waves in the upper and lower clouds of Venus from VEX-VIRTIS images, J. Geophys. Res., 113, E00B18, doi:10.1029/2008JE003185.

1. Introduction [2] Atmospheric internal gravity waves are wave disturbances whose restoration force is buoyancy under stable stratification of the atmosphere [Holton, 1992]. They are commonly observed in the stratosphere of most planets as oscillations on the temperature field and they have also been observed as wavy structures in cloud fields of the Earth [Houze, 1993], Jupiter [Hunt and Muller, 1979; Flasar and Gierasch, 1986; Reuter et al., 2007] and Venus [Gierasch et al., 1997; Markiewicz et al., 2007]. Since they are supported by such a fundamental force (buoyancy) they are of great interest to atmospheric dynamics. On the one hand, from their properties one can infer the static stability of the atmosphere (since these waves can only propagate in regions where the static stability is positive). On the other, they are responsible of a number of important dynamical phenomena including the transfer of energy and momentum vertically. They are of particular importance to the atmosphere of Venus, where their role as a mechanism able to transport momentum from the surface to the atmospheric 1 Departamento de Fı´sica Aplicada I, Escuela Tećnica Superior de Ingenierıós, Universidad del Paı´s Vasco, Bilbao, Spain. 2 Istituto di Astrofisica Spaziale e Fisica Cosmica, Istituto Nazionale di Astrofisica, Rome, Italy. 3 Laboratoire d’Etudes Spatiales et d’Instrumentation en Astrophysique, Observatoire de Paris, UPMC, Universite´ Paris-Diderot, CNRS, Meudon, France.


mean circulation has been examined by several teams, although without a final consensus on their role in powering the atmospheric superrotation [Hou and Farrell, 1987; Gierasch, 1987; Leroy and Ingersoll, 1995]. Space probes determined during their descent at different latitudes and local times the existence of at least two stable layers: between 30 and 48 km, and upward of 55 km [Kliore and Patel, 1980; Seiff et al., 1985; Gierasch, 1987]. These altitude levels are home to dense clouds on Venus [Esposito et al., 1997] and gravity waves arising on them can manifest as regular patterns on the clouds albedo fields [Markiewicz et al., 2007; McGouldrick and Toon, 2008]. The altitudes below 30 km and the intermediate layer between 48 and 55 km have small static stabilities and are not suitable candidates for the development of gravity waves. [3] Gravity waves observed at the upper cloud level were first inferred from analysis of Mariner 10 [Belton et al., 1976] and Pioneer Venus [Rossow et al., 1980] ultraviolet images of the upper cloud field at z 65 km. More recently, the Venus Monitoring Camera (VMC) onboard Venus Express has also detected regular cloud patterns identified as gravity waves from their observed characteristics [Markiewicz et al., 2007]. Mariner 10 images of equatorial elongated cloud patterns of about 5000 km in the longitudinal direction and separated 500 km in latitude (circumequatorial belts) were interpreted as evidence of internal gravity waves. These structures propagated southward near the equator with phase speeds of 20 m/s [Belton et al., 1976]. The same circumequatorial belts were seen during the Pioneer Venus mission in addition

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PERALTA ET AL.: VENUS GRAVITY WAVES OBSERVED BY VEX-VIRTIS

to a new type of train of wavelike features composed of dark streaks of 2000 km, separated by 200 km, but in this case lying at a large angle to the constant latitude circles [Rossow et al., 1980]. No evidence for small-scale internal gravity waves or for circumequatorial belts was found in images taken by the Galileo spacecraft in 1990 [Belton et al., 1991; Toigo et al., 1994; Peralta et al., 2007a]. This was linked to a lower abundance of convective clouds visible during the Galileo flyby. The high-resolution images obtained recently by the VMC instrument onboard Venus Express display vigorous convection in near equatorial latitudes and visible wave activity (wavelengths of just a few tens of kilometers) in tropical latitudes in the south hemisphere and in middle to subpolar latitudes in the north hemisphere [Markiewicz et al., 2007]. [4] Evidence for gravity waves in the Venus atmosphere does not come only from imagery. The four Pioneer Venus probes, as they descended through the atmosphere, obtained altitude profiles of temperature (upward of 110 km) and wind velocity (lower cloud region) that showed wavelike variations with vertical wavelengths of 5 – 10 km [Seiff at al., 1980; Counselman et al., 1980]. Similar vertical variations can be seen in infrared temperature soundings [Taylor et al., 1980], and in radio occultation temperatures from Pioneer Venus [Kliore and Patel, 1980], Venera 9 [Kolosov et al., 1980] and Magellan [Hinson and Jenkins, 1995] sensitive to mesospheric vertical levels between the cloud tops (z 65 km) and the thermosphere base (z 100 km). Above these levels, gravity waves with wavelengths between 100 and 600 km have also been detected on the thermosphere as density perturbations studied by the mass spectrometer on the Pioneer Venus orbiter [Niemann et al., 1980; Kasprzak et al., 1988], and from non LTE CO2 emissions observed by the Visible and InfraRed Thermal Imaging Spectrometer (VIRTIS) instrument on Venus Express (R. Garcıá et al., Gravity waves in Venus upper atmosphere revealed by CO2 non LTE emissions, submitted to Journal of Geophysical Research, 2008). [5] Although several candidates could act as sources of gravity waves (flow over a mountain range, KelvinHelmholtz instability around a jet stream), convective processes generated in the low-stability layers between 48 and 55 km and below 30 km are considered to play the main role for gravity wave generation in the middle and upper regions of the clouds layer [Schubert, 1983; Gierasch, 1987; Leroy and Ingersoll, 1995, 1996; Baker et al., 2000a, 2000b]. [6] In this paper we present the observed characteristics of wavelike features present in the atmosphere of Venus at two cloud levels detected on images at different wavelengths obtained by the VIRTIS instrument [Drossart et al., 2007] onboard Venus Express [Svedhem et al., 2007]. We identify these features as gravity waves and present a simple theoretical approach to interpret them in terms of a ‘‘textbook’’ classical linear model. This is the first time that the abundant and variable mesoscale gravity waves within the lower cloud level of Venus have been characterized. From the comparison between the observations and model we infer the existence of a widely (from latitudes 40° to 80°S) and temporarily extended vertical stable region between 30 and 48 km, in agreement with

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previous inferences [Gierasch, 1987; Leroy and Ingersoll, 1995].

2. Observations [7] The instrument VIRTIS on board Venus Express is an imaging spectrometer (VIRTIS-M) combined with a highresolution IR-spectrometer (VIRTIS-H). VIRTIS-M is capable of obtaining images over several visible and infrared wavelengths in two channels: visible (0.3 to 1 mm) and near infrared (1 to 5 mm) [Drossart et al., 2007]. Dayside observations in the ultraviolet show cloud features in the upper cloud (380 nm), corresponding to an altitude of (66 ± 4) km. At the near infrared 980 nm, solar photons are more penetrating and reach the base of the upper cloud at (61 ± 3) km [Belton et al., 1991; Peralta et al., 2007b]. Night side images of Venus at 1.74 mm are sensitive to the opacity of the lower cloud layer at 44– 48 km, with the radiance coming from altitude levels beneath the clouds and being attenuated as it passes through the lower cloud layer [Carlson et al., 1991; Crisp et al., 1991]. Sańchez-Lavega et al. [2008] present a detailed account of the global cloud dynamics observed in these filters and the relation between wavelength and cloud altitude determination. [8] Because of the highly elliptical orbit of the VEX spacecraft, with nearly 80°N pericenter, only the south hemisphere can be covered by the VIRTIS instrument. The spatial resolution of VIRTIS images vary from 15 km/pix at polar and subpolar latitudes to 45 km/pix at equatorial latitudes. The nightside images used in this study cover the period from 12 April 2006 (Orbit Insertion) to 9 March 2007. The dayside images cover the period from Orbit Insertion to 28 July 2007, extending during a large fraction of the nominal mission. The examined period for the visible images was extended compared to the infrared images because of the lower number of orbits containing high-resolution well-contrasted images of the dayside. From these periods, and attending to image quality and spatial resolution, we selected observations corresponding to 112 orbits in the visible and 116 orbits in the infrared. [9] Figure 1 shows maps of the number of observations for each latitude and local time with a high spatial resolution suitable for gravity waves search (spatial resolution better than 30 km/pix). This roughly corresponds to latitudes poleward of 30°S in both day and nightside. [10] The images were navigated, corrected from defects (mainly striations and hot and dark lines), processed and mapped into cylindrical and polar projections using the ‘‘Planetary Laboratory Image Analysis’’ (PLIA) software (R Hueso et al., The Planetary Laboratory for Image Analysis (PLIA), submitted to Planetary and Space Science, 2008.). Each image was processed individually by a combination of contrast enhancement, unsharp mask and smoothing filters.

3. Results [11] Examples of systems of waves observed in the cloud fields in ultraviolet (380 nm), near-infrared (980 nm) and infrared (1.74 mm) wavelengths (hereafter, UV, NIR and IR,

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Figure 1. Global resolution maps and frequency of high-resolution observations made with Visible and InfraRed Thermal Imaging Spectrometer (VIRTIS) on the (a) dayside and (b) nightside of Venus. The number of observations of a specific latitude-local time with a high spatial resolution of 30 km/pix is indicated. This spatial resolution is suitable for observations of waves with wavelengths larger than 100 km. Observations with better resolutions of the order of 15 –20 km/pix were also present in the data set with much less frequency. The position of the waves encountered is shown as symbols (triangles for the waves in the upper cloud, and crosses for the waves in the lower cloud) and correlates with regions with high number of observations at this resolution. respectively) are displayed in Figure 2. The spatially distributed regular patterns of alternative bright and dark stripes represent reflectivity variations in the ultraviolet and near infrared (observed during dayside) and opacity variations in the infrared (observed during nightside). The images show trains of crests and troughs generally ordered in longitude (oriented in the zonal direction) and extending a few degrees in latitude nearly perpendicularly to the wave packet. In many cases the troughs are wider than the crests, or they simply disappear. Some of the wave packets are very regular while others have nonequidistant crests, change their latitudinal dimensions over the packet or show a slight curvature. Beside on Venus, these irregularities have also been observed in mesoscale cloud waves observed in Jupiter and interpreted in terms of ducted gravity waves [Flasar and Gierasch, 1986]. [12] We performed a systematic search of waves (see Figure 1) as those shown on Figure 2, finding 6 distinctive and separate wave packets in ultraviolet images, one wave packet in the near infrared (this wave system was observed simultaneously in the ultraviolet and the infrared with nearly identical characteristics) and 30 wave packets in infrared images. In some cases, a single infrared image presents wave systems at various latitudes. The larger abundance of waves in the infrared images results in a larger diversity of wave properties. The average image contrast variations that characterize these wave systems are on the order of 1% in the ultraviolet, less than 1% in the near infrared and vary widely in the infrared with some faint wavy structures with contrast variations of 1%, subtle to detect, to clearly observable systems with contrast of 15%. This study does not rule out the possible existence of less conspicuous wave variations in the cloud fields with less contrast or shorter wavelengths as found in the upper cloud by the VMC instrument [Markiewicz et al., 2007]. [13] We measured the observable properties of the wave packets such as their latitude and local time location, packet length (i.e., their extension perpendicular to the wavefronts),

packed width (i.e., their extension in the wavefront direction) and orientation respect to the parallels (lines of constant latitude). Results are listed in Tables 1a–1c. [14] Figure 3 shows the location of the clouds in terms of latitudes, local times and surface topography. These maps show abundant waves at the lower cloud with no correlation in their position with surface topography (Figure 3a) or local time (Figure 3b). This is in contrast to the northern hemisphere, where the Vega balloon registered the influence of the surface in atmospheric motions with oscillations at the lower cloud level interpreted as gravity waves generated by the flow over specific mountainous regions [Sagdeev et al., 1986; Young et al., 1987, 1994]. In the southern hemisphere, where the surface is nearly flat compared to the northern hemisphere, especially in the sampled latitudes, the waves seem to be present all along the nightside hemisphere except inside the South Polar Dipole [Piccioni et al., 2007]. This could be due to an observational effect since the dipole interior is particularly dark on the infrared images. In any case, gravity waves have been detected up to the dipole’s outer edge (80°S in orbit 84) and the global dynamics of the polar area will be presented elsewhere. [15] The Venus Monitoring Camera on VEX has found abundant cloud waves of higher frequency (wavelengths of a few tens of km) in the upper clouds from south tropical to north polar latitudes [Markiewicz et al., 2007]. These waves, if also present in the south hemisphere, cannot be resolved by VIRTIS observations. The scarcity of large waves detected on the upper clouds does not allow extracting a clear conclusion about their distribution but they seem clustered in the early morning hours and late afternoon. In both cloud layers, we do not find wave packets equatorward of 40°S. This is probably caused by the comparatively low number of high-resolution observations of those latitudes. However, we would also like to point out that at those latitudes there seems to be a transition to different cloud structures, with dark midlatitude bands in the upper cloud (UV), and a mottled turbulent morphology in the lower

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Figure 2. Examples of cloud waves observed on VIRTIS images projected into polar maps for size comparison. Figures 2a and 2b show waves observed at 380 nm in the upper cloud level. The singular detection of a wave at 980 nm images is shown in Figure 2c and correlates with the same structure observed in 380 nm and shown in Figure 2b. Either this is a vertically extended wave, or the observed levels in those wavelengths can be closer than expected at specific latitudes. Large wave packets observed in 1.74 mm in the lower cloud are shown in Figures 2d and 2e. cloud (IR) that on the one hand may difficult the detection of gravity waves but could also represent a region of active cellular convection where gravity waves might not be generated. [16] The extension covered by the wave packets, i.e., the packet length, varies between 640 and 1200 km in the upper cloud (UV) with a mean value of 920 km and from 280 to 1700 km in the lower cloud (IR) with a mean value of 760 km. Their average width are 320 and 230 km in the in

the upper (UV) and lower cloud (IR), respectively. We further measured the wavelength of each packet, the number of crests and, when possible, their zonal motions. Since the winds at cloud level derived from VIRTIS are variable in terms of the local time [Sańchez-Lavega et al., 2008] we also measured the local velocity at nearby longitudes and latitudes of each individual wave packet to better estimate their phase speed relative to the local wind. This was done only on those orbits where we could observe the same cloud

Table 1a. Summary of Wave Packets Properties From UV Images by VEX-VIRTISa Orbit

Date (mm/dd/yy)

Latitude (deg)

Local Hour

Packet Length (km)

Packet Width (km)

Orientation (deg)

59 59 170 255 388

06/18/2006 06/18/2006 10/07/2006 12/31/2006 05/13/2007

41 58 62 70 74

16,4 15,7 06,8 15,0 08,1

>860 >865 1275 980 640

170 340 340 420 335

25 30 25 40 2

a

Visible and InfraRed Thermal Imaging Spectrometer (VIRTIS).

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Table 1b. Summary of Wave Packets Properties From NIR Images by VEX-VIRTIS

Orbit

Date (mm/dd/yy)

Latitude (deg)

Local Hour

Packet Length (km)

Packet Width (km)

Orientation (deg)

59

06/18/2006

58

15.4

>860

350

35

field with time differences large enough to track the motions (1 h). Repeated observations of the same wave features indicate they conserve their morphology and albedo variations over these timescales being nondispersive. These properties appear detailed in Tables 2a– 2c. [17] Observed wavelengths are within the range of 90 to 170 km in the upper cloud (UV) and 60 to 180 km in the lower cloud but we cannot rule out waves of higher frequencies (wavelengths shorter than 50 km) not resolved by our observations. At both cloud levels the phase velocities turn out to be generally low respect to the averaged zonal flow. This is in agreement with our interpretation in terms of gravity waves that will be later discussed. [18] The overall properties of these wave systems do not seem to vary with latitude or local time. The wavelength behavior and wave packet properties in terms of latitude appear on Figure 4. Neither the wavelength (Figure 4a), nor the wave packet length (Figure 4b) or the wave packet width (Figure 4b) seem to be correlated with latitude. Only the wave packet orientation (Figure 4d) seems to slightly grow at higher (more poleward) latitudes. The wave system detected on the outer edge of the dipole is aligned with this

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structure and presents the largest inclination with respect to the parallels. [19] The derivation of the properties detailed above critically depends on the quality of the data and, for the determination of the wavelengths and phase speeds, in the ability to accurately track specific structures in the wavefronts and outside them. We estimate the navigation error to be lower than 1 pixel but since all the measurements were done by a human operator the measurement error can be larger. We therefore estimate the wavelengths to be accurate to a 10%, packet lengths and widths accurate to a few tens of km and phase speeds to have a mean measurement error of 8 m/s. The sometimes subtle identification of wave patterns in ultraviolet images relied on the confirmation of all wave structures by a second operator.

4. Discussion [20] On the basis of the characteristics of the wave systems described above we interpret them as internal gravity waves lying at regions of positive static stability. On the one hand, the high frequency of the waves and the slow rotation of the planet discard the observed waves as manifestations of Rossby waves and on the other, these frequencies are much smaller than the acoustic cut-off frequency. Also their latitudes and short sizes discard Kelvin waves as the underlying mechanism for the regular cloud patterns. Roll convection on Earth sometimes produces structures similar in their shape to the regular alignment of clouds observed on our images but this phenomenon is characteristic of the planetary boundary layer and depends on a critical equilibrium between a small static stability and

Table 1c. Summary of Wave Packets Properties From IR Images by VEX-VIRTIS Orbit

Date (mm/dd/yy)

Latitude (deg)

Local Hour

Packet Length (km)

Packet Width (km)

Orientation (deg)

84 96 97 97 100 112 112 112 112 112 113 114 118 141 142 161 164 166 228 231 261 290 313 315 317 317 317 318 321 323

07/13/2006 07/25/2006 07/26/2006 07/26/2006 07/29/2006 08/10/2006 08/10/2006 08/10/2006 08/10/2006 08/10/2006 08/11/2006 08/12/2006 08/16/2006 09/08/2006 09/09/2006 09/28/2006 10/01/2006 10/03/2006 12/04/2006 12/07/2006 01/06/2007 02/04/2007 02/27/2007 03/01/2007 03/04/2007 03/04/2007 03/04/2007 03/05/2007 03/07/2007 03/10/2007

80 48 39 61 55 60 49 49 53 58 54 54 65 65 64 58 50 53 51 78 59 63 60 61 53 59 63 60 49 41

21,9 02,0 22,1 19,8 03,5 23,8 23,1 22,3 21,4 21,1 01,5 05,1 00,2 20,5 03,8 03,6 03,6 03,2 00,9 03,3 21,8 20,6 02,8 01,2 19,1 19,7 20,5 19,5 19,7 19,4

>600 1750 660 570 >1525 610 410 730 785 575 >1140 >645 635 520 >1415 1050 335 >790 690 280 620 >440 >860 760 >1230 685 450 640 >1300 615

225 520 160 160 270 105 170 320 200 205 180 205 265 370 160 310 380 225 370 130 240 250 315 335 170 200 145 150 210 200

50 15 35 40 2 20 40 25 8 7 17 14 2 11 13 17 20 12 10 30 13 4 11 0 10 15 7 0 12 0

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Table 2a. Summary of Wave Properties From UV Images by VEX-VIRTIS Orbit 59 59 170 255 388

Latitude Number of Wave Length Crest Width cx (deg) Crests (km) (km) (m/s) 41 58 62 70 74

8 7 5 11 4

95 115 210 90 170

45 60 125 45 100

113 108 62 29 76

j~ cju (m/s) 12 40 0.5 14 36

a dynamic instability due to the vertical wind shear (low values of the Richardson number). Our wave systems are located several tens of km above the surface and at regions of high Richardson numbers. [21] In order to gain insight into the nature of the waves and further determine their characteristics, we will examine their wave dispersion relation using a simple ‘‘textbook’’ classical linear model of gravity waves. The wave dispersion relation can be deduced by neglecting the effects of rotation under the Boussinesq approximation and assuming two-dimensional motion. Linear perturbations over a basic state with constant zonal flow and density yield the dispersion relation [Holton, 1992]: ðw uk Þ2 ¼

k2

k2 N 2; þ m2

ð1Þ

is the where w is the frequency of the wave perturbation, u mean wind projected in the direction of the wave phase propagation, N2 is the square of the buoyancy or BruntVaïsa¨lla¨ frequency, and k and m are the horizontal and vertical wave numbers related to the horizontal and vertical wavelengths, lx and lz, by k = 2p/lx and m = 2p/lz. From (1) it is straightforward to deduce the horizontal and vertical components of the phase speed vector relative to the mean flow as,

Figure 3. Polar map of cloud waves’ locations in terms of their longitude, local time, morphology of the clouds, and surface topography. Figure 3a shows the wave packets central positions (triangles for the waves in the upper cloud, and crosses for the waves in the lower cloud) over a surface map obtained by the Magellan IDRS instrument [Saunders et al., 1991]. The topography varies from 1.3 km in the darkest low plains to +2.5 km at the top of the highest mountains in this projection. The higher concentration of waves in the sector between longitudes 210 – 300° is due to a bias in the spatial sampling. Figure 3b shows the cloud waves in terms of latitude and local time displaying the typical cloud structure at these levels with a background image from the Venus Orbit Insertion covering the whole southern hemisphere. Given the spatial resolution and sampling of the polar region, the lack of cloud waves at these latitudes might be significant.

N cx ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 k þ m2

ð2aÞ

N k cz ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; k 2 þ m2 m

ð2bÞ

where cx is the phase speed relative to the mean wind flow. We now proceed to verify if those waves for which we have information about their phase speeds verify this simple model of internal gravity waves. [22] The clouds observed in the ultraviolet are located at 66 km altitude. These levels are close to the transition between the troposphere and the mesosphere, where the static stability of the atmosphere abruptly increases. A reasonable value of the Brunt-Vaïsa¨la¨ frequency from

Table 2b. Summary of Wave Properties From NIR Images by VEX-VIRTIS Orbit 59

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Latitude Number of Wave Length Crest Width cx j~ cju (deg) Crests (km) (km) (m/s) (m/s) 58

7

125

75

-

-


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Table 2c. Summary of Wave Properties From IR Images by VEX-VIRTIS

Orbit

Latitude (deg)

Number of Crests

Wave Length (km)

Crest Width (km)

cx (m/s)

j~ cju (m/s)

84 96 97 97 100 112 112 112 112 112 113 114 118 141 142 161 164 166 228 231 261 290 313 315 317 317 317 318 321 323

80 48 39 61 55 60 49 49 53 58 54 54 65 65 64 58 50 53 51 78 59 63 60 61 53 59 63 60 49 41

6 12 8 8 12 8 5 7 6 4 6 5 7 4 18 14 4 6 5 5 4 4 7 6 9 9 6 5 12 5

63 155 100 80 130 60 103 90 125 105 140 100 90 180 75 75 100 130 130 105 145 120 135 100 100 90 90 100 105 110

40 70 35 30 65 30 50 20 70 45 90 40 50 75 44 40 50 60 65 65 65 66 65 55 55 50 45 50 50 55

26 64 59 58 44 61 64 45 42 60 64 59 60 66 54 50

1.0 9.0 3.1 2.7 0.2 5.1 1.8 29.7 9.0 5.0 3.1 2.4 1.1 6.1 7.5 3.3

different probes for these levels is N2 260 106 s2 [Seiff et al., 1985; Del Genio and Rossow, 1990; Gierasch et al., 1997]. Above these levels the stability of the atmosphere increases to higher values characteristic of the mesosphere. Below the clouds the stability decreases to nearly zero allowing for a region between 48 and 55 km where vertical convection can develop. The lower clouds acting as a source of opacity for the atmospheric emitted radiation are located just below, in altitudes (43 – 48 km) characterized by high values of the static stability. We consider as a representative value for the Brunt-Vaïsa¨la¨ frequency in the lower cloud N2 65 106 s2. This is close to the peak values measured by the Pioneer Venus Night Sounder at the height of 45 km [Seiff et al., 1985; Gierasch et al., 1997] over the stable layer situated in the height range 30– 48 km, and it is within the range of 50– 100 106 s2 obtained at these altitudes by the ensemble of Pioneer Venus sounders. This stable layer may act as a duct where waves can form excited by convection from the lower levels or from downdrafts from the unstable levels above [Baker et al., 1998]. [23] Figure 5 shows the behavior of the gravity wave dispersion relationship for different values of the vertical wavelength lz and the assumed values of the Brunt-Vaïsa¨la¨ frequency. The upper cloud waves (Figure 5a) seem to be distributed in two groups of short (5 km) and large (15 km) vertical wavelengths or in regions with small and large values of the atmospheric static stability. We note that the cloud wave simultaneously detected in the ultraviolet and

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near infrared images corresponds to one of the two cases of long vertical wavelengths. The lower cloud waves (Figure 5b) are more abundant and are homogeneously distributed in the plot in regions of short vertical wavelengths (5 – 10 km). This is coherent with the vertical extent of the duct and consistent with the fact that more vertically extended waves would be dispersed by the interaction with the vertical wind shear [Leroy and Ingersoll, 1995]. [24] In both cases, phase velocities relative to the mean zonal flow are low, as one would expect, for convectively driven gravity waves typically have small phase speeds relative to the mean zonal wind where they are generated. Also the observed nondispersive behavior of the waves is easily explained by Figure 5 and points to relatively small variations of the vertical wavelength and vertical stability variations across the wave packet extending several hundred kilometers. [25] Although a full nonlinear approach is beyond the scope of this paper we note that in the limit jc – uj!0 the waves encounter a critical level where linearity does not hold and the waves break in the atmosphere. The more slowly traveling waves on Venus are therefore close to the critical level (but not breaking since we are able to observe them for 1 – 2 h in the VIRTIS frames of the same atmospheric area). Notably under the WKB approximation, as the critical level is approached the vertical wavelength diminishes and finally collapses. However, the wave stalls and the limit is only reached after a time that approaches infinite [Salby, 1996]. This process is accompanied by an increase of the wave amplitude. For the IR waves in our survey it seems that the better they can be seen the slower the phase velocities they have, what might be a signature of high-amplitude waves moving close to their critical level. [26] For the lower cloud waves we can study the double dependence of the phase speed on the two unknowns N2 (constrained from measurements from previous spacecrafts) and lz (constrained by using physically reasonable arguments). This is not feasible on the upper cloud with less observations and apparently larger variations on lz or N2. Figure 6 shows a plot of the total c2 deviation of the phase speeds of the waves from the predictions by the wave dispersion relation (equation (2a)). This was calculated for different values of N2 and lz using the equation below which also considers the estimated error in phase speeds determination. 2 N 0 2 X cx cx ðN 2 ; mÞ c N ;m ¼ ; sc i 2

ð3Þ

where the index i runs for all of the wave system measured, c0x is the measured phase speed, cx is the theoretical value from equation (2a) and sc is the variance of the measured phase speeds. [27] The contours of constant Dc2 around its minimum value can be used to determine confidence regions in the values of the varying parameters N2 and lz. A confidence region is a region within the space of parameters that contains a large percentage of the probability distribution of finding the true parameter values within it [Press at al., 1992]. If all the wave systems measured had the same values of N2 and lz, and measurement errors obeyed a

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Figure 4. Wave properties in terms of latitude. Figures 4a, 4b, 4c, and 4d show the wavelength, wave packet longitudinal extent, wave packet latitudinal extent, and the wave packet orientation relative to the parallels, respectively. Gravity waves at the upper and lower cloud level are displayed as triangles and crosses, respectively. normal error distribution, the region surrounded by the c2 = 2 curves would have a 68% probability of containing the right values of N2 and lz. The more extended regions surrounded by the c2 = 5 and c2 = 10 curves would have a 90 and 95% probability of containing the real values. From Figure 6 it seems that, when fixing N2 with the value introduced previously (N2 = 65 106 s2), the confidence region with the best fit between experiment and theory limits the range of vertical wavelengths lz between 2 and 5 km (consistent with Figure 5b). This range of vertical wavelengths matches the values of 2.5 km found from temperature scintillations measured by Magellan above the middle cloud deck [Hinson and Jenkins, 1995]. These vertically short waves have vertical wave numbers m much larger than the horizontal wave numbers k and equation (2a) can be further simplified to cx

N ; m

ð4Þ

which results in nondispersive horizontally traveling waves in accordance with the observations. [28] Convection seems the most plausible source of gravity waves in the lower atmosphere of Venus [Gierasch et al., 1997]. The possibility of internal gravity waves generated by penetrative convection has been previously considered in the terrestrial case [Stull, 1976], and it has been extensively studied in Venus through several theoretical and numerical works [Leroy and Ingersoll, 1995; Baker et al., 1998, 2000a, 2000b; Yamamoto, 2003; McGouldrick and Toon, 2008; Baker et al., 1998] which showed that compressible convection in the Venus atmosphere can lead to a significant penetration of the stable layer between 30 and 48 km both by upward convective movements from the unstable layer between 20 and 30 km, and by downward convective movements from the unstable layer between 48 and 55 km. They also pointed out the possibility that the latter convective downwellings could also affect the stable region above 55 km via convective entrainment. Baker et al. [2000b] showed that this mechanism works better

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producing enhanced overshooting of the convection in regions with high vertical wind shear, which may allow the interaction of upward and downward plumes within the 30– 48 km stable layer and generate gravity waves of higher wavelengths. In that case we could anticipate a

Figure 6. Chi-square tests exploring the space parameter defined by equation (2a) on the lower cloud waves. Confidence regions corresponding to values of chi-square larger than a given value are displayed with contour lines. The horizontal gray area indicates the range of possible buoyancy frequencies at the cloud waves’ location, and the vertical gray area indicates the corresponding range of vertical wavelengths within the confidence region of the best fit (bounded by c2 = 2 lines).

certain degree of correlation between the locations of gravity waves in the upper and lower clouds. Unfortunately the scarce gravity waves observed in the upper cloud layer may not be enough to closely examine this issue. Also, since the lower clouds are observed in the nightside hemisphere and the upper cloud are observed in the dayside hemisphere, our VIRTIS observations do not cover the same locations in high resolution and close in time.

Figure 5. Internal gravity wave dispersion relation applied to the cloud waves in the (a) upper cloud and (b) lower cloud. Phase velocities are relative to the zonal wind. Lines represent solutions to equation (2a) varying the vertical wavelengths in the ranges lz = 2 – 15 km for both clouds. We used a fixed value of the buoyancy frequency representative of the two cloud levels (N2 = 260 106 s2 for the upper cloud and N2 = 65 106 s2 for the lower cloud; details are given in section 4). 9 of 12

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Figure 7. Waves’ phase velocity and mean zonal wind profiles at the two cloud levels. Wave phase velocities and the mean zonal wind profile at the upper cloud level are displayed with dark triangles and continuous line. Wave phase velocities and the mean zonal wind profile at the lower cloud level are displayed with light crosses and discontinuous line. Those waves with no dynamical information are also displayed along a discontinuous line at zero zonal velocity. The error bars in the zonal wind profiles represent measurement errors and variance of the wind at each latitude [Sańchez-Lavega et al., 2008]; the error bars of the wave phases represent estimated measurement error in each wave packet.

[29] Our observations, coupled to data from previous spacecraft probes that extend in time and cover ample locations (Pioneer-Venus, Venera and Vega landers, see, e.g., Gierasch et al. [1997]), suggests that the presence of the two stable layers where the clouds lie are a persistent feature of Venus’s vertical cloud structure. Our gravity wave detections in the south hemisphere from latitudes 40°S to 75°S imply that the layer sampled with infrared images favors the vertical stability. The region equatorward of 40°S is not sampled well enough to extend this conclusion and could be dominated by stronger convective motions, whereas poleward of 75° the dipole feature may mask the presence of the waves. Although abundant and recurrent, the waves observed in the lower cloud are not permanent. In view of the stability measurements obtained from the

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different previous probes we interpret this variability as a perturbation distinct to changes in the atmospheric stability and affecting the mechanisms responsible of exciting the waves. [30] The relation between wave location and wind is examined in Figure 7. Here we show the zonal wind profiles in the upper and lower cloud layers obtained by extensive analysis of the VIRTIS data set [Sańchez-Lavega et al., 2008] compared to the zonal phase speeds of the wave systems in both levels. The position of the waves is correlated neither with changes in the zonal profile, nor with values of the vertical wind shear. Additionally, the width of the wave packets does not correlate with the meridional shear of the wind, what may imply that the packet width could be related with meridional variations in the atmospheric static stability. [31] The significantly larger phase speeds of the waves in the upper cloud is probably a consequence of the stronger stability in this cloud level, with much higher values for the buoyancy frequency [Seiff et al., 1985; Gierasch et al., 1997]. Contrary to the lower cloud waves, which are vertically confined in a duct of static stability, these upper cloud waves are not bounded vertically. This may result in vertical propagation to levels where they are critically absorbed by the vertically varying mean flow, or dissipated [Schubert and Walterscheid, 1984]. The absorbed waves transport energy and momentum. Their role in powering the atmospheric superrotation was examined by Leroy and Ingersoll [1995] who concluded that the waves are probably not responsible for the atmospheric global winds. [32] Most of the numerical studies of gravity waves on Venus focus on waves of smaller wavelengths (no larger than 30 km, see Baker et al. [1998, 2000a, 2000b]) than those here reported (typically 100 km). While our observations cannot distinguish these high-frequency waves (our best resolution images have a pixel size of 15– 20 km) we do detect significant wave activity of larger wavelengths (100 km). McGouldrick and Toon [2008] suggest that gravity waves with greater horizontal wavelengths comparable to our observations could be generated by broad convective plumes or a large magnitude of the vertical shear of the zonal wind but we do not observe significant convection on the upper or lower clouds. The vertical wind shear of the wind from VIRTIS data is mainly confined to the 61 to 66 km altitude range with values @ < u >/@z = 8 ± 2 ms1 per km at equatorial to midlatitudes and less than 2 ms1 per km at subpolar latitudes [Sańchez-Lavega et al., 2008]. [33] Gravity waves transmit energy and momentum perpendicularly to the direction of phase propagation in the direction of constant phase lines (considering the traveling direction, mainly zonal, and the vertical). Most of the waves are zonally aligned but some others are tilted and may transport some momentum meridionally. In the zonal direction some of the waves move slightly faster while others slightly more slowly than the average wind. In the meridional direction some waves move slightly northward and some other slightly southward. We also do not have information about the vertical component of the wave motion. For these reasons it was difficult to evaluate the

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average role of the waves in the global transport of energy and momentum in the atmosphere.

5. Conclusions [34] We have presented a study of the mesoscale waves present in the atmosphere of Venus at different cloud levels by using images at three wavelengths obtained by the VIRTIS instrument onboard Venus Express. We interpret them as gravity waves, and this is the first time that mesoscale waves within three different cloud levels are characterized in Venus at the same time, these levels corresponding to wavelengths 380 nm, 980 nm, and 1.74 mm. [35] The abundant waves observed in IR images display a large diversity of morphologies and wave properties with many observations revealing wave activity at a wide range of latitudes (40°S75°S). None of the wave properties seem to vary systematically with latitude or the zonal wind profile except from an observational bias of the VIRTIS data set that is unable to observe these waves at tropical and equatorial latitudes. In particular the location of the waves seems uncorrelated with topographic features on the surface or the local time that combined with the negligible effect of the solar heating at the lower cloud could indicate a crucial role of deep convection as the source of wave activity. [36] We have presented a first account on wave properties using a classical ‘‘textbook’’ linear model of gravity waves [Holton, 1992] that confirms the nature of the waves. From our statistical analysis of the wave dependence on the static stability and vertical wavelength (Figures 5b and 6) we concluded that the vertical wavelengths are on the order of 2 to 5 km in the lower cloud and 5 to 15 km in the upper cloud from the less numerous measurements of wavelength and phase speeds (Figure 5a). These results are consistent with previous determinations of the vertical wavelengths of temperature scintillations in the middle cloud measured by the Magellan spacecraft [Hinson and Jenkins, 1995]. [37] This study opens questions about the specific source of the wave activity observed, the latitudinal and temporal distributions of atmospheric stability and convection and the role of these waves in the overall dynamics of Venus atmosphere. [38] Acknowledgments. This work has been funded by Spanish MEC AYA2006-07735 with FEDER support and Grupos Gobierno Vasco IT-464-07. J.P. acknowledges a UPV fellowship, and R.H. acknowledges a ‘‘Ramoń y Cajal’’ contract from MEC.

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Peralta, J., R. Hueso, and A. Sańchez-Lavega (2007b), A reanalysis of Venus winds at two cloud levels from Galileo SSI images, Icarus, 190, 469 – 477, doi:10.1016/j.icarus.2007.03.028. Piccioni, G., et al. (2007), South-polar features on Venus similar to those near the north pole, Nature, 450, 637 – 640, doi:10.1038/nature06209. Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery (1992), Numerical Recipes in Fortran 77: The Art of Scientific Computing, pp. 687 – 693, Cambridge Univ. Press, New York. Reuter, D. C., et al. (2007), Jupiter cloud composition, stratification, convection and wave motion: A view from New Horizons, Science, 318, 223 – 225, doi:10.1126/science.1147618. Rossow, W. B., et al. (1980), Cloud morphology and motions from Pioneer Venus images, J. Geophys. Res., 85, 8107 – 8128, doi:10.1029/ JA085iA13p08107. Sagdeev, R. Z., V. M. Linkin, J. E. Blamont, and R. A. Preston (1986), The VEGA Venus balloon experiment, Nature, 328, 510 – 512. Salby, M. L. (1996), Fundamentals of Atmospheric Physics, 627 pp., Elsevier, New York. Sańchez-Lavega, A., et al. (2008), Variable winds on Venus mapped in three dimensions, Geophys. Res. Lett., 35, L13204, doi:10.1029/ 2008GL033817. Saunders, R. S., R. E. Arvidson, J. W. Head III, G. G. Schaber, E. R. Stofan, and S. C. Solomon (1991), An overview of Venus geology, Science, 252, 249 – 252, doi:10.1126/science.252.5003.249. Schubert, G. (1983), General circulation and the dynamical state of the Venus, in VENUS, pp. 681 – 765, Univ. of Ariz. Press, Tucson. Schubert, G., and R. L. Walterscheid (1984), Propagation of small-scale acoustic-gravity waves in the Venus atmosphere, J. Atmos. Sci., 41, 1202 – 1213. Seiff, A., D. B. Kirk, R. E. Young, R. C. Blanchard, J. T. Findlay, G. M. Kelly, and S. C. Sommer (1980), Measurements of thermal structure and thermal contrasts in the atmosphere of Venus and related dynamical observations: Results from the four Pioneer Venus probes, J. Geophys. Res., 85, 7903 – 7933, doi:10.1029/JA085iA13p07903. Seiff, A., J. T. Schofield, A. J. Kliore, F. W. Taylor, S. S. Limaye, H. E. Revercomb, L. A. Sromovsky, V. V. Kerzhanovich, V. I. Moroz, and M. Y. Marov (1985), Models of the structure of the atmosphere of

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Venus from the surface to 100 kilometers altitude, Adv. Space Res., 5(11), 3 – 58, doi:10.1016/0273-1177(85)90197-8. Stull, R. B. (1976), Internal gravity waves generated by penetrative convection, J. Atmos. Sci., 33, 1279 – 1286, doi:10.1175/15200469(1976)0332.0.CO;2. Svedhem, H., et al. (2007), Venus Express: The first European mission to Venus, Planet. Space Sci., 55, 1636 – 1652, doi:10.1016/j.pss.2007. 01.013. Taylor, F. W., et al. (1980), Structure and meteorology of the middle atmosphere of Venus: Infrared remote sensing From the Pioneer Orbiter, J. Geophys. Res., 85, 7963 – 8006, doi:10.1029/JA085iA13p07963. Toigo, A., P. J. Gierasch, and M. D. Smith (1994), High resolution cloud feature tracking on Venus by Galileo, Icarus, 109, 318 – 336, doi:10.1006/icar.1994.1097. Yamamoto, M. (2003), Gravity waves and convection cells resulting from feedback heating of Venus’ lower clouds, J. Meteorol. Soc. Jpn., 81, 885 – 892, doi:10.2151/jmsj.81.885. Young, R. E., R. L. Walterscheid, G. Schubert, A. Seiff, V. M. Linkin, and A. N. Lipatov (1987), Characteristics of gravity waves generated by surface topography on Venus: Comparison with the VEGA balloon results, J. Atmos. Sci., 44, 2628 – 2639, doi:10.1175/15200469(1987)0442.0.CO;2. Young, R. E., R. L. Walterscheid, G. Schubert, L. Pfister, H. Houben, and D. L. Bindschadler (1994), Characteristics of finite amplitude stationary gravity waves in the atmosphere of Venus, J. Atmos. Sci., 51, 1857 – 1875, doi:10.1175/1520-0469(1994)0512.0.CO;2.

P. Drossart, Observatoire de Paris, LESIA, 5 place Jules Janssen, F-92195 Meudon, France. R. Hueso, J. Peralta, and A. Sańchez-Lavega, Departamento de Fı´sica Aplicada I, Escuela Tećnica Superior de Ingenierıós, Universidad del Paı´s Vasco, Alameda Urquijo s/n, E-48013, Bilbao, Spain. (javier.peralta@ ehu.es) G. Piccioni and O. Lanciano, Istituto di Astrofisica Spaziale e Fisica Cosmica, INAF, via del Fosso del Cavaliere 100, I-00133 Rome, Italy.

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Venus Express/VIRTIS observations of middle and lower cloud variability and implications for dynamics K. McGouldrick,1 K. H. Baines,2 T. W. Momary,2 and D. H. Grinspoon1 Received 1 February 2008; revised 27 June 2008; accepted 20 August 2008; published 18 November 2008.

[1] We present an analysis of Venus Express Visible and Infrared Thermal Imaging

Spectrometer (VIRTIS) data, carried out to characterize the morphological, geographical, and evolutionary trends of the middle and lower cloud features that are observed in the atmosphere of Venus as variations in brightness temperatures in specific near-infrared wavelengths. In this preliminary study, we analyze only data collected over the span of 11 orbits. The mean radiance as a function of latitude is consistent with previous ground-based observations, indicating that the overall global distribution of mean cloud cover is stable, at least on a 10- to 20-year time scale. In contrast with the consistent level of radiance at high latitudes, a significant amount of variability to the radiance exists at lower latitudes, consistent with significant convective activity in the lower and middle cloud decks. The morphology of the holes tends from highly variable orientations of features with aspect ratios of nearly one at low latitudes, to very large aspect ratios and zonally oriented features at higher latitudes. The peak radiance of the holes appears not to demonstrate a latitudinal tendency. There is evidence of more variability to the morphology and radiance of features at lower latitudes. To investigate the evolution of the holes, we examine a sequence of images taken over a 5 h span of a single orbit. If this limited amount of data is representative, then the typical e-folding time scale for the evolution of a hole is about 1 day. Citation: McGouldrick, K., K. H. Baines, T. W. Momary, and D. H. Grinspoon (2008), Venus Express/VIRTIS observations of middle and lower cloud variability and implications for dynamics, J. Geophys. Res., 113, E00B14, doi:10.1029/2008JE003113.

1. Introduction [2] Holes in the middle and lower cloud decks of Venus were first discovered as spatial inhomogeneities in the nearinfrared brightness temperatures [Allen and Crawford, 1984]. Thermal emission from the lower atmosphere and surface of Venus escapes through narrow spectral windows among the near-infrared absorption bands of carbon dioxide and water vapor. This emission is scattered by the sulfuric acid cloud decks. From tracking of the features as they traversed the planet and detailed radiative transfer analysis of the spectra of those features, it was recognized that they represented variations in the lower and middle cloud opacity [Crisp et al., 1989]. [3] The clouds are a defining characteristic of the atmosphere of Venus. They completely enshroud the planet between the altitudes of about 50 and 70 km. Consequently, except for the variations noted in the previous paragraph, the clouds render the lower atmosphere difficult to observe in infrared, visible, and ultraviolet wavelengths. The clouds of Venus have been implicated as playing a significant role 1 Department of Space Sciences, Denver Museum of Nature and Science, Denver, Colorado, USA. 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA.


in the maintenance of the Venus greenhouse effect [Pollack et al., 1980]. Thus, breaks in the clouds, which allow a greater flux of radiation to escape (and absorb a smaller amount of solar radiation), can have a significant effect on the thermal balance of Venus. This can have significant ramifications, especially in terms of the long-term climate evolution of Venus [Crisp, 1989; Hashimoto and Abe, 2000; Bullock and Grinspoon, 2001]. [4] The atmosphere of Venus exhibits a global superrotation at most altitudes. In order to sustain the global superrotation, there must be vertical transport of horizontal momentum to the upper atmosphere, specifically between about 40 and 80 km. Vertical transport of momentum by eddies and by waves has been suggested [Schubert et al., 1980]. However, both the eddies and the waves are going to interact in some way with the clouds that occupy the altitudes between 50 and 70 km. Most likely, vertically traveling waves and eddies will be dissipated by the regions of instability in the vicinity of the clouds. The radiative dynamical feedback that supports the cloud [Pollack et al., 1980; McGouldrick and Toon, 2007], maintains a region of dynamic instability in the middle and lower cloud region, which can limit the efficiency of vertical momentum transport by wave propagation. The shear profile and the static stability profile, both of which affect the vertical transport of energy that is brought about by convection, will have the largest effect on what happens to these vertically propagating waves and eddies. Furthermore, the morphological appear-

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MCGOULDRICK ET AL.: VIRTIS OBSERVATIONS OF VENUS CLOUDS

ance of cloud features (aspect ratio and ‘‘tilt’’ relative to the zonal direction) can offer information on the nature of these atmospheric characteristics (shear profile, static stability profile, and the amount of convective overturning). Thus, the morphology of the clouds can provide insight to the dynamical environment; hence, the efficiency of vertical momentum transport by these processes. [5] For these reasons, an understanding of what sustains the clouds, and what sorts of changes are responsible for the formation of holes in those clouds, is necessary in order to understand the Venus atmosphere as a whole. In this paper, we compare the results and predictions of several recent theoretical studies of the clouds of Venus, to recent observations made by the Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) on Venus Express. We analyze the possible existence of latitudinal tendencies of cloud morphology and overall cloud opacity, as well as the typical cloud feature lifetimes. 1.1. Previous Observations of the Clouds and Holes [6] Crisp et al. [1989] showed that the near-infrared features discovered by Allen and Crawford [1984] were caused by variations in the opacity of the middle and lower cloud decks. The observations by Crisp et al. [1989] also provided an early general characterization of the morphology and distribution of the cloud features at wavelengths of 1.74 mm and 2.3 mm in the near infrared. Crisp et al. [1989] observed an apparent latitudinal dependence of the overall cloud opacity, in which the opacity was greatest at high latitudes, and a relatively featureless bright band associated with diminished cloud opacity that often existed at mid latitudes (around 40° to 60°). Crisp et al. [1989] also noted occasional significant asymmetry between the brightness (hence, opacity) of the northern and southern hemispheres. [7] The flyby of the Galileo spacecraft in February of 1990 provided an excellent opportunity for the study of these near-infrared cloud features. Belton et al. [1991] observed the clouds of Venus with the Galileo solid-state imaging (SSI) camera. It is important to note, however, that the features observed by SSI likely are due to cloud variability at a higher altitude (i.e., about 60 to 65 km) than the cloud variability observed in the near-infrared spectral windows on the nightside (i.e., around 50 to 55 km). This is partly because the dayside observations detected solar infrared radiation that has been reflected from near the tops of the clouds, whereas the nightside observations detected surface-emitted infrared radiation transmitted through the clouds from below. Gaseous absorption by the atmosphere extinguishes the emitted or reflected radiation at much higher altitudes outside of the narrow spectral windows. Thus, the broadband observation exhibits contributions from a greater range of altitudes (mostly higher) than does the narrow-band observation. Nevertheless, Belton et al. [1991] noted that cloud features tended to appear ‘‘patchy’’ at low latitudes and ‘‘streaky’’ at high latitudes. Viewing the cloudy atmosphere at spatial resolutions as fine as 17 km per pixel, Belton et al. [1991] detected changes in cloud morphology on time scales only in excess of an hour or two; generally on the order of 1 day. They also noted the presence of ‘‘bright-rimmed cellular features,’’ suggestive of cellular convective elements that had lifetimes in excess of a day, and sizes on the order of a few hundred kilometers.

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Blamont et al. [1986] also noted a tendency of cloud features at higher latitudes to tilt relative to the zonal direction in a way that suggested a spiral pattern. Such a spiral (i.e., the polar vortex) is known to exist over both the north and south poles of Venus [Taylor et al., 1979; Piccioni et al., 2007]. The latitudinal extent of the Piccioni et al. [2007] observations is only poleward of about 50° latitude. Analysis of the tilting of cloud features in images centered over middle and lower latitudes can place constraints on the equatorward influence of these polar vortices. [8] Carlson et al. [1991], observing the nightside of Venus with the Galileo Near-Infrared Imaging Spectrometer (NIMS), noted that contrast between the brightest and darkest regions of the nightside of Venus at 1.74 mm was about 5:1. Carlson et al. [1991] also noted the existence of linear features whose tilt relative to the parallels of latitude implied meridional transport, presumably by Hadley circulation. These features seem to be similar in appearance to those described by Blamont et al. [1986] and Belton et al. [1991]; but Carlson et al. [1991] noted that establishment of a direct correlation of features between the two instruments (NIMS and SSI) was uncertain. [9] While the Galileo Near-Infrared Mapping Spectrometer (NIMS) was able to obtain only, essentially, a snapshot of the planet during the flyby, ground-based observations by Crisp et al. [1991a], spanning the weeks leading up to and following the flyby, were able to provide some context for the NIMS observations. Crisp et al. [1991a] detected a latitudinal distribution of cloud opacity similar to that observed by Crisp et al. [1989]. That is, they noted that high-latitude regions tended to be ‘‘dark and featureless,’’ whereas midlatitude regions tended to be ‘‘occupied by bright quasi-zonal bands.’’ In addition, low latitudes were noted to exhibit the most variability, both in terms of feature opacity and size. Crisp et al. [1991a] also noted the existence of a very large (hemisphere-scale) dark region that had persisted throughout the duration of the observing sessions (about 7 weeks). Chanover et al. [1998] also noted the existence of such a hemispherical asymmetry. Finally, Crisp et al. [1991a] noted that the features that were visible to Galileo during the flyby were of a relatively small scale that tended not to persist for more than about a week (i.e., about one atmospheric rotational period). [10] Markiewicz et al. [2007b] described observations of Venus cloud structure observed with the Venus Monitoring Camera (VMC) [Markiewicz et al., 2007a] on board Venus Express. While the infrared channels of VMC can be used to probe the middle and lower clouds of Venus, the observations reported by Markiewicz et al. [2007b] are mostly in the ultraviolet channel. Thus, their observations are of cloud features located largely above 65 km altitude. They observe the polar vortex as a dark spiral structure in the ultraviolet images. They observe a mottled cloud structure at low latitudes, which they attribute to vigorous convection in the upper cloud deck, with the sizes of typical convective cells being about 30 km. They also report waves with wavelengths on the order of 10 to 30 km in the upper cloud region. 1.2. Previous Simulations of the Clouds and Holes [11] Simulations suggest that the global dynamics of Venus have a significant effect on the formation and distribution of the Venus clouds and their holes. Imamura

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and Hashimoto [1998] showed that the Hadley circulation could result in enhancements of cloud opacity in the vicinity of both the ascending branch and the descending branch of the near-global Hadley cell. McGouldrick and Toon [2008b] showed that differences in the structure of the vertical shear of the zonal winds could have an effect on the evolution of large (2000 km) holes in the clouds of Venus. McGouldrick and Toon [2008a] showed that convective cells with typical size and separation of about 30 km tended to generate holes with lifetimes on the order of an hour or two, and with optical depths that were consistent with observations. McGouldrick and Toon [2008a] also showed that gravity waves, with characteristics similar to those of waves putatively launched by convection that have been both observed [Hinson and Jenkins, 1995] and simulated [Baker et al., 2000], can generate wave-like features in the clouds with 10 to 30 km wavelengths, visible in the emitted 1.74 mm radiation. [12] Imamura and Hashimoto [1998] suggested that the Hadley circulation determines the latitudinal variation in the middle and lower cloud deck opacity. Upwelling in the ascending branch near the equator leads to enhanced opacity because of the influx of sulfuric acid vapor into the cloud region. Meridional transport toward the descending branch of the Hadley circulation, which they reasoned to occur poleward of 55° latitude, also leads to enhanced cloud opacity because of an accumulation of upper cloud mass that has been transported poleward by the upper level Hadley circulation (because the time scale for the sedimentation of the upper cloud particles was considerably longer than the time scale for poleward transport of those particles by the Hadley circulation). Furthermore, the bright region between about 40° and 60° latitude could be explained as an absence of these cloud-thickening mechanisms. They also found that if the Hadley circulation were slower, then the contrast between the brightness at midlatitudes and that at high latitudes would be less severe, as a result of a thinning of the upper cloud at high latitudes. If the Hadley circulation were less efficient, then the time for the upper level flow to travel from equator to pole no longer would be large compared with sedimentation times of the upper cloud particles. Hence, less photochemically produced mass is transported to high latitudes. They also found that the bright band was located more equatorward in the case of a less efficient Hadley circulation. Hence, it may be possible to determine the magnitude of the Hadley circulation by determining the latitudinal location of the high-latitude peak in total cloud opacity [Imamura and Hashimoto, 1998]. [13] The middle and lower cloud decks of Venus are sustained by a radiative dynamical feedback, whereby heating of the cloud base by the very warm deep atmosphere drives an unstable lapse rate within the region occupied by the middle and lower cloud decks [McGouldrick and Toon, 2007]. Furthermore, the time scales for growth and for dynamical transport (by winds, and by convection, when present) are much shorter than the radiative time scale. Hence, in the simulations by McGouldrick and Toon [2007], the structure of the clouds was dominated by brief, intense periods of vertical transport (i.e., convection), separated by longer periods of quiescence during which the thermal instability developed.

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[14] McGouldrick and Toon [2008b] suggested that the nature of the vertical wind profile affects the longevity and possibly the morphology of large holes (2000 km) in the clouds. Specifically, the vertical shear of the zonal wind, coupled with the radiative dynamical feedback, can effectively dissipate large holes in the clouds. Furthermore, the nature of the vertical shear profile affected the time required to smooth out a simulated hole in the clouds. In simulations exhibiting negligible vertical shear throughout the middle cloud deck (as have been observed at low latitudes and midlatitudes by Pioneer Venus [Counselman et al., 1980] and measured via multispectral wind tracking with VIRTIS on board Venus Express [Sańchez-Lavega et al., 2008]), the lifetimes of holes were longer than in simulations in which the shear was at a constant magnitude throughout the entire cloud domain (i.e., shear similar to what has been observed at higher latitudes, especially by the Pioneer Venus north probe). Although the radiative dynamical feedback was able to limit the zonal ‘‘stretching’’ of holes in the simulations by McGouldrick and Toon [2008b] that exhibited minimal shear within the middle and lower cloud decks, the holes were stretched out across large zonal distances in simulations that exhibited greater amounts of shear. Such differences in the aspect ratios of features in the Venus clouds observed by VIRTIS could indicate the vertical structure of the zonal winds in the region of the middle and lower cloud decks of Venus. That is, holes that become highly zonally elongated may be in regions of high vertical shear of the zonal wind; and holes that maintain a more blocky appearance may be in regions of low vertical shear of the zonal wind. [15] An alternative cause for this zonal stretching of cloud features could be horizontal shear of the zonal wind. However, observations by both the Venus Monitoring Camera and VIRTIS on Venus Express indicate that there is negligible meridional shear of the zonal wind equatorward of about 50° latitude [Markiewicz et al., 2007b; Sańchez-Lavega et al., 2008]. Thus, at latitudes equatorward of 50°, the only contribution to stretching by the meridional shear of the zonal wind arises from the change in planetary circumference with latitude (f): @w 86400 u tan f ¼ ; @f RV cos f

ð1Þ

where RV = 6100 103 m is the radius of the planet at the altitude of the clouds, w is the angular velocity of the atmosphere in units of ° longitude per day, and the factor 86400 is needed to convert the units of the RHS to day1. For f 50°, and u = 60 m s1 from Sańchez-Lavega et al. [2008], this rate does not exceed 1.6° longitude per day per degree of latitude. Thus, among features at midlatitudes to low latitudes, only those holes that persist for several days, can be stretched significantly by the meridional shear. Furthermore, the stretching of the cloud features cannot be the result of zonal shear of the zonal wind. Such a situation would result in horizontal convergence or divergence, which would force vertical motion of atmospheric parcels. Such vertical motion can create significant changes in cloud opacity [McGouldrick and Toon, 2007, 2008a], but will not cause a zonal elongation of the features. [16] McGouldrick and Toon [2008a] analyzed the effect of gravity waves and convection cells on the appearance of

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Table 1. Data Analyzed in Morphological Analyses File Name

Orbit

VI0363_00 VI0364_00 VI0365_00 VI0366_00 VI0367_00 VI0368_00 VI0369_00 VI0370_00 VI0371_00 VI0372_00

363 364 365 366 367 368 369 370 371 372

Start Date 18 19 20 21 22 23 24 25 26 27

Apr Apr Apr Apr Apr Apr Apr Apr Apr Apr

2007 2007 2007 2007 2007 2007 2007 2007 2007 2007

Start Timea

Exposure Timeb (s)

Distance (km)

2017:56 2017:10 2016:25 2015:42 2014:57 2014:12 2013:25 2012:41 2011:55 2011:10

0.36 3.3 0.36 3.3 0.36 3.3 0.36 3.3 0.36 3.3

67021.2 66775.9 66468.1 66226.3 67209.0 67226.6 67218.5 67223.6 67203.3 67220.6

a

Times and dates are UTC. b Exposure time is per slice.

the clouds of Venus. They found, from a simple kinematical model used to drive the microphysical model utilized by McGouldrick and Toon [2007], that convective cells with sizes on the order of 30 km and vertical velocities of ±2 m s1 produce optical depth variations comparable in magnitude to those that have been observed in the clouds of Venus [Grinspoon et al., 1993], and which experience lifetimes on the order of hours. These simulated convective cells are consistent with those that have been observed with the Vega balloons [Linkin et al., 1986], and that have been generated in convective simulations by Baker et al. [2000]. McGouldrick and Toon [2008a] also found that gravity waves, such as those found in the simulations of Baker et al. [2000], and in the occultation observations by Hinson and Jenkins [1995], would lead to the appearance of wave-like structures in the lower clouds of Venus, with wavelengths comparable to the size of the convective cells. 1.3. VIRTIS [17] The VIRTIS-M IR channel is a medium (spatial) resolution (0.25 mrad) infrared mapping spectrometer that covers the range of wavelengths from 1 mm to 5 mm, with a spectral resolution of roughly l/Dl250. This is sufficient spectral resolution to resolve the spectral windows [Drossart et al., 2007]; and the spatial resolution allows for the detection of features as small as about 30 km when the spacecraft is at apoapse (d66,000 km). When the spacecraft is within about 10,000 km of the planet during the course of its highly eccentric orbit, it is moving too quickly for VIRTIS to operate very effectively in its mapping modes [Drossart et al., 2007]. Thus, all of the data we analyze in this paper are obtained at spacecraft distances greater than 10,000 km. [18] With clever pointing over the course of multiple orbits, a significant latitudinal and longitudinal coverage of the clouds of the southern hemisphere may be built up, which allows for the determination of possible latitudinal tendencies in the location of and optical thickness of the clouds. In the course of a single orbit, it is possible to observe several hours worth of cloud evolution (up to about 6 h) of a small region of the Venus atmosphere (defined by the field of view common to all of the images in the series: the maximum possible region being the total field of view of the image taken when the spacecraft is closest to the planet). Analysis of longer-term evolution is possible, but requires a repointing of the spacecraft on successive orbits, since the

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previously observed region of the atmosphere will have rotated out of the relatively narrow field of view by the time the spacecraft next arrives in position for observation by the mapping modes of VIRTIS. Even with successive repointings that follow the rotation of the atmosphere, significant changes in cloud morphology during the time that VIRTIS-M is unable to observe the planet frequently make difficult the recovery of features common to observations on successive orbits.

2. Observational Goals and Methods [19] Specific latitudinal variations in overall cloud opacity have been predicted to be a consequence of the Hadley circulation [Imamura and Hashimoto, 1998], and have been recorded by ground-based observations [Crisp et al., 1991a]. Does a large-scale latitudinal dependence of cloud cover exist in the VIRTIS data, as suggested by previous ground-based observations? Are there significant spatial or temporal variations in the latitudinal distribution of cloud cover? Tendencies in cloud shape (aspect ratio) and orientation (angle of ‘‘long axis,’’ relative to the zonal direction) can indicate differences in the shear structure or in the level of convection that is occurring. Does a latitudinal tendency to cloud shape or orientation exist in the VIRTIS data? Since the clouds represent a significant contributor to the greenhouse effect on Venus, their variability can alter the thermal balance of the planet. How variable are the clouds, and how does cloud variability affect the thermal balance? We analyze a subset of the VIRTIS-M IR observations to attempt to answer these questions concerning the interactions between the atmospheric dynamics and the cloud structure of Venus. [20] In all cases, we perform our analyses using the 1.74 mm image of the VIRTIS-M IR data. We choose this wavelength band over those in the other spectral windows because the 1.74 mm band possesses the best combination of high peak radiance and sufficient contrast between cloudy and clear observations; thus, the signal-to-noise ratio of both the brighter holes and the darker clouds is greater than it is for any other spectral image. Furthermore, while the images in the 2.3 mm window region demonstrate a greater overall brightness contrast (around 20:1 in the Galileo NIMS data, compared with a roughly 5:1 ratio at 1.74 mm [Carlson et al., 1991]), the radiance in the cloudiest regions, is barely distinguishable (and possibly indistinguishable) from the background noise. In order to analyze the latitudinal distribution of cloud cover and cloud morphology on Venus, we use images/cubes taken near the apoapse (66,000 km) of several successive orbits (orbits 363 through 372), as summarized in Table 1. We use images obtained near apoapse in order to maximize our spatial coverage of the planet, with only a small loss in spatial resolution. The data are calibrated using the VIRTIS PDS/IDL software library, as described by Drossart et al. [2007]. [21] Figure 1a demonstrates the overall coverage of the middle atmosphere by these selected observations. In generating this plot, we assume that the atmosphere rotates at a solid body rate of once per 5.5 days (a speed of about 80 m s1 at the equator, or about 60 m s1 at 40° latitude), and shift the geographic longitudes of each observation to the east, according to the time separation of each image from

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[22] Figure 1b shows the frequency of observations by location in the atmosphere. We see that among these images there is significant overlap, such that about 35% of the atmosphere between +10° latitude and 90° latitude has been imaged at least twice; and some regions (such as the one centered near 50° longitude and 40° latitude) have been observed three times or more. Such repeated coverage of the same part of the atmosphere gives hope to the possibility of ultimately extending the analysis of the variation in feature morphology and evolution from time scales no greater than 6 h (the extent of observation possible during a single orbit) to time scales on the order of days.

Figure 1. Distribution and frequency of coverage of the atmosphere by the images analyzed in this paper. (a) The regions of the atmosphere that have been imaged at least once over the course of the considered images (crosshatched regions). (b) The frequency of coverage of the atmosphere. The numbered contours indicate the number of images among those considered here that view the same region of the atmosphere. Both plots assume the atmosphere rotates as a solid body with a period of 5.5 days.

the first image (orbit 363). We choose a planet-circling period of 5.5 days for the clouds because that period is consistent with previous observations of lower and middle cloud tracking [Allen and Crawford, 1984; Crisp et al., 1991b]. According to the recent analysis of wind speeds from VIRTIS data by Sańchez-Lavega et al. [2008], a zonal wind speed of about 80 m s1 corresponds to an altitude between 58 and 70 km, for latitudes equatorward of 50°. A 5.5 day period at 40° latitude is consistent with the 60 m s1 winds reported by Sańchez-Lavega et al. [2008] at altitudes between 46 and 61 km. Though it is an oversimplification to assume solid body rotation, doing so allows us to obtain at least a first-order estimate of the atmospheric coverage of the observations. It also helps to facilitate the identification of features from image to image, despite their sometimes significant evolution (see section 2). Figure 1a shows that the images that we consider for this particular analysis provide us with coverage of a significant fraction of the southern hemisphere atmosphere (about 72% coverage of the area between the latitudes of +10° and 90°, with most of the missing regions lying poleward of 70°). This extensive coverage of the atmosphere allows us to have confidence in the conclusions that we draw regarding the latitudinal distributions of the features. If these images did not cover so much of the planet longitudinally, our analyses might have been blind to hemispherical variations such as those that have been reported by Crisp et al. [1989, 1991b].

2.1. Latitudinal Distribution of Cloud Cover and Hole Morphology [23] Crisp et al. [1989, 1991b] noted the appearance of a significant latitudinal distribution of cloud cover in which midlatitudes tended to be brighter, and higher latitudes tended to be darker. Imamura and Hashimoto [1998] provided a theoretical explanation of such a latitudinal distribution of cloud opacity, which, they determined, is caused by the action of the Hadley circulation. Here again, the tremendous volume of the VIRTIS data can play a significant role in confirming or refuting the existence and consistency of such a distribution of cloud opacity. Additionally, the VIRTIS data can be used to more finely tune such simulations of the Hadley circulation on Venus. To analyze the latitudinal distribution of cloud cover, we remap the VIRTIS data to a cylindrical projection and correct for the emission angle by applying the correction used by Carlson et al. [1993]. We then find the mean and standard deviation of the radiance as a function of latitude for each image. We also calculate a longer-term average latitudinal distribution of radiance, in which we average over all 10 images considered here. [24] Belton et al. [1991] suggested that there was a trend in the shapes of the features as a function of latitude. Specifically, the features at lower latitudes appeared ‘‘blocky,’’ whereas those at higher latitudes appeared streaky. The great volume of VIRTIS data from Venus Express can indicate whether these trends noted by Belton et al. [1991] are real and/or persistent. We investigate these possible morphological tendencies of the clouds by identifying features in each image, and determining a longitudinal versus transverse aspect ratio for each feature. The features are identified by using the latitudinal profile of the mean radiance of each image, as described in section 2.1. We subdivide the image into latitude bands that are no greater than 15° in extent. We find the mean and the standard deviation of the radiance over those latitudes in that image, and draw contours of the mean and the mean plus one standard deviation onto the image (Figure 2). A feature is identified if the mean plus standard deviation contour is closed (or nearly so) and convex. At higher latitudes, where the longitudinal variation is smaller and the latitudinal variation is greater, we use narrower bands to determine the defining contours. We determine the longitudinal versus transverse aspect ratio by measuring the distances along the long and short axes of each feature. The feature is assigned latitude and longitude coordinates that identify its left (L), right (R), top (T), and bottom (B) extent. The aspect ratio is then the ratio of the LR arc to the TB arc. Thus, a large aspect ratio indicates a highly elongated feature,

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Figure 2. (right) 1.74 mm VIRTIS image VI0383_00. (left) The blowup highlights and zooms in on the boxed region of the VIRTIS image. The parameters measured in the course of the morphological analyses are shown in the zoomed image at left. whereas an aspect ratio closer to one indicates a more blocky feature. [25] We also accumulate information on the tilt of the features relative to the parallels of latitude. The tilt angle is taken to be the angle that the LR arc of the feature makes relative to the parallels of latitude. Depending upon the magnitude of the meridional and zonal winds, a tilt measured from the parallels of latitude can indicate meridional flow or shear of cloud material or energy or a significant vertical shear of the zonal wind. 2.2. Cloud/Hole Lifetimes [26] The duration of holes in the clouds of Venus can indicate the nature of the processes that sustain them. McGouldrick and Toon [2008b] suggested that differences in the vertical profile of the zonal wind could lead to differences in the longevity of the features. Furthermore, McGouldrick and Toon [2008b] noted that a combination of vertical shear of the zonal wind and the vertical motions driven by the radiative dynamical feedback was sufficient to dissipate a large (2000 km) hole in a time scale of under two weeks. Crisp et al. [1991b] reported that a 2000 km hole persisted for at least two weeks. However, since the initial formation of that particular feature was not observed, this is only a lower limit. Furthermore, the spatial resolution of VIRTIS on Venus Express, even at apoapse, is far

superior to what Crisp et al. [1991b] were able to achieve from Earth. Thus, the analysis of the evolution of smaller features is now possible. Since the absorption of upwelling infrared radiation plays a role in sustaining the middle and lower clouds [Pollack et al., 1980; McGouldrick and Toon, 2007], the longevity of holes affects the magnitude of possible horizontal temperature or pressure variations that would result from such heating. Strong horizontal temperature or pressure gradients will affect the magnitude of vertical motions triggered by the radiative dynamical feedback. [27] To determine the typical lifetime of the cloud features, we analyze the VIRTIS-M data from orbit 383. This orbit contains a sequence of cubes that spans 5 uninterrupted h, and each cube in the sequence exhibits exposure times long enough that the nightside (rather than the dayside) is exposed ideally (Table 2). Since the Venus Express spacecraft is approaching Venus throughout the series of acquired cubes in each orbit, there is only a small area of the planet’s atmosphere, which cannot exceed the field of view of the last cube acquired (i.e., when the spacecraft is at its closest to Venus), that contains features that can be tracked for the entirety of the sequence. [28] We first identify features that persist throughout the series of observations. That is, we limit our analysis to features that persist for at least 5 h, and do not stray beyond the instrumental field of view. The zonal superrotation

Table 2. Data Analyzed in Evolution Analyses File Name

Orbit

VI0383_00 VI0383_01 VI0383_02 VI0383_03 VI0383_04 VI0383_05 VI0383_06 VI0383_07 VI0383_08 VI0383_09 VI0383_10

383 383 383 383 383 383 383 383 383 383 383

Start Date 8 8 8 8 8 8 8 8 9 9 9

May May May May May May May May May May May

2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007

Start Timea

Exposure Timeb (s)

Distance (km)

Resolution (km pixel1)

2028:12 2058:12 2128:12 2158:12 2228:12 2258:12 2328:12 2358:12 0028:12 0058:12 0128:12

3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3

67106.1 66178.0 65055.9 63735.1 62426.3 60678.6 58710.8 56515.1 54343.1 51631.1 48668.4

16.8 16.5 16.3 15.9 15.6 15.2 14.7 14.1 13.6 12.9 12.2

a

Times and dates are UTC. Exposure time is per slice.

b

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Figure 3. All 10 images considered in the morphological analyses, combined into a single image. The atmosphere is assumed to rotate as a solid body, with a planet-circling period of 5.5 days, as in Figure 1. makes it difficult to be certain of the identity of each feature from image to image, so we crudely correct for the motion of the atmosphere by shifting each image longitudinally at a corresponding rate of about 2.73° hr1 (which equates to a 5.5 day period). This adjustment renders many of the features nearly stationary in the sequence of images, thus identification of the same hole in subsequent images was facilitated. The boundary of each feature is determined in the same manner as the feature boundaries in the morphological analysis above (section 2.1). We measure the peak radiance and the contrast between the peak radiance and the background radiance for each feature that we were able to track in this sequence of images.

well as the overall mean and standard deviation of all of the data considered here. The overall mean indicates that the 10 images that we have analyzed for this paper demonstrate the same tendencies that have been noted by previous

3. Results 3.1. Latitudinal Distribution of Cloud Cover [29] Figure 3 demonstrates the approximate spatial coverage of cloudy and clear regions in the data considered in this paper. All of the considered 1.74 mm images are plotted onto a cylindrical projection. As in Figure 1, the atmosphere is assumed to rotate as a solid body with a period of 5.5 days, so each image has been advected longitudinally according to the time it was acquired relative to the first image (orbit 363). Overlapping regions are averaged together. There is possibly a hint of longitudinal hemispherical asymmetry, similar to that observed by Crisp et al. [1991a], whereby longitudes east of 180° in these data are brighter than those west of 180°; but this conclusion might be biased by the existence of a single very large and very bright feature that spans the region from 220° to 300° longitude and 20° to 5° latitude. Although the data cover nearly two full atmospheric rotations, the coverage and consistency seems to be insufficient to determine whether a longitudinal hemispherical asymmetry exists in these data. [30] Figure 4 shows the latitudinal profile of the mean radiance for each of the images considered in this paper as

Figure 4. Latitudinal profile of the mean radiance for each of the 1.74 mm images considered in this paper. The profiles of the various images are plotted as points. The mean of all of the data considered is shown as a solid line, and range of ±1 standard deviation of all of the data considered is shown with a pair of dashed lines. Also shown here, for comparison, is an estimate of the emitted radiance that results from an application of the cloud properties as derived from the Pioneer Venus Lower Cloud Particle Size Spectrometer (LCPS) by Knollenberg and Hunten [1980]. The vertical dotted line is the calculated radiance; the horizontal dotted line is the latitude of the probe’s descent (the probe actually descended in the northern hemisphere, but we assume roughly hemispherical symmetry only for the purpose of making this comparison).

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Figure 5. Aspect ratio (long axis divided by short axis) as a function of latitude. The data also have been binned into 20° latitude regions, with error bars indicating the standard deviation of aspect ratio in each region. One feature was identified poleward of 60°, which is shown in Figure 5, but obviously could not be sorted into any of the defined latitude bins. observers [Crisp et al., 1991a; Chanover et al., 1998]. Low latitudes tend to be fairly bright, as well as middle to high latitudes (between about 40° to 60°). There is typically less radiance emitted in middle to low latitudes (between about 20° and 40°). The polar collar, which is not easily detected in ground-based observations, can be seen in these images as a region of significantly diminished radiance poleward of about 60°. [31] Also shown in Figure 4, for comparison, is the estimated radiance at the location of the Pioneer Venus Large Probe. Although the Large Probe descended in the northern hemisphere, the location is plotted, in Figure 4, at about 5° latitude. We assume a north-south atmospheric symmetry here because the planetary obliquity is only 3°, indicating minimal seasonal variations, and because previous observations are consistent with such a north-south symmetry, with only a few, probably transient, exceptions. Recent observations by Venus Express indicate that the dynamics of the south polar region are similar to those observed of the north polar region by Pioneer Venus, further supporting the likelihood of north-south hemispheric symmetry [Piccioni et al., 2007]. This calculation of radiance experienced by the Pioneer Venus Large Probe assumes the cloud properties derived by Knollenberg and Hunten [1980], along with a factor of three reduction in the number of mode 3 particles [Pollack et al., 1980]. This represents the radiance that results from a typical assumed set of Venus cloud properties and mass. The optical depth of the middle cloud (i.e., the optical depth below about 57 km) that was used to generate this radiance is approximately t 23. This radiance is calculated using the model of McGouldrick and Toon [2007, 2008a, 2008b]. For a lack of an alternative, previous studies have taken this in situ observation (or reanalyses thereof) of the Venus cloud to be representative of the planetary cloud cover, as a whole [Toon et al., 1982; Grinspoon et al., 1993; James et al., 1997; Imamura and

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Hashimoto, 1998; McGouldrick and Toon, 2007]. Figure 4 shows that this clearly is not the case, since the predicted radiance of the cloud observed in situ by the Pioneer Venus Large Probe is closer to that of the maximum cloudiness (minimum radiance) encountered in the course of the 10 orbits analyzed here. Furthermore, the significant variability in radiance demonstrates the necessity of taking into account both the spatial and temporal cloud variability when determining the radiative balance of the middle atmosphere of Venus. [32] As mentioned in section 1.2, Imamura and Hashimoto [1998] explained that the latitudinal brightness variations seen by Crisp et al. [1991a] likely were a consequence of the global-scale Hadley cell that exists in the atmosphere of Venus. The overall average of the latitudinal distribution of radiance in these data (solid line in Figure 4) is remarkably similar to the results of those simulations. The bright zone around 50° in the data corresponds with the minimum of opacity located around 45° in the simulations of Imamura and Hashimoto [1998]. The diminished radiance poleward of about 60° may be reaching a minimum around 75°. The location of this radiance minimum also corresponds well with the increase in opacity in the nominal simulations of Imamura and Hashimoto [1998], which peak around 70° to 75°. Imamura and Hashimoto [1998] also performed a simulation in which they reduced the effectiveness of the Hadley circulation by reducing by half the speed of the meridional circulation in their simulations. In contrast with the nominal simulation, this second simulation exhibited a peak of cloud opacity somewhat more equatorward, at about 60° latitude (compared with about 75° in their nominal simulation). Although the data considered here represent only 10 days worth of observation, these data indicate that, compared with their test case with reduced meridional speeds, the nominal case of Imamura and Hashimoto [1998] is a better representation of the Hadley circulation of Venus. The increase in the variability of the observed radiance at low latitudes is consistent with the observations by Crisp et al. [1991a] and also suggests the existence of significant convective activity in the clouds of Venus. 3.2. Latitudinal Distribution of Cloud Morphology [33] In Figure 5, we plot the aspect ratio that we measured for approximately 130 features in the 1.74 mm VIRTIS images from orbits 363 through 372, as a function of the latitude of the center of the feature. In addition to plotting each individually measured aspect ratio as symbols, we also bin the measured data into 20° latitude bins. The mean and standard deviation of these three bins are also plotted in Figure 5. We see in Figure 5 that the aspect ratio of the measured holes in the clouds increases with latitude. The typical hole becomes more elongated poleward of 40° (of the 16 features with aspect ratios greater than 5:1, all but three are poleward of 40°). At low latitudes, in addition to holes having aspect ratios closer to 1.0, there also are several features with aspect ratios less than one (i.e., longer in the transverse direction than in the longitudinal direction), something that does not at all occur poleward of 40° in the images considered here. Thus, we observe a preponderance of highly elongated holes at high latitudes, and a

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it will have affected the average brightness in this latitude bin, but not the variability.

Figure 6. Tilt angle (relative to parallels of latitude) as a function of latitude. The data also have been binned into 20° latitude regions, with error bars indicating the standard deviation of tilt angle in each region. An angle of zero means zonally oriented features. The one feature poleward of 60° was not sorted into any bin. tendency for more equal aspect ratios at lower latitudes. These results are consistent with the observations reported by Belton et al. [1991], as summarized in section 1.1. [34] In Figure 6, we plot the angle that each feature makes relative to the parallels of latitude, as a function of latitude. A small ‘‘tilt angle’’ indicates that a feature is aligned nearly zonally, whereas a large tilt angle indicates a feature whose long axis is more vertically oriented. As in Figure 5, we plot each feature individually, as well as the mean and standard deviation of the tilt angle in 20° latitude bins. In Figure 6, we see that the average tilt angle is somewhat larger at low latitudes, becoming smaller at higher latitudes. This, in combination with the trends in Figure 5, indicates that the features not only become more elongated at higher latitudes, they also become more zonally oriented. There is significantly greater variation in the tilt angle at latitudes equatorward of 40°. The smaller variability poleward of 40° suggests that the polar vortex exerts its influence mainly on the poleward side of 40°. Furthermore, the variability equatorward of 40° suggests a complex interaction, likely involving convection, between the local atmospheric dynamics and the formation and evolution of the lower and middle clouds at lower latitudes. [35] Figure 7 shows the peak radiance emerging from holes as a function of latitude. As in Figures 5 and 6, we plot both the individual features themselves, as well as the averages binned over 20° latitude bins. The mean of the feature peak radiance is about 0.2 W m2 mm1 sr1, fairly consistent across all three latitude bins considered here. However, there is significantly more variability in the peak brightness of holes at low latitudes, as compared with the variability seen at higher latitudes. This too suggests that lower latitudes are demonstrating the effects of convection. Since the lower latitudes are viewed most obliquely in these data, the lower latitudes will be most affected by any errors that may occur in the correction for emission angle. However, even if our correction for the emission angle is flawed,

3.3. Feature Lifetimes [36] We might also be able to obtain information about the ages (and, ultimately, the lifetimes) of holes in the clouds from the morphology analyses. Recall, from equation (1), that the change in the atmospheric rotation period with latitude is a function of latitude only, if the zonal velocity is approximately constant. Cloud-tracking analysis by Sańchez-Lavega et al. [2008] suggests that the zonal wind is indeed a relatively constant 60 m s1 at latitudes equatorward of about 50°. If we assume that a hole is circular when it is formed, and the zonal velocity is constant, then the poleward edge of the hole advances farther than the equatorward edge of the hole, at a rate given by equation (1) times the latitudinal extent of the hole (Df). If we assume that there is no meridional motion, then the ratio of this translation in degrees longitude to the latitudinal extent of the hole is precisely our previous definition of the tilt angle of the hole! For example, a circular hole centered at 50° latitude, having a meridional extent of 2°, exhibits a roughly 3.15° longitude day1 difference between its top and bottom edges. After 1 day, this equates to a tilt angle of q = tan1(2/3.15) = 32°. After two days, the tilt angle would be 18°, and after three days, 12°. Granted, this is an extreme case. Holes that are closer to the equator experience less shearing because of this effect, since the angular velocity changes less quickly with latitude. A corresponding effect on the aspect ratio can also be calculated. Thus, these measurements of the aspect ratio and of the tilt angle, coupled with knowledge of the horizontal winds in the atmosphere, can provide a rough estimate of the ages of the holes in the clouds. [37] Using the procedure outlined in section 2, we have identified 10 holes, in the series of images taken during orbit 383, whose evolution we have been able to analyze for this paper. This is such a small number of holes that it will be difficult to determine any global trends of hole evolution,

Figure 7. Peak radiance of the holes as a function of feature latitude. The data also have been binned into 20° latitude regions, with error bars indicating the standard deviation of feature peak radiance in each region. The one feature poleward of 60° was not sorted into any bin.

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Figure 8. Derived evolutionary time scales versus peak radiance in the holes. Both axes are parameters of a log linear fit to the measured data. The error bars indicate the standard deviation error estimates of the fitted parameters. whether as a function of position or size. Furthermore, the difficulty in establishing a method of hole definition that is consistent across successive images (i.e., that identifies the same hole, and is not fooled by changes in morphology or surroundings) may introduce small additional uncertainties. For the present investigation, we quantify these numerous error sources in the uncertainties of the fits to the data, as described in subsequent paragraphs. In future investigations we will improve the level of quantification of the error in these analyses of hole evolution, as well as improve our method of feature identification and tracking. Nevertheless, as the first quantitative measurement of cloud evolution, this analysis provides the first benchmark of the lifetimes of the holes in the clouds of Venus. [38] We fit the sequence of the measured peak radiance for each hole according to an exponential variation: ln Ipeak ðt Þ ¼ ln Ipeak ð0Þ

1 t t

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fit to the curve at the time of the first image acquisition. It does not represent an actual measurement of radiance, nor does the variance shown indicate a known measurement error. Figure 8 may exhibit a peak in the evolutionary time constant (i.e., both thicker and thinner holes than this tend to evolve more quickly) at about Ipeak 0.15 W m2 mm1 sr1. However, the small number of holes that have been analyzed, and the large error bars on some of the features, cause us to have only a little confidence in the existence of such a peak, at this time. [40] For Figure 9 we replace Ipeak in equation (2) with the contrast between the peak radiance and the background radiance. We define the contrast as the ratio between the peak radiance of the hole (which we have measured directly from the images), and the local background radiance. We determine the local background radiance to be the mean minus one standard deviation of the radiance in the image at the latitude of the center of the hole. Recall from Figure 4 that the mean minus one standard deviation as a function of latitude tracks remarkably close to the minimum radiance as a function of latitude. Thus, the mean minus one standard deviation stands as a reasonable proxy for the minimum radiance (or thickest cloud cover). In Figure 9, we see a possible trend indicating that holes with greater contrast evolve more quickly than holes with smaller contrast. This is consistent with simulations of hole evolution [McGouldrick and Toon, 2008b]. It also suggests that a uniform cloud cover is the equilibrium situation in the Venus atmosphere, since large contrasts between cloudy and clear regions are quickly reduced, and small contrasts evolve rather slowly. [41] In Figure 10, we plot the evolution time constant obtained from the analysis of the peak radiance as a function of the latitude of the analyzed holes. In Figure 10, the horizontal error bars indicate the range of latitudes at which a given measured hole was located during the course of the image sequence. That is, a large horizontal error bar may be indicative of a significant meridional motion. If meridional

ð2Þ

where the initial peak radiance [ln Ipeak(0)] and the evolutionary time constant (t) are free parameters to be fit to the data for each hole. We find that, for the holes we have analyzed during orbit 383, the average time scale for evolution of a typical hole is 0.95+4.88 0.51 days. Thus, since the typical e-folding time scale of a hole is approximately 1 day, the peak radiance of a typical hole will decrease to approximately 1% of its initial value in a time of about 4.5 days. Note, however, that there is a large upper error bar to this time scale, indicating that a significant fraction of holes can be expected to exhibit time constants for evolution as large as 5 – 6 days. Such slowly evolving holes will remain evident for many weeks. [39] Figure 8 shows the relationship between the initial peak radiance and the evolutionary time scale. Each point in Figure 8 represents the best linear fit to equation (2) for each measured hole. The vertical error bars indicate the one standard deviation variation in the slope (1/t), and the horizontal error bars indicate the one standard deviation variation in the initial peak radiance [Ipeak(0)]. Note that the initial peak radiance in this case refers to the peak radiance

Figure 9. Derived evolutionary time scales versus contrast between the peak hole radiances and the background radiances. Background radiance is defined in the text. Both axes are parameters of a log linear fit to the measured data. The error bars indicate the standard deviation error estimates of the fitted parameters.

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Figure 10. Derived evolutionary time scales versus the latitudes of the measured holes. The horizontal error bars indicate the range of latitudes measured for each hole during the sequence of images. The vertical error bars indicate the standard deviation error estimates of the fitted time scale. motion can be ruled out, then a significant variation in measured feature latitude suggests that we may not be measuring the same feature in each image. With sufficient statistics and independent means of determining wind velocities, such an analysis can provide a further check on the validity of the analysis of feature evolution. Even with the small number of features measured in this instance, there appears to be a trend in which holes at lower latitudes evolve more quickly than those at higher latitudes. This behavior is consistent with the earlier assessment of increased cloud variability at equatorial latitudes. [42] In each of these analyses, our conclusions are necessarily tentative, as they derive from measurements of only 10 holes over the span of only 5 h. We hope to improve on these statistics by measuring many more features in many more images in the future. Nevertheless, these analyses represent a first quantitative assessment of the evolution of holes in the middle and lower clouds of Venus.

4. Conclusions [43] We have analyzed a series of ten 1.74 mm VIRTIS-M IR images, taken on successive orbits, covering a span of 10 days, to identify tendencies to the morphology and the brightness of the holes in the clouds of Venus. We have also analyzed a separate series of eleven 1.74 mm VIRTIS-M IR images, taken over the course of a single orbit, spanning a time of 5 h, to investigate the evolution of the holes in the clouds of Venus. [44] We find that the latitudinal distribution of radiance in which midlatitudes are relatively brighter, and low latitudes exhibit somewhat greater overall variability, is consistent with that observed by Crisp et al. [1991a], indicating that this tendency is a fairly stable phenomenon. The latitudinal distribution of average radiance that we observe in the images considered here is largely consistent with the simulations of Imamura and Hashimoto [1998], indicating that the global Hadley circulation plays a significant role in the distribution of the cloud mass of Venus. We observe an

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increase in the variability of radiance at lower latitudes, suggestive of the importance of convection to the nature of the middle and lower clouds of Venus. While the distribution of potential cloud constituents and their precursors (i.e., H2SO4, SO2, H2O, and amorphous sulfur) are not constrained by the data and analyses presented here, variations in these constituents, driven either by chemistry or by the atmospheric dynamics already noted, might also play a role in the cloud variability. [45] We find that the morphology of the holes in the clouds of Venus trends from features having a wide variety of orientations (relative to the zonal axis) and aspect ratios of approximately 1.0 at lower latitudes, to highly elongated and zonally oriented features at high latitudes. The greater variability in the morphology of features at lower latitudes is possibly indicative of a greater amount of convective activity there. We fail to find a correlation between the peak radiance of a hole and its latitude. A hint of a longitudinal hemispheric asymmetry, similar to that observed by Crisp et al. [1991a] and Chanover et al. [1998] was seen; but the amount of data considered here is insufficient to be certain of its existence here. [46] We find that, if the data analyzed here are typical, holes in the middle and lower clouds of Venus evolve with a time constant of approximately 1 day. We find that holes exhibiting greater contrast between their peak radiance and the background radiance, and holes at lower latitudes evolve more quickly. This is consistent with the increased variability seen at lower latitudes that results from enhanced convection. It is also consistent with the notion that the middle and lower clouds are supported by a radiative dynamical feedback, whereby regions exhibiting large contrasts in clouds cover will also experience large contrasts in radiative heating because of the absorption of upwelling infrared radiation. However, the data considered here represent only 5 h of observation of a fairly small area (about 2% of the atmosphere). This is hardly sufficient data to allow us to draw any broad conclusions. We intend to carry out future efforts to improve both the methods of the analysis as well as the volume of analyzed data. [47] The features in the images analyzed here strongly indicate the activity of convection in the lower and middle cloud decks of Venus. The spatial variability and short time scale for their evolution are somewhat inconsistent with typical terrestrial stratus, to which the clouds of Venus are often compared. However, previous radiative modeling by Pollack et al. [1980] and microphysical modeling of the middle and lower clouds by McGouldrick and Toon [2007] suggests the existence of a radiative dynamical feedback, which can support convection in the lower and middle cloud decks of Venus. The clouds of Venus may be more analogous to terrestrial stratocumulus ‘‘cloud streets,’’ whereby convection exists in the midst of strong horizontal flow [Houze, 1993]. Depending upon the relative strengths of the horizontal flow and the convection, either rolls or cellular patterns may be formed. Similarly, the nature of terrestrial thunderstorm development (whether single-cell, multicell, or supercell) is known to be somewhat dependent upon the local wind shear and static stability [Houze, 1993]. Perhaps processes analogous to cloud street formation or the development of multicell thunderstorms are occurring in the atmosphere of Venus in the vicinity of the lower and middle clouds.

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[48] Here we have presented analyses of only a small subset of the VIRTIS data. We have analyzed data from only 11 orbits, from among more than 500: barely 2% of the total available VIRTIS data set. However, given that at this writing, Venus Express has been orbiting Venus for more than three full Venus years, there likely is sufficient data in the archive to seek out potential tendencies of hole formation and/or appearance as a function of geographic or solar longitude. We intend to carry out further analyses of the VIRTIS data to determine whether such tendencies exist. Furthermore, comparisons with observations at other wavelengths, and concurrent Venus Monitoring Camera observations, can be utilized to gain greater understanding of the properties and evolution of the Venus clouds. In the longer term, the anticipated arrival of the Japanese Space Agency’s Venus Climate Orbiter (VCO) in 2010 promises to enhance the study of the Venus clouds [Nakamura et al., 2007]. The understanding of the nature of the middle and lower clouds of Venus could gain much from the potential coordinated observation of Venus by the two spacecraft. Subsequent observations of the same region of the atmosphere could extend the analysis of cloud evolution beyond the limits that either VCO or Venus Express could accomplish by itself, by extending the baseline of time over which a particular feature is viewable. Concurrent observations of features by multiple spacecraft could further elucidate the cloud properties. For example, it may be possible to determine the difference between the properties of a profile of a hole versus that of a cloud by performing occultation measurements with one spacecraft, while the other identifies the types of features that are passing over the limb of the planet. Alternately, long-lived balloon observatories can be placed in the atmosphere and possibly maneuvered to sample the atmosphere in regions that are identified by the concurrently orbiting spacecraft. [49] Acknowledgments. We thank two anonymous reviewers for their careful reading of the manuscript and their suggestions for making this a stronger paper. We also acknowledge conversations with Colin Wilson, Con Tsang, and Robert Carlson that have helped to improve the analysis of the data with respect to the correction for the emission angle. We also thank The European Space Agency for the opportunity to work with Venus Express. We thank Pierre Drossart, Giuseppi Piccioni, and the VIRTIS team for their assistance in enabling us to reduce and analyze the VIRTIS data. A portion of the work described in this paper was carried out at the Jet Propulsion Laboratory, Pasadena, California, under contract with NASA. K.M. and D.H.G. were supported by NASA in support of ESA’s Venus Express mission under grant NNX07AI61G.

References Allen, D. A., and J. W. Crawford (1984), Cloud structure on the dark side of Venus, Nature, 307, 222 – 224. Baker, R. D., G. Schubert, and P. W. Jones (2000), Convectively generated internal gravity waves in the lower atmosphere of Venus, part II: Mean wind shear and wave-mean flow interaction, J. Atmos. Sci., 57, 200 – 215. Belton, M. J. S., et al. (1991), Images from Galileo of the Venus cloud deck, Science, 253, 1531 – 1536. Blamont, J. E., et al. (1986), Implications of the VEGA balloon results for Venus atmospheric dynamics, Science, 231, 1422 – 1425. Bullock, M. A., and D. H. Grinspoon (2001), The recent evolution of climate on Venus, Icarus, 150, 19 – 37, doi:10.1006/icar.2000.6570. Carlson, R. W., et al. (1991), Galileo infrared imaging spectroscopy measurements at Venus, Science, 253, 1541 – 1548. Carlson, R. W., L. W. Kamp, K. H. Baines, J. B. Pollack, D. H. Grinspoon, Th. Encrenaz, P. Drossart, and F. W. Taylor (1993), Variations in Venus cloud particle properties: A new view of Venus’s cloud morphology as observed by Galileo Near-Infrared Mapping Spectrometer, Planet. Space Sci., 41, 477 – 485, doi:10.1016/00320633(93)900306.

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Chanover, N. J., D. A. Glenar, and J. J. Hillman (1998), Multispectral nearIR imaging of Venus nightside cloud features, J. Geophys. Res., 103, 31,335 – 31,348. Counselman, C. C., S. A. Gourevitch, R. W. King, G. B. Loroit, and E. S. Ginsberg (1980), Zonal and meridional circulation of the lower atmosphere of Venus determined by radio interferometry, J. Geophys. Res., 85, 8026 – 8030. Crisp, D. (1989), Radiative forcing of the Venus mesosphere, Icarus, 77, 391 – 413, doi:10.1016/00191035(89)900961. Crisp, D., et al. (1989), The nature of the near-infrared features on the Venus night side, Science, 246, 506 – 509. Crisp, D., D. A. Allen, D. H. Grinspoon, and J. B. Pollack (1991a), The dark side of Venus: Near-infrared images and spectra taken from the Anglo-Australian Observatory, Science, 253, 1263 – 1266. Crisp, D., et al. (1991b), Ground-based near-infrared imaging observations of Venus during the Galileo encounter, Science, 253, 1538 – 1541. Drossart, P., et al. (2007), Scientific goals for the observation of Venus by VIIS on ESA/Venus Express mission, Planet. Space Sci., 55, 1653 – 1672, doi:10.1016/j.pss.2007.01.003. Grinspoon, D. H., J. B. Pollack, B. R. Sitton, R. W. Carlson, L. W. Kamp, K. H. Baines, Th. Encrenaz, and F. W. Taylor (1993), Probing Venus’s cloud structure with Galileo NIMS, Planet. Space Sci., 50, 515 – 542, doi:10.1016/00320633(93)90034Y. Hashimoto, G. L., and Y. Abe (2000), Stabilization of Venus’ climate by a chemical-albedo feedback, Earth Planets Space, 52, 197 – 202. Hinson, D. P., and J. M. Jenkins (1995), Magellan radio occultation measurements of atmospheric waves on Venus, Icarus, 114, 310 – 327, doi:10.1006/icar.1995.1064. Houze, R. A. (1993), Cloud Dynamics, Academic, San Diego, Calif. Imamura, T., and G. L. Hashimoto (1998), Venus cloud formation in the meridional circulation, J. Geophys. Res., 103, 31,349 – 31,366. James, E. P., O. B. Toon, and G. Schubert (1997), A numerical microphysical model of the condensational Venus cloud, Icarus, 129, 147 – 171, doi:10.1006/icar.1997.5763. Knollenberg, R. G., and D. H. Hunten (1980), The microphysics of the clouds of Venus: Results of the Pioneer Venus Particle Size Spectrometer Experiment, J. Geophys. Res., 85, 8038 – 8058. Linkin, V. M., et al. (1986), Vega balloon dynamics and vertical winds in the Venus middle cloud region, Science, 231, 1417 – 1419. Markiewicz, W., et al. (2007a), Venus Monitoring Camera for Venus Express, Planet. Space Sci., 55, 1701 – 1711, doi:10.1016/j.pss.2007.01.004. Markiewicz, W. J., et al. (2007b), Morphology and dynamics of the upper cloud layer of Venus, Nature, 450, 633 – 636, doi:10.1038/nature06320. McGouldrick, K., and O. B. Toon (2007), Investigation of possible causes of the holes in the condensational Venus cloud using a microphysical cloud model with a radiative-dynamical feedback, Icarus, 191, 1 – 24, doi:10.1016/j.icarus.2007.04.007. McGouldrick, K., and O. B. Toon (2008a), Observable effects of convection and gravity waves on the Venus condensational cloud, Planet. Space Sci., 46, 1112 – 1131, doi:10.1016/j.pss.2008.02.010. McGouldrick, K., and O. B. Toon (2008b), Modeling the effects of shear on the evolution of the holes in the condensational clouds of Venus, Icarus, 196, 35 – 48, doi:10.1016/j.icarus.2008.02.020. Nakamura, M., et al. (2007), Planet-C: Venus Climate Orbiter mission of Japan, Planet. Space Sci., 55, 1831 – 1842, doi:10.1016/j.pss.2007.01.009. Piccioni, G., et al. (2007), South-polar features on Venus similar to those near the north pole, Nature, 450, 637 – 640, doi:10.1038/nature06209. Pollack, J. B., O. B. Toon, and R. Boese (1980), Greenhouse models of Venus’ high surface temperature, as constrained by Pioneer Venus measurements, J. Geophys. Res., 85, 8223 – 8231. Sańchez-Lavega, A., et al. (2008), Variable winds on Venus mapped in three dimensions, Geophys. Res. Lett., 35, L13204, doi:10.1029/ 2008GL033817. Schubert, G., et al. (1980), Structure and circulation of the Venus atmosphere, J. Geophys. Res., 85, 8007 – 8025. Taylor, F. W., et al. (1979), Temperature, cloud structure, and dynamics of Venus middle atmosphere by infrared remote-sensing from pioneer orbiter, Science, 205, 65 – 67. Toon, O. B., R. P. Turco, and J. B. Pollack (1982), The ultraviolet absorber on Venus: Amorphous sulfur, Icarus, 51, 358 – 373, doi:10.1016/ 00191035(82)900896.

K. H. Baines and T. W. Momary, Jet Propulsion Laboratory, California Institute of Technology, M/S 183-601, 4800 Oak Grove Drive, Pasadena, CA 91109, USA. D. H. Grinspoon and K. McGouldrick, Department of Space Sciences, Denver Museum of Nature and Science, 2001 Colorado Boulevard, Denver, CO 80205, USA. ([email protected])

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Whistler mode waves from lightning on Venus: Magnetic control of ionospheric access C. T. Russell,1 T. L. Zhang,2 and H. Y. Wei1 Received 29 February 2008; revised 29 May 2008; accepted 19 June 2008; published 18 September 2008.

[1] The fluxgate magnetometer on Venus Express samples the magnetic field near

periapsis at 128 Hz. Bursts of plane-polarized magnetic waves in the vicinity of 100 Hz are observed propagating at small angles to the magnetic field. The magnetic field is generally horizontal in the region around periapsis, located at high northern latitudes. When the magnetic field remains within 15° of horizontal during the 2-min periapsis pass, no such waves are observed; but when there are brief periods during which the local magnetic field dips into the atmosphere by more than 15°, the bursts begin to appear. Such radial excursions of the magnetic field occur 25% of the time in the region around periapsis. The bursts are seen only on passes with these excursions. We interpret this magnetic control in terms of the coupling between the electromagnetic wave from lightning discharges refracted vertically by the increasing electron density and the nearly horizontal ionospheric magnetic field along which the energy is guided to the spacecraft. The inferred rate of electric discharges in the Venus atmosphere is about 20% of that seen in the Earth’s atmosphere. Citation: Russell, C. T., T. L. Zhang, and H. Y. Wei (2008), Whistler mode waves from lightning on Venus: Magnetic control of ionospheric access, J. Geophys. Res., 113, E00B05, doi:10.1029/2008JE003137.

1. Introduction [2] While the Venus atmosphere is quite dry, containing very little water, Venus is also quite cloudy, with hydrated sulfuric acid droplets shrouding the planet some 50 –60 km above the surface. At low altitudes, the winds are slow, but in the clouds, the horizontal velocities are observed to range to over 100 ms 1. These ‘‘4-d’’ winds produce much wind shear and probably are accompanied by significant vertical transport as well. Thus, the clouds may well be electrified as terrestrial clouds are. The observations reported herein are over the polar vortex that is possibly associated with rapid downwelling [Piccioni et al., 2007]. [3] Many researchers have reported optical and electromagnetic signals that could be produced by discharges in or from these clouds. Krasnopolsky [1983] reported flashes seen in the Venera-9 visible spectrometer; Hansell et al. [1995] reported detections with an Earth-based telescope. The landing probes of Venera 11, 12, 13, and 14 detected electromagnetic pulses as they descended through the atmosphere and as they sat on the surface [Ksanfomaliti, 1983]. They have been most extensively studied from orbit on Pioneer Venus (PVO) using an electric antenna [Taylor et al., 1979; Scarf et al., 1980]. Finally, at radio frequencies that are able to propagate through the ionosphere with minimal interaction with the plasma, Galileo recorded radio 1 Institute of Geophysics and Planetary Physics, University of California, Los Angeles, California, USA. 2 Space Research Institute, Austrian Academy of Sciences, Graz, Austria.

waves similar to terrestrial radio frequency emissions from lightning [Gurnett et al., 1991]. However, as Gurnett et al. [2001] later reported, these high-frequency radio waves are weaker in strength and less frequent in occurrence than terrestrial lightning. [4] The waves detected in the magnetized Venus ionosphere by PVO were of two distinct types [Russell, 1991]. One class appeared only when the magnetic field had a significant radial component and behaved like an electromagnetic wave would. This wave diminished only slightly in estimated electromagnetic energy flux with increasing altitude. The second class was apparently electrostatic and attenuated rapidly with altitude. It was seen in all four narrowband frequency channels: 100 Hz, 730 Hz, 5.6 kHz, and 30 kHz. We interpret these electrostatic waves as being associated with cloud-to-ionosphere strikes, probably ‘‘local.’’ The electromagnetic waves were most probably due to intracloud discharges, possibly at some distance. [5] The occurrence of lightning on Venus remained controversial after the Venera and PVO missions ceased because some searches were unsuccessful. A search for scattered light in the PVO star sensor came up empty [Borucki et al., 1991], albeit the total time the star sensor was active and able to see flashes was only a few minutes in total, with very little coverage over the region defined as active by the PVO electric field instrument. The photometer on the Venus balloons also did not observe flashes [Sagdeev et al., 1986], but the balloons were in the clouds and were not looking down on the clouds from above. Thus any light from flashes could be scattered before reaching the detectors on the balloons.


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RUSSELL ET AL.: LIGHTNING ON VENUS

Figure 1. Waveform of 8-ms samples of the magnetic field at periapsis on 1 July 2006, bandpass filtered from 42 to 60 Hz and rotated into principal axis coordinates.

[6] The waves observed by Pioneer Venus were quite strong. In a vacuum, the energy flux of electromagnetic waves is evenly divided between the electric and magnetic components. In the ionosphere where the index of refraction is high, the wave slows down and the energy is mainly carried by the magnetic component. On Pioneer Venus, the plasma density, the magnetic field, and the electric component of the wave were measured and we could calculate the electromagnetic energy of the waves identified as propagating in the whistler mode [Russell et al., 1989]. Furthermore, we could check this estimate with data obtained in the atmosphere beneath the ionosphere on the last few orbits when Pioneer Venus began to enter the atmosphere because of gravitational perturbations and eventually because of drag on the spacecraft [Strangeway et al., 1993]. These calculations indicated that the Pioneer Venus electric waves near 100 Hz could be detected by a state-of-the-art fluxgate magnetometer in the Venus ionosphere [Russell et al., 2006]. The Pioneer Venus magnetometer did not have the bandwidth to detect these waves. When it was decided to include a magnetometer on Venus Express, the decision was made to include a 128-Hz sampling mode that would allow magnetic waves near 100 Hz to be sampled [Zhang et al., 2006]. These data were obtained initially for only 2 min around periapsis. In late December 2006, this interval was extended. As expected, waves with the expected amplitude and temporal structure were observed at periapsis [Russell et al., 2007]. In this paper we report on the properties of the waves seen in these 2-min sampling intervals.

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has been described in detail by Zhang et al. [2006], and the cleaning process has been described by Zhang et al. [2008]. The Alfvenic character of interplanetary fluctuations has been used to establish the magnetometer’s DC level to better than 1 nT [Leinweber et al., 2008]. [8] In addition to the DC spacecraft fields, there is AC interference, in particular from the reaction wheels. These waves are found in the passband in which we expect to see the waves due to lightning. They also can vary in frequency from pass to pass, and at times, during a pass. Examples of these waves have been published by Russell et al. [2008]. A very simple test of whether a wave is in the ambient plasma or due to spacecraft noise is simply to subtract the measurements of the two magnetometers. Natural waves have the same amplitude at the two locations and disappear in the difference wave. The reaction wheel noise is stronger in the sensor on the spacecraft deck and does not disappear in subtraction [Russell et al., 2008]. As we demonstrate below in section 3, the bursts we discuss in this report pass this test. [9] In this study we use the outbound sensor and pass the time series through a bandpass filter from 42 to 60 Hz. This range of frequencies is chosen to avoid strong signals from the reaction wheels [Russell et al., 2008]. The magnetometer has an antialiasing filter, but this filter does allow strong signals above the Nyquist frequency of 64 Hz to enter the telemetry stream. These signals will appear to be below 64 Hz, but their polarization will be reversed. When reaction wheel noise appears in this passband, we simply eliminate this pass from consideration. We examine herein observations obtained in 2006, after the initial commissioning of the instruments in orbit.

3. Observations [10] In the Pioneer Venus observations, whistler mode waves could not be detected in the day-lit hemisphere because of solar-induced noise in the plasma wave electric antenna. One of the advantages of the Venus Express magnetic measurement is that the waves can be observed in sunlight as well as darkness. Figure 1 shows a filtered

2. Instrument and Data Processing [7] Because Venus Express was a low-cost mission and employed an existing bus, there was no magnetic cleanliness program. Instead, two magnetometer triads were installed: one on the top deck and the second on the end of a 1-m boom. The two magnetometers were aligned and used in a gradiometer mode to identify spacecraft-associated waves so that they could be removed. The magnetometer

Figure 2. Three seconds of the record shown in Figure 1 showing substructure in the burst.

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Figure 3. Hodogram of 400 ms of the burst shown in Figure 2 in the principal axis system.

waveform from the outer fluxgate sensor on 1 July 2006, at 308-km altitude and 0642 LT. The wave packets have been rotated into their principal axis system where the minimum variance direction is along the third axis, and the maximum variance direction is along the first. Clearly, the waves are confined to two directions and have similar amplitudes in these two directions. Figure 2 focuses on 3 s of this record revealing substructure within the burst. Overall, the wave packets last about 1/4 to 1/2 s, but clearly substructure can occur on 100-ms scales. A hodogram of these waves is shown in Figure 3, over 400 ms of data. The waves are confined to a plane as is expected of whistler mode signals. The waves’ phase velocity (k vector) along the minimum variance direction is propagating at only 9° to the background magnetic field. As mentioned above, the magnetometer samples continuously in a gradiometer mode with simultaneous samples at the end of the boom and on the spacecraft upper deck. Ambient waves will be the same

Figure 4. By component of the 128-Hz magnetic field in spacecraft coordinates measured on the outboard and inboard sensors and the difference between the two readings. Naturally occurring, nonspacecraft sources would be identical at the two locations and cancel in subtraction. The burst’s absence indicates that the burst in Figure 3 is not due to spacecraft sources.

Figure 5. Bandpass-filtered measurements in principal axis system for 400 ms at periapsis on 23 December 2006. Comments of Figure 1 apply.

amplitude at the two magnetometers so that subtracting the two time series will result in a time series of only noise. In Figure 4, we demonstrate this subtraction for the event shown in the hodogram of Figure 3. The top trace is the output of the y component outboard sensor. The middle trace is the y component inboard sensor. The bottom trace is the difference. In the difference trace, the burst of waves vanishes into the noise level, indicating it is an ambient signal. [11] An event observed on 23 December 2006, at 284-km altitude, 88° latitude, 1042 LT, and 88.5° solar zenith angle is shown in Figure 5. This event is propagating at only 6° to the local magnetic field. Figure 6 shows a hodogram of 250 ms of data at the maximum of the event. Figure 7 repeats our gradiometer difference test for the burst in the hodogram in Figure 6. Again the burst disappears in the difference trace indicating its source is not on the spacecraft. [12] Figure 8 shows projections of the background magnetic field along the orbit in the plane containing the solar direction and the orbit plane normal. The 8-s averaged magnetic field is shown every minute. The event shown in Figure 7 occurs at the second vector before 0750 UT

Figure 6. Hodogram of 250 ms of the burst shown in Figure 5.

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where the field is dipping most strongly into the atmosphere. This suggests that we should examine the control of the occurrence of these waves by the magnetic field orientation. We demonstrate this control statistically in section 4.

shows that on some days, there just are not waves present near the dipping field lines. July 2 has an ambient magnetic field that is quite horizontal and says little about the possible presence of lightning near the satellite at this time. We note that more reaction wheel noise is present here than on other passes, but we feel we would have seen a typical amplitude burst if present. [16] Figure 11 shows three consecutive days in December 2006, when the spacecraft was near 251 km, 0141 LT, and 96.8° solar zenith angle. December 22 has a horizontal ambient magnetic field and no wave events. December 23 has a field inclined nearly 40° to the horizontal and has a strong wave event, near 1 nT peak-to-peak. December 24 deviates nearly 20° to the horizontal, and no wave bursts are seen. Again we have a pattern of inclined field being associated with occasional wave events and horizontal fields being associated with quiet intervals. [17] Figure 12 summarizes our survey of the 2006 Venus Express data. Plotted is the peak-to-peak amplitude of the strongest bursts on each pass versus the maximum deviation of the ambient field from the horizontal. We see that when the field deviation was less than 15° from the horizontal direction everywhere on the pass, no whistler mode wave bursts were detected. When the deviation was greater than 17°, the wave events could occur. We attribute this magnetic control to the effect of the ionosphere on the electromagnetic waves propagating from the clouds below and the properties of the propagation of whistler mode waves at the conditions in the Venus ionosphere as discussed in section 5.

4. Magnetic Field Control of Burst Occurrence

5. Discussion

[13] To illustrate the nature of the magnetic field control of the appearance of the whistler mode waves, we display the entire 2-min passes for three consecutive orbits together with the angle between the ambient magnetic field and the local horizontal direction for the same interval plotted with 1-s resolution. These orbits have very similar altitudes and local times but may differ in the steadiness and orientation of the magnetic field. [14] Figure 9 shows the measurements on 8, 9, and 10 June 2006. On 10 June, the ambient magnetic field is quiet and close to horizontal for the entire 2-min pass. No wave bursts are seen in the right-hand panel. In contrast on 9 June, the ambient magnetic field is time varying moving up to 30° away from horizontal. Strong wave packets accompany these variations at the beginning of the interval. These wave packets are analyzed in detail by Russell et al. [2007]. June 8 has similar field variations to those of 9 June, but no wave events are seen. This observation indicates that the magnetic orientation is not the only controlling factor. In fact, the most probable explanation of the lack of waves on this pass is that there are no atmospheric discharges below the satellite on this day. [15] Another 3-day interval, 30 June, 1 and 2 July, is shown in Figure 10 in the same format as Figure 9. In this interval, there is a burst on 1 July, but no activity on 30 June or 2 July. The most inclined fields occur on 30 June, but they are not accompanied by whistler mode bursts. The 1 July ambient fields do deviate from the horizontal by about 20° and are sufficient to allow the entry of waves into the ionosphere as in the previous example, but clearly 30 June

[18] The evidence for the necessity of inclined ionospheric magnetic fields for the access of whistler mode

Figure 7. By component of the 128-Hz field in spacecraft coordinates measured on the outboard and inboard sensors and the difference between the two readings. The absence of the burst on the bottom trace indicates that the burst in Figure 6 is not due to spacecraft sources.

Figure 8. Venus Express orbit together with projections of 8-s samples of the magnetic field once per minute in the x-z plane in Venus Solar Orbital coordinates. Magnetic field is seen to be dipping into atmosphere around time of burst of noise.

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Figure 9. Three consecutive periapsis passes on 8, 9, and 10 June. The left-hand panels show the angle of the field to the radial direction with 90° being horizontal as calculated from 1-s samples of the field. The right-hand panels show bandpass-filtered 8-ms magnetic field measurements covering the frequency range from 40 to 62 Hz. The component shown is from the y axis sensor in spacecraft coordinates. This is the sensor with the lowest noise level. waves from the atmosphere into the ionosphere is clear from our statistical survey. However, in order to understand why this access control occurs, we need to examine the expected characteristics of the electromagnetic waves that enter the

ionosphere from below. Figure 13 shows a cartoon of the wavefronts produced by an intracloud discharge in Venus’ atmosphere. The waves at first propagate with spherical wavefronts at the speed of light. When they encounter the

Figure 10. Three consecutive passes on 30 June, 1 July, and 2 July 2006. Comments of Figure 9 apply. 5 of 8

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Figure 11. Three consecutive passes on 22, 23, and 24 December 2006. Comments of Figure 9 apply. ionosphere, the part of the wavefront that enters the ionosphere slows down considerably to a very small fraction of the velocity of light depending on the electron density and the strength of the magnetic field. The flat wavefront corresponds to vertical propagation of the wave. The

Figure 12. Peak-to-peak amplitudes of burst maxima on each pass versus maximum inclination of the magnetic field to the horizontal.

Figure 13. Cartoon of electric discharge in the Venus clouds and the resultant wavefronts of electromagnetic waves. When the wavefronts enter the ionosphere where the phase velocity decreases substantially, the wavefront flattens and the wavenormals become vertical.

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Figure 14. Phase and group velocities of whistler mode waves at 100 Hz propagating at various angles to the magnetic field in a plasma with a density of 5000 cm 3 and a magnetic field strength of 20 nT. magnetic field in the ionosphere, as we have seen, tends to be horizontal so long as the interplanetary magnetic field is steady. Inclined fields can be produced by time variations of the magnetic fields that in turn can lead to interconnection of different field directions within the ionosphere. [19] Figure 14 shows the phase and group velocities of a whistler mode wave at 100 Hz propagating at various angles to the magnetic field in a plasma with a density of 5000 electrons cm 3 and a magnetic field strength of 20 nT. These velocities are calculated from the full dispersion relation for cold plasma waves [Stix, 1962]. On the left is the phase velocity. This wave does not travel perpendicular to the magnetic field. In fact, there is a cone of nonpropagation of the order of 10°, an angle similar to the angle of inclination needed for the waves to reach Venus Express. On the right is the group velocity in a similar display. Once the energy couples to the magnetic field, it is strongly guided by the magnetic field. Thus, our observations are consistent with what one would expect for the waves in the ionosphere that would be produced as a result of atmospheric electrical discharges. [20] We can use the first year’s statistics of the magnetic field orientation and the number of discrete whistler wave bursts during the 2-min sampling intervals to make a rough comparison with terrestrial rates. If we use the conservative assumption that access to the ionosphere occurs only when the field is inclined 15° or more to the horizontal, then the statistics of cone angle occurrence indicates that access is allowed only 25% of the time. In the first Venus year of operation up to 16 December 2006, we received 12,223 s of 128-Hz data that could be analyzed. During this interval, 61 individual bursts of noise were detected, giving a rate of 0.005 per second. Normalizing this rate for access time, i.e., the duration of the fields inclined more than 15° to the horizontal, we get a rate of 0.02 per second below the clouds. If the spacecraft can see a circle of 200-km radius in the clouds below it, a distance equal to its height above them, it is seeing only 0.027% of the surface. If we use this ratio to normalize our observations, we get a global rate of 18 strokes s 1 or 20% the terrestrial rate. We do not know if our region of study near Venus’ polar vortex is representative of the entire planet or if our area of seeing is correct, but these numbers do tell us that lightning is a significant and important process in the Venus atmosphere. Finally, we note

that our measurements were gathered over a full Venus year covering all local times. While we would not necessarily expect the results of our observations to agree with the rate seen on a brief Cassini swing-by, observing radio waves and not the whistler mode waves studied here, we do agree qualitatively with the result of Gurnett et al. [2001] that the rate at Venus may be lower than on Earth.

6. Summary [21] In summary, Venus Express observes bursts of whistler mode noise in the Venus ionosphere. These waves are almost circularly plane-polarized and propagate at a small angle to the magnetic field. They were seen only when the local ionospheric magnetic field was inclined 17° or more to the horizontal. Such a cone of evanescence would be expected for electromagnetic radiation produced in the atmosphere and refracted vertically as it enters the ionosphere. The observations are consistent with lightning being prevalent in the Venus atmosphere and creating the whistler mode waves seen in the ionosphere, both those observed with the electric antenna on Pioneer Venus at low latitudes and those detected with the Venus Express magnetometer at high latitudes. [22] Acknowledgments. This work was supported by the National Aeronautics and Space Administration under research grant NNG06GC62G.

References Borucki, W. J., et al. (1991), Pioneer Venus Orbiter search for Venusian lightning, J. Geophys. Res., 96(A7), 11,033 – 11,043, doi:10.1029/ 91JA01097. Gurnett, D. A., et al. (1991), Lightning and plasma wave observations from the Galileo fly by of Venus, Science, 253(5027), 1522 – 1525, doi:10.1126/science.253.5027.1522. Gurnett, D. A., et al. (2001), Non-detection at Venus of high-frequency radio signals characteristic of terrestrial lightning, Nature, 409(6818), 313 – 315, doi:10.1038/35053009. Hansell, S. A. W. K. W., et al. (1995), Optical detection of lightning on Venus, Icarus, 117, 345 – 351, doi:10.1006/icar.1995.1160. Krasnopolsky, V. A. (1983), Lightnings and nitric oxide on Venus, Planet. Space Sci., 31, 1363 – 1369, doi:10.1016/0032-0633(83)90072-7. Ksanfomaliti, L. V. (1983), Electrical activity of the atmosphere of Venus: I. Measurements on descending probes, Kosm. Issled., 21, 279 – 296. Leinweber, H., C. T. Russell, K. Torkar, J. L. Zhang, and V. Angelopoulos (2008), An advanced approach to finding magnetometer zero levels in the interplanetary magnetic field, Meas. Sci. Technol., 19, doi:10.1088/09570233/19/5/055104.

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Piccioni, G., et al. (2007), South-pole features on Venus similar to those near the north pole, Nature, 450(7170), 637– 640, doi:10.1038/nature06209. Russell, C. T. (1991), Venus lightning, Space Sci. Rev., 55, 317 – 356. Russell, C. T., et al. (1989), VLF bursts in the night ionosphere of Venus: Estimates of the Poynting flux, Geophys. Res. Lett., 16(6), 579 – 582, doi:10.1029/GL016i006p00579. Russell, C. T., R. J. Strangeway, and T. L. Zhang (2006), Lightning detection on the Venus Express Mission, Planet. Space Sci., 54, 1344 – 1351, doi:10.1016/j.pss.2006.04.026. Russell, C. T., T. L. Zhang, M. Delva, W. Magnes, R. J. Strangeway, and H. Y. Wei (2007), Lightning on Venus inferred from whistler-mode waves in the ionosphere, Nature, 450(7170), 661 – 662, doi:10.1038/ nature05930. Russell, C. T., T. L. Zhang, R. J. Strangeway, H. Y. Wei, M. Delva, and W. Magnes (2008), Electromagnetic waves observed by Venus Express at periapsis: Detection and analysis techniques, Adv. Space Res., 41(1), 113 – 117, doi:10.1016/j.asr.2007.08.032. Sagdeev, R. Z., et al. (1986), Overview of VEGA Venus balloon in-situ meteorological measurements, Science, 231(4744), 1411 – 1414, doi:10.1126/science.231.4744.1411. Scarf, F. L., et al. (1980), Lightning on Venus: Orbiter detection of whistler signals, J. Geophys. Res., 85(A13), 8158 – 8166, doi:10.1029/ JA085iA13p08158.

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Stix, T. H. (1962), Theory of Plasma Waves, 283 pp., McGraw-Hill, New York. Strangeway, R. J., et al. (1993), Observation of intense wave bursts at very low altitudes within the Venus nightside ionosphere, Geophys. Res. Lett., 20(23), 2771 – 2774, doi:10.1029/93GL02702. Taylor, W. W. L., et al. (1979), Absorption of whistler mode waves in the ionosphere of Venus, Science, 205(4401), 112 – 114, doi:10.1126/ science.205.4401.112. Zhang, T. L., et al. (2006), Magnetic field investigation of the Venus plasma environment: Expected new results from Venus Express, Planet. Space Sci., 54, 1336 – 1343, doi:10.1016/j.pss.2006.04.018. Zhang, T. L., et al. (2008), Initial Venus Express magnetic field observations of the Venus bow shock location at solar minimum, Planet. Space Sci., 56, 785 – 789, doi:10.1016/j.pss.2007.09.012. C. T. Russell and H. Y. Wei, Institute of Geophysics and Planetary Physics, University of California, 603 Charles Young Drive East, 3845 Slichter Hall, Los Angeles, CA 90095-1567, USA. ([email protected]. edu) T. L. Zhang, Space Research Institute, Austrian Academy of Sciences, Schmiedlstrasse 6, A-8042 Graz, Austria.

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Morphology and dynamics of Venus oxygen airglow from Venus Express/Visible and Infrared Thermal Imaging Spectrometer observations R. Hueso,1 A. Sańchez-Lavega,1 G. Piccioni,2 P. Drossart,3 J. C. Ge´rard,4 I. Khatuntsev,5 L. Zasova,5 and A. Migliorini2 Received 21 January 2008; revised 21 March 2008; accepted 2 May 2008; published 26 July 2008.

[1] Images obtained by the Visible and Infrared Thermal Imaging Spectrometer

(VIRTIS)-M channel instrument onboard Venus Express have been used to retrieve maps and apparent motions of the O2 (1D) infrared nightglow on Venus at 1.27 mm. The nightglow distribution is highly inhomogeneous with the regions of brightest emission generally located at low latitudes near the midnight meridian. Unexpectedly some orbits show also intense airglow activity over the south polar region. The spatially resolved airglow is spectacularly variable not only in its morphology and intensity but also in the apparent motions of the airglow small- and large-scale structures. Visual tracking of the bright features allowed to obtain mean zonal and meridional motions related to the subsolar to antisolar circulation. The zonal velocity is dominated by an intense prograde jet (contrary to the retrograde planetary rotation) from dawn to midnight extending up to 22 hours in local time with lower velocities and reversed sign from dusk. Typical zonal velocities range between +60 (prograde) to 50 (retrograde) m/s, whereas most meridional velocities range from 20 (poleward) to +100 m/s (equatorward) with an average meridional circulation of +20 m/s toward low latitudes. The brightest small-scale (100 km) features appear correlated with locations of apparent convergence which may be a signature of compression and downwelling, whereas this is not evident for the largescale structures suggesting slow subsidence over large areas mixed with horizontal motions. We argue that part of the tracked motions are representative of real motions at the mesosphere over an altitude range of 95–107 km. Citation: Hueso, R., A. Sańchez-Lavega, G. Piccioni, P. Drossart, J. C. Ge´rard, I. Khatuntsev, L. Zasova, and A. Migliorini (2008), Morphology and dynamics of Venus oxygen airglow from Venus Express/Visible and Infrared Thermal Imaging Spectrometer observations, J. Geophys. Res., 113, E00B02, doi:10.1029/2008JE003081.

1. Introduction [2] The circulation of the upper atmosphere of Venus is traditionally decomposed into two distinct flow patterns at different altitudes: A subsolar-to-antisolar (SS-AS) thermospheric circulation cell driven by solar heating, stable at levels above the mesosphere (z > 120 km [Bougher et al., 1997], and a superrotating zonal (SZ) flow in the planetary rotation sense at the upper troposphere (z 65 km) [see, e.g., Gierasch et al., 1997; Markiewitz et al., 2007; Limaye, 1 Departamento de Fı´sica Aplicada I, E.T.S. Ingenieros, Universidad del Paı´s Vasco, Bilbao, Spain. 2 Istituto di Astrofisica Spaziale e Fisica Cosmica, INAF-IASF Roma, Rome, Italy. 3 Laboratoire d’ Etudes Spatiales et d’ Instrumentation en Astrophysique, Observatoire de Paris, Universite´ Paris-Diderot, CNRS, UPMC, Meudon, France. 4 Laboratoire de Physique Atmosphe´rique et Plane´taire, Universite´ de Lie`ge, Lie`ge, Belgium. 5 Space Research Institute, Moscow, Russia.


2007; Sańchez-Lavega et al., 2008]. In the transition region, the mesosphere (70 < z < 120 km), the interaction of both motions results in a complex circulation that greatly varies over time [Lellouch et al., 1997; Bougher et al., 2006]. The global circulation in the mesosphere is probably influenced by a number of physical processes such as breaking gravity waves [Alexander, 1992] and diffusive processes, but their effects over the mesospheric circulation are not well understood or quantified [Bougher et al., 2006]. Additionally, the return branch of the SS-AS upper circulation must lie somewhere in the mesosphere but its location has not been clearly identified. [3] While the dynamics of the mesosphere of Venus is hardly accessible to in situ measurements it can be studied by means of Doppler shifts in certain absorption lines sensitive to mesospheric levels [e.g., Clancy et al., 2007; Widemann et al., 2007] but this technique lacks spatial resolution, requires a sophisticated analysis of the observations and a precise treatment of systematic errors. A complimentary technique suited for exploration from a spacecraft is the study of nightglow molecular emissions which trace the motions of different chemical species in the

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HUESO ET AL.: DYNAMICS OF VENUS OXYGEN AIRGLOW

upper atmosphere with the drawback that the emissions are coupled with photochemical processes. [4] Among the many nonthermal emissions produced in Venus, the oxygen airglow at 1.27 mm is the most intense. Its was first detected in ground-based observations of Venus by Connes et al. [1979]. This airglow is produced by the recombination of oxygen atoms dissociated by photolysis of CO2 at thermospheric altitudes on the sunlit hemisphere. In the upper mesosphere (95 – 110 km) three-body recombination of O atoms leads to O2 formation in a particular excited state, a1Dg, which is followed by airglow emission as the molecule relaxes to its X 3Sg ground state. The radiative lifetime of O2 singlet state is 70 min. [Lellouch et al., 1997; Miller et al., 2001]. The O2 airglow is observed in the night-side hemisphere at 1.267 microns [Allen et al., 1992; Bougher and Borucki, 1994] and with far less intensity in O2 Herzberg II visible wavelengths [Krasnopolsky et al., 1976; Bougher et al., 1997]. [5] It was soon realized that the intense oxygen IR nightglow is highly variable from day to day [Crisp et al., 1996], its intensity distribution is generally not symmetric in latitude [Allen et al., 1992], often exhibits multiple local maxima, and shows variations on timescales as short as 1 hour [Lellouch et al., 1997]. The related nitric oxide nightglow emission was mapped during the Pioneer Venus mission [Stewart et al., 1980] statistically presenting larger brightness close to the Equator and shifted by about 2 hours from midnight toward dawn. Both nightglows are decoupled in height providing information about different vertical levels: 95– 105 km for O2 airglow (G. Piccioni et al., Venus oxygen airglow vertical profile, submitted to Journal of Geophysical Research, 2008) and 115 km for NO [Ge´rard et al., 2008b]. [6] In this work we have used night side nadir observations from the Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) [Drossart et al., 2007a] on board the Venus Express orbiter [Titov et al., 2006; Svedhem et al., 2007] to construct maps of the Venus atmosphere in the O2 emission band. The airglow maps were used to study the morphology and temporal variability of the airglow. In those orbits where the airglow is particularly intense we also measured the displacement of ‘‘cloud-like’’ airglow features. [7] Preliminary measurements of apparent motions were presented by Drossart et al. [2007b] for selected orbits. Ge´rard et al. [2008a] gave a precise account of the average distribution of the O2 nightglow. Other works in this special section (Piccioni et al., submitted manuscript, 2008) present results obtained in limb view which lead to vertical profiles of the airglow emission.

2. Observations and Methods [8] We analyzed data obtained during 28 consecutive orbits between 1 July 2006 and 28 July 2006, one orbit on 6 August 2006 presenting a good view of the equator, six orbits between December 2006 and January 2007 and five orbits from 7 April 2007 to 11 April 2007 that cover the polar region in higher detail than the rest of the VIRTIS data set. The data were selected according to the availability of high-resolution observations, fast repetition in each orbit (two or more images of the same region separated by 1hour) and the strong airglow emission present during most of the

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selected orbits. The Sun F10.7 flux (a convenient measurement of the solar activity in radio wavelengths commonly used as a proxy of the Sun’s irradiance in the Extreme UltraViolet) averaged over July 2006, August 2006, January 2007 and April 2007 was 76 10 22, 79 10 22, 84 10 22 and 72 10 22 W/m2 Hz respectively, reflecting homogeneous conditions for the Sun’s activity close to the solar minimum. [9] Oxygen airglow at 1.27 mm superimposes with thermal radiation coming from the lower atmosphere and partially filtered by the lower clouds. In order to subtract the thermal contribution from the total airglow signal we compare VIRTIS fluxes measured in the 1.26 – 1.28 mm wavelength range to the flux on the 1.18 mm observation window which provides a nearby region with flux dominated by the lower thermal emission. In addition, O2 photons emitted downward are subsequently backscattered by the underlying clouds and enhance the measured airglow radiation. A correction factor of 2.7 was used for nadir observations and geometry effects were corrected to first order by multiplying each pixel by the cosine of the emission angle [Crisp et al., 1996]. This crude estimation of brightness emission is adequate for VIRTIS observations of subpolar and midlatitudes (emission angles lower than 40°) but may fail in the few observations containing information about the tropics and the equator. Finally, depending on the latitudes being sampled we projected the images in longitude-latitude maps or polar stereographic projections to compare images obtained from different observational geometries. [10] Images separated by 1 hour allowed to track and retrieve the displacements of the airglow features in 23 of the 40 orbits, typically the orbits with higher intensity airglow structures, being the remaining orbits characterized by low intensity of the airglow or low contrast of the observed structures. The apparent motions of distinct airglow features were measured visually using the PLIA software [Peralta et al., 2005].

3. Morphology of the Oxygen Airglow and Temporal Variability 3.1. Low and Mid Latitudes [11] As a consequence of the quasi-polar VEX orbit, the airglow activity at low latitudes is only partially covered by VIRTIS observations with a resolution of 100 km pix 1 in some cases, while mid to subpolar latitudes are observed on many orbits with spatial resolution down to 15 km pix 1. [12] The statistical distribution of Venus oxygen nightglow is inhomogeneous with the regions of brightest emission located at low latitudes near and dawnward of the midnight meridian [Ge´rard et al., 2008b]. Maps of airglow brightness distribution of different regions are presented in Figure 1 using data from orbits that sample nearly simultaneously most of the Southern Hemisphere. On this data set the equatorial region at midnight always presents high values of brightness. Large structures with typical sizes of 3000 km extending from the equator to midlatitudes are often observed. In most cases the large-scale structures observed on Figure 1a are similar to the global structures seen in the global map of airglow brightness presented by Ge´rard et al. [2008a] and constructed by averaging several

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Figure 1. Airglow intensity map obtained during orbits (a) 87 (16 July 2006) and (b) 91 (20 July 2006). The wide coverage of the maps is close to half the night-side hemisphere and was obtained using data from three different VIRTIS data cubes for each map. The mean brightness of the sampled regions close to the equatorial midnight can change from 5 MR to 1 in a single day but the large values are more common. hundred maps from different dates. Remarkably, though largely expected, the mean airglow brightness in VIRTIS images (centered at the same latitude and local time and covering areas as big as 1.5 107 km2) changes dramatically from one orbit to other. Changes observed on Figure 1 are sometimes observed on timescales as short as 1 day over the VIRTIS data set. On the contrary, large-scale structures as seen on Figure 1a are stable on timescales of 2 hours or longer indicating sustained formation of excited oxygen molecules. 3.2. Polar Latitudes [13] Ge´rard et al. [2008a] give an average value of O2 airglow brightness at 70°S of 1.2 MegaRayleighs (MR). In contrast, intense airglow activity can also arise over polar latitudes in particular orbits. This was unexpected since the polar regions are far from the antisolar point and should contain lower densities of oxygen atoms. Figure 2 shows two images of a sequence of VIRTIS observations of the most intense airglow brightness over polar latitudes and viewed with an emission angle lower than 20° for all the sequence. A gaussian fit to the brightness temporal evolution of this structure following its motion gives a time decay of 3 hours with a peak brightness of 7 MR over a 100 km

elongated structure, moving toward lower latitudes with a meridional velocity of v = 50 ± 10 m/s. The brightness decay in time of this structure was about 2.5 times the radiative lifetime of the oxygen airglow indicating continuous production of excited molecules over this small region. [14] The period from 7 to 11 April 2007 was used for detailed research of the polar area finding intense airglow activity in all the VIRTIS images and sustained during all orbits. Figure 3 shows a sequence of observations where apparent motions of the airglow lead to a polar to low latitudes apparent motion with v 50 ± 5 m/s. Detailed motions in the whole movie during this orbit (composed of 12 images at time steps of 30 min to 1 hour) shows that the airglow evolves by a combination of apparent motions at small scale and activation and deactivation of airglow emission structures over extended regions.

4. Apparent Motions and Mesospheric Circulation [15] Figure 4 shows apparent motions tracked over airglow structures on different dates, latitudes and local times. The panels show that the morphology of the airglow is related with the apparent motions tracked. Large-scale

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morning hours (0600 0200 local time) and reversed in direction from 1800 to 2000 local time. The meridional motions are more intense at subpolar latitudes (70°S) with average velocities of v 20 m/s, decreasing toward lower latitudes and reaching a minimum value of v 5 m/s at 15°S and increasing again at equatorial latitudes. [18] In the orbits with high intensities of the airglow emission (notably on the equatorial case and the five polar orbits) the displacements of the airglow brightness patterns seem to take place at different altitude levels since the images show a mixture of structures moving with different speeds that visually seem to superimpose. In support of this interpretation, limb observations of airglow activity some-

Figure 2. Polar projections and time evolution of the brightest airglow structure seen on polar latitudes. Longitudes are given in terms of local times. Observations correspond to orbit 80, 9 July 2006, and the time difference between both observations is 3 hours. structures move slowly on one direction (generally perpendicular to the airglow brightness front) while smaller-scale structures move more rapidly in the direction defined by the front (see Figure 4a). [16] The equatorial region covered on Figure 4d looks turbulent with bright features colliding suggesting that the observed motions may come from a variety of different altitude levels. Some of the brightest structures (notably on Figure 4e, but also on Figures 4c and 4d and particular regions on Figure 4a) correlate with locations where convergence of the apparent motions is present. This behavior may not apply to the larger-scale structures, since they appear surrounded by far less intense airglow features and thus, it is not possible to retrieve motions around them. [17] Figure 5 shows the mean horizontal circulation in Venus’ night-side hemisphere obtained by spatially and temporally averaging the ensemble of apparent motions obtained in 23 orbits with 1000 tracked motions. The upper part of Figure 5 also shows the local time-latitude location of the specific regions displayed in Figure 4. The large variability in intensity and direction of the velocities, which sometimes can be as high as 100 m/s, results in low values of the average speeds. The most remarkable feature in this plot is a repeatedly observed jet at 35°S with an average zonal speed of u = 30 m/s opposite to the planetary rotation and with motions twice as intense in the early

Figure 3. Polar projections and time evolution of the 1.267 mm radiance over orbit 355 obtained on 27 August 2007 at local times: (a) 1539, (b) 1739, (c) 1939, and (d) 2209. The brightest airglow features have an intensity of 1.8 MR on the first image and longitudes are given in terms of local times. The global apparent motions are toward higher latitudes. Some of the structures like the bright arm at 68°S in Figure 3d appear suddenly and are not related with horizontal motions but with the sudden airglow brightening of new regions.

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Figure 4. Average apparent motions and correlation with the airglow intensity for selected orbits: (a) orbit 96 obtained on 25 July 2006; (b) orbit 99 obtained on 28 July 2006; (c) orbit 76 obtained on 5 July 2006; (d) orbit 108 obtained on 6 August 2006; (e) orbit 84 obtained on 13 July 2006; and (f) orbit 80 obtained on 9 July 2006. The maps cover a significant portion of the local time-latitude plane showing repeatedly observed motions, like the zonal jet at 30°S – 45°S opposite to the planetary rotation (Figures 4a, 4b, 4e, and 4f). Opposite motions between 45°S and 75°S that seem part of a close circulation are observed at different local times on different orbits (Figure 4b); meridional motions are toward the pole (Figure 4a) but generally directed toward low latitudes (Figures 4a, 4b, 4c, and 4f). The few observations of the equator show chaotic motions with a mean prograde (in the planetary rotation sense) component (Figure 4d). Brightness emission given are only approximative for Figure 4d with emission angles 70°. times presents peaks at two different altitude levels, one at 96 ± 1 km altitude and a second peak at 103 –107 km altitude (Piccioni et al., submitted manuscript, 2008).

5. Discussion [19] The average motions obtained are in agreement with a subsolar to antisolar circulation at the IR oxygen airglow levels (96 km) with evident meridional motions from polar to low latitudes and large variability of the zonal and meridional components. [20] Caution must be taken in interpreting the airglow brightness patterns displacements as tracers of flow motions in the lower mesosphere. They could be influenced by the activation and/or deactivation of airglow emission at specific regions by other phenomena (i.e., gravity waves affecting atmospheric density, localized or extended regions of downwelling, atmospheric diffusivity, etc). [21] An additional level of complexity is introduced by the radiative lifetime (70 min) of the O2 singlet state which introduces difficulties in the interpretation of the observed rates of brightness variation. The e-folding time constant of any local change (decrease or increase) cannot be less than 70 min without advection of air masses containing different proportion of glowing O2 molecules. For airglow brightness decreasing more slowly the brightness reduction could be

caused by a combination of horizontal transport and a decrease of the flux of O atoms modifying the local O density and airglow brightness. In those locations where the airglow intensity increases there must be enhanced downward flux of O atoms. [22] The observations show two types of behavior for the Venus oxygen airglow temporal evolution. On the one hand, the large-scale 1000 –3000 km structures survive at least for a few hours (longer than the radiative decay time) but change dramatically from day to day. Since limb scans of O2 emission slightly depend on latitude with higher latitudes characterized by higher altitudes of the airglow (Piccioni et al., submitted manuscript, 2008) these large structures may descend progressively as they travel from polar latitudes to the equator, compressing and increasing the concentration of free O atoms. On the other hand, smallscale bright structures with smaller scales (100 km) seem related with the apparent convergence of motions tracked in the airglow. In fact, the time evolution of the airglow maps seems composed of two types of motions, the large-scale structures moving like ‘‘fronts’’ or waves, and the bright structures immersed on them or isolated, apparently moving like passive tracers. We argue that at least part of the later motions are real motions at the mesosphere as the newly formed oxygen molecules are advected by the mesospheric circulation.

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Figure 5. Mean apparent circulation of the mesospheric oxygen airglow brightness. (a) Averages of all apparent motions tracked on the airglow and also marks the sampled regions appearing on Figure 4. A reference vector of 100 m/s is included on the upper left corner of the map. (b) A schematic model of the mean circulation of Venus mesosphere from these measurements. The background image corresponds to a map of airglow brightness obtained on orbit 93, 22 July 2007, that can be compared with those on Figure 1. Regions with no arrows indicate regions with not much available data. The apparent rotating motions indicated at 50°S are found at a variety of local times depending on the particular orbit. The jet from dawn to midnight is a repetitive structure while the jet from dusk to midnight is less regular from one orbit to another. [23] The brightest airglow patches may be regions of strong subsidence (and local adiabatic warming) associated with downdrafts that increase the volume concentration of O atoms. If this is correct, spectral maps of the O2 IR nightglow should show a positive correlation between the brightness and the rotational temperature of the emission line. Partial confirmation of this point comes from stellar occultation experiments performed by Venus Express [Bertaux et al., 2007] which result on warm temperatures at the airglow emission levels (95 – 100 km). Ground-based observations have been made to correlate the rotational temperature and brightness with results sometimes indicating such a correlation [Ohtsuki et al., 2005] and sometimes not with some dependence on the morphology of the airglow region [Bailey et al., 2007]. [24] Although the present work does not fully disentangle the different contributions to the airglow variability and therefore is not able to fully retrieve the mesospheric true motions it does show that such an approach is possible.

VIRTIS-M observations obtained with shorter time differences than the radiative lifetime of the oxygen airglow (70 min) will partially help to solve this problem. Although part of the airglow motions tracked on this work were obtained on image pairs separated by 30 min the short time difference between the images translate into larger errors in the derived motions and most of the data corresponds to images separated by 1 hour. Both data sets produce similar results on terms of the mean derived circulation. [25] Further analysis should concentrate on identifying any correlation between local temperature anomalies and O2 intensities. Also, detailed comparison with Venus GCMs should allow to better constrain the role of gravity waves as drivers of the airglow variability. The Venus Express Extended mission should run during 2008 – 2009 with higher levels of solar activity. According to NOAA Space Weather Prediction Center (SWPC) a reasonable estimate is an increase of a 60% of the F10.7 index for the end of 2009 (http://www.sec.noaa.gov/SolarCycle/SC24). This

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will allow a higher airglow activity and a better sampling of Venus mesosphere through its spatial distribution and variability. [26] Over the next years the combination of results from Venus Express instruments (VIRTIS O2 IR maps, SPICAV thermal profiles and NO nightglow observations, VeRa thermal data combined with cyclostrophic balance over mid to high latitudes and cloud top wind tracking with VMC and VIRTIS) and ground-based observations (nightglows and Doppler winds retrieval) will allow a significant improvement of our understanding of the mesospheric dynamics and the underlying mechanisms driving its variability. [27] Acknowledgments. Venus Express is a mission of the European Space Agency. We gratefully thank all members of the ESA Venus Express project and of the VIRTIS technical team. This work has been funded by Spanish MEC AYA2003-03216, with FEDER support and Grupos UPV 15946/2004 and is supported by the CNES and ASI national space agencies. R.H. acknowledges a Ramoń y Cajal contract from MEC. J.C.G. acknowledges funding from the Belgian Fund for Scientific Research and the PRODEX program managed by the European Space Agency with the help of the Belgian Federal Space Science Office.

References Alexander, M. J. (1992), A mechanism for the Venus thermospheric superrotation, Geophys. Res. Lett., 19, 2207 – 2210. Allen, D., D. Crisp, and V. S. Meadows (1992), Variable oxygen airglow on Venus as a probe of atmospheric dynamics, Nature, 359, 516 – 519. Bailey, J. A., V. S. Meadows, S. Chamberlain, A. Simpson, and D. Crisp (2007), Variability of the Venus Oxygen Airglow, Bull. Am. Astron. Soc., 38, 526. Bertaux, J. L., et al. (2007), A warm layer in Venus’ cryosphere and highaltitude measurements of HF, HCl, H2O and HDO, Nature, 450, 646 – 649. Bougher, S. W., and W. J. Borucki (1994), Venus O2 visible and IR nightglow: Implications for lower thermosphere dynamics and chemistry, J. Geophys. Res., 99, 3759 – 3776. Bougher, S. W., M. J. Alexander, and H. G. Mayr (1997), Upper atmosphere dynamics: Global circulation and gravity waves, in Venus II: Geology, Geophysics, Atmospheres, and Solar Wind Environment, edited by S. W. Bougher, D. M. Hunten, and R. J. Philips, Univ. of Ariz. Press, Tucson. Bougher, S. W., S. Rafkin, and P. Drossart (2006), Dynamics of the Venus upper atmosphere: Outstanding problems and new constraints expected from Venus Express, Planet. Space Sci., 54, 1371 – 1380. Clancy, R. T., B. J. Sandor, and G. H. Moriarty-Schieven (2007), Dynamics of the Venus upper atmosphere: Global-temporal distribution of winds, temperature, and CO at the Venus mesopause, Bull. Am. Astron. Soc., 39, abstract 61.07. Connes, P., J. F. Noxon, W. A. Traub, and N. P. Carleton (1979), O2 (1D) emisssion in the day and night airglow of Venus, Astrophys. J., 233, L29 – L32. Crisp, D., V. S. Meadows, B. Be´zard, C. deBergh, J.-P. Maillard, and F. P. Mills (1996), Ground-based near-infrared observations of the Venus nightside: 1.27 mm O2 (a1Dg) airglow from the upper atmosphere, J. Geophys. Res., 101, 4577 – 4594. Drossart, P., et al. (2007a), Scientific goals for the observation of Venus by VIRTIS on ESA/Venus express mission, Planet. Space Sci., 55, 1653 – 1672. Drossart, P., et al. (2007b), Venus upper atmospheric emissions from VIRTIS spectral imaging observations, Nature, 450, 641 – 645.

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Ge´rard, J.-C., A. Saglam, G. Piccioni, P. Drossart, C. Cox, S. Erard, R. Hueso, and A. Sańchez-Lavega (2008a), Distribution of the O2 infrared nightglow observed with VIRTIS on board Venus Express, Geophys. Res. Lett., 35, L02207, doi:10.1029/2007GL032021. Ge´rard, J.-C., C. Cox, A. Saglam, J.-L. Bertaux, E. Villard, and C. Nehme´ (2008b), Limb observations of the ultraviolet nitric oxide nightglow with SPICAV on board Venus Express, J. Geophys. Res., doi:10.1029/ 2008JE003078, in press. Gierasch, P. J., et al. (1997), The general circulation of the Venus atmosphere: An assessment, in Venus II: Geology, Geophysics, Atmosphere, and Solar Wind Environment, edited by S. W. Bougher, D. M. Hunten, and R. J. Philips, pp. 459 – 500, Univ. of Ariz. Press, Tucson. Krasnopolsky, V. A., A. A. Krysko, V. N. Rogachev, and V. A. Parshev (1976), Spectroscopy of the Venus night airglow from the Venera 9 and 10 satellites, Cosmic Res., 14, 789 – 795. Lellouch, E., T. Clancy, D. Crisp, A. Kliore, D. Titov, and S. W. Bougher (1997), Monitoring of mesospheric structure and dynamics, in Venus II: Geology, Geophysics, Atmospheres, and Solar Wind Environment, edited by S. W. Bougher, D. M. Hunten, and R. J. Philips, pp. 295 – 324, Univ. of Ariz. Press, Tucson. Limaye, S. S. (2007), Venus atmospheric circulation: Known and unknown, J. Geophys. Res., 112, E04S09, doi:10.1029/2006JE002814. Markiewitz, W. J., D. V. Titov, S. S. Limaye, H. U. Keller, N. Ignatiev, R. Jaumann, N. Thomas, H. Michalik, R. Moissl, and P. Russo (2007), Morphology and dynamics of the upper cloud layer of Venus, Nature, 450, 633 – 635. Miller, H. C., J. E. McCord, J. Choy, and G. D. Hager (2001), Measurement of the radiative lifetime of O2 (a1Dg) using cavity ring down spectroscopy, J. Quant. Spectrosc. Radiat. Transfer, 69, 305 – 325. Ohtsuki, S., N. Iwagami, H. Sagawa, Y. Kasaba, M. Ueno, and T. Imamura (2005), Ground-based observation of the Venus 1.27-mm O2 airglow, Adv. Space Res., 36, 2038 – 2042. Peralta, J., R. Hueso, N. Barrado, and A. Sańchez-Lavega (2005), Introducing PLIA: The planetary laboratory for image analysis, Bull Am. Astron. Soc., 37, 653. Sańchez-Lavega, A., et al. (2008), Variable winds on Venus mapped in three dimensions, Geophys. Res. Lett., 35, L13204, doi:10.1029/ 2008GL033817. Stewart, A. I. F., J. C. Gerard, D. W. Rusch, and S. W. Bougher (1980), Morphology of the Venus ultraviolet night airglow, J. Geophys. Res., 85, 7861 – 7870. Svedhem, H., et al. (2007), Venus Express—The first European mission to Venus, Planet. Space Sci., 55, 1636 – 1652. Titov, D. V., et al. (2006), Venus Express science planning, Planet. Space Sci., 54, 1279 – 1297. Widemann, T., E. Lellouch, and A. Campargue (2007), New wind measurements in Venus’ lower mesosphere from visible spectroscopy, Planet. Space Sci., 55, 1741 – 1756. P. Drossart, Laboratoire d’ Etudes Spatiales et d’ Instrumentation en Astrophysique, Observatoire de Paris, Universite´ Paris-Diderot, CNRS, UPMC, 5 place Jules Janssen, F-92195 Meudon, France. (pierre.drossart@ obspm.fr) J. C. Ge´rard, Laboratoire de Physique Atmosphe´rique et Plane´taire, Universite´ de Lie`ge, 5 Avenue de Cointe, B-4000 Lie`ge, Belgium. ([email protected]) R. Hueso and A. Sańchez-Lavega, Departamento de Fı´sica Aplicada I, E.T.S. Ingenieros, Universidad del Paı´s Vasco, Alameda Urquijo s/n, E-48013 Bilbao, Spain. ([email protected]; [email protected]) I. Khatuntsev and L. Zasova, Space Research Institute, Profsouznaya 84/ 32, Moscow, Russia. ([email protected]; [email protected]) A. Migliorini and G. Piccioni, Istituto di Astrofisica Spaziale e Fisica Cosmica, INAF-IASF Roma, Via del Foso del Cavaliere 100, I-00133 Rome, Italy. ([email protected]; giuseppe.piccioni@ iasf-roma.inaf.it)

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Limb observations of CO2 and CO non-LTE emissions in the Venus atmosphere by VIRTIS/Venus Express G. Gilli,1 M. A. Lo´pez-Valverde,1 P. Drossart,2 G. Piccioni,3 S. Erard,2 and A. Cardesıń Moinelo3 Received 11 February 2008; revised 29 October 2008; accepted 13 November 2008; published 7 March 2009.

[1] We report and analyze here observations of strong infrared emissions from the limb of

the Venus upper atmosphere during daytime, taken by the Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) aboard Venus Express. We focus on the measurements taken during the first 4 months of nominal operations. The emissions observed at 4.3 mm and at 2.7 mm are attributed to CO2 fluorescence of solar radiation and are detected up to about 160 km and 130 km, respectively, while the CO fluorescence at 4.7 mm is observed up to about 120 km. The emissions are detected in both the channels of VIRTIS, at different spatial and spectral resolutions (resolving powers about 1800 and 400), for the periapsis and the apoapsis of the Venus Express orbit. From these data sets we built up 2-D maps of the emissions as well as vertical profiles, which are then studied in order to characterize their variations with geophysical parameters, like solar illumination and emission altitude. Several analyses are performed in order to understand the VIRTIS behavior, to determine systematic effects in the data, and to propose appropriate corrections. We also present comparisons with a theoretical nonlocal thermodynamic equilibrium (non-LTE) model of the Venus upper atmosphere. The agreement is very encouraging, in general, and the main variability observed in the data, with solar zenith angle and altitude, can be understood with the model. We conclude that the present data set opens brilliant perspectives for deriving densities and rotational temperatures in the upper mesosphere and lower thermosphere of Venus. Citation: Gilli, G., M. A. Lo´pez-Valverde, P. Drossart, G. Piccioni, S. Erard, and A. Cardesıń Moinelo (2009), Limb observations of CO2 and CO non-LTE emissions in the Venus atmosphere by VIRTIS/Venus Express, J. Geophys. Res., 114, E00B29, doi:10.1029/2008JE003112.

1. Introduction [2] The European mission Venus Express arrived at Venus in April 2006, and started its scientific phase in June 2006, being the first mission in more than 20 years to study the Venus atmosphere systematically from orbit [Svedhem et al., 2007]. In contrast to the Earth’s mesosphere and lower thermosphere, the equivalent density layers on Venus from about 90 to 150 km, are regions scarcely observed using remote sounding in the infrared (IR). The most abundant species CO2, is well known to present strong IR vibrationalrotational bands, and more than 25 years ago, Pioneer Venus made systematic nadir observations using the fundamental band of CO2 at 15 mm in order to retrieve atmospheric temperatures in the mesosphere of Venus [Taylor et al., 1980]. Also, NIMS/Galileo permitted a sounding of the lower mesosphere temperature at 4.3 mm in nadir and nighttime [Roos-Serote et al., 1995]. However, at higher altitudes, these emissions are expected to be out of thermo1 2 3

Instituto de Astrofı´sica de Andalucıá, CSIC, Granada, Spain. Observatoire de Paris, Meudon, France. IASF, INAF, Rome, Italy.


dynamic equilibrium, with particularly large deviations during daytime, owing to solar pumping of the corresponding vibrational states [Dickinson, 1972; Deming et al., 1983; Roldań et al., 2000]. As a consequence, their inversion in terms of geophysical parameters is not straightforward, or in other words, their thermodynamic information content is limited [Lo´pez-Puertas and Taylor, 2001]. The understanding of these non-LTE emissions is important, not only for its own interest, but also they can give rise to a large radiative heating, or cooling, of the atmosphere. In the case of Venus, they are a key ingredient of the energy balance between 80 and 150 km [Bougher et al., 1994]. Roldań et al. [2000] showed that the CO2 bands at 4.3 mm and the ones at 2.7 mm dominate the solar heating at those altitudes; much less important are the CO absorptions at 4.7 mm, given the lower atmospheric abundance of CO. Further observational problems challenged an infrared sounding of these layers from the Venus orbit so far; among them, the pointing in the limb (the optimum geometry of observation at these low atmospheric densities) and the sensitivity of the infrared detectors. [3] Progress toward limb sounding of planetary atmospheres has been made during the last 2 decades, both theoretically and observationally. Triggered by ground-based

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observations of CO2 laser bands at 10 mm, a number of nonLTE models for atmospheric IR emissions were developed for Mars and Venus in the 1980s [Deming et al., 1983; Gordiets and Panchenko, 1983; Stepanova and Shved, 1985]. Later, more comprehensive tools were developed in preparation for sounding of the Martian atmosphere in the IR during the 1990s [Lo´pez-Valverde and Lo´pez-Puertas, 1994], which were adapted later to the Venus atmosphere [Roldań et al., 2000]. These models, which included many more radiative bands and CO2 energy states, represent adequate tools for simulation and analysis of limb data. Some of the few non-LTE observations available so far, and amenable for such studies, were taken by the Near Infrared Mapping Spectrometer (NIMS) instrument [Carlson et al., 1992] on board the Galileo sounder, during its flyby of Venus in 1990 [Carlson et al., 1991]. A few limb spectra of the CO2 4.3-mm band were obtained during daytime and were explained recently using an improved version of the non-LTE model of Roldań and colleagues [Lo´pez-Valverde et al., 2007] (hereinafter LVEA). Recent analysis of CO2 non-LTE emissions have also been carried out for Mars using data from the Planetary Fourier Spectrometer (PFS) on board Mars Express [see Lo´pez-Valverde et al., 2005; Formisano et al., 2006], which helped to partially validate the non-LTE model. Regarding CO, non-LTE emission lines in the near-IR were first discovered in the Venus atmosphere about 20 years ago by de Bergh et al. [1988]. They were recently modeled and analyzed by Crovisier et al. [2006], who identified the fundamental CO(1-0) and the first hot CO(2-1) vibrational-rotational bands, derived rotational temperatures from them, and predicted that the instrument VIRTIS/Venus Express might detect these emissions. [4] VIRTIS is one of the instruments aboard Venus Express with capabilities to sound the upper atmosphere of Venus [European Space Agency (ESA), 2001; Titov et al., 2006]. It is an imaging spectrometer in the visible and near infrared, inherited partly from the Rosetta mission [Coradini et al., 1998]. VIRTIS is devoted to a large number of investigations at Venus, like the cloud deck morphology and dynamics, the lower atmospheric composition, and the derivation of the temperature structure in the lower mesosphere. First studies of these aspects are presented in companion papers in this issue. Most of these studies are carried out by nadir observations. However, VIRTIS is designed to perform limb observations aboard Venus Express as well [Titov et al., 2006]. This mode of observation represents the first systematic sounding of the upper layers of the Venus atmosphere in the infrared, and is devoted to understand the Venus high-atmosphere emissions [Drossart et al., 2007b]. The detection of the non-LTE CO2 4.3-mm band by VIRTIS at thermospheric altitudes has been confirmed by Drossart et al. [2007a], in consonance with the theoretical expectations. [5] In this paper, we focus on a detailed and systematic analysis of VIRTIS observations of CO2 and CO non-LTE emissions from the upper atmosphere of Venus using such a limb geometry. This is a unique data set, and our goals include, first of all, performing an internal validation of the data of our interest, second, characterizing the behavior of the instrument during limb sounding, and also describing the data set available, its quality and quantity, and its

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scientific potential. These CO2 non-LTE emissions have also been observed by VIRTIS in nadir, which show signatures of gravity waves propagation into the thermosphere, as studied in a companion work in this issue [Garcıá et al., 2009]. The study of the O2 infrared system in the Venus mesosphere is also presented by Hueso et al. [2008] and in this issue (G. Piccioni et al., Near IR oxygen nightglow observed by VIRTIS in the Venus upper atmosphere, submitted to Journal of Geophysical Research, 2009). [6] In this paper we also present and discuss comparisons with the non-LTE model results in LVEA, using both their published results and a small number of specific simulations performed here with such model. Although a more detailed comparison between model and data is under preparation, this is the first time that this model is confronted with an extensive data set of such a high spectral and spatial resolution. This work is, therefore, also devoted to test the model’s main theoretical predictions, and to identify improvements and lines of exploration for non-LTE models. [7] In section 2, we first describe VIRTIS observations in the limb, including the presentation of 2-D maps and vertical profiles of radiances. Their validation analysis, including consistency tests, actual noise level determination, as well as internal correlations and comparisons with the non-LTE model, are presented in section 3. Further discussions, future prospects and conclusions, are presented in section 4.

2. VIRTIS Observations [8] VIRTIS on Venus Express is an imaging spectrometer whose precursor is currently en route to comet 67P/ Churyumov-Gerasimenko, as part of the scientific payload of the Rosetta mission. Its detailed description and calibration can be found elsewhere [Coradini et al., 1998; Piccioni et al., 2006; Titov et al., 2006] and see also the companion papers in this issue. We briefly describe here those characteristics and mode of operation on board Venus Express which are needed for our analysis. 2.1. VIRTIS/VEX Characteristics and Data Set [9] The instrument consists of two channels: VIRTIS-M (hereinafter V-M), a mapping spectrometer at a resolving power R 400, working in the visible (0.3 –1 mm) and in the near infrared (1 – 5 mm), and VIRTIS-H (hereinafter V-H), a high-resolution spectrometer (R 1800) working in the spectral range 2 –5 mm. Table 1 of the work by Drossart et al. [2007b], summarizes their main characteristics; let us recall some of them here. The nominal field of view (FOV) is 64 64 mrad2 for V-M (whole frame, with 256 spatial pixels in each direction), and 0.58 1.75 mrad2 for V-H. The V-M spectral range is sampled at 432 spectral wavelengths, while a typical V-H spectrum obtained in the nominal mode contains a sequence of 3456 measurements. The nominal noise equivalent radiance (NESR), for 1 second of integration time, is 5000 mW m 2 sr 1 mm 1 in both channels, according to the ESA [2001]. All V-H and V-M data taken during one orbit, or terrestrial day, are stored in the so-called ‘‘qubes.’’ The V-M ones generally contain a set of data in 3 dimensions, 2 spatial and 1 spectral. During each orbit the number of V-H/V-M data qubes

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Figure 1. Geographical coverage with VIRTIS-M (V-M) during orbit 25, from apoapsis. (top, left) Map of radiances at 4.32 mm from qubes 0, 1, and 2. (top, right) Altitude-SZA location of all the limb data above the cloud layer (all pixels of seven qubes of this orbit); (bottom) Location of the data in SZA, longitude, latitude, and local time. The numbers identify the different qubes. obtained varies from 1 to 20. A detailed description of data handling and archiving is presented by Drossart et al. [2007b]. [10] VIRTIS observations are separated into two categories, corresponding to distances of Venus Express to the planet lower than 12,000 km (Spectral Mode) or higher (Spectral Imaging Mode). During the Spectral Mode only a partial coverage of the surface is normally obtained, but thanks to the proximity to the planet, a very small field of view (projected) is achieved, ideal to study atmospheric variability and composition, and for tangential limb observations. Several instruments on board Venus Express share this operation mode. The Spectral Imaging Mode permits unique and unprecedented maps of the disk of Venus, and has been used to obtain global observations from apoapsis (‘‘mosaic construction’’). We will show below how these VIRTIS 2-D images contain useful information on the limb of the planet as well. [11] A note regarding the distinction between limb and nadir observations is needed in order to clarify that, in a general sense, a data qube from VIRTIS will contain both kind of observations. Our distinction merely tells whether the line of sight touches the disk of Venus (nadir data) or crosses its atmospheric limb. Therefore, some pixels may represent actual limb sounding while nearby pixels in the same V-M image will supply ‘‘disk’’ observations. This will become clearer in section 2.5. For this reason, analysis of nadir observations, described in companion papers of this issue, share similar difficulties to our study and all of them are beneficial to the overall validation of VIRTIS data.

2.2. Geographical and Temporal Coverage [12] At present, V-M data are available in calibrated form up to the orbit 631 (13 May 2006 to 1 Jan 2008), but in this work we focused our analysis on a selected number of orbits up to orbit 295 (10 February 2007). For V-H, at the time of preparing this paper, the data from orbit 23 to 127 (13 May 2006 to 25 August 2006) were already calibrated, so we focused on them, while only a fraction was calibrated and available for the rest of the mission. As mentioned above, our purpose is to perform an exploration of V-M and V-H limb data in the Venus upper atmosphere, from the cloud top, around 60 km, up to upper thermospheric layers. With this aim, we made a catalog of orbits/qubes/pixels with actual limb sounding and a number of diagrams of the geometrical and spatial coverage of all limb data obtained. Examples are shown and commented next. 2.2.1. VIRTIS-M [13] Starting with V-M, Figures 1 and 2 show two examples of the V-M coverage for two different observational geometries, both sampling the Venus limb, one from orbit 25 (15 May 2006), during the apoapsis, and the other from orbit 43 (2 June 2006) near the periapsis. They can be considered as representative of the two principal VIRTIS modes of observation (see section 2.1). [14] The first orbit, from apoapsis, contains 8 qubes of V-M data (7 with limb data), each one consisting of set of pixels, or a 2-D image at each wavelength, as mentioned above. Let us recall that each pixel is associated to one individual spectrum. In Figure 1a, data at the wavelength of 4.32 mm from three of the qubes, building up a map of radiances; we will discuss them below. Each qube includes a

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Figure 2. V-M coverage in tangent altitude versus SZA during periapsis of orbits 43 and 287. Data are shown between 60 and 180 km only. large number of pixels (245 256), and the fraction of those pixels with actual pointing to the Venus limb are shown in Figures 1b– 1d. We see that when the 8 qubes are considered, the latitude, longitude, and local time of the data cover an ample range of values. In this work we pay particular attention to the altitude and solar zenith angle (SZA) coverage. These two parameters are expected to produce the maximum variation in the non-LTE emission of both CO2 and CO in the atmosphere, according to the model of LVEA. The most intense signal is expected between 90 and 140 km, and for low SZA values; for nighttime the signal would be very low, and we confirmed all of this with VIRTIS data. Figure 1b shows that the number of limb daytime spectra is large for this orbit. The vertical resolution from each row of pixels (for each qube) is between 15 and 20 km, typically. This is larger than an atmospheric scale height, but the superposition of adjacent rows may improve significantly this resolution, as we will show below. Also the SZA sampling is not completely even, but the large density of points permits suitable averaging if they are needed. [15] Figure 2 illustrates the V-M coverage in the qube 0 of orbit 43 (2 June 2006), during the periapsis of Venus Express and in the qubes 0 and 1 of the orbit 287 (1 February 2007). We use again altitudes at the tangent point versus SZA, which describe our preferred data set for limb sounding. The available daytime limb data during the orbit 43 are about 5410 while this number is larger for the 287 (about 8000). The vertical resolution obtained at the limb from one single row of pixels (300 m), improves by a very large factor with respect to the apoapsis case, and is therefore particularly useful for studying the vertical variation of the non-LTE emissions and testing the theoretical predictions. 2.2.2. VIRTIS-H [16] Observations of the limb of the planet by VIRTIS in the Spectral Mode are not as frequent as in the Spectral

Imaging Mode owing to pointing constraints; the field of view of the V-M frame is much larger than the V-H. The amount of V-H spectra which actually touches the limb of the planet is therefore very limited in most orbits, sometimes only 2 or 3 spectra of our interest are available per orbit. [17] Figure 3 illustrates the V-H geometrical coverage at the limb, again as a function of SZA and tangent altitude, combining all the orbits during the first four months of operation. A total of 651 points are shown, each one representing a V-H spectrum. We have marked 4 specific orbits (43, 76, 80, and 88) to indicate 4 different special observational coverages. The first one corresponds to a special limb case, the so-called ‘‘Tangential Limb Scenario’’ [see Titov et al., 2006], where the satellite is fixed pointing to the limb of Venus during the periapsis. This is a specially useful kind of orbit for limb sounding and for non-LTE studies, given the high rate of tangent point observations. Orbit 76 (6 July 2006) corresponds to a ‘‘stellar occultation’’ case, where again a large number of observations is obtained at the limb, but they correspond to the nighttime hemisphere. They do not show a distinct emission level and have not been considered in the analysis. The other two orbits correspond to the most common limb scenario. [18] For a number of studies, averaging of these data may be required, in order to achieve a good SZA or altitude coverage. From the 651 V-H limb spectra available, a total of 145 are daytime observations (SZA < 90°), and 44 of these correspond to strong solar illumination conditions (SZA < 60°). The last ones are, in principle, optimum for non-LTE studies. Let us recall that, within Venus Express, V-H data are the measurements with the highest spectral resolution. The latitude and longitude coverage of the daytime spectra are shown in Figure 3 as well. Except for orbits 43 (2 June 2006) and 80 (10 July 2006), with highlatitude sampling of nearby points, all others are disperse, with a preference for low latitude.

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Figure 3. (left) Coverage in tangent altitude and SZA of the VIRTIS (V-H) data available from all the orbits from 23 to 127. Some orbits are indicated with special symbols. (right) Latitude-longitude location of the data, for daytime only. Symbols as in the left. See section 2.2.2 for details. 2.3. VIRTIS-H Spectra and Profiles [19] We describe here typical V-H limb spectra which show strong non-LTE signals, and we build up vertical profiles also, in order to characterize the emissions. We focus on three emission bands, those at 4.3 mm and 2.7 mm both due to CO2, and that at 4.7 mm, due to CO. All of them are suitable to be contrasted with the recent non-LTE model predictions of LVEA. 2.3.1. CO2 4.3 mm Emission [20] Figure 4 (left) shows a sample of seven V-H spectra in the 4.3-mm region, corresponding to different orbits, and pointing at different tangent altitudes. The selection corresponds to orbits 33, 40, and 47 (24 May, 31 May, and 7 June 2006, respectively) and to a SZA interval from 20° to 30°. First, it is noticeable an intensity of the signal well above the noise level at all wavelengths between 4.20 and 4.50 mm. The emission falls to zero shortward and longward of these values, respectively, where the small oscillation observed can be considered as the measurement noise and which seems to be close to 2000 mW m 2 sr 1 mm 1. Only the two lowest altitude spectra show signal in the wings, though surely from solar scattering in the clouds. At the uppermost altitude shown in Figure 4, 167 km, the spectrum is still above noise level, which demonstrate that V-H can sound up to those thermospheric altitudes in Venus before averaging.

[21] It is clear that the spectral shape of the 4.3-mm band, the peak intensity, and the wavelength of the peak of the band, vary with altitude. The maximum emission is obtained in this sample around 110 km tangent altitude. This is close to the altitude predicted by the CO2 non-LTE model by LVEA. Also the spectral shape is similar to the model prediction, with three clear peaks in the 110 km spectrum, around 4.28, 4.32 and 4.35 mm. According to the model, the first peak is due to the second hot band of the main CO2 isotope; the mechanism being solar absorption at 2.7 mm, exciting CO2 states which later relax radiatively, emitting in the 4.3-mm region. The second peak, around 4.32 mm, is produced by radiative relaxation of the CO2 vibrational states excited after solar absorption at 2.7 mm and 2.0 mm. The third peak is mostly due to solar pumping at 2.0 mm. The non-LTE model predicts that at high tangent altitudes the 4.3-mm band would change this shape to a simpler two emission peaks separated by a dent around 4.29 mm, due to the dominance of the 2.7 mm solar pumping. This is indeed observed in Figure 4, above about 130 km. At higher altitudes, the model predicts a further change, due to the direct solar absorption in the fundamental band of the main isotope, whose central dip at 4.26 mm is observed in Figure 4 in the 167 km spectrum. [22] Vertical profiles can be constructed combining spectra at different altitudes. They, however, will not represent

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Figure 4. (left) Selection of seven V-H spectra from orbits 33, 40, and 47 at different altitudes and for 20° < SZA < 30°. (right) Vertical distribution of the 145 daytime measurements at 4.32 mm from orbits 23 to 127 (May – August 2006). The solid line on the right represents a model simulation. Orbits and symbols as in Figure 3. real 1-D profiles of the Venus atmosphere. Only a couple of orbits (43 and 80) contain a sufficient number of limb spectra to build a vertical profile for one single orbit; in general, data from different dates and geographical locations will be combined. This will have to be taken into account when performing detailed comparisons with non-LTE models, which usually provide the radiance structure at a single geographical location. In Figure 4 (right) we combine diverse data at 4.32 mm. The data and the symbols correspond to those in Figure 3. A tentative grouping by SZA values did not show any clear trend, given the scarcity of data; the SZA variation is better observed in V-M data and discussed below. [23] The cloud of V-H data presents a dispersion not present in the 1-D radiance simulations by LVEA, also shown in Figure 4. The model simulation corresponds to SZA = 80° similar to that of orbit 80. This orbit can be considered as a good 1-D profile extending from 30 to 200 km. Notice that data from orbit 43 can also be used to build vertical profiles, but only above about 100 km. Both data and model present a clear increase with altitude in the lower mesosphere and a peak emission around 120 km. Above this altitude, the emission declines quickly to zero, following the decrease in atmospheric density. The actual atmospheric density profile is critical in determining the precise altitude of the peak emission, and this is the most likely reason for the mismatch with the prediction of LVEA. Regarding the peak radiances, not much comparison can be performed at this stage, as it should be the topic of a more rigorous comparison of the non-LTE model, and should account for suitable atmospheric variability in the density profiles.

2.3.2. CO2 2.7 mm Emission [24] According to LVEA, CO2 emissions at 4.3 mm and at 2.7 mm are both produced by solar pumping. The emission level at 2.7 mm is, however, much lower and we have only found a small number of cases/spectra with a distinct emission above noise levels. Figure 5 shows two of those spectra, which correspond to SZA of about 45° and 60°. One of them presents a bias, which is not corrected here. A non-LTE model simulation has been performed for these solar illumination conditions, using the VIRA reference atmosphere, and is also presented in Figure 5. The spectral shape of the two fundamental bands of the main CO2 isotope can be discerned, by comparing with the non-LTE model simulation. The data are very noisy for further conclusions at this stage. Hopefully, a more extended data set of V-H spectra will be obtained during the rest of the mission, and a statistically sounded comparison with the theoretical prediction will be a good test for non-LTE models. This 2.7 mm non-LTE emission has also been detected in the V-M signal, as we discuss in section 3.2. 2.3.3. CO 4.7 mm Emission [25] Figures 6 and 7 illustrate a similar study for the 4.7-mm spectral region which, according to LVEA is dominated by non-LTE emissions of CO in the upper mesosphere and lower thermosphere. The emission observed is lower than the CO2 emission, and the number of V-H CO spectra with good signal to noise ratio is much smaller. About 20 spectra, out of the 145 available at the limb during daytime, show a clear signal. Many of them show a small signal above the noise level or above the daylight scattered by the aerosols and clouds. This is why it is difficult to detect this non-LTE

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Figure 5. Two selected spectra at 2.7 mm observed by VIRTIS-H (thin lines) compared to a non-LTE model simulation (thick line). The upper spectrum is from orbit 88, qube 2 at an altitude of about 107 km and SZA about 60°. The lower spectrum is from orbit 96, qube 20 at an altitude of 118 km and SZA about 45°. The theoretical simulation has been performed at 110 km and for similar solar illumination conditions. No shift has been applied to the observed data. See section 2.3.2. emission below about 70 km. The highest altitude where the emission can be clearly identified is about 120 km. [26] Three of the most interesting spectra are shown in Figure 6 (top). The two spectra with the strongest emission correspond to two observations at approximately the same tangent height, around 85 km, but for SZA of 60° and 76°. The third one, shifted downward for clarity, is the spectrum at the highest altitude available in the data set explored so far, about 120 km. In spite of the noise level, very noticeable in the high-altitude spectrum, and which amounts to 5000 mW m 2 sr 1 mm 1, in all of them we can see two branches of what seems a typical vibrational-rotational band. The simulations of LVEA for the CO(1-0), or fundamental band (FB), of the main isotope showed such structure as well. However, their prediction has a minimum emission at the FB center, at 4.67 mm, while the spectra in Figure 6 present their central minimum around 4.73 mm. This points to a different band of CO, the CO(2-1), or first hot (FH), of the main isotope, as responsible for these data. Such emission was not simulated by LVEA, who focused their analysis in the FB. These V-H measurements indicate that, if the fundamental transition is present, its intensity is smaller than the first hot. A similar effect was observed with ground-based measurements at much higher spectra resolution [see Crovisier et al., 2006]. [27] The spectral resolution of V-H allows to identify individual lines of the P and R branches of the FH CO band, and this is shown Figure 6 (bottom). A number of vertical

lines are drawn at the wavelengths of the spectral lines centers of the two bands, according to HITRAN [Rothman et al., 2005]. For clarity, we focus on the spectral interval occupied by the P branch of the FH band. We can identify most of the lines of the P branch, up to P20 at least. It is interesting to notice, however, that in the center of the FH P branch, at 4.727 mm, a weak emission line is observed, which coincides with the line P7 of the CO fundamental band. No FH lines are present there. Moreover, all the 5 lines observed around this wavelength are better fit by the P5 –P9 lines of the FB than by the FH band. In other words, the emission from the FB seems to be also detected in the central portion of the FH band. The absence of FH lines may be due to the smaller strength of the lines with low rotational number. This is a tentative explanation which requires careful quantitative modeling. [28] As previously done by Crovisier et al. [2006], it might be possible to derive temperatures from this emission after a correct model fit. This is not possible with the model of LVEA in this moment, but we have attempted to estimate the rotational temperature from some VIRTIS data. First, we assumed that the rotational levels of CO are in thermal equilibrium at the pressures of relevance. Then, the usual expression for the rotational temperature, given by the ratio of line intensities (see an example for the Venus atmosphere by Deming et al. [1983]), was applied to the spectrum shown in Figure 6 (bottom). A selection of lines from the P branch of the FH band is required. The criteria we

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Figure 6. (top) Selection of three V-H spectra at 4.7 mm. The two closer ones correspond to about 85 km and to SZA of 60° (solid line) and 76° (dashed line). The lowest spectrum, shifted 20,000 radiance units downward for clarity, corresponds to a tangent altitude of 118 km and SZA of 46°. (bottom) Zoom of the SZA 60° spectrum, with lines marking the positions of the Hitran line centers for the fundamental (dashed marks) and first hot (solid marks) bands of CO. followed were a small overlapping with the FB lines and a peak emission well above the noise levels. This method rules out high and low J lines. Also, adjacent rotational lines should be avoided in order to reduce numerical error. For this spectrum, the best subset of lines that we found are those between P3 and P8. By forming pairs between the more extreme ones, P3, P4 and P7, P8, several values of rotational temperature were found, all around 237 K, but with uncertainties of 50 K or larger. This large error is due to the large sensitivity to the peak intensity in the center of the lines. This intensity value is uncertain because it is not directly available from the data, given the V-H spectral resolution and sampling, but obtained by an extrapolation to the radiance values in the centers of the selected lines. Notice that the P branch of this spectrum apparently has a maximum at P9 and a local minimum at P6; we have excluded these two lines in this analysis since they seem specially affected by the V-H sampling and noise. This error in the lines intensities can be reduced in the future, by using a non-LTE model simulation for the FH band, as was done by Crovisier et al. [2006].

[29] Regarding the vertical variation, profiles can be constructed from the two dozens of spectra with good S/N ratio. They are shown in Figure 7 at 4.678 mm. The data have a clear dispersion, and in contrast to the model prediction for the FB, it does not show any clear peak: the plateau between 95 and 120 km, at about 6000 mW m 2 sr 1 mm 1 is very close to the noise level. As it happened with the CO2 data in Figure 4, the small number of points does not allow to discern a clear SZA variation. 2.4. VIRTIS-M Maps During Periapsis [30] In this section we present measurements of V-M from the periapsis, where the maximum spatial resolution can be achieved, as shown in Figure 2. In the Spectral Mode, near periapsis, the V-M slit is fixed, no scanning is performed, and a row of pixels is taken. Therefore, the mapping is composed of a set of successive ‘‘V-M slit frames,’’ each one taken about 10 s apart, while the spacecraft is moving along the orbit. [31] This is illustrated in Figure 8, where we plotted a map of CO2 radiances at 4.32 mm, in the center of the strong

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Figure 7. Vertical distribution or V-H radiances at a central wavelength of the 4.7-mm band of CO. Orbits and symbols as in Figure 3. See section 2.3.3. CO2 bands system. The vertical lines correspond to the actual pointing of each pixel, similarly to Figure 2. We will build similar maps with V-M data but from apoapsis observations, in the next section. [32] Two strong variations are observed in the radiances, one along the vertical axis and the other along the horizontal, and both can be explained by the non-LTE model.

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Considering first the vertical, a peak emission is observed, in the lower thermosphere, 110– 120 km. As the SZA varies along the track (from 70° to nighttime), the peak intensity, and the whole profile, decrease. This is predicted by the non-LTE model and this data set offers a perfect benchmark for a quantitative validation of the model; a work which is in progress. [33] This is the first time that maps of IR radiation with this geometrical resolution are obtained in the Venus upper atmosphere. In order to illustrate more clearly such a resolution, we constructed a vertical profile with data from a single slit view, that indicated with an arrow in Figure 8. They are shown in Figure 9 (right), at three wavelengths. A log scale is used for the radiances, to confirm that a good signal to noise is achieved even at 160 km from one single spectrum. These data correspond to SZA 75°– 80°, a value typical of daytime observations at high latitudes. The altitude of the peak is maximum in the center of the band, at 4.32 mm, and it is lower in the wings, at 4.25 and 4.40 mm, according to model expectations. Figure 9 (left) shows three V-M spectra at three tangent altitudes. The spectral resolution is lower than V-H, but the change in the overall shape of the band with altitude is clearly observed, as discussed in the previous section. We have also tried to build up maps of radiances in the 2.7-mm and 4.7-mm region. Unfortunately such V-M maps are very noisy at all altitudes, contain a very strong component of scattering up to 90 km, and are not further studied here. 2.5. VIRTIS-M Maps and Profiles During Apoapsis [34] V-M observations from the apocenter of the Venus Express orbit are good examples to show the large potential of an imaging instrument like V-M for mapping large portions of the Venus disk. However, some images also contain information about atmospheric limb emissions, and

Figure 8. Map of V-M radiances from periapsis (orbit 43) at 4.32 mm versus SZA and tangent altitude. The color code ranges from 0.03 to 0.34 mW m 2 sr 1 mm 1. Each line corresponds to a column of pixels along the V-M slit. V-H data for the same orbit are overplotted (triangles). The arrow indicates the column of pixels from which the vertical profiles in Figure 9 have been extracted. 9 of 19

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Figure 9. V-M spectra and profiles at 4.3 mm from periapsis, orbit 43. (left) Spectra at three different altitudes, as indicated. (right) Profiles at three wavelengths extracted from the pixels indicated in Figure 8. See section 2.4 for details. allowed us to extract vertical profiles with an excellent vertical resolution. 2.5.1. 2-D Images From Apoapsis [35] Figure 10 shows a map of V-M radiances at 4.32 mm, built up with the data from three consecutive qubes or V-M images (2, 1, and 0) of orbit 25, all of them acquired from

apoapsis, at about 66,000 km from the center of Venus [Svedhem et al., 2007]. The instrument takes about 10 min to take every qube, or image, before the satellite pointing switches to the next. The three images coincide with the data qubes shown in Figure 1a. The current map looks very different for two main reasons. First, now we are plotting

Figure 10. Maps of V-M limb radiances at 4.32 mm from apoapsis, from the three qubes of orbit 25 shown in Figure 1, as a function of tangent altitude and longitude. Vertical lines indicate SZA in degrees. Color scale is given in Figure 11. 10 of 19

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Figure 11. Smiling effect in V-M images from apoapsis and its correction for two cases: (top) qube 1 of orbit 25 and (bottom) qube 0 of orbit 29. (left) Original map of radiances at 4.32 mm. (middle) Location of pixels with radiance between the ranges indicated, and fit of a parabolic line to the cloud of pixels. (right) Corrected maps. See section 2.5.2 for details. just the portion of the image which corresponds to actual limb sounding, i.e., tangent altitudes above about 60 km. And second, instead of pixel number, in Figure 10 we used altitude above the surface for the y axis, and a geographical parameter, longitude, in the x axis. Since the latitude is almost constant for all these limb data, Figure 10 represents a geometrically realistic view of such altitude-longitude cross section of the Venus upper atmosphere. [36] Although the data acquisition and the image construction are different, this map is similar to the map from periapsis shown in Figure 8. Again, we observe a strong emission in the lower thermosphere, with a peak around 110– 120 km. Also a large variation is observed in the horizontal axis. The SZA variation is, however, very small in each of the three qubes/images shown in Figure 10. Therefore, the horizontal variation may be of a different nature than that seen in the periapsis case, and might indicate a real atmospheric variability. [37] However, extreme care is required before such an interpretation. The observations are taken from very far away, in a geometry which the instrument was not optimized for limb sounding; the data might contain some geometrical effects. First of all, these maps contain what appears to be a systematic bending of the radiances in the central part of the image. This bump has also been observed at other wavelengths, qubes, and dates, and it seems to be systematic. It is specially apparent in the uppermost layers.

A second problem is shown by the sharp change in radiance between adjacent qubes. The three qubes shown here were taken with some minutes apart, and there is no obvious physical reason to expect changes of that magnitude between them. Investigation of these instrumental effects is ongoing. A likely explanation for the ‘‘smiling effect’’ is discussed in the next section. A probable cause for the second problem could be some flat field effect not yet identified. 2.5.2. Geometrical Correction of V-M Images [38] The apparent bending observed in Figure 10 is of the order of 20 km, precisely the vertical resolution per pixel from the apoapsis, in other words, this error is within the size of one pixel. Although we cannot rule out small variations of the spacecraft pointing, the effect may be due to a slight bending of the V-M slit. This ‘‘smiling effect,’’ which cannot be avoided, is of special importance for this limb sounding. Fortunately, some correction is possible, precisely by using these limb non-LTE emissions. We basically used two theoretical predictions of these emissions: first their strong decrease with tangent altitude, and second their variation with SZA, as explained below. The idea is to fit the bending observed with a simple function and then use that function to replot the radiances. We illustrate two examples of such a correction in Figure 11. [39] Figures 11a and 11d show two different V-M images, those for orbit 25 (15 May 2006), qube 1 and orbit 29

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Figure 12. Vertical profiles of V-M radiance at (right) 4.33 mm after combining the (left) rows of pixels. (top) Qube 2 of orbit 25. (bottom) Qube 1. (19 May 2006), qube 2; these are the original maps to transform. The correction has to be applied to them on a pixel basis, as we will assume it to be symmetric around the central pixel of the frame 128. Then we have selected all those pixels with radiances within a small bracket of values; they are marked with crosses in Figures 11b and 11e. According to the non-LTE model, if the atmospheric densities and the SZA illumination do not change very much, a given limb radiance corresponds to a given tangent altitude. Actually, this is the reason why we discovered this smiling effect. We can observe how the crosses marking the 0.15 – 0.20 radiance interval indicate a bending approximately symmetric around pixel 128. We fit a simple parabolic function to this cloud of pixels, and used it to correct for the tangent altitude of all the 256 pixels in each row of the frame. A perfect fit cannot be obtained owing to the different sources of error, which produce some dispersion in the measurements. In addition, the cloud of data does not present a random distribution but a peculiar dispersion produced by the particular geometry of the observations, which is specific for each qube. Some atmospheric variability cannot be ruled out either. For these reasons, we aim here at a first-order correction. Let us notice, also, that it will be difficult to find a single fit function with matches all the radiance intervals of all the orbits and qubes. In our example, the radiance maps resulting after the correction are shown in Figures 11c and 11f. The bending of about 20 km is greatly reduced. This correction function introduces some uncertainty in the absolute pointing altitudes. In the center of the frame, around pixel 128, the correction is very

small, but the pointing may be inaccurate by around 5 km for the pixels at the extremes of the V-M frame. Still, we believe that the corrected images can be used for scientific studies, like atmospheric variability, possible dependence of the emissions on non-LTE parameters, etc, which would be impossible without this geometrical correction. In the VIRTIS team we are considering the implementation of this procedure in future operational processing. 2.5.3. Vertical Profiles From V-M 2-D Images [40] Lets analyze now the vertical profiles of radiance from the limb that can be obtained from these V-M images at the apoapsis. As an example, Figure 12 shows two vertical profiles extracted from two different data qubes, already shown in Figure 1, qubes 2 and 1 of orbit 25. The profiles, at the wavelength of 4.32 mm, are shown on the right, while on the left we plot a small portion of the qube/ image and marked the precise pixels used to build the profiles. The portion of the Venus disk selected corresponds to an almost constant solar illumination, with a small change of SZA, from 10° to 20°. To build up the profiles, we considered two adjacent sets of 10 pixels, taken at the same time, from two adjacent rows of the V-M frame. The selected pixels are pointing at varying altitudes, from the planet’s surface up to the Venus thermosphere, with a vertical resolution around 15 km, and symbols in Figure 12 just point to the center of each pixel. If the orientation of the limb of Venus is optimum, as we can see in Figures 12a and 12b, a very good altitude resolution can be achieved by mixing adjacent pixels; otherwise, the gain in vertical resolution is lower or nonexistent. The improvement of

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Figure 13. (top) Vertical profiles of V-M radiances at four wavelengths for qube 2, orbit 25. (bottom) Spectra of the 20 pixels composing the vertical profiles shown on the top; they belong to 2 adjacent rows of pixels, marked with stars and circles as in Figure 12. Four triangles mark the wavelengths of the four vertical profiles. the vertical resolution is at the expense of losing horizontal resolution, obviously. [41] Other four vertical profiles are shown in Figure 13 (top), obtained from the same rows of pixels in the top of Figure 12 but at four different wavelengths in the 4.3-mm region. The wavelength values are marked in Figure 13 (bottom), which shows the 20 spectra considered. We can get a lot of useful information about the non-LTE emissions from an image like this, since it combines spectral and vertical variations. For instance, the peak altitude is shown to vary with wavelength, being higher at 4.29 mm or 4.33 mm (see Figure 12) than in the wings of the band. This was predicted by the non-LTE model, and was observed also in periapsis observations. Although the qualitative behavior seems to agree with the model, the vertical profile at 4.45 mm does not coincide with the model, but this suggests a much lower emission compared with the center of the band. We think this may be solved by increasing the number of weak CO2 bands in the non-LTE model, a task for a forthcoming revision of the non-LTE model that we are currently undertaking. [42] A systematic problem in V-M data can be noticed in Figure 13 (bottom). There seems to be an oscillation in the spectral domain, with high and low values of intensity from one spectral point to the next. We checked that this ‘‘oddeven effect’’ is present at all VM near-IR wavelengths, from 2 to 5 mm, and it amounts to about 0.05 radiance units in Figure 13, or 50,000 mW m 2 sr 1 mm 1, which is about 10 times larger than the measurement noise. The technical cause of the odd-even seems to be an asymmetry of the

Read-Out Integrated Circuit (ROIC) of the Focal Plane Array (FPA) of the instrument; see Coradini et al. [1998] for a more detailed description of the instrument. In particular, there is an asymmetry in the clock feedthrough and the unit cells between pixels in odd columns and pixels in even columns, which causes some differences in gain and offset. Most part of this effect is corrected through the radiometric calibration where the responsivity of each pixel is taken into account. However, there seems to be also a problem of the offset/reset being dependent with the signal, which makes the difference of the odd-even effect bigger for extreme conditions, which is the case of very high signals or very low exposure times. During periapsis, for example, the exposure time is about 10 times larger than in apoapsis. This is the reason why the odd-even effect is much smaller (at or below the noise level) in Figure 9. Let us recall that odd-even effects are common in imaging spectrometers, in the spectral or spatial domains [Moutou et al., 2003; Siebenmorgen et al., 2007]. We will return to this point in the next section.

3. Validation Analysis [43] Validation analysis traditionally comprises direct comparisons with independent observations. This is not possible with our VIRTIS limb data, although eventually, some indirect comparisons with related measurements by other Venus Express instrument, like neutral densities from VERA and SPICAV, will be possible. At this stage, however, our analysis focused on other aspects. These include (1) internal consistency tests and comparison with averages,

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Figure 14. V-H and V-M noise and bias in a selection of spectra around 4.3 mm from periapsis, at a tangent altitude of 118 km. Radiance units are mW m 2 sr 1 mm 1. See section 3.1 for further details. (2) estimation of noise/data quality, (3) cross correlation between the two VIRTIS signals, (4) qualitative comparisons with theoretical non-LTE models, and (5) examination of the repeatability and variability of the measurements. The goal is to characterize the behavior of the VIRTIS instrument, to confirm that the measurements are physically meaningful, and to detect potential bias or systematic errors. The most important of these analysis, and their conclusions, are presented next. 3.1. Noise and Bias in V-H and V-M [44] One of our basic objectives was to evaluate the measurement noise in orbit, in the spectral ranges and emissions of our interest, to compare it with the nominal one obtained by ground calibration, and at the same time, to analyze possible systematic effects. A first step was the examination of individual spectra for the orbits with limb data. A second task was to average homogeneous sets of data, considering appropriate boxes in SZA and tangent altitude, and to calculate their mean radiance and dispersion. [45] For the evaluation of the noise level and the bias in the 4.3-mm region, we looked at the two wings of this CO2 system of bands, shortward and longward of 4.20 and 4.50 mm, and at altitudes where no significant emission is expected there. The mean radiance of each spectrum can be considered as the bias, and its standard deviation (SD) as an estimation of the noise. An example for orbit 43 (periapsis) is shown in Figure 14, where two individual spectra are shown, one from V-H and a nearby one from V-M. The tangent altitude is around 120 km. [46] Regarding V-H, the noise values obtained for this particular example are similar in the left and right wings of the band, and slightly lower than the nominal noise of 5000 mW m 2 sr 1 mm 1. We have evaluated these values

for a diverse set of spectra, for different orbits, solar illumination conditions and tangent altitudes, and have found that they are basically constant up to orbit 47 (7 June 2006) and decrease to about half those values after orbit 79 (8 July 2006). This is probably related to the spacecraft thermal evolution along the mission, with higher noise levels at the beginning of the mission, when the cold box temperature was higher. [47] Regarding the V-M data, the noise in this example is also lower than the nominal value, although there is a significant difference between the left and right wings of the band. Regarding its bias, the availability of a large number of spectra from V-M at a given position, allows us to estimate it for a close subset of pixels. In Figure 14 we added another spectrum, that obtained by averaging 25 V-M pixels/spectra within a small range of tangent altitudes (118 –120 km) and SZA (78° –81°) around the V-M spectrum selected. The mean value coincides well with the bias obtained from one single spectrum. [48] Similar noise and bias analyses for V-M at apoapsis are difficult at present owing to the large odd-even effect mentioned above, much larger than those parameters. In fact, the averaged V-M spectrum in Figure 14 shows more clearly such oscillation in the intensity than one single spectrum. This effect has a magnitude (amplitude between peaks) of about 2000 mW m 2 sr 1 mm 1, of the order of the noise. The V-M calibration group of the VIRTIS team is currently working on this point, in order to reduce its impact on apoapsis data. 3.2. Correlation Between V-H and V-M Spectra [49] There is a small number of observations in the Spectral Mode which correspond to the special Tangential Limb sounding, with simultaneous measurements of V-H

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Figure 15. Correlation between V-M and V-H spectra taken during periapsis, orbit 43. (right) Location in tangent altitude and SZA, with approximate size of the V-H and V-M field of view. (left) Spectra of the 15 selected V-M pixels, those with SZA of 78.2°, 79.8°, and 81.3°, and of the V-H spectrum (convolved to V-M spectral resolution). Differences between V-H and V-M spectra are also plotted below. See section 3.2 for further details. and V-M. The proximity to the planet during periapsis, allows for a precise geographical collocation of these profiles, and therefore, permits a specially appropriate comparison between them. Orbit 43 offers one example, and Figure 8 shows how close V-M and V-H measurements can be. Figure 15 shows a number of VM spectra close to one particular V-H spectrum, obtained at that orbit, around SZA = 80° and 119 km tangent altitude. Five adjacent slit images were considered, with five pixels on each. The vertical size of the pixel’s FOV, about 300 m for V-M and 2 km for V-H, is shown on the right. On the left we plot 15 of the VM spectra, those from the groups at SZA of 78.2°, 79.9°, and 81.3°, and compare them with the V-H spectrum. This was degraded/convolved to the lower spectral resolution of V-M for the comparison. The shape of the whole CO2 band is very well reproduced by all of them. We think the overall comparison seems fairly good, given the dispersion among the 15 V-M spectra plotted. These present, first of all, a small dispersion within each group, which is similar or slightly larger than typical noise and bias levels. In addition there is a small variation between the three groups, significantly larger than the noise, which is surely due to the small SZA difference between them; the radiance at larger SZA being smaller, as expected. Comparing the V-H spectrum with its closest V-M group of spectra, there is a small shift between them, of about 40,000 mW m 2 sr 1 mm 1 around the peak at 4.33 mm, larger than the noise and bias of both, V-H and V-M. However, this difference is only a factor 2 larger than the SD of this particular group of V-M data, and equivalent to a small fraction of a degree in SZA. This gives us a good level of confidence on both signals.

[50] The emission at 2.7 mm can also be used to test the V-H and V-M agreement. Although the number of spectra with good signal is very limited, we found some spectra in the orbit 43 which are useful for the present discussion. They are shown in Figure 16 and correspond to SZA = 80° approximately. Figure 16 (top) shows vertical profiles at three wavelengths in the 2.7 mm spectral region and show a neat altitude variation, with a clear peak around 115– 120 km. This was predicted by the LVEA model. Figure 16 (bottom) shows the mean and standard deviation of a set of 15 V-M spectra, at altitudes around 120 km. The error bars of individual V-H and V-M are slightly larger than the nominal values, and they have been omitted in Figure 16. The agreement between V-H (convolved to V-M resolution) and V-M is good within error bars; there may be a slight mismatch around 2.8 mm but the data seem noisier there. Moreover, this emission is lower and noisier than that shown in Figure 5, at all wavelengths, since the solar excitation is lower now. We performed a simulation with the non-LTE model, also shown in Figure 16, for the same solar illumination conditions. The agreement is very encouraging, although the model seems to overestimate the radiance; this is due to the specific atmosphere structure assumed in the simulation, and therefore is of no relevance in this analysis. [51] Related to this correlation, we investigated the correlation between different V-H spectra, wherever they can be collocated. The amount of V-H spectra is not large, but we identified a couple of interesting correspondences between spectra with good S/N ratio. On Figure 17 (left), they correspond to four consecutive V-H measurements (acquisition 84 to 87) obtained at the closest approach to Venus

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Figure 16. (top) Vertical profiles of V-H and V-M radiances at three wavelengths in the 2.7 mm for qube 0, orbit 43 and SZA around 80°. Triangles and crosses mark V-H and V-M data, respectively. (bottom) One V-H spectrum, convolved to V-M resolution (solid line) and comparison with the mean of 15 V-M spectra (dashed lines), from the data composing the vertical profile in the top, at a tangent altitude of 120 km. The dashed-dotted line represents a model simulation. Radiance units are mW m 2 sr 1 mm 1 in both the top and bottom. Three triangles in the bottom indicate the wavelengths of the three vertical profiles. during orbit 43, and point to the lowest tangent altitudes for this orbit, around 99 km (as indicated in the picture) and around SZA = 83° (see Figure 8). The geometrical distance along the track of the satellite between the spectra 84 and 87 is about 250 km, and they correspond to latitudes of 73.0° and 71.8°, longitudes of 302° and 307°, respectively, and a difference in SZA of 1.4°. The four spectra are very similar, with differences of the order of the noise at almost all wavelengths, except in the central region. Around 4.3 mm there is a clear trend, from 84 to 87, the last one being about 30,000 mW m 2 sr 1 mm 1 lower than the former. We think, again, that this is due to SZA changes; close to the terminator small changes in SZA produce noticeable variations in the solar pumping of the CO2 vibrational states. This reflects small changes in the atmospheric structure between those four locations. [52] Figure 17 (right) presents another two V-H spectra, very closely located to each other, both at a latitude of 77.27°S and longitudes of 257.8°E and 260.8°E, respectively, but obtained 2 days apart, on orbits 40 and 42. Their tangent altitudes and SZA are also very close, at 108.4 and 107.8 km, and 82.7° and 82.3°, respectively. Their noise and bias levels are below 5000 mW m 2 sr 1 mm 1 and 8000 mW m 2 sr 1 mm 1, respectively. It is surprising the excellent agreement between them in the overall shape and in the wings of the band; there is only a small difference, in

the central portions of the CO2 emission, with the spectrum of orbit 42 slightly larger than the other and the noise. Again, the small SZA differences is a possible candidate for the observed difference. This points, once more, to a relatively calm and stable atmospheric structure, fully repeatable in times scales of 2 days, at least in this case. It would be interesting to explore further coincidences of this type in future measurements of V-H, since the number of them available to date is small (see Figure 3). 3.3. Comparisons With a Non-LTE Model [53] As discussed above, we have used in this work the non-LTE model of the Venus atmosphere developed at the Instituto de Astrof ´ısica de Andalucıá/CSIC, in Granada [Roldań et al., 2000; Lo´pez-Valverde et al., 2007], to perform a small number of simulations. These simulations and those reported by LVEA have guided most of the analysis presented in previous sections. From these comparisons we found a good overall agreement with the predictions of the spectral shape of V-H and V-M data (Figures 4, 9, and 17), and with the altitude profiles of the radiances in the central part of the 4.3-mm band (Figures 4 and 9). Also the SZA variation of the peak radiance (Figures 8 and 15) qualitatively agrees with expectations. The vertical profiles of the CO2 emission shown in Figures 9 and 13 do agree qualitatively with those of LVEA but a quantitative fit

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Figure 17. Geographical coincidences between different V-H spectra. (left) Four spectra from orbit 43, around 99 km. (right) Example of two spectra taken at very close points but 2 days apart. Variations attributable to SZA changes. See section 3.2 for details. would be desirable and future work should focus on this aspect and exploit such fit, once the weak bands required are implemented into the model. Other non-LTE emissions predicted by LVEA have also been examined, detected and compared with the model. Figures 5 and 16 show simulations of the CO2 2.7 mm bands for a given reference atmosphere; they indicate a good agreement in the spectral shape of the bands, and for both V-H and V-M spectral resolutions. Although these data are noisy, the main features are captured by the model, and the vertical profiles are also in agreement, with similar altitudes of the peak emission in the data and in the model. The absolute emission, as it happens with the 4.3 mm bands, is not considered in this comparison, since it depends on the actual atmospheric structure assumed in the non-LTE model. The third nonLTE emission detected and studied in this work, that of CO, contains a first hot component not simulated yet by the non-LTE model. [54] Let us highlight here some other results which, either were not predicted or seem to pose interesting challenges to the theoretical modeling. One of them is the intensity in the longer wavelength wing of the 4.3 mm band, where contributions from weak, isotopic and hot transitions are expected. The predictions by LVEA produce a relatively minor contribution of that band’s wings compared to the central band, but the VIRTIS spectra shown in Figures 4 and 9 indicate a more significant contribution. The model surely needs a larger number of CO2 bands in this spectral region. Such a model extension is underway. [55] Certainly, another extension of the non-LTE model is needed to include the first hot band of carbon monoxide, CO(2-1), in order to study the spectra shown in Figure 6.

This is also a minor band compared to the strongest fundamental transition, but it seems to be highly excited by direct solar radiation, like the weaker transitions of CO2 discussed above. As discussed in section 2.3.3, a proper simulation of the CO emission lines will be very valuable for the derivation of rotational temperatures. These could be obtained from this band’s structure, but only after fitting a theoretical model which permits small radiance errors in the line peaks. This is the only molecular band detected by VIRTIS in this infrared portion of the spectrum with well isolated lines, and is therefore a very interesting data set.

4. Conclusions and Future Work [56] VIRTIS on Venus Express is an innovative instrument which, combining imaging and spectroscopy in the near infrared, is obtaining very exciting new data on the upper atmosphere of Venus. We have analyzed the limb measurements taken by VIRTIS in the regions from 4 mm to 5 mm and around 2.7mm from both, the periapsis and the apoapsis of the Venus Express orbit, and in the two signals, V-H at high spectral resolution, and V-M at a lower resolution. We have focused in this work on a subset of VIRTIS data, which includes limb measurements with V-H taken at the periapsis from orbits 23 (13 May 2006) to 127 (25 August 2006), and a small sample of V-M data qubes between orbits 23 (13 May 2006) and 295 (10 February 2007). Extension of the analysis presented here to the rest of the data is ongoing. [57] We clearly identified the strong emission by CO2 at 4.3 mm in the upper mesosphere and lower thermosphere, and of the first hot band of CO at 4.7 mm in the upper

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mesosphere. Individual spectra show good signal to noise up to above 160 km in the case of CO2, and up to about 120 km in the case of CO. Single spectra show noise values slightly below the value expected from ground calibration, and a small bias too. We also showed that the V-H spectral resolution is good enough for a number of tests of our nonLTE models. The spectral resolution of V-M is lower, however the vertical resolution is much better from V-M data. During periapsis this is typically 300 m for a single row of pixels, while from apoapsis, the grouping of V-M adjacent rows of data permits a resolution at least two to three times better than the FOV of individual pixels, depending on the geometry of the V-M images and on the relative orientation of the limb of Venus. These observations characterize the Venus atmosphere directly, since they will permit to calculate densities and temperatures from measurements, and also indirectly, because they can give information about the collisional processes, energy transfer and the upper atmosphere energy balance, after a more detailed comparison with a non-LTE model. [58] The variability observed by VIRTIS and mentioned previously by Drossart et al. [2007a] have been confirmed and analyzed here for the first time with this new and extended data set. The maps studied in this paper permit to describe altitude variations at several wavelengths, variations with SZA, and the detailed spectral shape of the CO and CO2 bands. Actually, the good agreement between model and data, on the basic non-LTE features, indicates that our current understanding of the physical processes in the upper atmosphere is essentially correct. All these results will be perfect tests for future non-LTE models, and suggestions for more detailed future comparisons are given in section 3.3. We foresee that such detailed comparisons, specially of the shape of the V-H emissions, can give us information about non-LTE collisional parameters in the upper mesosphere and lower thermosphere, where these processes are specially relevant. Also, comparisons between spectra at similar conditions but obtained with a span of 2 days, and between data more than 200 km apart, show very good matches, which indicates a good behavior of VIRTIS. When applied to a future and extended data set, similar comparisons may supply interesting information about atmospheric variability in the lower thermosphere. The high density of points in the limb in some maps during periapsis offer excellent chances for derivation of density maps in the thermosphere. Such density profiles are foreseen to be obtained by a rigorous ‘‘retrieval’’ process which uses inversion techniques under non-LTE conditions, an exercise similar to other upper atmosphere experiment on Earth, like the MIPAS/Envisat data analysis [Funke et al., 2005]. Densities of CO would be derived in a limited altitude range (90 – 120 km) owing to the low signal of the CO 4.7 mm emission (see section 2.3.3). However, densities of CO2 can be derived in principle in a wider altitude range. Nevertheless, the accuracy of such derivation will depend on a sensitivity study to be performed with the revised non-LTE model. Limb views from V-M during apoapsis should also be very useful for density retrievals, although they are affected at present from a ‘‘smiling effect’’ which deforms the actual emission field by about 20 km, approximately the size of the projected FOV of one pixel. We showed how the non-LTE model has been essential to detect this effect which could be reduced in

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future processing thanks to a correction technique using these CO2 non-LTE emission. These V-M data from apoapsis are also affected by a large odd-even effect, about 10 times larger than the noise level. During periapsis this effect is below noise due to the much larger exposure time there. This aspect requires further work within the VIRTIS team in the future. [59] The study of the first hot CO(2-1) emission is also very promising, since the rotational structure is resolved and it can therefore be used to derive rotational temperatures in the upper mesosphere. This will be confined to daytime and to the altitude range 90– 120 km, where such signal is strongest. This derivation does not require a full non-LTE retrieval but a fit of the shape of the CO emission band with the model, as explained in section 2.3.3. At present, uncertainties in individual lines of a CO spectrum produce very large temperature errors. Comparisons with temperatures obtained from ground-based measurements of these bands [Crovisier et al., 2006] will also be interesting, as a validation exercise of these data and in order to future check the non-LTE models. [60] VIRTIS/Venus Express will continue acquiring new data at least until 2009. The exploration of the new data and their analysis will continue, hopefully with a version of the non-LTE model extended to weaker bands, and with better understanding of some of the current uncertainties in the data that we highlighted here. The application of retrieval techniques and systematic search for atmospheric variability, makes the VIRTIS observations a very promising data set for studying the non-LTE processes and the structure of the upper atmosphere of Venus. [61] Acknowledgments. We thank the space agencies ASI and CNES for their support. The work of the IAA-CSIC team has been carried out under project ESP2004-01556 and was partially funded by the project AYA2008-03498/ESP of the Spanish Ministery of Science and Innovation and by EC-FEDER funds. GG has been partially supported by a fellowship from Spanish National Research Council (CSIC-JAE Program).

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European Space Agency (ESA) (2001), Venus Express: An orbiter for the study of the atmosphere, the plasma environment, and the surface of Venus, Eur. Space Agency Sci. Tech. Rep., ESA STR 6, 46 pp. Formisano, V., A. Maturilli, M. Giuranna, E. D’Aversa, and M. A. LopezValverde (2006), Observations of non-LTE emission at 4 – 5 microns with the Planetary Fourier Spectrometer aboard the Mars Express mission, Icarus, 182, 51 – 67, doi:10.1016/j.icarus.2005.12.022. Funke, B., et al. (2005), Retrieval of stratospheric NOx from 5.3 and 6.2 mm nonlocal thermodynamic equilibrium emissions measured by Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat, J. Geophys. Res., 110, D09302, doi:10.1029/2004JD005225. Garcıá, R., M. A. Lo´pez-Valverde, P. Drossart, and G. Piccioni (2009), Gravity waves in Venus atmosphere revealed by CO2 non-LTE emission, J. Geophys. Res., doi:10.1029/2008JE003073, in press. Gordiets, B. F., and V. I. Panchenko (1983), Non-equilibrium infrared emission and the natural laser effect in the Venus and Mars atmospheres, Kosm. Issled., 21, 929 – 939. Hueso, R., A. Sańchez-Lavega, G. Piccioni, P. Drossart, J. C. Ge`rard, I. Khatuntsev, L. Zasova, and A. Migliorini (2008), Morphology and dynamics of Venus oxygen airglow from Venus Express/Visible and Infrared Thermal Imaging Spectrometer observations, J. Geophys. Res., 113, E00B02, doi:10.1029/2008JE003081. Lo´pez-Puertas, M., and F. W. Taylor (2001), Non-LTE Radiative Transfer in the Atmosphere, Ser. Atmos., Oceanic, Planet. Phys., vol. 3, World Sci., Singapore. Lo´pez-Valverde, M. A., and M. Lo´pez-Puertas (1994), A non-local thermodynamic equilibrium radiative transfer model for infrared emission in the atmosphere of Mars: 2. Daytime populations of vibrational levels, J. Geophys. Res., 99, 13,117 – 13,132. Lo´pez-Valverde, M. A., M. Lo´pez-Puertas, J. J. Lo´pez-Moreno, V. Formisano, D.Grassi, A. Maturilli, E. Lellouch, and P. Drossart (2005), Analysis of CO2 non-LTE emissions at 4.3 mm in the Martian atmosphere as observed by PFS/Mars Express and SWS/ISO, Planet. Space Sci., 53, 1079 – 1087, doi:10.1016/j.pss.2005.03.007. Lo´pez-Valverde, M. A., P. Drossart, R. Carlson, R. Mehlman, and M. RoosSerote (2007), Non-LTE infrared observations at Venus: From NIMS/ Galileo to VIRTIS/Venus Express, Planet. Space Sci., 55, 1757 – 1771, doi:10.1016/j.pss.2007.01.008.

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Moutou, C., A. Coustenis, J. Schneider, D. Queloz, and M. Mayor (2003), Searching for helium in the exosphere of HD 209458b, Astron. Astrophys., 405, 341 – 348. Piccioni, G., et al. (2006), VIRTIS: The Visible and Infrared Imaging Spectrometer, Eur. Space Agency Spec. Publ., ESA-SP 1295, 1 – 27. Roldań, C., M. A. Lo´pez-Valverde, M. Lo´pez-Puertas, and D. P. Edwards (2000), Non-LTE infrared emissions of CO2 in the atmosphere of Venus, Icarus, 147, 11 – 25, doi:10.1006/icar.2000.6432. Roos-Serote, M., P. Drossart, Th. Encrenaz, E. Lellouch, R. W. Carlson, K. H. Baines, F. W. Taylor, and S. B. Calcutt (1995), The thermal structure and dynamics of the atmosphere of Venus between 70 and 90 km from the Galileo-NIMS spectra, Icarus, 114, 300 – 309, doi:10.1006/icar.1995.1063. Rothman, L., et al. (2005), The Hitran 2004 molecular spectroscopic database, J. Quant. Spectrosc. Radiat. Transfer, 96, 139 – 204. Siebenmorgen, R., et al. (2007), Very large telescope paranal science operations CRIRES user manual, Rep. VLT-MAN-ESO-14500-3486, 56 pp., Eur. South. Obs., Garching, Germany. Stepanova, G. I., and G. M. Shved (1985), Radiation transfer in the 4.3-mm CO2 band and the 4.7-mm CO band in the atmospheres of Venus and Mars with violation of LTE: Populations of vibrational states, Sov. Astron., Engl. Transl., 29, 248 – 422. Svedhem, H., et al. (2007), Venus Express: The first European mission to Venus, Planet. Space Sci., 55, 1636 – 1652, doi:10.1016/j.pss.2007.01.013. Taylor, F. W., et al. (1980), Structure and meteorology of the middle atmosphere of Venus infrared remote sensing from the Pioneer orbiter, J. Geophys. Res., 85, 7963 – 8006. Titov, D. V., et al. (2006), Venus Express science planning, Planet. Space Sci., 54, 1279 – 1297, doi:10.1016/j.pss.2006.04.017. A. Cardesıń Moinelo and G. Piccioni, IASF, INAF, via del Fosso del Cavaliere 100, I-00133 Rome, Italy. P. Drossart and S. Erard, Observatoire de Paris, F-92195 Meudon, France. G. Gilli and M. A. Lo´pez-Valverde, Instituto de Astrofı´sica de Andalucıá, CSIC, Camino Bajo de Huetor 50, E-18008 Granada, Spain. ([email protected])

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Near-IR oxygen nightglow observed by VIRTIS in the Venus upper atmosphere G. Piccioni,1 L. Zasova,2 A. Migliorini,1 P. Drossart,3 A. Shakun,2 A. Garcıá Munõz,4 F. P. Mills,4,5 and A. Cardesin-Moinelo1 Received 29 February 2008; revised 9 January 2009; accepted 17 February 2009; published 30 April 2009.

[1] We present observations of both the (0–0) and (0–1) bands at 1.27 and 1.58 mm of the O2(a1Dg X3S g ) nightglow made with the Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) instrument aboard Venus Express. The observations were conducted in both nadir and limb viewing modes, the latter constituting the first systematic investigation into the vertical distribution of the volume emission rate of the infrared oxygen nightglow in Venus’ upper atmosphere. Limb measurements from 42 orbits covering the latitude range 7°S to 77°N are analyzed. The peak altitude of the volume emission rate occurs typically between 95 and 100 km, with a mean of 97.4 ± 2.5 km. The vertical profile is broader near the equator, with a full width at half maximum of 11 km, a factor 2 larger than at middle latitudes. A double peak is frequently observed, with the lower and upper peaks occurring near 96–98 km and 103–105 km, respectively. On average, the nightglow appears brightest in the vicinity of the antisolar point. This conclusion is consistent with past ground-based observations and nadir measurements by VIRTIS. We mapped the global mean O2 nightglow intensity from VIRTIS data collected during 880 orbits. Patchy features of the nightglow intensity observed in nadir view are correlated with the thermal brightness at 4.23–4.28 mm. The observed positive correlation is consistent with downwelling (upwelling) of oxygen atoms accompanying compressional heating (expansion cooling) or with modulation by gravity waves. Finally, from simultaneous measurements of the 1.27 and 1.58 mm bands, we have estimated the ratio of the transition probabilities A00/A01 to be 63 ± 8. Citation: Piccioni, G., L. Zasova, A. Migliorini, P. Drossart, A. Shakun, A. Garcıá Munõz, F. P. Mills, and A. Cardesin-Moinelo (2009), Near-IR oxygen nightglow observed by VIRTIS in the Venus upper atmosphere, J. Geophys. Res., 114, E00B38, doi:10.1029/ 2008JE003133.

1. Introduction [2] Intense nightglow emission in the (0 – 0) band of the O2 (a1Dg X3S g ) electronic transition near 1.27 mm was first detected on Venus in 1975 [Connes et al., 1979]. Subsequent ground-based observations of the airglow emission on the nightside (nightglow) have shown that the intensity and spatial distribution are highly variable on time scales of hours to days [Crisp et al., 1996; Ohtsuki et al., 2005, 2008; Bailey et al., 2008]. Nadir-looking observations by the Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) on Venus Express (VEX) have provided more

1

INAF, Istituto di Astrofisica Spaziale e Fisica Cosmica, Rome, Italy. Space Research Institute of Russian Academy of Sciences, Moscow, Russia. 3 LESIA, Observatoire de Paris, UPMC, Universite´ Paris-Diderot, CNRS, Meudon, France. 4 Research School of Physics and Engineering, Australian National University, Canberra, ACT, Australia. 5 Fenner School of Environment and Society, Australian National University, Canberra, ACT, Australia. 2


detailed coverage of the morphology of the nightglow, and visual tracking of bright features has enabled the estimation of apparent motions [Hueso et al., 2008]. Limb observations by VIRTIS of the O2 nightglow [Drossart et al., 2007a; Ge´rard et al., 2008] provide a complementary view of the chemistry and dynamics of Venus’ upper atmosphere by determining the vertical distribution of the O2 nightglow. However, no systematic study of a large number of limb observations of the O2 nightglow has been published to date. In this work, we examine limb observations spanning 7°S to 77°N latitude and 2000 to 0400 local times. These observations were collected during the period from July 2006 until August 2008. This much larger set of data has allowed us to determine how the vertical distribution of O2 nightglow varies with latitude. We have also been able to make an observational estimate of the ratio of the transition probabilities for the (0 – 0) and (0– 1) bands near 1.27 and 1.58 mm, respectively. Our results provide new observational insight into the chemical and dynamical processes occurring in Venus’ upper atmosphere and new constraints on chemical transport models. [3] On Venus’ nightside, O2(a) is believed to be produced primarily via reaction (1) [e.g., Connes et al., 1979; Mills

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PICCIONI ET AL.: VENUS O2 NIGHTGLOW FROM VIRTIS

and Allen, 2007], where O*2 denotes an electronically excited state of O2: 2O þ M ! O*2 þ M

ð1aÞ

O*2 þ M ! O2 ðaÞ þ M

ð1bÞ

There are two recent estimates of the net yield of O2(a) from reaction (1). Crisp et al. [1996] inferred net yields of 0.63 ± 0.19 and 0.6– 0.75 for M = N2 and CO2, respectively, on the basis of laboratory studies, while Huestis [2002] recommends a net yield of 0.94 – 0.99 for M = N2 and CO2 on the basis of a combination of laboratory and atmospheric studies. The atomic oxygen consumed in reaction (1) is transported to the nightside via the upper atmospheric circulation from the dayside [Bougher and Borucki, 1994] where atomic oxygen is produced by photodissociation of CO2 at wavelengths 0.8 are relatively well constrained by in situ measurements, adopting these values as e0 = 1 a0 and T0(0 km) = 735 K does not work well because lowest atmosphere scattering and absorption is not accounted for. Albedo a0 is expected to be greater than 0.15, which Meadows and Crisp [1996] used to represent the basaltic plains of the surface. Temperature T0 with respect to equation (3) will be less than real surface temperature since the lowest atmosphere is not fully transparent. [47] To estimate a0, the distribution data points in the F0, F30, and Z space is determined using all VIRTIS observations. Average value of F0 in each F30 interval is given with 0.5s error bars for different topography intervals in Figure 7. The position on the F30 axis translates with equation (2) into cloud reflectivity r. Figure 7 demonstrates that it is possible to reproduce the relation of observed fluxes F0 and F30 within 0.5s with a lowest atmosphere and surface albedo a0 = 0.22 independent from topography and temperature T0(Z) as monotonic function of altitude. Variation of a0 with band position l has not been investigated. Preliminary analysis of the windows at 1.10 and 1.18 mm not presented here yields similar values of lowest atmosphere albedos, which leads to the conclusion that this parameter does not vary strongly with wavelength. [48] Using this constant, a0, equation (3) is solved for T0 for each data point. Next, the distribution of T0 values in the topography Z and band position l space is determined from all processed data points. A two dimensional polynomial of degree 2 with coefficients knm and parameters Z and l is fitted to the averages of T0 to obtain an analytical approximation of Tl(Z):

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Tl ðZ Þ ¼

2;2 X i;j¼0;0

kij Z j li

ð10Þ

MUELLER ET AL.: VENUS SURFACE THERMAL EMISSION

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the following conditions are met: (1) emission angle is less than 80°, (2) incidence angle is greater than 95°, and (3) sunlight parameter I½36;39 is less than 0.01 W m2mm1sr1. [51] Two different quantities are retrieved, either surface temperature T0 or the thermal flux anomaly A0 which is thermal emission corrected for the global relation of surface temperature Tl(Z) to altimetry and band position. Figure 9 illustrates the process of retrieving surface and lowest atmosphere brightness temperature. Both results are map projected for each observation using Lambert’s azimuthal equal area projection centered on the south pole. The projected individual images are then combined by using the median of all different observations of the same place. The spatial distribution of processed images is shown in Figure 10.

5. Results Figure 7. Average of surface window flux for different altitudes as a function of cloud reflectivity r. Flux uncertainty is given as 0.5s. Size of topography bins is 500 m. Curves are calculated with equation (3) with a0 = 0.22 for all heights and T0(1 km) = 712 K, T0(0 km) = 708 K, T0(1 km) = 703 K, and T0(2 km) = 698 K. Figure 7 shows that equation (3) describes the relation between surface and atmosphere window flux well and thus can be used for removal of cloud contrast. The i j matrix of coefficients is 0 B B kij ¼ B B @

63:6 103

12:3 103

60:3 103

13:0

25:6

12:5

1:83 103

3:62 103

1:79 103

1 C C C ð11Þ C A

Units of kij are mjmmi K where i is index of column and j is index of row starting with zero. [49] The Z-l isolines determined directly from the data and from the fit of equations (10) and (11) are illustrated in Figure 8 for seven temperatures. The lapse rate of 4 to 5 K km1 is not consistent with the 8.06 K km1 in the VIRA model but this is not surprising since scattering, absorption and emission in the atmosphere lowers the lapse rate of observed flux. Furthermore scattering and absorption optical path from surface to top of atmosphere is determined by topography. Usually this is negligible, as topography is only a fraction of total optical path lengths, but extinction is here strongly concentrated toward the lowest part of the atmosphere owing to Rayleigh scattering being proportional to density and the continuum absorption being proportional to square of density [Pollack et al., 1993]. 4.5. Image Processing [50] After the parameters necessary for atmospheric correction are either assumed or determined from the statistical behavior of VIRTIS data points, the next step is the application of equation (3) on each observation to analyze the local thermal flux. Data pixels are processed only when

[52] Surface temperature parameter T0 averaged over time is highly correlated with altimetry in Figure 11. Signal to noise is better in the hemisphere west of 0° longitude as is expected owing to greater coverage (Figure 10). SNR generally decreases toward the limb, where less images as well as less spectra per unit area have been acquired owing to the generally oblique viewing angles. The polar region is usually seen at low emission angles but stray sunlight increases the error although spectra surpassing a certain threshold of stray light are discarded during processing. [53] The map of flux anomaly Figure 12 presents in principle the same data as Figure 11 but corrected for the global temperature to topography relation (Figure 8). There are however some correlations with topography remaining when comparing the altimetry contours in Figure 11 with flux anomaly in Figure 12. There is lower flux in the sector between 120° and 110° longitude starting at about 75° to 60°S latitude and extending at least to the equator. This coincides with an increased uncertainty in the Magellan altimetry investigated by Rappaport et al. [1999]. Several rifts, e.g., Artemis Chasma in V-48, show contrast in the flux anomaly map. This might be related to the difficulties in smoothing of the radar altimetry (section 4.3). The error

Figure 8. Relation of lower atmosphere surface brightness temperature T0 (for seven different values of T0) to topography Z and band position l. Solid lines are isolines of average data temperature. Dashed lines are polynomial fit to data.

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Figure 9. VIRTIS image VI0373_01 (second image acquired in orbit 373) during several processing steps: (a) band 0 at 1.02405 mm; (b) spectral median of bands 36 to 39 showing distributions of stray light; (c) band 0 corrected for stray light and limb darkening; (d) band 30 corrected for stray light and limb darkening; (e) brightness temperature of lower atmosphere and surface derived from band 0 and band 30; and (f) surface temperature map constructed from Magellan altimetry and Tl(Z). Horizontal dark lines are data points not processed owing to an offset of the whole frame. due to the smoothing is expected to be greatest in regions with steep slopes and large altitude differences. [54] The equatorial regions generally show a greater residual correlation of flux and topography. This might be indicative of a latitudinal component in the flux to topography relation, which might be due to latitudinal temperature or atmospheric composition variation. Most data points used for determination of the global flux to topography relation are in the midlatitude to low-latitude regions between 25° and 75°S where influence of topography seems to be well accounted for (compare Figure 10 and Figure 12). Spectrometer temperature and thus band position l varies

with orbital position. The equatorial regions have a bias to be observed at wavelengths displaced from the optimum. Determination of Tl(Z) is therefore possibly less reliable for the equatorial regions. The lower latitudes are, with some exceptions that are described in sections 5.1 – 5.3, very well represented by the global flux to topography relations as the flux anomaly is very near to zero. 5.1. In Situ Sites [55] There are four in situ measurements of surface rock composition on the southern hemisphere, see Figure 12. The Venera 8 landing site in quadrangle V-43 is in a region with

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Figure 10. Spatial distribution of processed images in Lamberts azimuthal equal area projection centered on the south pole and extended to the equator; labels denote Venus mapping quadrangles. average to low thermal flux. Venera 13 in quadrangle V-42 is in the region with low flux adjacent to Doliya tessera, Venera 14 is on the gradual transition from this same diffuse low flux area to the diffuse high flux area over Navka planitia. Vega 2 landed on Rusalka planitia in quadrangle V37 which shows an unreliably determined flux. All landing sites are in the equatorial regions and not very well covered by VIRTIS observations. Possible interpretations of rock types are summarized on the basis of the work of Kargel et al. [1993]. Venera 8 measured abundances of natural radioactive isotopes of K, U, and Th, which where consistent with several terrestrial analogues, including rhyolite (volcanic rock of felsic composition), monzonite (intermediary composition) and leucitite, an on Earth rare alkaline mafic volcanic rock in which felsic minerals are mostly replaced by the feldspathoid mineral leucite. Leucitite is also proposed for the Venera 13 landing site, where elemental abundances were more directly measured by X-ray fluorescence spectroscopy. The Venera 14 and Vega 2 samples also analyzed with XRF spectroscopy are interpreted to have basaltic composition similar to mid-ocean ridge basalts. Though it might be tempting to attribute areas of low flux anomaly to the exceptional samples of Venera 8 and Venera 13 situated in the vicinity, very little difference is seen in NIR flux anomaly between the Venera 8, Venera 13, and Venera 14 sites. 5.2. Relation to Geological Settings [56] The thermal flux anomaly shows some correlation with geomorphological terrain types and also individual features. Over large areas of tessera terrain the flux adjusted for topography is generally lower than in neighboring areas. The tessera terrain outlines in Figure 12 are taken from the

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electronic global geological map accompanying the work of Tanaka et al. [1997]. Large tessera terrains with relative good SNR are Phoebe regio and Doliya tessera in V-41, Alpha regio in (among others) V-32, Lhamo and Cocomama tessera in V-56. Tessera boundaries are however not well defined in NIR flux, negative flux anomalies are interspersed with average flux and occasional positive anomalies. For instance, at Doliya tessera no relation of its boundaries to flux is obvious, but instead the tessera area is situated directly on the boundary between larger areas of positive and negative flux anomalies. [57] The negative flux anomaly over tessera regions is consistent with the relative low surface emissivity at highlands retrieved by Hashimoto et al. [2008] from the Galileo NIMS flyby data. Tessera are more frequent in highlands [Ivanov and Head, 1996]. Their approach does not correct for topography empirically but uses a flux to topography relation derived from the adiabatic lapse rate and radiative transfer modeling and is therefore not directly comparable to this result. [58] Tessera are not extensively covered by VIRTIS observations; there is very little data yet of the large tessera highlands in Aphrodite terra. Most VIRTIS spectra were acquired on midlatitudes to low latitudes s as well as more sophisticated profiles from the literature [Fukabori et al., 1986; Perrin and Hartmann, 1989; Pollack et al., 1993; Meadows and Crisp, 1996; Tonkov et al., 1996], combined with different line cut assumptions (125 –1000 cm1), are currently under investigation with respect to individual band positions of CO2, but a final definition of a ‘‘best choice model’’ could not be provided up to now. As was mentioned above, pressure-induced absorption is currently not included in the simulations. [20] Empirical continuum absorption coefficients have been used so far in window ranges where it is needed. The continuum cross section kc is currently determined from a Venus ‘‘reference spectrum’’ (compare Figure 5) when the clouds are included in the simulation procedure. The bottom line (inverted triangle) in Figure 4 shows the influence of continuum absorption on a pure gas simulation in the 1.0– 1.45 mm range. With the continuum included, the simulated relative peak values and profiles of the window signatures are much closer to the observed ones. [21] Mie scattering theory is applied to derive the microphysical parameters of the H2SO4 clouds, where a four modal log normal size distribution is used (modes 1, 2, 20, and 3 according to Pollack et al. [1993]). Optical depths of cloud absorption, scattering and extinction are calculated on the basis of a particle number density model that essentially corresponds to that described by Moroz [2002b]. The total cloud opacity of a so-called standard model, which is applied throughout this paper, is 26.9 at 1 mm. Unity total optical depth occurs near the upper limit of the main cloud deck at an altitude of 70 km. There are many open questions with respect to the chemical composition and the particle size distribution of the clouds [Moroz, 2002b; Taylor, 2006]. It is well established that the upper cloud particles (mainly mode 2 and mode 20) consist of a water solution of sulphuric acid (75% H2SO4 by weight). The composition of the

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ARNOLD ET AL.: VENUS SURFACE DATA FROM VIRTIS/VEX

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Figure 5. Comparison of VIRTIS measurement for orbit 90, 30°N (solid line) and radiative transfer simulation (dashed line). The surface elevation is 1.0 km. The integers refer to the band number introduced in Figure 2. This spectrum was used as reference spectrum for the simulation model development. smaller mode 1 droplets is unknown. The large mode 3 particles are probably highly variable in composition and spatial and temporal distribution. [22] Rayleigh scattering optical depths are considered according to Hansen and Travis [1974]. The total Rayleigh optical depth at 1 mm is 1.54; that is, it is not negligible within the short-wavelength windows. Radiation multiple scattering in the dense cloudy atmosphere is considered by a successive order procedure where the source functions are calculated in a two-stream approximation of the angulardependent radiation field [Arnold et al., 2000; Haus and Titov, 2000]. [23] The synthetic quasi-monochromatic intensity spectra at the model top level of the atmosphere (140 km) that are generated by the radiative transfer code are convolved with the VIRTIS spectral response function. A constant spectral resolution of 10 nm and a Gaussian instrumental response function have been used so far. In the wave number frame, 10 nm correspond to 100 cm1 at 10000 cm1 (1 mm), 25 cm1 at 5000 cm1 (2 mm), and 4 cm1 at 2000 cm1 (5 mm), respectively. [24] Figure 5 compares a synthetic spectrum with the VIRTIS measurement performed at 30°N on orbit 90. The surface elevation according to Magellan topography is 1.04 km there. This spectrum was selected as the ‘‘reference spectrum’’ to investigate the combined effects of cloud scattering and gas continuum absorption. The spectral range from 1 to 2.6 mm shown in Figure 5 covers the atmospheric windows located at 1.02, 1.10, 1.18, 1.28, 1.31, 1.74, and 2.3 mm. The simulation was performed for the Venus standard atmosphere [Seiff et al., 1985]. An update of these VIRA data by VIRA-2 [Moroz and Zasova, 1997] was not required for the investigations intended here, since the temperature and pressure profiles below 50 km remained unchanged. The calculations indicate that temperature changes within and above the clouds do not influence the surface and deep atmosphere signals. [25] The preliminary quantitative simulation approach yields a more or less good fit to the observed spectrum,

although there are some discrepancies (mainly at 1.7 and 2.3 mm). They are probably due to an inappropriate choice of spectral line profiles, line cut and continuum parameters. Additionally, the lower atmospheric temperature profile may be slightly different from the VIRA standard profile. Small changes can result in significant window flank shifts as corresponding calculations have shown. This will be investigated in more detail in near future. The present gas simulation uses Voigt line shapes and a line cut of 125 cm1 everywhere for each molecular constituent. Pressure-induced continuum absorption is assumed to depend on the total molecular density squared. It is determined as a correction value that must be applied to fit the measured reference spectrum when the total aerosol and Rayleigh optical depths of the atmosphere are 26.9 and 1.54 at 1 mm, respectively. The following values (in units of [cm1 amagat2]) were used in the simulation shown in Figure 5: windows 3 – 5 (1.18, 1.28, 1.31 mm), 1.56 1009; window 6 (1.74 mm), 6.23 1009; window 7 (2.30 mm), 4.17 1008. Pollack et al. [1993] derived the values 7.00 1009 and 2.50 1008 for windows 6 and 7, respectively, while Marcq et al. [2006] used a value of 3.5 108 at 2.3 mm. The water vapor signatures, which appear in the pure gas simulation between 1.10 and 1.18 mm (compare Figure 4) due to the neglect of super-Lorentz line shapes, have been currently removed by applying a continuum in the order of 1.0 108 at 1.13 mm. All the continuum absorption coefficients are assumed to be independent of wavelength throughout a given window range. They are also not varied from spectrum to spectrum; that is, the values are held constant for simulations with respect to different VIRTIS measurements.

4. Results [26] Figure 6 illustrates the simulated radiances at the top level of the Venus atmosphere for cloud optical depths (opacities) that differ from the standard model by a factor of 0.7, 1.0, 1.4, 2.0, and 2.5, respectively. The surface

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Figure 6. Simulated nightside radiance spectra in dependence on cloud opacity factor. Radiances decrease with increasing factor. Factor 1 corresponds to a total cloud optical depth of 26.9 at 1 mm. The surface elevation is 1.0 km. elevation is constant at a value of 1.04 km. Each window is sensitive to changes in the cloud opacity, but the contrasts increase toward longer wavelengths. Cloud contrast is most apparent within the 2.3 mm window. The aerosol single scattering albedo below 1.5 mm is very close to unity; that is, conservative scattering is the dominating cloud feature. In other words, the clouds do not contribute to thermal emission, but act as a strong attenuator of radiation that originates from below the cloud deck and emerges into space. Little spectral dependence of upwelling radiation is added by the clouds below 1.5 mm. This is the reason why it should be possible to use multispectral radiation measurements of the Venus surface and lower atmosphere in the 1.0 – 1.35 mm window complex to eliminate cloud interferences. [27] Figure 7 illustrates the change of averaged window radiances in dependence on the cloud opacity factor. Averaging means that mean values over each window range are

used instead of the maximum value. The ranges [mm] were defined as follows. W1, 1.028– 1.035; W2, 1.090 – 1.110; W3, 1.160 – 1.190; W4, 1.255 – 1.289; W5, 1.300 – 1.309; W6, 1.691 – 1.780; W7, 2.200 – 2.290. Only the shortwavelength part of window 7 at 2.3 mm was used here to eliminate possible minor constituent variations (H2O, CO, COS, SO2, HF) in the long-wavelength part at the right. The left-hand part is exclusively determined by CO2. Water vapor concentration changes, however, which may affect the windows at 1.74, 1.18, and 1.10 mm, respectively, were not eliminated at present. [28] As expected, there is a dependence of radiation on cloud opacity in the windows 1– 5 that leads to constant radiance ratios. There is an increasing contribution of cloud absorption with increasing wavelength. Thus, the linear dependence is lost and the radiances at 1.7 and 2.3 mm decrease much faster with growing cloud opacity. As a

Figure 7. Simulated averaged window radiances as a function of cloud opacity factor. The integers in the box refer to the band number introduced in Figure 2. The surface elevation is 1.0 km. 7 of 13

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Figure 8. Comparison of measured (scatter points) and simulated (lines) averaged window radiance ratios as a function of 2.3 mm averaged window radiance. The scatterplot of binned measured ratios includes all latitudes from the 19 orbits of groups 1 – 3 (compare Figure 1), where the surface elevation is constrained to the interval [1500 m, 500 m]. The integers in the box refer to the band numbers introduced in Figure 2. Values included in parentheses denote the corresponding nominal wavelengths. F is the cloud opacity factor. Nearly constant ratios for the surface windows are apparent. consequence, a cloud correction algorithm, which makes use of radiance ratios, tends to fail for longer wavelengths. [29] Figure 8 compares measured and simulated averaged window radiance ratios as a function of the wavelengthaveraged radiance that was measured at 2.3 mm. The radiance at 2.3 mm is most sensitive to changes in cloud optical depth as is clearly visible in Figures 6 and 7, and also in Figure 3. It is insensitive to changes in surface elevation (compare Figures 9 and 10). Cloud opacity is not a free parameter in the measurements as it is in the simulations. It cannot be measured directly by the VIRTIS instrument. But the observed strong variability at the lefthand flank of the 2.3 mm window is a good indicator of cloud thickness on the Venus nightside. Thus, a scatterplot of measured radiance ratios against the measured 2.3 mm

radiance, which decreases with increasing cloud opacity, has the same physical background as a plot of simulated ratios against the cloud opacity. This is convincingly demonstrated in Figure 8. The scatterplot is restricted to measurements from the 19 orbits shown in Figure 1, and the surface elevation according to Magellan topography is constrained to the interval [1500 m, 500 m]. The restriction to a narrow elevation interval is necessary to omit strong radiation changes due to topographical changes (compare Figure 9). It is assumed that the quantitative differences between measurement plot and simulation result, which are visible in the case of 6/1 and 7/1 ratios, are due to present inaccuracies in the spectral line profiles and continuum coefficients at these wavelengths.

Figure 9. Simulated nightside radiance spectra in dependence on surface elevation for the standard cloud model (opacity factor 1.0). Radiances decrease with increasing elevation in the surface windows. The 1.74 and 2.3 mm windows are unaffected. 8 of 13

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Figure 10. Simulated averaged window radiances as a function of surface elevation. The integers in the box refer to the band numbers introduced in Figure 2. The cloud opacity factor is 1.0. [30] Another important conclusion can be derived from Figure 8. All the measurement points are distributed up to a maximum 2.3 mm radiance value of about 0.065 W/(m2 sr mm). This value corresponds to a cloud opacity factor of 0.95 (compare Figure 7) and seems to indicate that the assumed standard cloud optical depth of 26.9 (opacity factor 1.0) is rather the lower limit of what is present in the real Venusian atmosphere. Since continuum parameters were derived based on this standard model, a possible conclusion is that not only higher cloud opacities, but also lower continuum values are closer to reality than those adopted in the present ‘‘standard case model.’’ This will be addressed in future investigation. [31] The possibility to eliminate cloud signals from the VIRTIS measurements below 1.5 mm by the use of radiance ratios is of decisive importance for our future work that will mainly focus on Venus surface data extraction and the study of surface and lower atmosphere interactions. [32] Figures 9 and 10 illustrate the change of simulated radiances with surface elevation and wavelength. The cloud opacity was held constant in the simulations. The windows at 1.02, 1.10, and 1.18 mm exhibit a clear dependence of transmitted radiation on topographical features and, thus, on surface thermal emission. The radiance decreases considerably with increasing elevation, since high-elevation surfaces are substantially cooler and emit less thermal radiation. An elevation change of 12 km results in a surface temperature change of almost 100 K. The window at 1.28 mm still shows a minor influence of surface temperature, while it is mainly determined by features of the lower atmosphere (15 – 30 km). The surface elevation (temperature) does not observably influence the 1.31, 1.74, and 2.3 mm windows radiances, respectively. A simulation was performed, where the surface contribution was excluded assuming a surface emissivity value of zero. Thus, a quantitative estimate of the surface thermal emission contribution to emission within the Venus atmospheric windows was obtained. In the windows 1 (1.02 mm), 2 (1.10 mm), 3 (1.18 mm), and 4 (1.28 mm), the surface contributes 97 (95) %, 59 (60) %, 27 (40) %, and 1.3 ( e0 is/2pf = jej exp[i( f )], where e0 is the real part of the dielectric constant, s is the surface material conductivity, f is the wave frequency, and q( f ) is the argument of e. [13] The first step in finding the surface dielectric constant e from measurements is to solve (1) and (2) using the powers in the received RCP and LCP echo signals [Simpson et al., 2006]. The ratio of RCP to LCP power depends strongly on the magnitude of the dielectric constant but not its argument (Figure 4a). The angle at which RCP and LCP echo powers are equal, the Brewster angle for real e, increases from 60° to 75° to 84.3° for jej = 3, 10, and 100. [14] The phase of the received cross spectrum depends initially on the phase of the product of the circular reflection coefficients, RRRL*. With RCP incident on the surface, the difference in the two reflection coefficients imposes a phase shift between the RCP and LCP echo components. When the spacecraft rolls about the HGA axis during transmission, the phase difference between the RCP and LCP signals changes by 4p for each full rotation (VEX rolls for thermal protection). The two components may undergo further, small differential phase shifts as they pass through magnetized plasma between Venus and Earth. The separate paths through microwave circuits and electronics on the ground are certainly different as well. It is usually adequate to assume that spacecraft roll, plasma effects, and electronic drift add no more than a slowly time-varying bias to the

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SIMPSON ET AL.: VENUS EXPRESS BISTATIC RADAR

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Figure 2. Example VEX 13 cm RCP 1024-point 100 kHz power spectrum from 15 June. Spectrum is the average of 976 individual power spectra collected within ±5 s of 0156:16, when the specular point was near (78°N, 25°E) in a plains area northwest of Maxwell Montes. The surface echo is identified by the horizontal bar over 38– 47 kHz. Within the echo window a narrow carrier signal propagating directly from the spacecraft (Figure 1) is visible at 43 kHz. Baseline noise power density (No = 0.299 zeptowatts/Hz, where 1 zW is 1021 W) is calculated from the windows on either side of the echo. The carrier power, when present, is removed by linear interpolation across five frequency bins, after which the noise pedestal is removed and the echo power (Pe = 164.8 zW) is calculated by summing the remainders. The formal uncertainty in Pe (3.99 zW) is based on No and the amount of time and frequency averaging in the noise and echo windows. The uncertainty does not include the fluctuations in Pe, caused by the scattering process, which are typically 3 – 4 times larger (Table 3).

phase difference initiated by the RCP and LCP reflection coefficients. If the elevated regions on Venus are conducting while the surrounding plains are not, we would expect to see clear changes in the echo phase in hVRVL*i, mimicking the changes in the phase of hRRRL*i and occurring suddenly as the specular point moves into and out of the anomalously reflecting regions. [15] Figure 4b shows the predictions of cross-spectrum phase y from (1) and (2) for the nine choices of e (three each of magnitude and argument) used in Figure 4a. The cross-spectrum phase is ±180° for real dielectric constants. For imaginary e, y 135°, varying slightly with incidence angle 8 for the smallest e. The cross-spectrum phase is approximately the average of the real and imaginary values when e itself is midway between real and imaginary. Thus, to first order, y 180 q=2 þ bias terms

ð3Þ

The corresponding cases for Magellan’s 13 cm bistatic radar experiment are shown in Figure 5. Figure 5a shows the RCP/LCP power ratio for the three example choices of jej and three choices for q when the incident wave is linearly polarized at 45° with respect to the incidence plane. For the Magellan configuration the power ratio would be unity when the dielectric constant is real. If the dielectric constant were purely imaginary, RCP echo power can become 10 times larger than LCP near the Brewster angle for the corresponding real e, with the difference being most pronounced for the smallest jej. Figure 5b shows the phase of the echo cross spectrum, which is most sensitive to 8 and jej and least sensitive to q.

4. Data [16] Figure 6 shows example HGA ‘‘footprints’’ across Maxwell Montes for the bistatic radar experiments. The MGN footprints moved from southwest to northeast; since the experiments were conducted on alternating 93 min

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Figure 3. Example VEX 13 cm cross spectrum on 19 June at 0158:55 ± 05, when the specular point was near (76°N, 9°E). (a) The directly propagating carrier, visible at 43 kHz in Figure 2, can be seen in the magnitude plot at 73 kHz. (b) Phase values are clustered at y 100°, where the surface echo is well defined, but are distributed randomly elsewhere.

orbits, the tracks are nearly identical. The Venus Express footprints moved from northwest to southeast on alternating 24 h orbits. Of particular interest is the VEX track on 19 June, which crossed the MGN tracks near Cleopatra, where the specular echoes implying conducting surface material were obtained. Here we focus on 19 June. The results from 15 and 17 June and are similar but without the Cleopatra connection. [17] Figure 7 provides an overview of the 13 cm VEX results on 19 June. Details for Cleopatra Patera, Maxwell Montes, and the immediate surroundings are expanded in Figure 8 and listed in Table 3. The solid lines in Figure 7 show amplitudes and phases averaged over 25 frequency bins spanning 2441 Hz centered on the echo peak, consistent with the echo widths in Figures 2 and 3. Results for the peak bin alone (Df 98 Hz) are shown by the open circles. Averages of noise in 25 bins approximately 10 kHz above and below the surface echo are indicated by ‘‘plus’’ and ‘‘cross’’ symbols, respectively. [18] A predominantly specular echo is expected to have a cross-spectrum magnitude that is close to the geometric mean of the RCP and LCP echo powers and a crossspectrum phase that has a small standard deviation. As the scattering process becomes less specular, we expect the

correlation between RCP and LCP echo components to decrease, the cross-spectrum magnitude to drop below the RCP-LCP geometric mean, and the cross-spectrum phase to approach a uniform distribution. The extreme case of no echo is illustrated in the noise portions of spectra. For example, over 55 – 65 kHz in Figure 3, the RCP and LCP noise power densities are 0.299 and 0.416 zW/Hz, respectively, the cross-spectrum magnitude is small, and the phases are distributed randomly. In Figure 8a, the noise power has been subtracted to leave the RCP and LCP echo powers. The majority of measurements for t < 2.011 and t > 2.039 in Figure 8 satisfy the criteria for specular echoes in that the cross-spectrum magnitude is close to the RCP-LCP geometric mean and the crossspectrum phase is narrowly distributed. Also, measurements in this range have larger RCP than LCP echo power density, as is expected for reflection at incidence angles greater than the Brewster angle. [19] For 2.011 < t < 2.039, at least seven of the ten measurements in Figure 8a show LCP echo power density larger than RCP, implying a much larger e than in the surrounding terrain. Only the last measurement (t = 2.0375) meets the criteria for specular reflection, however. Half of the measurements have cross-spectrum magnitudes smaller

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Figure 4. (a) Ratio of hVRVR*i to hVLVL*i calculated from equations (1) and (2) for RCP incident (the VEX case), three choices of dielectric constant magnitude (jej = 3, 10, 100), and three choices of dielectric constant argument (q = 0°, 45°, and 90°). (b) Phase y of the cross spectrum hVRVL*i is given for the same conditions.

than both RCP and LCP echo powers, and only the last measurement has a cross-spectrum phase standard deviation less than 40° (Table 3, rows 5 – 14). Of particular concern is the cross spectrum that we associate most closely with Cleopatra (t = 2.0208; Table 3, row 8). There, a broad maximum in cross-spectrum magnitude (0.008 zW/Hz) is centered on Cleopatra; but the RCP and LCP echo power densities are also relatively large with values 0.018 and 0.025 zW/Hz, respectively (Figure 8a). Taken together, these measurements imply that less than 40% of the echo power is from specular scattering. The standard deviation of the cross-spectrum phase at t = 2.0208 is 40°, the second smallest within the Maxwell group, and not unreasonable if there is a mix of specular and ‘randomizing’ elements within the illuminated footprint. The cross-spectrum phase itself is 92°, midway between the four-row averages at the beginning (106°) and end (82°) of Table 3. Although 92° could be taken as evidence against a sudden shift in electrical properties, we note that the measurements immediately before and after yielded cross-spectrum phases of 154 ± 51° (Table 3, row 7) and 127 ± 44° (row 9), which diverge from the linear trend apparent in Figure 7d. However, all three values have overlapping one-sigma uncertainties. [20] To calculate the total echo power Pe and its uncertainty in each polarization we used the method illustrated in

Figure 2. The dielectric constant can then be obtained by solving (1) and (2). In practice, we used the numerical RCP/ LCP ratio and the incidence angle 8 to obtain e by interpolation from a precomputed lookup table. A total of nine solutions for e was sought for each (RCP, LCP) pair of Pe values (Table 3, Notes). When any of the nine (RCP, LCP) pairs failed to yield a solution, ‘N/A’ was entered in the table. [21] Before solving for e, we adjusted the incidence angle for the effects of atmospheric refraction, including differences in refraction resulting from surface elevation changes (specular point latitude and longitude are changed very little by refraction). The incidence angle correction is as much as 4° ten minutes before the specular point entered Maxwell Montes. The maximum topography of Maxwell reduces a 2.3° correction to 1.3°. The observational geometry is almost identical among the three VEX orbits except for a 30 s/day delay in time and a 1.8°/day eastward shift in longitude of the specular point, resulting primarily from the rotation of Venus. [22] The 10 s spacing of data points in Table 3 represents a compromise between long integration times needed to improve signal-to-noise ratio and short integration times needed to preserve temporal and spatial resolution. Averages over 30 s consistently resulted in solutions for e, but the enhanced echoes from Cleopatra were not well resolved.

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Figure 5. (a) Ratio of hVRVR*i to hVLVL*i when a linearly polarized wave is incident for three choices of dielectric constant magnitude (jej = 3, 10, 100) and three choices of the dielectric constant argument (q = 0°, 45°, and 90°). The incident angle of polarization is halfway between horizontal and vertical: the Magellan case. Note all ratios approach unity for small angles of incidence. In order to facilitate display, we have displaced the curves for jej = 3 upward and the curves for jej = 100 downward. (b) Phase y of the cross spectrum hVRVL*i under the same conditions, but without displacements.

The 10 s points are separated by about 85 km along the specular track (Figure 6). The circularly polarized antenna beam from VEX was about 84 km across, half power to half power, where it intercepted the surface at periapsis (slant range 820 km on 19 June at t = 2.033). The HGA pattern, projected onto the surface at 8 = 71°, results in an elliptical footprint with dimensions approximately 84 260 km; five minutes earlier and seven minutes later the ellipse dimensions were doubled, chiefly because of increased slant range. The long axis of the ellipse was approximately perpendicular to the specular point track during the experiment, so the measurements listed in Table 3 can be considered independent.

5. Discussion 5.1. Magellan [23] We reprocessed the Magellan bistatic radar data [Simpson, 1997] using the same software that was applied to the VEX BSR data. One source of concern has been a possible ground receiver problem, which might have resulted in an apparent excess in Magellan RCP echo power and led to the conclusion that the surface near Cleopatra is

electrically conducting. We also know that the ground antenna system is imperfect. However, our experience since 2004 with a number of bistatic radar experiments and tests at DSN 70 m antennas is that cross polarization between RCP and LCP is approximately 25 dB below the direct polarization level. [24] Our reprocessing of the Magellan data included calibration of the absolute power levels. Compared with an original ad hoc assumption that the two receiver channels had equal gains and system noise (neither of which was known), the reprocessed results are less stable than the originals because of variations in system gain (especially LCP) of up to 1 dB on time scales of seconds to minutes. Also, because RCP and LCP are not balanced, the differences between channels appear as a weakly polarized (and variable) component of the noise where none should be expected. These instabilities dominate the physical cross polarization in the Magellan data in both an average sense and in terms of minute-to-minute fluctuations. [25] When the specular point is outside Maxwell, typical Magellan echoes have power densities larger than 0.1 zW/Hz (Figure 9a). Because the transmitted polarization was linear, both RCP and LCP have similar echo powers at

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Figure 6. Magellan and Venus Express ground tracks, shown as a sequence of antenna beam footprints superimposed on a topographic map of the Maxwell area [U.S. Geological Survey, 1984]. Footprints are separated by 30 s. Magellan footprints moved from southwest to northeast along three almost identical tracks spaced by 186 min in time (only one orbit is shown). Venus Express footprints moved from northwest to southeast on 15 –19 June (only the first and last orbits are shown).

the receiver; the power ratio is unity for latitudes less than 60° and larger than 70° (Figure 9b). The echo powers from within Maxwell drop to about a tenth those from areas immediately adjacent to Maxwell. Nonetheless, it is possible to identify the excess RCP power during the latter half of the Maxwell passage (latitudes 65– 67° in Figure 9), which Pettengill et al. [1996] associated with Cleopatra. Although this excess may sometimes be associated with the short-term gain fluctuations, the amount of excess is clearly different in spectral regions occupied by echo signals as compared with nearby noise. In addition, the RCP excess recurs on each of the three Magellan orbits over Cleopatra Patera, and no similar feature appears in echoes at other surface locations. On this basis, we support the original conclusion by Pettengill et al. [1996] regarding the excess RCP power in the Maxwell echoes. [26] From Figure 5 we can confirm that the 100° crossspectrum phase change over Maxwell (Figure 9c) is consistent with jej 100 (or larger); a power ratio of 1.4 is consistent with equal real and imaginary parts, while a power ratio of 1.8 favors an entirely imaginary dielectric constant. Neither of these results is strongly dependent on incidence angle, which is approximately 2° larger than the latitude during this time.

5.2. Applicability of Fresnel Reflection [27] The methods presented here are based on an assumption that the surface echo from Venus is dominated by Fresnel reflection, equations (1) and (2), reflection by many surface ‘facets’ having radii of curvature that are large compared to the wavelength [Tyler, 1976]. Over most of Venus, the magnitude of the cross spectrum is near the geometric mean of the RCP and LCP powers and the crossspectrum phase is well defined, validating the assumption. The data from Cleopatra Patera, however, imply that less than 40% of the echo originates from such surface elements. Around Cleopatra the model may be inadequate, and over other parts of Maxwell the breakdown may be even more severe. [28] However, the VEX data do show that there is a reversal of the sense of circular polarization over Maxwell generally and near Cleopatra specifically. We do measure a nonzero cross spectrum with constrained (albeit not small) phase distributions: at least in the vicinity of Cleopatra. These are results we would expect from Fresnel reflection even though we admit that (1) and (2) cannot be applied rigorously. A literature of Fresnel alternatives exists, scattering by discrete objects (e.g., wires and rocks) and

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Figure 7. Summary results from Venus Express 13 cm bistatic radar experiment on 19 June. (a) RCP power density, (b) LCP power density, (c) cross-spectrum magnitude, and (d) and cross-spectrum phase. Horizontal axis is Earth receive time in hours past midnight. Solid lines are average values over 25 frequency bins (2441 Hz) centered on the surface echo peak. Open circles are values in the central echo bin (Df = 98 Hz). Plus and cross symbols are 25 bin averages 10 kHz above and below the echo, respectively, where only noise is present. Note in Figure 7d that surface echo phase drifts toward smaller values during the experiment because of spacecraft attitude and other slow changes. For more detail on Maxwell Montes, including Cleopatra Patera, see Figure 8 and Table 3.

irregular structure (edges and cracks), but the results do not explain the Venus observations. Dedrick et al. [1978] have shown that the cross-polarized radar cross section of an ‘‘atmosphere’’ of randomly oriented wires is one-third that of the direct cross section in both the forward and backward directions. This sets a limit on the amount of polarization change that can be attributed to discrete objects and fails to explain the polarization reversal we see at Maxwell Montes. Refraction has been proposed by Tryka and Muhleman [1992] and Peters [1992] to explain unexpected phenomena on Venus and icy satellites, respectively, but the models require a regolith which is nearly lossless and are more attractive for backscatter. [29] Cost [1965] and Ulaby et al. [1988] have studied the microwave bistatic response of terrestrial surfaces both with

and without vegetation. Their experiments were typically conducted in a controlled (outdoor laboratory) environment with orthogonal linear polarizations. This and other work has been summarized by Weiner [2007]. Almost without exception the maximum bistatic radar cross section of these surfaces was observed to be consistent with specular theory: even for ‘rough’ surfaces. For incidence angles 8 > 40° the response in the specular direction was typically 10 dB or more than in other directions. [30] Lacking a detailed, viable alternative model we propose that many of the key features of Fresnel reflection are being reproduced in Maxwell Montes by its very rough surface. Although (1) and (2) cannot be applied because none of the ‘facets’ is large enough to qualify as a quasi-

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Figure 8. The 13 cm VEX bistatic radar echo measurements collected near and within Maxwell Montes. Maxwell is assumed to extend over 2.011 < t < 2.039, with Cleopatra Patera at t 2.02. (a) RCP (plusses) and LCP (crosses) echo power density averaged over 25 frequency bins centered on the echo peak; noise power has been subtracted from total power listed in Table 3, columns 6– 7. Cross-spectrum magnitude density (circles) is taken from Table 3, column 8. (b) Cross-spectrum average phase (circles) and its standard deviation from calculations over the same 25 frequency bins.

specular reflector, the bulk properties of the surface material may still be sensed. A surface saturated with relatively flat rocks would have a hybrid scattering response, partly from the dipole nature of the rocks and partly from the exposed flat side, which can be responsible for polarization reversal in the bistatic geometry. Working the problem backward, we can interpret changes in the RCP/LCP ratio in terms of the material dielectric constant without being able to say exactly what the values are. We have previously investigated the scattering by populations of discrete rocks on and within a regolith [Baron et al., 1998]. Similar methods may be applied here; but the model must incorporate rock-rock interactions, which makes an already computationally intensive calculation an order of magnitude more difficult.

6. Conclusions [31] The three Venus Express bistatic radar experiments conducted over Maxwell Montes confirm the presence of high dielectric material in elevated regions through measurements of opposite sense, circularly polarized echo powers when a circularly polarized wave was incident.

The echo power ratio yielded values in the range 4 < jej < 25 for Maxwell, but the weak echoes and relatively low cross-spectrum magnitude caution against a strict Fresnel-based interpretation. Away from Maxwell Montes, where criteria for specular scattering are more easily met, values jej 4 are common. [32] Attempts to detect electrical phase changes in the VEX cross spectrum hVRVL*i, expected if the surface material were conducting, have not been successful. Magellan observations of excess RCP power, which require that e have a large imaginary component, have been confirmed near Cleopatra crater. VEX results from the same area are inconclusive because of inadequate signal-to-noise ratio. [33] Continuing the VEX BSR explorations, particularly over areas other than Maxwell and at smaller Earth-Venus distances remains a desirable goal. The VEX opportunity has been lost because of failure in the spacecraft radio system, which after November 2006 radiated only 200 mW at 13 cm wavelength. Future electromagnetic probing of the unusual high-elevation terrains on Venus must await the arrival of a new spacecraft.

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2.0014 2.0042 2.0069 2.0097 2.0125 2.0153 2.0181 2.0208 2.0236 2.0264 2.0292 2.0319 2.0347 2.0375 2.0403 2.0431 2.0458 2.0486

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

71.52 71.10 70.95 70.73 70.46 70.17 69.87 69.55 69.15 68.77 68.42 67.93 67.40 66.83 66.32 65.88 65.51 65.07

8 (°) 71.73 71.00 70.27 69.52 68.76 67.98 67.20 66.41 65.60 64.79 63.98 63.15 62.31 61.47 60.63 59.79 58.96 58.13

Lat (°N) 2.29 3.38 4.37 5.28 6.12 6.83 7.50 8.11 8.63 9.11 9.56 9.92 10.25 10.54 10.80 11.06 11.30 11.50

Lon (°E) 0.334 0.334 0.313 0.306 0.305 0.306 0.307 0.319 0.312 0.304 0.303 0.303 0.306 0.312 0.356 0.325 0.308 0.313

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.016 0.018 0.009 0.010 0.011 0.010 0.009 0.010 0.007 0.012 0.009 0.013 0.010 0.011 0.030 0.012 0.010 0.014

RCPd 0.441 0.434 0.417 0.418 0.429 0.424 0.431 0.443 0.434 0.425 0.419 0.421 0.430 0.434 0.464 0.438 0.429 0.426

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.021 0.017 0.011 0.011 0.010 0.010 0.013 0.012 0.015 0.010 0.016 0.014 0.013 0.015 0.025 0.013 0.014 0.015

LCPd 0.029 0.027 0.009 0.007 0.003 0.004 0.007 0.008 0.008 0.006 0.002 0.001 0.001 0.015 0.052 0.022 0.015 0.015

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.012 0.012 0.007 0.006 0.009 0.009 0.009 0.012 0.008 0.007 0.008 0.008 0.006 0.011 0.026 0.011 0.006 0.011

Peak Magnitude (zW/Hz) 110.2 ± 17.0 110.0 ± 18.3 93.4 ± 46.6 111.7 ± 50.2 74.6 ± 69.8 114.9 ± 60.6 154.5 ± 51.3 92.2 ± 40.0 126.5 ± 43.6 101.7 ± 49.2 137.7 ± 79.7 132.9 ± 85.7 25.9 ± 79.1 78.2 ± 21.9 83.3 ± 7.4 79.4 ± 15.0 86.1 ± 29.0 79.8 ± 26.2

Phase (°)

Cross-Spectrum Average

131.23 ± 10.87 96.69 ± 10.95 24.64 ± 10.89 62.69 ± 10.83 4.23 ± 10.86 43.22 ± 10.77 23.07 ± 10.83 60.97 ± 10.79 52.10 ± 10.73 36.38 ± 10.75 6.48 ± 10.75 12.53 ± 10.72 36.31 ± 10.68 54.49 ± 10.66 162.26 ± 10.64 100.07 ± 10.56 54.23 ± 10.54 72.33 ± 10.54

RCPt

85.28 ± 15.28 70.63 ± 15.16 36.92 ± 15.18 15.46 ± 15.11 75.36 ± 15.10 54.95 ± 15.03 37.68 ± 15.12 130.38 ± 14.98 70.14 ± 15.06 69.00 ± 14.97 41.05 ± 14.94 45.06 ± 14.83 40.92 ± 14.86 103.63 ± 14.77 187.01 ± 14.73 94.71 ± 14.74 39.56 ± 14.73 47.43 ± 14.73

LCPt

Total Echo Power per Figure 2 (zW)

e 6.150 ± 0.769 6.511 ± 1.014 N/A N/A N/A 9.849 ± 2.529 13.252 ± 6.386 14.619 ± 2.185 9.172 ± 1.865 12.363 ± 3.274 N/A 47.770 ± 51.2 6.682 ± 2.098 9.809 ± 1.640 5.877 ± 0.383 4.791 ± 0.554 3.803 ± 0.938 3.347 ± 0.646

From RCPt/LCPt

Columns 1 – 2: Values in each row are 10 s averages centered on the time in column 2. Columns 3 – 5: Geometry was corrected for atmospheric refraction (including differential refraction effects of topography) using a ray tracing model, the Magellan model atmosphere [Lyons, 1991], and topography derived from Magellan radar altimetry [Ford, 1992]. Columns 6 – 7: Power density and its standard deviation were computed using 25 frequency bins (2441 Hz) centered on the fitted position of the echo peak; of these totals, 0.299 – 0.301 and 0.416 – 0.420 zW/Hz were RCP and LCP noise power, respectively (see Figure 2). Columns 8 – 9: Cross-spectrum magnitude and phase averaged over 25 bins. Statistics were computed using real and imaginary parts; surface echoes are associated with small phase standard deviations, while noise has phase standard deviations 90°. Columns 10 – 11: Echo powers as calculated from components illustrated in Figure 2. Column 12: Dielectric constant was derived by solving equations (1) and (2) (see text) for each of nine RCP/LCP ratios and the corrected incidence angle (column 3). The nine ratios were constructed by pairing RCP power, RCP power less one standard deviation, and RCP power plus one standard deviation with the corresponding LCP values. Solutions were given a weighting of e0.5 for each occurrence of a standard deviation in the ratio. Values in column 12 are the weighted mean and standard deviation from the nine solutions. ‘N/A’ appears if any of the nine solution attempts failed. Rows 5 – 14 are associated with Maxwell Montes, row 8 with Cleopatra Patera.

a

Time (Hours Past 0 h)

Row

Average Power Density Noise + Echo Peak (zW/Hz)

Table 3. Venus Express Bistatic Radar Power and Cross-Spectrum Results 13 cm Wavelength 19 June 2006a

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Figure 9. Magellan bistatic radar results after reprocessing in the Maxwell Montes region, plotted versus specular point latitude, which is about 2° less than angle of incidence. Echo properties were averaged over 25 frequency bins (610 Hz at sampling rate 25 kHz). (a) Echo power from the sum of the RCP and LCP power densities. (b) Ratio of RCP to LCP power densities, expected to be near 1.0 for surfaces with real dielectric constants. (c) Cross-spectrum phase. Cleopatra Patera (65 – 67° latitude) is unique in having moderately strong echoes, excess RCP power (ratio 1.5), and a large phase change.

[34] Acknowledgments. This work was supported at Stanford University by the NASA Venus Express Participating Scientist Program (grant NNG06GG04G-000001). Estimates of absorption by the Venus atmosphere were based on a model kindly provided by J.M. Jenkins of the SETI Institute. Comments on an earlier version of the manuscript by Peter Ford, Gordon Pettengill, and an anonymous reviewer are gratefully acknowledged.

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Lyons, D. T. (1991), Magellan planetary constants and models, JPL D-2300 Rev. D, Jet Propul. Lab., Pasadena, Calif. Peters, K. J. (1992), The coherent backscatter effect: A vector formulation accounting for polarization and absorption effects and small or large scatterers, Phys. Rev. B, 46, 801 – 812, doi:10.1103/PhysRevB.46.801. Pettengill, G. H. (1962), Enhancement of radar reflectivity associated with the lunar crater Tycho, J. Geophys. Res., 67, 4881 – 4885, doi:10.1029/ JZ067i012p04881. Pettengill, G. H., E. Eliason, P. G. Ford, G. B. Loriot, H. Masursky, and G. E. McGill (1980), Pioneer Venus radar results: Altimetry and surface properties, J. Geophys. Res., 85, 8261 – 8270, doi:10.1029/ JA085iA13p08261. Pettengill, G. H., P. G. Ford, and R. J. Wilt (1992), Venus surface radiothermal emission observed by Magellan, J. Geophys. Res., 97, 13,091 – 13,102, doi:10.1029/92JE01356. Pettengill, G. H., P. G. Ford, and R. A. Simpson (1996), Electrical properties of the Venus surface from bistatic radar observations, Science, 272, 1628 – 1631, doi:10.1126/science.272.5268.1628. Rogers, A. E. E., and R. P. Ingalls (1970), Radar mapping of Venus with interferometric resolution of the range-Doppler ambiguity, Radio Sci., 5, 425 – 433, doi:10.1029/RS005i002p00425. Schaefer, L., and B. Fegley Jr. (2004), Heavy metal frost on Venus, Icarus, 168, 215 – 219, doi:10.1016/j.icarus.2003.11.023. Shepard, M. K., R. E. Arvidson, R. A. Brackett, and B. Fegley Jr. (1994), A ferroelectric model for the low emissivity highlands on Venus, Geophys. Res. Lett., 21, 469 – 472, doi:10.1029/94GL00392. Simpson, R. A. (1993), Spacecraft studies of planetary surfaces using bistatic radar, IEEE Trans. Geosci. Remote Sens., 31, 465 – 482. Simpson, R. A. (1997), Magellan Bistatic Radar Raw Data Records V1.0 (MGN-V-RSS-1-BSR-V1.0), NASA Planet. Data Syst., Greenbelt, Md. Simpson, R. A., G. L. Tyler, M. Pa¨tzold, and B. Haüsler (2006), Determination of local surface properties using Mars Express bistatic radar, J. Geophys. Res., 111, E06S05, doi:10.1029/2005JE002580. Stratton, J. A. (1941), Electromagnetic Theory, McGraw Hill, New York.

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Tryka, K. A., and D. O. Muhleman (1992), Reflection and emission properties on Venus: Alpha Regio, J. Geophys. Res., 97, 13,379 – 13,394, doi:10.1029/92JE01163. Tyler, G. L. (1976), Wavelength dependence in radio-wave scattering and specular-point theory, Radio Sci., 11, 83 – 91, doi:10.1029/ RS011i002p00083. Tyler, G. L., and D. H. H. Ingalls (1971), Functional dependencies of bistatic-radar frequency spectra and cross sections on surface scattering laws, J. Geophys. Res., 76, 4775 – 4785, doi:10.1029/JB076i020p04775. Ulaby, F. T., T. E. Van Deventer, J. R. East, T. F. Haddock, and M. E. Coluzzi (1988), Millimeter-wave bistatic scattering from ground and vegetation targets, IEEE Trans. Geosci. Remote Sens., 26, 229 – 243. U.S. Geological Survey (1984), Topographic and shaded relief maps of Venus (map I-1562), Reston, Va.

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Weiner, M. W. (2007), Clutter, in Advances in Bistatic Radar, edited by N. J. Willis and H. D. Griffiths, chap. 9, pp. 230 – 319, SciTech, Raleigh.

B. Haüsler and R. Mattei, Institut fu¨r Raumfahrttechnik, Universita¨t der Bundeswehr Mu¨nchen, D-85577 Neubiberg, Germany. (bernd.haeusler@ unibw-muenchen.de; [email protected]) G. L. Tyler and R. A. Simpson, Space, Telecommunications, and Radioscience Laboratory, Stanford University, Packard Building, MC 9515, Stanford, CA 94305-4020, USA. ([email protected]; rsimpson@ magellan.stanford.edu) M. Pa¨tzold, Rheinisches Institut fu¨r Umweltforschung, Abt. Planetenforschung, Aachener Strasse 209, D-50931 Ko¨ln, Germany. (mpaetzol@ uni-koeln.de)

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Felsic highland crust on Venus suggested by Galileo Near-Infrared Mapping Spectrometer data George L. Hashimoto,1 Maarten Roos-Serote,2 Seiji Sugita,3 Martha S. Gilmore,4 Lucas W. Kamp,5 Robert W. Carlson,5 and Kevin H. Baines5 Received 2 March 2008; revised 29 July 2008; accepted 18 September 2008; published 31 December 2008.

[1] We evaluated the spatial variation of Venusian surface emissivity at 1.18 mm

wavelength and that of near-surface atmospheric temperature using multispectral images obtained by the Near-Infrared Mapping Spectrometer (NIMS) on board the Galileo spacecraft. The Galileo NIMS observed the nightside thermal emission from the surface and the deep atmosphere of Venus, which is attenuated by scattering from the overlying clouds. To analyze the NIMS data, we used a radiative transfer model based on the adding method. Although there is still an uncertainty in the results owing to the not well known parameters of the atmosphere, our analysis revealed that the horizontal temperature variation in the near-surface atmosphere is no more than ±2 K on the Venusian nightside and also suggests that the majority of lowlands likely has higher emissivity compared to the majority of highlands. One interpretation for the latter result is that highland materials are generally composed of felsic rocks. Since formation of a large body of granitic magmas requires water, the presence of granitic terrains would imply that Venus may have had an ocean and a mechanism to recycle water into the mantle in the past. Citation: Hashimoto, G. L., M. Roos-Serote, S. Sugita, M. S. Gilmore, L. W. Kamp, R. W. Carlson, and K. H. Baines (2008), Felsic highland crust on Venus suggested by Galileo Near-Infrared Mapping Spectrometer data, J. Geophys. Res., 113, E00B24, doi:10.1029/2008JE003134.

1. Introduction [2] Venus is the nearest neighbor of Earth, and these twins are close in mass, size, and bulk composition. However, the surface environment of Venus is completely different from that of Earth, where the mean surface temperature is 735 K and water is almost absent. In contrast, the Earth surface is covered with ocean and is habitable. It is not clear whether the surface environments on these twin planets differed from beginning or they evolved from a similar environment into the current different states. Understanding the evolution of these twin planets is one of the most important and elusive problems in planetary climatology. [3] The chemical and mineralogical composition of the Venus surface can provide valuable information on the climatic evolution of Venus. If geologic signatures that are produced only under unique conditions are found in the chemical and mineralogical composition, they could be used 1 Laboratory for Earth and Planetary Atmospheric Science, Organization of Advanced Science and Technology, Kobe University, Kobe, Japan. 2 Lisbon Astronomical Observatory, Lisbon, Portugal. 3 Department of Complexity Science and Engineering, Graduate School of Frontier Science, University of Tokyo, Kashiwa, Japan. 4 Department of Earth and Environmental Sciences, Wesleyan University, Middletown, Connecticut, USA. 5 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA.


to reconstruct the Venus paleoenvironment. Additionally, a global map of surface composition will be a very important diagnostic of bulk planetary composition, chemical differentiation, and evolution of the planetary interior. [4] It has been also argued that the controlling mechanism of the present Venus surface environment is a connection between the atmosphere and the chemical characteristics of planetary surface [e.g., Hashimoto and Abe, 2005, and references therein]. The high surface temperature of Venus enhances the importance of the gas-solid reactions on the planetary surface in controlling the atmospheric composition, including infrared-active species which control the greenhouse effect. Venus climate models that combine chemical reaction and radiative transfer in the atmosphere have demonstrated that such a coupling between surface chemical reactions and greenhouse effect will play an important role in controlling the surface environment [e.g., Hashimoto and Abe, 2000; Bullock and Grinspoon, 2001]. [5] However, chemical characteristics of the Venus surface are not well constrained. In situ measurements of the surface using gamma-ray and X-ray fluorescence spectroscopy were performed by Venera and Vega landers [Surkov et al., 1984, 1986, 1987], but it is difficult to infer the global characteristics from lander data collected at 7 sites on the planet. Although the radar reflectivity and radiothermal emissivity were measured nearly globally [e.g., Pettengill et al., 1982, 1992], origins of the microwave emissivity differences are highly controversial and appear to be related to both grain size and dielectric constant of constituent

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minerals [e.g., Campbell et al., 1997; Pettengill et al., 1997]. We need a new observation capable of determining the chemical and mineralogical composition of the Venus surface. [6] It has been discovered that thermal emission from the surface of Venus is detectable at the CO2-free windows in the near-infrared wavelengths (0.85, 0.90, 1.01, 1.10, and 1.18 mm) [e.g., Carlson et al., 1993a, 1993b; Meadows and Crisp, 1996; Baines et al., 2000]. Thermal emissivity is a function of surface temperature, grain size, and mineral composition. Previous investigations of surface emissivity using Earth-based telescopic data have demonstrated variations that appear to be correlated with topography and therefore surface temperature [Lecacheux et al., 1993; Meadows and Crisp, 1996]. Although the variations in the surface emissivity have been investigated, no signature of surface emissivity differences was found at near-infrared wavelengths in contrast with the microwave wavelengths [Lecacheux et al., 1993; Meadows and Crisp, 1996]. However, these analyses neglect multiple reflection between the planetary surface and the clouds which has been demonstrated to significantly obscure variations in surface emissivity [Moroz, 2002; Hashimoto and Sugita, 2003]. Reexamination of previous analyses demonstrated that there may be a significant spatial variation in the surface emissivity as large as 20% [Hashimoto and Sugita, 2003]. [7] Here in this study, we evaluated the Venus surface emissivity at 1.18 mm wavelength using data obtained by the Near-Infrared Mapping Spectrometer (NIMS) aboard the Galileo spacecraft. Because emissivity depends on the mineralogy, a mapping of surface emissivity may constrain the chemical characteristics of the Venus surface. In the following, we first describe the procedure of our analysis which includes the reflection by the planetary surface. Then, we evaluate the surface emissivity, and discuss the errors and assumptions that may affect the estimation. Finally, the existence of paleo-ocean on early Venus is inferred from the spatial variation of surface emissivity.

2. Data and Analysis 2.1. Data [8] We analyzed the multispectral nightside image identified as VPDIN-1 (for Venus Partial Disk Imaging-NightSide) which covers the disk from approximately 20°W to 90°E longitude [Carlson and Taylor, 1993] and is distributed by the Planetary Data System (PDS). Those data were obtained by Galileo NIMS during its flyby of Venus on 10 February 1990. NIMS is an imaging spectrometer, operating in the spectral range from 0.7 to 5.2 mm [Carlson et al., 1992], and images of nightside are obtained with 17 spectral channels [Carlson et al., 1991]. Some channels measured the thermal radiation in the well-known spectral windows at 1.18, 1.74, and 2.3 mm. In these windows, thermal emission originating from both the surface and lower atmosphere are observed [e.g., Allen and Crawford, 1984; Crisp et al., 1989; Carlson et al., 1991; Lecacheux et al., 1993; Pollack et al., 1993; Baines et al., 2000]. [9] NIMS channel 3 observed the radiation of the 1.18 mm window, in which thermal emission from the surface leaks through the Venus clouds (Figure 1a). Initial results from the VPDIN observations are described by Carlson et al.

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[1991], which mention that the high-altitude (12 km above mean planetary radius) surface feature Maxwell Montes is detectable in the NIMS channel-3 image. The dark features near the top and the right of the image are likely due to the Ishtar and Aphrodite terra, respectively (Figures 1a and 1e). However, there are also some features irrelevant to the surface. They are probably produced by the effects of scattering by the Venus clouds, which is the dominant effect in channels 5 and 7 (Figures 1c and 1d). Other effects detectable in channel 3 are scattered sunlight and limb darkening. These effects need to be corrected separately. 2.2. Overview of Analysis [10] The analysis procedure for evaluating surface emissivity is the following. (1) We first remove the contribution of scattered sunlight, (2) we make a correction for emission angle, and (3) we evaluate the surface emissivity. Since the radiation at 1.18 mm window is affected by both clouds and surface topography, these contributions are evaluated to isolate the contribution of surface composition to thermal emissivity. [11] In this analysis procedure, we use the NIMS channels 4, 5, and 7 in addition to channel 3. Figure 2 provides a schematic representation of the characteristics of radiation observed by the four channels. NIMS channel 4 is used to remove the contribution of scattered sunlight, and the NIMS channels 5 and 7 are used to estimate the cloud properties. 2.3. Radiative Transfer Model [12] We calculated synthetic spectra by means of a lineby-line radiative transfer program including both scattering and absorption. The computational code was developed by G. L. Hashimoto and others at the University of Tokyo. The algorithm known as the adding method is used to calculate the radiation in a vertically inhomogeneous plane-parallel atmosphere [Goody and Yung, 1989]. The number of radiative streams is 12 in each hemisphere. Taking into account a triangular spectral bandpass with the width of 0.025 mm [Carlson et al., 1992], we calculate radiances for each channel from synthesized spectra in which molecular absorption features are resolved. [13] Following the method by Pollack et al. [1993], opacities of gases and particles were calculated. In all our simulations, we used the vertical profiles of gas mixing ratios developed by Pollack et al. [1993]. Line absorption data for CO2, H2O, CO, SO2, HF, and OCS were taken from the HITEMP and HITRAN2000 [Rothman et al., 2003; L. S. Rothman et al., HITEMP, the high-temperature molecular spectroscopic database, manuscript in preparation, 2008]. Total internal partition sums (TIPS) are calculated by the TIPS code developed by Fischer et al. [2003]. Rayleigh scattering cross sections for atmospheric molecules are calculated from the refractive indices [Keady and Kilcrease, 2000; Penndorf, 1957; Vardavas and Carver, 1984]. Optical properties of cloud particles are calculated by the Mie scattering code [Bohren and Huffman, 1983] based on the baseline cloud model [Pollack et al., 1993] and the optical constants of 75% H2SO4 solution [Palmer and Williams, 1975]. [14] We also introduced a continuum absorption to reproduce an acceptable fit to the NIMS data, since an additional source of continuum opacity is present owing to the far

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Figure 1. Multispectral images of nightside of Venus. These data were obtained during the first NIMS imaging sequence (VPDIN-1) at a resolution of 50 km/pixel. (a) An image at 1.18 mm (channel 3). (b) An image at 1.47 mm (channel 4). (c) An image at 1.74 mm (channel 5). (d) An image at 2.31 mm (channel 7). (e) A Magellan topographic map. This image covers from 20°W to 90°E of longitude. The topographic high near the north pole is the Ishtar Terra. The western edge of Ovda Regio can be seen near the rightmost part of the image. wings of strong CO2 bands and collision-induced CO2 opacity [Pollack et al., 1993]. Binary absorption coefficients for continuum opacity are determined to match the measured flux with the cloud model of Pollack et al. [1993]. The values

used in our simulations are 1.0 109 cm1 amagat2 for 1.18 mm window, 1.0 108 cm1 amagat2 for 1.74 mm window, and 7.0 108 cm1 amagat2 for 2.3 mm window, respectively, though these values are somewhat different

Figure 2. A schematic picture of radiation in Venus’s atmosphere and clouds. 3 of 10


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from those used in other analyses [e.g., Be´zard et al., 1990; Pollack et al., 1993]. 2.4. Scattered Sunlight [15] Nightside emissions at the near-infrared wavelengths contain scattered sunlight reflected off the dayside of the planetary disk. In particular, a strong component that brightens gradually toward the terminator is found in channel 4 (1.47 mm) (Figure 1b). Atmospheric radiative transfer models demonstrate that Venus does not emit thermal radiation at this wavelength. The Venusian CO2 atmosphere is too opaque to allow radiation to penetrate the atmosphere, though the surface and lower atmosphere of Venus are hot enough to emit thermal radiation at this wavelength. The radiance observed in channel 4 can serve as a good approximation for the scattered sunlight. [16] We corrected the effect of scattered sunlight, by assuming that the contributions of this effect to channel 3, 5, and 7 are proportional to that of channel 4. Taking into account the solar spectrum, we use the following correction: IxS ¼ Ix

Sx I4 S4

ð1Þ

where suffix denotes the NIMS channel, ISx is the radiance after the correction of scattered sunlight, Ix is the radiance observed by the NIMS, and Sx is the solar spectrum. 2.5. Limb Darkening [17] We computed limb darkening by using our radiative transfer model. Figure 3 shows our calculated limb darkening curve and NIMS observation that is corrected for the scattered sunlight. The observed limb darkening is well reproduced by our simulation. Although the particle size and number density in the lower clouds would vary in time and space, they affect only scales of radiance and do not affect the shape of limb darkening. Since the upper cloud layer is opaque enough to determine the distribution of emission angle at the cloud top, the shape of limb darkening curve is independent of the property of the lower clouds. [18] The equation for limb darkening correction is given by IxSL ¼

Jx ð0Þ S Ix Jx ðqÞ

ð2Þ

where ISL x is the radiance after the correction of scattered sunlight and limb darkening, Jx (q) is the computed radiance for emission angle of q, respectively. 2.6. Estimation of Surface Emissivity [19] To evaluate the surface emissivity at 1.18 mm window, we need to take into account the effects of clouds and surface topography. The overlying clouds modulate the radiation mostly by scattering. The lower atmosphere of Venus absorbs the radiation emitted by the surface and also emits thermal radiation, since the opacity of the thick Venus atmosphere is not negligible at 1.18 mm wavelength [e.g., Taylor et al., 1997]. The effects of atmospheric absorption and emission increase with the column density of the atmosphere, which is largely determined by the surface

Figure 3. Theoretical and observed limb darkening for the NIMS channel 3. The radiances are plotted as a function of the cosine of the emission angle. The NIMS observation is corrected for the scattered sunlight. topography owing to hydrostatic balance. For example, the atmospheric column density is smaller in a highland area, leading to smaller atmospheric absorption and emission. In addition the intensity of thermal radiation from the surface is a function of the temperature which in turn decreases with height, so that highlands are cooler and emitting less. [20] We cannot separate the clouds’ correction from the evaluation of surface emissivity, since multiple reflection between the surface and clouds affects the intensity of radiation observed above the clouds [Hashimoto and Sugita, 2003]. Here, multiple reflection refers to phenomenon where a fraction of the upward incident radiation at the bottom of cloud layer has experienced reflections between clouds and the planetary surface more than once. This effect has a significant influence on the observed radiance under the Venus conditions, where the reflectivity of overlying clouds is high. Although multiple reflection also occurs on Earth and Mars, the degree of its effect is very small. Since the atmospheric absorption affects the multiple reflection, the correction for modulation induced by clouds is also connected to the surface topography via atmospheric opacity. [21] To estimate the cloud properties we follow the method of Carlson et al. [1993b] which used NIMS channels 5 and 7 (1.74 and 2.31 mm, respectively). At these wavelengths, visible features are almost entirely due to clouds, which modulate the emission from the lower atmosphere, with no significant surface contribution [e.g., Taylor et al., 1997]. It is not necessary to take into account the effect of multiple reflection at these channels, since photons reflected by clouds are absorbed by lower atmosphere (Figure 2). Using the NIMS channel 5 and 7, we can estimate the cloud properties without the influence from the surface emissivity. [22] Then, we calculate the upward radiance at the top of the atmosphere in channel 3 as a function of surface emissivity. The cloud properties estimated from the NIMS channels 5 and 7 and the surface topography are input to the radiative transfer model. The surface emissivity is evaluated in accordance with the computed relation between

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the surface emissivity and the radiance at the top of the atmosphere. 2.6.1. Evaluation of Cloud Properties [23] The clouds of Venus have been observed by remote sensing as well as in-situ measurement techniques [e.g., Esposito et al., 1983]. The observations indicate that the clouds of Venus are vertically stratified with three layers, which are called the upper, middle, and lower clouds, respectively [e.g., Knollenberg and Hunten, 1980]. These clouds are made up of a few different particle size components, which are often referred with mode 1, 2, 20, and 3 [e.g., Knollenberg and Hunten, 1980; Pollack et al., 1993]. The upper and middle clouds are generally uniform and featureless, while considerable spatial variations in cloud properties are observed in the lower cloud [Marov et al., 1980; Ragent and Blamont, 1980]. [24] We start with a cloud model developed by Pollack et al. [1993] and assume that the spatial variations in cloud properties are caused by variations in the number densities of mode 20 and 3 particles in the lower clouds. Carlson et al. [1993b] demonstrated that the NIMS observation is well reproduced by varying the number densities of mode 20 and 3 particles. Their results are verified by our radiative transfer model (Figure 4). Since the wavelength dependence of scattering varies with cloud particle size, channels 5 and 7 enable us to distinguish the variation in the abundance of mode 20 and 3 particles (Figure 5). 2.6.2. Surface Topography and Atmospheric Structure [25] The surface topography of Venus is measured by the Magellan spacecraft with vertical accuracy of about 50 m [Pettengill et al., 1991]. We assume that the Venusian lower atmosphere is horizontally uniform and the vertical temperature profile is given by the Venus International Reference Atmosphere (VIRA) [Seiff et al., 1985]. Below the zero altitude (6052.0 km from the center of Venus), the temperature lapse rate in the lowermost layer is extrapolated. For

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Figure 5. Venus cloud maps. (a) A map of the optical thickness of lower clouds. (b) A map of the modal radius of lower cloud particles. pressure extrapolation, the hydrostatic pressure relation is integrated using the temperature profile. We also assume that the temperature of the planetary surface equals to the temperature of atmosphere in contact with it, since the temporal variation of the temperature in the Venusian lower atmosphere is expected to be extremely small [e.g., Seiff, 1983]. 2.6.3. Emissivity Estimate and Spatial Smoothing [26] Using our radiative transfer model, we can estimate the surface emissivity based on the band-3 radiance which has been corrected for the effects of scattered day-side light and limb darkening. However, because the band-3 radiance depends only weakly on the surface emissivity due to multiple reflection between clouds and planetary surface [Hashimoto and Sugita, 2003], small errors in both observation and correction lead to a large error in surface emissivity estimate. In fact, not all the data points in the band-3 radiance have values that correspond to surface emissivity values between 0 and 1. Thus, we calculated the surface emissivity beyond the physically significant range (i.e., 0 through 1) by extrapolating the results of radiative transfer model and discarding data points whose is smaller than 1 or larger than 2. Such a criterion for data selection increases the available data and helps us to obtain a sufficient sample to infer the Venus surface composition. We reduced the random noise by averaging the data within a circle of radius 250 km. This smoothing procedure should not change general trends of the data since the spatial resolution of the Venus surface observation is limited to about 100 km owing to cloud blurring [Hashimoto and Imamura, 2001].

3. Results and Discussion Figure 4. Scatterplot of the NIMS channel 5 against the channel 7. The NIMS data are corrected for the effect of limb darkening. Six theoretical curves are also shown. For each curve, the ratio of mode 3 to mode 20 content is constant, and the total opacity of the lower cloud is varied. The lowermost curve is for a cloud that contains no mode 3 particles, while the uppermost curve is for a cloud in which the lower cloud is thoroughly composed of mode 3 particles.

3.1. Surface Emissivity [27] The estimated surface emissivity at 1.18 mm window wavelength is shown in Figure 6. Although the effect of scattered sunlight increases toward the terminator, our result does not show such a bias. Also there is no noticeable tendency varying with the emission angle, while the effect of limb darkening depends on the emission angle. These facts indicate that both the corrections for the scattered sunlight and limb darkening work fairly well.

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erties. However, the Northern Hemisphere does not exhibit such a behavior associated with the modal radius in the lower clouds. [33] The good fit of Carlson’s method (Figure 4) strongly suggests that it works well for evaluating the cloud properties. We did not identify any apparent error in the estimation of surface emissivity, though there may be a problem with the correction for the effect of clouds. It would be an important future work to develop a declouding method that uses the spectra of 1.73 and 2.3 mm window. Some instruments aboard the Venus Express spacecraft are observing the spectra of 1.73 and 2.3 mm window [e.g., Baines et al., 2006], though the Galileo NIMS did not observed them in a mapping mode [Carlson and Taylor, 1993]. Figure 6. Surface emissivity at 1.18 mm window wavelength. (a) A map of surface emissivity. (b) A map of surface emissivity that is averaged for a region of a circle of radius 250 km. [28] However, the mean surface emissivity in the Southern Hemisphere is greater than that in the Northern Hemisphere. This feature may be an artifact due to incorrect evaluation of cloud properties, since the modal radius of lower clouds also exhibit such behavior (Figure 5b). The result of declouding that is a correction for this effect is discussed further in section 3.2. [29] Although the surface emissivity map exhibits a considerable amount of noise, there is a substantial regional variation. For example, Ishtar Terra, Eistla Regio, and Alpha Regio have relatively low emissivity. In contrast, Bell Regio and a band of region from Tahmina Planitia to Fonueha Planitia have higher emissivity values. We will not further discuss a regional difference in emissivity, since surface emissivity estimation might be affected by overlying clouds. 3.2. Declouding [30] We created a declouded image corrected for cloudinduced contrast (Figure 7a). The cloud-induced contrast was corrected on the basis of the cloud properties evaluated by the method by Carlson et al. [1993b]. Although the cloud-induced contrast depends on the surface emissivity, we assumed that surface emissivity is uniform ( = 0.85) during this declouding procedure. [31] It is clearly shown that all the major features in the topography can be recognized in the declouded image, even though the declouding process is apparently a conspicuous noise source. For comparison, the thermal emission at the NIMS channel 3 wavelength was computed on the basis of the Magellan surface topography, assuming no clouds and uniform surface emissivity (Figure 7b). The correspondence between the declouded image (Figure 7a) and the synthesized image (Figure 7b) indicates that our declouding procedure effectively removes the influence of clouds. [32] The declouded image shows a north-south asymmetry that is not noticeable before the declouding. Since there is also a hemispheric asymmetry in the modal radius of lower clouds, their appearance of high surface emissivity in the southern hemisphere, especially the region along the limb, might be attributed to insufficient evaluation of cloud prop-

3.3. Temperature in the Lower Atmosphere [34] The intensity of leaking radiation at the 1.18 mm window depends not only on the surface emissivity but also on the temperature of lower atmosphere [e.g., Taylor et al., 1997]. A deviation from the assumed temperature profile will cause an error in the surface emissivity estimation. To obtain a rough estimate of the sensitivity to the temperature variation, we computed the intensities of the leaking radiation with varying the temperature of the lower atmosphere. The range of temperature deviation from the VIRA profile is roughly estimated to be about ±2 K, if the spatial variation in the declouded NIMS channel 3 image is entirely attributed to the deviation in the atmospheric temperature. [35] In other words, our analysis of the NIMS channel 3 indicates that horizontal temperature variation in the lower atmosphere is no more than ±2 K. This is consistent with results from observational and theoretical studies. While measurement accuracies are no less than ±4 K, temperature data from the Venera probes indicated that surface temperatures scatter over no more than a few K [Seiff et al., 1985]. Extrapolation of the temperature profile measured by Pioneer probes indicates surface temperatures from 731 to 735 K [Seiff et al., 1985]. Theoretical study also indicated that variations of temperature in the deep atmosphere is as

Figure 7. Thermal emission at 1.18 mm window wavelength from the surface and the lower atmosphere. (a) A declouded image that is corrected the cloud-induced contrast. (b) A synthesized image based on the Magellan topographic map.

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small as 0.1 K, because of the large heat capacity of the deep atmosphere [Stone, 1975]. [36] The NIMS channel 3 image also shows no clear evidence for a variation in temperature in the deep atmosphere with latitude or with local time; temperatures in the deep atmosphere are almost constant in spite of the variation in the insolation as a function of distance from the subsolar point. There might be a small hemispheric asymmetry in the temperature of deep atmosphere causing the north-south asymmetry in the declouded image, though that asymmetry would be attributed to the declouding process. A further study is needed to evaluate the actual horizontal variation in the temperature of deep atmosphere and its influence on the circulation of Venusian atmosphere. 3.4. H2O Abundance [37] In the estimation of surface emissivity, we assumed that H2O mixing ratio is 30 ppmv and is constant below the cloud deck. Since water vapor absorbs some radiation at 1.18 mm window, inhomogeneous distribution of water vapor causes an error in surface emissivity estimation. We computed the intensities of the leaking radiation with varying H2O mixing ratio, and found that variation of about ±10 ppm in H2O mixing ratio is required to reproduce the observed contrast in the declouded image. [38] However, it is unlikely that variation in the H2O mixing ratio is a major contributer to the spatial variation in the NIMS channel 3 image. Spatial variation of the H2O abundance in the lower atmosphere had been evaluated by Drossart et al. [1993]. They searched for its spatial variations from the analysis of the NIMS complete spectra at maximum spectral resolution using the 1.18 mm window. The result of their analysis is that the water vapor abundance shows no horizontal variation exceeding 20% over a wide latitude range on the nightside of Venus. Hence, the variation in the H2O mixing ratio is not enough to explain the spatial variation in the NIMS channel 3 image. 3.5. Emissivity and Altitude [39] Probability densities of surface emissivity (after smoothing) for three different altitude ranges are shown in Figure 8. We can see a general trend of negative correlation between emissivity and altitude; the emissivity of lowlands is generally higher than that of highlands. Although it is difficult to discuss the difference in emissivity in local scales (e.g., a few hundred km) due to noise emerging from the declouding procedure, the majority of lowlands have higher emissivity than the majority of highlands. Whereas there is an uncertainty in the results due to the not well known parameters of the atmosphere, the difference of emisivity between lowlands and highlands is more than 0.3. [40] The temperature lapse rate of the lowermost atmosphere can cause an altitude-dependent bias in the surface emissivity estimation. When a larger temperature lapse rate was used in the surface emissivity estimation, emissivities of highlands and lowlands are estimated to be higher and lower, respectively. If we use the temperature profile in which the lapse rate of the lowermost atmosphere is about 1 K/km larger than that of VIRA, the difference in the estimated surface emissivity between lowlands and highlands will disappear. However, such a temperature profile

Figure 8. Surface emissivity distribution for three different altitude ranges. Solid curve is for lowlands (z < 0 km); dashed curve is for intermediate altitudes (0 km < z < 2 km); and dotted curve is for highlands (z > 2 km). is statically unstable compared with the adiabatic lapse rate [Seiff et al., 1985]. It is unlikely that unstable stratification is observed all over the nightside of Venus, since there is no heat source which maintains the superadiabatic lapse rate. [41] It is also unlikely that horizontal variations in the H2O abundance and/or atmospheric temperature cause an altitude-dependent bias in the surface emissivity estimation. If H2O abundance and/or atmospheric temperature vary in association with surface topography, the estimated emissivity might have a bias related to the altitude. However, no observation indicates that the variation in the H2O abundance or atmospheric temperature correlates with the surface topography. Thus, it is likely that there is a difference in surface emissivity between lowlands and highlands. [42] Although we will not argue a regional difference in emissivity, a regional analysis will be useful to test the validity of emissivity estimate independent from any uncertainties in the retrieval. For example, there is no reason to expect that the volcanoes and coronae would be granitic, particularly as they are geologically young (erupted during today’s dry Venus). It would be useful to plot the emissivity of the tesserae separately from the other highlands to see if they are similar. Also, a study on the correlation of morphology and retrieved emissivity would be interesting, since morphology of many volcanic structures allows us to infer the chemical composition of the lavas [e.g., Head et al., 1992]. Using the gravity data, we can examine whether there is a systematic difference in emissivity in the same altitude region between isostatically compensated and dynamically supported highlands [e.g., Smrekar and Phillips, 1991]. [43] It is worth noting that material properties such as grain size could alter the emissivity, which may correspond to surface roughness or age (older terrains may acquire impact debris over time). It is also possible some differences might be due to weathering or that older terrains are covered by more impact debris which subdues the spectral absorptions. Chemical weathering is a function of altitude, and might be important in connection with anomalous radar

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reflectivity [e.g., Klose et al., 1992; Schaefer and Fegley, 2004]. These issues will require further consideration. 3.6. CO2 Continuum Absorption [44] The opacities of pressure-induced continuum absorption are important and poorly known factor for calculating the emission from the lower levels of the Venus atmosphere. We used the cloud model of Pollack et al. [1993] to determine the continuum absorption coefficients, but these values are somewhat different from those used in other analyses [e.g., Be´zard et al., 1990; Pollack et al., 1993]. The emissivity contrast between highlands and lowlands revealed by our analysis will decrease with decreasing the continuum opacity. Since more radiation is absorbed above lowlands than above highlands, the decrease in the continuum opacity allows lowlands to emit more radiation. Therefore, smaller continuum opacity leads to an estimation of smaller emissivity in the lowlands. There is still a large uncertainty in the parameters of the clouds, and ambiguity between average cloud opacity and continuum opacity. If the total cloud opacity is larger than that of the Pollack’s cloud model, the emissivity difference between highlands and lowlands retrieved by our analysis may decrease or even disappear. [45] To analyze the thermal emission from the Venus nightside, Meadows and Crisp [1996] used a different approach for modeling the CO2 far wings, instead of a constant binary continuum absorption coefficient. They determined the coefficients of a sub-Lorentzian profile by fitting laboratory data. Assuming constant surface emissivity, they derived that temperature lapse rate is dynamically stable (subadiabatic) as opposed to the dynamically unstable (superadiabatic) lapse rate found in this study. If temperature of the Venusian lower atmosphere is horizontally uniform, the results of Meadows and Crisp [1996] indicates a trend of emissivity increasing with altitude. The difference between their and our results indicates that modeling of the CO2 far wings is crucial to sense the surface and lower atmosphere of Venus. [46] It is quite obvious that we need detailed modeling and more accurate laboratory measurements for pressureinduced continuum absorption. Although the approach of Meadows and Crisp [1996] is no less empirical than the constant binary coefficient, it may provide insights into the processes responsible for the CO2 continuum absorption. There is also certainly a need for constraining the total cloud opacity. Either by new in situ measurements or possibly by spectroscopy with higher spectral resolution, more reliable estimates of the Venusian surface and lower atmosphere will be obtained.

4. Implication for Venus Geology and Evolution [47] Near-infrared emissivity can be used to discriminate the rock compositions [e.g., Ross et al., 1969; Baird, 1984b]. The emissivity, , is related to the reflectivity, r, by Kirchhoff’s law ( = 1 r). The reflectance spectra at near-infrared wavelength is most sensitive to the presence of iron-bearing mafic minerals [e.g., Burns, 1993]. For mafic rocks with large amounts of mafic minerals, the emissivity around 1 mm is high; while for felsic rocks with small amounts of mafic minerals, the emissivity around 1 mm is low [e.g., Baird, 1984a].

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[48] Our results indicate that there is a significant difference in emissivity between highlands and lowlands. The relative difference in surface emissivity between lowlands and highlands are consistent with lowlands materials being composed of generally mafic rocks, whereas highlands materials are composed of generally felsic rocks. We cannot yet determine which value of corresponds to which types of rocks, such as basalt, andesite, and granite. Nevertheless, the observed large disparity in between the lowland and highland is very difficult to be accounted for by the emissivity variation of basalts alone but requires presence of more felsic rocks, such as granites [e.g., Hashimoto and Sugita, 2003]. [49] This inference is consistent with several observations. The radar images of the Venusian surface by Magellan revealed that most landforms in the lowland plains suggest low-viscosity materials, which are characteristic of mafic magmas [e.g., Head et al., 1992]. The data of Venera and Vega landers are interpreted as that lowland materials are dominated by mafic rocks [e.g., Pieters et al., 1986; Kargel et al., 1993]. The inferred composition contrast between lowlands and highlands is also consistent with the principle of isostasy that is the application of Archimedes’ principle to the crust. Isostatic compensation at the surface can be achieved either by variation of crustal density or by variation of thickness of homogeneous crust. The estimated emissivity indicates that highland materials are less dense than lowland materials, since density of mafic rocks is higher than that of felsic rocks. [50] One interpretation for the felsic highland crust is that highlands are composed of granitic rocks. The presence of granitic rocks would imply that Venus had have an ocean and subduction in the past, since it has been suggested that remelting of oceanic crust combined with water along subduction would have caused the formation of granitic magmas [e.g., Campbell and Taylor, 1983]. If Venus had been an Earth-like planet where the surface was covered with ocean and subduction processes were working, granitic magmas would be generated like the present Earth. Although the present Venus is a dry-planet where granitic magmas would not be generated any more, ancient continents might have survived until today. [51] A wet origin for Venus is also suggested by numerical simulations of late-stage accretion which begins after the end of oligarchic growth of planetary embryos [e.g., Morbidelli et al., 2000; Raymond et al., 2004]. Such models indicated that Venus’s initial endowment of volatiles is likely similar to that of Earth. In fact, there is similarities between Venus and Earth in the near-surface inventories of carbon and nitrogen [e.g., Lećuyer et al., 2000]. It indicates that Venus’s inventory of water was once similar to that of the Earth at the end of planetesimal accretion. In addition, loss of Venus’s hydrogen, that is necessary for early wet Venus to evolve into the current dry Venus, is suggested by the hydrogen isotopic ratio (D/H) measured in the atmosphere of Venus [e.g., DeBergh et al., 1991; Donahue and Hodges, 1992]. The factor of 100 enrichment in D/H compared to the Earth’s ocean is generally considered to be evidence that Venus started with at least 100 times as much hydrogen as it has now [Donahue et al., 1982]. Since early Sun’s luminosity is fainter than the present, early Venus

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would have avoided going into a state of runaway greenhouse [e.g., Kasting, 1988]. If Venus was endowed with sufficient water, early Venus would have had an ocean for more than a billion years [Hashimoto et al., 2007]. [52] It would be an important future work to look for other evidences of ancient ocean on Venus. Unfortunately, we cannot infer the ancient Venus from the observation of Venusian surface morphology, since the age of the Venus surface is no more than 1 billion years [e.g., Basilevsky et al., 1997, and references therein]. However, chemical signatures of water such as hydrous minerals may remain over a longer time period [e.g., Johnson and Fegley, 2000], even as the surface morphology has been erased. It is also reasonable to expect that ancient ocean has left its traces in the planetary interior, since the presence of water should have a great influence on the evolution of planets through controlling plate tectonics and mantle convection.

5. Summary [53] We evaluated the emissivity of the Venus surface at 1.18 mm wavelength using multispectral images obtained by the Galileo NIMS. Although the surface of Venus is shrouded in a thick atmosphere and clouds, our declouding procedure adequately remove the influence of clouds. Our analysis has revealed that the emissivity of lowlands is generally higher than that of highlands. This observation indicates that highland materials are generally composed of felsic rocks, while lowland materials are dominated by mafic rocks. Such a composition contrast between lowlands and highlands is consistent with the principle of isostasy. Since formation of granitic magmas is likely related to water, there is an implication of ancient wet Venus in the presence of granitic terrains. [54] It would be an important future work to confirm the presence of granitic rocks with the intention of revealing the history of the Venus surface environment. There are five spectral windows in the Venus atmosphere between 0.85 and 1.18 mm that are sensitive to the surface property. We will be able to evaluate the surface emissivity at these spectral windows in the same way as described in this paper. Such spectral information will help us estimate the mineralogic composition of the Venus surface. [55] It is also worth mentioning that the declouding procedure used in this study is also useful in retrieving temperature profiles in the Venus lower atmosphere. There are several spectral windows in the near-infrared wavelengths that can sense temperatures at several altitude levels below the cloud top. A multilevel global temperature fields retrieved from multiple-wavelength images will provide new insights into the general circulation of the Venus atmosphere. [56] Acknowledgments. The authors express their thanks to anonymous reviewers for their constructive comments on this manuscript. G.L.H. would like to thank Teruyuki Nakajima for detailed discussion of radiative transfer modeling, Naomoto Iwagami for helpful discussion of gas absorption, Yutaka Abe for fruitful discussion of early evolution of terrestrial planets, Yukari Tsutsumi for her help in developing computational code, and Constantine Tsang for his technical aid in preparing figures. G.L.H. and S.S. thank Grants-in-Aid for Scientific Research and The 21st Century COE Program of Origin and Evolution of Planetary Systems of Ministry of Education, Culture, Sports, Science and Technology (MEXT) for support. M.R.S. is supported by Portuguese FCT project PDCTE/FNU/49822/2003 and FCT scholarship BSAB/584/2006.

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Hashimoto, G. L., and T. Imamura (2001), Elucidating the rate of volcanism on Venus: Detection of lava eruptions using near-infrared observations, Icarus, 154, 239 – 243. Hashimoto, G. L., and S. Sugita (2003), On observing the compositional variability of the surface of Venus using nightside near-infrared thermal radiation, J. Geophys. Res., 108(E9), 5109, doi:10.1029/2003JE002082. Hashimoto, G. L., Y. Abe, and S. Sugita (2007), The chemical composition of the early terrestrial atmosphere: Formation of a reducing atmosphere from CI-like material, J. Geophys. Res., 112, E05010, doi:10.1029/ 2006JE002844. Head, J. W., L. S. Crumpler, J. C. Aubele, J. E. Guest, and R. S. Saunders (1992), Venus volcanism: Classification of volcanic features and structures, associations, and global distribution from Magellan data, J. Geophys. Res., 97, 13,153 – 13,198. Johnson, N. M., and B. Fegley Jr. (2000), Water on Venus: New insights from tremolite decomposition, Icarus, 146, 301 – 306. Kargel, J. S., G. Komatsu, V. R. Baker, and R. G. Strom (1993), The volcanology of Venera and VEGA landing sites and the geochemistry of Venus, Icarus, 103, 253 – 275. Kasting, J. F. (1988), Runaway and moist greenhouse atmospheres and the evolution of Earth and Venus, Icarus, 74, 472 – 494. Keady, J. J., and D. P. Kilcrease (2000), Radiation, in Allen’s Astrophysical Quantities, edited by A. N. Cox, pp. 95 – 120, Springer, New York. Klose, K. B., J. A. Wood, and A. Hashimoto (1992), Mineral equilibria and the high radar reflectivity of Venus mountaintops, J. Geophys. Res., 97, 16,353 – 16,369. Knollenberg, R. G., and D. M. Hunten (1980), The microphysics of the clouds of Venus: Results of the Pioneer Venus particle size spectrometer experiment, J. Geophys. Res., 85, 8039 – 8058. Lecacheux, J., P. Drossart, P. Laques, F. Deladerrie`re, and F. Colas (1993), Detection of the surface of Venus at 1.0 mm from ground-based observations, Planet. Space Sci., 41, 543 – 549. Lećuyer, C., L. Simon, and F. Guyot (2000), Comparison of carbon, nitrogen and water budgets on Venus and the Earth, Earth Planet. Sci. Lett., 181, 33 – 40. Marov, M. Y., V. E. Lystsev, V. N. Lebedev, N. L. Lukashevich, and V. P. Shari (1980), The structure and microphysical properties of the Venus clouds: Venera 9, 10 and 11 data, Icarus, 44, 608 – 639. Meadows, V. S., and D. Crisp (1996), Ground-based near-infrared observations of the Venus nightside: The thermal structure and water abundance near the surface, J. Geophys. Res., 101, 4595 – 4622. Morbidelli, A., J. Chambers, J. I. Lunine, J. M. Petit, F. Robert, G. B. Valsecchi, and K. E. Cyr (2000), Source regions and timescales for the delivery of water to the Earth, Meteorit. Planet. Sci., 35, 1309 – 1320. Moroz, V. I. (2002), Estimates of visibility of the surface of Venus from descent probes and balloons, Planet. Space Sci., 50, 287 – 297. Palmer, K. F., and D. Williams (1975), Optical constants of sulfuric acid: Application to the clouds of Venus?, Appl. Opt., 14, 208 – 219. Penndorf, R. (1957), Tables of the refractive index for standard air and the Rayleigh scattering coefficient for the spectral region between 0.2 and 20.0 m and their application to atmospheric optics, J. Opt. Soc. Am., 47, 176 – 182. Pettengill, G. H., P. G. Ford, and S. Nozette (1982), Venus: Global surface radar reflectivity, Science, 217, 640 – 642. Pettengill, G. H., P. G. Ford, W. T. K. Johnson, R. K. Raney, and L. A. Soderblom (1991), Magellan: Radar performance and data products, Science, 252, 260 – 265. Pettengill, G. H., P. G. Ford, and R. J. Wilt (1992), Venus surface radiothermal emission as observed by Magellan, J. Geophys. Res., 97, 13,091 – 13,102. Pettengill, G. H., B. A. Campbell, D. B. Campbell, and R. A. Simpson (1997), Surface scattering and dielectric properties, in Venus II, edited by S. W. Bougher, D. M. Hunten, and R. J. Phillips, pp. 527 – 546, Univ. of Ariz. Press, Tucson.

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Pieters, C. M., et al. (1986), The color of the surface of Venus, Science, 234, 1379 – 1383. Pollack, J. B., et al. (1993), Near-infrared light from Venus’ nightside: A spectroscopic analysis, Icarus, 103, 1 – 42. Ragent, B., and J. Blamont (1980), The structure of the clouds of Venus: Results of the Pioneer Venus nephelometer experiment, J. Geophys. Res., 85, 8015 – 8089. Raymond, S. N., T. Quinn, and J. I. Lunine (2004), Making other earths: Dynamical simulations of terrestrial planet formation and water delivery, Icarus, 168, 1 – 17. Ross, H. P., J. E. M. Adler, and G. R. Hunt (1969), A statistical analysis of the reflectance of igneous rocks from 0.2 to 2.65 microns, Icarus, 11, 46 – 54. Rothman, L. S., et al. (2003), The HITRAN molecular spectroscopic database: Edition of 200 0 including updates through 2001, J. Quant. Spectrosc. Radiat. Transfer, 60, 665 – 710. Schaefer, L., and B. Fegley Jr. (2004), Heavy metal frost on Venus, Icarus, 168, 215 – 219. Seiff, A. (1983), Thermal structure of the atmosphere of Venus, in Venus, edited by D. M. Hunten et al., pp. 215 – 279, Univ. of Ariz. Press, Tucson. Seiff, A., J. T. Schofield, A. J. Kliore, F. W. Taylor, S. S. Limaye, H. E. Revercomb, L. A. Sromovsky, V. V. Kerzhanovich, V. I. Moroz, and M. Y. Marov (1985), Models of the structure of the atmosphere of Venus from the surface to 100 kilometers altitude, Adv. Space Res., 5, 3 – 58. Smrekar, S. E., and R. J. Phillips (1991), Venusian highlands — Geoid to topography ratios and their implications, Earth Planet. Sci. Lett., 107, 582 – 597. Stone, P. H. (1975), The dynamics of the atmosphere of Venus, J. Atmos. Sci., 32, 1005 – 1016. Surkov, Y. A., V. L. Barsukov, L. P. Moskalyeva, V. P. Kharyukova, and A. L. Kemurdzhian (1984), New data on the composition, structure, and properties of Venus rock obtained by Venera 13 and Venera 14, Proc. Lunar Planet. Sci. Conf. 14th, Part 2, J. Geophys. Res., 89, suppl., B393 – B402. Surkov, Y. A., L. P. Moskalyova, V. P. Kharyukova, A. D. Dudin, G. G. Smirnov, and S. Y. Zaitseva (1986), Venus rock composition at the Vega 2 landing site, Proc. Lunar Planet. Sci. Conf. 17th, Part 1, J. Geophys. Res., 91, suppl., E215 – E218. Surkov, Y. A., F. F. Kirnozov, V. N. Glazov, A. G. Dunchenko, L. P. Tatsy, and O. P. Sobornov (1987), Uranium, thorium, and potassium in the Venusian rocks at the landing sites of Vega 1 and 2, Proc. Lunar Planet. Sci. Conf. 17th, Part 2, J. Geophys. Res., 92, suppl., E537 – E540. Taylor, F. W., D. Crisp, and B. Be´zard (1997), Near-infrared sounding of the lower atmosphere of Venus, in Venus II, edited by S. W. Bougher, D. M. Hunten, and R. J. Phillips, pp. 325 – 351, Univ. of Ariz. Press, Tucson. Vardavas, I. M., and J. H. Carver (1984), Solar and terrestrial parameterizations for radiative-convective models, Planet. Space Sci., 32, 1307 – 1325.

K. H. Baines, R. W. Carlson, and L. W. Kamp, Jet Propulsion Laboratory, California Institute of Technology, M/S 183-601, 4800 Oak Grove Drive, Pasadena, CA 91109, USA. ([email protected]; rcarlson@lively. jpl.nasa.gov; [email protected]) M. S. Gilmore, Department of Earth and Environmental Sciences, Wesleyan University, 265 Church Street, Middletown, CT 06459, USA. ([email protected]) G. L. Hashimoto, Laboratory for Earth and Planetary Atmospheric Science, Organization of Advanced Science and Technology, Kobe University, 1-1 Rokkodai, Nada, Kobe 657-8501, Japan. ([email protected]) M. Roos-Serote, Lisbon Astronomical Observatory, Tapada da Ajuda, P-1349-018, Lisbon, Portugal. ([email protected]) S. Sugita, Department of Complexity Science and Engineering, Graduate School of Frontier Science, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan. ([email protected])

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Climate evolution of Venus F. Taylor1 and D. Grinspoon2 Received 19 December 2008; revised 5 March 2009; accepted 16 March 2009; published 20 May 2009.

[1] The processes in the atmosphere, interior, surface, and near-space environment that

together maintain the climate on Venus are examined from the specific point of view of the advances that are possible with new data from Venus Express and improved evolutionary climate models. Particular difficulties, opportunities, and prospects for the next generation of missions to Venus are also discussed. Citation: Taylor, F., and D. Grinspoon (2009), Climate evolution of Venus, J. Geophys. Res., 114, E00B40, doi:10.1029/2008JE003316.

1. Introduction [2] A dramatically hot and dry climate is found on Venus at the present time, probably the result of the atmosphere following a divergent evolutionary path from a more Earthlike beginning. A key question is to what extent the two planets and their early inventories of gases and volatiles were physically alike, given their common origin in the same region of the young Solar System. Venus and Earth have nearly the same size and mass, and most current models suggest they have similar chemical compositions and interior structures. However, factors such as the small discrepancy in mean density (after allowing for compressional effects, see Ringwood and Anderson [1977]), and the absence of an internal dynamo [Luhmann and Russell, 1997], as well as discrepancies in the abundances of the noble gases [Pepin, 2006], fuel a lively debate about the extent to which the two planets can be considered to be a close match owing to essentially identical origins. The common assumption of identical origins is also clouded by the possibility of stochastic variations in late accretion history leading to unequal volatile inventories [Morbidelli et al., 2000] or volatile loss and interior processing through catastrophic early impacts [Davies, 2008; Zahnle, 2006; Alemi and Stevenson, 2006]. Even if we understood these issues, deriving the path and time scales of Venus’ divergent evolution to its present state would still present numerous challenges, not least in terms of explaining the high temperature and pressure and low water abundance at the surface. [3] Taylor et al. [2007, p. 160] posed a thought experiment: Suppose Venus and Earth had been swapped at birth – that is, at the time when they had accumulated virtually all of their present mass but before their atmospheres were fully evolved. Venus, with its slow retrograde rotation would then be at one astronomical unit from the Sun, and Earth somewhat closer. Venus would still have any bulk compositional differences it may have acquired as a result of forming at the closer position to the center of the protosolar cloud. What would this part of the Solar System look like today?

1 2

Department of Physics, Oxford University, Oxford, UK. Denver Museum of Nature and Science, Denver, Colorado, USA.


In particular, would the atmospheres of Earth and Venus still be as dramatically different in composition, surface temperature and pressure? Would a repositioned Earth, its climate provoked by twice as much solar irradiance and its atmosphere exposed to a more aggressive solar wind, have developed a climate like that of present-day Venus? Would Venus, left more to its own devices at 1 AU from the Sun, be the planet with plate tectonics and oceans, and the one whose inhabitants were conducting our current explorations? To what extent might the chance formation of Earth’s singular moon have broken any potential symmetry? [4] In seeking to understand questions like this about the formation and initial states of the two planets, and how and by what stages their atmospheres progressed to the presently observed conditions, considerable use is made of the findings of each mission flown to Venus by spacecraft. Many of the measurements still needed (for instance, seismic determinations of the interior structure of Venus) will require substantial investment in new technologies, while other important investigations (such as the history and present activity of volcanism) can be addressed with available techniques. In this paper, we look at the current state of understanding of the atmosphere, interior, and nearspace environment of Venus, and the interactions between them that produce the climate at the surface and control its evolution (Figure 1). We review actual and potential progress on these topics, particularly that beginning to be made in the light of new results from the European Venus Express mission [Svedhem et al., 2009], and the important gaps that could be addressed by future planned projects. [5] The plan of the paper is as follows. Section 2 summarizes briefly what is known about the present-day climate, implicitly defined as the state of the atmosphere, and the main processes thought to be responsible for the energy balance and stability of the system. In section 3, some major topics relevant to possible long-term changes in the stability of the atmosphere are tackled, in particular the state of the interior of Venus, its thermal evolution and consequent resurfacing rates and outgassing into the atmosphere; surface-atmosphere chemical reactions and possible equilibrium states; and the loss processes at the top of the atmosphere, in particular as they concern the history of water on Venus. These are all very uncertain and hard to understand with the current very limited measurement base,

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However, the internal processes leading to critical climate elements like volcanism, global tectonics and magnetic field generation seem to be quite different, at least in outcome. New data from missions more advanced and challenging than anything attempted to date will be needed to address these; they are the subject of section 5.

2. Current Atmosphere and Climate

Figure 1. A conceptualization of the problem addressed in this paper and the formidable nature of its long-term goals. The properties and processes in and between the interior, surface, atmosphere, and near-space environment are all known to be important for determining the climate, in particular, the surface temperature. We want to understand these and their interrelationships not only for current conditions, but also throughout time from the formation of the planet to some future state when Venus may become more, or less, Earth-like. Each box in the diagram can be expanded into a range of different disciplines; for instance, the role of the atmosphere depends on its radiative transfer properties, on heterogeneous chemistry involving gas-liquid and gas-solid reactions, and on the circulation and dynamics. A further hidden dimension exists in the form of the same matrix for the Earth, and perhaps Mars also, and their relationship to Venus through common origins and processes. but incremental advances are made with each successful mission, and Venus Express was developed with the goal of making a key contribution. Section 4 reviews the incorporation of key processes into evolutionary climate models, which, in principle at least, are capable of simulating the present-day climate and showing how this may have developed over the history of the Solar System to date. Such models can also be used, within their limitations, to obtain some idea of how the climate may evolve in the future. A further dimension is added by our wish to understand, within the same physical framework, the important similarities and differences between this climate system and the corresponding one for the Earth (and also Mars and Titan, although we will not consider them in depth here). [6] The tools at our disposal for Venus climate modeling include not only the fruits of previous Venus missions and studies but also the methods that have been derived as part of the huge contemporary effort to predict the evolution of climate on the Earth. While the current emphasis in the latter is mainly on the very short term, decades to centuries, the similarities in size, composition, internal, and atmospheric processes mean that much of the same physics applies. Important contributions to the surface temperature result from an atmospheric CO2 – H2O – aerosol driven greenhouse effect, for example, and the input of solar energy to the climate system on each planet is similar.

2.1. Historical Background [7] Before the space age, many astronomers expected that the surface environment on Venus would resemble a more tropical version of the Earth. The Swedish Nobel Laureate Svante Arrhenius wrote nearly a century ago that the surface temperature ‘‘is calculated to be about 47°C’’, compared to an average of around 26°C in the Congo on Earth, and he inferred that the humidity on Venus is about six times higher than on Earth [Arrhenius, 1918]. Patrick Moore, in his book The Planet Venus, published in 1954, wrote that Venus could be a world in a ‘‘Cambrian’’ state, possibly complete with primitive organisms. At about the same time, however, Urey [1952, p. 222] noted that ‘‘the presence of carbon dioxide in the planet’s atmosphere is very hard to understand unless water were originally present, and it would be impossible to understand if water were present now.’’ [8] Beginning in 1956 at the U.S. Naval Research Laboratory, Earth-based microwave observations of Venus showed that the planet had an equivalent blackbody temperature of about 575 K. The scientists planning the microwave radiometer to be carried on the first spacecraft mission to Venus, Mariner 2, took this to be the probable surface temperature of the planet and, weighing up all of the available observational and theoretical evidence, planned their experiment around atmospheric models in which the surface pressure ranged from 2 to 20 bars and the composition was 75% CO2, 24% N2, and 1% H2O [Barrett et al., 1961]. The results from the experiment confirmed a high surface temperature [Barath et al., 1964] and the first direct measurements by the lander Venera 4 in 1967 delivered an improved estimate of around 675 K. Modern values for the mean surface temperature on Venus are around 730 K, which is higher than the melting point of the metals lead and tin, with excursions of more than 100 K, due primarily to topography. [9] Much of our current knowledge of the details of the Venus atmosphere and climate system was accrued by the Pioneer Venus orbiter and entry probe missions of the late 1970s and early 1980s [Russell, 1992]. Four probes sounded the clouds and lower atmosphere, returning chemical, physical, and meteorological data on the Venus atmosphere. The orbiter observed the surface of Venus with a radar altimeter and sounded the atmosphere in the infrared and ultraviolet regions of the spectrum. It also provided in situ data on the upper atmosphere, ionosphere and solar wind interaction. [10] After the Soviet Venera and VEGA missions in the early 1980s (notwithstanding the Magellan surface mapping mission which arrived at Venus in August 1990) there was a gap of 2 decades before another mission with an atmospheric focus was launched. In May 2006 Venus Express became the 28th spacecraft to arrive successfully at Venus since Mariner 2 in 1962, and the first mission to employ the near infrared spectral windows, discovered in the 1980s (see

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Figure 2. A comparison of measured atmospheretemperature profiles on Earth and Venus, where the vertical scale is pressure in millibars (1000 mb equals the mean surface pressure on Earth). The solid line is derived from remote sounding measurements made by the Pioneer Venus Orbiter Infrared Radiometer [Taylor et al., 1979a], extrapolated assuming a dry adiabatic lapse rate below 500 mb, and the dashed profile is derived from similar measurements by the Improved Stratospheric and Mesospheric Sounder on the Upper Atmosphere Research Satellite [Taylor et al., 1993]. the review by Taylor et al. [1997]) from orbit to carry out systematic remote sensing observations of the Venusian atmosphere below the clouds. Potential climate-related advances and serendipitous discoveries were to be used, inter alia, as a basis for (1) producing improved greenhouse models of the energy balance in the lower atmosphere, (2) validating and improving general circulation models of the atmosphere, with improved treatment of the zonal superrotation, the meridional Hadley circulation, and the polar vortices, (3) generating new climate evolution models using simple physics and chemistry constrained by measurements, and (4) comparative studies in all three areas with the other terrestrial planets including Earth. 2.2. Atmospheric Temperature Structure [11] The Bond albedo of Venus is around 2.5 times that of Earth (about 0.76 compared to about 0.3), so that Venus absorbs rather less radiative energy than Earth, despite its greater proximity to the Sun. The net effect of this, and the differences in atmospheric composition, is that the temperature profiles for the atmospheres of Earth and Venus are actually quite similar in the range where they overlap in pressure (Figure 2). The main difference is due to the effect of radiative heating in the Earth’s ozone layer, which produces a local maximum near the 1 mb pressure level that has no equivalent on Venus. [12] The remarkably un-Earth-like climate at Venus’ surface is a consequence of the fact that the pressure, and hence the temperature, both continue to rise with depth below the 1 bar level. The profile roughly follows the

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hydrostatic and adiabatic formulae, as would be expected, leading to a temperature increase of about 10 K for each km of depth below the 1 bar level. This amounts to some 450 K altogether at Venus’ surface pressure of 92 bars. If the Earth had such a high surface pressure, it too would be extremely hot, even without the increased proportion of greenhouse gases that is found on Venus. About 96% of this is carbon dioxide, which, along with water vapor and other minor constituents, and the net radiative effect of the ubiquitous cloud cover, drives the radiative energy balance at the surface in the direction of elevated temperatures. [ 13 ] Measured temperature profiles for both Venus and Earth conform reasonably well to the predictions of radiative-convective model calculations like those discussed in section 4. This confirms that the processes at work are basically the same in both cases and that, unlike many aspects of the climate on Venus, there are no mysteries, at least to first order. The factor that was so surprising when it was first discovered, the high surface temperature on Venus, is largely a consequence of the large mass of the atmosphere, rather than any mysterious thermal process. As discussed below, the atmospheric bulk may not be too surprising either, provided we can account for the history of water on Venus. [14] Conditions in the upper atmosphere are crucial for determining loss rates for atmospheric species and hence understanding the composition as a function of time, specifically issues such as the long-term water budget and the evolution of the atmospheric oxidation state and surface pressure. A number of processes are involved: dissociation, ionization, thermal and nonthermal escape, solar wind and cosmic ray erosion, meteoritic and cometary impacts. For determining the nature and extent of current losses, key measurements are temperature and composition as a function of height in the upper atmosphere, and the abundance and distribution of atoms and ions of atmospheric origin in the magnetosphere (Figure 3). [15] Temperature profiles from 80 to 140 km altitude deduced from stellar occultations observed by the SPICAV instrument on Venus Express, combined with previous measurements, show the maximum temperature (90 – 100 km), increasing with the value of solar zenith angle. A sharp maximum is seen in the temperature profile near the antisolar point, corresponding to the adiabatic heating expected in the subsolar to antisolar circulation regime that occurs above the mesopause at around 90 km [Bertaux et al., 2007]. Overall, the thermosphere of Venus is cooler than Earth’s, because of the greater abundance of carbon dioxide, which is very efficient at radiating heat to space. Above about 150 km, the temperature is approximately constant with height on the dayside at about 300 K. The terrestrial thermosphere is the seat of rapid winds, up to 1000 m s1 or more, and this tends to redistribute energy originally absorbed from the Sun over the dark as well as the sunlit hemisphere. The result is a day-night difference of around 200 K about a mean temperature of 1000 K. On Venus however, the nighttime temperature in the thermosphere is very low, around 100 K, in contrast to 300 K on the dayside. The transition from the day to nightside values of temperature on Venus also show remarkably steep gradients [Keating et al., 1979] and modelers have great difficulty

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Figure 3. Thermal structure, composition, and cloud measurements have defined the main parameters of the circulation and other key features of the climate system, as illustrated schematically here and discussed in the text. in reproducing both the minimum temperature and the short distance across the terminator with which it is attained. 2.3. Atmospheric Composition [16] The primordial atmosphere of Venus that originally formed with the solid body, like those of the other terrestrial planets, will have been lost or radically altered in the distant past as both the young Sun and the hot, postaccretional young planets went through phases of high activity. The present atmosphere would have been produced much later by outgassing associated with volcanism, a process that may still be ongoing on Venus, as it is to some extent on Earth, augmented by the influx of unknown amounts of cometary and meteoritic material. The relative contributions of these distinct sources can, to some extent, be deduced from the data which are gradually being accrued on the composition, and in particular the isotopic ratios, in the contemporary terrestrial planet atmospheres, and in comets and meteorites. [17] Comets are a rich source of volatile compounds such as carbon dioxide, water vapor, methane, and ammonia. If the last of these was the source of the nitrogen now present, allowing for processes such as the production of argon by the decay of radioactive potassium in the crust, the contemporary atmosphere could all be of external origin. On the other hand, the high abundance of sulfur in Venus’

clouds indicates extensive volcanic activity, as discussed below. Volcanoes are also prolific sources of carbon dioxide and the other gases required to explain the present-day climate of Venus, SO2 and H2O in particular. [18] Apart from carbon dioxide and water vapor, Venus’ atmosphere consists primarily of inert gases, particularly nitrogen and argon (Table 1). The amount of water present as gas and bound up with sulfuric acid and other compounds in the clouds is roughly one hundred thousand times less than exists in the oceans and atmosphere of the Earth. Thus, assuming most of the primordial water is not retained in the interior, Venus is overall very dry compared to the Earth while, at the same time, deuterium is more than one hundred times more abundant on Venus than Earth. This suggests that large amounts of water have escaped and that, unless the inventory is dominated by nonprimordial sources, Venus had much more water initially [Grinspoon, 1993; Donahue, 1999]. Loss takes place by dissociation of the water in the upper atmosphere by solar ultraviolet radiation, and the subsequent escape of the hydrogen. Both deuterium and normal hydrogen escape from the atmosphere, but the heavier isotope escapes less efficiently, leading to the observed fractionation. [19] The loss rate of the water depends strongly on its abundance in the relatively cool middle atmosphere, as well

Table 1. Composition of the Atmospheres of Venus and Earth as Fractional Abundances Except Where Parts Per Billion Is Stateda Species

Venus

Earth

Climate Significance

Carbon dioxide Nitrogen Argon Neon Water vapor Sulfur dioxide Carbonyl sulfide Carbon monoxide Atomic oxygen Hydroxyl Atomic hydrogen

0.96 0.035 0.00007 0.000005 0.000030 0.00015 0.000004 0.00004 trace trace trace

0.0003 0.770 0.0093 0.000018 .01 0.2 ppb

Major greenhouse gas Similar total amounts Evolutionary clues Evolutionary clues Volcanic, cloud, greenhouse Volcanic, cloud, greenhouse Volcanic, cloud Deep circulation High circulation, escape processes High circulation, escape processes Escape processes

a

0.00000012 trace trace trace

All except the noble gases argon and neon are observed by Venus Express instruments.

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as the intensity of the solar ultraviolet flux. Models of the process suggest, with considerable uncertainty however, that Venus could have lost an ocean of present-day terrestrial proportions in only a few hundred million years [Kasting et al., 1984]. Such potentially important processes as cloud-albedo feedback in the water-rich early atmosphere have yet to be included in models of early water loss from Venus. The oxygen produced at the same time is too massive to escape at any significant rate, according to Jeans’ formula, and would remain on the planet, presumably most of it bound chemically within the crust, if thermal escape were the only process available to remove it. However, recent results from the ASPERA instrument on Venus Express show that oxygen is escaping at a rate nearly half that of the hydrogen escape flux, suggesting that large amounts of O could have escaped over time through nonthermal processes. So long as liquid water remained available, the formation of carbonates would remove atmospheric carbon dioxide efficiently, as it does on the Earth. Once the surface water was gone, the mixing ratio of water vapor in the upper atmosphere would have fallen sharply and the loss rates of both forms of hydrogen, and the take up of oxygen into minerals, would have begun declining toward the present relatively low levels. With the loss of water, the removal mechanism for CO2 would be eliminated, and carbonate rocks on the surface would presumably eventually be subducted and lost to thermal decomposition, with the CO2 being irreversibly returned to the atmosphere through outgassing. [20] In the modern atmosphere of Venus, chemical cycles coupled with transport and radiative processes control the abundances of the minor constituents. The most important are the cycles involving water vapor, sulfuric acid, and their products, which maintain the cloud layers and probably also involve reactions between the atmosphere and the surface. Another is the sequence that gives rise to the observed distribution of carbon monoxide. CO is very abundant (mixing ratios on the order of a few parts per thousand by volume) in the upper atmosphere of Venus, as would be expected from the action of solar ultraviolet radiation on carbon dioxide. It is strongly depleted in the cloud layers (2.0.CO;2. Zahnle, K. J. (2006), Earth after the Moon forming impact, Geochim. Cosmochim. Acta, 70, A729, doi:10.1016/j.gca.2006.06.1311.

D. Grinspoon, Denver Museum of Nature and Science, 2001 Colorado Boulevard, Denver, CO 80205, USA. F. Taylor, Department of Physics, Oxford University, Oxford OX1 3PU, UK. ([email protected])

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Retrieval of air temperature profiles in the Venusian mesosphere from VIRTIS-M data: Description and validation of algorithms Davide Grassi,1 P. Drossart,2 G. Piccioni,3 N. I. Ignatiev,4 L. V. Zasova,4 A. Adriani,1 M. L. Moriconi,5 P. G. J. Irwin,6 A. Negraõ,1,7 and A. Migliorini3 Received 14 January 2008; revised 9 May 2008; accepted 2 June 2008; published 1 October 2008.

[1] We present here methods developed for the retrieval of air temperature profiles in the

Venusian mesosphere from the absolute radiances measured by the Visual and Infrared Thermal Imaging Spectrometer (VIRTIS) on board the Venus Express satellite. The infrared M channel of the instrument acquires multispectral images between 1000 and 5000 nm. In nighttime measurements, radiance in the range 3800–5000 nm is dominated by the thermal emission and absorption by the clouds and carbon dioxide. Since the latter is the main atmospheric component, it is possible to exploit the strong variability of its opacity in this spectral range, as resolved by the instrument, to reconstruct the vertical air temperature profile as a function of pressure. In this context we decided to adopt the Twomey et al. (1977) relaxation scheme. The resulting code was extensively tested on a set of simulated VIRTIS-M data. Comparison of the known input conditions with the results of analysis code allowed us to evaluate the systematic and random errors affecting the retrievals procedures on a statistical basis. The code returns the vertical air temperature profile with an uncertainty of less than 1 K in the region between 70 and 7 mbar (66 and 77 km above the reference surface) and less than 4 K throughout the entire range 100–0.1 mbar (64–95 km). Finally, we present the first examples of the code applied to actual measured Venusian data, demonstrating its capability to achieve a satisfactory modeling of the observations and provide physically reasonable results. Citation: Grassi, D., P. Drossart, G. Piccioni, N. I. Ignatiev, L. V. Zasova, A. Adriani, M. L. Moriconi, P. G. J. Irwin, A. Negraõ, and A. Migliorini (2008), Retrieval of air temperature profiles in the Venusian mesosphere from VIRTIS-M data: Description and validation of algorithms, J. Geophys. Res., 113, E00B09, doi:10.1029/2008JE003075.

1. Introduction [2] The Visual and Infrared Thermal Imaging Spectrometer (VIRTIS) is one of the experiments included in the scientific payload of the Venus Express ESA mission [Titov et al., 2006]. Its design is largely based on the heritage of the twin instrument on board the ESA Rosetta satellite. The instrument actually consists of an ensemble of different

1 Istituto di Fisica dello Spazio Interplanetario, Istituto Nazionale di Astrofisica, Rome, Italy. 2 Laboratoire d’Etudes Spatiales et d’Instrumentation en Astrophysique, Observatoire de Paris, Universite´ Pierre et Marie Curie, Universite´ Paris 7, CNRS, Meudon, France. 3 Istituto di Astrofisica Spaziale e Fisica Cosmica, Istituto Nazionale di Astrofisica, Rome, Italy. 4 Space Research Institute, Russian Academy of Sciences, Moscow, Russia. 5 Istituto di Scienze dell’Atmosfera e del Clima, Consiglio Nazionale delle Richerche, Rome, Italy. 6 Clarendon Laboratory, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK. 7 Faculdade de Engenharia da Universidade do Porto, Porto, Portugal.

subsystems, with specific measuring capabilities. VIRTIS-H is a high-resolution grating spectrometer operating in the range 3000 –5000 nm, with a typical resolution and sampling step of 1.5 nm (variable along the range and with spectral order). VIRTIS-M is a spectro-imager, able to acquire a stack of monochromatic images that allow the spectrum to be reconstructed for each pixel. VIRTIS-M operates simultaneously in the visible and infrared part of the spectrum. The ranges covered are 280– 1100 and 1000– 5000 nm, with sampling steps of 19 and 11 nm, respectively. The VIRTIS-M and -H instantaneous fields of view (IFOV) are 0.25 0.25 mrad (for an individual pixel) and 1.74 0.58 mrad. These figures, together with the Venus Express orbital parameters, lead to a horizontal resolution for individual pixels of 16.5 16.5 and 115 38 km, respectively, in the case of measurements acquired at the apocenter. A complete description of the instrument and its radiometric performances is given by Piccioni et al. [2007a]. The noise equivalent radiance pertinent to each measurement depends on several factors such as the instrument temperature and exposure time. For the purpose of this study, a value of 5104 erg/(sec. cm2 ster. nm) at 4300 nm was assumed.


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Figure 1. Example of a VIRTIS-M IR spectrum acquired on the Venus nightside. [3] Once calibrated, the radiation field measured by VIRTIS in orbit around Venus is given by the radiative transfer equation [Hanel et al., 2003, chap. 4]:

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environment has been demonstrated by the wide literature derived from the data acquired by Pioneer Venus Infrared Radiometer and Venera 15 Fourier Transform Spectrometer (FTS). Some examples include a first study of air temperature fields in the mesosphere as a function of latitude and altitude [Taylor et al., 1980; Moroz et al., 1986], aerosol vertical distributions [Zasova et al., 1999], and water vapor mapping [Ignatiev et al., 1999]. The experience accumulated by the corresponding scientific teams represents therefore the fundamental starting point for this kind of study. [5] The full exploitation of the VIRTIS data is a task well beyond the ambition of an individual article. In this paper we present the first successful retrievals derived from the VIRTISM IR subsystem data, aiming to determine the temperature structure of the Venusian atmosphere. Several concepts presented here have their roots in the previous studies by Grassi et al. [2005], Zasova et al. [1999] and Roos-Serote et al. [1995]. For the sake of brevity, we will hereinafter use the term ‘‘VIRTIS’’ as synonymous of ‘‘VIRTIS-M IR subsystem.’’

2. Information Content of VIRTIS Data 2.1. Example of Venusian VIRTIS Spectra [6] Figure 1 presents an example of an actual VIRTIS Venusian nighttime observation. A visual inspection allows

In t n;total ; m; f ¼ tZn;total 0 1 A: 1 vo;n t n ¼ t 0n eðtn;total tn Þ=m Bn T t n ¼ t 0n dt 0n þ m 0

B:

C:

D:

1 4pm Fn;0 4p 1 4pm

tZn;total Z2p

Z1

0 0 tZn;total

1

0 eðtn;total tn Þ=m~ pn t n ¼ t 0n ; m; f; m0 ; f0 In t n ¼ t 0n ; m0 ; f0 dm0 df0 dt 0n þ ð1Þ

0 0 ~pn t n ¼ t 0n ; m; f; m0 ; f0 eðtn;total tn Þ=m eðtn;total tn Þ=m0 dt 0n þ

0 tZn;total Z2p

0

0

Z1

0 eðtn;total tn Þ=mSn t n ¼ t 0n ; m; f; m0 ; f0 dm0 df0 dt 0n

1

ð1Þ

where t n is the optical thickness, pn is the aerosol phase function, v0, n is the aerosol single scattering albedo, Bn is the Planck function and F0,n is the solar flux density. [4] This expression includes the terms describing the thermal emission by the surface and atmosphere (A), as well as a modeling of the reflection of the solar radiation (C), multiple scattering phenomena (B) and other nonthermal emissions (D) such as radiation due to non-LTE (local thermal equilibrium) phenomena. Term (B) makes (1) an integral equation, without analytical solutions. Even with a complete knowledge of the vertical distributions of aerosols and gases, air temperatures (driving thermal emission described by Planck function B) and optical properties of suspended materials, the analytical determination of the expected radiation field is not usually possible; consequently, it has to be estimated numerically. The inversion of (1), aiming to retrieve the above mentioned quantities, becomes therefore an extremely complex problem, often without unique solution [Rodgers, 1976]. Nevertheless, the scientific potential of infrared measurements in the study of Venusian

us to identify the main spectral features and relate them with specific parameters of Venusian environment. [7] 1. For wavelengths less than 3000 nm, the H2SO4 droplets (the main cloud component) become much less absorbing and they behave, effectively, as conservative scatterers. Thermally emitted radiation from lower atmosphere (i.e., below the main cloud deck) and even from the surface may therefore escape to space in the windows such as those at 1180, 1740 and 2300 nm that lie between strong atmospheric absorption bands. [8] 2. The signal level measured in the range 3500 – 5100 nm follows roughly a Planckian shape (in its Wien tail), corresponding to the temperature of clouds where the optical thickness reaches a unity value. [9] 3. Two strong CO2 absorptions bands are centered at 4300 and 4800 nm. [10] 4. The main CO band is evident as a continuum depression centered at 4600 nm. [11] 5. Again in the range 3500 – 5100 nm, deviations from an ideal Planckian shape, in the spectral intervals not

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Figure 2. Levels of unit optical depth of different VIRTISM IR channels for typical Venusian conditions. The absorption of both clouds and CO2 is included in the computations. severely affected by gases, are ascribed to variations of cloud optical properties. 2.2. Assessment of Retrieval Capabilities [12] The use of Bayesian formalism [Rodgers, 2000] allows a precise assessment of retrieval capabilities offered by individual radiometric measurements, but its usage in the Venusian case is de facto precluded by the lack of reliable a priori information. The data sets collected by previous missions, despite their obvious scientific values, do not allow the compilation of a set of climatology statistics with adequate seasonal, longitudinal and local time coverage. Consequently, a heuristic analysis, to be confirmed by numerical tests, represents the most reasonable method to assess the information content offered by VIRTIS data. [13] At VIRTIS resolution, the CO2 4300 – 4800 nm bands are covered by about 90 sampling channels (hereinafter, the term ‘‘channel’’ will be used as a synonym for ‘‘sampling position along the wavelength grid’’). For typical Venusian temperatures and aerosol densities (Seiff [1983] and models references therein), the VIRTIS channels where S/N exceeds 3 reach a unity opacity in the pressure range between 100 and 0.1 mbar ( 64 – 95 km), when both aerosols and CO2 are considered (Figure 2). These opacity values are derived from averages of monochromatic opacities weighted by the instrumental line shapes of individual channels. Our data may therefore carry information of the thermal structure of the atmosphere in the same pressure range, as far as term (A) in equation (1) dominates the emerging radiation field. [14] This condition is true only for nighttime data. During daytime, the 4300-nm band is affected by the scattering of solar radiation by the Venusian clouds as well as by intense non-LTE emission by CO2 molecules in the higher atmosphere [Lopez-Valverde et al., 2007]. The cumulative effects of these terms may easily reach a value of 40% of the total signal at 4500 nm. Even if both effects could possibly be included in the modeling of observed radiances, the induced computational load is so heavy that we prefer to limit

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ourselves to the analysis of nighttime data in this first phase of data processing. [15] Analysis of the deep atmosphere temperature structure is also severely limited by current modeling issues. Radiances measured in the atmospheric windows commonly show variations of the order of 30 –40%, which are interpreted as being due to variable thickness of the cloud decks [Taylor et al., 1997]. On the other hand, these lower clouds are mainly composed of mode 3 particles [Esposito et al., 1983], for which substantial uncertainties of optical properties, shape and composition exist. In this study, we considered the Venus aerosols model given by Crisp [1989]: (Mode 1: reff = 0.49 mm, Mode 20: reff = 1.18 mm; Mode 2: reff = 1.14 mm; Mode 3: reff = 3.85 mm). [16] Absorption properties of CO2 at the high temperatures and pressures of Venus lower atmosphere are poorly known at the present date [Taylor et al., 1997] and current data analysis attempts largely rely on empirical corrections. In these conditions, even if a temperature retrieval scheme may possibly be envisaged, its application to actual data processing would be questionable. Its development has therefore been postponed to later phases of our work and will not be described here.

3. Characteristics of Retrieval Code 3.1. Preliminary Considerations [17] According to equation (1), for nighttime observations, the observed radiance level depends on the behavior of atmospheric temperatures T as function of total opacity t, i.e., I = f(T(t)). For a given vertical distribution of optically active species, this is equivalent to the atmospheric temperatures as a function of altitude or pressure. As long as we neglect the CO band, the opacity between 3500 and 5100 nm is due to CO2 and clouds. [18] 1. Carbon dioxide is the main constituent of Venus atmosphere, and has a constant mixing ratio of 0.965 in the mesosphere. Therefore, once a temperature profile is assumed, we can compute its density on a fixed pressure grid (and the related opacities) on the basis of hydrostatic equation and the perfect gas law. [19] 2. In the absence of CO2 absorption, unity optical thickness would be reached in the spectral range of our interest in the upper cloud deck [Esposito et al., 1983]. Even though this structure appears less variable than its lower counterpart, variations of effective altitude and density decay scale heights have been reported by Zasova et al. [1999] on the basis of Venera 15 FTS data. [20] Since both air temperatures and aerosol densities are variable and contribute to total opacity, it appears that the two unknowns have to be retrieved simultaneously in order to achieve a self-consistent modeling of experimental data. Unfortunately, the optical properties of mode 2 particles (the main components of upper cloud deck) are almost constant in the VIRTIS spectral range and therefore it is extremely hard to discriminate between the effects of these two parameters. For example, in the case of a positive temperature lapse rate close to the cloud top, the same radiance level can be achieved either with a higher air temperature and high cloud or with a lower temperature and lower cloud. In numerical (noise-free) radiance simulations these two cases lead to different spectral intensities and shapes.

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However, in the case of actual data, the magnitude of these differences is comparable to the modeling errors due to uncertainties on aerosol optical properties, radiative transfer approximations or residual calibration offset. Therefore, a ‘‘nonuniqueness-of-solution’’ problem exists at altitudes where the cloud opacity reaches unity values. On the basis of analysis of aerosol densities retrieved from Venera 15 FTS data [Zasova et al., 1999], we can see that this level of unity opacity is located, for most cases, around 100 mb. 3.2. Initialization [21] The initialization of the state vector (i.e., input quantities for the radiative transfer computation) is performed in two main phases: (1) definition of the quantities to be assumed as known and (2) setting of a first guess for the quantities to be retrieved. [22] The following list describes the preferred initialization options for phase 1 adopted during our analysis of measured Venusian spectra: [23] 1. Clouds were assumed to consist of liquid droplets of a mixture composed of 75% of H2SO4 and 25% H2O, whose complex refractive indices were taken from Palmer and Williams [1975]. [24] 2. Since the upper cloud is mostly composed by Mode 2 particles, only this aerosol component has been included in our simulations. Following Crisp [1989], a lognormal distribution with reff = 1.14 mm and variance of 0.23 mm was adopted. [25] 3. Gaseous CO2 lines data were extracted from the HITRAN 2004 database [Rothman et al., 2005]. These data were used to build an expansion of preconvolved transmittance profiles in the Venusian mesosphere, according to the methods described by Zasova et al. [1999]. [26] During phase 2 of initialization, we should keep in mind that owing to the nonlinearity of the radiative transfer equation, an initial guess as close as possible to the true value is highly desirable to avoid convergence toward a purely mathematical, but unphysical solution. The analysis of VIRTIS data currently conducted in our team adopts the following guidelines: [27] 1. A study carried out on a wide population of simulated spectra (see section 4.1) allowed us to assess the correlation coefficient between the air temperatures at different pressure levels of the atmosphere with the observed brightness temperature at the different VIRTIS sampling points. The maximum correlation level as a function of sampling point matches closely the level of unit optical depth, plotted in Figure 2. About fifteen sampling points outside the CO contaminated region were selected to provide initial guesses of air temperatures at corresponding levels. These estimates were then fitted with a third degree polynomial in the entire pressure range considered in our computations. [28] 2. The aerosol density as a function of pressure was initialized to the average values derived from Venera 15 FST data. In the version of the retrieval code presented here, no effort was made to provide a more refined guess on the basis of the data. Future improvements of our code could benefit from a correlation analysis on simulated radiances carried out during our validation tests. This study demonstrated that the ratios of brightness temperatures measured in symmetric positions of the 4800 nm CO2 band with

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respect to its center are highly sensitive to effective scale height and altitude of the cloud deck. 3.3. Retrieval Algorithms [29] In our code, air temperatures and aerosol densities are retrieved in two separate steps, nested inside an outer iterative loop. This design is justified by the different retrieval algorithms employed for the two unknowns. [30] 1. The computation of vertical air temperature profile has been widely discussed in literature for the case of the Earth’s atmosphere as well as for other planets. A general introduction on these topics is given by Hanel et al. [2003, chap. 7]. In our case we adopted the algorithm proposed by Twomey et al. [1977], in the formulation presented by Zasova et al. [1999]. In this scheme, the temperature update between two consecutive iterative cycles is given by m P

Tliþ1

¼

TBch ðIobs;ch Þ

i ch¼1 TBch Iexp ected;ch Tli m P

Wch;l ð2Þ

Wch;l

ch¼1

where j, ch, and l are the cycle, sampling channel and pressure level indices, respectively, and TB are the brightness temperatures corresponding to the radiances observed by VIRTIS (Iobs) or modeled according the current temperature profile (Iexpected). W is the weighting function, i.e., @Tch Wch;l ¼ @ log10 pp¼pl

ð3Þ

where T is the total atmospheric transmittance between space and a given pressure level. The Twomey et al. [1977] algorithm falls into the general class of relaxation methods, which are based on empirical corrections of a first guess profile, aiming mainly to achieve a perfect correspondence between the observed and synthetic spectrum, computed on the basis of the current value of the T(p) function. These methods are usually very robust, but suffer from weak theoretical basis. They are not able to take into account correlations between the temperatures at different levels of the atmosphere or the instrumental noise equivalent radiance (NER hereinafter). The latter point implies that they cannot provide an analytical estimation of the error on the retrieved solution, which shall therefore be assessed by means of numerical tests. Consequently, the only criteria to stop their iterations comes from the comparison between the NER, observed and modeled spectra. In our case, the atmosphere is modeled by a stack of 67 pressure levels logarithmically spaced between 1200 and 0.005 mbar ( 49– 106 km). The air temperature retrieval takes into account the VIRTIS radiances measured between 4250 and 5000 nm, excluding the spectral region dominated by CO centered roughly at 4600 nm. The low-wavelength shoulder of the 4300 nm CO2 band proved very difficult to model, as observed by Roos-Serote et al. [1995]. These authors suggested line mixing as a possible cause of misfit. Since our radiative transfer model is not yet able to account for this effect, the 4000 – 4250 nm region was not considered for our analysis purposes.

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Figure 3. Distribution of cases considered for code validation in the latitude-local time space. Points, Venera 15 FTS measurements; triangles, selected cases considered for this study.

Figure 4. Random (dashed line) and systematic (solid line) components of air temperature retrieval errors for the developed algorithm. Values refer to VIRTIS-M data once spatial pixels have been averaged in 10 10 bins.

[31] 2. The aerosol density profile is modeled as an exponential decay:

[34] These data were used as input for a direct radiative transfer code with the same characteristics of that used in the retrieval algorithm, namely: [35] 1. CO2 transmittances have been evaluated according to the preconvoluted transmittances approach. [36] 2. Aerosols have been assumed to be fully described by mode 2 particles. [37] 3. Scattering has been treated according to the TWOSTR algorithm by Kylling et al. [1995]. [38] For all cases, an emission angle equal to zero has been assumed. [39] The correspondence of the radiative transfer scheme in simulation and retrieval phases ensures that we are

nðzl Þ ¼ nðz0 Þe

ðzl z0 Þ ha

ð4Þ

where z0 is the nominal altitude of 1200 mbar pressure level. With this assumption, n(z0) and ha become the two free parameters to be computed on the basis of the data. In this case, we adopted a classical Gauss-Newton retrieval scheme, where the partial derivatives of simulated radiances with respect to the two free parameters are evaluated by explicit differentiation at each iterative step. The iteration is stopped when variations of both free parameters between two successive iterative steps become less than 1%. The aerosol retrieval takes into account the VIRTIS radiances measured between 4450 and 5100 nm, but again excluding the spectral region dominated by CO.

4. Retrieval Code Performances [32] The retrieval code performances were assessed on statistical basis by applying the software on a wide population of simulated spectra. The comparison between the retrieval outcomes and the known input conditions allowed us to estimate the random and systematic components of the retrieval errors for Venus conditions. 4.1. Reference Simulated Spectra Set [33] Temperature and aerosol density profiles derived from analysis of the Venera 15 FTS were selected in order to achieve, as far as possible, a uniform coverage in the latitude and local time space. A total of 187 cases have been considered for the subsequent analysis (Figure 3). The low values of Venus orbit eccentricity and inclination of rotation axis ensure limited seasonal cycles as well as, to a first approximation, a symmetry in the atmospheric structures of the two hemispheres. Since the profiles are derived from actual data, we are confident that they retain the actual characteristics of Venus mesosphere and its variability.

Figure 5. Correlation coefficients between retrieval errors at different altitudes.

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systematic error component in our retrieval scheme, while the standard deviation gives a measure of its random part. Both are presented in Figure 4. Cumulative errors remain below 4 K in the entire range 100– 0.1 mbar ( 64– 95 km), and below 1 K in the indicative span 70– 7 mbar ( 66– 77 km). [42] Further tests, not shown here for shake of brevity, allowed us to identify the main causes of limitation in retrieval performance. [43] 1. At the higher altitudes, the limiting factor is represented by the instrumental noise. The higher part of the atmosphere is probed by the channels with stronger CO2 absorption located at the center of the band, which have a very low signal level. A test carried out using the VIRTIS NER pertinent to individual spectra (i.e., not averaged on 10 10 bins) demonstrated that the upper boundary of region effectively probed by retrieval shrinks downward from 0.1 to 1 mbar (from 95 to 85 km).

Figure 6. (a) Comparison between the retrieved and true value of cloud deck altitude, defined as the pressure level where unity optical thickness at 5000 nm is achieved. Code performances are heavily affected by the nonuniqueness of the solution once we try to determine simultaneously air temperatures in the same pressure range. (b) The same as Figure 6a, but assuming the true air temperature profile during retrieval. actually investigating the performances of the algorithm itself and not other possible sources of error due to the required assumptions. [40] Random noise, with the same statistic of instrumental NER, was finally added to the simulated data. In actual operative conditions, VIRTIS-M monochromatic images are spatially degraded by averaging pixels on 10 10 bins, to achieve acceptable processing times. To account for that, the actual instrumental NER has been divided by 10 in order to simulate the increase in signal-to-noise ratio derived from the averaging process. 4.2. Performance Test Results [41] The case-by-case comparison between the retrieved and input temperature profiles allowed us to build, for each pressure level, a statistical population of retrieval errors. The average value of this population is interpreted as a

Figure 7. Examples of the retrieval code capability to properly model the simulated observations. In the bottom panels, the solid line is the true spectrum, and the dashed line is the spectrum as modeled by retrieval code. A positive offset of 5 103 was added to the latter curve for clarity. The top panels show the difference between true and modeled spectra.

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retrieved temperature profiles were compared on a separated test and demonstrated to be less than 1 K above the reference level of 100 mbar. [48] The code, in its current implementation, performs a tentative retrieval of aerosol density profile, where actual free parameters are n(z0) and ha (see section 3.3 and equation (4)). This modeling is, however, intended only to define a continuum level for the CO2 absorption. Numerical tests on simulated radiances actually demonstrated that the code is unable to retrieve properly the altitude of cloud deck; retrieved and true values of pressure level where unity optical thickness is achieved appear completely uncorrelated (Figure 6a). This fact has to be related with the nonuniqueness of solution discussed in section 3.1; assuming for each test case the corresponding ‘‘true’’ temperature profile allows us to properly retrieve aerosol densities in a variety of situations (Figure 6b). Similar numerical tests demonstrated Figure 8. Statistics of c2 values computed from the fit between observed (simulated) and modeled spectra in the wavelength range [4250, 4450] nm. [44] 2. Errors in the retrieved aerosol densities are the main cause of temperature retrieval errors in the lower mesosphere. 100 mbar ( 64 km) is the indicative level where aerosol clouds reach a unity optical thickness at 4500 nm. Assuming for each test case the corresponding ‘‘true’’ aerosol density (i.e., retrieving only the temperature profile) leads to a decrease from 100 to 300 mbar (from 64 to 59 km) of the lower boundary of the region effectively probed by retrieval. [45] Different options of the temperature profile initialization have a very limited impact on the retrieval performance, affecting mainly the behavior of systematic components outside the reference 100– 0.1 mbar pressure range. Inside these boundaries, differences in retrieval performance with respect to the reference case of Figure 4 are less than 0.3 K. [46] Further insights on code behavior are provided by correlation studies on retrieval errors at different altitudes (Figure 5). This plot (1) shows how errors at different levels may compensate each other (regions of negative correlations) and (2) illustrates the vertical resolution of retrieval scheme. Actually, nonzero correlations between different levels indicate the occurrence of systematic components in retrieval errors along the vertical grid. These components have their ultimate cause in the finite width of weighting functions in equation (2), or, in other words, in the limited vertical resolution of retrieval. [47] The latter point is quite ambiguous owing to the presence of negative lobes, but it provides at least in the lower atmosphere an indicative figure similar to a gaseous scale height (of the order of 10 km for typical Venus conditions) and slightly less above 2 mbar ( 83 km). The error estimate shown in Figure 4 is considered as a minimum, since other potential sources of errors such as (1) radiative transfer approximations, (2) possible systematic calibration uncertainties or artifacts, and (3) uncertainties in the HITRAN parameters were not considered in this validation test. The effects of different radiative transfer schemes (namely, two streams and multi streams) on

Figure 9. Examples of retrieval code capability to properly model the actual Venusian VIRTIS observations. In the bottom panels, the solid line is the true spectrum, and the dashed line is the spectrum as modeled by retrieval code. A multiplicative factor of 1.5 103 was applied to the latter curve for clarity. The top panels show the difference between true and modeled spectra. Both examples are extracted from the VI0038_02 frame.

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Figure 10. Two-dimensional air temperature maps at (a) 90 and (b) 35 mbar levels ( 65 and 70 km), derived from the analysis of the VI0038_02 frame. that in most cases, errors in the aerosol retrieval do not add errors in the temperature retrieval greater than 3 K above the 100 mbar level. [49] The code has been demonstrated to be capable of providing satisfactory modeling of simulated data in a variety of situations (Figures 7a and 7b). Nevertheless, the percentage of cases where the code was not able to reproduce observations within instrumental error is far from being negligible (Figure 8). A direct inspection shows how the most problematic cases are those related to weak thermal inversion and low altitude of cloud deck. In these conditions, the initial guess of aerosol density was demonstrated to be particularly poor and the code was not able to converge toward a realistic solution, oscillating until the maximum number of iteration was achieved. In these cases, however, systematic differences between observed and modeled radiances well above NER level appear around 5000 nm, allowing the results to be effectively filtered.

pling grid or to approximate values for instrumental resolution of individual sampling channels. [51] Figure 10 shows two typical two-dimensional maps of air temperature at two different pressure levels, derived from an individual frame acquired during orbit 38. The maps present quite smooth behavior, despite the fact that the analysis of each pixel was carried out completely independently from the surrounding ones. This fact demonstrates the stability of the retrieval code and its robustness against occasional calibration issues (dead columns, caused mainly by impinging cosmic rays). This fact is further confirmed by the analysis of partially overlapping frames that, when available and acquired within small time intervals, show

5. Examples of Temperature Mapping From Venus Data [50] The purpose of this section is to provide to the reader an idea of the actual behavior of the code once applied to real Venus measurements. The modeling of Venus’ spectra provides usually very satisfactory fits (Figure 9). Systematic effects can be appreciated in the following spectral regions: (1) main CO band around 4600 nm, not included in our simulations, (2) above 5000 nm, in a region with weak CO2 absorption, probably related to the approximate aerosol models adopted during retrieval or radiative transfer algorithms not being completely adequate, and (3) around the radiance local maximum at 4270 nm, likely to be ascribed to residual registration errors in the VIRTIS wavelength sam-

Figure 11. Average temperature field along the meridian 180°, as derived from frames VI0063_01 to VI0063_07. Local time is about 1900.

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Figure 12. Comparison of a VIRTIS frame documenting (a) the occurrence of the polar dipole (l = 5 mm) and the air temperature fields derived from the same frame at (b) 90 mbar and (c) 35 mbar. Temperature fields were derived from the VI0038_02 frame and represent higher-resolution versions of Figure 10.

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Figure 13. Sequence of air temperature fields along the 0° meridian. The arrows highlight the patterns of the air temperature isosurface that appears to propagate between consecutive frames (cubes VI0038_01 to VI0038_04). The time interval between frames is exactly 1 hour.

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Figure 14. (a) Standard deviation of the air temperature fields, as derived from the time sequence of Figure 13, local time 0400. (b) The same as Figure 14a, but sequence VI0063_01 to VI0063_07, local time 1900. differences in the temperature fields in the order of retrieval errors described in section 4.2.

6. Preliminary Analysis of Air Temperature Fields [52] A complete scientific analysis of results is currently being undertaken by the VIRTIS team, but cannot be considered as conclusive, owing to the limited numbers of observations processed so far. Moreover, owing to the orbital characteristics of the Venus Express orbiter, the available temperature fields document mainly the atmosphere conditions close to the southern pole. Preliminary findings are as follows: [53] 1. The main features of the air temperature fields (Figure 11) are in quantitative agreement with the previous results of Pioneer Venus Infrared Radiometer and Venera 15 FTS for the northern hemisphere [Taylor et al., 1980; Zasova et al., 1999]. Namely, the occurrence of a cold collar at 100 mbar ( 64 km) centered at 65°, and the rise of temperature toward the pole for a wide range of altitudes are observed. Further discussion about the general characteristics of the atmospheric temperature fields, their local time dependences and the derived winds are presented in the companion paper by Piccialli et al. [2008]. [54] 2. The pattern of the polar dipole, as observed in VIRTIS frames at 5 mm [Piccioni et al., 2007b], has an immediate correspondence in the thermal fields only in the lowest parts of probed range (around 90 mbar, 65 km), and is already barely visible at 35 mbar, i.e., 70 km (Figure 12). This evidence may put constraints on the vertical scale at which the dipole develops. The relevant pressure range is characterized by moderate to negative lapse rates. The higher part of probed pressure range is usually much less rich in detail. [55] 3. Comparison of temperature fields derived from frames closely spaced in acquisition times shows the occurrence of constant patterns, with a consistent evolution in time (Figure 13), which strongly suggest a wavelike activ-

ity. Related temperature variations are of the order of 5 K. Bearing in mind the retrieval errors of Figure 4, these values are considered as statistically meaningful. [56] 4. The standard deviation of air temperature derived from these sequences of frames allows us to assess the region of the atmosphere characterized by the strongest variability on timescales of an hour (Figure 14). The region around 1 mbar ( 86 km) is apparently characterized by the strongest variability, well above the random retrieval errors given in Figure 4. Notably, the variability of the atmosphere appears higher just after sunset than before dawn.

7. Conclusions [57] This paper describes the retrieval code in the form that is currently adopted by our team for the analysis of Venus VIRTIS-M data. The extensive validation and error characterization described here make it suitable for routine data processing for scientific purposes. Planned or inprogress studies include (1) phenomenological characterization of air temperature fields as a function of season, (2) their correlations with topography, airglow emissions, cloud altitudes and UV markings, (3) mesosphere energy budget and, in a longer-term perspective, (4) validation and assimilation in Venus global circulation models. Moreover, several improvements are already under development or can be envisaged for the near future: (1) testing of an adding/ doubling algorithm for radiative transfer, (2) extension of the algorithm concept to VIRTIS-H, (3) better initialization of the aerosol densities on the basis of thermal brightness ratios and statistics derived from Venera 15 FTS data, and (4) use of more realistic initial guesses for retrievals, as derived from the analysis of the measurements by other Venus Express instruments (SPICAV and VeRA). These improvements should allow a better characterization of the thermal status and aerosol densities in the lower mesosphere and therefore a more complete exploitation of the information content offered by the VIRTIS data set.

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[58] Acknowledgments. D.G. carried out this research during an 11 month postdoctoral period in LESIA-Observatoire de Paris, funded by a grant kindly provided by the Paris City Hall (Ville de Paris). VIRTISVenus Express is an experiment developed jointly by IASF-INAF (Italy) and LESIA-Observatoire de Paris (France). The project is funded by ESA, ASI, and CNES. Russian coauthors acknowledge Russian Foundation of Basic Research for financial support (grant RFFI 08-02-01383). The work presented here would not have been possible without the efforts of IASF and LESIA VIRTIS technical staffs.

References Crisp, D. (1989), Radiative forcing of the Venus mesosphere. II. Thermal fluxes, cooling rates, and radiative equilibrium temperatures, Icarus, 77(2), 391 – 413, doi:10.1016/0019-1035(89)90096-1. Esposito, L. W., et al. (1983). The Clouds and Hazes on Venus, in Venus, edited by D. M. Hunten et al., pp. 484 – 564, Univ. of Ariz. Press, Tucson. Grassi, D., N. I. Ignatiev, L. V. Zasova, A. Maturilli, V. Formisano, and M. Giuranna (2005), Methods for the analysis of data from the planetary Fourier spectrometer on the Mars Express Mission, Planet. Space Sci., 53(10), 1017 – 1034, doi:10.1016/j.pss.2005.01.006. Hanel, R. A., B. J. Conrath, D. E. Jennings, and R. E. Samuleson (2003), Exploration of the Solar System by Infrared Remote Sensing, 2nd ed., Cambridge Univ. Press, Cambridge, U. K. Ignatiev, N. I., V. I. Moroz, L. V. Zasova, and I. V. Khatuntsev (1999), Water vapour in the middle atmosphere of Venus: An improved treatment of the Venera15 IR spectra, Planet. Space Sci., 47(8 – 9), 1061 – 1075, doi:10.1016/S0032-0633(99)00030-6. Kylling, A., K. Stamnes, and S.-C. Tsay (1995), A reliable and efficient two-stream algorithm for spherical radiative transfer: Documentation of accuracy in realistic layered media, J. Atmos. Chem., 21(2), 115 – 150, doi:10.1007/BF00696577. Lopez-Valverde, M., P. Drossart, R. Carlson, R. Mehlman, and M. RoosSerote (2007), Non-LTE infrared observations at Venus: From NIMS/ Galileo to VIRTIS/Venus Express, Planet. Space Sci., 55(12), 1757 – 1771, doi:10.1016/j.pss.2007.01.008. Moroz, V. I., D. Spankunch, and V. M. Linkin (1986), Venus spacecraft infrared radiance spectra and some aspects of their interpretation, Appl. Opt., 25(10), 1710 – 1719. Palmer, K. F., and D. Williams (1975), Optical constants of sulfuric acid: Application to the clouds of Venus?, Appl. Opt., 14(1), 208 – 219. Piccialli, A., D. V. Titov, D. Grassi, I. A. Khatunsev, P. Drossart, G. Piccioni, and A. Migliorini (2008), Cyclostrophic winds from the VIRTIS temperature sounding: A preliminary analysis, J. Geophys. Res., doi:10.1029/ 2008JE003127, in press. Piccioni, G., et al. (2007a), VIRTIS: The Visible and Infrared Thermal Imaging Spectrometer, Eur. Space Agency Spec. Publ., in press. Piccioni, G., et al. (2007b), South-polar features on Venus similar to those near the north pole, Nature, 450(7170), 637 – 641, doi:10.1038/ nature06209.

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Rodgers, C. D. (1976), Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation, Rev. Geophys., 14, 609 – 624, doi:10.1029/RG014i004p00609. Rodgers, C. D. (2000), Inverse Methods for Atmospheric Sounding: Theory and Practice, World Sci., Singapore. Roos-Serote, M., P. Drossart, T. Encrenaz, E. Lellouch, R. W. Carlson, K. H. Baines, F. W. Taylor, and S. B. Calcutt (1995), The thermal structure and dynamics of the atmosphere of Venus between 70 and 90 KM from the Galileo-NIMS spectra, Icarus, 114(2), 300 – 309, doi:10.1006/ icar.1995.1063. Rothman, L. S., et al. (2005), The HITRAN 2004 molecular spectroscopy database, J. Quant. Spectrosc. Radiat. Transfer, 96(2), 139 – 204, doi:10.1016/j.jqsrt.2004.10.008. Seiff, A. (1983), Thermal structure of the atmosphere of Venus, in Venus, edited by D. M. Hunten, et al., pp. 215 – 279, Univ. of Ariz. Press, Tucson. Taylor, F. W., et al. (1980), Structure and meteorology of the middle atmosphere of Venus: Infrared remote sensing from the Pioneer orbiter, J. Geophys. Res., 85(A13), 7936 – 8006. Taylor, F. W., D. Crisp, and B. Be´zard (1997), Near infrared sounding of the lower atmosphere of Venus, in Venus II, edited by S. W. Bougher, D. M. Hunten, and R. J. Phillips, pp. 215 – 279, Univ. of Ariz. Press, Tucson. Titov, D. V., et al. (2006), Venus Express science planning, Planet. Space Sci., 54(13 – 14), 1279 – 1297, doi:10.1016/j.pss.2006.04.017. Twomey, S., B. Herman, and R. Rabinoff (1977), An extension of the Chahine method of inverting the radiative transfer equation, J. Atmos. Sci., 34, 1085 – 1090, doi:10.1175/1520-0469(1977)034 2.0.CO;2. Zasova, L. V., I. A. Khatountsev, V. I. Moroz, and N. I. Ignatiev (1999), Structure of the Venus middle atmosphere: Venera 15 Fourier spectrometry data revisited, Adv. Space Res., 23(9), 1559 – 1568, doi:10.1016/ S0273-1177(99)00169-6.

A. Adriani, D. Grassi, and A. Negraõ, Istituto di Fisica dello Spazio Interplanetario, Istituto Nazionale di Astrofisica, Via del Fosso del Cavaliere 100, I-00133 Rome, Italy. ([email protected]) P. Drossart, Laboratoire d’Etudes Spatiales et d’Instrumentation en Astrophysique, Observatoire de Paris, Universite´ Pierre et Marie Curie, Universite´ Paris 7, CNRS, 5 place Jules Janssen, F-92195 Meudon CEDEX, France. N. I. Ignatiev and L. V. Zasova, Space Research Institute, Russian Academy of Sciences, Profsojuznaja 84/32, 117997, Moscow, Russia. P. G. J. Irwin, Clarendon Laboratory, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Parks Road, Oxford OX1 3PU, UK. A. Migliorini and G. Piccioni, Istituto di Astrofisica Spaziale e Fisica Cosmica, Istituto Nazionale di Astrofisica, Via del Fosso del Cavaliere 100, I-00133 Rome, Italy. M. L. Moriconi, Istituto di Scienze dell’Atmosfera e del Clima, Consiglio Nazionale delle Richerche, Via del Fosso del Cavaliere 100, I-00133 Rome, Italy.

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Structure of the Venus neutral atmosphere as observed by the Radio Science experiment VeRa on Venus Express Silvia Tellmann,1 Martin Pa¨tzold,1 Bernd Haüsler,2 Michael K. Bird,3 and G. Leonard Tyler4 Received 23 May 2008; revised 2 February 2009; accepted 18 February 2009; published 23 April 2009.

[1] The European Space Agency Venus Express Radio Science experiment (VeRa)

obtained 118 radio occultation measurements of the Venusian atmosphere between July 2006 and June 2007. Southern latitudes are uniformly sampled; measurements in the northern hemisphere are concentrated near the pole. Radial profiles of neutral number density derived from the occultations cover the altitude range 40–90 km, which are converted to profiles of temperature (T) and pressure (p) versus height (h). Profiles of static stability are found to be latitude-dependent and nearly adiabatic in the middle cloud region. Below the clouds the stability decreases at high latitudes. At an altitude of 65 km, the VeRa T[p(h)] profiles generally lie between the Venus International Reference Atmosphere (VIRA) and VIRA-2 models; the retrieved temperatures at any given pressure level typically are within 5 K of those derived from the Pioneer Venus Orbiter Radio Occultation experiments. A large equator-to-pole temperature contrast of 30 K is found at the 1-bar (1000 hPa) level. The VeRa observations reveal a distinct cold collar region in the southern hemisphere, complementing that in the north. At the latitudes of the cold collars, the tropopause altitude increases relative to higher and lower latitudes by 7 km while the temperature drops roughly 60 K. The observations indicate the existence of a wave number 2 structure poleward of ±75° latitude at altitudes of 62 km. Citation: Tellmann, S., M. Pa¨tzold, B. Haüsler, M. K. Bird, and G. L. Tyler (2009), Structure of the Venus neutral atmosphere as observed by the Radio Science experiment VeRa on Venus Express, J. Geophys. Res., 114, E00B36, doi:10.1029/2008JE003204.

1. Introduction [2] The exploration of Venus from space began in the early 1960s. Several American and Soviet flyby missions, orbiters, landers, balloons, and probes explored the Venusian atmosphere over the following decades, supported by ground-based observations. The Pioneer Venus Orbiter (PVO) spacecraft orbited Venus between 1978 and 1992. Extensive data returned by PVO and the related atmospheric probes, together with results from the Venera and Mariner missions, provided the base for the Venus International Reference Atmosphere (VIRA) [Seiff et al., 1985]. [3] The first radio occultation investigation of Venus was conducted during the flyby of Mariner 5 in 1967 [Stanford Mariner Group, 1967; Kliore et al., 1967; Fjeldbo et al., 1971]. This was followed by Mariner 10 [Howard et al., 1974; Nicholson and Muhleman, 1978], Venera-9 and -10 [Kolosov et al., 1979], Venera-15 and -16 [Yakovlev et al., 1 Abteilung Planetenforschung, Rheinisches Institut fu¨r Umweltforschung, Universita¨t zu Ko¨ln, Cologne, Germany. 2 Institut fu¨r Raumfahrttechnik, Universita¨t der Bundeswehr Mu¨nchen, Neubiberg, Germany. 3 Argelander Institut fu¨r Astronomie, Universita¨t Bonn, Bonn, Germany. 4 Department of Electrical Engineering, Stanford University, Stanford, California, USA.


1991], and Magellan [Jenkins et al., 1994; Steffes et al., 1994; Hinson and Jenkins, 1995]. To date, the most extensive campaign of radio occultation experiments at Venus was carried out with PVO [Kliore and Patel, 1980, 1982; Kliore, 1985]. A number of authors have addressed important issues arising from investigation of the Venus atmosphere [e.g., Hunten et al., 1983; Bougher et al., 1997; Moroz, 2002; Taylor et al., 1979, 1980; Taylor, 2002, 2006; Titov et al., 2001; Svedhem et al., 2007]. [4] The European Space Agency (ESA) spacecraft Venus Express (VEX) began orbiting Venus in April 2006. The Venus Express Radio Science experiment (VeRa) uses radio signals at 3.6 and 13 cm wavelength (X and S bands, respectively) to investigate the surface, atmosphere, and ionosphere of Venus. A detailed description of the experiment can be found in the work by Haüsler et al. [2006, 2007]. Pa¨tzold et al. [2007] give preliminary VeRa results from the first year in orbit. [5] Temperature changes in lower atmosphere, the troposphere, and the transition region above the tropopause, the middle atmosphere or the mesosphere, are mainly dependent on latitude; several regions of temperature variation, presumably resulting from eddy motions, can be found throughout the atmosphere [Seiff, 1983; Hinson and Jenkins, 1995; Young et al., 1987]. Both small- and global-scale variations associated with gravity waves are present in the lower and middle atmosphere of Venus [Hinson and Jenkins, 1995]. Also, latitude-dependent temporal fluctuations with

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periods of 2.48, 2.9, and 5.3 days are apparent in the middle atmosphere between the cloud tops and the upper atmosphere above 100 km [Taylor et al., 1980; Seiff, 1983; Piccioni et al., 2007]. [6] The circulation in the lower and middle atmosphere below the cloud tops is driven mainly by retrograde zonal circulation in near cyclostrophic balance with the pressure gradient [Schubert et al., 1980; Counselman et al., 1979]. A strong retrograde jet with wind speeds exceeding 100 m/s can be found between latitudes ±50 and 55° at a height of about 70 km. This is equatorward of the cold collar regions, located between ±65 and 75° latitude at 60-km altitude, where the zonal retrograde rotation gives way to strong polar vortex structures [Newman et al., 1984]. Hadley circulation is found above the clouds, presumably the result of radiative heating in the clouds. The combination of the zonal rotation and the descending branches of the Hadley circulation, which move poleward at the cloud tops [Limaye and Suomi, 1981; Rossow et al., 1980; Svedhem et al., 2007], forms the strong polar vortex [Suomi and Limaye, 1978; Taylor et al., 1980; Taylor, 2006]. [7] In the remainder of this paper, following an introduction to the method, section 2, the data set, section 3, and an overview of the lower atmosphere, section 4, we provide specifics as follows: [8] 1. Section 5 investigates the static stability in the lower and middle atmosphere of Venus which is a key parameter for understanding the dynamical behavior of an atmosphere. [9] 2. Section 6 compares the tropopause values retrieved from VeRa with previous results from Pioneer Venus Orbiter Radio Occultation (PV-ORO) [Kliore and Patel, 1982] and Venera-15 and -16 [Yakovlev et al., 1991]. The static stability of an atmosphere is closely associated with the behavior of the tropopause. [10] 3. Section 7 investigates the dominant variability of the atmosphere as manifest in changes within the temperature structure as a function of latitude. The VeRa data are compared with earlier data from the PV-ORO experiment, which provided the previously most extensive radio occultation data set [Kliore and Patel, 1980, 1982; Kliore, 1985]. [11] 4. Section 8 describes an investigation of the thermal structure between 75 and 85° latitude for evidence of solarinduced structures in the VeRa data. Diurnal variations are significant above an altitude of 100 km, but circulation features within the Venus middle atmosphere, such as the polar collar and the hot dipoles, also show some solar related variability [Taylor et al., 1980; Piccioni et al., 2007]. [12] 5. Section 9 compares the VeRa profiles with the predictions from two previously established atmospheric reference models, VIRA [Seiff et al., 1985] and VIRA-2 [Moroz and Zasova, 1997; Zasova et al., 2006]. Data retrieved after the publication of the Venus International Reference Atmosphere were used by Zasova et al. [2006] to develop an improved version, VIRA-2.

planetary disk only to reappear at the opposite limb of the planet. During the early and late stages of these events, radio signals from VEX, sound the ionosphere and neutral atmosphere, and are received in closed-loop receiver mode at an ESA or NASA ground station. VeRa employs a oneway, dual-frequency radio link stabilized by a dedicated onboard ultrastable oscillator (USO), which provides precise control of the transmitted signal frequency so that perturbations in the received signal frequencies as small as 3 – 4 1013 can be attributed to propagation effects within the atmospheric path. [14] Within the atmosphere and ionosphere the gradient of refractive index of the gas incrementally deflects the direction of propagation, resulting in a curved propagation path. In this way atmospheric refraction alters the path geometry by an amount that is controlled by the radial variation of refractivity. The result is a subtle but easily measured shift in the frequency of the signal received on Earth. [15] Assuming local spherical symmetry, the degree of bending, the ray periapsis and impact parameter, and the projection of the raypath onto the planetary surface can be computed from geometrical optics and the known Earth spacecraft geometry. The refractivity of the atmosphere at the ray periapsis is obtained from the bending angle via an Abel transform [Fjeldbo et al., 1971]. [16] The refractivity profile m(h) as a function of the altitude h is determined by the local state of the neutral atmosphere and the electron density distribution Ne(h) in the ionosphere as

mðhÞ ¼ C3

Ne ðhÞ þ C1 nðhÞ k f02

ð1Þ

where k is the Boltzmann constant, n(h) is the vertical distribution of neutral number density, C3 = 40.31 m3/s2 and fo is the frequency of the radio signal. The first term in (1) describes the effect of the ionospheric electron density. In (1), the neutral number density is related to the refractivity profile through the constant factor C1 which depends on the atmospheric composition of the atmosphere. This proportionality factor has the value C1 1312 106 Kms2/kg for an atmospheric composition of 96.5% CO2 and 3.5% N2 [Essen and Froome, 1951]. At Venus the ionospheric contribution is much weaker than the one from the atmosphere given by the second term in (1). In the reduced data, the neutral and ionized regions are well separated in altitude, the atmospheric term dominating below about 100 km, the ionosphere above 100 km. The VeRa investigations of the ionospheric vertical structure are described by Pa¨tzold et al. [2007]. [17] The ideal gas law relates the pressure, temperature, and neutral number density of an atmosphere pðhÞ ¼ k nðhÞ T ðhÞ

2. Radio Science Observation Method [13] VeRa radio occultation observations make use of the spacecraft radio subsystem working with Earth receivers to study the atmosphere. During these observations, as seen from the Earth, the spacecraft disappears behind the

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ð2Þ

Since hydrostatic equilibrium is very accurate throughout a well-mixed planetary atmosphere, height profiles of pressure and temperature can be derived directly from the neutral number density profile [Eshleman, 1973; Jenkins et

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Figure 1. Example T[p(r)] at high latitudes (day of year (DOY) 150 2007). The occultation point at the 1-bar level (50 km) corresponds to 8 = 85.85° N, solar zenith angle is 89.5°, and local time (LT) is 1550 h. Altitude is approximate relative to the mean Venus radius of 6051.8 km. Three profiles, calculated for upper boundary temperatures of 170, 200, and 230 K, merge at an altitude near 90 km (0.3 hPa). Mean lapse rate 10 K/km obtains below the tropopause. The middle atmospheric lapse rate in the middle atmosphere is much lower; the temperature is nearly isothermal up to the 10-hPa level (10 mbar at roughly 75 km). Small-scale fluctuations are often found in the middle atmosphere. Temperature continues to decrease at higher altitudes up to the upper boundary of VeRa sensitivity near 0.02 hPa (100 km). al., 1994; Ahmad and Tyler, 1998]. In practice, the temperature profile is derived from mup m T ðhÞ ¼ Tup þ mðhÞ k nðhÞ

Zhup

nðh0 Þ gðh0 Þdh0

ð3Þ

h

and the pressure follows from 1 pðhÞ ¼ mðhÞT ðhÞ C1

ð4Þ

where m represents the mean molecular mass of the mixed neutral atmospheric species, and g(h) the altitude-dependent acceleration of gravity [Lipa and Tyler, 1979]. [18] The integration parameter Tup = T(hup) in (3) represents the temperature at the upper boundary of the detectable atmosphere. Figure 1 shows the influence of this

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parameter on the retrieval of the temperature profiles. The three temperature curves have been computed from (3) but with different upper boundary conditions separated by 30 K. The curves merge below about 80– 90 km so that the effect from the upper boundary temperature is negligible below this altitude level. For the work below we chose a boundary temperature of 200 K at an altitude level of 100 km for our retrieval. Except as noted, we relate all altitudes to the mean Venus radius of 6051.8 km. [19] The dense Venus atmosphere is critically refractive at an altitude of roughly 32 km, rendering these lower-altitude levels inaccessible to occultation measurements [Fjeldbo et al., 1971; Haüsler et al., 2006]. With reference to the convergence in temperature for solutions with different boundary conditions (Figure 1), we see that the VeRa profiles cover the altitude range from about 40 km up to about 90 km, within which the dependence on the upper boundary condition can be neglected. [20] In this paper we have not provided an analysis of errors. Previous work has shown that occultation measurements at Venus are accurate [e.g., Jenkins et al., 1994; Hinson and Jenkins, 1995; Lipa and Tyler, 1979; Steffes et al., 1994]. The uncertainties in retrieved values of temperature and pressure vary with altitude, but characteristically are 1 K and 0.1 percent, respectively. Appendix B provides a brief discussion of error sources and citations to pertinent literature for understanding this topic.

3. Data Set [21] A total of 118 profiles from the ionosphere and the neutral atmosphere were retrieved from the VeRa observations over the three occultation seasons between July 2006 and June 2007 of the nominal VEX mission. Figure 2 shows the latitudinal distribution of the occultation ingress and egress locations. While the southern hemisphere could be observed with good coverage in latitude and longitude during each occultation season, observations in the northern hemisphere are mainly constrained to latitudes near the pole. The concentration of occultation events near the northern pole, however, provides a good spatial coverage owing to the large number of measurements with high vertical resolution in this area. The VEX orbital configuration presents the opportunity to cover each latitude range at different local times and illumination conditions during the same occultation season. In fact, half of the neutral atmospheric observations are for solar zenith angles (SZA) >90°. This large range of variations supports systematic study of latitudinal, local time and long-term variations in the retrieved profiles.

4. Large-Scale Temperature Structure of the Middle and Lower Venus Atmosphere [22] Figure 1 shows a typical temperature profile of the Venus atmosphere between 40 and 100 km at high latitudes. It is clear that the atmosphere in this altitude range can be divided into two distinct regions according to the thermal structure [Pa¨tzold et al., 2007]: (1) the troposphere, extending from the surface to the large temperature inversion in Figure 1, the tropopause at about 60 km, which is also the upper boundary of the middle clouds [Seiff, 1983], and (2) a

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Figure 2. Spatial distribution of ingress and egress occultation points. Points for the neutral atmosphere are given for ray periapsis location at 1-bar level (altitude 50 km). (top) Latitudinal distribution of ingress and egress occultation points by orbit number. (bottom) Latitude versus solar zenith angle of ingress and egress occultation points. transition region, the middle atmosphere (sometimes called the mesosphere), extending from the temperature minimum at the tropopause in Figure 1 to about 100 km, where the diurnal temperature variation becomes extremely pronounced. [23] The temperature in the troposphere decreases monotonically from a surface value of about 735 K [Seiff et al., 1985; Moroz and Zasova, 1997] to values in the range 210 –280 K at the tropopause (Figure 1). The lapse rate has a value near 10 K/km below the tropopause and shows several small abrupt changes. These changes coincide roughly with the boundaries of the highly variable cloud layers in the lower and middle atmosphere at 50– 70 km. The cloud layers, consisting mainly of sulphuric acid droplets, have a strong influence on the thermal structure and stability of the Venus atmosphere, even at heights well above and below the clouds. The circulation patterns in the troposphere and mesosphere are believed to be also strongly dependent on the thick cloud layers. [24] Stratification of the middle atmosphere, the transition region between the tropopause and about 100 km (0.02 hPa), is stable over the VEX observation time, but displays a high thermal variability, especially with regard to latitudinal

changes. The lapse rate is always lower than in the troposphere and nearly isothermal in the latitude range close to the poles. Several small inversions and wave-like structures are also seen (Figure 1). The latter have been attributed to dynamical variations on local or global scales, eddy motions, or gravity waves [Hinson and Jenkins, 1995].

5. Static Stability [25] The static stability S of an atmosphere is defined as the difference between the temperature gradient of the atmosphere dT/dz and its dry adiabatic temperature gradient G: S¼

dT G dz

ð5Þ

where z is the geometric height of the atmosphere above the mean radius. The value of G used for this comparison is from Seiff et al. [1980] using a pure CO2 atmosphere; the difference between a mixed atmosphere and the pure CO2 atmosphere is negligible in this context. While positive values of S indicate a stably stratified atmosphere, negative

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Figure 3. Height profiles of static stability for the first occultation season OCC-1. (left) Low, (middle) middle, and (right) high latitudes, respectively (see legend). values of S represent an atmosphere that is unstable against convective overturning. [26] For the first occultation season (OCC-1), Figure 3 compares the stability in three latitude ranges in both hemispheres: 0° > 8 > ±30° (equatorial), ±30° > 8 > ±60° (midlatitudes), ±60° > 8 > ±90° (polar latitudes). Figures 4 and 5 show similar plots for OCC-2 and OCC-3. The atmosphere is generally stable at all altitudes above 45 km, although there is considerable variation in the lowest 20 km probed. The altitude levels displaying the most abrupt changes coincide with the boundaries of the cloud layers, as determined by cloud tracking instruments [Ragent and Blamont, 1980]. [27] A stable region in the troposphere, beginning just below the middle cloud deck near 50 km, extends well below the clouds. Results from in situ measurements have shown that the stability is maximum at about 45 km, just below the cloud layer; the entire stable region is about 20 km in vertical extent [Seiff et al., 1980; Young et al., 1987]. VeRa results show that the upper boundary may depend on the latitude, as upper boundary rises at high latitudes during the three occultation seasons, in good agreement with the tropopause altitude (section 6). [28] The lapse rate is close to adiabatic over the altitude range of the middle cloud, approximately from 49 to 59 km, and is latitude-dependent. Convective regions (S < 0) are found only in very shallow layers within the middle cloud

deck. Figure 4 (middle), between 52 and 54 km, shows typical examples where S 1. This result is in good agreement with previous observations that show a stable, stratified atmosphere with a shallow convective region in the middle cloud deck [Schubert et al., 1980; Seiff et al., 1980; Seiff, 1983; Hinson and Jenkins, 1995]. The altitude range of adiabatic behavior appears to expand toward higher latitudes and the upper boundary increases from the equator toward the cold collar region. While the convection region thickness is close to 5 km at equatorial latitudes, it doubles to 10 km at polar latitudes. [29] We note the presence of two regions of high stability. The uppermost occurs above 64-km altitude, coincident with the upper boundary of the cloud layer. The lower is located just below the middle cloud layer. All profiles show a highly stable layer above the middle cloud in the altitude range 60– 65 km. The regions above about 70 km have a mean stability of S 10 K/km with several superimposed wavelike structures that might be associated with local- or planetary-scale dynamics such as gravity waves or eddy motions. The stable layer below the clouds does not appear in all high-latitude profiles, in agreement with Yakovlev et al. [1991]. [30] The transition between stable and adiabatic regions is fairly smooth at equatorial latitudes, but abrupt at middle and polar latitudes. The change in lapse rate from adiabatic to stable defines the tropopause altitude (section 6).

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Figure 4. Same as for Figure 3, but for the second occultation season OCC-2.

[31] The approximately adiabatic behavior in the middle cloud can be explained by the influence of clouds on atmospheric heating. Infrared radiation from the deep atmosphere is trapped within the clouds only to be partly reemitted at longer wavelengths to adjacent areas below. This process cools the upper part of the middle cloud, leading to the observed adiabatic behavior in the cloud. This process can explain the thick stable layer just below the clouds and the nearly adiabatic layer within the cloud [Schubert et al., 1980; Seiff, 1983]. [32] The existence of the highly stable layer, S > 0, just below the clouds may have an important connection with the dynamics acting to separate the altitude ranges above and below with regard to atmospheric wave propagation and meridional circulation [Schubert et al., 1980]. Hadley circulation, which may extend into the cloud deck, with equatorward flow at an altitude of 45– 55 km [Seiff et al., 1980] and poleward flow above the upper cloud, is cut off below this level, thereby isolating the deeper atmosphere from the dynamics of the upper regions. At the same time, convective motions may occur at the same time in the nearly adiabatic region in the middle cloud.

6. Tropopause [33] The lapse rate in the troposphere is slightly less than the dry adiabatic lapse rate at most altitude levels, indicating

an atmosphere which is stable against convective overturning. The tropopause level can be defined as the altitude at which the temperature lapse rate shows a significant decrease (Figures 3 – 5). In order to specify a working threshold, the tropopause is identified as the altitude at which the absolute difference between the observed temperature lapse rate and the adiabatic lapse rate exceeds a value of 8 K/km. This threshold is consistent with that used by Kliore [1985]. [34] The overall stability profile above about 65 km, in the middle atmosphere, is invariant within each VeRa occultation season (Figures 3 – 5) with only occasional small-scale differences from profile to profile. Large-scale differences from occultation season to occultation season occur above the cloud tops, however. These differences do not affect the overall stability of the atmosphere above the tropopause. [35] Comparing the upper boundaries of the nearly adiabatic region in Figures 3– 5 illustrates the latitudinal change of the tropopause altitude. The tropopause structure is very weak and occurs at lower altitudes near the equator. The neutrally stable layer below the tropopause expands vertically from equator to pole. The lapse rate in the middle cloud is extremely close to adiabatic, especially at midlatitudes. The high latitudes show an enhanced level of smallscale fluctuations in the lapse rate, possibly connected with the intermittent polar vortex structures.

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Figure 5. Same as for Figure 3, but for the third occultation season OCC-3. [36] The tropopause and the temperature inversion region in this altitude range depend strongly on latitude [Kliore and Patel, 1982; Yakovlev et al., 1991]. This behavior is coincident with the strong zonal winds and the superposition of the zonal retrograde rotation and the descending branch of the Hadley circulation in the collar region leading to the strong polar vortex structures in the polar regions. [37] Figure 6 shows the variation of the tropopause temperature as a function of latitude for both hemispheres. The cubic spline fits are calculated for both hemispheres separately in order to bring out possible hemispheric asymmetries. Each profile is assigned to the latitude at which the closest approach of the radio raypath crosses the tropopause altitude as defined by the stability criterion. The spatial coverage in the northern and southern hemispheres is quite different owing to the geometrical distribution of the occultation points over the occultation season. While the VEX orbit provides a broad spatial distribution of measurements in the southern hemisphere, measurements in the northern hemisphere are mainly clustered in the polar region. [38] Our observations show that the higher latitudes of the two hemispheres containing the cold collar and the polar regions are very similar. The tropopause temperature decreases from a maximum in the lower latitudes near ±15° to a minimum near ±70° latitude, the cold collar region, and rises again going closer to the poles. The minimum temperatures of the spline curves in both cold collar regions are 219.0 and 218.0 K at 73°N and 74°S,

respectively. The tropopause temperatures within 5° of the poles are higher, with 232.8 ± 12.4 K and 233.6 ± 4.1 K for the northern and southern hemispheres, respectively. An overview of tropopause conditions can be found in Table 1. The VeRa tropopause temperatures in the polar region are slightly lower than those reported from PV-ORO [Kliore and Patel, 1982], and those from Yakovlev et al. [1991], but the two are in agreement within the limitations of the data. In the low latitudes the VeRa values are lower than those from Kliore and Patel [1982]. These differences reach values of 20 K (3.0 km) where the data are sparse. VeRa, PV-ORO, and Venera-15 and -16 show good agreement in the cold collar region. The data sets from both hemispheres seem to indicate a lower tropopause temperature at the equator than at roughly ±15° latitude. More precisely, the spline fit indicates on average a maximum temperature of 280 K at 8 = 17° in the north and 267 K at 8 = 15° in the south. The number of profiles in the equatorial region, however, is definitely too small to draw any definite conclusion. [39] We cannot adequately compare the two hemispheres between the equator and about 8 = ±60° because of the lack of data in the northern hemisphere. Another uncertainty at lower latitudes comes from the weakness of the temperature inversion at the tropopause because the change in the measured temperature lapse rate is not as sharp as in the higher-latitude region. The tropopause altitude and temperature thus depend critically on the choice of the

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Figure 6. Tropopause temperature as a function of latitude. (top left) Northern hemisphere (dots) and the resulting least median of squares (LMS) spline fit (dashed line). (bottom left) Southern hemisphere data (circles) and the resulting spline fit (solid line). (top right) Same as left, but sorted by OCC season and entry/exit. (bottom right) Superposition of northern (dots) and southern (circles) hemisphere data together with LMS spline fits (northern hemisphere, dashed line; southern hemisphere, solid line). stability threshold criterion. The latitudinal behavior of the tropopause temperature is virtually mirrored by the tropopause altitude shown in Figure 7. [40] The tropopause altitude rises from 59 km at low latitudes to about 62 km in the cold collar region, and then drops toward the near polar and polar region to a mean value of 58 km. The cubic spline fits for both hemispheres are slightly different, again mainly induced by the different spatial distribution of the data sets. [41] The northern polar region appears in some profiles warmer than the corresponding region in the south (Figure 6), but the cold collar region on both hemispheres are in good agreement. Large fluctuations in the thermal structure of this latitude range are induced by strong fluctuations of the temperature near the pole resulting from the rotating hot dipole structures. The polar dipoles, which follow a wave-2 dependence with a rotation period of 2.9 days in the north [Taylor et al., 1980] and 2.48 days in the south [Piccioni et al., 2007], are poleward of the cold collar and contrast with its predominantly wave-1 behavior [Taylor et al., 1980]. The VeRa tropopause altitudes in this region are in good

agreement with those reported by Kliore and Patel [1982] and Yakovlev et al. [1991] (Table 1). We note the positive bias of 0.2 km in our values resulting from the revised radius used here. These differences do not affect the

Table 1. Tropopause Conditions Region 8 8 8 8

= 0° – 40° = 40° – 55° = 55° – 77° 77°

8 8 8 8

= 0° – 40° = 40° – 55° = 55° – 77° 77°

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a

Altitude (km)

Temperature (K)

Pressure (hPa)

Northern Hemisphere 58.9 ± 1.0 269.5 ± 8.5 291.0 ± 47.2 60.6a 244.7 190.7 61.5 ± 0.9 220.0 ± 8.4 149.7 ± 26.5 59.3 ± 1.5 228.5 ± 11.7 210.8 ± 63.2 Southern Hemisphere 59.3 ± 1.2 263.6 ± 10.6 270.0 ± 52.1 61.1 ± 1.2 241.5 ± 11.3 191.6 ± 40.0 62.3 ± 0.9 220.5 ± 8.1 141.3 ± 19.2 59.7 ± 1.3 228.2 ± 7.6 196.9 ± 45.5

Only one value.

Depth Inversion (K) 3.1 ± 1.3 N/A 20.9 ± 7.7 16.5 ± 6.1 3.0 ± 2.1 7.8 ± 3.7 19.9 ± 5.6 16.3 ± 5.4

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Figure 7. Tropopause altitude as a function of latitude. (top left) Northern hemisphere (dots) and resulting spline fit (dashed line). (bottom left) Southern hemisphere data (circles) and LMS spline fit (solid line). (top right) Same as left, but now separated according to OCC season and entry/exit measurements. (bottom right) Superposition of northern (dots) and southern (circles) hemispheric data together with LMS spline fits (northern hemisphere, dashed line; southern hemisphere, solid line). Values are relative to 6051.8 radius. determination of the temperature and pressure values in Table 1. [42] The tropopause pressure as a function of latitude is shown in Figure 8. The same characteristics can be observed in the pressure differences as in the altitude differences. The significant differences in the spline fits are affected in an unknown way by the differences in the hemispheric data distribution. [43] The depth of the temperature inversion, defined as the temperature difference between the tropopause and the temperature maximum in the middle atmosphere above, shown in Figure 9, also suggests a strong latitudinal dependency. The inversions in the northern hemisphere are somewhat deeper than those in the southern cold collar region but, again, this impression may be caused by the limited set of data points in this highly variable region. Given a longitudinal open cold collar and a high variability in the observed inversion depths at fixed high latitudes, we

speculate that the cross section in latitude varies and is at its thickest opposite the opening. [44] The three occultation seasons cover 350 Earth days, 1.5 Venus years. No significant long-term temporal changes are observed in the thermal structure of the atmosphere within this period (Figures 6 –9, top right).

7. Latitudinal Variations in the Venus Atmosphere [45] Latitudinal variations dominate the structure of the lower and middle atmosphere of Venus. These are evident at an altitude of 30 km [Seiff, 1983] and are significant in the cloud layer (45– 70 km) and above, mainly in association with cyclostrophic balance of the high-velocity zonal winds [Newman et al., 1984]. [46] Figure 10 shows a temperature map based on 12 temperature profiles from the southern hemisphere obtained during the third occultation season during nighttime (day of

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Figure 8. Tropopause pressure as function of latitude. See Figures 6 and 7 for explanation. year (DOY) 116– 146 2007). Some typical features of the Venusian atmosphere are apparent in this map. [47] The temperature in the troposphere from the equator to 60° latitude remains nearly constant for a constant pressure level below 2 102 hPa, 60-km altitude. In the polar region (j8j > 60°), the temperature drops significantly, for example, at the pressure level 1000 hPa (50 km) DT 40 K between 60° and 90°. The temperature at pressure levels above 30 hPa (70 km) increases with increasing latitude. The ‘‘cold collar’’ [Taylor et al., 1980] is evident at the pressure level 100 hPa (ca. 64 km) between 60° and 75°. [48] The characteristic poleward decrease in temperature on surfaces of constant pressure is apparent in Figure 11, which shows the temperature at the 1-bar level as a function of latitude for the three occultation seasons. The temperature is nearly constant equatorward of 50° latitude and decreases from 50° poleward. We see no significant differences between the three occultation seasons, nor between the two hemispheres at any pressure level in the troposphere (the differences between the mean temperature values of the three occultation seasons are not greater than 2 K (8 ±50°, p = 1 bar)). These results are consistent with the PV-ORO results [Kliore and Patel, 1982], suggesting that

there have been no significant long-term temporal changes in the four decades since 1967 in the lower atmosphere of Venus. [49] The most striking feature in Figure 10 is the presence of a strong inversion layer at a pressure level near 100 hPa (60– 70 km) that becomes apparent in the latitude range between 60 and 80° latitude. This inversion layer corresponds to the cold collar region first observed in the northern hemisphere by Pioneer Venus [Taylor et al., 1980; Kliore and Patel, 1982; Newman et al., 1984]. The cold collar was not seen in some southern hemisphere profiles from Pioneer Venus [Kliore and Patel, 1982] and was not detected in the three northern hemisphere height profiles retrieved from Magellan occultation data [Jenkins et al., 1994]. The Radio Science experiment on Venera-15 and -16, however, did observe the cold collar in the southern hemisphere [Yakovlev et al., 1991]. The Orbiter Infrared (OIR) experiment on Pioneer Venus described this inversion region as a ‘‘crescent-shaped collar region’’ [Taylor et al., 1980], which seems to have a solar-fixed component and is also influenced by spiral streaks from the hot dipole regions around the poles. [50] The dynamics supporting the cold collar inversions are not understood. The strong zonal winds in and below the

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Figure 9. Depth of temperature inversion in the middle atmosphere (mesosphere) as a function of latitude. The depth of inversion is defined as the temperature difference between the tropopause and the maximum temperature in the middle atmosphere above this level. See Figures 6 and 7 for explanation. clouds in connection with the downwelling branch of the Hadley circulation likely have an association with the observed thermal behavior and particularly the transition to pronounced vortices around the poles. [51] Cooling of the atmosphere at 60-km altitude is affected by the rotating pair of infrared emission windows found at slightly higher latitudes than the cold collar structures. This, together with radiative absorption at higher levels, could be related to the strong inversion [Taylor et al., 1980]. The emission windows have been attributed to cloud depressions resulting from the downwelling Hadley circulation. [52] The collar region also divides the atmosphere vertically. Below the collar, the atmosphere cools with increasing latitude. Above, the temperature gradient is reversed (Figure 10). Again the origin and dynamics of this structure remain unknown. Diabatic heating or dynamically controlled processes could be responsible for the observed structures [Schubert et al., 1980]. At the altitude of the collar and closer to the pole the atmosphere is almost isothermal and also much warmer than the surrounding areas.

[53] Reconstructed orbit solutions for Venus Express have formal accuracies along the projected path in the plane of the sky of less than 100 m, which are more precise than earlier Venus missions. Uncertainties resulting from the orbit inaccuracy and further retrieval approximations, such as the assumption of spherical symmetry, lead to errors in the altitude of the retrieved occultation profiles that are at the same level of 100 m or less, which contributes to systematic errors in the retrieved profile parameters, especially the temperature. [54] Much weaker limitations on the altitude determination were reported by Kliore and Patel [1980] and Yakovlev et al. [1991], who indicated discrepancies of some kilometers. Yakovlev et al. [1991] report disagreements in the altitude assignment in the order of ±1 km in most cases up to ±5 km in certain orbits. Kliore and Patel [1980] observe inconsistencies of 2 – 3 km, although some orbits show differences by about 10 km. On the basis of a lapse rate of 10 K/km, this translates to temperature uncertainties of several kelvins in the lower atmosphere. [55] Systematic errors due to solar tidal distortions of the Venus atmosphere arising both from heating of the atmo-

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Figure 10. Nighttime temperature in the southern hemisphere for the third occultation season. Local times range from 2230 at low latitudes to 0500 at high latitudes. Contour map is based on measurements from 12 occultations in the interval DOY 116-146 2007. sphere and the Sun’s gravity gradient are thought to be small but need further analysis. [56] For reasons of convenience and because the uncertainties associated with earlier measurements are not well documented, we choose to present our results preferably as a function of the pressure, even though in most cases we indicate the approximate altitude level. The agreement between the VeRa temperatures and previous results under

similar conditions and at the same pressure level is typically within a few kelvins. [57] In this connection, it should be mentioned that the reduction of the PV-ORO occultation measurements employed a different atmospheric gas constant in (1), on the basis of the accepted atmospheric composition at that time (see Appendix A). [58] Figure 12 shows the pressure at 60-km altitude versus latitude for VeRa, PV-ORO, and VIRA. The VeRa

Figure 11. Temperature at the 1-bar level (50 km) as a function of latitude. Different symbols mark the different occultation seasons separated for ingress (entry) and egress (exit). 12 of 19

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Figure 12. Pressure at the 60-km altitude. VeRa (solid dots), PVO (open dots), and VIRA model (dashed line). Compare text. VeRa data here are for a Venus radius of 6052 km for consistency with PVO and VIRA. points are derived from the available observations after the first three OCC seasons, which have been averaged over ten latitude bins before comparison with the PV-ORO data. Hemispheric differences are neglected. The VeRa results fall between the VIRA model and the ORO observations. The

difference between VeRa and VIRA at the 60 km level is in the order of 10 hPa at low latitudes and increases to 20 hPa at the higher latitudes. [59] Figure 13 shows a comparison of VeRa and PV-ORO profiles from both the northern and southern hemispheres,

Figure 13. Mean VeRa temperature profiles (solid lines) from three occultation seasons binned for 10 latitude ranges in both hemispheres. PVO mean temperature profiles from ORO data (dashed lines) are from Seiff et al. [1985]. 13 of 19

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Figure 14. Temperature and pressure as a function of solar-fixed longitude. 0° is subsolar point; 90° is 0600; 180° is antisolar point; 270° is 1800. Data from latitude range 75° – 85°. averaged over 10° latitude bins. Although Figure 12 shows substantial differences in the pressures at fixed altitude, the overall agreement below the 100 hPa altitude is within ±5 K with maximum differences of 13 K. Overall, the VeRa results indicate a slightly warmer atmosphere at pressure levels > 1000 hPa ( 80°), solar-fixed longitude 90° –130°.

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Figure 17. Temperature distribution as a function of latitude and pressure for the first occultation season. The approximate altitude levels are also shown. (left) Daytime profiles from DOY 196 – 216 2006. (right) Nighttime profiles from DOY 216– 239 2006. Figure is revised version of Figure 3 from Pa¨tzold et al. [2007]. 0.2 km. In particular the reanalysis of OCC-1 has resolved the unexpectedly large day/night temperature variations reported earlier. [70] Figure 17 provides corrected results from the first occultation season. In addition to the correction of the geometry, the following other changes were included in the processing: (1) Every temperature value is assigned to the specific latitude and pressure level of the measurement point given by the projection of the closest approach of the radio link to the planetary surface. In the work by Pa¨tzold et al. [2007] each temperature profile was assigned to a single latitude value. (2) The profiles are now displayed as a function of log pressure. No significant local time differences are observed in the temperature-pressure profiles. The data sets are slightly different from those used by Pa¨tzold et al. [2007] owing to the refined latitude assignments of the profiles. [71] The improvements applied to Figure 17 are now incorporated in our standard data reduction procedures. We now find the temperature-pressure profiles from the three occultation seasons to be highly consistent among each other, as can be seen in the earlier sections of this paper.

11. Summary and Conclusions [72] The VeRa experiment sounded the Venus neutral atmosphere over three occultation seasons during the Venus Express nominal mission. In total, 118 profiles of neutral number density were retrieved over a meaningful range of local times. These were used to derive density, temperature, and pressure profiles with fairly uniform coverage of the southern hemisphere and high northern latitudes. [73] Comparing the results of the VeRa observations with those from PV-ORO, we find the latitudinal variations from

the two missions to be in general agreement. Overall, the VeRa temperatures are slightly lower than those from PV-ORO by differences that do not exceed a few kelvins, with the exceptions noted above. A comparison of the pressure distributions at 60-km altitude, VeRa vis-a-vis VIRA, reveals small deviations, with a difference of about 10 hPa in the lower latitudes, but with larger deviations in the vicinity of the poles (25 hPa). [74] The high vertical resolution of the VeRa radio occultation profiles offers the opportunity to compare the observed temperature gradient with the adiabatic lapse rate in order to investigate the stability of the atmosphere. These studies show an extremely stable atmosphere in the topmost cloud layer and above, a nearly adiabatic behavior in the middle cloud, and a stably stratified atmosphere below the middle clouds. These results are consistent with former results from the Pioneer Venus probes [Seiff et al., 1980] and Magellan [Hinson and Jenkins, 1995], which show only very small altitude regions of instability in the middle cloud. The unstable regions are extremely thin in the VeRa profiles. The static stability shows a latitudinal dependency corresponding to the more pronounced inversion structures in the higher-latitude range. The nearly adiabatic lapse rate in the middle cloud at altitudes of 50 –55 km is most obvious at midlatitudes. The near-polar regions show several small-scale perturbations and the stable layer below the middle cloud is almost undetectable. [75] The static stability is used to determine the variability of the tropopause with latitude. Referring to Figures 6 and 7 (bottom, left), any differences appear to be concentrated in the lower midlatitude and within a few degrees of the poles. Additional observations are needed to sort this out. [76] The VeRa results are consistent, in an overall sense, with the VIRA and VIRA-2 models as the temperature differences at the same pressure seldom exceed 10 K. From

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the limited data available at the current stage it is not possible to determine whether or not overall the VeRa data agree better with the VIRA or the VIRA-2 model. In particular, it is not clear that VIRA-2 is an improvement over VIRA. At present the VeRa observations are too limited to draw final conclusions about a possible improvement from the implementation of diurnal effects. Additional data are required in order to determine the extent to which temporal effects resulting from rotating structures, eddies or gravity waves are affecting the VeRa observations. [77] A first investigation of the latitude range between ±75° and 85° indicates the presence of a solar-induced effect above the tropopause. VeRa observations imply the presence of a wave-2 structure with the coldest temperatures located at the subsolar and antisolar points. Further observations are required to refine and extend these preliminary results.

Appendix A [78] Kliore and Patel [1980, 1982] used an atmospheric composition of 96.0% CO2 and 4.0% N2. Using the refractive indices from Essen and Froome [1951] this leads to an atmospheric constant of C1 = 1.30943 106 Kms2/kg. The implementation of this C1 value in our retrieval procedure results in temperature changes less than 2 K at all altitude levels in comparison with our usual results. The VeRa temperature profiles would be colder by 2 K if we would use the atmospheric composition used by Kliore and Patel [1980, 1982]. Therefore, differences in the atmospheric composition cannot explain the systematic bias between the VeRa results and the ORO results in the middle atmosphere.

Appendix B [79] The occultation results presented here are derived from observations of a series of measurements of the radio signal traveling between the Venus Express spacecraft and the ground station; for VeRa the measurement interval typically is 0.5 s. During this interval, the observed occultation ray always remains in the instantaneous plane defined by the positions of the spacecraft and Earth receiver with respect to Venus and the direction of local gradient of atmospheric refractivity, essentially the direction to the center of Venus. The altitude and lateral position of the ray varies with changes in the locations of the spacecraft and receiver. The atmosphere sampled by the occultation signal corresponds to the geometric area swept out during the measurement interval. Typical changes in the location of the ray during the measurement interval are of the order of kilometers in the horizontal, and at most a few hundred meters in the vertical, decreasing rapidly with lowering altitude. As the average horizontal atmospheric gradient is very small, this generally is not of concern except for measurements made at very large bending angles. Vertical gradients are quite large, so a reported measurement must be considered as an average over the altitude range seen by the ray during the measurement interval. [80] More difficult to visualize is the manner in which the atmosphere is represented in profile retrieved by Abelian inversion. Using ray theory, Fjeldbo et al. [1971, Appendix C] showed that the vertical refractivity profile of a spherically symmetric atmosphere could be retrieved by Abelian inver-

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sion from measurement of the ray asymptotes obtained by radio occultation. That is, it is possible to obtain the value of refractive index at the lowest level of each ray in a continuous sequence of observed rays. Given the variation in refractive index one can then calculate the corresponding state of the atmosphere from hydrostatic equilibrium and the gas laws. Lipa and Tyler [1979] provided an analysis of statistical error for Mariner 10 radio occultation measurements of Venus atmosphere. More recently, Ahmad and Tyler [1998] found the inversion kernel for atmospheric occultation and used this to calculate the weighting function for errors due to departures from spherical symmetry. A perturbation theory of systematic errors Abelian inversion due to systematic departures from spherical symmetry in ‘‘thick’’ and ‘‘thin’’ atmospheres is given by Ahmad and Tyler [1999], with results using the atmospheres of Earth and Mars as example cases. The Mars example is comparable to the Venus case above about the 1-bar pressure level; a detailed comparison should include a correction for the electrical and mass differences between the predominately nitrogen and carbon dioxide gases. [81] Acknowledgments. This paper presents results of a research project partially funded by the Deutsches Zentrum fu¨r Luft- und Raumfahrt (DLR) under contract 50 OV 0601. Our investigation could not have been successful without the efforts of the ESA Venus Express Science and Mission Operations teams. We particularly appreciate the support of the VEX Project scientist H. Svedhem (ESTEC) and VEX Mission Operations Manager F. Jansen (ESOC). The VeRa experiment has benefited greatly from the continued support of the NASA Deep Space Network. In this respect, it is a pleasure to thank T. W. Thompson and D. P. Holmes (JPL) for their special efforts.

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M. K. Bird, Argelander Institut fu¨r Astronomie, Universita¨t Bonn, Bonn D-53121, Germany. B. Haüsler, Institut fu¨r Raumfahrttechnik, Universita¨t der Bundeswehr Mu¨nchen, Neubiberg D-85577, Germany. M. Pa¨tzold and S. Tellmann, Abteilung Planetenforschung, Rheinisches Institut fu¨r Umweltforschung, Universita¨t zu Ko¨ln, Ko¨ln D-50931, Germany. G. L. Tyler, Department of Electrical Engineering, Stanford Universtity, Stanford, CA 94305-4020, USA.

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Evidence for anomalous cloud particles at the poles of Venus C. F. Wilson,1 S. Guerlet,2 P. G. J. Irwin,1 C. C. C. Tsang,1 F. W. Taylor,1 R. W. Carlson,3 P. Drossart,2 and G. Piccioni4 Received 30 January 2008; revised 25 May 2008; accepted 24 July 2008; published 4 November 2008.

[1] An analysis of near-infrared emissions on the nightside of Venus observed by the

Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) instrument on board Venus Express reveals anomalous cloud particles in the polar regions of Venus. These anomalous particles are found within the centers of polar vortices at both poles and are either larger or different in composition from those elsewhere in the planet. We find no persistent latitudinal variation in cloud properties at low to midlatitudes, nor do we find asymmetry between the southern and northern hemispheres. These findings arise from analysis of the relative brightness of 1.74 and 2.30 mm infrared radiation thermally emitted from the deep atmosphere of Venus. Larger cloud particles cause relatively more attenuation at 2.30 mm than at 1.74 mm, so we use a ‘‘size parameter,’’ m = (I1.74mm)/(I2.30mm)0.53, as a proxy for particle size. This methodology follows that of Carlson et al. (1993), supported by new radiative transfer modeling. Citation: Wilson, C. F., S. Guerlet, P. G. J. Irwin, C. C. C. Tsang, F. W. Taylor, R. W. Carlson, P. Drossart, and G. Piccioni (2008), Evidence for anomalous cloud particles at the poles of Venus, J. Geophys. Res., 113, E00B13, doi:10.1029/2008JE003108.

1. Introduction [2] The clouds of Venus have been extensively studied, by means of spacecraft entry probes, orbiter and flyby missions, and ground-based observation (see comprehensive reviews by Esposito et al. [1983, 1997]). Entry probes revealed that almost all particulate matter is located in a series of cloud layers starting at 45 km altitude, stretching up to 65 – 70 km. Data from the Pioneer Venus lander particle size spectrometer (LCPS) instrument [Knollenberg and Hunten, 1980] were interpreted as indicating four populations of particles. Mode 1 particles have a mean radius of 0.3 mm, and make up the bulk of the upper cloud layers. The larger mode 2 particles (mean radius 1.0 mm) make up the particulate mass in the upper clouds, while the slightly larger mode 20 particles (mean radius 1.4 mm) are found in the middle and lower clouds. The scattering properties of the mode 2 and 20 particles indicate that they are composed of liquid H2SO4/H2O mixture, with 75– 85% H2SO4 by weight [Esposito et al., 1983]. [3] Mystery still surrounds the large mode 3 particles found in the base of the clouds at altitudes 45– 52 km. The original analysis of LCPS data concluded that these must be crystalline particles [Knollenberg and Hunten, 1980]; however, a later study [Toon et al., 1984] concluded that the mode 3 particles could indeed be composed of liquid 1

Department of Physics, Oxford University, Oxford, UK. Laboratoire d’Etudes Spatiales et d’Instrumentation en Astrophysique, Observatoire de Paris, Meudon, France. 3 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA. 4 Istituto di Astrofisica Spaziale e Fisica Cosmica, Rome, Italy. 2


sulfuric acid droplets, once possible instrumental errors were taken into account. [4] Although it is clear that sulfuric acid comprises the majority constituent of the mode 2 and 20 cloud particles and possibly the mode 3 particles as well, there is also evidence that minority constituents are present. X-ray fluorescence measurements on the Vega probes found significant abundances of chlorine, phosphorus, and iron in lower cloud particles [Andreychikov et al., 1987]; an analysis from Krasnopolsky [1989] concluded that this suggested that the clouds contained compounds including Fe2Cl6, H3O4, and Al2Cl6. For the present work, though, we have ignored these minority constituents and assumed clouds comprising of 85 wt % H2SO4 to 15 wt % H2O, copying the modal size distributions as used by Grinspoon et al. [1993], as shown in Table 1. [5] In this work we take advantage of the near-infrared spectral window regions at wavelengths of 1.0 –2.5 mm, in which thermal emission from the deep atmosphere of Venus escapes through the clouds to space. These window regions exist in spectral regions between broad CO2 and H2O absorption features. The radiation is attenuated by a combination of gaseous absorption and scattering as it passes upward through the clouds to space; therefore the optical thickness and scattering properties of the clouds both have a significant effect on the observed radiation, as will be shown. Initially discovered fortuitously by Allen and Crawford [1984], the near-infrared emission windows have been extensively exploited in ground-based observations [Be´zard et al., 1990; Pollack et al., 1993], as well as by the Galileo Near Infrared Mapping Spectrometer (NIMS) during that spacecraft’s Venus flyby in 1990 [Carlson et al., 1991]. However, Venus Express is the first Venus orbiter equipped with a spectrometer capable of observing at these wavelengths.

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Table 1. Cloud Parameters Used in This Worka Mode Mode Mode Mode

1 2 20 3

Mean Radius r (mm)

Variance s (mm)

Altitude Range (km)

0.3 1.0 1.4 3.65

1.56 1.29 1.23 1.28

48 – 84 57 – 84 48 – 57 48 – 57

a Parameters are as per Grinspoon et al. [1993]. Unless otherwise noted, all cloud particles are assumed to be composed of 85% H2SO4 15% H2O.

[6] Radiative transfer modeling has been quite successful at replicating the observed spectra of these nightside emissions [Pollack et al., 1993; Grinspoon et al., 1993; Be´zard et al., 1990; Tsang et al., 2008a]. However, while the spectrally narrow near-IR window regions include enough spectral features to allow unambiguous mapping of several constituents of the deep atmosphere, they do not provide much information on cloud composition, because variations in cloud composition tend to cause broad changes in the spectra which cannot be observed within the narrow spectral range of a single near-IR transmission window. [7] All the thermal emissions in all of the window regions are attenuated as they pass upward through the clouds. This attenuation arises both because of absorption by cloud particles themselves (nonconservative scattering), and by gaseous absorption along the path taken by the photons. Because the particles are liquid and thus spherical, and because the wavelength of the light is of the same order as the radius of the particles, one can assume Mie scattering for the calculation of the scattering parameters. The single scattering albedo and extinction cross sections for the different modes are shown in Figure 1. The first thing to note is that the single scattering albedo, w0, is extremely close to 1 for all these particles at wavelengths below 2.5 mm, particularly at the shorter wavelength windows near 1.0 mm. The optical depth of the clouds is typically very high (t 30 as defined at l = 0.63 mm, in the model

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cloud defined by Pollack et al. [1993]), so the thermal emissions undergo a large number of scattering events as they pass upward through the clouds; it is only due to the high albedo of the H2SO4 droplets at wavelengths 100 km. However, it is only recently (more than 1 year after the beginning of the mission) that the three spectrometers can operate simultaneously in the solar occultation mode. These observations have enabled the study of the properties of the Venusian mesosphere over a broad spectral range. In this manuscript, we briefly describe the instrument characteristics and the method used to infer haze microphysical properties from a data set of three selected orbits. Discussion focuses on the wavelength dependence of the continuum, which is primarily shaped by the extinction caused by the aerosol particles of the upper haze. This wavelength dependence is directly related to the effective particle radius (cross section weighted mean radius) of the particles. Through independent analyses for the three channels, we demonstrate the potential to characterize the aerosols in the mesosphere of Venus. The classical assumption that the upper haze is only composed of submicron particles is not sufficient to explain the observations. We find that at high northern latitudes, two types of particles coexist in the upper haze of Venus: mode 1 of mean radius 0.1 rg 0.3 mm and mode 2 of 0.4 rg 1.0 mm. An additional population of micron-sized aerosols seems, therefore, needed to reconcile the data of the three spectrometers. Moreover, we observe substantial temporal variations of aerosol extinction over a time scale of 24 h. Citation: Wilquet, V., A. Fedorova, F. Montmessin, R. Drummond, A. Mahieux, A. C. Vandaele, E. Villard, O. Korablev, and J.-L. Bertaux (2009), Preliminary characterization of the upper haze by SPICAV/SOIR solar occultation in UV to mid-IR onboard Venus Express, J. Geophys. Res., 114, E00B42, doi:10.1029/2008JE003186.

1. Introduction [2] Venus is completely enshrouded in clouds which show an enormous vertical extent of more than 50 km. Venus’ clouds are mainly found in a cloud deck located between 45 and 70 km of altitude, with thin hazes above and below. Much of the clouds are composed of H2SO4 aerosol particles showing a multimodal particle size distribution. The upper haze (70 – 90 km) is composed of submicron aerosol particles with an effective radius below 0.3 mm [Lane and Opstbaum, 1983; Sato et al., 1996]. The composition of these particles was derived from the Pioneer Venus Orbiter Cloud photopolarimeter experiment [Kawabata et al., 1980]. A refractive index of 1.4 was measured, which is in agreement with a haze consisting of 1

Belgian Institute for Space Aeronomy, Brussels, Belgium. Space Research Institute, Moscow, Russia. 3 Service d’Ae´ronomie du CNRS, Verrie`res-le-Buisson, France. 4 Institut Pierre Simon Laplace, Universite´ de Versailles Saint Quentin, Guyancourt, France. 2


concentrated sulfuric acid (75%). Kawabata et al. [1980] showed that the haze is thicker at high latitudes and very diffuse in the tropics. Larger particles are found at lower altitudes. Particles with a mean radius of about 1.0 mm are the principal constituent of the main cloud deck. Even bigger particles with a mean radius larger than 4.0 mm have been observed at levels below the tropopause. [3] The Spectroscopy for Investigation of Characteristics of the Atmosphere of Venus (SPICAV) instrument [Bertaux et al., 2007], which is composed of three independent spectrometers, namely the UV and IR spectrometers and the Solar Occultation at Infrared (SOIR) instrument, was developed in view of investigating the Venus atmosphere from the ground up to the uppermost limit of the atmosphere. Each channel of the instrument can perform, vertical profiling by mean of solar occultation, this is the first time at Venus. Besides the detection of CO2, H2O, and HDO, as well as other minor constituents, this instrument provides unrivalled information on the aerosol loading of the atmosphere. Taken separately all three channels, operating in the solar occultation mode, can contribute to the characterization of the aerosol extinction as well as, in some cases, of

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the aerosol particle size. We will show that if we combine their measurements, it is moreover possible to define a bimodal distribution of the aerosol particles size. This information is of great interest as it is known that aerosol particles, through absorption and scattering of solar radiation, play a major role in the energetic balance of the atmosphere.

2. SPICAV/SOIR Instrument [4] SPICAV is a suite of three spectrometers in the UV and IR range dedicated to the study of the atmosphere of Venus from ground level to the outermost hydrogen corona at more than 40,000 km. It is derived from the SPICAM instrument flying on board Mars Express (MEX), with the addition of a new IR high-resolution spectrometer SOIR [Bertaux et al., 2007]. The UV spectrometer (118– 320 nm, resolution 1.5 nm) is identical to the MEX version. It is dedicated to nadir viewing, limb viewing and vertical profiling by stellar and solar occultation. The SPICAV IR sensor (0.65– 1.7 mm, resolution 0.5– 1.2 nm) which makes use of an Acoustooptical tunable filters (AOTF), investigates the dayside solar radiation reflected from the clouds and the nightside thermal emission coming through the clouds in nadir mode, and in solar occultation mode, this channel studies the vertical profiles of H2O, CO2, and aerosols. The pioneer SOIR spectrometer is a solar occultation IR spectrometer with the highest spectral resolution on board VEX. This new concept of instrument includes a combination of an echelle grating and an AOTF crystal to select one order at a time. One of the main objectives of SOIR is to measure HDO and H2O in solar occultation [Bertaux et al., 2007; Fedorova et al., 2008a; Svedhem et al., 2007], in order to characterize the escape of D atoms from the upper atmosphere and give more insight about the evolution of water on Venus. However, the instrument is also well adapted to investigate the isotopologues of CO2 [Bertaux et al., 2008; Wilquet et al., 2008] and other minor species [Vandaele et al., 2008]. The three channels of the SPICAV/SOIR instrument will be described in the following sections more specifically in view of their capabilities to characterize the aerosols present in the Venus atmosphere. The instrument is on the Venus Express spacecraft operating on the orbit around Venus from April 2006. This orbit lasts about 24 h and is highly elliptical with nearly 80°N pericenter [Titov et al., 2006]. The vertical resolution of the occultation is better when the distance to the limb is small, which is the case, for such an elongated orbit, at high northern latitudes. 2.1. UV Channel [5] The ultraviolet channel of SPICAV is a versatile instrument, operating in a wide range of modes: nadir, limb, and solar/stellar occultations. A detailed instrument description can be found in the paper by Bertaux et al. [2007]. The UV channel covers the 118 to 320 nm spectral range with a spectral sampling of 0.54 nm per pixel while the actual spectral resolution determined by optical aberration is three pixels (i.e., 1.5 nm). The UV channel is a compact imaging spectrometer equipped with an image intensifier and a Thomson CCD array of 288 384 pixels.

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[6] The focal length of the UV telescope is such that one CCD pixel covers a field of view (FOV) of 0.01 0.01°. The total useful FOV of the instrument is, however, limited by a slit located in the focal plane, whose width equals two CCD pixels as imposed by Nyquist-Shannon sampling theorem. Total FOV varies between 0.02° 0.1° and 0.02° 0.4° depending on CCD readout configuration. [7] Owing to downlink limitations, the equivalent of only 5 out of the 288 available pixel lines can be transmitted to Earth. To comply with this restriction while making use of a larger portion of the photosensitive area, pixel lines are first binned together during readout. The number of lines per bin (also referred to as bands) is determined upon consideration of photometry. For solar occultations, five adjacent bins of two to eight lines are used. In order to maintain the same vertical resolution at the limb of 1 km, a particular combination of integration time (ranging between 80 and 450 ms) and line binning (from two to eight) is set to adjust with the altitude of the spacecraft during solar occultation. See the numerous articles describing the binning procedure in more detail [Bertaux et al., 2007; Montmessin et al., 2006a, 2006b; Que´merais et al., 2006]. [8] The signal-to-noise ratio (SNR) has been determined by observing the sun outside the atmosphere where the signal is stable and varies only according to photon statistical noise. This analysis showed that pixel SNR can exceed 150 (SNRmax) in the range 200 to 300 nm for a single measurement. Below 200 nm, solar emission drops rapidly and yields no detector count shortward of 170 nm. As the line of sight traverses progressively deeper regions of the atmosphere, SNR decreases as SNRmaxx(exp(t))1/2 where t is the slant opacity of the atmosphere. [9] UV data treatment is performed through a succession of steps. First, dark charge (DC) and offset are subtracted from pixel content using masked pixels located at the extremity of each pixel line and dedicated to dark charge estimation. A predetermined non uniformity pattern allows extraction of an instantaneous DC value for any pixel of the CCD from reading of the masked CCD pixels. In addition, when a significant portion of the occultation sequence remains behind Venus with respect to the sun, i.e., SPICAV line of sight aims at Venus nightside, the electronic readout noise pattern (with a periodicity of 14 pixels) can be isolated and subtracted throughout the sequence, thereby improving the scientific analysis of low-statistics signals. [10] Atmospheric transmission spectrum is computed as the ratio of the spectrum collected at a given altitude to the reference spectrum of the sun formed by coadding hundreds of solar spectra recorded outside the atmosphere (Figure 1a.). [11] On some sequences, the solar spectrum recorded outside the atmosphere shows a linear drift of the signal amplitude throughout the sequence. The cause of this drift is still not clearly established but appears related to center-tolimb darkening effect of the sun. Intensity variation is then produced by spacecraft line of sight scanning across the solar disk as a result of either the spacecraft slewing in one direction or, most likely, the orbital course of the spacecraft itself projecting at different locations at the surface of the sun. In any case, this effect is corrected for by performing a linear regression of the solar signal variation outside the atmosphere and by extrapolating the linear variation to the reference spectrum over the whole sequence.

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126 km

a 1

104 km 95 km

0.8

Transmittance

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91 km

0.6

88 km

0.4

0.2 82 km

0

60 km

200

220

240

260 wavelength (nm)

280

300

Figure 1. Example of transmittance spectra for the three channels during occultation 486. (a) SPICAVUV: transmittance at several altitudes during the occultation. (b) SPICAV-IR: sequential scanning of 664 spectral points recorded by defining three windows (spectral ranges 1288.5 –1309.3 nm, 1334.4 – 1442.5 nm, and 1461.5 – 1471.1 nm) and 11 individual measurements described as ‘‘dots’’ (wavelengths 650.8, 757.2, 852.7, 982.3, 1101.1, 1159.6, 1197.2, 1273.5, 1323.0, 1553.7, and 1626.0 nm). Last dot is not used for the retrieval owing to its sensitivity to a CO2 absorption band. (c) SOIR: a series of transmittances throughout the occultation. Each image corresponds to one selected diffraction order (spectral window). 2.2. Near-IR Channel [12] The SPICAV-IR instrument is part of the SPICAV/ SOIR experiment on board the Venus Express mission. It is a new generation of the AOTF spectrometer (SPICAM-IR) on MEX [Fedorova et al., 2008b; Korablev et al., 2006].

SPICAV-IR works in the range 0.65 –1.7 mm with a spectral resolution of 5.2 cm1 in the LWL channel (1 – 1.7 mm) and 7.8 cm1 in the SWL channel (0.6 –1.05 mm). Two photodiodes register light in the two orthogonal polarizations at the AOTF output. The spectrometer is designed to work in

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Figure 1. (continued) nadir or solar occultation mode. The entry optics (a small lens with an useful diameter of 3 mm and a circular diaphragm) provides an angular field of view of about 40 or 0.07° for solar occultation observations, which corresponds to a vertical resolution of 1 –15 km on the limb of Venus, depending on the spacecraft altitude. SPICAV- IR works on the principle of sequential scanning of the spectrum. Each spectral point is recorded in 2.8 ms. To shorten the spectrum recording time to 2 s (664 spectral points), a regime of ‘‘windows’’ and ‘‘dots’’ has been used (Figure 1b) [see also Korablev et al., 2006]. First, the instrument scans the spectral windows (from longer wavelength downward) then the reference wavelengths are recorded in reversed order. In solar occultation mode, the possible windows are 1461.5 – 1471.1 nm, 1334.4 – 1442.5 nm and 1288.5 – 1309.6 nm, primarily dedicated to measurements of CO2 and H2O abundances from the 1.43 mm and 1.38 mm bands, respectively. Aerosol properties and vertical distributions are determined using 10 continuum points outside the gas absorption bands (Figure 1b). 2.3. SOIR Channel [13] The instrument [Bertaux et al., 2007; Nevejans et al., 2006] and its in-flight performances [Mahieux et al., 2008], including data handling, onboard background subtraction, calibrations of the AOTF and the echelle spectrometer, have already been extensively described, whereas the retrieval technique and some results are discussed in the paper by Vandaele et al. [2008]. SOIR is an echelle grating spectrometer operating in the IR, combined with an AOTF for the selection of the diffraction grating orders. It is designed

to measure the atmospheric transmission in the IR (2.2 – 4.3 mm) at high resolution (0.15 cm1), using the method of solar occultation (Figure 1c). [14] The SOIR detector has 320 columns along the wave number axis and 256 rows along the spatial axis. To avoid saturation, short integration times are used (20 to 30 ms), depending on the wavelength at which the measurement is taken. The background signal is measured and subtracted onboard. In order to improve the signal-to-noise ratio (SNR), a number of measurements can be accumulated as long as the total measuring time remains below 250 ms. [15] The slit height is 30 arc min and it is projected onto 32 rows of the detector. The slit width is equivalent to 2 detector pixels in the spectral direction. The spectral resolution of the spectrometer is very high and varies between 0.13 cm1 at 4.0 mm (order 111) and 0.27 cm1 at 2.3 mm (order 192). The corresponding resolving power [l/Dl = u/Du] exceeds 20,000 [Mahieux et al., 2008]. [16] Owing to telemetry limitations, only eight spectra, each 320 pixels long, can be downloaded per second. During most observations of Venus, these eight spectra are taken in four different diffraction orders (four different RFs applied to the AOTF), each corresponding to two large bins of 16 or 12 rows on the detector.

3. Data Sets and Observation Strategy [17] Figure 2 shows the spatial distribution of all solar occultations performed by the three channels of SPICAV/ SOIR since March 2007, i.e., since it was rendered possible to perform simultaneous observations for SOIR and SPI-

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Figure 2. Spatial map of measurements in the solar occultation mode, acquired since March 2007, for the three channels of the SPICAV/SOIR instrument. Coordinates of the limb tangent points at an altitude of 120 km for SPICAV-UV (circles), SPICAV-IR (inverted triangles), and SOIR (asterisks). CAV in the solar occultation mode. This paper is aimed at demonstrating the capability of the instrument to sound the vertical structure of aerosols in the upper haze through occultation spectroscopy. We will therefore concentrate on a few observations for which we had simultaneous data for the three channels (see Table 1). The line of sight to the Sun is slightly different for the three channels so that the portion of the atmosphere probed is not exactly the same, yet the geophysical positions of the tangent point (the point along the optical path that is the closest to the surface of the planet) are very close. The analysis of the data sets from each channel of the instrument is made independently in order to eliminate any instrumental issues. All these observations are located at high northern latitudes (between +60° and +75°). [18] During a solar occultation, e.g., for a sunset, the measurement cycle is started before the instrument’s line of sight intersects with the top layers of the atmosphere, and a reference spectrum can be recorded for each channel. Below that level, solar light is absorbed by atmospheric components and the intensity of the recorded signal starts to decrease. As spectra obtained during the occultation are divided by the reference solar spectrum recorded outside the atmosphere, any solar or instrumental signature gets removed and the transmittance contains only information about the Venus atmosphere. This is illustrated in Figure 1c where the transmittances obtained during occultation 486 for 4 different spectral windows centered at 2715, 3343, 3838, and 4264 cm1 (orders 121, 149, 171, and 190) are plotted. The evolution of the light transmission during solar

occultation 485 is depicted in Figure 3 for the three channels of the SPICAV/SOIR instrument: two wavelengths for the UV channel, two wavelengths for the IR channel, and the average signal for two diffraction orders recorded during this occultation for SOIR. The vertical resolution depends on the geometry of the solar occultations and the distance to the limbs of VEX. In the orbits analyzed in this paper the vertical resolution is 3.0 km for SPICAV-UV, 3.4 km for SPICAV-IR, and 2.0 km for SOIR.

4. Method of Analysis 4.1. Retrieval of the Extinction Coefficients [19] All three instruments, taken together, provide useful information on the absorption due to aerosols. In this section, we will first indicate how aerosol extinction coefficients are obtained from the measurements, which are Table 1. Distance to the Limb, Longitude, and Latitude for Each Orbit at a Tangent Height of 65 km and 120 km Showing How These Values Vary During the Occultation Distance to the limb (km) Longitude (deg) Orbit

Date

482 484 485 486 487

16 Aug 2007 18Aug 2007 9 Aug 2007 20 Aug 2007 21 Aug 2007

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Latitude (deg)

65 km 120 km 65 km 120 km 65 km 120 km 2474 2657 2790 2973 3249

2131 2277 2380 2515 2702

242 246 249 251 253

243 248 250 252 254

+73 +70 +68 +65 +61

+75 +73 +71 +69 +66

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Figure 3. Solar light transmission through the Venus atmosphere of the solar occultation performed during orbit 485 by the SPICAV/SOIR instrument. Two wavelengths for each channel are plotted here: SPICAV-UV at 220 nm (black circles), SPICAV-UV at 300 nm (gray circles), SPICAV-IR at 757 nm (black inverted triangles), SPICAV-IR at 1553 nm (gray inverted triangles), SOIR at 2560 nm (black asterisks), and SOIR at 3978 nm (gray asterisks). different in kind for the three channels of the instrument. Then we will investigate how particle size and number density can be inferred from these measurements. 4.1.1. UV Channel [20] The inversion procedure is almost identical to the one applied to the SPICAM stellar occultations at Mars, with the exception that solar occultations provide a narrower spectral range to extract the CO2 concentration. The reader is therefore referred to the numerous publications dedicated to the subject [Montmessin et al., 2006a, 2006b; Que´merais et al., 2006]. Inverting occultation data only requires consideration of the simple Beer-Lambert’s law, which states that source attenuation scales exponentially with opacity. Spectral inversion is performed independently for every altitude. For every level, a least squares fitting technique (Levenberg-Marquardt) is used to infer the amount of CO2, which possesses a prominent electronic transition between 120 and 200 nm, and the aerosol opacity; both quantities being integrated over the line of sight. Profiles of CO2 local density and aerosol local extinction (b) are obtained after vertical inversion using Abel’s integrals. [21] For each integration, five spectra are recorded simultaneously by the instrument, each corresponding to a distinct position relative to the limb. Each band of the

detector therefore provides an independent profiling of the atmosphere. However, it was found in a number of cases (usually when the spacecraft was far from the limb) that a significant shift existed between the transmission profiles of each band. This was most likely caused by a relative error of altitude registration between the pixels of the CCD. A 5-point boxcar average has therefore been applied to the aerosol profiles to smooth out the scatter of values. 4.1.2. SPICAV IR and SOIR Channels [22] The first step is to determine the extinction (b) in each atmospheric layer, supposed to be homogenous, where we have a measurement by using an onion-peeling method R [Vandaele et al., 2008] and by assuming that bdz = ln(T) where T is the observed transmittance from which line absorptions have been removed, and dz is the thickness of the layer under consideration. Cumulative and local extinction coefficients were retrieved from each of the series of transmittances recorded during one occultation: the values correspond to the average value of the signal, for SOIR, in each selected diffraction order and, for SPICAV IR, for each selected spectral point. Except for some details due to the instrument specificity, the inversion procedure for SPICAV-IR is identical to the one described for the SPICAM solar occultations on Mars [Fedorova et al., 2008b].

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Figure 4. Spectral dependence of the extinction coefficient b during orbit 485. The extinction was obtained from data acquired at two wavelengths with the SPICAV-UV channel (circles), at 10 wavelengths with the SPICAV IR channel (inverted triangles), and at four wavelengths with the SOIR channel (asterisks). The various altitudes of the solar occultation are mentioned in the top right corner. 4.1.3. Spectral Dependence [23] First, we investigate the spectral dependence of the extinction for the different wavelengths probed by the instrument. The extinction coefficients deduced, as explained earlier, from the measurements performed by the three channels are plotted as a function of wavelength in Figure 4, for various altitudes of solar occultation performed during orbit 485. [24] The Angstro¨m exponent (a) is usually used to describe the dependency of the aerosol extinction coefficient on wavelength: b ¼ b0

a l l0

aerosol extinction for the SOIR data is slight and not monotonic, and therefore is not described by equation (1). The occultations chosen for this paper are selected so that the 4 spectral windows probed by SOIR are well spread over its entire spectral range, in order to maximize the aerosol extinction variations in function of the wavelength (Figure 4). 4.2. Determination of the Radius and of the Total Number Density of the Particles [27] The extinction coefficient is expressed as Zþ1 bð z; lÞ ¼ N ð zÞ f ðrÞQðr; lÞdr

ð1Þ

ð2Þ

0

[25] In the case of the UV channel only two points are shown to facilitate reading. The spectral dependence follows a power law (see Table 2). Usually, all the other points fall in between the two plotted points, defining a clear spectral dependence which allows the unambiguous determination of the particle size, see thereafter. [26] The SPICAV near-IR data also exhibit an obvious spectral dependency, while the spectral dependency of the

Table 2. Values of the Angstro¨m Exponent in the Near-IR and in the UV for Various Sizes of Particles r (mm)

l = 0.3 mm; l0 = 0.2 mm

l = 0.75 mm; l0 = 0.85 mm

l = 1.00 mm; l0 = 0.85 mm

l = 1.55 mm; l0 = 0.85 mm

0.2 0.7 1.0

2.3 0 0

2.07 0.65 0.3

2.4 0.48 0.56

2.8 0.12 0.50

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Figure 5. Refraction index model (T = 215 K) assuming 75% H2SO4 spherical particles and classical Mie theory [Hummel et al., 1988] in the wavelength range covered by the SPICAV/SOIR instrument. The boxes show the spectral windows of the instrument; dark gray box for the SPICAV-UV channel, gray for the SPICAV-IR channel, and light gray for the SOIR channel. where N(z) is the particle total number density at altitude z and Q(r; l) is the extinction cross section at wavelength l. A lognormal distribution has been used to describe the size distribution of particles of radius r in the Venus upper haze: 2 ! ln r ln rg 1 f r; rg ; sg ¼ const r exp 2 ln2 sg

ð3Þ

where rg and sg are the mean radius and variance. [28] The calculation of the extinction cross section Q(r, l) is based on Mie theory, assuming that the upper haze aerosols are composed of 75% H2SO4 aqueous spherical particles, whose refraction index is derived from Hummel et al. [1988] for T = 215 K (see Figure 5). The spectral ranges probed by the three channels of the SPICAV/SOIR instrument are also indicated in Figure 5 and clearly show the complementarity between them. [29] The aerosol extinction coefficients b derived for each of the sampled wavelength ranges (i.e., for one channel) are then normalized to a reference wavelength lr: ben ¼

bð z; lÞ bð z; lr Þ

ð4Þ

This reference is chosen as b (852.7 nm) for SPICAV IR and b (250 nm) for SPICAV UV. In the case of SOIR, the reference value corresponds to the higher wave number probed, since the four spectral intervals in one occultation are not always the same. [30] Finally, an estimation of the particle radius was obtained as described in the paper by Bingen et al. [2003], by fitting the normalized experimental aerosol extinctions ben, obtained from equation (4), to their corresponding theoretical values: bt rg ; sg ; l ¼

Zþ1

f r; rg ; sg Qðr; lÞdr

ð5Þ

0

The fit was obtained by minimization of the merit function: 2 4 e X bn ð z; li Þ btn rg ; sg ; li M rg ; sg ; z ¼ Dben ð z; li Þ i¼1

ð6Þ

This leads to the determination of the aerosol parameters sg (z) and rg (z), where Db is the 1-sigma error on the retrieved extinction coefficients. Once the radius that gives the best fit at each altitude has been extracted, the

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Figure 6. Vertical profiles of the b extinction coefficient obtained with the three channels of the SPICAV/SOIR instrument for the orbits (a) 485, (b) 486, and (c) 487. The extinction was obtained from data acquired with SPICAV-UV at 220 nm (black circles), SPICAV-UV at 300 nm (gray circles), SPICAVIR at 757 nm (black inverted triangles), SPICAV-IR at 1553 nm (gray inverted triangles), on Figure 6a with SOIR at 2560 nm (gray asterisks) and SOIR at 3978 nm (black asterisks), and on Figure 6b and Figure 6c with SOIR at 2345 nm (gray asterisks) and SOIR at 3682 nm (black asterisks). corresponding model of particle number density N(z) that best fits the data can also be found.

5. Results 5.1. Vertical Profiles of Aerosol Extinctions [31] The extinction coefficients b retrieved as described in section 4.1 are plotted in Figure 6 for the selected orbits, when all three channels of the instrument were measuring simultaneously. For clarity, b for only two wavelengths per channel of the SPICAV/SOIR instrument are shown. [32] The general trend is a decrease of the extinction with increasing altitude except when aerosols layers are observed. This phenomenon was more pronounced at longer wavelengths (SOIR channel) than at shorter wavelengths (SPICAV-UV). It could indicate the presence of particles with a size larger than the wavelengths of the UV channel (>300 nm), which is less sensitive to such particles. In general, less temporal variability of the b coefficient was observed in the UV than in the IR. [33] These aerosol layers have been observed in several occultations, mainly during summer 2007, showing temporal and/or spatial variations in terms of the altitude of the layer (Figure 7). Figure 7 illustrates the apparition and the displacement of such layers observed with the SOIR channel in the upper haze during orbits 482 and 484 to 487. While the layers were almost not visible in the UV, they

were also detected with the SPICAV-IR channel (Figure 6), yet a small difference in the altitude of the layer is observed between the SPICAV-IR and the SOIR channels. This can be explained by a weak accuracy of the calibration of the FOV of the SPICAV-IR channel. For orbit 482, the extinction coefficient, for the four spectral windows investigated during the solar occultation, increased fairly smoothly with decreasing altitude. In addition, the extinction showed little spectral dependency; that is, the extinction is very similar for the four diffraction orders. In contrast, for orbits 484 to 487, one or two layers appeared in the extinction profiles in which extinction decreased with decreasing altitude before it increased further (Figure 7). The altitude of the layers varied from orbit to orbit, they were located at 82 km and 74 km for orbit 484, 78 km and maybe 74 km for orbit 485 and, at 84 km and 78 km for orbit 486 and at 83 km and maybe 72 km for orbit 487. Moreover, the spectral dependency of the extinction was greater for these occultations than for the one during orbit 482 and was strikingly higher within the layers than at other altitudes (Figure 7). For example, in orbit 486, at an altitude of 78.5 km, there is about 1 order of magnitude for b between diffraction order 171 (4 105 km1) and order 121(3 104 km1). We are currently retrieving temperature, density and pressure from these peculiar orbits, as a layer was not observed in all occultations, to see if there is any correlation with the observed variations of the layer.

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Figure 7. Temporal variation of a layer in the extinction profiles observed with the SOIR channel. Extinction coefficients in function of the altitude for orbit 482 (brown), diffraction orders 111 (circles), 121 (triangles), 137 (squares), 171 (inverted triangles); orbit 484 (blue), diffraction orders 125 (circles), 149 (triangles), 172 (squares), 180 (inverted triangles); orbit 485 (green), diffraction orders 112 (circles), 119 (triangles), 149 (squares), 174 (inverted triangles); orbit 486 (gray), diffraction orders 121 (circles), 149 (triangles), 171 (squares), 190 (inverted triangles); and orbit 487 (orange), diffraction orders 121 (circles), 149 (triangles), 171 (squares), 190 (inverted triangles).

[34] Detached layers of aerosols were already observed in the past above the Venus clouds [Lane and Opstbaum, 1983]. It is preliminary here to speculate on the mechanisms of formation of such layers but gravity waves are known to create stratified layers in the atmosphere. Recently, internal gravity waves have been identified in the lower and upper clouds of Venus [Peralta et al., 2008]. 5.2. Retrieval of Particle Size [35] The use of the three channels allowed us to investigate a broad range of wavelengths in the UV and the IR and to make use of the spectral dependence of the extinction (Figure 5) to infer aerosol size(s). It is worth mentioning that the simultaneous use of the three channels in the solar occultation mode was not possible at the beginning of the VEX mission but through a programming adaptation, it has been in operation for about 1 year. This means that for the earlier measurements, aerosol characterization has to be performed from data acquired by two channels (SOIR and SPICAV-IR). We therefore make first an attempt to derive independently particle size(s) and number densities of the Venusian upper haze from the extinction coefficients obtained for each of the three channels of the instrument. This analysis is illustrated using a number of orbits with coordinated solar occultations and using the method described in section 4.3, i.e., by fitting the aerosol extinction

curve with a Mie theory model (equation (5)). A lognormal distribution (equation (3)) was assumed where sg was variable for SPICAV-UV and fixed to 0.1 for SPICAV-IR and SOIR (similar particle sizes were found for a fixed variance of 0.2 or of 0.3, data not shown), and the refractive indices of H2SO4 compiled by Hummel et al. [1988] was used. [36] Figure 8 shows the vertical size distribution of the particles for solar occultations performed during orbits 485, 486, and 487. The channels cannot discriminate particles with a radius higher than the wavelength at which the data were acquired, i.e., for these particular orbits, a maximal radius of 0.3 mm for SPICAV-UV, of 1.6 mm for SPICAV-IR and of 3.7 mm for SOIR. [37] The data obtained with the UV channel indicate the presence of particles with a radius increasing from 0.1 mm at 100 km up to 0.3 mm at 75 km (Figure 8). The vertical distribution for this channel exhibits low variability from one orbit to another. [38] From the SOIR and SPICAV-IR channels, the vertical distribution of particle size is limited to altitudes comprised between 70 km and 90 – 95 km. The radii determined with both channels are in fairly good agreement and are comprised between 0.4 and 1.0 mm. Particles of 1 mm were detected at 74– 77 km of altitude during all orbits. For each orbit analyzed here, the maxima in the

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Figure 8. Vertical distributions of particle size for orbits (a) 485, (b) 486, and (c) 487. Radii derived from normalized extinction values obtained at 2 wavelengths with the SPICAV-UV channel (circles), at 10 wavelengths with the SPICAV-IR channel (inverted triangles), and at 4 wavelengths with the SOIR channel (asterisks). vertical particle size distribution (Figure 8) correlate with the maxima of the extinction vertical profiles (Figure 6). The aerosol layers also exhibited temporal and/or spatial variations in terms of the size of the particles within the layer, from 0.4 up to 1.0 mm radius. [39] The particle sizes found with the UV channel in this study are in good agreement with values obtained previously. Sato et al. [1996] compiled Venus cloud and haze properties deduced from Pioneer Venus, Venera, and Vega mission results. Sato et al. [1996] reported haze particles in the north polar region of rg = 0.25 mm ± 0.05 mm and sg = 0.25 mm ± 0.05, while Esposito [1983] identified particles of rg = 0.2 mm and Kawabata et al. [1980] estimated haze particles of rg = 0.23 mm ± 0.04 mm and sg = 0.18 mm ± 0.1. They all refer to an altitude range comprised between 70 and 90 km, called the upper haze. However, our measurements tend to suggest that the two modes found in the upper clouds (properties summarized by Esposito [1983]), usually defined as mode 1 (rg = 0.2 mm) and mode 2 (rg = 1.0 mm), are still both present at altitudes higher than 70.0 km. Earlier works reported that modes 1 and 2 only coexist in the upper cloud region (55 – 70 km) or below and that only mode 1 is found above. Whether the merging of these two modes account for only one global

mode of particles or two separate distributions cannot be firmly established on the basis of this analysis alone. 5.3. Total Particle Number Density [40] Figure 9 shows the particle number densities derived in the UV spectral range for aerosol particles of about 0.1– 0.3 mm size from the SPICAV-UV channel and in the IR for aerosol particles of about 0.4– 1.0 mm size from the SPICAV-IR and the SOIR channels depending on the altitude. SPICAV-UV measurements lead to mode 1 particle number density between 10 and 30 cm3 below 90 km, decreasing at higher altitudes. These values are 1 order of magnitude lower than those reported by Esposito [1983] but more consistent with the measurements from Pioneer Venus limb scans [Lane and Opstbaum, 1983] at northern midlatitude, close to the orbit latitudes analyzed in this paper (Table 1). [41] Number density for the larger particles (mode 2) decreases smoothly from 10 to 15 cm3 at 70 km down to less than 1 cm3 at 90 km. It is important to mention that the results obtained for the two IR channels are very comparable although they were calculated from independent data sets, yet from simultaneous measurements. The values found at about 70 km, which is the boundary between the upper cloud and the upper haze, is close to, yet smaller than the one reported by Esposito [1983] for

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Figure 9. Vertical profiles of the particle total number density in the Venus upper haze during two successive orbits of Venus Express. The radius of particles found by fitting normalized extinction to a haze model allowed derive N for mode 1 particles with the SPICAV-UV channel (circles), N for mode 2 particles with the SPICAV-IR channel (inverted triangles), and N for mode 2 particles with the SOIR channel (asterisks) during orbit 485 (black) and orbit 486 (gray). mode 2 particles in the upper cloud region, i.e., 50 cm3. Nevertheless, the values found in this restricted analysis (only three orbits) should be taken with caution and are indicative of an order of magnitude for the number density for mode 2 particles. A systematic multichannel retrieval is currently ongoing. The combination of extinction coefficients obtained from UV up to mid-IR and the use of a bimodal particle size distribution will probably allow retrieving unique aerosols parameters and solving the discrepancies between nadir [Esposito, 1983] and limb viewing [Lane and Opstbaum, 1983].

6. Conclusion [42] The number of orbits analyzed was restricted for this paper, aimed at proving the capability of the SPICAV/SOIR instrument on board Venus Express to characterize the upper haze of Venus’ atmosphere. The ability of the instrument since March 2007 to perform simultaneous solar occultations with the three channels will allow us to investigate the upper haze in greater detail in the future and to plan solar occultations specifically for this purpose. This could be achieved by making use of the full spectral range of the instrument, from 170 nm up to 4 mm, by choosing appropriate, regularly spread wavelengths for each channel, and by taking maximum advantage of the spectral dependency of the extinction studied in this paper. [43] One of the main advantages of the solar occultation technique is the high vertical resolution and in comparison

with nadir viewing, a much longer optical path resulting in an improved sensitivity. In addition, data collected at terminators could lead to a better understanding of the known morning/evening asymmetry of the upper haze [Esposito, 1983]. [44] The determination of the particles sizes performed for this paper was made independently for the different channels of the SPICAV/SOIR instrument. The first results look promising for further analysis of the aerosols in the mesosphere of Venus as, in the future, the radii of particles could be retrieved in one single minimization of the merit function (see section 4.3) from the extinction coefficients obtained with the three channels. This study demonstrated the existence of at least two types of particles, one type with a radius comprised between 0.1 and 0.3 mm as inferred by the UV channel and the second type, detected in the IR, with a radius varying between 0.4 and 1 mm depending on the altitude. Therefore, the model describing the upper haze on Venus should include a bimodal population for putting together the three channels in retrieving particle sizes from the extinction profiles. To our knowledge, this is the first time that the existence of mode 2 particles at altitudes above 70 km in the atmosphere of Venus is shown. It was found independently for the two IR channels of the SPICAV/SOIR instrument, giving strong evidence for its existence. [45] Our measurements also showed the high temporal variability of the aerosol loading in the Venus mesosphere. The SPICAV/SOIR is well suited to study the vertical distribution of the different types of aerosols, as well as such temporal and latitudinal variability.

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[46] Acknowledgments. The research program was supported by the Belgian Federal Science Policy Office and the European Space Agency (ESA-PRODEX program contracts C 90268, 90113, and 17645). The Russian team acknowledges RFBR grant 06-02-72563.

References Bertaux, J. L., D. Nevejans, O. Korablev, E. Villard, E. Que´merais, E. Neefs, F. Montmessin, F. Leblanc, J. P. Dubois, and E. Dimarellis (2007), SPICAV on Venus Express: Three spectrometers to study the global structure and composition of the Venus atmosphere, Planet. Space Sci., 55(12), 1673 – 1700, doi:10.1016/j.pss.2007.01.016. Bertaux, J.-L., A. C. Vandaele, V. Wilquet, F. Montmessin, R. Dahoo, E. Villard, O. Korablev, and A. Fedorova (2008), First observation of 628 CO2 isotopologue band at 3.3 mm in the atmosphere of Venus by solar occultation from Venus Express, Icarus, 195(1), 28 – 33, doi:10.1016/ j.icarus.2008.01.001. Bingen, C., F. Vanhellemont, and D. Fussen (2003), A new regularized inversion method for the retrieval of stratospheric aerosol size distributions apllied to 16 years of SAGE II data (1984 – 2000): Method, results and validation, Ann. Geophys., 21, 797 – 804. Esposito, L. W. (1983), The clouds and hazes on Venus, in Venus, edited by D. M. Hunten et al., pp. 484 – 564, Univ. of Ariz., Tucson. Fedorova, A., et al. (2008a), HDO and H2O vertical distribution and isotopic ratio in the Venus mesosphere by Solar Occultation at Infrared spectrometer on board Venus Express, J. Geophys. Res., 113, E00B22, doi:10.1029/2008JE003146. Fedorova, A. A., O. I. Korablev, J.-L. Bertaux, A. V. Rodin, F. Montmessin, D. A. Belyaev, and A. Reberac (2008b), Solar infrared occultations by SPICAM experiment on Mars-Express: Simultaneous measurement of the vertical distributions of H2O, CO2 and aerosol, Icarus, 200, 96 – 117. Hummel, J. R., E. P. Shettle, and D. R. Longtin (1988), A new background stratospheric aerosol model for use in atmospheric radiation models, AFGL-TR-88 – 0166, Air Force Geophys. Lab., Hanscom Air Force Base, Mass. Kawabata, K., D. L. Coffeen, J. E. Hansen, W. A. Lane, M. Sato, and L. D. Travis (1980), Cloud and haze properties from Pioneer Venus polarimetry, J. Geophys. Res., 85(A13), 8129 – 8140, doi:10.1029/JA085iA13p08129. Korablev, O., et al. (2006), SPICAM IR acousto-optic spectrometer experiment on Mars Express, J. Geophys. Res., 111, E09S03, doi:10.1029/ 2006JE002696. Lane, W. A., and R. Opstbaum (1983), High altitude Venus haze from Pioneer Venus limb scans, Icarus, 54, 48 – 58, doi:10.1016/00191035(83)90071-4. Mahieux, A., et al. (2008), In-flight performance and calibration of SPICAV SOIR on board Venus Express, Appl. Opt., 47(13), 2252 – 2265, doi:10.1364/AO.47.002252.

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Montmessin, F., et al. (2006a), Sub-visible CO2 ice clouds detected in the mesosphere of Mars, Icarus, 183, 403 – 410, doi:10.1016/j.icarus. 2006.03.015. Montmessin, F., E. Que´merais, J. L. Bertaux, O. Korablev, P. Rannou, and S. Lebonnois (2006b), Stellar occultations at UV wavelengths by the SPICAM instrument: Retrieval and analysis of Martian haze profiles, J. Geophys. Res., 111, E09S09, doi:10.1029/2005JE002662. Nevejans, D., et al. (2006), Compact high-resolution space-borne echelle grating spectrometer with AOTF based on order sorting for the infrared domain from 2.2 to 4.3 micrometer, Appl. Opt., 45(21), 5191 – 5206, doi:10.1364/AO.45.005191. Peralta, J., R. Hueso, A. Sańchez-Lavega, S. Pe´rez-Hoyos, G. Piccioni, O. Lanciano, P. Drossart, and t.V.-V.E. team (2008), Characterization of gravity waves in the upper and lower clouds of Venus using VIRTISVEX images, Geophys. Res. Abstr., 3, Abstract EPSC2008-A-00095. Que´merais, E., J.-L. Bertaux, O. Korablev, E. Dimarellis, C. Cot, B. R. Sandel, and D. Fussen (2006), Stellar occultations observed by SPICAM on Mars Express, J. Geophys. Res., 111, E09S04, doi:10.1029/ 2005JE002604. Sato, M., L. D. Travis, and K. Kawabata (1996), Photopolarimetry analysis of the Venus atmosphere in polar regions, Icarus, 124, 569 – 585, doi:10.1006/icar.1996.0231. Svedhem, H., et al. (2007), Venus Express—The first European mission to Venus, Planet. Space Sci., 55(12), 1636 – 1652, doi:10.1016/j.pss.2007. 01.013. Titov, D. V., et al. (2006), Venus Express science planning, Planet. Space Sci., 54(13 – 14), 1279 – 1297, doi:10.1016/j.pss.2006.04.017. Vandaele, A. C., et al. (2008), Composition of the Venus mesosphere measured by SOIR on board Venus Express, J. Geophys. Res., 113, E00B23, doi:10.1029/2008JE003140. Wilquet, V., A. Mahieux, A. C. Vandaele, V. I. Perevalov, S. A. Tashkun, A. Fedorova, O. Korablev, F. Montmessin, R. Dahoo, and J.-L. Bertaux (2008), Line parameters for the 01111 – 00001 band of 12C16O18O from SOIR measurements of the Venus atmosphere, J. Quant. Spectrosc. Radiat. Transfer, 109, 895 – 905, doi:10.1016/j.jqsrt.2007.12.021.

J.-L. Bertaux, F. Montmessin, and E. Villard, Service d’Ae´ronomie du CNRS, BP3, F-91371, Verrie`res-le-Buisson, France. R. Drummond, A. Mahieux, A. C. Vandaele, and V. Wilquet, Belgian Institute for Space Aeronomy, 3 Avenue Circulaire, B-1180 Brussels, Belgium. ([email protected]) A. Fedorova and O. Korablev, Space Research Institute, 84/32 Profsoyuznaya Street, 117997, Moscow, Russia.

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Venus Express observations of atmospheric oxygen escape during the passage of several coronal mass ejections J. G. Luhmann,1 A. Fedorov,2 S. Barabash,3 E. Carlsson,3 Y. Futaana,3 T. L. Zhang,4 C. T. Russell,5 J. G. Lyon,6 S. A. Ledvina,1 and D. A. Brain1 Received 29 January 2008; revised 21 April 2008; accepted 27 May 2008; published 26 August 2008.

[1] The solar wind interaction contributes to the loss of Venus atmospheric constituents,

especially oxygen, by direct antisunward acceleration of planetary ions and possibly by related sputtering of neutrals. Both comet-like ‘‘ion pickup’’ and related sputtering processes may have played a key role in Venus’ atmosphere evolution, but the significance of their effects, as well as other proposed escape mechanisms, is still uncertain. In particular, recent reports of only modest ion escape rates measured on Venus Express (VEX) during the current low-activity phase of the solar cycle make it important to reconsider the evidence seen in both Pioneer Venus Orbiter and VEX observations suggesting significantly enhanced escaping O+ ion fluxes during periods of disturbed interplanetary conditions. At present, the most extreme interplanetary conditions result from the effects of coronal mass ejections (CME), which may have been more prevalent in the first 1–2 billion years of the Sun’s history. The Analyser of Space Plasmas and Energetic Atoms 4 (ASPERA-4) Ion Mass Analyzer and Magnetometer on Venus Express have now made detailed measurements during several periods when CME disturbances encountered Venus. The observations and models described in this report provide further insights into the possible response of oxygen ion escape to solar activity. In particular, they illustrate nuances of in situ sampling of large-gyroradius pickup ions, related to the atmospheric source properties, spacecraft orbit geometry, and the prevailing interplanetary conditions, that make the estimation of the variable global escape fluxes due to that process particularly challenging. In three of the four cases examined in some detail, ionospheric oxygen ions were either unobservable or below the limit of detectability for passes well within interplanetary coronal mass ejection (ICME) intervals. In the fourth example, where ionospheric ions were observed as expected from the model, O+ pickup ions were observed in greater abundance than is typical in undisturbed solar wind. Other escape processes are not considered here, although it is assumed the source population for the modeled pickup ions is a preaccelerated upper atmosphere component. Analysis of many more ICME events, expected to be obtained as the Sun becomes more active in future years, are necessary to resolve the question of the importance of ICME to Venus oxygen escape. Citation: Luhmann, J. G., A. Fedorov, S. Barabash, E. Carlsson, Y. Futaana, T. L. Zhang, C. T. Russell, J. G. Lyon, S. A. Ledvina, and D. A. Brain (2008), Venus Express observations of atmospheric oxygen escape during the passage of several coronal mass ejections, J. Geophys. Res., 113, E00B04, doi:10.1029/2008JE003092.

1. Introduction 1

Space Sciences Laboratory, University of California, Berkeley, California, USA. 2 Centre d’Etude Spatiale des Rayonnements, Toulouse, France. 3 Swedish Institute of Space Physics, Kiruna, Sweden. 4 Space Research Institute, Austrian Academy of Sciences, Graz, Austria. 5 Institute of Geophysics and Planetary Physics, University of California, Los Angeles, California, USA. 6 Department of Physics and Astronomy, Dartmouth College, Hanover, New Hampshire, USA. Copyright 2008 by the American Geophysical Union. 0148-0227/08/2008JE003092$09.00

[2] One of the key planetary science issues for Venus (and Mars) is the evolutionary importance of the solar wind interaction [e.g., Chassefiere, 1997; Lammer et al., 2006]. Our current picture of planetary atmospheric ion scavenging by direct interaction of the solar wind with weakly magnetized planets was developed in large part from observations obtained by the Pioneer Venus Orbiter, PVO [e.g., Moore et al., 1990; Brace et al., 1995], although the Venera mission first detected Venus’ planetary ion wake [e.g., Vaisberg et al., 1995]. The observations confirmed the presence of a comet-like interaction where the convection electric field of the solar wind, E = VXB (where V and B are the bulk

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velocity of the solar wind and the interplanetary magnetic field vector, respectively) accelerates or ‘‘picks up’’ planetary ions near and above the exobase (200 km altitude).The ions that become pickup ions may be either transported into the acceleration region by other forces such as those associated with pressure gradients, or they may be produced in situ from the local neutral gases. If they are accelerated to at least the 11 km/s escape velocity for Venus, and are on outward trajectories that do not reimpact the exobase [e.g., Luhmann and Kozyra, 1991], they can be permanently lost. Other mechanisms for Venus ion escape have also been suggested that generally fall under the headings of ‘ionospheric ion outflow’ [e.g., Barabash et al., 2007a] or ‘bulk ionospheric escape’ [e.g., Terada et al., 2002]. However, many of the PVO observations at both high ( keV) and low (10s of eV) ion energies can be modeled by assuming that the pickup process dominates [Luhmann et al., 2006, 2007] we focus on that process in the present analysis. [3] It was also inferred from PVO measurements of both the upper atmosphere (observed by UV remote sensing and by the Orbiter Neutral Mass Spectrometer), the ionosphere (observed by the retarding potential analyzer, thermal ion mass spectrometer, and Langmuir Probe), and the ion fluxes moving antisunward in the nightside wake (observed by the Plasma Analyzer and Neutral Mass Spectrometer in its ion mode), that singly ionized atomic and molecular oxygen ions are the primary species involved in the solar windinduced escape of the Venus upper atmosphere [e.g., Brace et al., 1987, 1995; Mihalov et al., 1995]. This is a consequence of the ion chemistry of a CO2 atmosphere, where oxygen forms the bulk of the upper atmosphere and ionosphere [e.g., Fox and Kliore, 1997]. [4] Typical escape rates of oxygen ions from Venus today are low from an atmospheric evolution standpoint, little more than the equivalent of a weak cometary ion tail at approximately 1024 1026 O+ s 1 [e.g., Moore et al., 1991; Barabash et al., 2007a]. However, the Venus solar wind interaction represents one of the only ways to accelerate oxygen and other heavy atmospheric elements to escape speeds. The need for an effective loss mechanism for Venus oxygen has lately become a matter of special importance as arguments mount for a possible early ocean [e.g., Grinspoon and Bullock, 2007; Kulikov et al., 2006]. One key question is whether this solar wind-enabled escape mechanism could have been sufficient, or whether instead, important quantities are sequestered and buried in oxidized regolith. This question bears on the expectations for future surface and subsurface mineralogy measurements at Venus in pursuit of a history of water on the planet. The broader consequences relate to general unmagnetized and weakly magnetized planetary bodies and satellites with atmospheres that interact directly with surrounding magnetized external plasma flows of any origin. [5] Luhmann et al. [2006, 2007] revisited the PVO escaping O+ ion observations to investigate the possible impact of disturbed solar wind plasma and interplanetary magnetic field conditions on escaping ion fluxes. The observations used in that study consisted of > 36 eV O+ ion fluxes (10 eV is required for O+ escape from Venus) measured by the Orbiter Neutral Mass Spectrometer in its ion detection mode. The results suggested that enhanced

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incident solar wind dynamic pressures and interplanetary magnetic fields, such as those that occur during passage of interplanetary coronal mass ejections (ICME) and solar wind stream interaction regions are sometimes followed by detection of enhanced (by 100) escaping O+ fluxes. What could not be determined from PVO measurements owing to the limited instrument capabilities, and orbital and temporal sampling coverage, is both the full range of the escape fluxes and the definitive escaping ion composition. In particular, evaluation of the escape flux requires knowledge of the details of its spatial and temporal variations, which are difficult to obtain with in situ measurements, while the composition measurements require ion mass spectrometry over an energy range from 10 eV to > several 10s of keV. [6] While several statistical analyses carried out on the PVO data revealed general patterns of the inferred O+ escape flux spatial distribution, and average values of the fluxes along the spacecraft orbit [e.g., Mihalov and Barnes, 1982; Moore et al., 1990; Luhmann et al., 2006], it is not possible to reconstruct from these the global response to relatively rare and/or short-lived extremes in the solar and interplanetary conditions. Observation-validated models that allow the interpretation of global loss rates from local measurements provide the best alternative. In addition, the ASPERA-4 Ion Mass Analyzer, IMA, on Venus ExpressVEX [Barabash et al., 2007b] now routinely measures escaping O+ fluxes with definitive mass identification in an energy range (0.01 – 30. keV) exceeding that available on PVO (e.g., see the discussion by Luhmann et al. [2006]). Since its orbit insertion in April 2006, Venus Express has witnessed a number of significant solar events [e.g., Futaana et al., 2007], providing an excellent opportunity to investigate, with the supporting VEX magnetometer observations [Zhang et al., 2006], the response of the escaping ions. [7] The motivation for the study described here is to further constrain present rates of oxygen ion escape during ICME passages, toward evaluating the evolutionary importance of these conditions for Venus. VEX ASPERA-4 IMA and magnetometer observations are used, together with an MHD field-based test particle model of O+ pickup ions, to analyze the escaping ion response during several events. The results illustrate that making the case for increased loss rates at these times depends critically on our ability to use models to interpret the observations. However, the observations reported here are independent of the assumption of any particular process and are of interest in themselves. Truly definitive results on the ion escape response will depend on the analysis of data obtained during a large number of different ICME as well as case studies. This will require measurements over at least one solar maximum period, the next of which will be underway in 2010 – 2012, together with models that take into account whatever processes are found to be significant.

2. ICME Observations on VEX [8] Details of the VEX instruments used in this study can be found in papers by Barabash et al. [2007b] and Zhang et al. [2006]. The magnetometer is a dual triaxial fluxgate magnetometer where one triad is used to correct the

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Figure 1. A set of four single orbit VEX magnetic field time series showing the ICME cases used here: (a) 5 July 2006, (b) 18 July 2006, (c) 11 September 2006, and (d) 14 February 2007. Near the beginning of each time series Venus perturbation is seen, including the bow shock, magnetosheath, dayside magnetic barrier, and magnetotail as sampled by VEX. The ICME field components exhibit an initial interplanetary shock and field pileup in the ICME sheath, followed by the smoothly rotating, often enhanced, solar ejecta fields sometimes lasting a day or more, although the start of the ICME is not always visible in these plots. measurements for the spacecraft-generated magnetic fields [Zhang et al., 2006]. The ASPERA-4 instrument is a close copy of the ASPERA-3 instrument on Mars Express [Barabash et al., 2006], and includes both the ion mass spectrometer, called the IMA for ion mass analyzer, and an electron spectrometer, although here we concentrate on the data from the former. The mass range of the IMA is from 1 to 60 amu and the energy range is from 10 eV to 30 keV. The angular response of the IMA, which includes a deflection system, allows detection of ions coming from all azimuthal directions with respect to the Venus-Sun line, and from elevation angles of ±45°. Although the IMA is also a capable solar wind ion analyzer, ASPERA-4 operates only around periapsis to conserve instrument resources and to stay within its telemetry allotment (although there are occasional special full-orbit operations). Additional details of the IMA characteristics and operations are given by Fedorov et al. [2006] and Barabash et al. [2007b].

[9] VEX orbits Venus in a high-inclination elliptical orbit with 250 – 300 km periapsis close to the north pole [Svedhem et al., 2007]. While the plasma analyzer operates only around periapsis, the magnetometer makes continuous measurements. To identify occurrences of ICME events in the VEX data we visually scanned magnetometer vector time series from 2006 and 2007 for the typical field signatures of ICME. These often include a leading shock jump and sheath (compressed solar wind) region, followed by larger than average, low-variance, smoothly rotating ejecta or driver fields. Typical duration of the entire disturbance is 1 – 2 days. Examples found in the PVO data at 0.73 AU are described in earlier papers by Lindsay et al. [1995], Mulligan et al. [1998] and in a recent paper by Jian et al. [2008]. The same signatures are used to identify ICME near the Earth [e.g., Jian et al., 2006]. [10] Most unambiguous ICME events found in the VEX data occurred in 2006, when the Sun was more active. From

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Figure 2a. Case 1: 5 July 2006 ICME ASPERA-4 IMA observations, together with the preceding and following orbits (days). The top panels show a 2-h orbit segment for these data, near periapsis. The top energy-time spectrograms include ions with masses 12– 60 amu, while the bottom spectrograms include all ions detected. The latter is dominated by solar wind H+. The bottom panels show the magnetic field magnitude from the magnetometer for context. these, three cases were selected for detailed analysis because they exhibit the smooth, slowly rotating ejecta fields useful for analyzing and modeling the effects of the ICME conditions on pickup ions. A single case from 2007 was also included. In addition to the large, smooth ejecta fields, high dynamic and thermal pressures are found in the preceding postshock sheath of an ICME due to the ejecta’s high velocity, solar wind density compression ahead of the fast-moving ejecta, and shock heating of the sheath solar wind plasma. However, these sheath periods last only a

fraction of a day and so it is difficult to capture the Venus interaction at the VEX periapsis during the times of their passage. However, the high bulk speeds, and related highconvection electric fields and enhanced solar wind pressures, can persist throughout the passage of the ejecta, although they have typical solar wind densities. These attributes are not easily distinguished in the perturbed planetary interaction environment that ASPERA-4 samples near the VEX periapses, but their presence can still be inferred from the intervals of IMA data obtained outside the

Figure 2b. Case 2: Same as Figure 2a but for the 18 July 2006 ICME. 4 of 15

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Figure 2c. Case 3: Same as Figure 2b but for the 11 September 2006 ICME. (In this case the pass in the first panel is a day earlier because ASPERA-4 data were not available the day before this ICME). bow shocks. In the cases of the four ICME events analyzed here, the plasma velocities outside of the Venus bow shock were not especially high, an attribute not surprising for events occurring near solar minimum. Thus the main feature of these particular ICME is their steadier and larger than normal (by 2x) interplanetary magnetic fields lasting a day or more, plus the expected (but not observed) shorter period of enhanced dynamic pressure from the preceding sheath higher densities. [11] What are the possible consequences of ICME passage expected for Venus? In addition to a reduced ionopause altitude and ionospheric magnetization in response to the increased solar wind pressures in the ICME sheath [e.g.,

Luhmann and Cravens, 1991], ICME conditions are known to produce the largest convection electric fields and interplanetary field magnitudes detected in the interplanetary medium at 0.73 AU [e.g., Lindsay et al., 1995]. Thus any processes affected by the convection electric field or ionospheric magnetization should be enhanced. In addition, the interplanetary shock-heated solar wind is expected to result in higher than usual Venus sheath electron temperatures and related impact ionization of any neutrals exposed to them, while the increased solar wind plasma densities in the ICME sheath can increase the rate of charge exchange (creating both additional ionization of the neutral O and energetic neutral hydrogen atoms, ENAs). The slowly varying, un-

Figure 2d. Case 4: Same as Figure 2c but for the 14 February 2007 ICME. 5 of 15

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Figure 3a. ASPERA-4 IMA energy-mass diagrams for ICME cases 1 and 2. These show the ion composition for the 15 min around periapsis for the three passes (before, during, and after the ICME passage, from left to right), for (top) case 1 (Figure 2a) and (bottom) case 2 (Figure 2b). The heavy ions can be seen in the right part of the diagrams for the passes on either side of the central passes in Figures 2a and 2b. They consist mainly of O+ and O2+, but heavier ions are also present. usually steady magnetic fields of the ejecta meanwhile provide exceptionally simple conditions for analyzing and interpreting the behavior of observed ions in terms of the pickup process. [12] Time series of VEX magnetometer vector components in the VSO system (x directed toward the Sun, y opposite the direction of planetary motion, and z northward from the Venus orbit plane) are shown in Figures 1a– 1d for the days containing the selected passes. In all of these cases the ICME is already in progress when the day begins. The ICME are easily distinguished here by the previously mentioned low variance, slowly rotating ejecta fields, and by their field magnitudes, especially in the third example. For the first case shown, from 5 July 2006, the conditions surrounding the Venus interaction are dominated by highinclination, northward (+Bz, clock angle 0) ejecta fields, while the 18 July 2006 case is dominated by highinclination, southward ( Bz, clock angle 180) ejecta fields. The 11 September 2006 case has By and Bz (clock angle 225) dominated fields, while the 14 February 2007 case has +By and +Bz (clock angle 45) fields. Thus the selected cases provide a variety of external magnetic field orientations during ICME passages. [13] ASPERA-4 IMA energy-time spectrograms for the four selected passes within the ICME events are shown in Figures 2a– 2d, together with the total magnetic field and orbit near periapsis in the cylindrical view (where the y and

z positions are converted into a cylindrical coordinate projected into a plane perpendicular to the Venus-Sun (x) axis). As controls, we also show the spectrograms for the adjacent periapsis passes the day before and the day after the main event passage. (The VEX orbital period is approximately an Earth day, also the approximate duration of a typical ICME.) The orbit segments at the top of each pass illustrate the spacecraft sampling in local time, solar zenith angle, and radius. The spectrograms are shown for both all detected ions (0– 60 amu) and heavy (>12 amu) ions only. The total ion spectrograms are included to provide the solar wind plasma context of the measured heavy planetary ions. Note that the light ions may also include some picked-up planetary H+, H+2 and He+ ions [e.g., Barabash et al., 2007a], but we exclude these from consideration in our present analysis focused on oxygen. The total ion spectrograms are dominated by the standard solar wind interaction features including proton heating at the bow shock (seen as broadening of the main solar wind proton band in the energy-time spectrogram), the compression and deceleration in front of the ionospheric obstacle, and the reduction or disappearance of solar wind proton densities in the wake. The periodic dropouts in the detected fluxes are a result of the directional sampling cycle of ASPERA-4 and the anisotropy of the detected ions, most of which flow approximately antisunward. Near-periapsis magnetic field magnitude time series are plotted at the bottom to provide

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Figure 3b. Same as Figure 3a but for ICME cases 3 and 4 in Figures 2c and 2d. For case 3 (top row), the pre-ICME pass is two days ahead. For case 4 (bottom row) it is a day ahead. Case 4 is the only one of the four with high (saturated counts) oxygen ion content in the central diagram, during the main portion of the ICME passage. a perspective on the location of the ion features relative to the magnetic pileup signatures in the sheath. [14] The heavy ion spectrograms in the second panels from the top of each example in Figures 2a, 2b, 2c, and 2d are dominated by atomic oxygen, but may also contain contributions from O+2 , CO+, and CO+2 [Carlsson et al., 2006]. As shown by the IMA mass-energy plots of Barabash et al. [2007b], these also include ‘shadows’ of the solar wind due to the high flux of protons and the resulting overlap in mass-energy space. The species separations used for the heavy ion spectrograms are obtained with Gaussian distribution fits to the overlapping mass peaks in the mass-energy plots [e.g., Carlsson et al., 2006]. These spectrograms exhibit several distinctive features including a period dominated by lo—energy ions within the 10– 20 min interval around periapsis (roughly the plot center), and a more energetic heavy ion signature with a highly variable appearance. The low-energy ions are typically in the 10eV to a few 10s of eV energy range, and are thus already at escape energies for oxygen. The latter may appear as a faint high-energy component during the same time period that the low-energy ions are observed, or it may be observed outside the periapsis interval in the adjacent magnetosheath, together with the solar wind protons. The magnetosheath cases closely resemble what was observed in the PVO plasma analyzer data in similar timeenergy spectrograms without mass separation [e.g., Luhmann et al., 2006]. In a few passes the energies of the

heavy ions around periapsis appear to dip below the IMA energy threshold. A possible relationship between the lowenergy and high-energy components will become apparent in the discussion of the modeling results below. [15] The key observational result derived from these events is best seen with the help of mass-energy analyses of the data in Figures 2a– 2d. Figures 3a and 3b show massenergy plots obtained from the 15 min around periapsis for the four event passes and for passes before and after the main event pass. The color scale indicates the detector counting statistics for these samples, which are approximately proportional to the flux. It is seen that three of the four event passes show a near or total absence of counts at the heavy ion masses compared to the passes during relatively typical interplanetary conditions. The one exception out of the four, on 14 February 2007, in contrast shows an exceptionally high (saturated) count rate for the heavy ions in the same near-periapsis sample. The possible reasons for the nondetection of the mainly oxygen ions at these times for the other cases, both at periapsis and in the adjacent sheath, are best investigated with models of the global distribution of escaping ions.

3. Model Analysis of the VEX Ion Observations [16] As noted in the introduction, we here assume that the pickup process describes the bulk of escaping heavy ions observed by the ASPERA IMA around Venus. This as-

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sumption provides a starting point for analysis that has observational support from PVO results, and can also lead to insights on whether other processes are important. [17] Venus pickup ions include all planetary ions that are accelerated by the action of the underlying convection

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electric field, whether they are escaping or on trajectories that return them to the atmosphere. It is important to appreciate that the pickup process is not restricted to the magnetosheath and solar wind outer regions. If the solar wind magnetic field penetrates the upper atmosphere between the ionopause and the exobase (or even below it), and a flow is present, an ion can still be accelerated by E = VXB. Different processes may also combine. For example, some ions may first move in response to other forces such as pressure gradient forces, and then attain escape speed when they are transported into a region where the convection electric field leads to their further acceleration. It is also important to remember that, unless they are observed in the mid-to-distant wake, pickup ions detected along a spacecraft orbit do not necessarily contribute to an escape flux. In determining whether an observed O+ ion is escaping or not, one must consider its energy, its location with respect to the planet, and its direction of motion. One also needs to know how representative the orbital sampling of the escaping ions is of the total escaping ion population. A global model of the ions is thus extremely useful for determining both whether a particular observed O+ flux is escaping, and what the implications of the local measurements are for the overall loss rates. [18] As also mentioned above, one of the striking features of the heavy ion spectrograms in Figures 2a–2d is their diverse appearances. Most show a period of enhanced fluxes around periapsis at the lowest energies and altitudes measured. These probably represent the pickup O+ source region in the upper ionosphere that is dominated by ionization and nightward transport of the dayside thermosphere neutral O source, rather than by the much more rarefied exospheric O from dissociative recombination of ionospheric O+2 . The coexistence of cold and hot oxygen atmospheric components in the pickup ions has been discussed previously (e.g., recently by Luhmann et al. [2007]). The ion spectrogram features at the higher altitudes exhibit the inbound-outbound asymmetries expected from the pickup process and the finite heavy ion gyroradius effects found for the more energetic pickup ions. When the interplanetary magnetic field component perpendicular to the solar wind velocity rotates from one angle (or clock angle) to another, the appearance of the pickup ion wake signature can change dramatically [e.g., Kallio et al., 2006]. But we do not even know how the low- and higher-energy ion observations are related to one another, or if their variation on the global scale is coupled. Such key outstanding questions only emphasize the difficulty of inferring global ion escape rates from one or two orbits during special events such as ICME passages.

Figure 4. Orthogonal views of some MHD model field lines (black) with superposed test particle O+ trajectories for MHD fields without (red) and with (blue) mass loading near the Venus obstacle. The model interplanetary field is northward (clock angle = 0). (a) Noon– midnight plane projection; (b) ecliptic plane projection; and (c) view from the Sun, looking tailward. The trajectories are traced to 3 Rv. These illustrate both the asymmetry and the spatial concentration of the oxygen pickup ion ‘‘wake.’’ 8 of 15

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Figure 5. (top) Cross sections of the picked up O+ ion wake at the (right) terminator plane and (left) down-tail color coded by ion energy. This case uses the MHD model with mass loading effects and is for 90° (+y) interplanetary field clock angle. The northward pointing convection electric field produces the north-south asymmetry. (bottom) Same as top but using the MHD fields and flows without mass loading effects. [19] The model of the global pickup process that we use here has been validated by comparisons with magnetic fields measured on PVO [Kallio et al., 1998], and with PVO plasma analyzer measurements of O+ [Luhmann et al., 2006]. The background fields and flows are provided by an MHD model of the typical 0.73 AU solar wind interacting with a spherical conductor whose radius is at the nominal subsolar ionopause height for Venus, with an interplanetary magnetic field perpendicular to the incident flow direction. The trajectories of planetary O+ ions are traced as test particles in these background magnetic fields and flows. We use the same numerical equation of motion solver, including the Lorentz force and Venus gravity, as used by Luhmann et al. [2006]. The test particle pickup ions’ properties (energy and directional distributions) are the collective result of the assumed distribution of O+ starting points, their initial velocities, the geometry and strength of the background MHD model magnetic fields, and the bulk plasma flows that together with these magnetic fields describe the E = VXB convection electric field. The effects of the Venus-Sun (x) component of the interplanetary magnetic field are not included in this model. This

allows us to rotate the model results around the Venus-Sun or x axis to approximate the effects of orbital sampling of the pickup ion population for different interplanetary field clock angles (relative to Venus North). The x component of the field is known to introduce some dawn-dusk asymmetries into the plasma interaction with Venus and so its neglect should be kept in mind, but for many analyses this is a secondary effect. [20] Two versions of the MHD model were used to calculate the pickup ion trajectories: one assumes the solar wind interacts with the conducting spherical obstacle with no effect of the production of heavy ions in the inner magnetosheath. Such a scenario might apply if the ionopause is at higher altitudes due to a low solar wind pressure or an enhanced ionospheric pressure. The other version assumes mass loading of the underlying flow by heavy O+ ions consistent with a simplistic approximation to photoion production from both thermospheric and exospheric atomic oxygen above the dayside of the planetary obstacle. (See Kallio et al. [1998] for further details.) [21] To illustrate the basic nature of the pickup ion spatial distributions and their sampling by the VEX orbit, Figure 4

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Figure 6. Illustration of VEX periapsis pass sampling of model O+ trajectories (obtained with the MHD field and flow model with no mass loading) for four interplanetary field clock angles: (a) 0°; (b) 45°; (c) 90°; and (d) 270°. The view is from the Sun. These suggest how an in situ ion observer’s perspective could be biased by a particular orbit’s geometry of intersection with the ion wake.

compares the trajectories of several hundred O+ ions launched on a spherical shell within a few hundredths of a planetary radius of the MHD model’s inner boundary for MHD model fields with (blue) and without (red) mass loading. The trajectories are traced to 3 Rv (Rv = the Venus obstacle in the model (planet plus ionosphere) 6300 km), a distance that covers the VEX orbit sampling of near-Venus space. Three projections, one as seen from the Sun, one in the ecliptic, and one in the noon –midnight plane are shown. The pickup ion distribution global asymmetry seen here, with the highest concentration of large-gyroradius trajectories initially moving toward the hemisphere where E = VXB points, is a well known signature of the pickup process. While the two cases produce similar trajectories, the mass loaded case trajectories are more spatially concentrated when seen in cross section from the Sun. In both cases it is apparent that one’s assessment of the escaping ion fluxes in such a distribution will depend on where and when they are sampled as the spacecraft orbits and the interplanetary field (and ion wake) rotate. [22] Figure 5 displays complementary plots of the locations where the modeled pickup ions cross thin cross-

sectional slabs at the terminator and downstream for the cases with (Figure 5a) and without (Figure 5b) mass loading. Here the interplanetary field is along the +y axis so that the maximum density of the asymmetric pickup ion wake seen in Figure 4 is in the north or +z half plane. The existence of a wedge-like feature and a low-energy central wake concentration at several Rv downstream is consistent with VEX results reported by Barabash et al. [2007a] from statistical analyses of the oxygen ions in the Venus wake, while the terminator cross section asymmetric bulge in the direction of the upstream convection electric field is consistent with reported ionopause cross section asymmetries seen on PVO [Phillips et al., 1988]. These cross sections, together with the ion trajectories in Figure 4, emphasize the asymmetry and limited cross section of the pickup ion wake, properties that are critical when evaluating global escape rates from limited orbital sampling. This is further emphasized by Figures 6 and 7 which show the four selected periapsis orbit segments together with the pickup ion trajectories. The ion trajectories have been rotated to three orthogonal positions to represent the effect of a range of interplanetary field clock angles on the orbital sampling

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Figure 7. Same as Figure 6 but for the ecliptic plane projection: (a) 0°; (b) 45°; (c) 90°; and (d) 270°.

geometry. These illustrate that one could infer a large range of ion escape fluxes, from negligible to representative of the maximum, from what is measured along the same VEX orbit, depending on the location of the orbital plane and the prevailing interplanetary field. Models provide a way to better understand the implications of the observations and also allow numerical experiments to determine which model assumptions produce results consistent what is observed. [23] To obtain the general character of the VEX heavy ion energy-time spectrograms, we found the following choices for the O+ starting grid were necessary: (1) starting points only in a narrow altitude range near the inner (spherical) model boundary; (2) a starting point grid including both dayside and nightside hemispheres, rather than just the dayside; (3) nonzero test particle starting velocities. The first of these was necessary to reproduce the narrow (in energy) pickup ion signatures; the nightside grid was necessary to obtain the observed deep wake fluxes of O+ near the base of the induced magnetotail current sheet separating the tail lobes; the nonzero starting velocities allowed the consistent reproduction, with the same model, of the low-altitude, low-energy ion signature and the sheath energetic ion signatures for the same pass. This last assumption also has the merit that PVO Retarding Potential Analyzer observations [Miller and Whitten, 1991] showed significant (5 – 10 km/s, up to nearly 10 eV) antisunward

thermal O+ flows in the upper ionosphere at solar zenith angles near and beyond the terminator plane, interpreted to result from day-to-night ionospheric plasma pressure gradients. [24] To illustrate the general characteristics of the modeling results we show in Figure 8 simulated O+ energy-time spectrograms for eight interplanetary field clock angles, spaced at 45° intervals, for the trajectory associated with the 18 July 2006 periapsis. This pass provided a particularly good trajectory for illustrating the asymmetries in the pickup ion distribution because it roughly samples the northern hemisphere in the terminator plane from dawn to dusk. We found it was not necessary to use many ions or a closely specified altitude of the starting point grid to establish the general patterns in the spectrograms, thus we started approximately 1200 O+ ions uniformly distributed over a spherical grid at 1.05 times the obstacle radius. The results are shown for two cases: the background MHD model without mass loading and an initial O+ velocity of a few 10s of km/s at the terminator, and an MHD model with some rudimentary ionospheric mass loading (see discussion of these two models by Kallio et al. [1998]), and a lower (by 0.1x) initial O+ velocity. The spectrograms were constructed by calculating the number of constant time step ion trajectory points falling within spherical volumes of 0.1 Rv radius, stepped along the VEX orbit through the

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Figure 8. Simulated ASPERA-4 IMA O+ spectrograms for the nominal (no mass loading) MHD field and flows case, for clock angles spaced 45° apart, obtained by ‘‘flying’’ the 18 July 2006 orbit through the modeled ion wake. As expected, the spectrograms look very different, although the ion wake is the same in all cases. periapsis pass. The velocity vectors of the particles entering the spherical volumes were filtered to accept only those within the ±45° elevation angles accepted by the IMA. The color scale and its range are analogous to the log (counts) scale in the VEX spectrograms. Here the model statistics provide an idea of the numbers of O+ trajectory points falling within the sampling spheres. These statistics are proportional to the IMA counts. As mentioned above, the assumption of the initial velocity of the ions affects the low-

energy near-periapsis O+ signatures, as does the use of mass loaded versus not loaded MHD field and flow models. [25] The general patterns in the modeled heavy ion spectrograms in Figure 8 resemble those in the observations in Figures 2a– 2d. In particular they reproduce the occasionally observed narrow bands of high intensity O+ in the sheath on inbound and/or outbound legs with velocities (energies) that smoothly increase (decrease) as the spacecraft altitude increases (decreases). In the model, these features do not represent a time-dependent acceleration

Figure 9. Energy-mass diagrams for the narrow layers (5 min duration) visible on (a) the outbound leg of case 1 and (b) the inbound leg of case 2 ICME pass spectrograms shown in the middle of Figures 2a and 2b. O+ appears to be present in these features. 12 of 15

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Figure 10. (top) Views from the Sun of the O+ trajectories in the nominal model (red) and for a mass-loaded, increased (3) interplanetary field model (blue). (bottom) VEX ICME orbit sampling of the latter model. The VEX orbit intersects much less of the pickup ion wake owing to the reduced O+ gyroradius and the already confining effects of the mass loading on the O+ wake extent. signature, but rather sampling of a stationary spatial distribution of ions, with locally narrow energy spectra at each location. They are analogous to the features referred to as beams in the literature [e.g., Carlsson et al., 2006], although the ions are beam-like only in the sense that they have narrow energy spectra and are traveling in approximately the same directions at a given location. The term ring beam distribution, widely used in pickup ion studies, is the better descriptor. [26] A key result is that for some clock angles practically no model pickup ions are detected along the VEX orbit even though the global pickup ion population, including the escaping component, is identical. In these cases the nar-

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rowness and low altitude of the production layer, coupled with the magnetic field clock angle, minimize the orbit’s intersection with the pickup ion wake. Of special interest for the ICME observations are the spectrograms for 0° and 180° clock angles. One characteristic of ICME ejecta fields is rotation to large inclinations out of the ecliptic plane. The model results suggest that the prominent pickup ions features in the sheath should become observable as the ICME fields sweep through the large inclinations. However, the observations in Figures 2a– 2d show that this is generally not the case during the periods of the selected ICME passes, which are short on the ICME timescales (compare Figure 1). The approximate clock angles at the times of the IMA data are 0° for the 2006 5 July case, 180° for the 2006 18 July case, 225° for 2006 11 September case, and 45° for 2007 14 February. Only in the last case does the VEX heavy ion spectrogram show the O+ feature suggested by its model counterpart in Figure 8, although the orbital sampling differs from cases 2 and 3. [27] These model results also assume fixed typical solar wind B and V magnitudes of about 14 nT and 400 km/s, respectively, while the ICME often have higher B by a factor of 2 or more, and higher V by up to a factor of 4. The combined higher V and B effects on the ion trajectories will offset one another somewhat because the gyroradius is proportional to particle v, which is accelerated to up to twice the ambient flow speed, and inversely proportional to B. However, the higher V means that the energies attained by the picked up O+ can be much larger than is typical. Thus the O+ trajectories can be the same but the O+ energies may exceed the energy range of the ASPERA-4 IMA. In such cases the IMA would still observe the lower altitude, lower energy portions of the picked up O+ features, and just not detect the full energy-dispersed features in the sheath. However, this does not seem to be the case in our first three examples. In addition, the solar wind velocities for the four selected ICME, obtained by inspection of the IMA energy-time spectrograms in Figures 2a– 2d, seem to rule out an energy range limitation to the picked up O+ detection in the sheath. For the pass on 5 July 2006 the center of the solar wind spectral band prior to and after the broadening due to the shock suggests a nominal solar wind proton energy only slightly greater (1.3 keV) than the typical 1 keV. In fact many ICME travel only modestly faster than the ambient solar wind. On 18 July 2006, only the outbound shock is visible in the spectrogram, but it too suggests solar wind proton energies at or slightly less than the typical 1 keV. The 11 September 2006 case also shows only outbound solar wind, again near 1 keV, and the ICME segment passing on 14 February 2007 appears to have only 800 eV solar wind protons. However, while none of the cases under consideration here should be subject to the IMA energy range upper limit mentioned above, we expect the strongest events to be affected in this way. [28] A more likely explanation for the apparent absence of significant O+ in the IMA spectrograms may be the increased magnetic fields. Figure 9 shows energy-mass analyses of the very narrow features inbound on 5 July 2006 and outbound on 18 July 2006, where the spacecraft apparently enters and leaves the ionosphere. These reveal that O+ is indeed present for those short periods (5 min.). In addition, hints of O+ below the 10 eV lower threshold of

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Figure 11. Simulated ASPERA-4 IMA O+ spectrograms for the four selected ICME. The left column is the nominal (not-loaded) model; the right is for loaded plus high field. The most striking features consistent with the observations in Figures 2a– 2d are the energy-dispersed, spectrally narrow features occasionally seen in the sheath regions. The other notable features also present in the VEX data are the occasional low-energy ion enhancements observed throughout the lowest altitude portion of the pass. The origins of these features are discussed in the text. the IMA energy range in both cases suggest that intensive mass loading of the solar wind interaction boundary at these times reduces the initial velocities of the source O+. In addition, the larger magnetic field magnitudes during the ICME reduce the pickup ion gyroradii so that the pickup ion wake spatial distribution may be more closely confined to the optical wake. As an approximate model of these conditions we used the mass loaded MHD field and flow results with the magnetic field multiplied by 3, and the lower O+ starting velocities (2 km/s) used for the mass-loaded case as described earlier. The particle trajectories for this case are compared with the nominal case and with the spacecraft near-periapsis trajectories in Figure 10. The larger magnetic field reduces the O+ gyroradius as expected, altering the ion wake cross section and its orbital intersection. In Figure 11 the resulting O+ spectrograms for the four ICME cases are compared with the same orbital samples of the normal (standard magnetic field, no mass loading) model for the event pass trajectories. The high field, mass loaded case on the right shows attributes that tend toward the observed ICME heavy ion spectrogram behavior: an absence of the

long, energy-dispersed sheath ion features, and O+ concentrated in nearly vertical features at the boundaries of the source region entry and exit. A feature that remains puzzling, and for which we have no explanation, is the apparent opposite sides of these latter features from what would be expected for the 5 July and 18 July 2006 cases with 0° and 180° clock angles respectively. Clearly there is a need for carrying out more specific simulations using the observed ICME external parameters, with a detailed treatment of the ionospheric source, in order to obtain more realistic background fields and with them, more accurate models of the picked up O+ behavior.

4. Conclusion [29] So is there any evidence that the ICME selected for study in this paper produced an increase in the escaping pickup O+ fluxes? For the four selected events examined here, perhaps only in one case (case 4). Although the flux conversion for IMA counts is still under discussion, a quantitative estimate of the increase can be made from the

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color contours in the bottom middle panel of Figure 3b. Assuming this 15 min sample in the mass-energy analysis contains the bulk of the escaping oxygen ions for the pass, there are at least 10 times more present on this occasion compared to their counterparts in the other panels. The fact that the bulk of the ions are at low energies may be an artifact of the sampling, or it may be key to the physical process underlying their escape. Models of the pickup ion spatial distribution discussed above suggest a possible relationship between the low-altitude, lower energy ions and the higher-altitude more energetic ions. They also provide an explanation for the nonobservation of O+ signatures for the three other events, related to the alteration of the pickup ion wake geometry by the enhanced ICME magnetic field and VEX orbital sampling. But a more detailed examination of this question requires further model development of the ICME-disturbed Venus interaction that is beyond the scope of the present report. We also expect an increase in the number of CME as solar activity rises to give us a picture of the responses of Venus picked up O+ to much stronger (faster) events. [30] Acknowledgments. Several of the authors (J.G.L., C.T.R., and D.A.B.) acknowledge support from the Venus Participating Scientist and Instrument Scientist Programs through NASA grants: NNG06GC61G (J.G.L.), NNG06GC62G (C.T.R.), NNX07AI62G S01 (D.A.B.). We are grateful to ESA and NASA for this opportunity to work with the Venus Express mission data.

References Barabash, S., et al. (2006), The Analyzer of Space Plasmas and Energetic Atoms (ASPERA-3) for the Mars Express Mission, Space Sci. Rev., 126, 113 – 164, doi:10.1007/s11214-006-9124-8. Barabash, S., et al. (2007a), The loss of ions from Venus through the plasma wake, Nature, 450, 650 – 653, doi:10.1038/nature06434. Barabash, S., et al. (2007b), The Analyser of Space Plasmas and Energetic Atoms (ASPERA-4) for the Venus Express mission, Planet. Space Sci., 55, 1772 – 1792, doi:10.1016/j.pss.2007.01.014. Brace, L. H., W. T. Kasprzak, H. A. Taylor, R. F. Theis, C. T. Russell, A. Barnes, J. D. Mihalov, and D. M. Hunten (1987), The ionotail of Venus: Its configuration and evidence for ion escape, J. Geophys. Res., 92, 15 – 26, doi:10.1029/JA092iA01p00015. Brace, L. H., R. E. Hartle, and R. F. Theis (1995), The nightward ion flow scenario at Venus revisited, Adv. Space Res., 16, 99 – 112, doi:10.1016/ 0273-1177(95)00255-D. Carlsson, E., et al. (2006), Mass composition of the escaping plasma at Mars, Icarus, 182, 320 – 328, doi:10.1016/j.icarus.2005.09.020. Chassefiere, E. (1997), Loss of water on the young Venus: The effect of a strong primitive solar wind, Icarus, 126, 229 – 232, doi:10.1006/ icar.1997.5677. Fedorov, A., et al. (2006), Structure of the Martian wake, Icarus, 182, 329 – 336, doi:10.1016/j.icarus.2005.09.021. Fox, J. L., and A. J. Kliore (1997), Ionosphere: Solar activity variations, in Venus II, edited by S. Bougher, D. L. Hunten, and R. Phillips, pp. 161 – 188, Univ. of Ariz. Press, Tucson. F u t a a n a , Y. , S . B a r a b a s h , M . Ya m a u c h i , R . L u n d i n , a n d S. McKenna-Lawlor (2007), Geo-effective solar flare events in December 2006: Space weather effect on Mars and Venus oxygen loss to space, Eos Trans. AGU, 88(52), Fall Meet. Suppl., Abstract P23A-1087. Grinspoon, D. H., and M. A. Bullock (2007), Searching for evidence of past oceans on Venus, paper presented at DPS Meeting 39, Am. Astron. Soc., Washington, D. C. Jian, L., C. T. Russell, J. G. Luhmann, and R. M. Skoug (2006), Properties of interplanetary coronal mass ejections at one AU during 1995 – 2004, Sol. Phys., 239, 393 – 436, doi:10.1007/s11207-006-0133-2. Jian, L., C. T. Russell, J. G. Luhmann, and R. M. Skoug (2008), Evolution of solar wind structures from 0.72 to 1 AU, Adv. Space Res., 41, 259 – 266, doi:10.1016/j.asr.2007.03.023. Kallio, E., J. G. Luhmann, and J. G. Lyon (1998), Magnetic field near Venus: A comparison between Pioneer Venus Orbiter magnetic field

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observations and an MHD simulation, J. Geophys. Res., 103, 4723 – 4737, doi:10.1029/97JA02862. Kallio, E., R. Jarvinen, and P. Janhunen (2006), Venus solar wind interaction: Asymmetries and the escape of O ions, Planet. Space Sci., 54, 1472 – 1481, doi:10.1016/j.pss.2006.04.030. Kulikov, Y. N., et al. (2006), Atmospheric and water loss from early Venus, Planet. Space Sci., 54, 1425 – 1444, doi:10.1016/j.pss.2006.04.021. Lammer, H., et al. (2006), Loss of hydrogen and oxygen from the upper atmosphere of Venus, Planet. Space Sci., 54, 1445 – 1456, doi:10.1016/ j.pss.2006.04.022. Lindsay, G. M., C. T. Russell, and J. G. Luhmann (1995), Coronal mass ejection and stream interaction region characteristics and their potential geomagnetic effectiveness, J. Geophys. Res., 100, 16,999 – 17,014, doi:10.1029/95JA00525. Luhmann, J. G., and T. E. Cravens (1991), Magnetic fields in the ionosphere of Venus, Space Sci. Rev., 55, 201 – 274, doi:10.1007/ BF00177138. Luhmann, J. G., and J. U. Kozyra (1991), Dayside pickup oxygen ion precipitation at Venus and Mars – Spatial distributions, energy deposition and consequences, J. Geophys. Res., 96, 5457 – 5467, doi:10.1029/ 90JA01753. Luhmann, J. G., S. A. Ledvina, J. G. Lyon, and C. T. Russell (2006), Venus O+ pickup ions: Collected PVO results and expectations for Venus Express, Planet. Space Sci., 54, 1457 – 1471, doi:10.1016/j.pss.2005.10.009. Luhmann, J. G., W. T. Kasprzak, and C. T. Russell (2007), Space weather at Venus and its potential consequences for atmosphere evolution, J. Geophys. Res., 112, E04S10, doi:10.1029/2006JE002820. Mihalov, J. D., and A. Barnes (1982), The distant interplanetary wake of Venus- Plasma observations from Pioneer Venus, J. Geophys. Res., 87, 9045 – 9053, doi:10.1029/JA087iA11p09045. Mihalov, J. D., C. T. Russell, W. T. Kasprzak, and W. C. Knudsen (1995), Observations of ionospheric escape on Venus’ nightside, J. Geophys. Res., 100, 19,579 – 19,584, doi:10.1029/95JA01342. Miller, K. L., and R. C. Whitten (1991), Ion dynamics in the Venus ionosphere, Space Sci. Rev., 55, 165 – 200, doi:10.1007/BF00177137. Moore, K. R., D. J. McComas, C. T. Russell, and J. D. Mihalov (1990), A statistical study of ions and magnetic fields in the Venus magnetotail, J. Geophys. Res., 95, 12,005 – 12,018, doi:10.1029/JA095iA08p12005. Moore, K. R., D. J. McComas, C. T. Russell, S. S. Stahara, and J. R. Spreiter (1991), Gasdynamic modeling of the Venus magnetotail, J. Geophys. Res., 96, 5667 – 5681, doi:10.1029/90JA02251. Mulligan, T., C. T. Russell, and J. G. Luhmann (1998), Solar cycle evolution of the structure of magnetic clouds in the inner heliosphere, Geophys. Res. Lett., 25, 2959 – 2962, doi:10.1029/98GL01302. Phillips, J. L., J. G. Luhmann, W. C. Knudsen, and L. H. Brace (1988), Asymmetries in the location of the Venus ionopause, J. Geophys. Res., 93, 3927 – 3941, doi:10.1029/JA093iA05p03927. Svedhem, H., et al. (2007), Venus Express-The first European mission to Venus, Planet. Space Sci., 55, 1636 – 1652, doi:10.1016/j.pss.2007. 01.013. Terada, N., S. Machida, and H. Shinagawa (2002), Global hybrid simulation of the Kelvin-Helmholtz instability at the Venus ionopause, J. Geophys. Res., 107(A12), 1471,doi:10.1029/2001JA009224. Vaisberg, O. L., et al. (1995), Ion populations in the tail of Venus, Adv. Space Res., 16, 105 – 118, doi:10.1016/0273-1177(95)00217-3. Zhang, T. L., et al. (2006), Magnetic field investigation of the Venus plasma environment: Expected new results from Venus Express, Planet. Space Sci., 54, 1336 – 1343, doi:10.1016/j.pss.2006.04.018. S. Barabash, E. Carlsson, and Y. Futaana, Swedish Institute of Space Physics, Box 812, SE-981, 28 Kiruna, Sweden. ([email protected]; [email protected]; [email protected]) D. A. Brain, S. A. Ledvina, and J. G. Luhmann, Space Sciences Laboratory, University of California, 7 Gauss Way, Berkeley, CA 94720-7450, USA. ([email protected]; [email protected]; [email protected]) A. Fedorov, Centre d’Etude Spatiale des Rayonnements, BP-4346, F-31028 Toulouse CEDEX 4, France. ([email protected]) J. G. Lyon, Department of Physics and Astronomy, Dartmouth College, 6127 Wilder Laboratory, Hanover, NH 03755-3528, USA. (john.lyon@ dartmouth.edu) C. T. Russell, Institute of Geophysics and Planetary Physics, University of California, Box 951567, Los Angeles, CA 90095, USA. (ctrussel@igpp. ucla.edu) T. L. Zhang, Space Research Institute, Austrian Academy of Sciences, 8042 Graz, Austria. ([email protected])

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Tailward flow of energetic neutral atoms observed at Venus A. Galli,1 M.-C. Fok,2 P. Wurz,1 S. Barabash,3 A. Grigoriev,3 Y. Futaana,3 M. Holmstro¨m,3 A. Ekenba¨ck,3 E. Kallio,4 and H. Gunell5 Received 31 January 2008; revised 11 April 2008; accepted 26 August 2008; published 2 December 2008.

[1] The Analyzer of Space Plasma and Energetic Atoms (ASPERA-4) experiment on

Venus Express provides the first measurements of energetic neutral atoms (ENAs) from Venus. The results improve our knowledge on the interaction of the solar wind with a nonmagnetized planet and they present an observational constraint to existing plasma models. We characterize the tailward flow of hydrogen ENAs observed on the nightside by providing global images of the ENA intensity. The images show a highly concentrated tailward flow of hydrogen ENAs tangential to the Venus limb around the Sun’s direction. No oxygen ENAs above the instrument threshold are detected. The observed ENA intensities are reproduced with a simple ENA model within a factor of 2, indicating that the observed hydrogen ENAs originate from shocked solar wind protons that charge exchange with the neutral hydrogen exosphere. Citation: Galli, A., M.-C. Fok, P. Wurz, S. Barabash, A. Grigoriev, Y. Futaana, M. Holmstro¨m, A. Ekenba¨ck, E. Kallio, and H. Gunell (2008), Tailward flow of energetic neutral atoms observed at Venus, J. Geophys. Res., 113, E00B15, doi:10.1029/2008JE003096.

1. Introduction [2] As a part of the Venus Express (VEX) scientific payload, the Analyzer of Space Plasma and Energetic Atoms (ASPERA-4) experiment, consisting of the Electron Spectrometer (ELS), the Ion Mass Analyzer (IMA), the Neutral Particle Detector (NPD) and the Neutral Particle Imager (NPI), is designed to study the plasma environment of Venus. It provides the first observations of energetic neutral atoms (ENA) from Venus, in a similar manner as the ASPERA-3 experiment on Mars Express (MEX) provided the first ENA observations of Mars [Futaana et al., 2006a, 2006b; Galli et al., 2006b]. Unlike the MEX spacecraft, VEX is also equipped with a magnetometer (MAG) allowing for a better characterization of the plasma environment. [3] The first results of IMA, ELS, and MAG data from Venus have been published by Barabash et al. [2007b], Coates et al. [2008], Martinecz et al. [2008], and by Zhang et al. [2007]. The location and temporal variation of the plasma boundaries are found to be similar to the results derived from Pioneer Venus Orbiter (PVO) data [Martinecz et al., 2008]. Figure 1 shows the location of plasma boundaries derived in that study: at the bow shock the solar wind is slowed down to subsonic velocities, the magnetosheath between the bow shock and the inner plasma boundary is dominated by slow, heated up solar wind. The inner plasma boundary can either be defined as the 1

Physikalisches Institut, Universita¨t Bern, Bern, Switzerland. Geospace Physics Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA. 3 Instituted fo¨r Rymdfysik, Kiruna, Sweden. 4 Finnish Meteorological Institute, Helsinki, Finland. 5 Department of Physics, West Virginia University, Morgantown, West Virginia, USA. 2


boundary where the planetary ions start to dominate the plasma or as the boundary where the interplanetary magnetic field BIMF piles up around the ionosphere. The two different concepts are equivalent. In this paper we shall use the term ‘‘Induced Magnetosphere Boundary’’ (IMB) for the inner plasma boundary. It is close to the geometrical shadow behind Venus as is shown in Figure 1. [4] Charge exchange between energetic ions from the plasma and neutral atoms in the exosphere produces ENAs. For a flux of monoenergetic protons jp(r) that are neutralized along the line of sight (LOS) through atmospheric hydrogen with a density nH(r) the ENA production equation reads Z jENA ¼ s

dr nH ðrÞjp ðrÞ;

ð1Þ

LOS

with jENA the ENA intensity in cm2 sr1 s1, and s the charge exchange cross section. For a proton-hydrogen reaction at 1 keV energy s 2 1015 cm2, and s 1 1015 cm2 for O+-hydrogen charge exchange [Lindsay and Stebbings, 2005]. ENA measurements therefore reflect the plasma flux distribution as well as the neutral densities along the LOS of the sensor. The motivation of the ENA observations at Mars and Venus is to better understand the interaction of the solar wind with the neutral atmosphere of nonmagnetized planets. To interpret the observations, comparison with ENA models is needed. Such comparisons in turn constrain the uncertainties about the exospheric densities and the plasma populations implemented in the model. [5] For Venus, so far only the ENA model predictions by Fok et al. [2004] and by Gunell et al. [2005] are available. For Mars, the list is longer. The ENAs originating from solar wind and magnetosheath protons were modeled by Holmstro¨m et al. [2002]. ENAs originating from planetary

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Figure 1. The two plasma boundaries around Venus, bow shock and Induced Magnetosphere Boundary (IMB), as measured with Analyzer of Space Plasma and Energetic Atoms/Ion Mass Analyzer (ASPERA-4/IMA) and electron spectrometer (ASPERA-4/ELS) during the first 5 months of observations [Martinecz et al., 2008]. The coordinates are given relative to the Venus Solar Orbital (VSO) referenceq frame in units of planetary radii RV. The x axis is the Sun-Venus line XVSO, and the y axis is the ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 to XVSO : distance YVSO þ ZVSO

hydrogen [Lichtenegger et al., 2002] and from planetary oxygen [Barabash et al., 2002] were also investigated, but both for Mars and Venus the total ENA production is expected to be dominated by ENAs originating from solar wind protons. Because charge exchange hardly affects the energy of the fast particle, the typical energy of ENAs around Mars and Venus is predicted to be comparable to the 1 keV of the solar wind. The ENA models also show that the ENA outflow originating from planetary ions is coaligned with the convective electric field E = vSW BIMF. On the other hand, the ENA outflow caused by solar wind protons is homogeneous around the planetary limb with respect to the Sun’s direction, irrespective of the BIMF direction. Gunell et al. [2006] show that this prediction does not change when an MHD or a hybrid code instead of an empirical code is implemented. The observations of the Martian ENAs made with MEX/NPD agree with the basic characteristics of the ENA models. The ENA emission of the dayside atmosphere and the tailward flow of ENAs from shocked solar wind are easily recognizable [Futaana et al., 2006a, 2006b; Galli et al., 2006b]. By analogy, we expect to see above the dayside of Venus solar wind ENAs that are scattered back from the atmosphere and a narrow ENA stream of shocked solar wind right above the subsolar point in the magnetosheath. On the nightside we expect to

observe a tailward flow of ENAs, for which unperturbed solar wind, magnetosheath plasma, or accelerated planetary ions are possible parent ions. The latter parent population is the only one that also produces a measurable amount of oxygen ENAs. [6] Because of the UV sensitivity of the NPD sensor, ENA measurements outside the Venus shadow have to be avoided in most cases. We will therefore concentrate on the tailward ENA flow observed when the spacecraft is inside the shadow. First examples of such measurements were published by Galli et al. [2008]. Here, we will provide a complete account of the nightside ENA observations. We will briefly describe the NPD sensor in section 2, before presenting the database that underlies this work (section 3). In section 4 we will show the global intensity images of tailward-flowing hydrogen ENAs; they will be compared to model predictions in section 5. The search for oxygen ENAs at Venus will be summarized in section 6, followed by the conclusions in section 7.

2. NPD Instrument [7] NPD is one of the four sensors constituting the ASPERA-4 [Barabash et al., 2007a] experiment on ESA’s VEX spacecraft (orbit insertion in May 2006). NPD is designed to measure hydrogen ENAs at energies between

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Figure 2. Typical energetic neutral atom (ENA) energy spectrum, integrated over 8 min of observation time in Venus eclipse on 24 May 2006. Top shows the spacecraft centered view. The black cross is the Sun’s direction, the thick red line is the Venus limb. The red stars that cover the edge of the field of view (FOV) of sector NPD2_0 denote the limb of the solar panel. Middle and bottom show the time-of-flight (TOF) spectrum and the derived ENA intensity spectrum observed in channel NPD2_2 (see text for further explanations). 0.2 and 10 keV and oxygen ENAs between 0.4 and 10 keV, using the time-of-flight (TOF) technique. Angular resolution is provided by having two NPD sensors (NPD1 and NPD2), each with three angular channels with a field of view (FOV) of 30° 5° giving a total instantaneous FOV of 180° 5° [Barabash et al., 2007a]. We can distinguish between hydrogen and oxygen ENAs mainly because the velocities of hydrogen and oxygen ENAs of equal energy are very different. Only oxygen ENAs more energetic than several keV have TOF values that overlap with the ones of hydrogen ENAs between 0.2 keV and 0.5 keV.

[8] Beside ENAs, NPD is also sensitive to UV photons. Observations with the Sun in the FOV therefore have to be avoided. Unfortunately, the dayside hydrogen exosphere of Venus proves to be too bright (more than 20 kR Lyman-a [Bertaux et al., 1978]) for the NPD sensor as well. [9] The spectrum reconstruction from raw data follows the same method described by Galli et al. [2006a]. As improvement over the first report on tailward ENAs observed at Venus [Galli et al., 2008] we have completed the database for observations inside the IMB and we have now incorporated the final laboratory calibration informa-

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Figure 3. Venus Express orbit trajectory on 24 May 2006, plotted in the same coordinate system as Figure 1. The green rectangle highlights the time period (30 min around 0200 UT) during which the spacecraft is inside the shadow of Venus. tion for NPD, which became available recently [Grigoriev, 2007]. The instrument response, efficiency, and geometrical factors have remained the same within the error bars since our earlier publication [Galli et al., 2008].

3. Database [10] The database of nightside observations includes all suitable ENA observations made inside the eclipse of Venus from orbit insertion until the end of 2007. We will not discuss observations for which the FOV was directed away from Venus, but up to now no signals above the detection threshold have been encountered under such circumstances. [11] Observations with poor counting statistics or with other technical problems were excluded from further analysis. For instance, several of the six sectors of NPD may be obstructed by the solar panel, as is illustrated in Figure 2 by the red stars adjacent to the two sectors NPD2_0 and NPD1_0. To complicate things further, the ASPERA-4 main unit is mounted on a rotating platform. This allows for a faster coverage of the sky but drastically blurs the ENA signal for a given pointing direction. By default, the NPD FOV is rotated by 180° every 32 seconds. The minimum integration time of two minutes for a FOV sector of 30° therefore necessitates an observation time longer than 6 2 minutes. In the meantime the spacecraft position relative to Venus may vary considerably. [12] The following VEX orbits belong to the database of nightside NPD observations: [13] 1. Eclipse season from 16 to 29 May 2006, 11 different suitable orbits. Detection threshold is 1 104 cm2 sr1 s1. [14] 2. Eclipse season from 19 August to 3 September 2006, 5 suitable orbits. For this period the raw ENA intensities have been multiplied by a factor 4 ± 2 to compensate for a decrease in detection efficiency caused by an overexposure to UV light. Detection threshold is 4 104 cm2 sr1 s1. [15] 3. Eclipse season from 27 November 2006 to 7 January 2007: never a reliable ENA signal above the detection threshold of several 104 cm2 sr1 s1. [16] 4. Eclipse season from 8 July to 16 August 2007. Detection threshold is several 104 cm2 sr1 s1. All observations are taken into account that have suitably high count rates to synthesize a TOF spectrum. The ENA intensity derived from these measurements is to be understood only as a relative number to be multiplied with a yet

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unknown correction factor because the NPD sensor settings have been adjusted to make up for the decrease in detection efficiency. This gives us 12 different orbits from 8 July to 4 August 2007, and four further suitable orbits from 5 to 16 August 2007 when the end of this eclipse season approached. [17] We have statistically evaluated (see section 5) only the data obtained during the first two eclipse seasons (16 different orbits), for which absolute intensities can be derived. All ENA intensities were integrated over the energy range from 0.2 to 10 keV, the uncertainty of the final value being 30% typically. For analysis, all measurements were divided into intervals of two to ten minutes integration time, two minutes being enough only for very intense ENA signals. Moreover, we restricted the data evaluation to sector NPD2_2 because of the six NPD channels only this one was directed at the tailward ENA flow. All observations correspond to low solar activity.

4. Results [18] Figure 2 shows a typical NPD observation of the tailward hydrogen ENA flow, obtained on 24 May 2006. The VEX orbit trajectory for that date is sketched in Figure 3. As for most observations in our database, the time window for NPD observations inside the Venus eclipse was roughly 30 min (green rectangle in Figure 3). Figure 2 (top) shows the spacecraft centered view for 0154:46 UT, when VEX was on the nightside of Venus at an altitude of 2900 km. The thick red line indicates the Venus limb, the cross marks the Sun’s position. Shown in black are the six NPD sectors, the red stars close to sectors NPD2_0 and NPD1_0 outline the solar panel. Figure 2 (middle) shows the original and the reconstructed TOF spectrum of the hydrogen ENAs (peak between TOF bin 10 and 100) measured in sector NPD2_0. The flat background in the TOF spectrum (dashed line) is caused by UV light. Figure 2 (bottom) shows the ENA intensity spectrum derived from the TOF spectrum in the energy range from 0.2 to 10 keV. The ENA intensity, integrated from 0.2 to 10 keV, calculates to 1.4 105 cm2 sr1 s1. [19] The intensity spectrum shown in Figure 2 (bottom) is typical for the hydrogen ENA signals observed with NPD. The intensity spectrum shown in Figure 2 may be described as a two-component power law with a low-energy slope of 1.8 ± 0.2, a rollover at 1.0 ± 0.1 keV, and a slope at energies above 1.0 keV of 3.3 ± 0.1. Taking into account all ENA measurements from 2006 with a high signalto-noise ratio we find that most intensity spectra can be characterized by a two-component power law with a rollover between 0.4 and 2.0 keV. The median rollover lies at 1.1 keV. Unfortunately, for many measurement intervals poor counting statistics avoid a reliable reconstruction of the spectral shape of the ENA signal. Therefore, we do not use the shape of the measured spectrum as observational constraint for the ENA model. In section 5 we will illustrate the problem of interpreting measured energy spectra by showing a modeled ENA spectrum (see Figure 8). To make a quantitative comparison between observations and model, we will only use the integral ENA intensities because they are more reliable. [20] We have synthesized images of the ENA intensities taking into account all measurement intervals that fulfill the

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Figure 4. Hydrogen ENA (H-ENA) intensity image 1 of Venus, including 54 measurement intervals from 16 to 29 May 2006. During the measurements the Venus center varies between 32° ± 10° longitude and 34° ± 5° latitude, the spacecraft altitude is 0.25 ± 0.05 RV. The average FOV footprint equals 38° 36°. criteria listed in section 3. The data from May to September 2006 resulted in four different images for different vantage points and altitudes of the spacecraft. To create the images, the single intensity measurements were averaged over a 10° 10° square mesh. The three most comprehensive images are shown in Figures 4, 5, and 6. The plots are shown in a cylindrical projection of the Venus Solar Orbital (VSO) reference frame, the x axis being the VSO longitude, the y axis being the VSO latitude in degrees. The VSO reference frame is defined as follows: XVSO points from Venus to the Sun, ZVSO points to the North pole of the Venusian orbital plane, and YVSO closes the right-handed reference frame. By this definition, the Sun’s direction is always at 0° longitude and 0° latitude. Pixels that are covered more than once by the NPD FOV are shown in color corresponding to the observed ENA intensity. In units of 104 cm2 sr1 s1, the colors represent the following:

black is below 1, purple is 1 to 2, blue is 2 to 4, light blue is 4 to 6, green is 6 to 8, yellow is 8 to 10, orange is 10 to 12, and red is above 12. The identical color scale is used for observations and for models. Pixels that have not been covered more than once are left white. Because of the fast spacecraft proper motion during the pericenter passage of VEX and because of the simultaneous scanner operations, single measurements are associated with a FOV footprint of typically 40° 40°. This has to be taken into account when comparing model images with a much higher resolution to the observations (see section 5). [21] The evaluation of the first and second eclipse season (see list in section 3) yields the following statistics: (1) In 15 of the 16 different orbits the hydrogen ENA signal exceeds the detection threshold. (2) In 14 out of 15 cases the ENA intensity reaches its maximum in the ecliptic plane (20° to +20°) within 40° around the Sun’s direction.

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Figure 5. H-ENA intensity image 2 of Venus, including 35 measurement intervals from 16 to 24 May 2006. The Venus center varies between 27° ± 6° longitude and 9° ± 5° latitude, the spacecraft altitude is 0.57 ± 0.05 RV. The average FOV footprint equals 30° 30°. [22] In Figure 4 the maximum ENA outflow seems to be shifted 30° away from the Sun’s direction. However, this shift should not be overinterpreted as it may also be caused by an observational bias: for the image shown in Figure 4 the orange region between 30° and 60° VSO longitude has been covered dozens of times. The region closest to the Sun between 0° and 30° VSO longitude, which is predicted to produce even higher ENA intensities, was covered only once. [23] The median of all 16 orbits calculates to 1.2 105 cm2 sr1 s1 for the direction of most intense ENA outflow; the highest intensity ever measured is (4.2 ± 1.6) 105 cm2 sr1 s1. From a direction more than 60° away from the Venus limb we never (taking into account all NPD observation inside and outside the eclipse) see an unambiguous signal >4 104 cm2 sr1 s1.

5. Comparison to an MHD Model [24] To predict the ENA production of a planet, one requires a plasma model for the ion flux distribution around the planet and a model of the neutral exosphere. For Venus only few ENA model calculations have been done so far.

The models presented by Fok et al. [2004] and by Gunell et al. [2005] are technically similar since both are based on MHD calculations of the plasma and PVO measurements of the neutral oxygen and hydrogen exosphere. The resulting maximum ENA intensities on the nightside and the total production rates are consistent with each other within a factor of 2. [25] To interpret the NPD observations, we use the Venus ENA model developed by Fok et al. [2004]. This model predicts hydrogen and oxygen ENA images based on the MHD plasma simulation by Tanaka and Murawski [1997] assuming the neutral exosphere parameters from the Venus International Reference Atmosphere (VIRA) model [Keating et al., 1985]. A plot of the ion densities, velocities and temperatures calculated by Tanaka and Murawski [1997] is shown in Figure 7. The bow shock is easily recognizable in all three images; the IMB corresponds to the location in the middle where the velocity of the solar wind protons sharply drops from 200 to 0 km s1. No planetary hydrogen ions are included in the MHD model, all predicted hydrogen ENAs (H-ENAs) originate from solar wind protons. The oxygen ENAs (O-ENAs) are caused by accelerated planetary oxygen from the ionosphere (not shown in Figure 7). The most

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Figure 6. H-ENA intensity image 3 of Venus, including 14 measurement intervals from 20 August to 3 September 2006. The Venus center varies between 3° ± 12° longitude and 24° ± 10° latitude, the spacecraft altitude is 0.64 ± 0.20 RV. The average FOV footprint equals 70° 45°. important model parameters are the solar wind strength and the density profile of the hydrogen exosphere. [26] The parameters of the exosphere model by Keating et al. [1985] are summarized in Table 1. We implement only the neutral hydrogen and oxygen components, all other species have very low scale heights and particle densities above the exobase. The exobase height is set to 250 km. The vertical density profiles at altitudes above 3500 km, where the VIRA model is not defined any more, are extrapolated using the Chamberlain model [Chamberlain and Hunten, 1987]. If T be the exospheric temperature, MV the planetary mass, and r the radial distance from the planet center, the scale height H of the density profile is given by H¼

kB Tr2 : GmMV

ð2Þ

[27] For the thermal hydrogen, equation 2 yields about 200 km given the temperatures in Table 1, whereas for the hot hydrogen component H 1000 km. The scale height of the thermal oxygen is only 20 km at most for the hot subsolar region. Therefore, the exospheric oxygen hardly affects the production of ENAs, and the NPD observations

will be sensitive only to the neutral hydrogen parameters listed in Table 1. The only exception to this rule applies to ENA observations at low altitudes above the subsolar region, but this region is never crossed by the LOS of NPD. Above the terminator and the nightside the neutral hydrogen density is higher than the one of oxygen even at the exobase, and the charge exchange cross section for H-ENA production is twice as large as for neutral oxygen [Lindsay and Stebbings, 2005]. Table 1 also indicates that it is the thermal hydrogen component that dominates the ENA production on the nightside. Because of the low exobase density, the particle density of the hot component exceeds the one of the thermal component only at altitudes above 2000 km according to equation 2. [28] As a starting point we will compare the NPD observations to the model predictions using the values in Table 1. Later on we can easily simulate the effects of a thinner or denser neutral exosphere by simply varying the ENA model output. Because we deal with an ENA thin medium around Venus, the predicted ENA outflow depends linearly on the exobase density. [29] As far as the solar wind is concerned, we have to stick to the parameters implemented in the plasma model by Tanaka and Murawski [1997]: vsw = 400 km s1, nsw =

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Figure 7. Plasma MHD model by Tanaka and Murawski [1997]. The left shows the proton density, the middle shows the ion velocity along the Sun-Venus line, and the right shows the temperature. The top half corresponds to values in the meridional plane, whereas the bottom half corresponds to values in the equatorial plane of the Venus Solar Orbital (VSO) reference frame. The color scales are linear. 14 cm3, and BIMF = (0, 0, 14) nT. As mentioned in the introduction, knowledge of BIMF becomes only important when interpreting ENAs from planetary ions. On the other hand, the intensity of the solar wind ENAs and magnetosheath ENAs directly scales with the flux of the unperturbed solar wind. Unfortunately, we do not know yet the solar wind flux at Venus during the NPD measurements because the absolute calibration of the IMA sensor (and therefore density and speed of the solar wind ions) has not been finished yet. In a recent statistical evaluation of the magnetic field data measured with PVO, Jarvinen et al. [2008] find that the particle flux of 0.6 109 cm2 s1 assumed by Tanaka and Murawski [1997] is close to the most probable flux and is thus a good choice for a single observation. But the median particle flux, observed during solar minimum at Venus, is close to 0.9 109 cm2 s1. The probability that the actual median particle flux encountered during the 16 VEX orbits in 2006 was lower than 0.7 109 cm2 s1 or higher than 1.1 109 cm2 s1 is 25% in both cases [Jarvinen et al., 2008]. At the end of this section we will discuss how the choice of a solar wind flux that is 1.5 times smaller than the expected median value affects the comparison between model prediction and measurements. [30] In Figure 8 we compare the ENA intensity spectrum derived from the ENA model to the NPD observation from

24 May 2006. To achieve a direct comparison, we have rebinned the NPD observation shown in Figure 2 (bottom) to the 12 bins between 0.2 and 10 keV used in the ENA model (red curve). Obviously, the modeled integral intensity is higher than the observed one (black curve), around 1 keV the differential intensity is four times higher. The spectral shapes of the NPD measurement and of the model agree in so far as the peak of the model spectrum at 0.8 keV corresponds to a rollover in the NPD spectrum at the same energy. This is the energy of ENAs originating from weakly shocked solar wind. The slopes of the two-component power law that characterizes the measured spectrum seem to be quite different from the model. Note, however, the error bars of the measured spectrum. We find in general that the slopes of NPD energy spectra strongly depend on the instrument efficiency [Grigoriev, 2007] and on the exact algorithm used to reconstruct differential intensities. The position of the rollover and the integral intensity are much more robust parameters. Figure 8 is a further indication that the position of the rollover may be the only trustworthy spectral parameter to be derived from NPD measurements. Moreover, the integration over several minutes blurs all spectral features. We caution the reader against overinterpreting Figure 8. It only indicates that the observed rollover between 0.4 and 2.0 keV (see section 4) is consistent with the hypothesis that the majority of the observed ENA

Table 1. Default Model Parameters of the Neutral Exosphere for Varying Solar Zenith Anglea SZA (deg)

nH (cm3)

TH (K)

16 34 61 90 119 146 164

5.6e4 7.0e4 2.0e5 1.1e6 2.9e7 1.7e7 7.5e6

284 289 292 230 141 124 127

nH,

hot

(cm3)

1.0e3 – – – – – 1.0e3

TH,

hot

(K)

1000 – – – – – 1500

nO (cm3)

TO (K)

1.1e7 1.1e7 8.8e6 1.1e6 1.2e4 2.7e3 3.0e3

284 289 292 230 141 124 127

nO,

hot

(cm3)

6.6e4 – – – – – 2.0e3

TO,

hot

(K)

4800 – – – – – 4800

a SZA, solar zenith angle; see Keating et al. [1985]. The most important parameters are the exobase density and the temperature of the thermal hydrogen component.

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Figure 8. Observed (solid curve) versus modeled (dotted curve) ENA intensity spectrum for the maximum ENA outflow observed on 24 May 2006 (see Figure 2 for the pointing direction).

tailward flow originates from shocked solar wind. Because the derived spectral features are not very reliable except for the rollover, we based the evaluation of NPD observations on the images of the modeled ENA intensities, integrated from 0.2 to 10 keV. [31] We now compare the four ENA intensity images from the first two eclipse seasons (three of them shown in Figures 4, 5, and 6) to the ENA model predictions. The square sum over all image pixels of observed ENA intensity jobs,i minus predicted ENA intensity jmod,i, divided by the uncertainty of the observed ENA intensity sj,i, serves as our merit function: c2 ¼ Si

jobs;i jmod;i sj;i

2 :

ð3Þ

The goodness of fit is measured with the probability P c2 ; f ¼ g 0:5c2 ; 0:5f :

ð4Þ

It indicates if the square sum of deviations c2 between model and observations is tolerable or if the model prediction is very unlikely to be consistent with the observations. As probability threshold below which a model has to be rejected we choose P(c2, f) = 1%. The degree of freedom f is the number i of statistically reliable image pixels (approximately the number of independent measurement intervals) minus the number of model parameters. For the number of model parameters we set 12, appropriate to describe the solar wind strength and the exospheric densities of the thermal and hot hydrogen. Before evaluating equation 3, the modeled ENA images of 2.5° 2.5° resolution are convolved with the same FOV footprints (typically 40° 40°) from which the NPD images were composed. The effect of this smearing process is shown for instance in Figure 10. The typical uncertainties sj,i in equation 3 are as large as jobs,i: the same location in the image is only covered a few times by independent measurements, each of which with a typical uncertainty of

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30%, and one measurement corresponds to a FOV footprint that covers a dozen image pixels. [32] Figure 9 shows the ENA model result for the observer position of NPD image 3 (Figure 6). Assuming the original VIRA exobase densities listed in Table 1 the smeared ENA model images show up to 3.5 105 cm2 sr1 s1 (Figure 9, top). The NPD images, however, do not exceed 1.4 105 cm2 sr1 s1 (red pixels in Figure 6). The corresponding fit probabilities calculated from equation 4 yield P 1% for three of the four NPD images. The VIRA model thus is consistent with most NPD measurements but it seems to be an upper limit at low solar activity, at least for the thermal hydrogen component. [33] Because the default ENA model predicts too high ENA intensities and because we did not have the time to calculate plasma simulations with changed input parameters, we test how a reduction of exospheric densities and temperatures affects the ENA model predictions. We choose two temperature regimes, one as stated in Table 1, the other one with all exospheric temperatures reduced by 20%. This reduction is motivated by the 25% error bars of the default temperatures suggested by Keating et al. [1985]. That is, an exospheric model with a 20% lower exobase bulk temperature is a plausible lower limit compared to observations. We then search for the best fitting exospheric density with the crude approach c nH with c one free parameter, identical for all solar zenith angles (SZA). The optimized c is defined by the product of the fit probabilities (equation 4) for the four NPD images. Since most hydrogen ENAs owe their existence to the thermal hydrogen component, the outcome is not changed much whether the correction factor c is applied only to the thermal hydrogen or to all components listed in Table 1. The resulting ENA intensities change linearly with c. [34] For the default temperatures we find that the agreement between the ENA model and the four NPD images is optimized for neutral hydrogen exobase densities two times lower than in Table 1. The confidence level of 1% implies for the range of uncertainty c = 0.5 ± 0.25. For c = 0.5 we find P(c21 = 43, 7212) = 96%, P(c22 = 45, 3912) = 1%, but only 2 of 39 pixels deviate from the measurement by more than 2 sj,i. P(c23 = 15, 4012) = 98%, P(c24 = 13, 4612) = 99.97%, whereby 0 of 46 pixels deviate from the measurement by more than 2 sj,i. The latter is not a very selective image; because of large error bars and poor spatial coverage it puts only loose constraints on the best fitting model. Obviously, only image 2 (Figure 5) has a really low probability of agreement. This is because of the row of purple pixels to the upper right of the Sun’s direction: two independent NPD measurements show for this direction an ENA intensity of (1 ± 1) 104 cm2 sr1 s1 whereas the ENA model, convolved with the same FOV footprints, predicts an order of magnitude higher ENA intensities (see Figure 10, bottom). [35] Two examples for the model with c = 0.5 are shown in Figures 10 and 11. These images were calculated for the same SZA and planetary distance as the NPD images 2 and 3 in Figures 5 and 6. While the model image with default exospheric densities in Figure 9 has still a fit probability larger than 1%, the fit probability of the ENA model shown in Figure 11 is significantly higher. The model with reduced hydrogen exobase densities is also consistent with the

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Figure 9. Model prediction of the tailward flow of H-ENAs on the nightside of Venus for image 3 in Figure 6 with the original exospheric densities and temperatures as proposed by Keating et al. [1985]. The top shows the model image in a resolution of 2° 2°, the bottom is the same image, convolved by the same FOV footprints appropriate for the observed image 3. 10 of 17

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Figure 10. Model prediction of the tailward flow of H-ENAs on the nightside of Venus for image 2 in Figure 5. The hydrogen exobase densities have been reduced by a factor of 2. The format is the same as in Figure 9. 11 of 17

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Figure 11. Model prediction of the tailward flow of H-ENAs on the nightside of Venus for image 3 in Figure 6. The hydrogen exobase densities have been reduced by a factor of 2. The format is the same as in Figure 9. 12 of 17

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Figure 12. Model prediction of the tailward flow of H-ENAs on the nightside of Venus for image 3 in Figure 6. All exospheric temperatures have been reduced to 80% of the default values listed in Table 1. The format is the same as in Figure 9. 13 of 17

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Figure 13. Modeled oxygen ENA intensity spectrum for the pixel of maximum ENA outflow in Figure 14. observational upper limit on O-ENAs, whereas the original VIRA parameters lead to O-ENA intensities that should be detectable with NPD (see section 6). [36] Reducing the exospheric temperatures is an alternative possibility to reduce the modeled ENA intensities. Switching on and off the hot hydrogen component has no significant effect on the predicted ENA outflow. The maximum ENA intensities along the Venus limb (the red pixels in Figures 10 and 11) change by 20% at most. But if the bulk exobase temperatures are reduced by 20% (including the thermal hydrogen) we find that the predicted ENA intensities drop by a factor of 2. An analogous evaluation as for the models with default exobase temperatures yields a correction factor of c = 1 with a factor of 2 uncertainty. The range of uncertainty 0.5 < c < 2 again corresponds to the confidence level of 1%. [37] We find that the agreement between the ENA model prediction and the NPD observations is optimized if two times lower hydrogen densities, lower exobase temperatures or a combination thereof is assumed. The low number of independent pixels along the Venus limb and the error bars of the measured images make it impossible to determine which effect offers the better explanation. Comparing Figure 11 (default temperatures, reduced hydrogen densities) to Figure 12 (default densities, reduced temperatures) shows that the two parameter sets lead to very similar ENA intensity images. The only visible difference is that the cool exosphere model leads to intensities that decrease faster for directions away from the maximum ENA outflow. [38] The only discrepancy between NPD and model images that cannot be solved by adjusting the exospheric parameters is the fact that in two images the observed maximum of the ENA outflow is shifted along the Venus limb away from the Sun’s direction. One example of this behavior is shown in Figure 4. Probably it can only be reproduced with a more refined plasma simulation. In all other respects the model images, after smudging them by the NPD FOV footprints, reproduce the four NPD images from the Venus eclipse observations in 2006. [39] About the influence of the unknown solar wind strength on the model results we can make the following estimate: for low solar activity a median solar wind flux of (9 ± 2) 108 cm2 s1 is expected [Jarvinen et al.,

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2008] whereas the plasma simulation was run for jSW = 6 108 cm2 s1. In the case of jSW = 9 108 cm2 s1 the ENA model would predict hydrogen ENA intensities approximately 1.5 times higher than shown here. This would imply that the exospheric densities or temperatures should be reduced even further. Keep in mind, however, that a factor of 2 is usually considered the accuracy in ENA modeling. The model used in this work predicts an ENA production rate that is almost two times higher than the value predicted by Gunell et al. [2005] although the input parameters for the solar wind and the neutral exosphere are identical in both models. Therefore, we do not change the optimized fit parameters for the exospheric hydrogen. [40] We conclude that the MHD plasma model, including only solar wind protons, reproduces the observed hydrogen ENA intensities and can therefore be relied upon for further work. The observed ENA outflow can be explained as shocked solar wind protons that charge exchange with the neutral hydrogen in the magnetosheath. Hydrogen ENAs of planetary origin are not needed to reproduce the required intensities, and ENAs of unperturbed solar wind protons cannot be observed in the shadow. The high temperature of the shocked solar wind is the reason why NPD detects an ENA signal although the spacecraft is inside the Venus shadow. This can be illustrated with the following comparison: the aperture angle of the ENA outflow around the Sun’s direction is observed to be typically 30° (see Figure 6). We obtain the same result if we refer to Figure 7 for the modeled temperature and flow velocity of the protons inside the magnetosheath at the terminator (T = (2 ± 1) 106 K, v = (250 ± 50) km s1) and calculate a proton scatter angle of rffiffiffiffiffiffiffiffi 2kT ¼ 25 . . . 50 : a ¼ arctan mv2

ð5Þ

[41] To conclude this section, we now calculate the total HENA production rate of Venus. The ENA model with default exobase densities and temperatures yields 1.2 1025 s1. Since a hydrogen exosphere with reduced density or temperature fits better to the observations we recommend for low solar activity an H-ENA production rate of 0.6 1025 s1, with a factor of 2 uncertainty. Because the neutral hydrogen exosphere is the dominant neutralizing agent to produce these H-ENAs, the total hydrogen loss of Venus due to charge exchange has to be also at least QH;CX 0:6 1025 s1 :

ð6Þ

This is consistent with a preliminary evaluation of IMA data [Barabash et al., 2007b] that suggests a lower limit of 1025 s1 for the H+ loss through the tail region.

6. Upper Limit of Observed Oxygen ENAs at Venus [42] Observing oxygen ENAs at Venus would be interesting as it would directly reveal an atmospheric erosion process. If there is an observable O-ENA signal it is truly planetary since in the solar wind the oxygen abundance is 5000 times lower than hydrogen. If Venus produces intense O-ENA signals anywhere, they are expected to appear in the

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Figure 14. MHD simulation for the tailward flow of O-ENAs on the nightside of Venus for image 2 in Figure 5. The neutral hydrogen exospheric densities have been reduced by a factor of 2 compared to Keating et al. [1985]. The top shows the model image in a resolution of 2° 2°, the bottom is the same image, convolved by the same FOV footprints appropriate for the observed image shown in Figure 5. 15 of 17

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tailward flow (see Figure 7 by Fok et al. [2004]). According to the calibration in laboratory already an O-ENA signal of more than 1 104 cm2 sr1 s1 can be observed even in the presence of UV noise and an H-ENA peak if the O-ENA energy lies above 1 keV. [43] The entire data set of Venus eclipse measurements (see list in section 3) has up to now not provided any distinct O-ENA signal. A thorough analysis of the first eclipse season in May 2006, with the laboratory instrument response and efficiency for O-ENAs [Grigoriev, 2007] yields as upper limit for O-ENAs in the tailward flow of Venus the following numbers: 1 104 cm2 sr1 s1 for energies between 1 and 10 keV, and 5 104 cm2 sr1 s1 for the interval 1.5 down to 0.4 keV. [44] For the default exospheric parameters in Table 1 the model predicts oxygen ENA intensities above the NPD detection threshold. When discussing ENAs originating from planetary ions the ignorance of the BIMF direction is troublesome (see section 1), but it is very unlikely that during each of the 16 different orbits the convective electric field pointed to a direction not covered with NPD2_2. The median solar wind flux was probably even stronger than assumed in the plasma model, which deepens the discrepancy between model predictions and observations. However, the model predicts oxygen ENA intensities that are consistent with the observed upper limits if two times lower hydrogen exobase densities are implemented (see previous section 5). Figure 13 shows the modeled O-ENA spectrum: only 1/3 of the integral intensity is attributed to energies above 0.4 keV. If we take that into account and if we reduce the exospheric density of the thermal hydrogen by a factor of 2, the modeled O-ENA outflow results in Figure 14. The predicted ENA intensities are just below or equal to the observational limit of 5 104 cm2 sr1 s1, corresponding to the dark blue pixels. Alternatively, reducing the exobase bulk temperature by 20% also leads to oxygen ENA intensities that are consistent with the observations. The shape of the O-ENA outflow is in general very similar to the H-ENA outflow; it is only slightly narrower. Both the O-ENA and the H-ENA outflow are predominantly caused by charge exchange with neutral hydrogen. [45] The NPD observations do not rule out that the oxygen exosphere in 2006 was also more tenuous than assumed in the VIRA model. All we can say with the NPD images is that the presented plasma model is consistent with the nonobservation of oxygen ENAs for energies larger than 0.4 keV if we assume hydrogen exobase densities two times lower or exobase temperatures 20% cooler than the VIRA values. The total O-ENA production rate predicted by the MHD model with reduced hydrogen densities (illustrated in Figure 14) calculates to 7 1024 s1 over the energy range from 0.2 to 10 keV. [46] The total loss of oxygen due to charge exchange reactions at Venus will be higher than the total O-ENA production rate since the loss of oxygen pickup ions that are not neutralized before leaving the exosphere has to be accounted for as well. For the sum of losses due to charge exchange processes Fok et al. [2004] found for the default ENA model 1.5 1025 s1 but they cautioned that this should be understood as a lower limit. The reason is that the MHD model does not take into account finite gyroradius pick up of O+, which will heat up the ionospheric

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oxygen. On the basis of our current knowledge we estimate the total oxygen loss rate due to charge exchange to be of the order of QO;CX 1025 s1 :

ð7Þ

7. Conclusions [47] 1. The first ENA images of Venus have been compared to an ENA model. The model, combining the canonical VIRA exosphere model with an MHD plasma code of solar wind ions and planetary oxygen ions, reproduces the observed tailward flow of ENAs within a factor of 2. The observed ENA tailward flow can be understood as neutralized magnetosheath plasma. Although, the uncertainties of the measurements and of the model results limit us to an accuracy of a factor of 2 some exospheric parameters can be constrained. [48] 2. The MHD approximation seems appropriate to predict the ENA outflow. The resolution of the observed ENA images is insufficient to motivate the use of kinetic models. The images in most cases do not even allow to statistically discriminate the shape of H-ENA outflow against the one of O-ENAs. [49] 3. The VIRA model by Keating et al. [1985], extrapolated to higher altitudes assuming a Chamberlain profile, is consistent with most NPD measurements. However, the agreement between model and observations is optimized if the exobase densities or temperatures of the thermal hydrogen proposed by Keating et al. [1985] are reduced. If the exobase temperatures are not changed, the NPD observations are reproduced by multiplying the VIRA densities by a factor of 0.5 ± 0.25 for all SZA. If the bulk temperatures are reduced by 20%, the possible range for the thermal hydrogen is 1.0+1.0 0.5 times the VIRA densities. The inherent uncertainties of the observed ENA images do not allow to determine whether the exobase density or the exospheric temperature should be reduced with respect to Keating et al. [1985] for low solar activity. The other neutral components of the Venusian exosphere, including the hot hydrogen component, hardly affect the ENA outflow and therefore cannot be constrained with NPD measurements. [50] 4. Unfortunately, we cannot say anything about a possible contribution of planetary hydrogen and the influence of the interplanetary magnetic field on the ENA outflow, lacking high spatial and temporal resolution. The present model, which assumes that all H-ENAs originate from solar wind or magnetosheath ions, produces already higher H-ENA intensities than actually observed. Therefore, the contribution of planetary H-ENAs should be of minor importance. [51] 5. From the model we derive a total hydrogen ENA production rate of 0.6 1025 s1 with a factor of 2 uncertainty. This agrees well with Gunell et al. [2005], who predicted, prior to VEX, an average of 0.7 1025 s1 from a different MHD model. The authors also noted that this number does not vary much over the solar cycle. The H-ENA production rate of Venus is similar to the one measured at Mars with MEX/NPD [Galli et al., 2006b]. For the total oxygen ENA production of Venus at low solar activity an upper limit of 0.7 1025 s1 between 0.2 and 10 keV can be derived.

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[52] 6. We estimate the total loss rates of oxygen and hydrogen due to charge exchange reactions to be of the order of 1025 s1 for low solar activity. This seems to be the same order of magnitude as found in preliminary evaluations of IMA data [Barabash et al., 2007b]. This number will be better constrained in the near future when absolute calibration of the IMA sensor becomes available. Lammer et al. [2006] calculated similar loss rates from ion pickup of 1 1025 s1 for hydrogen and 2 1025 s1 for oxygen. For oxygen this is already close to the total loss rate derived by Lammer et al. [2006]; minor contributions from ion sputtering and detached plasma clouds are predicted to add up to a total of 3 1025 s1. For hydrogen, Lammer et al. [2006] find that the thermal escape of the photochemically produced hot hydrogen component is more important. For this erosion process they predict, on the basis of hydrogen exobase densities of Rodriguez et al. [1984], a loss rate of 4 1025 s1. Rodriguez et al. [1984] studied models with thermal hydrogen densities similar to the default values incorporated in our MHD model. We therefore recommend for the hydrogen loss caused by photochemical processes QH,UV = (2 ± 1) 1025 s1 for low solar activity. [53] 7. ENA production on its own is not important for atmospheric loss, neither for oxygen nor for hydrogen. But ENA imaging enables researchers to obtain within short time a global overview on the interaction of the solar wind with planetary atmospheres and ionospheres. [54] Acknowledgments. The ASPERA-4 experiment on the ESA Venus Express mission is a joint effort between 16 laboratories in 11 countries, all sponsored by their national agencies. We thank all these agencies as well as the various departments/institutes hosting these efforts. The lead author wishes to acknowledge the support of the Swiss National Science Foundation.

References Barabash, S., M. Holmstro¨m, A. Lukyanov, and E. Kallio (2002), Energetic neutral atoms at Mars: 4. Imaging of planetary oxygen, J. Geophys. Res., 107(A10), 1280, doi:10.1029/2001JA000326. Barabash, S., et al. (2007a), The analyzer of space plasmas and energetic atoms (ASPERA-4) for the Venus Express mission, Planet. Space Sci., 55, 1772. Barabash, S., et al. (2007b), The loss of ions from Venus through the plasma wake, Nature, 450, 650. Bertaux, J. L., J. Blamont, M. Marcelin, V. G. Kurt, N. N. Romanova, and A. S. Smirnov (1978), Lyman-Alpha observations of Venera-9 and 10 I. The non-thermal hydrogen population in the exosphere of Venus, Planet. Space Sci., 26, 817. Chamberlain, J. W., and D. M. Hunten (1987), Theory of Planetary Atmospheres, 2nd ed. Academic, Orlando, Fla. Coates, A. J., et al. (2008), Ionospheric photoelectrons at Venus: Initial observations by ASPERA-4 ELS, Planet. Space Sci., 56, 802.

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Fok, M.-C., T. E. Moore, M. R. Collier, and T. Tanaka (2004), Neutral atom imaging of solar wind interaction with the Earth and Venus, J. Geophys. Res., 109, A01206, doi:10.1029/2003JA010094. Futaana, Y., et al. (2006a), First ENA observations at Mars: Subsolar ENA jet at Mars, Icarus, 182, 413. Futaana, Y., et al. (2006b), First ENA observations at Mars: ENA emissions from the Martian upper atmosphere, Icarus, 182, 424. Galli, A., et al. (2006a), Direct measurements of energetic neutral hydrogen in the interplanetary medium, Astrophys. J., 644, 1317. Galli, A., P. Wurz, S. Barabash, A. Grigoriev, H. Gunell, R. Lundin, M. Holmstro¨m, and A. Fedorov (2006b), Energetic hydrogen and oxygen atoms observed on the nightside of Mars, Space Sci. Rev., 126, 267. Galli, A., et al. (2008), First observation of energetic neutral atoms in the Venus environment, Planet. Space Sci., 56, 807. Grigoriev, A. (2007), The neutral particle detector on the Mars and Venus Express missions, Ph.D. thesis, Inst. of Space Phys., Kiruna, Sweden. Gunell, H., M. Holmstro¨m, H. K. Biernat, and N. V. Erkaev (2005), Planetary ENA imaging: Venus and a comparison with Mars, Planet. Space Sci., 53, 433. Gunell, H., M. Holmstro¨m, S. Barabash, E. Kallio, P. Janhunen, A. F. Nagy, and Y. Ma (2006), Planetary ENA imaging: Effects of different interaction models for Mars, Planet. Space Sci., 54, 117. Holmstro¨m, M., S. Barabash, and E. Kallio (2002), Energetic neutral atoms at Mars 1. Imaging of solar wind protons, J. Geophys. Res., 107(A10), 1277, doi:10.1029/2001JA000325. Jarvinen, R., E. Kallio, I. Sillanpaä¨, and P. Janhunen (2008), Hybrid modelling the Pioneer Venus Orbiter magnetic field observations, Adv. Space Res., 41, 1361. Keating, G. M., et al. (1985), Models of Venus neutral upper atmosphere: Structure and composition, Adv. Space Res., 5, 117. Lammer, H., et al. (2006), Loss of hydrogen from the upper atmosphere of Venus, Planet. Space Sci., 54, 1445. Lichtenegger, H. I. M., H. Lammer, and W. Stumptner (2002), Energetic neutral atoms at Mars: 3. Flux and energy distributions of planetary energetic H atoms, J. Geophys. Res., 107(A10), 1279, doi:10.1029/ 2001JA000322. Lindsay, B. G., and R. F. Stebbings (2005), Charge transfer cross sections for energetic neutral atom data analysis, J. Geophys. Res., 110, A12213, doi:10.1029/2005JA011298. Martinecz, C., et al. (2008), Location of the bow shock and ion composition boundaries at Venus: Initial determinations from Venus Express ASPERA-4, Planet. Space Sci., 56, 780. Rodriguez, J. M., M. J. Prather, and M. B. McElroy (1984), Hydrogen on Venus: Exospheric distribution and escape, Planet. Space Sci., 32, 1235. Tanaka, T., and K. Murawski (1997), Three-dimensional MHD simulation of the solar wind interaction with the ionosphere of Venus: Results of two-component reacting plasma simulation, J. Geophys. Res., 102, 19,805. Zhang, T.-L., et al. (2007), Little or no solar wind enters Venus’ atmosphere at solar minimum, Nature, 450, 654.

S. Barabash, A. Ekenba¨ck, Y. Futaana, A. Grigoriev, and M. Holmstro¨m, Instituted fo¨r Rymdfysik, P.O. Box 812, SE-98128 Kiruna, Sweden. M.-C. Fok, Geospace Physics Laboratory, NASA Goddard Space Flight Center, Code 673, Greenbelt, MD 20771, USA. A. Galli and P. Wurz, Physikalisches Institut, Universita¨t Bern, Sidlerstrasse 5, CH-3012 Bern, Switzerland. ([email protected]) H. Gunell, Department of Physics, West Virginia University, P.O. Box 6315, Morgantown, WV 26506, USA. E. Kallio, Finnish Meteorological Institute, P.O. Box 503, FIN-00101 Helsinki, Finland.

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Induced magnetosphere and its outer boundary at Venus T. L. Zhang,1,2 M. Delva,1 W. Baumjohann,1 M. Volwerk,1 C. T. Russell,3 H. Y. Wei,3 C. Wang,2 M. Balikhin,4 S. Barabash,5 H.-U. Auster,6 and K. Kudela7 Received 4 June 2008; revised 11 August 2008; accepted 29 September 2008; published 20 December 2008.

[1] The induced magnetosphere at Venus consists of regions near the planet and its wake

for which the magnetic pressure dominates all other pressure contributions. Initial Venus Express measurements indicate a well-defined outer boundary, the magnetopause, of the induced magnetosphere. This magnetopause acts as an obstacle to deflect the solar wind. Across this boundary, the magnetic field exhibits abrupt directional changes and pronounced draping. In this paper, we examine the structure of the magnetopause using Venus Express magnetic measurements. We find that the magnetopause is a directional discontinuity resembling either a tangential or a rotational discontinuity depending on the interplanetary magnetic field orientation. Citation: Zhang, T. L., et al. (2008), Induced magnetosphere and its outer boundary at Venus, J. Geophys. Res., 113, E00B20, doi:10.1029/2008JE003215.

1. Introduction [2] It is well known that the solar wind interaction with a planet produces a magnetosphere-like structure near the planet with common features such as bow shock, magnetosheath, magnetotail, and boundary layers. These magnetosphere-like structures are found at all planets in the solar system regardless if the planet has an intrinsic global magnetic field or not. In the case of planets like Mercury, Earth, Jupiter, Saturn, Uranus and Neptune, a magnetosphere is formed by the interaction of the solar wind with the planet’s large intrinsic magnetic field. For a planet like Venus or Mars, which has no global intrinsic magnetic field but with atmosphere, an induced magnetosphere is created by the solar wind interaction with the highly conducting ionosphere. The induced magnetosphere is therefore analogous to the magnetosphere of an intrinsically magnetized planet, but occupies a smaller volume. [3] The term ‘‘induced magnetosphere,’’ to our knowledge, was coined first by Podgorny et al. [1980] in describing the solar wind interaction with comets and Venus. Ness et al. [1982] used this term in studying the interaction between Titan and the Saturnian magnetospheric flow. The induced magnetosphere was used because although 1 Space Research Institute, Austrian Academy of Sciences, Graz, Austria. 2 State Key Laboratory of Space Weather, Center for Space Science and Applied Research, Chinese Academy of Sciences, Beijing, China. 3 Institute of Geophysics and Planetary Physics, University of California, Los Angeles, California, USA. 4 Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK. 5 Swedish Institute of Space Physics, Kiruna, Sweden. 6 Institut fu¨r Geophysik und Extraterrestrische Physik, Technische Universita¨t, Braunschweig, Germany. 7 Institute of Experimental Physics, Slovak Academy of Sciences, Kosˇice, Slovakia.


there is no intrinsic magnetic field at Titan, it possesses a magnetotail with similar configuration as the magnetotail produced from an intrinsic magnetic field. Further, Ness et al. [1982] called the outer boundary of the induced magnetosphere the magnetopause. [4] Recently, Luhmann et al. [2004] revitalized the terminduced magnetosphere by giving it a global picture. Luhmann et al. [2004] gave a working definition of an induced magnetosphere as ‘‘everything between an outer boundary outside of which the obstacle has no effect on the external medium, and an inner boundary inside of which there is no effect of the external conditions.’’ The terminduced magnetosphere has been widely used by the recent Venus Express publications [Barabash et al., 2007; Zhang et al., 2007; Kallio et al., 2008; Fedorov et al., 2008]. Zhang et al. [2007] further updated the definition of the induced magnetosphere to be the regions near the planet and its wake in which magnetic pressure dominates the other pressure contributions. Figure 1 illustrates the current understanding of Venus induced magnetosphere and its boundaries. The outer boundary of the induced magnetosphere is the magnetopause and the inner boundary is the ionopause. The dayside portion of the induced magnetosphere is often called the magnetic barrier and the nightside portion of the induced magnetosphere the magnetotail. [5] Russell et al. [1979] noticed that the magnetic field piles up to form a magnetic barrier in the inner magnetosheath on the dayside from the initial observations of Pioneer Venus Orbiter (PVO). Zhang et al. [1991] found that the magnetic barrier acts as an obstacle to the solar wind in analogy to the Earth’s magnetosphere. They found that the magnetic barrier is bounded by the ionopause as the lower boundary and a pressure balanced magnetic barrier upper boundary. [6] An initial survey of the Venus Express (VEX) magnetic field time series data indicates a well-defined boundary, the magnetopause, located at the outer edge of the magnetic barrier region [Zhang et al., 2007]. This magne-

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Figure 2. Magnetic field measurements around periapsis (0153 UT, altitude is 302 km, SZA is 88.8°) on 27 June 2006. The shadowed area is the induce magnetosphere.

Figure 1. Schematic of Venus induced magnetosphere and its boundaries. topause is easily identified as a sudden cessation of magnetosheath wave activity and an abrupt start of pronounced field draping in the inner magnetosheath where the field magnitude is enhanced. The magnetic barrier, an induced magnetosphere on the dayside, is found with the same thickness at both solar minimum and solar maximum [Zhang et al., 2008a]. Finally, it was found that the magnetopause has no signature in the field magnitude and the thickness is about 200 km in vertical extent. It is the purpose of this paper to present a more detailed analysis of the magnetic field data on the induced magnetosphere and its outer boundary. A complete description of the induced magnetosphere will require a combination of all relevant magnetic and plasma data. However, at time of writing, the plasma moment data are still not available, thus we restrict our analysis to the magnetic field data only.

carefully designed to cope with this great challenge of a ‘‘dirty’’ spacecraft with a large dynamic range, large automatic compensation range, and high digital resolution. [8] We process and clean the data based on dual-sensor measurements. The detailed description of the data cleaning method can be found by Zhang et al. [2008b]. Basically, we examine the time series DB, of the differences of the inboard and outboard magnetic field measurements in

2. Instrumentation and Data [7] The Venus Express mission is the first European misson to Venus [Titov et al., 2006]. The spacecraft has a highly elliptical polar orbit with a period of 24 h. Among other instruments, it carries a magnetometer to investigate the Venus plasma environment [Zhang et al., 2006]. The Venus Express magnetometer (MAG) consists of two triaxial fluxgate sensors. Because of schedule and budget constraint, no magnetic cleanliness program was implemented. The outboard sensor is mounted to the tip of a one meter deployable boom whereas the inboard sensor is directly attached to the top panel of the spacecraft. The magnetometer is designed using dual sensor or gradiometer method [Ness et al., 1971]. Both sensors sample simultaneously, to enable separation of spacecraft originated stray field effects from the ambient space field. The instrument has been

Figure 3. The magnetic field in VSO coordinates plotted along the trajectory through the induced magnetosphere on 27 June 2006. The thick line marks the induced magnetosphere part of the trajectory. Magnetopause crossings are indicated at 0146 and 0200 UT.

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Figure 4. (a) Altitude profiles of the magnetic field components and magnitude in the inbound magnetopause crossing on 27 June 2006. MVA is applied on the shaded area. (b) Hodogram of the magnetopause crossing.

spacecraft coordinates for all three components. In the DB time series, all variations are caused by the spacecraft disturbances, no natural signal appears on these time series. This allows us to categorize the spacecraft disturbances using various mathematical approaches such as fuzzy logic, neural network etc. In general, there are three kinds of disturbances in the DB time series: jumps, regular drift variations, and irregular variations. Once all disturbances are categorized from the DB time series, we examine the effect of each kind of disturbance on the outboard sensor

measurements. Finally, automatic data cleaning software has been developed to remove all identified disturbances at the outboard sensor measurements and the offset determination algorithm is applied to remove the large strayfield [Leinweber et al., 2008]. [9] One of the advantages of the VEX magnetometer (MAG) measurements is the higher time resolution compared with that of PVO. During a nominal 24 h VEX orbit, the MAG data are sampled at 1, 32, and 128 Hz. Initially, the sampling rate was set at 128 Hz for 2 min and 32 Hz for

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Figure 5. (a) Altitude profiles of the magnetic field components and magnitude in the outbound magnetopause crossing on 27 June 2006. MVA is applied on the shaded area. (b) Hodogram of the magnetopause crossing. 120 min around the pericenter. Later, the fast modes have been increased to 10 min for 128 Hz and 4 h for 32 Hz. Furthermore, all 128 and 32 Hz data are merged into the 1 Hz data set by applying the exact same averaging algorithm as was done onboard. So far, all 1 Hz data are cleaned with an absolute field accuracy of 1 nT, and the variable field accuracy better than 0.1 nT. Thus the data are of a quality comparable to magnetic field measurements made onboard magnetically clean spacecraft.

3. Observations of Induced Magnetosphere [10] Figure 2 displays VEX MAG measurements during a typical induced magnetosphere encounter while the interplanetary magnetic field (IMF) was very steady indicated by the little variation in both orientation and magnitude between inbound and outbound IMF. The observations are 1 Hz averages in Venus Solar Orbital (VSO) coordinates where the x axis points from Venus to the Sun, the Y axis is

opposite to the Venus orbital motion and Z axis is northward. The spacecraft moves near the terminator region, defined as where the solar zenith angle (SZA) near 90°, from dusk (Y) to dawn (+Y), enters the magnetosheath at about 0128 UT and exits the magnetosheath at about 0232 UT. Around periapsis (0153 UT, altitude 302 km, SZA 88.8°) in the inner magnetosheath, the field is enhanced to form a magnetic barrier, the induced magnetosphere on the dayside (shaded region in the Figure 2). Apparently, the wave activity is suppressed in the magnetic barrier. Here we define the magnetopause as where the magnetosheath waves stop. Along with the cessation of wave activity at the magnetopause, the field direction exhibits an abrupt change as shown in Figure 2. Using also the dynamic power spectrum of the 32 Hz data (not shown in this paper [see Zhang et al., 2007]), we identify the magnetopause crossings at 0146 UT (altitude 823 km, SZA 87°) for inbound and 0200 UT (altitude 905 km, SZA 91°) for outbound. As expected for solar minimum conditions

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planet. We note that the inbound magnetopause crossing (0140 UT) is much better defined than the outbound magnetopause crossing (0200 UT). The reason for this difference is due to the location and nature of the magnetopause structure which we will examine in detail in the following section.

4. Magnetopause Structure [12] The minimum variance analysis (MVA), pioneered for space magnetic field applications by Sonnerup and Cahill [1967], has been widely used in the determination of the normal direction of the magnetopause. Since the divergence of B is zero, the variation of B along the normal to a thin current sheet is generally small, unless there is small-scale, patchy reconnection that is easily recognized. Thus MVA allows us to estimate the normal to simple planar current sheets from magnetic field data measured with a single spacecraft. Here we also test the stability of the normal orientation determined from MVA by performing the MVA with several nested time intervals around the discontinuity and checked for a consistent result. [13] Ideally, when the magnetic component along the current sheet normal, Bn, is zero, the current sheet can be categorized as tangential discontinuity; otherwise, it is a rotational discontinuity [Burlaga, 1969]. However, this definition is not valid when one applies MVA to determine the normal component of the current sheet using single spacecraft magnetic field measurements since the obtain Bn is never a zero number. Lepping and Behannon [1980] found that the ratio between Bn, obtained from MVA, and the averaged magnetic field magnitude B across the discontinuity can be used in distinguishing tangential from rotational discontinuity. They determined a threshold of jBn/Bj = 0.3 as the upper bound of a tangential discontinuity.

Figure 6. (a) Magnetic field observations of the induced magnetosphere and its outer boundary on 11 June 2006. (b) The magnetic field plotted along the spacecraft trajectory between 0132 and 0142 UT, 11 June 2006. The thick line marks the induced magnetosphere part of the trajectory. The magnetopause occurs at 0138 UT. [Zhang et al., 2006], no ionopause is observed for this periapsis passage since the ionopause is generally lower than the 250 – 350 km pericenter altitude of the spacecraft. In Figure 3 the magnetic field direction is plotted along the spacecraft trajectory. The reversal of the field in the X direction around the periapsis 0153 UT indicates the formation of the ‘‘magnetic pole’’ at terminator [Russell et al., 1982]. In describing the geometry of an induced magnetosphere, it is common to refer the magnetic pole and ‘‘magnetic equator’’ with respect to the orientation of IMF. The magnetic equator lies in plane which contains the IMF. The magnetic pole is in the direction perpendicular to the magnetic equatorial plane. [11] Clearly evident in the induced magnetosphere portion in the Figure 3 is the draping of the field lines about the

4.1. Case 1: 27 June 2006, Inbound [14] Figure 4 shows the magnetic field altitude profile of the inbound crossing on 27 June 2006. Between 700 and 900 km, the field direction changes dramatically with the tendency of By and Bz toward zero and increasing Bx, an indication of more draping. We apply MVA on the data between 0145:30 and 0147:00 UT, corresponding to the altitude between 898 and 685 km indicated by the shaded band in the plot. We find that the normal vector is [0.346 0.331 0.878]. The ratio of the intermediate to the minimum eigenvalues, l2/l3, is found to be 50.1, indicating a well-defined minimum variance direction. Here we have also tested the stability of the normal orientation determined from MVA by performing the MVAwith nested time intervals and the result is consistent. The magnetic field component along the minimum variance direction Bn = 6.01 nT and the average magnetic field magnitude across the magnetopause Bt = 37.07 nT. Since the ratio Bn/Bt is 0.16, which is much less the threshold of 0.3 [Lepping and Behannon, 1980], this magnetopause crossing is a tangential discontinuity. 4.2. Case 2: 27 June 2006, Outbound [15] Figure 5 shows the magnetic field altitude profile of the outbound magnetopause crossing on 27 June 2006. The crossing is not well identified as expected for a nightside magnetopause crossing of an induced magnetosphere. We

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Figure 7. (a) Altitude profiles of the magnetic field measurements on 11 June 2006. MVA is applied on the shaded area. (b) Hodogram of the magnetopause crossing. apply MVA on the data between 0158:35 and 0159:55 UT. We find that the normal vector is [0.478 0.571 0.668], the ratio of the intermediate to the minimum eigenvalues, l2/l3, is 11.5. Thus the minimum variance direction is well defined. Again the stability of the normal was tested with nested time interval and we found that the obtained minimum variance direction is stationary. For this magnetopause crossing, the magnetic field component along the minimum variance direction Bn = 13.9 nT and the average field magnitude Bt = 32.9 nT, the ratio is 0.42. Thus here we have a rotational discontinuity with a large field component along the normal direction.

4.3. Case 3: 11 June 2006, Inbound [16] Figure 6a shows magnetic field time series of a welldefined magnetopause crossing at 0138 UT, 11 June 2006 (altitude 862 km, SZA 76°). Similar as in the example of 27 June 2006, the IMF is mainly in Y direction. Figure 6b shows the spacecraft trajectory and field vector along it. The magnetopause crossing is evident by the field directional change with pronounced draping. Also the wave activity clearly stops at the magnetopause. Figure 7 shows the magnetic altitude profile. We apply MVA on the data between 0137:10 and 0139:00 UT, corresponding to the altitude between 994 and 718 km indicated by the shading in the

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We apply MVA on the data between 0134:15 and 0135:45 UT, corresponding to the altitude between 829 and 627 km indicated by the shading in Figure 9. We find that the normal vector is [0.347 0.158 0.925] and ratio l2/l3 of 3.79. Although the minimum variance direction is not well defined from MVA in this case, the normal direction is reasonably well determined since it is normal to the expected magnetopause at solar minimum. Furthermore, we have also tested the stability of the normal orientation determined from MVA by performing the MVA with nested time intervals and the result is consistent. The magnetic field component along the minimum variance direction Bn = 29.45 nT and the average field magnitude Bt = 41.5 nT, the ratio Bn/Bt is 0.71. Thus this magnetopause crossing resemble to a rotational discontinuity.

5. Discussion and Conclusions

Figure 8. (a) Magnetic field observations of the induced magnetosphere and its outer boundary on 1 June 2006. (b) The magnetic field plotted along the spacecraft trajectory between 0130 and 0140 UT, 1 June 2006. The thick line marks the induced magnetosphere part of the trajectory. The magnetopause crossing occurs at 0134 UT. plot. We find that the normal vector is [0.046 0.259 0.841]. The minimum variance direction is well defined with l2/l3 of 24.3. The magnetic field component along the minimum variance direction Bn = 6.04 nT and the average field magnitude Bt = 35.93 nT, the ratio is 0.17. Similar as case 1, this magnetopause crossing is a tangential discontinuity. 4.4. Case 4: 1 June 2006, Inbound [17] In Figure 8a we show the inbound magnetopause crossing on 1 June 2006. One main reason to select this event is that the IMF is mainly in the Z direction. From Figures 8a and 8b, the magnetopause crossing at 0134 UT (altitude 864 km, SZA 71°) is evident by the magnetosheath wave activity abrupt ending and pronounced field draping.

[18] The induced magnetosphere of Venus is a region within which the magnetic pressure dominates all other pressure contributions. It is strongest at the subsolar point and weakens with increasing solar zenith angle. Although the lower boundary of the induced magnetosphere can normally be defined by the ionopause, this is not always the case. For example, at solar minimum or at solar maximum but when the solar wind ram pressure is high, the ionosphere is fully magnetized, and the induced magnetosphere is found deeply inside the ionosphere. In such case, the lower boundary of the induced magnetosphere is below the nominal ionopause location. Furthermore, the nightside ionopause is ill-defined and so is the lower boundary of the induced magnetosphere at the nightside. [19] In contrast to the lower boundary of the induced magnetosphere at Venus, the upper boundary, a magnetopause separating the magnetosheath solar wind plasma from the induced magnetosphere, is well defined as shown by initial Venus Express measurements [Zhang et al., 2007]. While the VEX measurements characterize the magnetopause in the terminator region, previous studies show that the same magnetopause, although other names might have been given, extends both to the subsolar region and the distant magnetotail. In a study of the dimension and magnetic structure of the distant Venus magnetotail, Saunders and Russell [1986] identified the magnetopause as a directional discontinuity. By examining four unambiguous tail boundary crossings, they showed that Venus magnetopause is either a rotational or a tangential discontinuity depending on the location of the observation. They pointed out that the tangential discontinuity occurs at a plasma sheet/magnetosheath interface, wheras the rotational discontinuity occurs at a tail lobe/magnetosheath interface. Therefore, Saunders and Russell [1986] first revealed the IMF control of the magnetopause structure at the distant magnetotail. [20] A more recent study of the structure of the magnetopause, i.e., the magnetic pileup boundary, at Mars and Venus was performed by Bertucci et al. [2005]. Using six magnetopause crossings both at dayside Venus and Mars, they concluded that this boundary resembles a tangential discontinuity rather than a rotational discontinuity. However, no IMF orientation control of the structure of the magnetopause was considered in their investigation.

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Figure 9. (a) Altitude profiles of the magnetic field measurements on 1 June 2006. MVA is applied on the shaded area. (b) Hodogram of the magnetopause crossing. [21] In the present study, we examined four magnetopause crossings of the induced magnetosphere of Venus. We found that Venus magnetopause exhibits properties resembling both tangential and rotational discontinuity, a conclusion consistent with the results of Saunders and Russell

[1986], but different from the results from Bertucci et al. [2005]. In order to resolve this difference, we further examine the possible controlling factor of the structure of the magnetopause. In Table 1, we list the magnetopause crossing location and the IMF conditions. Further we list the

Table 1. Magnetopause Crossing Locations and IMF Conditions Case

Magnetopause Crossing in VSO (km)

IMF (nT)

Altitude (km)

SZA

Clock Angle

Magnetopause Structure

1 2 3 4

390 2853 6245 177 4895 4940 1645 2535 6219 2284 2007 6213

0.18 7.94 0.97 0.11 8.69 2.05 0.73 5.20 0.18 0.48 3.84 6.62

823 905 862 864

87° 91° 76° 71°

108° 32° 114° 168°

tangential rotational tangential rotational

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altitude, the SZA and the clock angle of the magnetopause crossing. The clock angle is defined as the angle between the radius vector to the spacecraft at the magnetopause and the IMF both projected in the YZ plane. At terminator region, it is usual to call a clock angle of 90° or 90° as the magnetic pole and a clock angle of 0° or 180° as the magnetic equator. Because of the VEX orbital geometry, our magnetopause examples are limited to the terminator region covering a SZA between 71° to 91°. From Table 1, we can see that the magnetopause in a magnetic pole region exhibits properties of a tangential discontinuity, whereas a rotational discontinuity is found for a magnetopause at the magnetic equator. Therefore, it is evident that the IMF orientation exerts strong control on the structure of the magnetopause of the induced magnetosphere at Venus. [22] Ideally, the magnetopause is defined as pressurebalanced boundary where the magnetic pressure in the induced magnetosphere equals the ram pressure of the magnetosheath solar wind. However, due the insufficient temporal resolution of the PVO plasma instrument, such a definition could not be applied to data analysis in practice. A modified pressure balance boundary definition was proposed by Zhang et al. [1991] to determine the outer boundary of the magnetic barrier as the altitude where the magnetosheath magnetic pressure is equal half of the upstream solar wind dynamic pressure. Indeed, initial analysis of the ASPERA data shows that the so-called ion composition boundary [Martinecz et al., 2008] is colocated with the magnetopause. When the combined plasma and magnetic field data set is available, we plan to investigate the pressure balance nature of the magnetopause. [23] Acknowledgments. The work at China was supported by CAS grant KJCX2-YW-T13 and NNSFC grants 40621003 and 40628003. The work at UCLA was supported by the National Aeronautics and Space Administration under research grant NNG06GC62G. The work in Slovakia was supported by Slovak Research and Development Agency under the contract APVV-51-053805.

References Barabash, S., et al. (2007), The loss of ions from Venus through the plasma wake, Nature, 450, 650 – 653, doi:10.1038/nature06434. Bertucci, C., C. Mazelle, M. H. Acuna, C. T. Russell, and J. A. Slavin (2005), Structure of the magnetic pileup boundary at Mars and Venus, J. Geophys. Res., 110, A01209, doi:10.1029/2004JA010592. Burlaga, L. F. (1969), Directional discontinuities in the interplanetary magnetic field, Sol. Phys., 7, 54 – 71, doi:10.1007/BF00148406. Fedorov, A., et al. (2008), Comparative analysis of Venus and Mars magnetotails, Planet. Space Sci., 56, 812 – 817, doi:10.1016/j.pss.2007.12.012. Kallio, E., et al. (2008), The Venusian induced magnetosphere: A case study of plasma and magnetic field measurements on the Venus Express mission, Planet. Space Sci., 56, 796 – 801, doi:10.1016/j.pss.2007.09.011. Leinweber, H. K., C. T. Russell, K. Torkar, T. L. Zhang, and V. Angelopulos (2008), An advanced approach to finding magnetometer zero levels in the interplanetary magnetic field, Meas. Sci. Technol., 19(5), 055104, doi:10.1088/0957-0233/19/5/055104.

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Lepping, R. P., and K. W. Behannon (1980), Magnetic field directional discontinuities: 1. Minimum variance errors, J. Geophys. Res., 85, 4695 – 4703, doi:10.1029/JA085iA09p04695. Luhmann, J. G., S. A. Ledvina, and C. T. Russell (2004), Induced magnetospheres, Adv. Space Res., 33, 1905 – 1912, doi:10.1016/j.asr.2003.03.031. Martinecz, C., et al. (2008), Location of the bow shock and ion composition boundaries at Venus: Initial determinations from Venus Express ASPERA-4, Planet. Space Sci., 56, 780 – 784, doi:10.1016/j.pss. 2007.07.007. Ness, N. F., K. W. Behannon, R. P. Lepping, and K. H. Schatten (1971), Use of two magnetometers for magnetic field measurements on a spacecraft, J. Geophys. Res., 76, 3565 – 3573. Ness, N. F., M. H. Acuna, K. W. Bahannon, and F. M. Neubauer (1982), The induced magnetosphere of Titan, J. Geophys. Res., 87, 1369 – 1381, doi:10.1029/JA087iA03p01369. Podgorny, I. M., E. M. Dubinin, and P. L. Israelevich (1980), Laboratory simulation of the induced magnetospheres of Comets and Venus, Earth Moon Planets, 23, 323 – 338, doi:10.1007/BF00902047. Russell, C. T., R. C. Elphic, and J. A. Slavin (1979), Initial Pioneer Venus magnetic field results: Dayside observations, Science, 203, 745 – 748, doi:10.1126/science.203.4382.745. Russell, C. T., J. G. Luhmann, R. C. Elphic, F. L. Scarf, and L. H. Brace (1982), Magnetic field and plasma wave observations in a plasma cloud at Venus, Geophys. Res. Lett., 9, 45 – 48, doi:10.1029/GL009i001p00045. Saunders, M. A., and C. T. Russell (1986), Average dimension and magnetic structure of the distant Venus magnetotail, J. Geophys. Res., 91, 5589 – 5604, doi:10.1029/JA091iA05p05589. ¨ ., and L. J. Cahill (1967), Magnetopause structure and Sonnerup, B. U. O attitude from Explorer 12 observations, J. Geophys. Res., 72, 171 – 183, doi:10.1029/JZ072i001p00171. Titov, D. V., et al. (2006), Venus Express science planning, Planet. Space Sci., 54, 1279 – 1297, doi:10.1016/j.pss.2006.04.017. Zhang, T. L., J. G. Luhmann, and C. T. Russell (1991), The magnetic barrier at Venus, J. Geophys. Res., 96, 11,145 – 11,153, doi:10.1029/ 91JA00088. Zhang, T. L., et al. (2006), Magnetic field investigation of the Venus plasma environment: Expected new results, Planet. Space Sci., 54, 1336 – 1343, doi:10.1016/j.pss.2006.04.018. Zhang, T. L., et al. (2007), Little or no solar wind enters Venus’ atmosphere at solar minimum, Nature, 450, 654 – 656, doi:10.1038/nature06026. Zhang, T. L., et al. (2008a), Initial Venus Express magnetic field observations of the magnetic barrier at solar minimum, Planet. Space Sci., 56, 790 – 795, doi:10.1016/j.pss.2007.10.013. Zhang, T. L., et al. (2008b), Initial Venus Express magnetic field observations of the Venus bow shock location at solar minimum, Planet. Space Sci., 56, 785 – 789, doi:10.1016/j.pss.2007.09.012.

H.-U. Auster, Institut fu¨r Geophysik und Extraterrestrische Physik, Technische Universita¨t, Mendelssohnstrasse 3, D-38106 Braunschweig, Germany. M. Balikhin, Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK. S. Barabash, Swedish Institute of Space Physics, Box 812, SE-98128 Kiruna, Sweden. W. Baumjohann, M. Delva, M. Volwerk, and T. L. Zhang, Space Research Institute, Austrian Academy of Sciences, A-8042 Graz, Austria. ([email protected]) K. Kudela, Institute of Experimental Physics, Slovak Academy of Sciences, Watsonova 47, 040 01 Kosˇice, Slovakia. C. T. Russell and H. Y. Wei, Institute of Geophysics and Planetary Physics, University of California, Los Angeles, CA 90095, USA. C. Wang, State Key Laboratory of Space Weather, Center for Space Science and Applied Research, Chinese Academy of Sciences, Beijing 100080, China.

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Proton cyclotron waves in the solar wind at Venus M. Delva,1 T. L. Zhang,1 M. Volwerk,1 Z. Vo¨ro¨s,2 and S. A. Pope3 Received 20 March 2008; revised 30 May 2008; accepted 24 June 2008; published 18 September 2008.

[1] Magnetometer data from two Venus years of the Venus Express mission in orbit are

investigated for the occurrence of ion cyclotron waves. Proton cyclotron waves were recently detected in the upstream region of Venus by the spacecraft, indicating pickup of planetary protons from Venus’s exosphere by the solar wind and loss of hydrogen to interplanetary space. A study of representative cases illustrates the waveform, spectrum, duration, and higher-order resonances of the transverse waves with left-hand circular polarization and propagation nearly along the magnetic field; their properties in the magnetic field principal axes system are determined. A statistical approach studies the wave properties as a function of the angle between the solar wind and magnetic field direction, as a function of their occurrence in space, and with respect to the motional solar wind electric field. Proton cyclotron waves are found up to 9 Venus radii from the planet, for a large range of angles between the solar wind and magnetic field direction, independent from foreshock geometry and independent from the direction of the motional electric field. This reveals that cyclotron wave generation from local pickup of neutral hydrogen is efficient over a large volume of space upstream of the planet and imposes the existence of an extended reservoir of planetary neutral hydrogen at Venus. Citation: Delva, M., T. L. Zhang, M. Volwerk, Z. Vo¨ro¨s, and S. A. Pope (2008), Proton cyclotron waves in the solar wind at Venus, J. Geophys. Res., 113, E00B06, doi:10.1029/2008JE003148.

1. Introduction [2] The existence of waves at the proton cyclotron frequency in the solar wind at Venus has long been a puzzling question. The absence of a Venus-intrinsic magnetic field leads to an induced magnetosphere around a small impenetrable obstacle and a bow shock at close distance to the planet. This leaves a substantial part of the upper neutral exosphere outside and upstream of the bow shock and thus directly accessible to the solar wind [Luhmann and Bauer, 1992]. Several processes can ionize the neutrals, mainly photoionization, electron impact ionization and charge exchange [Zhang et al., 1993]. The newborn ions form a secondary ion population in the solar wind, its interaction with the background plasma enables wave generation through different mechanisms; mainly scattering from a ring distribution in velocity space [Huddleston and Johnstone, 1992] or interaction of the new ‘‘beam’’ through the ion/ion beam plasma microinstability [Gary, 1991] generate waves at the cyclotron frequency in the plasma system. The cyclotron waves can be expected at any location where pickup in adequate quantity from the neutral atmosphere is possible, especially upstream of the bow shock. In contrast to waves 1 Space Research Institute, Austrian Academy of Sciences, Graz, Austria. 2 Institute of Astro- and Particle Physics, University of Innsbruck, Innsbruck, Austria. 3 Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK.


generated by back streaming solar wind ions which are confined to the foreshock region defined by the tangent magnetic field line to the bow shock, cyclotron waves can occur everywhere upstream [Mazelle et al., 2004]. Therefore, the observation of cyclotron waves in the upstream region is proof that the specific ion is present in the exosphere and is being lost to the solar wind and into interplanetary space. This is especially important for hydrogen at Venus, whose very low water and hydrogen content in comparison to Earth is not yet fully understood. [3] At Mars the situation is similar to Venus with absence of an intrinsic magnetic field and the solar wind accessing the upper exospheric layers outside of the bow shock directly. There, upstream proton cyclotron waves were reported already from the Phobos-2 spacecraft data [Russell et al., 1990]. From the Mars Global Surveyor magnetometer data extensive confirmation was obtained and a thorough study of the wave properties was made [Brain et al., 2002]. The proton cyclotron waves are concentrated near the subsolar point and at the flanks of the solar wind interaction region, outside the bow shock in the unperturbed solar wind, and found up to a distance of at least 12 planetary radii on the sides of the planet; they are left-hand elliptically polarized and propagate at a small angle with respect to the ambient magnetic field direction. The waves originate from the pickup process of protons from the hydrogen corona of Mars, which extends well beyond the Martian bow shock [Barabash et al., 1991]. An overview of all observed cyclotron wave features at Mars is given by Mazelle et al. [2004]. [4] Ion cyclotron waves can also occur at planetary satellites, they were reported at Io and Europa within the Jovian

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magnetosphere [Huddleston et al., 1997; Volwerk et al., 2001]. The same situation occurs for pickup in the environment of active comets, as was observed at, e.g., Comet Halley [Johnstone et al., 1987; Glassmeier et al., 1989]. [5] At Venus, ion cyclotron waves from pickup protons as the lightest species have been reported from Pioneer Venus Orbiter (PVO) data in the magnetosheath, but never in the upstream region [Russell et al., 2006], although they are expected there too from the existence of a Venus hydrogen corona [Nagy et al., 1990]. Recently, the first observations of upstream cyclotron waves were reported [Delva et al., 2008] from the magnetometer MAG [Zhang et al., 2006] aboard the Venus Express spacecraft. This proof of existence of upstream proton cyclotron waves at Venus resolves the question why they would not be there, and adds a point of similarity to the plasma environment at Mars and Venus. [6] In this paper, we present a detailed study of the proton cyclotron waves observed by the magnetometer MAG during the first two Venus years of the Venus Express mission. The typical wave properties are discussed for several representative examples. For the long-term observations a statistical approach was performed, with emphasis on the wave properties and on their spatial occurrence.

2. MAG Instrumentation and Analysis Methods [7] The magnetometer consists of two sensors, mounted at different distances from the spacecraft body and operating synchronously. The dual sensor configuration enables separation of the spacecraft generated time-dependent fields from the ambient space field [Zhang et al., 2008; Leinweber et al., 2008]. Data are sampled over the entire 24 h orbit, delivering 1 Hz data over 22 h and 32 Hz data over 2 h around pericenter. The polar orbit with its pericenter at 78° north at an altitude of 260 km leaves the spacecraft most of the time in the solar wind and therefore in optimal position to detect the upstream waves. [8] The magnetometer is operated continuously and longterm 1 Hz data are now available, mainly without data gaps. We used the data from 10 May 2006 through 10 August 2007 making up two Venus years, i.e., two full revolutions of the spacecraft orbital plane around Venus’s polar axis; these are two revolutions in the Venus solar orbital (VSO) coordinate system (centered at Venus, xVSO axis toward the Sun, zVSO axis perpendicular to Venus’s orbital plane and positive to ecliptic north, yVSO axis completing the righthand system), which provides rotationally uniform data coverage. [9] Theoretically, pickup ions can generate different ULF waves including resonant and nonresonant instabilities. The resonant instabilities are the most likely since they have usually the highest linear growth rates. From linear theory, the highest growth rates are predicted for propagation parallel to the magnetic field, either the right-hand or the left-hand circular polarized mode [Gary, 1991; Brinca, 1991]. [10] In the solar wind frame, a pickup ion with gyrofrequency Wi (Wi = q/m B; mass m, charge q, magnetic field strength B) and drift velocity V// along the magnetic field B will be in resonance with a solar wind wave of frequency w and wave vector k if [Brinca, 1991]: w k V == ¼ n Wi

ð1Þ

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where the prevailing case is the fundamental resonance for n = 1. Resonance occurs mainly with the right-hand mode (minus sign) in the plasma frame, through the ion/ion righthand resonant instability with dispersion. The resonant solar wind wave parallel to B and with frequency w will be observed in the spacecraft system with a Doppler-shifted frequency: wsc ¼ w þ k== V SW

ð2Þ

Combination of both equations for the fundamental resonance and taking into account that the planetary ion’s injection velocity into the plasma system is VSW (neglecting any original velocity, being small with respect to the solar wind velocity) we obtain for the frequency observed at the spacecraft [Brinca, 1991; Gary, 1991]: wsc þ Wi

ð3Þ

which is independent from the pitch angle a (VSW, B); the wave generation mechanism is independent from the solar wind electric field E = Vsw B [Gary, 1991]. [11] Therefore, the waves will be observed at the local ion gyrofrequency in the spacecraft frame and with specific left-hand polarization due to the anomalous Doppler effect [Mazelle and Neubauer, 1993]. This fact immediately excludes confusion with ULF waves generated by solar wind protons back streaming from the bow shock. It is known from numerous studies of foreshock ULF waves at Earth, both observational [e.g., Paschmann et al., 1979] and theoretical [e.g., Gary et al., 1981] that these particles have streaming velocities very different from VSW; they generate waves at frequencies different from the gyrofrequency, which are observed at the spacecraft at frequencies much lower than the cyclotron frequency. Studies of the ion ring beam in velocity space generated by the pickup process showed that the increase of observed wave power is at a frequency just below the local ion gyrofrequency. [12] The MAG data are investigated for enhanced wave power at or just below the proton cyclotron frequency fp fp ¼ ð2pÞ1 q=mB ¼ kB:

ð4Þ

The magnetic field at Venus in the range 5 – 40 nT and the MAG data accuracy of ±DB = ±1 nT in the total field value lead to an accuracy in the proton cyclotron frequency of ±0.015 Hz in the frequency range 0.076 – 0.610 Hz. [13] After transferring the VSO data to the magnetic principal axes (PA) system, where the wave oscillations occur in the (PAx, PAy) plane, power spectra are calculated and analysis of the waves regarding ellipticity, polarization and direction of propagation with respect to the mean magnetic field is performed [McPherron et al., 1972]. [14] The position of the wave observations with respect to their possible connection along a field line to the bow shock is also investigated. Since no ion data are yet available, a static model bow shock from a fit of MAG observations of the bow shock crossings [Zhang et al., 2008] was used and the tangential field line determined to see if the spacecraft is

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Figure 1. Proton cyclotron waves observed on 6 July 2006 (0040 to 0050 UT) at solar zenith angle 100°. (a) Waveform in Venus solar orbital coordinates showing three components and total field. These are measured data without any filtering. (b) Compressional power and left- and right-hand polarized transverse power as a function of frequency. Vertical dashed line indicates local proton cyclotron frequency (fp = 0.15 Hz), and horizontal bar denotes (fp ± Dfp). The strong peak in the left-hand power at left of this line is due to locally generated proton cyclotron waves. located inside or outside (upstream) of the model foreshock region, i.e., was magnetically connected with the model bow shock or not. Distinction of proton cyclotron waves from waves generated by back streaming particles from the

bow shock is possible from two characteristics. First, the latter occur only within the foreshock. Second, waves generated by back streaming particles will be observed at

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Figure 2. Wave properties in principal axes coordinates for proton cyclotron waves on 21 January 2007. (a) Waveform in principal axes coordinates, (Bx and Bx)PA components for 40 s of time. (b – d) Hodogram in each plane of the principal axes coordinate system for the same time of approximately four cycles. Markers are every 20 s, starting with 1 and in increasing order with time. The wave is clearly plane and nearly circular in (x, y)PA plane. the spacecraft at frequencies much lower than the cyclotron frequency [Gary, 1991].

3. Representative Proton Cyclotron Wave Cases [15] Analysis of the data of 450 orbits of Venus Express shows enhanced power in the transverse, left-hand polarized waves at or just below the proton cyclotron frequency many times. To enable better insight in the characteristic properties of PCWs, we discuss representative examples which illustrate specific interesting aspects of the waves. 3.1. Waveform and Spectrum [16] As a typical example for the waveform and the spectrum, we discuss an observation on 6 July 2006 (0040 to 0050 UT) at a solar zenith angle (SZA) of 100°. The VSO data are displayed in Figure 1a; the mean field B 10.2 nT yields a calculated local proton frequency fp = 0.15 Hz, the waveform with period 9 s is clearly seen (fpobs 0.11 Hz), overlaid with a slow modulation of 5 min period. The properties in the magnetic principal

axes system are as follows: polarization 60%, ellipticity 0.55 (minus for left-hand polarization); propagation with small angle to the mean magnetic field q (k, B) 6°. The spacecraft was located inside the modeled foreshock on the side of the parallel shock, the cone angle was q (xvso, B) 149°. [17] The spectrum in Figure 1b shows the compressional power, and left- and right-hand polarized transverse components; the strong enhancement of the power just below the calculated proton cyclotron frequency is only seen in the left-hand transverse component. The timely evolution of the enhanced left hand power is shown as dynamical spectrum for another example further below. 3.2. Properties in the Principal Axes System [18] A second example on 21 January 2007 illustrates that the waves are generally plane in the transverse direction to the magnetic field and almost circular. In Figure 2 the two transverse components in the principle axes system are shown as a function of time (Figure 2a), for a time span of about 4 cycles (0624:40 to 0625:20 UT). Figures 2b– 2d

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display hodograms for the same time interval; the wave is plane in the (PAx, PAy) plane and clearly left-handed polarized. [19] The mean values over the 10 min time interval (0620 to 0630 UT) for the wave properties are as follows: ellipticity 0.69, polarization 78%, angle of propagation to the mean magnetic field direction q (k, B) 8°, ratio of eigenvalues l1/l2 = 1.34, l2/l3 = 16.3. This again illustrates the left-hand nearly circular polarization of the plane but transverse waves. 3.3. Duration [20] The proton cyclotron wave (PCW) duration spans from a few minutes (5– 10 min) to more than 1 h (maximum observed 1:30 h) in the considered period of two Venus years. The main part of the events (75%) is rather short (10 min), with a mean value of 16 min. An example of long and persistent wave occurrence of 1:20 h duration on 11 June 2006 (2240 to 2400 UT) is shown in Figure 3a as dynamic spectrum. Only part of the time interval is shown in VSO coordinates in Figure 3b, to make the waveform more visible. The spacecraft was at SZA 73° to 70° and approached Venus from altitude 6 to 4 Venus radii (RV) during the time interval, it was located in the (model) foreshock for cone angle q (xvso, B) 145°. 3.4. Observation of Higher-Order Resonances [21] On several occasions not only the fundamental resonance (n = 1) is observed, but also higher-order harmonics. Similar observations were reported from the Giotto spacecraft magnetometer data at Comet Giacobini-Zinner [Glassmeier et al., 1989]; these authors state that harmonics are seen less frequently for nearly perpendicular configurations or cone angle q (xvso, B) 90°. [22] In Figure 4a the spectrum is displayed for 30 July 2006 (2210 to 2220 UT); the fundamental resonance frequency is seen as peak in the left-hand polarized power at the proton cyclotron frequency (fp = 0.95 Hz) and a second peak occurs at the first higher-order resonance (fp1 = 1.90 Hz). In Figure 4b, in the dynamic spectrum the enhanced power is clearly visible at both frequencies in Figure 4b (top) for the left-hand power. The magnetic field had a nearly antiparallel configuration with the solar wind direction or cone angle q (xvso, B) 20°, the waves propagate at an angle q (k, B) 16° to the mean magnetic field direction; the spacecraft was at an altitude of 7 RV and SZA = 117°, not located in the modeled foreshock; the bow shock inbound crossing was observed on the next day at 31 July 2006 0043 UT.

4. Statistical Approach for Long-Term Observations [23] To gain an overview of the PCW occurrences for the long time span of two Venus years, a statistical approach was applied. For each 24 h orbit of the spacecraft, 4 h of data before and after the model bow shock crossing [Zhang et al., 2008], corresponding to a maximal distance of 9 RV from the Venus-Sun line, were investigated in intervals of 10 min. For each time interval the frequency fp and the error range Dfp = k (s (B) + DB) were determined from the mean total field m (B), its standard deviation s (B) and the data

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accuracy DB. In the magnetic principal axes system, power spectra were calculated and the power per component was integrated in the frequency interval [0.8 (fp Dfp), fp + Dfp], in order to account for power maxima just below the calculated cyclotron frequency. Detection of the waves was done on basis of enhanced power in the given frequency interval. PCWs are characterized by a larger power transverse to the mean magnetic field and a strong left-hand circular polarization of this component; selection was performed on the criteria PTransverse =PCompressional > 1:5 and PLeft =PRight > 1:5 Ellipticity < 0:5

ð5Þ

where PCompname denotes the power in the respective spectrum component. Only observations lying far enough outside of the model bow shock crossing were considered, at least 10 min apart from the crossing time. These criteria and time selection ensure that only upstream waves and with the typical properties expected from PCWs are taken into account. [24] In the two Venus years of data (450 orbits) from numerous wave occurrences, only 153 PCW occurrences from 10 min intervals fulfilled our criteria; this corresponds to 1% of the investigated time intervals. It has to be pointed out that the use of the automated approach with fixed time setting of 10 min intervals and selection according to (5) reduces the number of selected ‘‘good’’ samples. If a wave occurrence happens near the edge of the 10 min time interval or overlaps over two intervals, its integrated power components may not fulfill the criteria, although careful selection of the integration time would identify the event as PCW. Therefore, the automated approach will systematically underestimate the number of PCW occurrences taken into account. However, a precise selection of time intervals and generation of spectra by hand is not feasible for the time span of 450 8 h under investigation. [25] We now discuss the occurrence of the PCWs as a function of the most significant parameters for the generation mechanism of the waves, leaving other possible investigations beyond the scope of this paper. 4.1. Statistics of PCW Properties [26] Median values for the main characteristic wave parameters are listed in Table 1. The amplitudes are generally moderate (median amplitude 0.56 nT) and the ratio amplitude to B is small. The waves have much more power in the transverse component (PT/PC 4.34) and the lefthand component is severely stronger (PL/PR 3.9). The angle of propagation with the mean magnetic field is generally small q (k, B) 13°, and polarization and ellipticity are strong. [27] These values are characteristics of proton cyclotron waves [Mazelle et al., 2004; Bertucci et al., 2005] and prove that they are generated from pickup of local neutral hydrogen. From the properties listed in Table 1, only the amplitude and amplitude/B show a dependence on the solar zenith angle with larger values and more variability for larger SZA (see Figure 6), which is in accordance with the observations at Mars [Brain et al., 2002]. The other prop-

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Figure 3. (a) Dynamic spectrum for waves on 11 June 2006 with duration of 1:20 h. The spacecraft was at solar zenith angle 72° and approached Venus from 7 to 5 RV in the time interval (2240 to 2400 UT). White line indicates local proton cyclotron frequency fp. Enhanced power near fp is seen only in left-hand polarized component. (b) Waveform in VSO coordinates of the event on 11 June 2006. Only a limited part of the wave occurrence is shown (2300 to 2340 UT) to enable clear view to the waveform. 6 of 12

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Figure 4. (a) Observation of cyclotron waves at the fundamental resonance and first harmonics on 30 July 2006, at distance of 8 RV and SZA 115°. Dashed line and horizontal bar are as in Figure 1b. The vertical arrows indicate the peak in the power at these frequencies. (b) Dynamic spectrum for waves on 30 July 2006. The white line indicates local proton cyclotron frequency fp; enhanced power near fp and the first harmonics fp1 is observed in the left-hand polarized component.

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Table 1. Statistics of PCW Propertiesa Wave Property Amplitude (nT) Amplitude/B PT/PC PL/PR Ellipticity Polarization (%) Propagation angle q (k, B) (deg) Eigenvalue ratio (l1/l2) Eigenvalue ratio (l2/l3)

Median First Quartile Third Quartile 0.56 0.06 4.34 3.92 0.61 63.40 13.08 1.49 6.93

0.32 0.04 2.91 2.98 0.56 55.33 8.56 1.35 5.05

0.82 0.08 6.43 6.01 0.67 72.49 19.35 1.66 9.70

a

ns = 153.

erties are rather uniformly distributed (not shown) in the observed range of SZA (20° to 140°). 4.2. PCW Occurrence as a Function of Cone Angle [28] An important question is, if there is any dependence of PCW occurrence on the angular configuration of Vsw and B. Equation (3) for the local proton cyclotron frequency in the spacecraft frame is independent from the angle q (Vsw, B) and we principally can expect to observe PCWs at all cone angles q (xvso, B). From the theory, we know that PCWs are generated as soon as a large enough number of newborn ions form a secondary ion population in the solar wind which interacts with the background plasma, where two different mechanisms are effective with a gradual transition between them [Huddleston and Johnstone, 1992; Gary, 1991]. To investigate this, we consider the injection of the planetary ions into the solar wind flow in velocity space. Neglecting any (small) original velocity of the neutral particle, in the plasma frame the ion has an

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injection velocity Vinj = Vsw, which is in fact in direction of xVSO. Figure 5 shows Vinj in velocity space, where the x axis is parallel to the magnetic field direction, i.e., decomposition into components parallel and perpendicular to the local mean field B; the angle from the B direction to the injection velocity Vinj is equal to the cone angle q (xvso, B). [29] In rather parallel or antiparallel configurations of Vsw and B (cone angle < 40° or > 140°) the ion’s velocity component V? in the plane vertical to the B direction is small, the ion/ion beam plasma microinstability for (anti-) parallel beams is very efficient and strong waves can be expected [Gary, 1991]. Indeed, the largest amplitudes are observed for this range of cone angles (Figure 5). [30] In more oblique cases the ions gyrate with V? in the plane vertical to B and form a ring distribution. This ion ring is unstable and scattering from the ring to a shell distribution takes place, supplying the energy for cyclotron wave generation. However, the time scale for scattering and wave generation is longer than in case of the more effective (anti-) parallel beam mechanism. Furthermore, Huddleston and Johnstone [1992] have shown that for ring distributions and small ratio VA/VSW, where VA is the Alfveń velocity, cyclotron wave generation is less efficient for approximately perpendicular configurations or pitch angles q (Vinj, B) 90°. At Venus, using nominal solar wind conditions VSW = 400 km s1, VA = 55 km s1 (for B = 8 nT and solar wind proton number density npSW = 10 cm3) we obtain VA/VSW 0.1375 and the condition for more efficient wave generation at smaller pitch angles is fulfilled. This means that we expect less and weaker waves for quasiperpendicular cases of the solar wind velocity and magnetic field and stronger waves for oblique configurations.

Figure 5. Injection velocity of the pickup ion into the solar wind flow for all PCWs observed from 10 May 2006 to 10 August 2007, in velocity space in the plasma frame. The x axis is in direction of the magnetic field B; colors are according to wave amplitude. The inner part shows the distribution of observations with cone angle q (xvso, B); no PCWs are observed for cone angle 90°, more and stronger waves for cone angles corresponding to the prevailing Parker spiral angles. 8 of 12

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Figure 6. Positions of observations of PCWs from 10 May 2006 to 10 August 2007 in cylindrical VSO coordinates. Colors are according to wave amplitude, and black circles denote observations upstream of the foreshock region. Gray lines denote approximately the limiting orbits of the spacecraft; regions outside of these lines were not accessed. [31] The histogram in the center of Figure 5 displays the PCW observations as a function of the cone angle in bins of 20°; the lack of observations for cone angle q (xvso, B) 90° is obvious. In the intervals [20°, 40°] and [140°, 160°], for rather antiparallel or parallel configurations of the solar wind velocity and B, more and stronger PCWs are observed; this corresponds also to the prevailing magnetic field orientation of the Parker spiral at Venus. 4.3. Spatial Distribution of PCW Observations [32] The spatial occurrence of the PCWs is displayed in Figure 6 in cylindrical VSO coordinates: the ordinate is the distance from the Venus-Sun line, color coding is according to amplitude, black surrounding circles indicate PCWs observed outside the modeled foreshock region; the gray lines are the approximate limiting orbits of the spacecraft; regions outside of the gray lines were not scanned by the spacecraft. Several ‘‘trains’’ of observation points indicate observations during longer times, i.e., subsequent intervals of 10 min. [33] 1. From Figure 6, we see that more observations occur at larger SZA. This is as expected since the waves are swept downstream by the solar wind; these waves are not

observed at the position of their generation, which lays further upstream, but have propagated along the field lines in the flowing solar wind plasma. This is in accordance with observations at Mars [Brain et al., 2002]. [34] 2. Furthermore, larger amplitudes are observed closer to the bow shock and/or toward larger SZA. Again, this is in accordance with predictions: stronger waves occur for higher pickup ion density, i.e., closer to the planet, or if there was time for wave growth during propagation to larger SZA. Similar effects were reported from observations at Mars [Brain et al., 2002]. [35] 3. The observations in or outside of the modeled foreshock region show no difference in spatial distribution, proving that the waves are not a foreshock-related phenomenon. The discrepancy in sample size in and out of the foreshock (ns_in = 93; ns_out = 60) is an observational effect due to the orbit of the spacecraft. The pericenter of the orbit lays at 78° north in the VSO coordinate system and for zVSO > 1 RV the spacecraft is always close to the planet and therefore mostly in the foreshock region, because of the Parker spiral angle of the IMF (37° or 143°) at Venus. Moreover, this region is scanned during every orbit, which

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Figure 7a. Distribution of observed wave energy as a function of altitude above the Venus surface. Colors are according to wave amplitude. Weak waves occur from low up to high altitudes. explains more observations within the foreshock. The region further away from the line Venus-Sun is scanned only for zVSO < 1 RV and here the spacecraft can be in or outside of the foreshock, depending on the cone angle q (xvso, B). [36] 4. In Figure 7a the wave energy is shown as a function of the altitude above the Venus surface. Waves with low energy occur at all altitudes from close to Venus up to 8 RV; more energetic waves are confined to lower altitudes, between 2 and 5 RV.

field direction for cone angle < 90°, more PCWs in negative E field regions for cone angle > 90°) is an observational effect due to the orbit of the spacecraft with pericenter around zVSO 1 RV and PCW observations mainly for zVSO < 0. We therefore conclude that no asymmetry effect is observed in the PCW distribution for positive or negative motional electric field directions; this can be explained by the small gyroradius of pickup hydrogen with respect to the size of the planet.

4.4. PCW Observations as a Function of Motional Electric Field [37] We now investigate the PCW observations as a function of the direction of the motional electric field E = Vsw B. We transform the positions of PCW observations from the VSO system to a local electromagnetic coordinate system, where xEM is positive in direction of Vsw, the (x, y)EM plane contains Vsw and B and zEM is positive in direction of the motional electric field. Figure 7b (top) shows the positions in zEM as a function of the cone angle: waves occur up to large positive and negative zEM values or large distances on either side of the (Vsw, B) plane; again, observations are lacking for cone angle q (xvso, B) 90°. In Figure 7b (bottom), the histogram of observations is separated into regions with positive or negative E field; an approximately equal number of observations is in negative (44%) as in positive direction (56%), so no preferential effect for positive E field direction is found. The unequal distribution (more PCWs in positive E

5. Discussion [38] The studied cases of observed PCWs give insight into the main characteristics of the waves. For the fundamental resonance, the observed properties are typical for what is known from cyclotron waves at other planets. In addition to the fundamental resonance, also the first harmonics is observed in several cases. The duration of the cyclotron wave occurrences ranges from some min (5 to 10) to more than 1 h, the mean value is 16 min. We expect that solar wind changes cause this variability, which needs further investigation but is beyond the scope of this paper. Similar and even longer durations have been reported for Mars from long-term Mars Global Surveyor (MGS) observations, with no dependence on the cone angle [Brain et al., 2002; Mazelle et al., 2004; Bertucci et al., 2005]. However, for some specific MGS orbits, more short duration cyclotron wave occurrences were reported by Wei and Russell [2006]. The authors explain this as result of an asymmetry effect in direction of positive electric field due to secondary ioniza-

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Figure 7b. (top) Distribution of zEM component of PCW observation positions in electromagnetic coordinate system as a function of cone angle q (xvso, B). (bottom) Histogram of zEM component, separated for positive and negative direction of motional electric field. A comparable number of cases is observed for each direction. tion from a disk of fast reneutralized hydrogen near and parallel to the (Vsw, B) plane, both downstream and to the side of Mars. From the observations at Venus presented in this paper, with also long duration of the PCWs and no such asymmetry, the suggested mechanism is not confirmed; rather the here observed PCWs are generated at initial ionization of local planetary hydrogen at any position around Venus. [39] The statistical approach for the long time interval uses severe criteria, which leads to a selection of 153 cases of PCWs corresponding to wave occurrence during 1% of the investigated time, but with systematical underestimation of the number of PCW occurrences. The statistical properties derived from the data set fulfill the general requirements for cyclotron waves. PCWs are observed mainly for nearly parallel or antiparallel configurations of the magnetic field and solar wind direction, with no observations for perpendicular conditions; this is in accordance with theoretical predictions.

[40] The waves are observed up to high altitudes (8 RV) from the planet and also up to 4 RV in direction toward the Sun, as far as the spacecraft scanned the sunward side of the planet. No asymmetry is found with respect to positive or negative directions of the motional electric field. This is important, because PCWs propagate generally (anti-) parallel to the B field lines [Mazelle et al., 2004] and pickup ions can only move in direction of positive motional electric field; no mechanism is known to move ions across the magnetic field lines or against the electric field. Therefore the waves can be only generated by local pickup also for large distances on the negative side of the (Vsw, B) plane and a sufficient density of local neutral hydrogen must be available. A detailed study of the consequences of these findings for the neutral hydrogen exosphere at Venus is made by M. Delva et al. (An extended neutral hydrogen exosphere at Venus, submitted to Geophysical Research Letters, 2008), giving escape rates of 1.4 1023 s1 up to

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5.6 1023 s1 and claiming that an extended neutral hydrogen exosphere must exist at Venus.

6. Conclusions [41] The MAG data from the two first Venus years of the Venus Express mission in orbit (May 2006 to August 2007) were analyzed for the occurrence of proton cyclotron waves in the solar wind. Proton cyclotron waves are important as precursor of the approaching planet and indicator of pickup from local neutral planetary hydrogen and loss of the ions to the solar wind. The general properties of the waves were found to fulfill all the criteria as known from upstream cyclotron waves at other planets or comets. The observations indicate that wave generation takes place at initial ionization of planetary hydrogen, by the ion/ion beam plasma microinstability for (anti-) parallel beams or by scattering from a ring distribution for more oblique configurations of the solar wind velocity and magnetic field direction, and a gradual transition between both mechanisms. A statistical approach showed that the waves are present up to large distances (9 RV) from the planet and exhibit no asymmetry with respect to the motional electric field direction, proving that pickup of planetary hydrogen takes place everywhere upstream of the bow shock in a large volume of space. This leads to a permanent escape of hydrogen from the extended environment of the planet, which may have a significant impact on the overall evolution of the water content of Venus over the age of the Solar System. [42] Acknowledgments. The authors thank all members of the MAG team for development of the magnetometer with its outstanding performance and accuracy, the ASPERA-4 team for valuable discussions, and K. Kudela (University of Kosicˇe, Slovakia) and W. Zambelli (IWF Graz, Austria) for assistance in preparation of the VSO data. The work by Z.V. is supported by the Austrian Wissenschaftsfonds under grant P20131-N16.

References Barabash, S., et al. (1991), Picked up protons near Mars: Phobos observations, Geophys. Res. Lett., 18, 1805 – 1808, doi:10.1029/91GL02082. Bertucci, C., C. Mazelle, and M. Acuna (2005), Interaction of the solar wind with Mars from Mars Global Surveyor MAG/ER observations, J. Atmos. Sol. Terr. Phys., 67, 1797 – 1808, doi:10.1016/j.jastp.2005.04.007. Brain, D. A., et al. (2002), Observations of low-frequency electromagnetic plasma waves upstream from the Martian shock, J. Geophys. Res., 107(A6), 1076, doi:10.1029/2000JA000416. Brinca, A. L. (1991), Cometary linear instabilities: From profusion to perspective, in Cometary Plasma Processes, Geophys. Monogr. Ser., vol. 61, edited by A. P. Johnstone, pp. 211 – 221, AGU, Washington, D. C. Delva, M., et al. (2008), First upstream proton cyclotron wave observations at Venus, Geophys. Res. Lett., 35, L03105, doi:10.1029/2007GL032594. Gary, P. (1991), Electromagnetic ion/ion instabilities and their consequences in space plasmas: A review, Space Sci. Rev., 56, 373 – 415, doi:10.1007/BF00196632. Gary, S. P., J. T. Gosling, and D. W. Forslund (1981), The electromagnetic ion beam instability upstream of the Earth’s bow shock, J. Geophys. Res., 86, 6691 – 6696, doi:10.1029/JA086iA08p06691.

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Glassmeier, K. H., et al. (1989), Spectral characteristics of low-frequency plasma turbulence upstream of Comet Halley, J. Geophys. Res., 94, 37 – 48, doi:10.1029/JA094iA01p00037. Huddleston, D. E., and A. D. Johnstone (1992), Relationship between wave energy and free energy from pickup ions in the Comet Halley environment, J. Geophys. Res., 97, 12,217 – 12,230, doi:10.1029/92JA00726. Huddleston, D. E., et al. (1997), Ion cyclotron waves in the Io torus during the Galileo encounter: Warm plasma dispersion analysis, Geophys. Res. Lett., 24, 2143 – 2146, doi:10.1029/97GL01203. Johnstone, A. D., et al. (1987), Waves in the magnetic field and solar wind flow outside the bow shock at Comet Halley, Astron. Astrophys., 187, 47 – 54. Leinweber, H. K., C. T. Russell, K. Torkar, T. L. Zhang, and V. Angelopoulos (2008), An advanced approach to finding magnetometer zero levels in the interplanetary magnetic field, Meas. Sci. Technol., 19, 055104, doi:10.1088/0957-0233/19/5/055104. Luhmann, J. G., and S. Bauer (1992), Solar wind effects on atmosphere evolution at Venus and Mars, in Venus and Mars: Atmospheres, Ionospheres and Solar Wind Interactions, Geophys. Monogr. Ser., vol. 66, edited by J. G. Luhmann, M. Tatrallyay, and R. O. Pepin, pp. 417 – 430, AGU, Washington, D. C. Mazelle, C., and F. M. Neubauer (1993), Discrete wave packets at the proton cyclotron frequency at comet P/Halley, Geophys. Res. Lett., 20, 153 – 156, doi:10.1029/92GL02613. Mazelle, C., et al. (2004), Bow shock and upstream phenomena at Mars, S p a c e S c i . R e v . , 11 1 ( 1 – 2 ) , 1 1 5 – 1 8 1 , d o i : 1 0 . 1 0 2 3 / B:SPAC.0000032717.98679.d0. McPherron, R. L., C. T. Russell, and P. J. Coleman (1972), Fluctuating magnetic fields in the magnetosphere, II, ULF waves, Space Sci. Rev., 13, 411 – 454, doi:10.1007/BF00219165. Nagy, A. F., J. Kim, and T. E. Cravens (1990), Hot hydrogen and oxygen atoms in the upper atmospheres of Venus and Mars, Ann. Geophys., 8, 251 – 256. Paschmann, G., N. Sckopke, S. J. Bame, J. R. Asbridge, J. T. Gosling, C. T. Russell, and E. W. Greenstadt (1979), Association of low frequency waves with suprathermal ions in the upstream solar wind, Geophys. Res. Lett., 6, 209 – 212, doi:10.1029/GL006i003p00209. Russell, C. T., et al. (1990), Upstream waves at Mars: Phobos observations, Geophys. Res. Lett., 17, 897 – 900, doi:10.1029/GL017i006p00897. Russell, C. T., S. S. Mayerberger, and X. Blanco-Cano (2006), Proton cyclotron waves at Mars and Venus, Adv. Space Res., 38, 745 – 751, doi:10.1016/j.asr.2005.02.091. Volwerk, M., M. G. Kivelson, and K. K. Khurana (2001), Wave activity in Europa’s wake: Implications for ion pickup, J. Geophys. Res., 106, 26,033 – 26,048, doi:10.1029/2000JA000347. Wei, H. Y., and C. T. Russell (2006), Proton cyclotron waves at Mars: Exosphere structure and evidence for a fast neutral disk, Geophys. Res. Lett., 33, L23103, doi:10.1029/2006GL026244. Zhang, M. H. G., J. G. Luhmann, A. F. Nagy, J. R. Spreiter, and S. S. Stahara (1993), Oxygen ionization rates at Mars and Venus: Relative contributions of impact ionization and charge exchange, J. Geophys. Res., 98(E2), 3311 – 3318, doi:10.1029/92JE02229. Zhang, T. L., et al. (2006), Magnetic field investigation of the Venus plasma environment: Expected new results from Venus Express, Planet. Space Sci., 54, 1336 – 1343, doi:10.1016/j.pss.2006.04.018. Zhang, T. L., et al. (2008), Initial Venus Express magnetic field observations of the Venus bow shock location at solar minimum, Planet. Space Sci., 56, 785 – 789, doi:10.1016/j.pss.2007.09.012.

M. Delva, M. Volwerk, and T. L. Zhang, Space Research Institute, Austrian Academy of Sciences, A-8042 Graz, Austria. S. A. Pope, Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S10 2TN, UK. Z. Vo¨ro¨s, Institute of Astro- and Particle Physics, University of Innsbruck, A-6020, Innsbruck, Austria.

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Mirror-mode-like structures in Venus’ induced magnetosphere M. Volwerk,1 T. L. Zhang,1 M. Delva,1 Z. Vo¨ro¨s,2 W. Baumjohann,1 and K.-H. Glassmeier3 Received 26 March 2008; revised 1 July 2008; accepted 5 September 2008; published 10 December 2008.

[1] The solar wind interaction with Venus creates an induced magnetosphere around the

planet. It is shown that within the space bound by Venus’ bow shock and ionopause, there is a rich occurrence of mirror-mode-like structures in the magnetic field data. The dayside magnetosheath and nightside magnetosheath/wake regions are investigated separately. It is shown that the probability to observe mirror mode structures is much higher at the dayside, where it is also strongly dependent on the angle between the solar wind magnetic field and the bow shock normal. In Venus’ wake the chance to observe these structures is low, most likely because of the fully developed turbulence in this region, which will decrease temperature anisotropies. The results stand in contrast to the very low occurrence rate claimed from data taken by the Pioneer Venus Orbiter mission. Citation: Volwerk, M., T. L. Zhang, M. Delva, Z. Vo¨ro¨s, W. Baumjohann, and K.-H. Glassmeier (2008), Mirror-mode-like structures in Venus’ induced magnetosphere, J. Geophys. Res., 113, E00B16, doi:10.1029/2008JE003154.

1. Introduction [2] The mirror instability occurs in plasmas with strong temperature anisotropies, i.e., plasmas in which the perpendicular temperature is higher than the parallel temperature [Hasegawa, 1969; Gary et al., 1993; Southwood and Kivelson, 1993]. These waves have been found in various objects, e.g., the Earth’s magnetotail [Rae et al., 2007], the magnetosheath of the Earth [e.g., Tsurutani et al., 1982; Baumjohann et al., 1999; Lucek et al., 1999a; Constantinescu et al., 2003], Mar’s [Bertucci et al., 2004] and of Jupiter [Erdo¨s and Balogh, 1993; Joy et al., 2006], in Saturn’s middle magnetosphere [Russell et al., 2006a], in cometary tails [e.g., Russell et al., 1987] and magnetic pile up boundaries [Glassmeier et al., 1993] and in the ion pickup region near Io [e.g., Huddleston et al., 1999]. Lately, Volwerk et al. [2008] have shown that mirror-mode-like structures appear at different locations of Venus’ magnetosheath, and Zhang et al. [2008] have shown that mirror mode waves also appear in the solar wind at Venus’ orbit. [3] The instability criterion for mirror mode (MM) waves, i.e., high-b plasma and T? > Tk, however, is shared with another wave mode, namely the ion cyclotron (IC) instability (see for IC waves at Venus Delva et al. [2008a]). Gary et al. [1993] have shown that the growth rate for IC is usually greater than for MM. Southwood and Kivelson [1993] have 1 Space Research Institute, Austrian Academy of Sciences, Graz, Austria. 2 Institute for Astro- and Particle Physics, University of Innsbruck, Innsbruck, Austria. 3 Institut fu¨r Geophysik und Extraterrestrische Physik, Technische Universita¨t, Braunschweig, Germany.


proposed that the inhomogeneities in the background plasma, created by the MM, will inhibit the IC growth in planetary magnetosheaths. Also, the presence of He ions suppresses the growth rate for proton IC waves [Gary et al., 1993]. The MM instability generates compressional waves that grow preferentially in the direction perpendicular to the ambient magnetic field [see Treumannn and Baumjohann, 1996, section 3.5]. [4] At frequencies below the IC frequency in the plasma frame, the MM behaves in such a way that the perpendicular pressure p? of the plasma will be in antiphase with compressional variations in the magnetic field [Hasegawa, 1969]. In a bi-Maxwellian plasma with perpendicular temperature T? and parallel temperature Tk this means that T? dB dp? ¼ 2p? 1 ; Tk B

ð1Þ

which leads to the instability criterion T? < 0: 1 þ b? 1 Tk

ð2Þ

[5] The temperature asymmetry in the (Earth’s) magnetosheath can be created by two mechanisms [Lucek et al., 1999b]: (1) enhanced gyratory motion of the ions caused by (multiple) reflection(s) at the bow shock under quasiperpendicular conditions before entering the magnetosheath [shock drift acceleration [see, e.g., Kirk et al., 1994, chapter 2.2] or (2) compression of the magnetosheath close to the magnetopause, where the increased magnetic field strength leads to a gyration velocity increase through the first adiabatic invariant [see, e.g., Baumjohann and

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Treumannn, 1996, chapter 2.5]. Volwerk et al. [2008] presented two events in Venus’ magnetosheath which show that these two mechanisms create MM there. [6] From the case studies presented by Volwerk et al. [2008] it was found that the MM waves in Venus’ magnetosheath have a large amplitude, with variations in magnetic field strength sometimes greater than 50%. The waves were shown to be highly compressional (i.e., the maximum variance direction of the waves was almost aligned with the background field) and the propagation direction of the waves was perpendicular to the background field (i.e., the minimum variance direction of the waves was almost perpendicular to the background field). The duration of the events is only up to several minutes, instead of up to hours at Earth. At the same time the wave period of the MM waves is shorter, e.g., near the bowshock 5 s at Venus and 30 s at Earth. These two facts combined with the fact that Venus’ magnetosheath is approximately a factor 10 smaller than the Earth’s led to the empirical assumption that Venus’ magnetosheath may be similar to the Earth’s magnetosheat, only scaled down by a factor of 10. When the MM waves are present in the data they are highly compressional and propagating perpendicular to the ambient field direction. [7] In this paper, the case study paper presented by Volwerk et al. [2008] is extended to a full statistical study of the MM-like structures in Venus’ magnetosheath and magnetotail for 1 Venus year of data. The occurrence rate for these structures in Venus’ induced magnetosphere will be determined on the basis of the location of the spacecraft and it will be shown that also under quasi-parallel bow shock there is a chance for MM like structures to appear. The occurrence rate as a function of the angle between solar wind magnetic field and bow shock normal will be shown.

2. Venus Express [8] Magnetometer data from the Venus Express mission (VEX) [Svedhem et al., 2007] are used; the spacecraft is in a polar orbit around Venus with periapsis at 300 km and therefore will enter deeply into Venus’ induced magnetosphere, as shown by Zhang et al. [2007a]. [9] Magnetic field data from VEXMAG [Zhang et al., 2006] are used with a sampling rate of 1 Hz. During the nominal mission of VEX data are also available at 32 Hz sampling rate (and for short intervals also at 128 Hz). The plasma data from ASPERA [Barabash et al., 2007] have a resolution of 3 min for ions and 4 s for electrons. The MM waves were shown to have periods 4 T 15 s [Volwerk et al., 2008] which means that the ion plasma data cannot be used for identification of the waves on the basis of equilibrium between magnetic and plasma pressure. Yet, the plasma data could give information on the temperature asymmetry of the magnetospheric plasma. However, as this paper is written, plasma data with the required accuracy are not yet available.

3. Mirror Mode Identification [10] In the absence of high-resolution ion data, MM waves need to be identified from the magnetic field data only. This situation is similar to that by Lucek et al. [1999a, 1999b] for Equator-S, and in this paper the same identifi-

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cation method will be adopted and enhanced. MM waves are identified as having strengths DB/B 10%, and having small angles qBmv between the maximum variance and the magnetic field direction qBmv 30° [Price et al., 1986]. These two quantities are determined for sliding windows of 30 s width and 1 s shift. [11] To investigate the wave activity the mean magnetic field is determined first through low-pass filtering of the data, with a shortest period of 1.5 min. After this the data and the mean magnetic field are subtracted such that only the amplitude of the field oscillation is left. Within each 30 s window the amplitude of the waves DB in the data is then determined as twice the maximum difference between the data and mean field to get an upper estimate of the peak-topeak value of the waves. This means that the value for the strength of the MM-like structures, DB/B, can reach a maximum value of 2, and DB/B = 0.2 10% variation around the mean value. This method is used as the MM-like structures in Venus’ magnetosheath are not as nicely ordered as in the Earth’s magnetosheath. In the latter, it is clear what the base value of the magnetic field is from which the MM waves depart [see, e.g., Lucek et al., 1999a]. This is dependent on whether the MM waves are ‘‘dips’’ or ‘‘peaks’’ with respect to the background field, which are two different appearances for this instability. Califano et al. [2008] investigate this dip-peak phenomenon numerically and show that it depends on the plasma b and the distance of the system from the instability threshold. Joy et al. [2006] have shown that, in the Jovian magnetosheath, the dips occur mainly in the low plasma-b regions near the magnetopause and on the flanks, whereas the peaks show up mainly in the dayside high plasma-b regions in the middle magnetosheath. [12] For each window a minimum variance analysis [Sonnerup and Scheible, 1998] is performed and the angle qBmv between the maximum variance direction of the waves and the mean magnetic field is determined. Here we use a more strict directional requirement qBmv 20° for the selection of the compressional waves. Additionally, the angle fBmv between the minimum variance direction and the magnetic field is determined, which is expected to be nearly perpendicular for MM waves. As there is no possibility for the determination of pressure balance in the MM waves, because of the insufficient data resolution of the plasma instrument, an extra requirement on the structures is introduced, to make sure only MM-like structures are selected, here a limit is set at fBmv 80°.

4. Data [13] Before a statistical study of the MM waves is performed, it is useful to discuss one event in detail, observed on 5 May 2006, which is Volwerk et al.’s [2008] event 1. 4.1. One Quasi-Perpendicular Bow Shock Event [14] On 5 May 2006 VEX entered from the solar wind (SW) through the bow shock (BS) into the magnetosheath (MS). At the beginning of the event the spacecraft was located near (1.69, 0.05, 0.48) RV in Venus-Sun Orbital (VSO) coordinates implying a local time of 1200 (noon) and the solar wind magnetic field was BSW = (2.04, 4.73, 1.37) nT. The angle qBn between the solar wind

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Figure 1. The magnetic field data for 5 May 2006. (a –d) The magnetic field components Bx,y,z in VSO coordinates and magnitude Bm. The smooth curve shows the low-pass filtered (periods longer that 1.5 min) magnetic field, which is used as the baseline to determine the amplitude of the magnetic fluctuations. The spacecraft moves from the solar wind (SW), through the BS into the magnetosheath (MS). (e and f) The fluctuation of the magnetic field DB/B and the angle qBmv between the maximum variance direction and the mean magnetic field (dots), and the angle fBmv between the minimum variance direction and the mean magnetic field (pluses). The two grey shaded areas I and II show the intervals for which the MM wave condition is met (i.e., qBmv < 20, fBmv > 80 and DB/B large). magnetic field and the local shock normal is calculated by using the best fit for the BS, given by Zhang et al. [2007a] RBS ¼

2:169505 ; 1 þ 0:617330 cos g SZA

ð3Þ

where g SZA is the solar zenith angle. One can calculate the normal direction in the X - R plane through 0 @

nX nR

1

0

A¼B @

g Þ sin g @RBS ð@g @RBS ðg Þ cos g @g

1

0 1 2:17 cos g þ 2:17 0:62 C @ A: A 2:17 sin g ð4Þ

[15] The angle g SZA is easily determined from the location of the spacecraft in the solar wind. The solar wind magnetic field is determined 30 min before VEX enters the bow shock. The R component of the normal is easily split into two components ny and nz through multiplication with cosd and sind respectively, where d is the angle between the positive y axis and the direction of the spacecraft in the YZ plane. [16] For this event it is found that qBn 106°, implying a quasi-perpendicular BS. Immediately after crossing the BS large amplitude compressional waves occur as shown in shaded region I in Figure 1. In Figures 1e and 1f DB/B and the angles qBmv and fBmv are shown. In Figure 1f, the angle qBmv drops well below 20°, indicating that the waves are

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Figure 2. All MM-like events plotted in cylindrical VSO coordinates. The thick solid line depicts Venus’ bow shock, and the dotted line depicts Venus’ magnetopause/ionopause, as determined by Zhang et al. [2007a]. mainly compressional. At the same time fBmv increases to well above 80°. This region shows compressional waves at a period of 5 s propagating perpendicular to the field. [17] Sightly later, 0117– 0118:30 UT, with the spacecraft deeper into the MS, there is another region (shaded region II in Figure 1) displaying compressional waves propagating almost perpendicular to the ambient magnetic field. These waves, in contrast to shaded region I, now have a period of 15 s. 4.2. Statistical Analysis [18] To perform a statistical analysis of MM-like structures in Venus’ magnetosphere, 1 Venus year of data (224 Earth days, from 24 April to 31 December 2006) is used from the VEX magnetometer. For this study which identifies the MM-like structures automatically, an extra limitation to the data has been put into the analysis software. Only MM-like structures that are observed for Bm 10 nT are included in the database, to avoid similar structures observed while the spacecraft is still in the solar wind [see, e.g., Zhang et al., 2008]. In Figure 2 all the events are shown in a cylindrical coordinate system (i.e., (Y2VSO + Z2VSO)1/2 versus XVSO, where VSO is the Venus-centered Venus-Sun Orbital coordinate system, where X is in the direction to the Sun, Y opposite to the orbital velocity of Venus and Z along the rotational axis of Venus). [19] In order to find how many separate events happen in this 1 Venus year a limit distance in time is set between separate events of 30 s, also ‘‘events’’ of one point are discarded from the database. This leaves 1637 events in the database varying strongly in strength in the range 0.2 DB/ B 2. The total distribution of the MM-like structures as a function of DB/B is shown in Figure 3. of VEX is sampled in cylindrical coordi[20] The orbit pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi nates into X Y 2 þ Z 2 = 0.25 0.25 RV boxes to find the time the spacecraft spends in each box over 1 Venus year.

This is used to find the chance of measuring MM waves in each box. For each box one should calculate the ratio number of events in box total time in MS

) number of total events time spent in box number of events in box P0 ¼ : time spent in box

P¼

[21] As in the calculation of the chance P to measure MM-like structures in a box the values for ‘‘number of total events’’ and ‘‘total time in MS’’ are constant, these values are taken out of the equation, which leads to the determination of P0. The chance P0 of events in each box is shown in Figure 4 on a log scale. Clearly, the greatest chance to observe MM is on the dayside, where close to Venus the maximum chance seems to follow the ionopause and further outward it follows the bow shock, with little chance in between. Down Venus’ magnetotail is another region of enhanced MM-like wave signatures, in the wake of the planet, between 1.75RV XVSO 2.75RV. Interestingly, the wake is also the region of developed turbulence, where the power spectra showed individual peaks at low frequencies, which may be correlated to MM-like structures [Vo¨ro¨s et al., 2008]. Two regions of enhanced chance P0 were identified, the dayside magnetosphere and the wake region, which are discussed separately below. 4.3. Dayside Activity [22] The dayside activity is assumed to be at XVSO 0. Volwerk et al. [2008] have shown two case studies for MMlike structures in this region, one near the bow shock for quasi-perpendicular conditions (reproduced in this paper) and one near the ionopause/magnetopause for an assumed compressed magnetosphere. In this paper a third event is shown of MM-like waves just behind the bow shock, for 11 June 2006. In this case the angle between solar wind magnetic field and local bow shock normal is 148°, which

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Figure 3. A histogram showing the distribution of the MM-like events as a function of their strength DB/B. means quasi-parallel bow shock conditions. Although the MM instability favours a quasi-perpendicular BS, at a moderately quasi-parallel BS, as in this case, the chance of MM waves is not zero. [23] Figure 5 shows the magnetic field data from VEX and the low-pass filtered data, with respect to which the strength DB/B is calculated. The spacecraft moves from the solar wind (SW), through the bow shock (BS) into the magnetosheath (MS). The location of the bow shock could be argued to be slightly later than the left boundary of shaded box I. The shaded boxes labelled ‘‘I’’ and ‘‘II’’ are two locations where the maximum variance direction of the waves makes an angle qBmv 20° with the background magnetic field (circles in Figure 5f), which is one of the requirements for the identification of MM-like structures being compressional waves. However, only in box II the minimum variance direction and the background magnetic field have an angle fBmv 80°, whereas in box I the value is slightly below 80°. In this case, the waves measured in box II are (automatically) identified as MM-like structures, whereas the waves in box I are not. [24] The strength of the MM-like structures in box II is rather small, DB/B 0.7 with a median 0.3. Therefore, these are not well developed waves, i.e., the driving mechanism is not efficient. This is to be expected, as this case represents a quasi-parallel shock, for which the shock drift acceleration of the particles, perpendicular to the magnetic field, by the bow shock is less efficient. [25] Interesting, too, are the waves between 0129 and 0130 UT, which have a minimum variance direction (pluses) along the magnetic field and a maximum variance direction (dots) perpendicular to the magnetic field. These are transverse waves at a period of 4 s, which would agree with

proton cyclotron waves at that location just below the local gyro frequency [see, e.g., Delva et al. 2008a], Bm 19 nT ! fp 0.29 Hz. These waves will be discussed in a separate paper. [26] Now, a closer look will be taken to all events on the dayside of Venus. It is expected that the MM-like structures near the bow shock occur mainly during quasi-perpendicular shock conditions [Lucek et al., 1999a, 1999b]. Therefore, as stated above, the angle between the solar wind magnetic field direction and the bow shock normal, qBn, is determined for each orbit of VEX. The dayside events are projected onto the YZVSO plane, and are sorted into five bins on the basis of qBn. In Figure 6 all dayside events are shown in Figure 6a and the cases of quasi-parallel shock (0° < qBn < 45° and 135° < qBn < 180°) are shown below it, whereas in Figure 6 (right) the events for the quasi-perpendicular shock for the angular bins 45° < qBn < 75°, 75° < qBn < 105° and 105° < qBn < 135° are shown. In Table 1 the number of events per bin is listed, with a total number of 1071 events on the dayside of Venus, covering 190 days of the whole data set (compared to 224 days for 1 Venus year). [27] Table 1 shows that quasi-parallel bow shock conditions, as defined by the bins in this paper, occur for approximately 30% of the time in the current data set. Figure 6 displays the chance to observe MM-like structures as a function of qBn, with all cases and the quasi-parallel cases in the left column and the quasi-perpendicular cases in the right column. The angle qBn between the local normal and the solar wind magnetic field has a clear influence on the chance of observing MM waves. Interestingly, the greatest chance to measure MM-like structures is for 75° < qBn < 135° (Figures 6d and 6f).

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Figure 4. The ‘‘normalized’’ occurrence rate of MM-like structures around Venus. Shown is the number of events in boxes 0.25 0.25RV divided by the number of seconds that VEX is located in each box on a log scale. (top) The occurrence rate for events with DB/B 0.2, and (bottom) the occurrence rate for events with DB/B 0.6. The white areas within the BS mean that there were no events matching the criteria of the figure. [28] In Figure 6 there is a preference for the MM-like structures to appear at positive ZVSO. This effect is created by the highly elliptical polar orbit of the spacecraft, with periapsis near the north polar region. Indeed, on the flanks (large YVSO), where the MS is wider, there are also data for negative ZVSO. [29] The nominal angle of the Parker spiral of the solar wind magnetic field qP 36°, which means that at the nose of the BS qBn 36° or 144°, depending on the direction of the magnetic field. From the current data set the direction of

the solar wind magnetic field can be determined, to obtain an estimate of the ‘‘Parker spiral direction’’ 0qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1 B2y þ B2z A: qP ¼ atan@ Bx

ð5Þ

[30] A histogram of the solar wind magnetic field direction, measured 30 min before VEX enters the bow

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Figure 5. Magnetic field data for 11 June 2006 in the same format as Figure 1. shock, see Figure 7, shows that the distribution peaks near the nominal values of the Parker spiral (indicated by the two vertical white lines), however, at large angles there is a broad variation. This distribution is comparable to the one found by Delva et al. [2008b]. The broad spread in directions of the solar wind magnetic field, with main occurrence between 90° qP 160°, this gives enough variation to create the observed distribution shown in Figure 6. 4.4. Wake Activity [31] The wake [Lundin and Barabash, 2004] will be qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 1. assumed to be at XVSO 1.5 and YVSO þ ZVSO

This region coincides with the optical shadow of the planet. From Figure 4 it is clear that at the night side the events occur mainly in the wake of Venus. This may complicate the investigation, as Vo¨ro¨s et al. [2008] have shown that as soon as VEX enters Venus’ shadow (or wake) the plasma turbulence spectral density (P( f ) / f a) changes from little developed (scaling index a 0.8) to almost fully developed (scaling index a 2.5). Two events of MM-like structures in the wake are discussed in detail below. [32] As first example, the data from 15 August 2006 are shown in Figure 8 (left), when the spacecraft was located near (2.1, 0.5, 0.1) RV, in the Venusian wake. The solar wind magnetic field was B (7, 6, 9) nT. In Figure 8 the low-pass

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Figure 6. Normalized occurrence rate of MM-like structures at the dayside of Venus in the YZ plane. (a) All events and (b – f) individual events sorted with respect to the angle between the solar wind magnetic field and the bow shock normal. The white areas mean that there were no events matching the criteria of the figure. filtered magnetic field has been subtracted from the nonfiltered data, to make the waves of interest better visible because of the strong variation in the amplitudes of the magnetic field components as can be seen from the variation in Bm. [33] Two intervals are shaded, where the angle of B to the maximum variance direction is below 20° and to the minimum variance direction larger than 80°. Box I shows weak waves with a mean DB/B 0.2, being small amplitude compressional waves, with the correct characteristics for MM waves. In box II is a superposition of ‘‘high-’’ frequency waves onto a large ‘‘low-’’ frequency oscillation of magnetic field in the X and Z component. The strength of the structure (as defined by DB/B 2) in this case clearly does not describe the situation correctly. The mean magnetic field magnitude approaches zero at the centre of the structure, which on such a large scale may indicate the crossing of a current sheet. In the case of box II, the difference

between filtered and nonfiltered data is misleading and the total magnetic field data needs to be looked at (shown in Figure 9). [34] From the above considerations it is clear that in reality we have a current sheet/neutral sheet crossing in Venus’ magnetotail, and not a MM-like structure. This is the only case where the automated approach led to misidenti-

Table 1. Number of Events in Each qBn Bin on the Dayside of Venusa qBn Bin

0 – 45°

45 – 75°

75 – 105°

105 – 135°

135 – 180°

Number of events Number of days

122 21

151 31

287 52

330 51

181 36

a In total there are 1071 events on the dayside covering 190 days of the whole data set.

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Figure 7. Histogram of the solar wind magnetic field ‘‘Parker angle’’ qP for 1 Venus year. The two vertical white lines indicate the nominal Parker spiral direction of 36° and 144°. fication. The structure is very interesting because it shows that on that day the Venusian current sheet was similar to the Earth’s current sheet in the YZ plane, as often observed by Cluster [e.g., Baumjohann et al. 2007]. Depending on the solar wind magnetic field and its draping around the planet, the current sheet at Venus can rotate between the XZ plane and the YX plane [Luhmann et al., 1991]. [35] As a second example in the wake, the data from 1 September 2006 are shown in Figure 8 (right), when the spacecraft was located near (2.3, 0.5, 0.3) RV and the solar wind magnetic field B (10, 1, 5) nT. There are two intervals, shaded boxes I and II, which satisfy the demands for MM-like structures. In box I is a very nice MM structure, with a strong decrease in Bm of 50%. Such a structure is reminiscent of so-called magnetic holes in the solar wind [see, e.g., Zhang et al., 2008, and references therein] and a clear indication of MM. In box II, again there is a very weak structure with DB/B 0.15. [36] Most of the MM-like structures in Venus’ wake are rather weak in strength. Indeed, limiting the data set to only stronger MM-like structures with DB/B 0.6 (corresponding to a 30% decrease in field magnitude) drastically reduces the occurence rate in the wake region by 1–1.5 orders of magnitude (Figure 4 (bottom)), whereas for the dayside this limit increase barely changes the occurrence rate. Most likely, the turbulence in the wake region disturbs the plasma too much to allow full development of MM-like structures

because of isotropization of any ion temperature asymmetry [see, e.g., Zimbardo et al., 2003].

5. Summary and Discussion [37] Venus’ magnetosheath has been investigated by a slew of spacecraft and the results from these missions have been well summarized by Phillips and McComas [1991]. Interestingly, most of the investigated wave activity therein was for regions under quasi-parallel bow shock conditions. This means that the mirror mode instability has not been investigated in any of the papers mentioned in the review. Up to the arrival of VEX there has been little discussion of MM waves in Venus’ magnetosheath. The reason could be that they either have not been observed, or they were not looked for in the various data sets. Indeed, identification of MM-like structures can be difficult, but not impossible, without high enough resolution plasma data to accompany the magnetic field data. Rae et al. [2007] have confirmed the existence of MM waves by using both magnetic field and plasma data from Equator-S, for cases that Lucek et al. [1999b] had identified first from magnetic field data only. [38] The (non)occurrence of MM waves in Venus’ magnetosheath is discussed by Luhmann [1995]. Using Pioneer Venus Orbiter (PVO) data for selected orbits between numbers 625 and 650, which probed the subsolar point of the magnetosheath, the authors found that the waves in the magnetic field were mainly linearly polarized and trans-

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Figure 8. (left) Waves in the Venusian wake for 15 August 2006. (right) Magnetic field data for 1 September 2006. (a – c) The difference between the measured magnetic field and the low-pass filtered data describing the mean field (DBx,y,z. (d) The magnitude of the magnetic field Bm is plotted with the smooth curve showing the low-pass filtered data. (e and f) The fluctuation of the magnetic field DB/B and the angle qBmv between the maximum variance direction and the mean magnetic field (circles) and the angle fBmv between the minimum variance direction and the mean magnetic field (pluses). The two grey shaded areas I and II show the intervals for which the MM wave condition is met (i.e., qBmv < 20, fBmv > 80, and DB/B large). verse, with no evidence for regularly occurring slow mode like waves structures (to which the MM waves belong) standing in the flow of the magnetosheath. However, this was for low-altitude regions in the magnetosheath. It could well be argued that some of the peaks that appear in the compressional component of the power spectra, presented by Luhmann [1995], are related to MM like structures. There seem to be spectral peaks between 0.07 and 0.1 Hz, which would translate to 10– 14 s period, which are slightly longer than were shown by Volwerk et al. [2008] for MMlike structures near the magnetopause/ionopause. However, without reevaluating the PVO data, no definite conclusions can be drawn on these waves. [39] In the present study, 1 Venus year of VEX data have been investigated on mirror-mode-like structures in Venus’ induced magnetosphere. The structures appear when in a high-b plasma a temperature asymmetry perpendicular and parallel to the ambient magnetic field exists. Such asymmetry can either occur through ion drift shock acceleration at the bow shock under quasi-perpendicular conditions or by magnetic pumping through the first adiabatic invariant

during compression of the induced magnetosphere. Also the motion of the plasma through the magnetosheath is creating the conditions favoring the MM instability. Treumannn and Baumjohann [1996, p. 59] write about the global phenomenon in the magnetosheath: ‘‘The shocked solar wind, on its path from the bow shock to the magnetopause, is adiabatically heated in the perpendicular direction, while at the same time field-aligned outflow toward the flanks of the magnetopause cools the plasma adiabatically in the parallel direction. The main effect is parallel cooling, leading to pressure anisotropy of the plasma which increases toward the magnetopause.’’ These statements are in agreement with the results from the current paper, with respect to the occurrence of the MM-like structures. However, to truly check this phenomenon, one has to wait for the plasma data to become available. [40] The occurrence rate of the MM-like structures is much higher at the dayside of Venus’ magnetosheath, in comparison to the night side wake region, when looking at structures of reasonably large strength (DB/B 30%). The lack of strong events in the wake is most likely caused

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Figure 9. The magnetic field for 15 August 2006 in VSO coordinates. (a – d) Shown are Bx, y, z, m and the smooth curve shows the low-pass filtered magnetic field. Grey shaded interval I shows the interval where there is MM activity (as determined in Figure 8) and grey shaded interval II shows the interval where erroneously MM was inferred but is shown to be a current sheet crossing. by the fully developed turbulence in that region, which will reduce temperature anisotropies. Another reason for the lower chance to observe these waves could be the stretching of the magnetic flux tubes that are ‘‘hung-up’’ at the magnetopause/ionopause. Russell et al. [2006b] describes how in such a case the length of the flux tubes becomes long relative to the velocity of the plasma along the magnetic field, leading to a decrease in the plasma b. If the plasma b becomes too low, then the MM instability does not occur anymore. [41] On the dayside, the occurrence rate is maximum near the bow shock and along the magnetopause/ionopause boundary, which is expected, following the above comment by Treumannn and Baumjohann [1996]. Near the bow shock the drift shock acceleration will generate temperature asymmetries under quasi-perpendicular situations. Near the magnetopause/ionopause the asymmetry will be created

through compression of the induced magnetosphere and by adiabatic parallel cooling. Unfortunately, because of the lack of plasma data at time of preparation of this paper, it is not possible to find out when the solar wind compresses the magnetosphere or how the temperature anisotropy develops through the magnetosheath. However, it is possible to obtain some indication about whether the magnetosheath is compressed or not by looking at the magnetometer data. For one, the location of the bow shock crossing can be compared with the best fit model of Zhang et al. [2007b], or the magnetic field strength near periapsis and can be compared with a long time average of the magnetic field strength at that location, e.g., Volwerk et al.’s [2008] second event shows a Bm 50 nT, whereas the orbit (day) before VEX measured Bm 40 nT. [42] The direction of the solar wind magnetic field has a strong influence on the occurrence rate and location of the

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MM-like structures. As expected, the occurrence rate drops for quasi-parallel bow shock conditions. [43] Comparing the MM-like structures studied here with those observed in the Earth’s magnetosheath by Lucek et al. [1999b] on the dawnside (their Figure 1) shows that there the MM waves appear mainly around the magnetopause. Most likely, this is an orbital effect as the apogee of Equator-S is 11 RE and is thus not expected to reach the bow shock. Also, the duration of the events in the current paper is much shorter up to minutes instead of up to hours there. [44] Joy et al. [2006] discussed the MM waves in the Jovian magnetosheath. The authors find that ‘‘between the bow shock and the magnetopause, the fluctuations appear to increase in amplitude and decrease in frequency.’’ From the first event presented in this current paper (see Figure 1) it is seen that close to the BS, in interval I, the period of the wave is 5 s, whereas further away from the BS, in interval II, the period of the wave is 15 s, which is in agreement with what is observed at Jupiter. This effect can also be seen in the second event of this current paper (see Figure 5). However, one event presented by Volwerk et al. [2008], with VEX close to the magnetopause/ionopause shows that the period is 10 s, which is longer that near the BS, but shorter than in the mentioned interval II above. The fact that the period is shorter is not a contradiction with the results from Joy et al. [2006], as the plasma parameters in Venus’ magnetosheath may be different in the two events and therefore the frequency of the MM-like structures. [45] Interestingly, Bavassano Cattaneo et al. [1998] find at Saturn that the MM waves directly behind the BS, in the outer magnetosheath, have a ‘‘significant transverse component’’, and that they become almost purely compressional first in the inner magnetosheath. This is different from what is found here at Venus, where the MM-like structures are almost purely compressional also in the outer magnetosheath (near the BS). This could be an effect of the specific identification criteria used here. [46] Bertucci et al. [2004] studied the magnetic pile up region at Mars, where they find mirror mode waves at the upstream side of the pile up region, whereas on the downstream side the waves are fast magnetosonic waves. In our study at Venus it was found that the chance to observe MM-like structures follows the magnetopause/ionopause, which can be identified with the magnetic pile up boundary. Volwerk et al. [2008] showed that MM-like structures occur near the magnetopause/ionopause (their Figure 3). Near periapsis there is still evidence of wave activity at a similar period as the MM-like structures. However, the polarization of these waves changes over the magnetopause/ionopause from compressional, through intermediate propagating perpendicular to the field, to transverse with propagation direction more aligned with the background field. This could imply fast magnetosonic waves, but need further investigation. Bertucci et al. [2004] also find that MM waves are always present, independent of the BS nature. However, the MM waves are always ‘‘attached’’ to the magnetic pile up boundary.

6. Conclusions [47] A statistical study of MM waves for 1 Venus year of magnetometer data showed that at the dayside mirror-mode-

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like structures in Venus’ magnetosheath occur frequently, preferably under quasi-perpendicular bow shock conditions. This is in contrast to the findings during the PVO era, where no such waves were detected. In the wake the occurrence rate is much lower, caused by turbulence (reducing temperature asymmetries) and/or low plasma-b (suppressing the mirror mode instability). An explanation for the difference with the PVO era could be found in that previous research was concentrating on quasi-parallel bow shock conditions, which are unfavorable for mirror mode generation. [48] Acknowledgments. The authors thank Simon Pope at the University of Sheffield for preparing the data. The work by Z.V. is supported by the Austrian Wissenschaftsfonds under grant P20131-N16.

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Luhmann, J. G., C. T. Russell, K. Schwingenschuh, and Y. Yeroshenko (1991), A comparison of induced magnetotails of planetary bodies: Venus, Mars, and Titan, J. Geophys. Res., 96, 11,199 – 11,208. Lundin, R., and S. Barabash (2004), The wakes and magnetotails of Mars and Venus, Adv. Space Res., 33, 1945 – 1955, doi:10.1016/j.asr.2003. 07.054. Phillips, J. L., and D. L. McComas (1991), The magnetosheath and magnetotail of Venus, Space Sci. Rev., 55, 1 – 80. Price, C. P., D. W. Swift, and L.-C. Lee (1986), Numerical simulation of nonoscillatory mirror waves at the Earth’s magnetosheath, J. Geophys. Res., 91, 101 – 112. Rae, I., I. Mann, C. Watt, L. Kistler, and W. Baumjohann (2007), Equator-S observations of drift mirror mode waves in the dawnside magnetosphere, J. Geophys. Res., A11203, doi:10.1029/2006JA012064. Russell, C. T., W. Riedler, K. Schwingenschuh, and Y. Yeroshenko (1987), Mirror instability in the magnetoshpere of Comet Halley, Geophys. Res. Lett., 14, 644 – 647. Russell, C. T., J. S. Leisner, C. S. Arridge, M. K. Dougherty, and X. BlanoCano (2006a), Nature of magnetic fluctuations in saturn’s middle magnetosphere, J. Geophys. Res., 111, A12205, doi:10.1029/2006JA011921. Russell, C. T., J. G. Luhmann, and R. J. Strangeway (2006b), The solar wind interaction with Venus through the eyes of the Pioneer Venus orbiter, Planet. Space Sci., 54, 1482 – 1495, doi:10.1016/j.pss.2006.04.025. ¨ ., and M. Scheible (1998), Minimum and maximum Sonnerup, B. U. O variance analysis, in Analysis Methods for Multi-Spacecraft Data, edited by G. Paschmann and P. Daly, pp. 185 – 220, Eur. Space Agency, Noordwijk, Netherlands. Southwood, D. J., and M. G. Kivelson (1993), Mirror instability: 1. Physical mechanism of linear instability, J. Geophys. Res., 98, 9181 – 9187. Svedhem, H., D. V. Titov, F. W. Taylor, and O. Witasse (2007), Venus as a more Earth-like planet, Nature, 450, 629 – 632, doi:10.1038/nature06432. Treumannn, R., and W. Baumjohann (1996), Advanced Space Plasma Physics, Imperial Coll. Press, London. Tsurutani, B. T., E. J. Smith, R. R. Anderson, K. W. Ogilvie, J. D. Scudder, D. N. Baker, and S. J. Bame (1982), Lion roars and nonoscillatory drift

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mode mirror waves in the magnetosheath, J. Geophys. Res., 87, 6060 – 6072. Volwerk, M., T. L. Zhang, M. Delva, Z. Vo¨ro¨s, W. Baumjohann, and K.-H. Glassmeier (2008), First identification of mirror mode waves in Venus’ magnetosheath, Geophys. Res. Lett., 35, L12204, doi:10.1029/ 2008GL033621. Vo¨ro¨s, Z., T. L. Zhang, M. P. Leubner, M. Volwerk, M. Delva, W. Baumjohann, and K. Kudela (2008), Magnetic fluctuations and turbulence in the Venus magnetosheath and wake, Geophys. Res. Lett., 35, L11102, doi:10.1029/2008GL033879. Zhang, T. L., et al. (2006), Magnetic field investigation of the venus plasma environment: expected new results, Planet. Space Sci., 54, 1336 – 1343. Zhang, T. L., et al. (2007a), Little or no solar wind enters venus atmosphere at solar minimum, Nature, 450, 654 – 656, doi:10.1038/nature06026. Zhang, T. L., et al. (2007b), Initial Venus express magnetic field observations of the Venus bowshock location at solar minimum, Planet. Space Sci., 56, 785 – 789, doi:10.1016/j.pss.2007.09.012. Zhang, T. L., et al. (2008), Characteristic size and shape of the mirror mode structures in the solar wind at 0.72 AU, Geophys. Res. Lett., 35, L10106, doi:10.1029/2008GL033793. Zimbardo, G., A. Greco, A. L. Taktakishvili, P. Veltri, and L. M. Zelenyi (2003), Magnetic turbulence and particle dynamics in the earth’s magnetotail, Ann. Geophys., 21, 1947 – 1953.

W. Baumjohann, M. Delva, M. Volwerk, and T. L. Zhang, Space Research Institute, Austrian Academy of Sciences, Schmiedlstraße 6, A-8042 Graz, Austria. ([email protected]) K.-H. Glassmeier, Institut fu¨r Geophysik und extraterrestrische Physik, Technische Universita¨t, D-38106 Braunschweig, Germany. Z. Vo¨ro¨s, Institute for Astro- and Particle Physics, University of Innsbruck, Technikerstraße 25/8, A-6020 Innsbruck, Austria.

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O+ ion flow below the magnetic barrier at Venus post terminator K. Szego,1 Z. Bebesi,1 Z. Dobe,1 M. Fra¨nz,2 A. Fedorov,3 S. Barabash,4 A. J. Coates,5 and T. L. Zhang6 Received 20 April 2008; revised 21 August 2008; accepted 14 October 2008; published 1 January 2009.

[1] Venus forms an obstacle in the streaming solar wind; inside the obstacle boundary

(that is below the magnetic barrier) the ions of planetary origin dominate the plasma. The objective of this study is to investigate the properties of the O+ ions inside the obstacle boundary of Venus in the wake; we are especially interested in the characterization of the different plasma regions the O+ ions occupy. The study is based on the data collected by the ASPERA 4 plasma analyzer flying onboard of the Venus Express mission in a region never explored before experimentally. The obstacle boundary was approximately identified from the dropout of magnetospheric electrons and the sharp decrease of the proton speed; the entry point correlated well with the location of the magnetic barrier derived by eyes from magnetometer data. The most characteristic structures seen during the various flybys were (1) the tailward continuation of the mantle was evident; (2) in the mantle near Venus the O+ ion flow was significantly intense in low-energy counts; (3) the inbound and outbound crossings of the tailward boundary were sharp, characterized by less intense but higher-energy O+ beams; (4) the crossing of the central tail region (current sheet) was marked by the change of the sign of Bx and by an intense low-energy O+ ion flux; (5) it is remarkable that the O+ ion outflow was not confined to the central tail region; the intensity elsewhere was highly variable, resulting in a ray-like outflow pattern in most of the cases. Citation: Szego, K., Z. Bebesi, Z. Dobe, M. Fra¨nz, A. Fedorov, S. Barabash, A. J. Coates, and T. L. Zhang (2009), O+ ion flow below the magnetic barrier at Venus post terminator, J. Geophys. Res., 114, E00B26, doi:10.1029/2008JE003170.

1. Introduction [2] It is well known that Venus forms an obstacle in the streaming solar wind. Above the obstacle the solar wind dominates the plasma flow, below the obstacle boundary the ions of planetary origin play the major role. Zhang et al. [1991] identified the magnetic barrier as the obstacle boundary at Venus on the dayside, and Spenner et al. [1980] proved that below the magnetic barrier there is a region, the mantle, where solar wind and planetary populations coexist. The mantle is bounded below by the ionopause, inside the mantle; in the lower regions of the mantle, the planetary ions become dominant. Intuitively, and according to models, it is clear that the mantle continues tailward, but experimentally this was not verified. The objectives of this study are to investigate whether the mantle exists in the wake, to investigate the properties of the O+ 1 KFKI Research Institute for Particle and Nuclear Physics, Budapest, Hungary. 2 MPI fu¨r Sonnensystemforschung, Katlenburg-Lindau, Germany. 3 Centre d’Etude Spatiale des Rayonnements, Toulouse, France. 4 Swedish Institute of Space Physics, Kiruna, Sweden. 5 Mullard Space Science Laboratory, University College London, London, UK. 6 Space Research Institute, Austrian Academy of Sciences, Graz, Austria.


ions below the obstacle boundary of Venus in the wake; especially we are interested in the characterization of the different plasma regions the O+ ions occupy, their boundaries, the motion of these ions and the process of tail formation (this latter question will be discussed elsewhere). Concerning the magnetic field, Vo¨ro¨s et al. [2008] pointed out recently that in the magnetic data the boundaries in question are evident in most of the cases, but with different signatures than on the dayside: they appear as enhanced perturbations rather than jumps in magnitudes. [3] The analysis presented here for O+ ions is based on the data collected by the Analyzer of Space Plasmas and Energetic Atoms (ASPERA-4) carried onboard of the Venus Express mission [Barabash et al., 2007a]. The ASPERA-4 sensor is unique relative to all previous instruments flown around Venus because it measures 3D plasma distributions above 10 eV, making possible to derive the moments of the major ion components, for the first time since the exploration of Venus started. The Venus Express (VEX) spacecraft has polar orbit (the latitude of the pericenter is at 76° North relative to Venus orbit, the period is 24 h) with pericenter altitude 220 – 350 km and apocenter altitude 66,000 km; this orbit is inertially fixed. The VEX spacecraft is 3-axes stabilized, the attitude varies with observation modes, in most cases it is nadir pointing. ASPERA-4 onboard VEX is exploring the near Venus plasma regions. The orbit constraints of earlier missions did not allow the detailed exploration of this region. We review the previous

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SZEGO ET AL.: O+ ION FLOW AT VENUS

Figure 1. The plasma boundaries at Venus, in Venus coordinate system (VSO) cylindrical coordinates, as observed on Venera 9 and 10 [Vaisberg et al., 1995]. space mission results therefore only in the region relevant for this study, that is in the post terminator mantle and in the near plasma tail. [4] The first two spacecraft that studied the solar wind interaction with Venus were Venera 9 and 10 [Vaisberg et al., 1995]. They started orbiting Venus on 20 and 23 October 1975, respectively. Their main task was to support the lander missions, but they also studied the tail of Venus. The satellites had two plasma spectrometers: a 2-D ion spectrometer consisting of 6 narrow-angle cylindrical electrostatic analyzers and a combination of sunward-looking differential ion Faraday cup and antisunward oriented integral electron Faraday cup. The energy ranges were 50 eV/Q – 20 keV/Q, and 0 – 4 keV/Q for ions and 0– 400 eV for electrons. The time resolution of both spectrometers was 160 s. [5] The main regions identified downstream, based on the data collected by the onboard electron and ion spectrometers, in the close vicinity of the planet are shown in Figure 1. The investigators found a boundary layer separating the Venus magnetosheath and the tail; within this boundary layer low-energy plasma streamed tailward. This boundary layer seems to be the continuation of the plasma mantle identified during the Pioneer Venus mission by Spenner et al. [1980]. The tailward boundary of this layer (tail boundary) was sharp. Having crossed the tail boundary the Veneras measured higher-energy, E > 50 eV ion flow; ions were permanently seen within the tail. In the innermost region, near the central part of the tail no ions were visible above 50 eV, this region was called cavity. The Venera investigators reported that the tail thickness depends on the magnetic latitude, i.e., from the angular distance of the plane containing the component of B perpendicular to the SW velocity vector and the center of Venus. The tail at distances 4 –5 RV was studied only during two passes of

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Venera 10, and no boundary crossing was observed. The tail plasma is of planetary origin, as the investigators assessed it. During the two passes at distances 4– 5 RV in the tail plasma ions were permanently observed; no hot ions were seen at Bx reversals. [6] The Pioneer Venus Orbiter (PVO) mission started to operate in December 1978 and functioned till the fall of 1992. At the beginning of the mission (which coincided with low solar activity) the periapsis of the spacecraft was lowered to 150 km, and maintained there for about 19 months. Later the periapsis was raised till the reentry before the end of the mission. The orbit plane of the Pioneer Venus spacecraft (sc) was nearly polar (with a periapsis at 17°N latitude); the spacecraft was spinning with its spin axis normal to the plane of the Venus orbit. The analog experiment to ASPERA-4 onboard PVO, concerning low-energy ions, was the Orbiter Plasma Analyzer (OPA). It was a quadrispherical electrostatic analyzer; the energy/charge range was 50– 8000 eV (ions) in 32 steps and 1 – 500 eV (electrons) in 16 steps. The angular range covered was ±85° elevation. However, OPA was primarily designed for solar wind monitoring with a low time resolution of about 10 min [Intriligator et al., 1980]. [7] In the tail the PVO plasma instruments either explored the nightside ionosphere and the very near tail region, or the far tail; that is 150 km to 2500 km altitude, or 5 – 11 Venus radii altitude. Here we focus primarily on the near tail region. The most important findings were summarized by Phillips and McComas [1991] and Brace and Kliore [1991]. The first important fact is that the ionotail exhibits large solar cycle and orbit to orbit variations. The solar cycle variation of the tail is connected to the variation of the electron density on the dayside, which is also controlled by the solar EUV flux, and hence by the solar cycle. The height of the nightside ionopause correlates with solar cycles. The PVO spacecraft between July 1984 and November 1986, during solar cycle minimum conditions found that the ionopause was at low height; between December 1978 and October 1981 during solar cycle maximum conditions it found a more extended ionosphere. The mechanism of the orbit-to-orbit variation is yet unexplained. The VEX data, in 2006, were taken in solar minimum, that is high solar wind pressure conditions. It was argued by Slavin et al. [1989] and others that the tail structure observed in the ion spectra correlates with the magnetic field variations; the low-energy plasma in the tail is controlled by the IMF orientation. The current sheet in the tail is parallel to the vSW BSW direction. In the central tail, at the current sheet, whenever Bx changed sign, the energy of ions increased. Ions, measured within the tail contained very cold ions (and moving at half solar wind speed if they were assumed to be protons). PVO observations confirmed that the plasma flow speed is characteristically slowest in the center of the tail, and faster outside the tail. It has been established that the origin of the O+ ions in the tail is the day-to-night O+ flow, and the escape is due to the polarization field that develops in the ionosphere [Hartle and Grebowski, 1995]. The curvature force of the magnetic field and the magnetic pressure force both act to accelerate the plasma down the tail [Russell, 1999]. McComas et al. [1986] determined the average structure of the Venus magnetotail from PVO magnetometer observations. They have found that in the tail the tailward velocity varied from 250 km/s at 8 RV

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Figure 2a. The field of view (FOV) of the Ion Mass Analyzer (IMA) is 90° 360°. This can be modeled as a cylinder; its y axis is the direction about which the FOV is 360°, and the height of the cylinder models the ±45° FOV in the perpendicular direction. The horizontal arrow is the x direction in the spacecraft frame of reference. Owing to location of IMA on the spacecraft, the spacecraft body and the solar panels partially obscure the full FOV. The elevated surfaces illustrate the blocked portion of the sensor. to 470 km/s at 12 RV, the current sheet density was 0.06 O+/cc, while the lobe density was approximately 15% of that. A strong flapping was identified; at 12 RV it might reach the average cross section of the tail. [8] Recently, Barabash et al. [2007b] published the first results on ion escape through the Venusian tail. In that paper the fluxes of the escaping ions were collected from 33 orbits (basically from the same orbits we use in the present study), and integrated for the near tail. These integrated fluxes revealed structures in the tail: in the central part of the tail, called plasma sheet, the fluxes were the highest, and the plasma sheet separated the tail lobes inside the magnetosphere boundary, that is inside the magnetic barrier boundary layer. In the current paper, using mostly the same orbits as in the work by Barabash et al. [2007b], we give a detailed analysis of the O+ ions along the individual orbits in the tail plasma, pointing out also certain difficulties to have a full picture at this point of data analysis. [9] The plasma data provided by the onboard instrumentation of VEX differ significantly from the data we received from PVO or from other missions. The most important difference is that ASPERA-4 does not measure cold ions since the lowest energy limit of the instrument is 10 eV. This means that the ion spectrometer does not give direct information on the ionospheric plasma, and it is difficult to resolve the location of the ionopause in the ion data owing to our time resolution. On the other hand, as discussed above, on PVO the ion moments were measured sparsely, and the mass resolution was low. Hence, the ASPERA data significantly enrich our knowledge on the ion environment of Venus relative to previous missions in a region never explored before.

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[10] The Ion Mass Analyzer (IMA) instrument is part of the ASPERA-4 experiment package, the general scientific objective of ASPERA-4 is to study the solar windatmosphere interaction and characterize the plasma and neutral gas environment in the near-Venus space. The IMA instrument is a separate part attached to the spacecraft body, it provides ion measurements in the energy range 0.01– 36 keV/q for the main ion components with M/q > 40 amu/q. Its energy resolution is DE/E = 0.06, the field of view (FOV) is 90° 360°, the angular resolution is 10° 22.5°; and the time resolution for a full 3-D data acquisition is 192 s. The IMA FOV can be modeled as a cylinder; its axis is the direction about which the FOV is 360°, and the height of the cylinder models the ±45°FOV in the perpendicular direction (compare Figure 2a). However, owing to location of IMA on the spacecraft, the spacecraft body and the solar panels partially obscure the full FOV. The elevated surfaces in Figure 2a illustrate the blocked portion of the sensor in first approximation (Fedorov, internal team report, 2008). We do not need a refined obscuration model in this paper, though work is in progress to this end. [11] The Electron Spectrometer (ELS) on VEX [Barabash et al., 2007a] is an axially symmetric quadrispherical analyzer. It has a field of view of 360° 4°, with the 360° measurement plane divided into 16 sectors, each 22.5° wide; the energy resolution (DE/E) is 7%. The ELS operation varies, for the observations referred in this paper ELS covered the spectral range from 0.8 to 30 keV in 127 logarithmically spaced energy steps. ELS is located on a scanning platform providing a 4p coverage. Electron spectra can be measured about every 4 s. The magnetometer of VEX is described by Zhang et al. [2006], in this paper we use the 1-s resolution data product of the magnetometer.

2. Data Analysis [12] We use two coordinate systems for analyzing our results. The first is the standard Venus coordinate system (VSO), the second one is a Venus magnetic coordinate system (VMSO), where the x’ axis is parallel to the actual vSW velocity vector (hence aberrated to the actual flow B plane, direction), and the y’ axis is in the vSW perpendicular to vSW. The current sheet is parallel to the vSW B direction. This coordinate system was introduced by Saunders and Russell [1986]. (In the VSE system the x axis points toward the Sun, the z axis is the same.) [13] In this paper we analyze data of the Ion Mass Analyzer (IMA) part of VEX, taken between 20 November and 31 December 2006. We also use data from orbits half a Venus year earlier and later. The analysis presented here is preliminary owing to several reasons: (1) the IMA data we are using are partially calibrated, the effects due to the saturation of the sensor at 104 counts per energy step has not yet been fully eliminated; (2) the characterization of the plasma requires the comparison of the data to other VEX sensors like ELS, and to other instruments such as the magnetometer [Zhang et al., 2006, 2007]. Though those data are available, and we are using them in this paper, a full joint analysis is a task ahead of us. The spacecraft potential is yet unknown at this point outside the ionosphere (in the ionosphere it is about 11 eV), whereas it has a significant influence on low-energy ion detection. As we stated, our

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Figure 2b. The change of the view geometry of IMA on 14 December 2006 between 0646 and 0752 UT. The frame is parallel to the VSO frame of reference (the Sun-Venus line is the horizontal axis); the arrow at the corner of the obscured region is parallel to the spacecraft x axis; the axis of the cylinder is the y axis. Above each plot the time of the actual orientation is shown. The azimuth angles (the vertical axis in Figure 4b) is in the x-z plane, measured from x toward z, whereas the elevation angle is measured relative to the x-z plane, its maximal value in Figure 4b is toward the y axis of the spacecraft. objective is to identify different plasma regions, and the signatures we shall rely on in this analysis will be the sudden changes in the counts of ASPERA-4 in neighboring regions. The uncalibrated nature of the data, in our opinion, will not mask these sudden changes; even if refinements might be required later. [14] Within the ASPERA-4 team the data flow from the spacecraft to the user is well organized through different Web sites where supporting data are also available, and quick look is made possible. Fra¨nz et al. [2006] have developed moment calculation for ASPERA-3 flying onboard of Mars Express, and also a code that has been upgraded for ASPERA-4, and made available for the team for data analysis. This is the code we have been using for this paper as well. [15] The VEX spacecraft has a polar orbit (the latitude of the pericenter is at 76°N relative to Venus orbit) and since Venus orbits the Sun in 224.7 days, the inclination of the spacecraft orbit plane to the Sun-Venus line changes daily by about 1.6°. For the current analysis we selected 16 orbits around a special one (orbit 237, pericenter at 0729:34 UT on

14 December 2006, the CA altitude was about 375 km) which is more or less parallel to the Sun-Venus line, and VEX was flying tailward; and the others were selected from a time interval +17/ 24 days around it. This means that for these orbits the smallest distance between the Sun-Venus line and the actual orbits changed from 0 to 1 Venus radius; and this set of orbits provides reasonable tail coverage. [16] After the pericenter passage the spacecraft attitude varies strongly; in Figure 2b we illustrate the attitude variation on 14 December 2006, between 0745 and 0752 UT. From these plots it can be seen that neither the attitude variation, nor the obscuration blocks the FOV for particles arriving from the ram or from planetary directions. Hence, we are convinced that if we measure low counts, it really means that in the appropriate tail region the ions are of very low density (we assume here that in the tail the sc potential is near zero, the data of the other space missions support this assumption). [17] There are a number of orbits appropriate fully or partially for the analysis of the plasma regions below the obstacle boundary. In Figures 3a and 3b we provide an overview of the electron spectra (collected by ELS), the

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Figure 3a. Summary plot of electron and all-ion spectra, total magnetic field, and sc position on 14 December 2006 between 0700 and 0830 UT. The top and middle are dynamic spectrums (vertical axis is energy in eV units and the horizontal is time in minutes), with color coded count rated for the electrons and ions, respectively. The magnetic field values were measured by the magnetometer onboard Venus Express, the time resolution of the data is 1 s.

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total ion spectra (IMA), the total magnetic field, and the sc position along the flyby on 14 December 2006, to illustrate our text below. An important consequence of this plot is that whenever the ion counts are low, the electron counts are low as well. Since the electron motion is not directional, low electron density means low plasma density, substantiating our claim that the measured low ion densities are real. [18] The spacecraft speed near pericenter is close to 9 km/s. The O+ ions moving with 10 km/s relative speed to the spacecraft would impact with 8.35 eV. Therefore the ion speed and consequently the measured ion energies should have been corrected with the spacecraft motion. Since the ion velocity directions are not well known, these corrections could not be implemented for the data presented in this paper; we have to bear in mind the uncertainties due to that. [19] At the beginning of the flyby the spacecraft first was in the unperturbed solar wind, after it crossed the bow shock as witnessed by an increase of particle intensity and the increase of the magnetic field. Then Btot piles up; the magnetic barrier (that is the obstacle boundary) is identifiable on this and usually on all the other orbits we investigate. Further down the warm sheath electrons (E > 30eV) drop out (the region where the electrons are not detected is called the electron biteout region), the solar wind ions also drop out where the electrons do, clear signatures that the obstacle was crossed and the solar wind particles do not dominate the flow any longer, and the spacecraft reached the inner edge of the magnetic barrier behind terminator. Zhang et al. [1991] showed that the magnetic barrier is in effect the obstacle that diverts the bulk of the solar wind plasma from the inner regions of Venus. On PVO, below the magnetosheath of Venus on the dayside the Orbiter Retarding Potential Analyzer [Knudsen et al., 1980] identified a region called mantle where both sheath electrons and ionospheric electrons were present [Spenner et al., 1980]. A more detailed analysis of the ELS data of ASPERA-4 on 18 May 2006 [Coates et al., 2007] identifies the transition region, between 700 and 900 km in altitude, in the same way as Spenner et al. [1980] introduced the mantle, that is by the presence of both ionospheric photoelectrons (between 21 and 24 eV energy range) and sheath electrons. On that orbit VEX crossed the transition region in less than 3 min, that is in less time than the duration of a full IMA cycle (192 s). This illustrates well the difficulty we are confronted if we want to

Figure 3b. The spacecraft orbit is plotted in cylindrical coordinates, with tick marks for every 10 min. The distance unit is 1 Venus radius (RV = 6051 km). The other curves are the theoretical bow shock and magnetic pileup boundary locations, respectively. The circle represents Venus. 5 of 12

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Figure 4a. Energy versus time spectrograms with color coded H+ and O+ energy fluxes derived from ASPERA-4 IMA data for the 14 December flyby, between 0710 and 0830, below the obstacle boundary. The O+ ions that appear at solar wind energies are due to crosstalk. At low proton densities the crosstalk is negligible. For the calculation we used the calibration of A. Fedorov (internal team report, the IMA bible, 2007) and also a weak noise reduction. study ions in the mantle along these orbits. We are also aware of a crosstalk between the proton and O+ channel, to be cleaned at a later phase of data processing. This crosstalk is not strong, and it is not very disturbing in the tail plasma where the proton density is low. On 14 December 2006, according to the plasma regions exhibited in Figure 1, the spacecraft, after having crossed the mantle, possibly dipped briefly into the ionosphere, than it entered into the tail region, and for a short period of time it crossed the central tail. When exiting the tail, the mantle in the tail was crossed again before returning to the magnetosheath. In Figures 4a – 4c we present further details of the data collected along this orbit. We note here that in the literature the term ‘‘plasma sheet’’ and ‘‘current sheet’’ are not used coherently; and because of lack of data, there is no experimental distinction for Venus. In order to avoid misunderstanding, in this paper we frequently

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use the term ‘‘central part of the tail’’, since our resolution certainly does not allow the ion sensor to discriminate between them. The current sheet is the surface that separates the two different polarities of the magnetic field in the tail (lobes). The plasma sheet is a broader region including the current sheet as seen from the analysis of Barabash et al. [2007b], but for individual flybys the plasma sheet boundary is not properly defined yet. [20] Below first we analyze in detail the flyby on 14 December 2006; then four more flybys will be discussed. On 14 December 2006 the pericenter altitude was 370 km, reached at 0731 UT (7.51 UT). On that day the vSW BSW convective electric field pointed to the (0.46, 0.84, 0.3) direction in VSO, and the y’ axis of VMSO pointed to ( 0.175, 0.235, 0.96) in VSO. The projection of the orbit to the y z plane was parallel to the z axis in VSO; that is the magnetic latitude is almost identical to the geometric one. We shall use different plots to exhibit the data. The most conventional ones are probably the time-energy fluxes for the different plasma species, and we shall display counts, integrated pixel by pixel for all pixels of the IMA sensor in its attitude-elevation frame (compare Figure 2a). We shall plot also the variation of the magnetic field vectors along the orbit. From the time-energy fluxes shown in Figure 4a and from the angular distribution of the counts (Figure 4b), it can be concluded that the craft was below the obstacle boundary between 0725 and 0810 UT. Before that, as seen from the first attitude-elevation mosaic of Figure 4b collected at 0721:50, the beam was observed about the middle of the sensor, where the footprint of the shocked solar wind is expected. Later, below the boundary, as the next mosaic of Figure 4b shows, though the beam reached the detector, it was only partially in our field of view. The energy of the O+ ions can be obtained quite correctly, but the direction of the flow cannot be deduced in a simple way. What is evident, however, that the flow direction sensed onboard changed almost to the opposite direction right after entering into the boundary layer at 0725:02. Between 0721 and 0731 the sc attitude was stable; hence, the variation of the location of the peak counts on the detector plane is due to a change in the flow direction. We interpret this directional change by that the spacecraft was overpassing the ions that arrived from the ram direction. In this region the spacecraft was in the sunlight, hence it is expected that the overall sc potential was positive in the low-density plasma, and in addition to the ram effect, the ions must have been repelled from the spacecraft. Below the magnetic barrier we encountered low-energy O+ ions. The flow direction changed again at 0731:26, as well as the ion energy increased sharply, we interpret this by that the sc crossed the tail boundary. We could not associate any magnetic signature with this crossing. [21] The magnetic field vectors along the orbit, as viewed from the direction perpendicular to the Sun-Venus line is shown in Figure 4c. The field vector changes sign at 0750 UT, this coincides with the low-energy peak flux in Figures 4a and 4b. [22] We do not exhibit the mass-energy spectrum for this orbit. For count rates above 104 on these plots we have a saturation effect similar to the ‘‘blooming’’ of the CCD’s, peaks with such high count rates overflow the neighboring energy channels. Though we have already a fix to this

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Figure 4b. Azimuth-elevation plot for the 14 December flyby. The vertical axis is azimuth from 0° to 360° (corresponding to the equator of the cylinder in Figure 3), the horizontal axis is elevation (corresponding to the height of the cylinder in Figure 3), see the legend of Figure 2 for further details on the geometry. The azimuth is measured from the x axis of the spacecraft; the elevation (Theta) is measured from the y axis of the spacecraft. problem, this is not yet applied for mass production of data. Nevertheless, from such plots we are confirmed that the identification of the major tail ion species are correct. [23] As seen from Figure 4c, around 0750 UT Bx changed sign, corresponding to the geometric location of the crossing of the current sheet, and at that time the flux composed

of low-energy ions had a sharp maximum; we believe that we crossed the current sheet. There was also a peak for higher-energy ions as well. The angular plot at 0750:38 is consistent with a low-energy flow from about the ram direction (though in this case as the craft is in the shadow, the spacecraft potential is negative), but there are peaks in the

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Figure 4c. The magnetic field vectors projected on the VSO y-z plane. The length of the vectors is proportional to Btot. The blue lines show the bow shock and magnetic pileup boundary. Along the orbit the O+ ion counts are shown in color. azimuth elevation plots from more than one direction as well. (This, however, might be due to ions reflected from the sc body or from the solar arrays.) The mantle, outbound, is different from the inbound mantle (the magnetic field never piles up for that transition), the ions are more energetic and the tail boundary overlaps with a distant mantle. Outbound the sc crossed the boundary almost perpendicularly. [24] Next we present the analysis of four more flybys, 20 November, 26 November, 20 December, and 30 December 2006. The altitudes of the pericenter were 26.5 km at 0632 (6.54) UT, 335 km at 0647 (6.79) UT, 381 km at 0748 (7.8) UT, and 263 km at 0745 (7.75) UT, respectively. We selected these, because they are at the flank of the tail, and this way we can compare the flybys through the flank regions with a central one. Whereas geometrically the first two and the last two orbits were close, magnetically they were not, because the vSW B directions are ( 0.08, 0.195, 0.98), (0.40, 0.91, 0.06), (0.37, 0.78, 0.5), and ( 0.10, 0.46, 0.88), respectively. In Figure 5a we exhibit four more time-energy plots for O+ ions collected along these orbits, the corresponding trajectories in VSO and in VMSO are shown in Figure 5b, and the variations of the magnetic fields along the orbits are shown in Figure 5c (the black lines show Bx, whereas the gray lines exhibit the variations of Btot). On these orbits the inbound/outbound crossings of the tail boundaries occurred at about 0625/ 0652 UT, 0640/0720 UT, 0740/0825 UT, and at about 0738/ 0820 UT. As it can be seen, on 20 November the current sheet was crossed right at the inbound crossing of the tail, and no further crossing occurred in the tail. On 26 November the current sheet was crossed three times around the entrance point, on 20 and 26 December the current sheet crossings took place in the middle of the tail flyby. [25] On 20 November we observe a strong O+ signal near 0635 UT. We analyze this further using the 3d velocity distribution plots collected in each 192 s shown in Figure 5d. The 3-D velocity distributions are projected onto the x-y, x-z,

and y-z planes, respectively (from the non obscured part of the sensor); however, these are not yet corrected for spacecraft velocity. These plots exhibit the velocity interval between 50 and 50 km/s, in the VSO frame of reference. We can conclude that in the sheet (0630 UT) the ion velocities are low, in the 10 km/s range. The asymmetry seen is due to the sensor obscuration. At 0635 UT when we observe the second ion peak in the time-energy plot, the velocity distribution show ions of higher velocity; the origin of these ions is definitely outside of the sheet, but not yet clarified. Similar peaks outside the sheet are observed on other days as well. [26] On 20 December the current sheet was crossed at 0811 UT (8.18 UT); and on 30 December it was crossed twice, at 0813 (8.22) and 0826 (8.43) UT, indicating that the current sheet can be wavy. Intense cold O+ ion beams are associated with these crossings. On all the orbits the outbound magnetic barrier is distinguishable, but characteristically different from the inbound one. The tail boundaries are distinguishable as sharp, higher-energy signals. There is no easy pattern to describe the higher-intensity fluxes which frequently are not correlated with current sheet crossing. This indicate, that the current sheet is not the only ion outflow location, though long-term averages of the ion outflow certainly preferential at current sheet crossings, as shown by Barabash et al. [2007b]. [27] The variability of the O+ spectra, in correlation with variations in the electron data indicate that the tail plasma might have ‘‘ray-like’’ structures, these will be analyzed later. It is certainly to be investigated what is the flow directions of the various ion species in the tail.

3. Discussion and Conclusions [28] In this paper we analyzed the plasma properties in the tail of Venus, using the IMA sensor data of the ASPERA-4 instrument, focusing on the O+ ions. We have used data taken between 20 November and 31 December

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Figure 5a. Energy versus time spectrograms with color coded O+ energy fluxes derived from ASPERA-4 IMA data for 20 and 26 November and 20 and 30 December below the obstacle boundary. We used the calibration method of Fedorov (internal team report, the IMA bible, 2007) and also a weak noise reduction. Since the data were not corrected for the effects of crosstalk, some O+ counts appeared at solar wind energies: we excluded these from further calculations. 2006, but we also looked into data taken half a Venus year before and after; those data show basically the same features reported here. We have investigated that portion of the orbits when the craft was below the obstacle boundary nightside, as witnessed by the biteout of magnetosheath electrons. The objective of the investigation was to identify the characteristic plasma structure of the tail that is identifiable despite the high orbit-to-orbit variations. Because of the limitations of the data processing mentioned above, we confine the discussion to the most basic properties of the tail plasma to its boundary structure. [29] It is somewhat surprising that though the pericenter altitude was at about 300– 400 km, we did not see any clear signature in the ion data that the craft entered into the ionosphere. In the electron data the ionosphere is seen by the characteristic electron excitation of the ionospheric O+ ions (S. M. E. Tsang et al., Ionospheric photoelectrons at Venus: Case, studies and first observations in the tail, submitted to Journal of Geophysical Research, 2008). The ionopause crossing could be derived from the drop of Btot. Very likely the spacecraft dipped into the ionosphere for brief periods shorter than our data collection cycle, 192 s. During the PVO flybys, from the analysis of the median

height profiles of the Orbiter Retarding Potential Analyzer it was concluded that the ion density between 90° and 150° solar zenith angle (SZA) was about 1000 ion/cc at 800 km altitude, and the ionopause altitude near 90° SZA was about 800– 1000 km [Brace and Kliore, 1991; Zhang et al., 2007]. Even though we are in a different solar activity phase, we would have expected larger count rates than those observed by IMA, unless the sensor saturation causes these lower rates; this could be substantiated only at a later stage of data processing. It is also known from the PVO data that the relative ion composition for the antisolar ionosphere is dominantly O+; hence, the ions we observe there must be dominantly of planetary origin. We need a better understanding of the spacecraft potential to clarify this discrepancy. Using only the ASPERA-4 data, in this orbit we cannot resolve the difference between the mantle and the ionosphere. [30] All of our data confirm that the tail plasma is very variable. The electron density profiles measured by PVO in the ionotail at 2000 – 3000 km range typically showed large enhancements separated by deep troughs. The time resolution of ASPERA-4 does not allow comparable spatial ion resolution, but the large variations in the O+ ion counts are

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Figure 5b. The orbits of the four flybys in the Pioneer Venus Orbiter frame of reference as viewed toward the Sun. The gray sphere is Venus; the large dots indicate the entry/ exit points into the tail. The black arrows show the nominal current sheet directions.

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consistent with the PVO observations; indicating a ray-like ion structure in the region we investigate. [31] The most characteristic structures seen during the various flybys are the following: [32] 1. The continuation of the mantle in the wake; the near-Venus and far-Venus mantle crossings; the inbound being significantly more intense in low-energy counts, outbound the ions are more energetic than inbound. This is very likely connected with how ions in the tail mantle develop with increasing distance. [33] 2. The inbound and outbound crossings of the tail boundary were sharp, characterized by less intense but higher-energy ion beams. [34] 3. In most cases we observed the crossing of the current sheet within the central tail region where the lowenergy ion counts peak. [35] 4. In several cases the change of the sign of Bx is correlated with high-energy O+ ion peaks. [36] 5. A further important feature we have observed is the ray-like structure of the tail, regions with higher flux are separated by regions of very low flux; and the ion outflow outside of the sheet probably not negligible. These characteristic features together do appear roughly in most of the cases we have analyzed, the rest is more variable. [37] The central tail was characterized by the Venera investigators as a cavity. As opposed to this, in the lowenergy ion spectra we encountered relatively high-flux plasma what the Venera investigators could not detect

Figure 5c. Magnetic fields are shown for the four flybys. Btot is the gray line; Bx is the black line. On these orbits the inbound/outbound crossings of the tail boundaries occurred at about 0625/0652 (6.42/6.87) UT, 0640/0720 (6.66/7.33) UT, 0740/0825 (7.66/8.42) UT, and at about 0738/0820 (7.63/8.33) UT, respectively. 10 of 12

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Figure 5d. 3-D velocity distributions projected on to the x-y, x-z, and y-z planes, respectively (from the nonobscured part of the sensor), for 20 November 2006. It was evaluated for the velocity interval between 50 and 50 km/s, in the VSO frame of reference. The 16 8 directions are all the anodes and sectors of IMA; the individual points are the energy channels of the instrument (projected onto the respective planes). because of the energy limit of their sensors. Though these findings are not completely surprising, ASPERA-4 not only enriches but firmly establishes some of the previous results in a region never explored before; and our data have been derived in a much broader geometrical volume since the

VEX orbits scan through the whole tail. It is also important that the tail region is scanned from two opposite directions half a Venus year apart, because this allows us to use the change in the spacecraft velocity direction to sort out properties of the low-energy plasma.

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[38] In the tail there are signatures of pickup ions, the discussion of those is deferred to later work. The pickup ions either originate from the neutral O corona of Venus; the hot O density being 3.4 1010/m3 at 400 km with a scale height of 400 km [Nagy et al., 1981], or they are ions leaked out of the ionosphere. [39] There are questions we shall answer in subsequent papers. We crossed the mantle twice; this allows clarifying how the mantle plasma develops in the tail as we move farther out. The physical nature of the mantle-tail boundary also requires further investigation, as well as how the planetary ions in the tail reach the observed energies. The location of the central tail region is also variable. Some of the reasons must be connected to the temporal variations in the solar wind, but the other participating partner in the interaction, Venus, might have some share as well.

References Barabash, S., et al. (2007a), The Analyser of Space Plasmas and Energetic Atoms (ASPERA-4) for the Venus Express mission, Planet. Space Sci., 55, 1772 – 1792, doi:10.1016/j.pss.2007.01.014. Barabash, S., et al. (2007b), The loss of ions from Venus through the plasma wake, Nature, 450, doi:10.1038/nature06434. Brace, L. H., and A. J. Kliore (1991), The structure of Venus ionosphere, Space Sci. Rev., 55, 81 – 164, doi:10.1007/BF00177136. Coates, A. J., et al. (2007), Ionospheric photoelectrons at Venus: Initial observations by ASPERA-4 ELS, Planet. Space Sci., 56, 802 – 806. Fra¨nz, M., E. Dubinin, E. Roussos, J. Woch, J. D. Winningham, R. Frahm, A. J. Coates, A. Fedorov, S. Barabash, and R. Lundin (2006), Plasma moments in the environment of Mars, Space Sci. Rev., 126, 165 – 207, doi:10.1007/s11214-006-9115-9. Hartle, R., and J. Grebowski (1995), Planetary loss from light ion escape on Venus, Adv. Space Res., 15, 117 – 122, doi:10.1016/0273-1177(94) 00073-A. Intriligator, D. S., J. H. Wolfe, and J. D. Mihalov (1980), The Pioneer Venus Orbiter plasma analyzer experiment, IEEE Trans. Geosci. Remote Sens., GE-18, 39 – 43, doi:10.1109/TGRS.1980.350258. Knudsen, W. C., K. Spenner, J. Bakke, and V. Novak (1980), Pioneer Venus Orbiter planar Retarding Potential Analyzer plasma experiment, IEEE Trans. Geosci. Remote Sens., GE-18, 54 – 59. McComas, D. J., H. E. Spence, C. T. Russell, and M. A. Saunders (1986), The average magnetic field draping and consistent plasma properties of the Venus magnetotail, J. Geophys. Res., 91, 7939 – 7953, doi:10.1029/ JA091iA07p07939.

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Nagy, A. F., et al. (1981), Hot oxygen atoms in the upper atmosphere of Venus, Geophys. Res. Lett., 8, 629 – 632, doi:10.1029/GL008i006p00629. Phillips, J. L., and D. McComas (1991), The magnetosheath and magnetotail of Venus, Space Sci. Rev., 55, 1 – 80, doi:10.1007/BF00177135. Russell, C. T. (1999), Magnetic stress in solar system plasma, Aust. J. Phys., 52, 733 – 751. Saunders, M. A., and C. T. Russell (1986), Average dimension and magnetic structure of the distant Venus magnetotail, J. Geophys. Res., 91, 5589 – 5604. Slavin, J. A., D. S. Intriligator, and E. J. Smith (1989), PVO magnetic field and plasma observations in the Venus magnetotail, J. Geophys. Res., 94, 2383 – 2398, doi:10.1029/JA094iA03p02383. Slavin, J. A., et al. (2008), MESSENGER observations of the solar wind interaction with Mercury, paper A-04431 presented at General Assembly, Eur. Geosci. Union, Vienna, 14 – 18 April. Spenner, K., W. C. Knudsen, K. L. Miller, V. Novak, C. T. Russell, and R. C. Elphic (1980), Observation of Venus mantle, the boundary region between solar wind and ionosphere, J. Geophys. Res., 85, 7655 – 7663, doi:10.1029/JA085iA13p07655. Vaisberg, O., A. Fedorov, F. Dunjushkin, A. Kozhukhovsky, V. Smirnov, L. Avanov, C. T. Russell, and J. G. Luhmann (1995), Ion populations in the tail of Venus, Adv. Space Res., 16, 105 – 118, doi:10.1016/02731177(95)00217-3. Vo¨ro¨s, Z., T. L. Zhang, M. P. Leubner, M. Volwerk, M. Delva, W. Baumjohann, and K. Kudela (2008), Magnetic fluctuations and turbulence in the Venus magnetosheath and wake, Geophys. Res. Lett., 35, L11102, doi:10.1029/ 2008GL033879. Zhang, T. L., J. G. Luhmann, and C. T. Russell (1991), The magnetic barrier at Venus, J. Geophys. Res., 96, 11,145 – 11,153, doi:10.1029/ 91JA00088. Zhang, T. L., et al. (2006), Magnetic field investigation of the Venus plasma environment: Expected new results, Planet. Space Sci., 54, 1336 – 1343, doi:10.1016/j.pss.2006.04.018. Zhang, T. L., et al. (2007), Little or no solar wind enters Venus atmosphere at solar minimum, Nature, 450, 654 – 656, doi:10.1038/nature06026. S. Barabash, Swedish Institute of Space Physics, Box 812, SE-98128 Kiruna, Sweden. Z. Bebesi, Z. Dobe, and K. Szego, KFKI Research Institute for Particle and Nuclear Physics, Konkoly Thege Miklo´s u´t 29-33, H-1121 Budapest, Hungary. ([email protected]) A. J. Coates, Mullard Space Science Laboratory, University College London, London RH5 6NT, UK. ([email protected]) A. Fedorov, Centre d’Etude Spatiale des Rayonnements, BP-4346, F-31028 Toulouse, France. ([email protected]) M. Fra¨nz, MPI fu¨r Sonnensystemforschung, Max-Planck-Strasse 2, D-37191 Katlenburg-Lindau, Germany. ([email protected]; [email protected]) T. L. Zhang, Space Research Institute, Austrian Academy of Sciences, Schmiedlstrasse 6, Graz, Austria. ([email protected])

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Plasma environment of Venus: Comparison of Venus Express ASPERA-4 measurements with 3-D hybrid simulations C. Martinecz,1 A. Boesswetter,2 M. Fra¨nz,1 E. Roussos,1 J. Woch,1 N. Krupp,1 E. Dubinin,1 U. Motschmann,2,3 S. Wiehle,2 S. Simon,2 S. Barabash,4 R. Lundin,4 T. L. Zhang,5 H. Lammer,5 H. Lichtenegger,5 and Y. Kulikov6 Received 23 April 2008; revised 15 September 2008; accepted 30 December 2008; published 7 March 2009.

[1] We use data of the ASPERA-4 ion and electron spectrometers onboard Venus Express

to determine the locations and shapes of the plasma boundaries (bow shock, ion composition boundary, and mantle) at Venus. We also investigate the variation of the terminator bow shock position as a function of the solar wind dynamic pressure and solar EUV flux. We compare the results with a 3-D hybrid simulation. In the hybrid model, ions are treated as individual particles moving in self-consistently generated electromagnetic fields and electrons are modeled as a massless charge neutralizing fluid. The planetary heavy ion plasma is generated by an oxygen ionosphere and exosphere adapted to a profile, which depends on the solar zenith angle (Chapman layer). A comparison between observations and simulations indicates that the hybrid model is able to produce an adequate picture of the global plasma environment at Venus. The positions of the plasma boundaries are well reproduced by the model but a significant disagreement appears in the absolute values of the considered parameters. Citation: Martinecz, C., et al. (2009), Plasma environment of Venus: Comparison of Venus Express ASPERA-4 measurements with 3-D hybrid simulations, J. Geophys. Res., 114, E00B30, doi:10.1029/2008JE003174.

1. Introduction

1.1. Knowledge Based on Observations [2] For almost 50 years, planet Venus is an attractive target in planetary space science and thus, was the focus of several space missions (Mariner, Venera, and Vega) and ground-based observations. Although we owe most of our current knowledge of the solar wind interaction with Venus to the long-lasting Pioneer Venus Orbiter (PVO) mission (1978 – 1992), providing observations over more than a complete solar cycle, a lot of questions remain unanswered. Venus Express (VEX), which is the first European mission to planet Venus [Titov et al., 2006], aims at a comprehensive investigation of the Venusian atmosphere and its plasma environment in order to fill the gaps left by PVO. One of the central questions, addressed by the plasma instrument 1 Max Planck Institut fu¨r Sonnensystemforschung, Katlenburg-Lindau, Germany. 2 Institut fu¨r Theoretische Physik, Technische Universita¨t Braunschweig, Braunschweig, Germany. 3 Deutsches Zentrum fu¨r Luft- und Raumfahrt, Institut fu¨r Planetenforschung, Berlin, Germany. 4 Swedish Institute of Space Physics, Kiruna, Sweden. 5 Space Research Institute, Austrian Academy of Sciences, Graz, Austria. 6 Polar Geophysical Institute, Russian Academy of Sciences, Murmansk, Russia.


Analyzer of Space Plasmas and Energetic Atoms (ASPERA-4) onboard VEX, is the evolution of the Venus atmosphere under the combined effects of escape and interaction with the solid planet [Barabash et al., 2007a]. The basic morphology of the ionosphere and the plasma environment of Venus have been studied by PVO and a detailed discussion of the results can be found in the work by Luhmann and Cravens [1991] and Phillips and McComas [1991]. [3] Venus does not possess an intrinsic magnetic field to shield the upper atmosphere against the incoming solar wind flow. The lack of such a magnetic cavity results in an highly structured plasma environment, similar observed at Mars and comets, characterized by the direct interaction of the solar wind with the top of the ionosphere. This leads to atmospheric escape processes, solar wind induced current systems and a complex nightside ionosphere consisting of tail rays, filaments, ionospheric holes and plasma clouds. [4] Owing to the supersonic and super-Alfveńic solar wind a bow shock is formed upstream of the planet in order to heat, slow down and deflect the solar plasma flow around the obstacle. Solar radiation ionizes the neutral atmosphere of Venus creating a substantial ionosphere. The upper boundary of the ionosphere, known as ionopause, is characterized by a sharp gradient in the electron density and forms at an altitude where the thermal plasma pressure of the ionosphere equals incident solar wind pressure. The ionopause was found to be located about 300 km above the surface in the subsolar region and to have an altitude about 1000 km

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near the terminator at solar maximum [Phillips and McComas, 1991]. The region between the fast magnetosonic shock wave and the ionopause is called the magnetosheath, where an increased and turbulent magnetic field behavior is observed. Since the interplanetary magnetic field (IMF) is frozen into the magnetosheath flow, it piles up at the dayside of the obstacle producing a so-called magnetic barrier [Zhang et al., 2008b; Bertucci et al., 2003] as it is carried around the planet. The magnetic barrier is also called plasma depletion layer. The solar wind plasma is significantly excluded from this region and the magnetic pressure dominates over the incident ram pressure. The IMF lines drape around the obstacle and as a result, create an induced magnetotail consisting of two lobes of opposite polarity separated by a plasma sheet [Luhmann and Cravens, 1991]. Observations revealed another boundary layer, located between the magnetosheath and the ionopause, the so-called mantle region or transition zone, characterized by the presence of solar wind protons and planetary ions [Phillips and McComas, 1991]. Above the upper boundary of the mantle the shocked solar wind plasma is found whereas beyond the lower boundary of the mantle planetary ions become the main particle population (ion composition boundary). 1.2. Numerical Models [5] The global Venus-solar wind interaction can be studied by different numerical investigations such as gasdynamic, magnetohydrodynamic (MHD) and kinetic models. [6] In general, a gas dynamic model [Spreiter and Stahara, 1992] is able to reproduce some of the global characteristics of the interaction (bow shock, magnetosheath region) but it neglects the individual particle behavior as well as the effects of the magnetic field on the plasma dynamics. Thus, this fluid approximation does not include the magnetic barrier in a self-consistent manner and, additionally, does not consider the exospheric ion pickup process owing to the convective electric field [Zhang et al., 1993]. [7] More sophisticated treatments can be done using MHD models because here the magnetic field is taken into account in a self-consistent manner [Wu, 1992; Tanaka, 1993; Murawski and Steinolfson, 1996a, 1996b; Tanaka and Murawski, 1997; Kallio et al., 1998; Bauske et al., 1998]. The position and shape of the bow shock, the formation of the magnetic barrier, the magnetosheath and the magnetotail could be reproduced in these studies and are in a good agreement with observational data (e.g., from PVO). While MHD simulation provide an adequate picture of the large-scale solar wind interaction with Venus, including the mass loading of the solar wind with heavy oxygen ions, several important kinetic effects are not considered in these studies owing to the single fluid description. Thus, asymmetries in the form of the bow shock, the magnetic barrier intensity and the planetary pickup distribution as well as the formation of the ion composition boundary (separation between solar wind plasma and planetary plasma) and multiple shocklets (explained in section 4) cannot be reproduced by MHD models. [8] The gyroradii of the solar wind protons are in the range of several hundred kilometers, and therefore comparable with the characteristic scales of the subsolar interaction region. The gyroradii of the heavy pickup ions are in

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the range of several thousand kilometers, and therefore comparable with the radius of the planet. Thus, a kinetic treatment seems to be mandatory and is used here. A full particle code would be the most suitable numerical investigation but with the current computational resources, a fully kinetic approach is not feasible. Hence, a hybrid model is a very useful tool for studying the kinetic effects because it treats ions as gyrating particles. [9] Several hybrid simulations were carried out over the past years and, in general, demonstrated a good qualitative agreement between the simulation results and observational data. [10] The work by Moore et al. [1991] is one of the first important studies investigating the solar wind interaction with the dayside of Venus by means of a three-dimensional hybrid model. Their simulation results provided an asymmetrical bow shock depending on the upstream IMF orientation as well as asymmetries in the magnetic barrier location and field intensity. Furthermore, they found that the finite gyroradius of the planetary pickup ions causes an asymmetry in number density along the direction of the motional electric field. [11] The results of a three-dimensional hybrid simulation from Brecht and Ferrante [1991], including the ionosphere by assuming the planet as a conducting sphere, showed that the bow shock and magnetic barrier are asymmetrical at Venus. [12] In the hybrid model of Shimazu [2001] the Venusian planet was treated as an ionized gaseous body with uniform and constant supply of plasma. The simulation results provided an asymmetric bow shock with a multiple-shock structure, a magnetic barrier in front of the planet with asymmetries along the solar wind electric field, a magnetotail, tail rays and a plasma sheet. They found that the calculated asymmetry in the magnetic barrier intensity was consistent with observations, but the direction of the calculated asymmetry in the shock size did not concur with the observed asymmetry. Additionally, they demonstrated that ions escape to the magnetotail through the tail rays and that the tail rays were connected with the plasma sheet. [13] Other hybrid approaches deal with the investigation of the ion escape processes with a particular emphasis placed on the processes occurring at the ionopause associated with the Kelvin-Helmholtz instability [Terada et al., 2002, 2004]. Their 2-D simulation model results yielded an asymmetrical convection pattern of the ionosphere due to the asymmetrical momentum transport across the ionopause and showed the dynamic nature of the interaction. Moreover they concluded that most probably, the dynamic ion removal process associated with the Kelvin-Helmholtz instability plays a significantly role in the ion escape from the planet. [14] Kallio et al. [2006] studied the solar wind interaction with Venus using a global three-dimensional self-consistent quasi-neutral hybrid model (QNH) and focused on the asymmetries in the direction of the convection electric field caused by kinetic effects, the role of the interplanetary magnetic field (IMF) x component and the properties of the escaping planetary O+ ions. On the one hand, they found a notable north-south asymmetry of the magnetic field and plasma due to ion finite gyroradius effects and escaping O+ ions. On the other hand they showed that an asymmetry between the quasi-perpendicular bow shock hemisphere and the quasi-parallel bow shock hemisphere, which results

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Figure 1. ASPERA-4 data recorded on 15 July 2006, about an hour before and after the pericenter of that concerned orbit (1RV = 6051.8 km). The top shows the total counts of energetic electrons measured by the Electron Spectrometer (ELS) sensor and the middle and bottom present the total counts of the proton and heavy ion channels of the Ion Mass Analyzer (IMA) sensor, respectively. The heavy ion channel contains proton counts whenever the proton channel saturates. The black vertical arrows mark the plasma boundaries separating the different interaction regions (solar wind, magnetosheath, mantle, and ionosphere). from the IMF x component, exists. In general, the QNH model is able to reproduce the main observed plasma and magnetic field regions such as the bow shock, the magnetosheath, the magnetic barrier and the magnetotail. [15] In this study we investigate the plasma environment of Venus using plasma moments obtained by ASPERA-4 instrument onboard VEX in order to study the solar windatmosphere interaction of an unmagnetized planet and the results of this analysis is put in context of a numerical model. The paper is organized as follows. Section 1 deals with our identification of the plasma boundaries based on ASPERA-4 measurements and the dependency of the terminator shock distance on the solar wind ram pressure as well as on the solar EUV flux. Section 2 describes in short our 3-D hybrid model which we use to study the global plasma environment of Venus. In section 3 we present the simulation results for a specific orbit, compared with our observations along the VEX trajectory in the following section and finally, the paper closes with a conclusion.

2. Observations [16] For this paper we used data from the ASPERA-4 experiment onboard of Venus Express which consist of an

Electron Spectrometer (ELS), an Ion Mass Analyzer (IMA), a Neutral Particle Imager (NPI), and two Neutral Particle Detectors (NPD1 and NPD2). We refer the reader to Barabash et al. [2007a] for a detailed description of the plasma instrument. In this study we present data from the electron and ion spectrometers, exclusively. Figure 1 displays data obtained on 15 July 2006 showing the main plasma features of the solar wind interaction with Venus about 1 h before and after the closest approach of orbit 85. [17] Figure 1 (top) shows an energy spectrogram of measured electrons in the energy range of 0.1 eV – 20 keV obtained by ELS. But electrons below 5 eV are reflected to avoid saturation of the counters. The sensor has 16 anodes covering a total field of view of 4 360. Shown are counts obtained during 4s sampling intervals integrated over anodes 5 – 15 of the sensor because anodes 0 – 4 are more noisy. The data shown in Figure 1 (middle and bottom) represent protons and heavy ions, respectively, measured by IMA, integrated over all 16 anodes and 16 spatial sectors covering a total field of view of 90 360. Note that signatures above 50 eV energy in Figure 1 (bottom) in the solar wind and magnetosheath regions are not caused by heavy ions but by saturation of the proton channels. A spatial scan during 192 s by electrostatic deflection produces the

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Figure 2. This plot displays axisymmetric bow shock (BS), upper mantle boundary (UMB), and lower mantle boundary (LMB) fits derived using the first 19 months of ASPERA-4 observations in an aberrated Venus Solar Orbital coordinate system. The BS crossings (red circles) were fitted to a conic function. The UMB (green diamonds) and LMB (blue triangles) crossings were fitted by a circle on the dayside and by linear regression on the nightside.

repeatable pattern visible in the spectrogram. The x axis shows the distance, position and time of the spacecraft along the orbit. [18] First, VEX is located inside the solar wind before crossing the bow shock (BS) at 0115 UT, identified by an increase in counts of energetic electrons (E > 35eV) in the magnetosheath with respect to the solar wind. Passing the BS, the spacecraft enters the magnetosheath, characterized by the shocked, slowed down and heated solar wind. At 0148 UT, VEX crosses the upper mantle boundary (UMB), identified by a strong decrease in electron counts (E > 35eV), and is located in a so-called mantle region or transition zone, where we observe a mixture of solar wind protons and planetary ions. The lower boundary of the mantle (LMB), crossed at 0157 UT, we also call the ion composition boundary (ICB), because at this boundary the solar wind protons disappear and the planetary ions become the main population. LMB is identified in ELS by the appearance of ionospheric photoelectrons (E > 10eV). Passing the LMB, the spacecraft is located in the ionosphere between 0157 UT and 0201 UT. On the outbound pass, VEX crosses again all the mentioned plasma regions and boundaries, but in reverse order, i.e., at 0201 the LMB, at 0208 UT the UMB and at 0222 the BS, and finally, is back in the solar wind. [19] From Martinecz et al. [2008] we obtained initial determinations of the mean plasma boundary locations (BS and ICB) based on the first 5 months of observations. After VEX being almost 2 years in orbit around Venus, we now analyzed the first 19 months (14 May 2006 to 31 December 2007) of electron and ion data in order to obtain better fits based on better statistics (see Figure 2). Furthermore, we defined a third plasma boundary (UMB) because a

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so-called mantle region (or transition zone) is visible in the data, especially on the nightside, where we observe a mixture of solar wind and planetary plasma. The lower boundary of the mantle coincides with the former ICB. The upper boundary of the mantle marks the transition from the magnetosheath to the mantle region. [20] The fitting techniques used is described by Martinecz et al. [2008]. The new fit parameters obtained by this analysis is presented in this paper. For the BS we applied a conic function in polar coordinates, assuming cylindrical symmetry along the x axis (solar wind flow direction) which is least-squares fitted to the observed BS positions [r = L/(1 + cosJ)]. Accordingly, we got for the semilatus rectum L = 1.515 RV (s ± 0.024), the eccentricity = 1.018 (s ± 0.008), the conic focus x0 = 0.664 RV (s ± 0.039), and the terminator shock distance rtsd = 2.088 RV. The standard deviations are a result of the fitting procedure assuming that there is no error in the individual position determinations. Since the radial coverage in the subsolar region is not homogeneously sampled, we also corrected the BS fit by omitting crossings below 30 solar zenith angle. The new three-parameter BS fit is in good agreement with the previous fit by Martinecz et al. [2008] and with the BS model of Slavin et al. [1984] rather than with the twoparameter BS fit from Zhang et al. [2008a] based on magnetic field observations. [21] For modeling the positions of LMB and UMB we used circular fits (rLMB = 1.076 RV, rUMB = 1.130 RV) for the dayside observations (x > 0) and linear regressions [ y = k x + d] (kLMB = 0.122 RV, dLMB = 1.076 RV; kUMB = 0.101 RV, dUMB = 1.130 RV) for the nightside measurements (x < 0). Note that, currently we do not have data for the mantle region below about 50 solar zenith angle and thus, both fits provide boundaries which are too far away from the planet on the dayside. In order to get more realistic mantle fits it is necessary to include crossings in the subsolar region, expected later during the VEX mission. [22] Dubinin et al. [2008] determined the magnetospheric boundary (MB) and photoelectron boundary (PEB) at Mars based on ASPERA-3 and MARSIS observations on board Mars Express. MB is identified by a strong decrease (inbound pass) or increase (outbound pass) in energetic electron density. PEB marks the outer boundary of the ionosphere which is identified by the appearance of energy peaks in the range between 20 and 30 eV in the electron spectrograms. Consequently, the physics of UMB and LMB can be compared with MB and PEB, respectively. However, the upper and lower boundary positions of the magnetic barrier determined by Zhang et al. [2008b] on the basis of the magnetometer observations on board VEX are partly in agreement with the UMB and LMB locations. At the terminator the average thickness of the mantle region is around 500 km and hence, 300 km less thick than the magnetic barrier. The thickness of the magnetic barrier in the subsolar region is around 200 km [Zhang et al., 2008b] but currently we cannot estimate this thickness for the mantle region owing to the lack of mantle crossings below around 50 solar zenith angle. [23] Additionally, Martinecz et al. [2008] presented the variation of the BS position at the terminator as a function of the solar wind dynamic pressure. Owing to improved

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strength since the BS position should depend on the upstream Mach number [Zhang et al., 2008a].

3. Hybrid Model

Figure 3. The dependence of the BS position at the terminator on the dynamic pressure of the solar wind derived from ASPERA-4 measurements. All BS crossings (plus signs) were extrapolated to the terminator plane using a conic section curve with a fixed eccentricity ( = 1.018) and a fixed focus (x0 = 0.664) and with a variable semilatus rectum. No normalization has been applied to the data. The circles represent median values over pressure bins.

plasma moment calculations as well as a larger data set under consideration in this study, we decided to carry out the investigation again. Figure 3 demonstrates even better the independence of the BS position from the ram pressure during solar minimum conditions where the plus signs are the observed BS crossings extrapolated to the terminator plane using the conic section and the circles represent the median values. [24] From PVO observations we learned that the solar wind interaction with Venus is very dependent on the phase of the solar cycle [Russell et al., 2006]. During solar minimum the BS is found to be closer to the planet than during solar maximum owing to lower ionization and ion pickup rates caused by EUV flux changes over the 11-year solar cycle. Figure 4 shows the terminator BS position as a function of the solar EUV flux. Since the VEX spacecraft does not carry a solar UV monitor we here use the integrated flux of photons/cm 2 s 1 (F50) observed in the 0.1– 50 nm wavelength range by the Solar Extreme Ultraviolet Monitor (SEM) on board the Solar Heliospheric Observatory (SOHO) spacecraft (data from http://www. usc.edu/dept/space science/sem data) and shift the observation time according to the difference in solar longitude between the SOHO and Venus. This monitor is a highly stable photodiode spectrometer that continuously measures the full solar disk absolute photon flux at the He II 30.4 nm line as well as the absolute integral flux between 17 and 70 nm. Although this index is an excellent indicator of overall solar activity levels, we do not observe yet an effect on the terminator BS location in our data set, because the EUV flux variation is small over the period of observation as expected for solar minimum. [25] We attribute the large variations visible in the bow shock locations to the variations in the magnetic field

[26] The results of our data analysis is put in context of numerical investigations by means of a 3-D hybrid model which has been originally developed by Bagdonat and Motschmann [2002a, 2002b] to study the solar wind interaction with weak comets. Furthermore, the present version of this hybrid code has already been successfully applied for modeling the solar wind interaction with magnetized asteroids [Simon et al., 2006a], for studying the plasma environment of Mars [Bo¨ßwetter et al., 2004, 2007] and Titan [Simon et al., 2006b, 2007], as well as for the simulation of Rhea’s magnetospheric interaction [Roussos et al., 2008]. [27] In the hybrid model the ions are treated as individual particles moving in self-consistently generated electromagnetic fields, whereas the electrons are modeled as a massless charge-neutralizing fluid. For a detailed description of the code we refer the reader to the earlier studies just mentioned above. In the following we will only explain the main basic features of the model for completeness of this paper. [28] As already mentioned, the ions are treated as individual particles in order to cover ion dynamics. Hence, the code solves the equation of motion for the ions: and

d~ xs ¼~ vs dt d~ vs qs ~ E þ~ vs ¼ dt ms

~ B

ð1Þ

kD nn ð~ vs

~ un Þ

ð2Þ

xs and ~ vs are the charge, mass, position and where qs, ms, ~ velocity of an individual particle of species s, respectively;

Figure 4. This plot shows the terminator BS position as a function of solar EUV flux (F50 index, 0.1– 50 nm integrated photons cm 2 s 1) derived from Solar Heliospheric Observatory and Solar Extreme Ultraviolet Monitor observations and shifted to Venus. All BS crossings (asterisk signs) were extrapolated to the terminator plane by means of the conic function.

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[30] We are using two different electron pressure terms in order to consider the different electron temperatures in the solar wind and ionosphere, respectively. Both electron populations are assumed to be adiabatic: Pe;SW ¼ be;SW

ne;SW n0

Pe;HI ¼ be;HI

ne;HI n0

k

k

ð5Þ

ð6Þ

where k is the adiabatic exponent which has a value of k = 2 instead of 5/3, because the thermodynamic coupling is only effective in the two dimensions perpendicular to the field [Bo¨ßwetter et al., 2004]. Finally, the set of hybrid equations is completed by Faraday’s law @~ B ¼r @t

Figure 5. The neutral number density (green line) and the ion production rate (red line in subsolar direction and blue line at the terminator) of oxygen as a function of altitude at solar minimum (ionospheric photoionization frequency n = 4.55 10 7 s 1 [Torr and Torr, 1985]). whereas ~ E and ~ B are the electromagnetic field quantities. The first term on the right in equation (2) expresses the Lorentz term and the second the drag force, which takes into account collisions of ions and neutrals (kD is a constant un are the taken from Israelevich et al. [1999]). nn and ~ number density and bulk velocity of the neutrals. We use ~ un = 0. [29] The electron fluid description is based on the momentum equation 0 ¼ ne me

d~ ue ¼ dt

E þ~ ue ene ~

~ B

rPe;sw

rPe;hi

ð3Þ

from which the electric field can be derived ~ E¼ ~ ui

~ Bþ

r

~ ~ B B m0 ene

rPe;sw þ rPe;hi ene

ð4Þ

where ~ ui denotes the mean ion velocity. The mean ion density (ni) is equal to the electron density (ne) owing to the assumption of quasi-neutrality.

" ~ ui

~ B

r

r

# ~ ~ B B m0 ene

ð7Þ

expressing the time evolution of the magnetic field. [31] The simulation is applied to a curvilinear grid in three spatial dimensions which allows high spatial resolution in the vicinity of the planetary atmosphere. This so-called Fisheye Grid is obtained from an equidistant Cartesian grid by means of a nonlinear coordinate transformation. For the simulations presented in this study the box size was chosen 6 RV with 101 grid cells in each spatial direction. By using this grid one obtains a spatial resolution of about 100 – 150 km per grid cell in the vicinity of the obstacle. Beyond the distorted grid (equidistant cartesian grid) a resolution of 250– 300 km per grid cell is achieved. A detailed description of the grid generation is given by Bo¨ßwetter et al. [2004] and Simon et al. [2006b]. [32] The Venusian atmosphere is modeled as a spherical symmetric gas cloud around the planet consisting of atomic oxygen which is the dominant neutral in the upper atmosphere. Radial density distribution includes atmospheric and exospheric exponential profiles. The profile for the cold oxygens is based on the model from Kulikov et al. [2006] and the hot oxygen profile is based on PVO measurements compared with model calculations obtained by Nagy et al. [1990]. The heavy planetary oxygen ions are incorporated into the simulation by means of a so-called Chapman production function (see Figure 5), that means, the local ion production rate q depends on the altitude above the surface as well as on the solar zenith angle. This production function results from the ionization of the neutral profile by means of solar EUV radiation (photoionization frequency for atomic oxygen, measured at Earth and scaled to the heliocentric distance of Venus). Other ion production processes, like electron impact ionization and charge exchange are not taken into account in our model. On the nightside of the planet, a constant ion production function was assumed, yielding about 10% of the dayside production rate. Consequently, one obtains a dense ionosphere and a less dense exosphere. [33] Any ion hitting the so-called inner boundary at an altitude of 150 km above the planetary surface is removed from the simulation. No boundary conditions are imposed

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Table 1. Input Parameters for Simulation Run for VEX Orbit 85 on 15 July 2006 Parameter

Values

Comments

Solar wind density, nsw Solar wind velocity, vsw Magnetic field magnitude, Bsw IMF orientation, 8sw Electron beta, b e,sw Proton beta, b i,sw Ionospheric electron beta, b e,hi Alfeń veloctiy, vA Alfeńic mach number, MA Ionospheric photoionization frequency, n smin Total ion production, QO+ Lower boundary of atmosphere Upper boundary of atmosphere

5.0 cm 3 360.0 km s 1 5.0 nT 22.5 deg 0.81 1.61 0.02 48.77 km s 1 7.38 4.55 10 7 s 1 1.037 1026 s 1 1 RV + 150.0 km 2 RV

— in x direction in y direction in VSO Te,sw = 10.0 eV Ti,sw = 20.0 eV Te,hi = 0.3 eV [Miller et al., 1980] — dimensionless [Torr and Torr, 1985] Chapman production function RV = 6051.8 km —

on the electromagnetic fields, which means, the equations for ~ E and ~ B are solved outside as well as inside of the obstacle. An artificial inner density is assumed and increased while the simulation proceeds in order to match the surrounding ionospheric heavy ion density at the subsolar point as well as to avoid electric fields arising from density gradients [Bo¨ßwetter et al., 2007].

4. Simulation Results for the VEX Orbit 85 [34] Table 1 shows a list of input parameters mainly measured on orbit 85 (15 July 2006) by ASPERA-4 and Magnetometer [Zhang et al., 2006] onboard VEX and the rest of the parameters are taken from literature. The plasma moments are estimated either by integrating over a given range in the phase space (integration method), or by assuming a Boltzmann distribution for the phase space density (fitting method) [Fra¨nz et al., 2007]. The ionospheric electron temperature is the only parameter which was taken from the literature [Miller et al., 1980] (based on PVO observations) owing to the cutoff energy in the ELS sensor. A deflection voltage of about 5 eV prevents the measurement of low-energy electrons (spacecraft photoelectrons) in order to avoid saturation of the counters. [35] The simulation box has three spatial dimensions x, y, z 3 RV) in which the undisturbed solar ( 3 RV wind flows in the positive x direction, the convection electric field (~ E= ~ v ~ B) points in the negative z direction and thus, the background magnetic field completes the righthanded system pointing in the positive y direction (tilted 157.5 in the equatorial plane), as shown in Figure 6. Note that the positive y axis in the simulation coordinate system points into the direction of orbital motion while in the Venus Solar Orbital (VSO) system it is orientated opposite to the orbital motion. For practical reasons in discussing the simulation results we denote the hemisphere to which the solar wind electric field is pointing as the E+ hemisphere (southern hemisphere) and the other as E hemisphere (northern hemisphere). [36] Figures 7 and 8 represent global 3-D views of the simulation results for the magnetic field configuration and heavy ion density, respectively. The cutting planes through the simulation box are taken at x = 0 (terminator plane), y = 0 (polar plane) and z = 0 (equatorial plane). [37] In Figure 7, showing the magnetic field configuration at Venus, the solar wind enters the simulation box from the left forming a magnetosonic shock wave upstream of

the planet. On the dayside the IMF piles up in front of the obstacle, drapes around Venus and creates an induced magnetotail consisting of two lobes on the nightside. In the equatorial plane one can see the dusk magnetic field lobe where the IMF points toward the planet (visible in Figure 11). [38] A global picture of the planetary plasma is shown in Figure 8. In the northern hemisphere (E ) one can see a very sharp ion composition boundary (ICB) whereas in the southern hemisphere (E+) this boundary is less pronounced because the heavy ions are accelerated along the convective electric field and dragged away from the planet (pickup process). The formation of the ICB is discussed in more detail below. Figures 9, 10, and 11, represent 2-D cuts of the 3-D simulation results at the cutting planes x = 0 (terminator

Figure 6. Cross sections of the simulation box at the plane x = 0 (terminator plane), y = 0 (polar plane), and z = 0 (equatorial plane) presented in section 4. The undisturbed solar wind flows in the positive x direction, the convection electric field (~ E= ~ v ~ B) points in the negative z direction, and the background magnetic field completes the righthanded system pointing in the positive y direction (tilted 157.5 in the equatorial plane).

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Figure 7. Global 3-D view of simulation results showing the magnetic field strength (nT). The cutting planes through the simulation box are taken at x = 0 (terminator plane), y = 0 (polar plane), and z = 0 (equatorial plane). plane), y = 0 (polar plane) and z = 0 (equatorial plane), respectively. [39] Figure 9 shows from left to right the solar wind density nsw (cm 3), heavy ion density nhi (cm 3) and the magnetic field jBj (nT) along the terminator plane. The increased solar wind density indicates a bow shock formation in front of the obstacle due to the supersonic solar wind

Figure 8. Global 3-D view of simulation results showing the heavy ion density (O+) (cm 3). The cutting planes through the simulation box are taken at x = 0 (terminator plane), y = 0 (polar plane), and z = 0 (equatorial plane).

Figure 12. Projections of the trajectory of the Venus Express orbit 85 on the equatorial, polar, and terminator plane of our 3-D hybrid simulation. The undisturbed plasma flow is directed along the (+x) axis, the convection electric field ~ (~ E = ~ v B) is orientated antiparallel to the z axis, and the background magnetic field completes the right-handed system pointing in the positive y direction (tilted 157.5 in the equatorial plane).

*The numbering of Figures 9 – 12 has been corrected here. The article as originally published appears online.

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Figure 9. Simulation results in the terminator plane, i.e., looking from behind on Venus. The solar wind is orientated out of the plane (+x axis), the convective electric field is antiparallel to the (+z) axis and points away from the planet; thus, the background magnetic field completes the right-handed system (+y axis). Figure 10 displays (left) the solar wind density (nsw), (middle) the heavy ion density (nhi), and (right) the background magnetic field. An asymmetric bow shock, exhibiting a shocklet structure, is formed in front of the obstacle owing to the supersonic solar wind (left plot). The interplanetary magnetic field is draping around Venus generating an induced magnetotail on the nightside of the planet (right plot). See text for details.

Figure 10. Cut through the simulation box along the polar plane, i.e., looking from the western (dusk) side (in the sense of orbital motion) on Venus. The plasma flow comes in from the left (+x axis), the solar wind electric field points away from the planet ( z axis), and the interplanetary magnetic field completes the right-handed system pointing into the plane (+y axis). Figure 11 illustrates from left to right in the first row (a) the solar wind density (nsw), (b) the heavy ion density (nhi), (c) the background magnetic field, (d) the solar wind bulk velocity (jvswj), (e) heavy ion bulk velocity (jvhij), and (f) the convective electric field. In the density plots a clear separation between the solar wind and ionospheric plasma takes place forming the so-called ion composition boundary (ICB) in the northern hemisphere. In the southern hemisphere there is no clear ICB owing to the mass loading effect; that is, the planetary ions are accelerated in the direction of the convective electric field and are picked up by the solar wind flow. The solar wind electric field ~ E= ~ v ~ B vanishes where the heavy ion plasma dominates. See text for details. 9 of 15

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flow (left). The fast magnetosonic shock wave exhibits several asymmetries. On the one hand, the shock geometry offers a small asymmetry with respect to the north – south direction due to the pickup of oxygen ions in the direction of the convective electric field. As a consequence, the massloading effect decelerates the plasma flow in the E+ hemisphere. On the other hand, the bow shock is also asymmetric with respect to the dawn-dusk direction which results from the chosen interplanetary magnetic field (IMF) angle of 22.5 in VSO (for symmetric case, 90). Furthermore, the bow shock is not circular at the terminator plane because the propagation velocity of the fast magnetosonic wave is greatest in the direction of 90 to the magnetic field (positive z axis). Thus, the plasma is less compressed and requires a greater volume in order to flow around the obstacle in this direction [Russell et al., 1988]. Besides these asymmetries there are two more features visible in the solar wind density. Behind the bow shock, so-called shocklets [Omidi and Winske, 1990] or multiple shocks [Shimazu, 2001] occur which are kinetic shock substructures due to the finite gyroradius of the solar wind protons where the proton density locally increases (at the reversal point) and the proton velocity decreases at the same time. Last, the shock is very weak (or almost not present) on the left side of the obstacle owing to a quasi-parallel shock scenario. The shock normal is parallel to the IMF orientation (see jBj in Figure 11) so that solar wind protons can be reflected there and are gyrating back into the plasma flow. In the heavy ion density (Figure 9) one can see clearly an exospheric region (hot oxygen corona) which is in direct interaction with the oncoming plasma flow, as well as a weak ionosphere (cold oxygens) because of the low solar activity condition. The global configuration of the magnetic field is illustrated in the last plot in Figure 9. As already mentioned in section 1, Venus does not possess an intrinsic magnetic field and thus, the IMF lines are draping around Venus leading to an induced magnetotail on the nightside of the planet. [40] Figure 10 displays the simulation results of the solar wind density nsw (cm 3) and velocity jvswj (km s 1), heavy ion density nhi (cm 3) and velocity jvhij (km s 1), the magnetic field jBj (nT) and the solar wind electric field jEj (V km 1) in the polar plane. In the polar plane the simulation produces an almost symmetric flow of the shocked and slowed down solar wind around the obstacle. Additionally, a plasma wake is formed behind the planet where the proton density vanishes (proton cavity). [41] The solar wind bulk velocity is accelerated downstream above the north pole (E hemisphere) whereas downstream below the south pole (E+ hemisphere) it is significantly decelerated. The same features appeared in the 3-D hybrid simulation of the plasma environment of Mars and Bo¨ßwetter et al. [2004] found the following explanation for that picture. The magnetic tension and the pressure force, which are caused by the curvature and magnetic field gradients, are operating on the solar wind protons and heavy ions. These forces are strongest in the vicinity of the poles where the draped magnetic field lines can unwind. As a result, it leads to an acceleration of the solar wind plasma in the E hemisphere while the same acceleration force is compensated by the decelerated solar plasma flow stemming from the mass loading of heavy ions in the E+ hemisphere.

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[42] By comparing the solar wind and heavy ion densities one observes a clear separation of the different plasmas in the E hemisphere which is divided by the so-called ion composition boundary (ICB), as also already mentioned in section 2 in the course of the determinations of the plasma boundaries based on spacecraft measurements. The formation of the ICB has already been extensively studied by Simon et al. [2007] in the framework of global 3-D hybrid simulations of the plasma environments of Mars and Titan. They concluded that the underlying physical mechanism which is giving rise to the ICB is associated with the Lorentz forces that act on protons and heavy ions and can be explained in terms of kinetic models. The decisive role for the development of the ICB plays the combination of the convective electric field and the electron pressure forces. That means, in the E hemisphere where both forces are antiparallel it leads to the formation of a sharply pronounced boundary layer inhibiting the mixing of the plasmas. In contrast, in the E+ hemisphere no ICB emerges owing to the parallelism of both forces. As a result heavy ions are dragged away from the planet in the direction of the convective electric field and thus, causing a significant extension of the ionospheric tail. In other words, on the one hand, at the ICB the solar wind ions are reflected because the electron pressure gradient points away from the planet owing to the high electron density in the ionosphere and on the other hand, the heavy ion particles are accelerated toward the ICB in the direction of the convective electric field which points to the planet. [43] Inside the ionospheric tail the particles gyrate with a small gyroradius owing to the vanishing solar wind electric field while the picked up oxygen ions move away from Venus on large cyclodial paths as can be seen in the bulk velocity of the heavy ions. Owing to the acceleration of the planetary particles in the direction of the convective electric field, heavy ions are picked up by the solar wind, mass is added to the plasma flow (mass loading effect) and atmospheric material leaves from the planet. [44] The intensity of the magnetic field shows an asymmetric behavior which also has been confirmed by observations [Zhang et al., 1991] as well as by previous simulations [Shimazu, 2001; Bo¨ßwetter et al., 2004]. The piling up of the magnetic field in the E+ hemisphere is a consequence of the mass loading by planetary ions which leads to a deceleration of the shocked solar wind. The convective electric field, carried by the solar wind, vanishes in the plasma wake because it is inhomogeneously filled with ionospheric plasma as can be seen in Figures 10 and 11. In the E hemisphere the electric field is orientated perpendicular to the ICB and thus, pointing toward the planet while in the E+ hemisphere it is pointing away from Venus, as already mentioned above in the course of explaining the ICB formation. [45] Figure 11 illustrates the simulation results of the solar wind density nsw (cm 3) and velocity jvswj (km s 1), heavy ion density nhi (cm 3) and velocity jvhij (km s 1), the magnetic field jBj (nT)and the solar wind electric field jEj (V km 1) in the equatorial plane. The bow shock is asymmetrical also in the equatorial plane, as mentioned above when describing the simulation results of the terminator plane. Again, one finds the multiple shocklet structure as well as the quasi-parallel shock scenario, on the western

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Figure 11. Simulation results in the equatorial plane, i.e., looking from ecliptic North onto Venus. The undisturbed solar wind flow is parallel to the positive x axis, the convective electric field points into the plane ( z axis), and the background magnetic field completes the right-handed system (+y axis) and is tilted 157.5 in the equatorial plane. Figure 12 shows (a) the solar wind density (nsw), (b) the heavy ion density (nhi), (c) the background magnetic field, (d) the solar wind bulk velocity (jvswj), (e) heavy ion bulk velocity (jvhij), and (f) the convective electric field. Behind the bow shock the solar wind density is increased and the solar wind bulk velocity is decelerated, characterizing the magnetosheath region. Atmospheric material is lost through the plasma wake, thereby forming tail rays and filaments on the nightside ionosphere. The interplanetary magnetic field piles up on the dayside of the planet, producing a so-called magnetic barrier region wherein the magnetic pressure exceeds the thermal pressure of the solar wind (plasma beta below unity). See text for details. (dusk) side (in the sense of orbital motion). In addition, the solar wind bulk velocity is significantly decreased in the downstream region which is a characteristic of the magnetosheath. The geometry of the magnetic field on the nightside is related to the draping of the interplanetary magnetic field over the obstacle on the dayside [Luhmann and Cravens, 1991]. As a result, an induced magnetotail is formed behind the planet, consisting of 2 lobes of opposite polarity separated by a plasma sheet. Additionally, the IMF piles up on the dayside of the planet, producing a so-called magnetic barrier region, wherein the magnetic pressure exceeds the thermal pressure of the solar wind. This is also consistent with spacecraft measurements [Zhang et al., 1991] as well as global hybrid simulations of weakly magnetized bodies like, e.g., Mars [Bo¨ßwetter et al., 2004; Modolo et al., 2006], Venus [Jarvinen et al., 2008], and comets [Bagdonat and Motschmann, 2002b].

5. Comparison of the Simulation Results With VEX Orbit [46] Figure 12 shows that the spacecraft trajectory on orbit 85 lies in the terminator plane. We take a cut through

the simulation box along the VEX trajectory in order to discuss the parameters as a function of spacecraft time. A direct comparison between observations and simulation results are shown in Figures 13, 14, and 15. We take a cut through the simulation box along the VEX trajectory in order to discuss the parameters as a function of spacecraft time. All data have been resampled with 1 min resolution. Additionally, the positions of the plasma boundaries are marked by dashed vertical lines, namely the bow shock and the upper and lower boundary of the transition zone. [47] Figure 13 compares the magnetic field intensity (nT) extracted from the simulation with the measured value obtained by the Magnetometer along the spacecraft orbit 85. Most of the features are at least in qualitative agreement; that is, plasma boundary positions are well reproduced by the simulation, especially on the outbound pass. However, there are some disagreements regarding the absolute value where the real physics is still more complex than our model. During the inbound pass the simulation does not exhibit the jump in the magnetic field, indicating the BS crossing which is visible in the data. By comparing the VEX trajectory in Figure 9 with the simulation results in the terminator plane one can see clearly that this originates

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Figure 13. Direct comparison of the magnetic field parameter derived from the simulation (black line) with the magnetometer data (green line) along the VEX orbit 85 on 15 July 2006 from inbound to outbound. In addition, the positions of the plasma boundaries (red, bow shock; dark blue, upper mantle boundary; light blue, lower mantle boundary (ICB)) are marked by dashed vertical lines.

Figure 15. Direct comparison of the solar wind density from the simulation (black line) with ELS data (green line, fitted density) along the VEX orbit 85 on 15 July 2006 from inbound to outbound. In addition, the positions of the plasma boundaries (red, bow shock; dark blue, upper mantle boundary; light blue, lower mantle boundary (ICB)) are marked by dashed vertical lines.

from the quasi-parallel shock situation. In the simulation we assumed a constant IMF angle of 22.5 (in VSO) but in reality the magnetic field orientation is strongly fluctuating. Our constant magnetic field boundary is surely too raw. [48] Figure 14 displays the comparison of the solar wind velocity derived from the simulation and observed by the IMA sensor. The simulation is more or less in agreement with the data, except for the transition zone and ionosphere where IMA does not resolve the double peak feature which is visible in the simulation. The source of this disagreement

could be the location of accelerated shocked solar wind plasma after the terminator (see Figure 10, jvswj as well as the polar projection in Figure 12). That means, in the simulation the spacecraft would transit this region of accelerated plasma during the inbound pass, traverse the ionosphere (sharp drop in bulk velocity) and then, cross the fast plasma flow once again during the outbound pass. Though we never see proton bulk speeds accelerated above solar wind speed in the transition zone. Probably the solar wind electric field is much weaker in reality because of the fluctuating magnetic field. The positions of the plasma boundaries are well reproduced by the hybrid model, however, again there are significant discrepancies regarding the absolute values. [49] Figure 15 compares the solar wind density extracted from the simulation with the fitted density measured by the ELS sensor which shows for the most part a good agreement between data and model (from 0148 to 0320 UT) regarding the plasma boundaries as well as the absolute value, while on the outbound pass the agreement is almost perfect. During the inbound pass, in the solar wind and magnetosheath regions, there is a large difference between the simulated and measured densities due to the quasiparallel shock. Although the features are similar, the absolute values do not match the observations, maybe also due to the absolute calibration of the instruments. [50] Figure 16 shows the result of the convective electric field extracted from the simulation along the VEX trajectory which demonstrates that the hybrid model is able to reproduce the positions of the plasma boundaries.

Figure 14. Direct comparison of the solar wind bulk velocity from the simulation (black line) with IMA data (green line; integrated velocity) along the VEX orbit 85 on 15 July 2006 from inbound to outbound. Additionally, the positions of the plasma boundaries (red, bow shock; dark blue, upper mantle boundary; light blue, lower mantle boundary (ICB)) are marked by dashed vertical lines.

6. Conclusions [51] In this paper we investigated the global plasma environment of Venus using a 3-D hybrid model, treating the electrons as a massless, charge-neutralizing fluid, whereas

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Figure 16. Convective electric field resulting from simulation. The positions of the plasma boundaries (red, bow shock; dark blue, upper mantle boundary; light blue, lower mantle boundary (ICB)) are marked by dashed vertical lines. a completely kinetic approach is used to cover ion dynamics. The Venusian atmosphere is modeled under the assumption of an effective ionization rate mainly caused by solar EUV radiation. The production rate is a function of the altitude above the surface and of the solar zenith angle on the dayside, but depends only on the altitude on the nightside. Two different electron populations are incorporated in the hybrid code, in order to take into account the significantly different temperatures of the solar wind and ionospheric electrons. Using a curvilinear grid in the vicinity of the planet enables a high resolution of the plasma structures near the planetary surface. [52] Emphasis has been placed on the direct comparison of the plasma and magnetic field observations made onboard the Venus Express spacecraft with the results of the hybrid simulation. A first comparison between measurements and simulations indicates that the hybrid approach is capable of providing an adequate picture of the global plasma processes at Venus. [53] The common features of the solar wind-atmosphere interaction of an unmagnetized planet are fairly good reproducible by the hybrid code. The simulated bow shock, exhibiting shocklets structuring (kinetic nature), is formed in front of the planet and is equal in shape, size and position with the observed bow shock. Behind the obstacle a plasma wake is developed, filled with planetary ions and split into a central tail (plasma sheet) with high density and several rays with smaller density. Owing to the lack of an intrinsic magnetic field the IMF lines are draping around the planet, generating a magnetic barrier on the dayside as well as an induced magnetotail, consisting of two lobes of opposite polarity separated by a plasma sheet, on the nightside. The most pronounced characteristics are asymmetries with respect to the convection electric field affecting the heavy ion pickup region, magnetic field intensity and the shock geometry. A first determination of the tail boundaries (mantle region) indicates a very broad transition zone from solar wind to planetary ions, which also is confirmed by the simulation results.

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[54] Additionally, we determined the positions of the plasma boundaries at Venus at solar minimum based on the ASPERA-4 observations. We conclude from our measurements that the bow shock position is relatively stable whereas the upper and lower boundary of the mantle are highly variable on the nightside. In comparison to the large variations observed for the UMB, the variations of the BS position is smaller (about 15% of the terminator distance). The variations visible in the BS locations can be attributed to the variations in the magnetic field strength, since the BS position should depend on the upstream Mach number [Zhang et al., 2008a]. Previous studies based on PVO data [Russell et al., 1988] showed that the main factors influencing the BS position are the solar cycle, the upstream magnetosonic Mach number and the IMF clock angle (arctan(By/Bz)). But to detect the relatively small influences on magnetosonic Mach number and IMF clock angle, Russell et al. [1988] use only small subset of the large PVO data set (3300 orbits) with crossings within 0.5 RV close to the terminator. If we would apply the same selection to our data set, our statistics would be insufficient in order to analyze the dependency of the terminator BS position on the magnetosonic Mach number and IMF clock angle. For that reason we leave these issues for a later investigation. On the other hand, if we cannot decrease the number of data points we introduce a larger error in the dependency analysis caused by the projection of all BS crossings onto the terminator plane as well as by the assumption of a constant aberration angle. [55] Moreover, the main nonthermal loss processes of oxygen, namely the ion pickup, plasma clouds and momentum transport, are self-consistently included in the hybrid model. Thus, we can also compare the escape rates resulting from our simulation with measurements and estimations based on other models. Lammer et al. [2006] provide a theoretical escape rate for averaged solar activity of about 1.7 1026 s 1 (Monte Carlo particle simulations and gas dynamic test particle model). Recently, Barabash et al. [2007b] gave an initial estimation of the oxygen escape rate at Venus based on the ASPERA-4 measurements and concluded that the lower limit is about 1025 s 1. The simulation run (orbit 85) for solar minimum conditions yields an escape rate of about 0.9 1026 s 1 which establishes only a lower limit because the dayside ionospheric densities produced in the simulation ( 102 cm 3) are about a factor of 100 less than the observed values ( 104 cm 3) [Pa¨tzold et al., 2007]. In the simulated ionosphere the radial density gradient is extremely high and the spatial resolution of the simulation grid yields always mean values over 100 km in radial distance reducing the maximum values of the heavy ion density observed in the simulation. However in summary, our 3-D hybrid model yields an escape rate estimation within the limits of observations and theoretical calculations, and thus, seems to be able to provide an insight into the loss processes of oxygen at Venus induced by the solar wind. [56] To summarize, the positions of the simulated plasma boundaries are in good agreement with the observations but we found significant discrepancies between the observed compression of the plasma and the magnetic field on the western (dusk) side (in the sense of orbital motion) of the orbit. Possible reasons are on the one hand, the calculated input parameters because there are still uncertainties in the

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estimation of the plasma moments and on the other hand, the atmospheric model has also to be refined. Another reason for the disagreements between simulations and measurements can be ascribed to fluctuations in the upstream solar wind parameters which are assumed to be completely homogeneous in the simulation. [57] Kallio et al. [2006] investigated the global plasma environment of Venus for solar minimum conditions (based on PVO measurements) with a quasi-neutral hybrid (QNH) model which is similar to our hybrid approach. The QNH model is also able to reproduce the basic observed plasma and magnetic field regions and boundaries near Venus. However, their grid resolution is not fine enough to model the position and shape of the bow shock, multiple shocklets, the inner structure of the magnetic barrier, tail rays and filaments. Nevertheless, they also find a north – south asymmetry in the direction of the convective electric field due to mass loading of the solar wind by heavy ions as stated in section 4 explaining the simulation results. Additionally, they also observe a dawn-dusk asymmetry and conclude that this asymmetry is associated with the IMF x component because they use a Parker spiral IMF where the IMF x component is larger in magnitude than the magnetic field components perpendicular to the flow. They also yield an asymmetry in the magnetic field strength along the solar wind electric field because of the mass loading effect but it is less pronounced than in our simulation results. As in our case study, Kallio et al. [2006] also use homogeneous upstream solar wind parameters. But since Venus shows a strong dependence on the phase of the solar cycle it is necessary to study the solar wind-ionosphere interaction with varying plasma and magnetic field parameters in order to carry out more realistic investigations. In addition, they obtain a total escape rate of oxygen ions from the simulation box of about 2.2 1024 s 1. This value lies beyond the recently estimated lower limit based on ASPERA-4 observations [Barabash et al., 2007b] and is much lower compared to our simulated escape rate. The reason is the coarse grid resolution used in the QNH approach which inhibits the corporation of a self-consistent ionosphere. [58] A further technical limitation is associated with the spatial resolution. Although we use a curvilinear grid to enhance the spatial resolution near the planetary surface, it is not high enough to resolve the ionopause since the ionosphere is just a few bins in size. In the last years our code was parallelized by Josef Schu¨le from the Institute of Scientific Computation at the Technical University of Braunschweig (Germany). By means of this MPI (messagepassing interface) parallelization one can improve the spatial grid resolution in order to resolve small-scale structures of the solar wind interaction region, such as for example the ionopause. [59] Acknowledgments. We thank all members of the ASPERA-4 team for the big effort which led to the successful operation of the instrument and the calibration of the data. For this paper we are especially grateful to Emmanuel Penou and Andrei Fedorov at CESR for data preparation and IMA calibration and Andrew Coates at MSSL for calibration of the ELS sensor data. We thank the CELIAS/SEM experiment on the Solar Heliospheric Observatory (SOHO) spacecraft for providing solar EUV data. The ASPERA ELS was constructed under NASA contract NASW-0003. This work was supported by DFG grant MO539/17-1.

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Phillips, J. L., and D. J. McComas (1991), The magnetosheath and magnetotail of Venus, Space Sci. Rev., 55, 1 – 80. Roussos, E., et al. (2008), Plasma and fields in the wake of Rhea: 3D hybrid simulation and comparison with Cassini data, Ann. Geophys., in press. Russell, C. T., E. Chou, J. G. Luhmann, P. Gazis, L. H. Brace, and W. R. Hoegy (1988), Solar and interplanetary control of the location of the Venus bow shock, J. Geophys. Res., 93, 5461 – 5469. Russell, C. T., J. G. Luhmann, and R. J. Strangeway (2006), The solar wind interaction with Venus through the eyes of the Pioneer Venus Orbiter, Planet. Space Sci., 54, 1482 – 1495, doi:10.1016/j.pss.2006.04.025. Shimazu, H. (2001), Three-dimensional hybrid simulation of solar wind interaction with unmagnetized planets, J. Geophys. Res., 106, 8333 – 8342. Simon, S., T. Bagdonat, U. Motschmann, and K.-H. Glassmeier (2006a), Plasma environment of magnetized asteroids: A 3D hybrid simulation study, Ann. Geophys., 24, 407 – 414. Simon, S., A. Bo¨ßwetter, T. Bagdonat, U. Motschmann, and K.-H. Glassmeier (2006b), Plasma environment of Titan: A 3-D hybrid simulation study, Ann. Geophys., 24, 1113– 1135. Simon, S., A. Boesswetter, T. Bagdonat, and U. Motschmann (2007), Physics of the ion composition boundary: A comparative 3-D hybrid simulation study of Mars and Titan, Ann. Geophys., 25, 99 – 115. Slavin, J. A., R. E. Holzer, J. R. Spreiter, and S. S. Stahara (1984), Planetary mach cones: Theory and observation, J. Geophys. Res., 89, 2708 – 2714. Spreiter, J. R., and S. S. Stahara (1992), Computer modeling of solar wind interaction with Venus and Mars, in Venus and Mars: Atmospheres, Ionospheres, and Solar Wind Interactions, Geophys. Monogr. Ser., vol. 66, edited by J. G. Luhman, M. Tatrallyay, and R. O. Pepin, pp. 345 – 383, AGU, Washington D. C. Tanaka, T. (1993), Configurations of the solar wind flow and magnetic field around the planets with no magnetic field: Calculation by a new MHD simulation scheme, J. Geophys. Res., 98, 17,251 – 17,262, doi:10.1029/ 93JA01516. Tanaka, T., and K. Murawski (1997), Three-dimensional MHD simulation of the solar wind interaction with the ionosphere of Venus: Results of two-component reacting plasma simulation, J. Geophys. Res., 102, 19,805 – 19,822, doi:10.1029/97JA01474. Terada, N., S. Machida, and H. Shinagawa (2002), Global hybrid simulation of the Kelvin-Helmholtz instability at the Venus ionopause, J. Geophys. Res., 107(A12), 1471, doi:10.1029/2001JA009224.

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Terada, N., H. Shinagawa, and S. Machida (2004), Global hybrid model of the solar wind interaction with the Venus ionosphere: Ion escape processes, Adv. Space Res., 33, 161 – 166, doi:10.1016/j.asr.2003.05.011. Titov, D. V., et al. (2006), Venus Express science planning, Planet. Space Sci., 54, 1279 – 1297, doi:10.1016/j.pss.2006.04.017. Torr, M. R., and D. G. Torr (1985), Ionization frequencies for solar cycle 21: Revised, J. Geophys. Res., 90, 6675 – 6678. Wu, C. C. (1992), MHD flow past an obstacle: Large-scale flow in the magnetosheath, Geophys. Res. Lett., 19, 87 – 90. Zhang, T. L., J. G. Luhmann, and C. T. Russell (1991), The magnetic barrier at Venus, J. Geophys. Res., 96, 11,145 – 11,153. Zhang, T. L., C. T. Russell, J. G. Luhmann, J. R. Spreiter, and S. S. Stahara (1993), On the spatial range of validity of the gas dynamic model in the magnetosheath of Venus, Geophys. Res. Lett., 20, 751 – 754. Zhang, T. L., et al. (2006), Magnetic field investigation of the Venus plasma environment: Expected new results from Venus Express, Planet. Space Sci., 54, 1336 – 1343. Zhang, T. L., et al. (2008a), Initial Venus Express magnetic field observations of the Venus bow shock location at solar minimum, Planet. Space Sci., 56, 785 – 789, doi:10.1016/j.pss.2007.09.012. Zhang, T. L., et al. (2008b), Initial Venus Express magnetic field observations of the magnetic barrier at solar minimum, Planet. Space Sci., 56, 790 – 795, doi:10.1016/j.pss.2007.10.013. S. Barabash and R. Lundin, Swedish Institute of Space Physics, P.O. Box 812, SE-981 28, Kiruna, Sweden. A. Boesswetter, U. Motschmann, S. Simon, and S. Wiehle, Institut fu¨r Theoretische Physik, Technische Universita¨t Braunschweig, Mendelssohnstrasse 3, D-38106 Braunschweig, Germany. E. Dubinin, M. Fra¨nz, N. Krupp, C. Martinecz, E. Roussos, and J. Woch, Max Planck Institut fu¨r Sonnensystemforschung, Max-Planck-Strasse 2, D-37191 Katlenburg-Lindau, Germany. ([email protected]) Y. Kulikov, Polar Geophysical Institute, Russian Academy of Sciences, Khalturina Street 15, 183010 Murmansk, Russia. H. Lammer, H. Lichtenegger, and T. L. Zhang, Space Research Institute, Austrian Academy of Sciences, Schmiedlstrasse 6, A-8042 Graz, Austria.

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Correction to ‘‘Plasma environment of Venus: Comparison of Venus Express ASPERA-4 measurements with 3-D hybrid simulations’’ C. Martinecz, A. Boesswetter, M. Fra¨nz, E. Roussos, J. Woch, N. Krupp, E. Dubinin, U. Motschmann, S. Wiehle, S. Simon, S. Barabash, R. Lundin, T. L. Zhang, H. Lammer, H. Lichtenegger, and Y. Kulikov Received 12 March 2009; published 29 April 2009.

Citation: Martinecz, C., et al. (2009), Correction to ‘‘Plasma environment of Venus: Comparison of Venus Express ASPERA-4 measurements with 3-D hybrid simulations,’’ J. Geophys. Res., 114, E00B98, doi:10.1029/2009JE003377.

[1] In the paper ‘‘Plasma environment of Venus: Comparison of Venus Express ASPERA-4 measurements with 3-D hybrid simulations’’ by C. Martinecz et al. (Journal of Geo-

physical Research, 114, E00B30, doi:10.1029/2008JE003174, 2009), Figures 9–12 were numbered incorrectly. The correct numbered figures and their captions are shown here.

Figure 9. Simulation results in the terminator plane, i.e., looking from behind on Venus. The solar wind is orientated out of the plane (+x axis), the convective electric field is antiparallel to the (+z) axis and points away from the planet; thus, the background magnetic field completes the right-handed system (+y axis). Figure 10 displays (left) the solar wind density (nsw), (middle) the heavy ion density (nhi), and (right) the background magnetic field. An asymmetric bow shock, exhibiting a shocklet structure, is formed in front of the obstacle owing to the supersonic solar wind (left plot). The interplanetary magnetic field is draping around Venus generating an induced magnetotail on the nightside of the planet (right plot). See text for details.


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Figure 10. Cut through the simulation box along the polar plane, i.e., looking from the western (dusk) side (in the sense of orbital motion) on Venus. The plasma flow comes in from the left (+x axis), the solar wind electric field points away from the planet (z axis), and the interplanetary magnetic field completes the right-handed system pointing into the plane (+y axis). Figure 11 illustrates from left to right in the first row (a) the solar wind density (nsw), (b) the heavy ion density (nhi), (c) the background magnetic field, (d) the solar wind bulk velocity (jn swj), (e) heavy ion bulk velocity (jn hij), and (f) the convective electric field. In the density plots a clear separation between the solar wind and ionospheric plasma takes place forming the so-called ion composition boundary (ICB) in the northern hemisphere. In the southern hemisphere there is no clear ICB owing to the mass loading effect; that is, the planetary ions are accelerated in the direction of the convective electric field and are picked up by the solar wind flow. The solar wind electric field ~ E = ~ v~ B vanishes where the heavy ion plasma dominates. See text for details.

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Figure 11. Simulation results in the equatorial plane, i.e., looking from ecliptic North onto Venus. The undisturbed solar wind flow is parallel to the positive x axis, the convective electric field points into the plane (z axis), and the background magnetic field completes the right-handed system (+y axis) and is tilted 157.5° in the equatorial plane. Figure 12 shows (a) the solar wind density (nsw), (b) the heavy ion density (nhi), (c) the background magnetic field, (d) the solar wind bulk velocity (jn swj), (e) heavy ion bulk velocity (jn hij), and (f) the convective electric field. Behind the bow shock the solar wind density is increased and the solar wind bulk velocity is decelerated, characterizing the magnetosheath region. Atmospheric material is lost through the plasma wake, thereby forming tail rays and filaments on the nightside ionosphere. The interplanetary magnetic field piles up on the dayside of the planet, producing a so-called magnetic barrier region wherein the magnetic pressure exceeds the thermal pressure of the solar wind (plasma beta below unity). See text for details.

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Figure 12. Projections of the trajectory of the Venus Express orbit 85 on the equatorial, polar, and terminator plane of our 3-D hybrid simulation. The undisturbed plasma flow is directed along the (+x) axis, the convection electric field ~ E = ~ v~ B is orientated antiparallel to the z axis, and the background magnetic field completes the right-handed system pointing in the positive y direction (tilted 157.5° in the equatorial plane).

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Intermittent turbulence, noisy fluctuations, and wavy structures in the Venusian magnetosheath and wake Z. Vo¨ro¨s,1 T. L. Zhang,2 M. P. Leubner,1 M. Volwerk,2 M. Delva,2 and W. Baumjohann2 Received 2 April 2008; revised 5 August 2008; accepted 18 September 2008; published 25 December 2008.

[1] Recent research has shown that distinct physical regions in the Venusian induced

magnetosphere are recognizable from the variations of strength of the magnetic field and its wave/fluctuation activity. In this paper the statistical properties of magnetic fluctuations are investigated in the Venusian magnetosheath and wake regions. The main goal is to identify the characteristic scaling features of fluctuations along Venus Express (VEX) trajectory and to understand the specific circumstances of the occurrence of different types of scalings. For the latter task we also use the results of measurements from the previous missions to Venus. Our main result is that the changing character of physical interactions between the solar wind and the planetary obstacle is leading to different types of spectral scaling in the near-Venusian space. Noisy fluctuations are observed in the magnetosheath, wavy structures near the terminator, and in the nightside near-planet wake. Multiscale turbulence is observed at the magnetosheath boundary layer and near the quasi-parallel bow shock. Magnetosheath boundary layer turbulence is associated with an average magnetic field which is nearly aligned with the Sun-Venus line. Noisy magnetic fluctuations are well described with the Gaussian statistics. Both magnetosheath boundary layer and near-shock turbulence statistics exhibit non-Gaussian features and intermittency over small spatiotemporal scales. The occurrence of turbulence near magnetosheath boundaries can be responsible for the local heating of plasma observed by previous missions. Citation: Vo¨ro¨s, Z., T. L. Zhang, M. P. Leubner, M. Volwerk, M. Delva, and W. Baumjohann (2008), Intermittent turbulence, noisy fluctuations, and wavy structures in the Venusian magnetosheath and wake, J. Geophys. Res., 113, E00B21, doi:10.1029/ 2008JE003159.

1. Introduction [2] The structured plasma environment of Venus is a natural plasma laboratory where the planetary ionosphere acts as an obstacle to the supersonic solar wind flow carrying a magnetic field. The absence of an intrinsic magnetic field ensures that this interaction is more cometlike than Earth-like. It is known from the previous missions to Venus [e.g., Russell and Vaisberg, 1983] that the most prominent features include the direct interaction of ionized magnetosheath flow with the ionosphere/quasi-neutral atmosphere, mass loading of the magnetosheath flux tubes, and the transport/convection of magnetic flux to the wake region, representing also the dominant source for the magnetotail fluxes. However, for most of the time, mainly during time intervals of low solar wind dynamic pressure, 1 Institute of Astro- and Particle Physics, University of Innsbruck, Innsbruck, Austria. 2 Space Research Institute, Austrian Academy of Sciences, Graz, Austria.


the induced and piled up magnetic field around the planetary obstacle represents an effective magnetic barrier, preventing free entrance of solar wind plasma to the Venusian ionosphere [Zhang et al., 2007]. The draping of the interplanetary magnetic field (IMF), the accretion of magnetic flux by the planet, the characteristic spatial scales of physical processes as well as the location and specific features of boundaries, all depend on dynamical processes in the solar wind and make this environment unique. The results of Venus Express spacecraft (VEX) provide an altitude for the induced magnetopause of about 300 km at the subsolar point, while the subsolar bow shock distance from the surface of the planet is about 1900 km [Zhang et al., 2007]. These values exhibit a solar cycle dependence; that is, the thickness of the Venusian magnetosheath varies around 1500 km at the subsolar point and widens up to 5000– 7000 km at the terminator. During time intervals of high solar wind dynamic pressure, the ionopause moves to low altitude ( 250 km) and mainly the ionosphere forms the obstacle responsible for deflecting the solar wind flow. In this case the thickness of the ionopause increases and more direct interaction between the ionosphere and solar wind is possible [Elphic et al., 1981; Russell and Vaisberg, 1983].

*Author names correctly given here. The article as originally published appears online.

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[3] In the dayside magnetosheath strong magnetic fluctuations and waves are present [e.g., Luhmann et al., 1983]. Mirror mode waves were observed here in case studies [Volwerk et al., 2008a], and also investigated statistically [Volwerk et al., 2008b]. On the other hand, the limited spatial scale of the magnetosheath on the dayside might not support a development of a turbulent cascade, the fluctuations rather resemble 1/f noise (f is the frequency). 1/fa noise is a signature of the presence of independent physical mechanisms driving fluctuations in the magnetosheath [Vo¨ro¨s et al., 2008]. Keeping in mind that the ion inertial length is of the order of 100 km, the magnetohydrodynamic (MHD) spatial scales in the Venusian magnetosheath are limited to one or two decades in wave number space. [4] The terminator and, further downstream, the nightside region is of particular interest [Spreiter and Stahara, 1992]. Here plasma instabilities, vortices, and turbulence can develop near boundaries. For example, Wolff et al. [1980] have shown that the distortion of the ionopause by KelvinHelmholtz instability might lead to the formation of magnetic flux ropes inside the ionosphere as well as ionospheric bubbles embedded in the solar wind. Numerical simulations indicate that the Kelvin-Helmholtz instability can occur at the terminator ionopause of Venus [Terada et al., 2002], capable of producing wave structures over 1000 km in size [Amerstorfer et al., 2007; Biernat et al., 2007]. In fact, the initial VEX observation detected these wavy structures (M. A. Balikhin et al., Giant vortices lead to ion escape from Venus, submitted to Geophysical Research Letters, 2008.). A recent study shows that MHD turbulence near and immediately after the terminator is not fully developed because of the rapid decrease of spectral power toward higher frequencies, resulting in spectral scaling indices a > 2 [Vo¨ro¨s et al., 2008]. Further downstream, in the magnetotail/ magnetosheath region, the spectral analysis indicates the presence of developed inertial range turbulence, with a spectral scaling index a 1.6, close to the values expected for hydrodynamic or magnetohydrodynamic turbulent flows. However, in the same region, non-Gaussian probability density functions (PDF) with typical long tails, corresponding to intermittent turbulence, were not observed [Vo¨ro¨s et al., 2008]. This can be explained by the shortness of turbulent time series during a tail crossing, in particular not allowing a full reconstruction of PDF tails. [5] The near-Venus tail is highly structured. There is no significant plasma inflow into the near-Venusian wake. The wake magnetic field is known to be stronger than the IMF and from the cavity hot plasma is excluded [Bauer et al., 1977]. The solar wind-ionosphere interaction in the presence of the draped interplanetary magnetic field, however, produces an extended boundary tailward from the terminator, at the inner edge of the magnetopause, or outer edge of the wake downstream, where boundary layer turbulence can develop and heat locally the plasma. The width of this boundary layer is limited, does not include the whole plasma sheet. In order to understand the energy content and energy dissipation of underlying processes, it is necessary to investigate systematically the statistical features of fluctuations in the structured near-Venusian plasma environment. In this paper we statistically analyze the spectral scaling features of fluctuations in the magnetosheath, wake and near-boundary regions using VEX magnetometer data

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during the first 20 days in May 2006. The time resolution of the magnetic data is 1 s. Because of rapid crossing of different structures, this time resolution is necessary for obtaining statistically reliable scaling results. The main emphasis is on the analysis of scalings, i.e., on the evaluation of the continuous part of magnetic power spectra. The analysis of waves (when the continuous parts of the spectra are not considered) is equally important, but out of the scope of this paper. In addition to spectral estimations, we will compare the probability distribution functions (PDFs) and evaluate the scale dependency of their shapes, associated with noisy and turbulent fluctuations. This helps to obtain a more reliable differentiation between turbulence and noise, occurring along the VEX trajectory. The nearpolar orbit of VEX with a periapsis altitude of 250– 350 km allows for the first time observations at terminator and midmagnetotail regions [Zhang et al., 2006]. These two important regions were not covered by the previous missions, e.g., Pioneer Venus Orbiter (PVO) [Russell, 1992].

2. Near-Venus Plasma Regions With Varying Spectral Scaling Properties [6] It was reported by Vo¨ro¨s et al. [2008] that the value of spectral scaling index a, describing the self-similarity of the power spectrum of the magnetic field data in the frequency range 0.03 – 0.5 Hz, exhibits different values in different regions near Venus. The magnetic fluctuations are nonstationary, e.g., in the magnetosheath the magnetic field strength is increasing toward the induced magnetopause, which introduces a trend into magnetic field data. In order to estimate a robustly, we used a wavelet method proposed by Abry et al. [2000] and applied it successfully to the description of magnetic fluctuations in the Earth’s plasma sheet [Vo¨ro¨s et al., 2004]. In this paper we use the Daubechies wavelets, for which finite data size effects are minimized and the number of vanishing moments can be changed. The latter feature of Daubechies analyzing wavelets is essential to cancel the influence of polynomial trends or periodic structures in the data on the estimation of the scaling index. [7] Let us demonstrate first how the spectral scaling features vary along the VEX trajectory during its journey from the dayside magnetosheath through the terminator region and wake to the post-terminator magnetosheath. As an example, we show the variation of total magnetic field strength B on 19 May 2006 (Figure 1, top) together with the corresponding power spectral densities (PSD) calculated during equally long time intervals (a, b, c, d and e, Figure 1, bottom subplots) along the spacecraft trajectory. Zhang et al. [2007] have already demonstrated that a crossing of each physical region in the near-Venus plasma environment is recognizable from the variations of strength and wave/fluctuation activity of the magnetic field. Indeed, B is not disturbed before t1 = 0115 UT, and after t2 = 0310 UT, the spacecraft is in the solar wind (Figure 1). The planetary obstacle perturbs the magnetic field only between t1 and t2. During the interval a, VEX enters the dayside magnetosheath from the solar wind (VEX trajectories and the intervals a – e are shown in Figure 2, left bottom subplot). Here the magnetic field strength is strongly fluctuating and its value increases up to 50 nT. The

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Figure 1. (top) Magnetic field strength (B) on 19 May 2006. The horizontal black lines correspond to the time intervals a– e of equal length in the dayside magnetosheath (interval a), nightside near-planet wake (interval b), magnetosheath boundary layer (interval c), tailward magnetosheath (interval d), and in the vicinity of the bow shock. (bottom) Power spectra and spectral scalings estimated within the intervals a – e. Pronounced wavy structures are present mainly in the wake (interval b). The vertical arrow points to the spectral peak at 15 s. estimated spectral scaling index a = 1 ± 0.2 (the first bottom subplot in Figure 1) is low, indicating that fully developed turbulence is absent in this region [Vo¨ro¨s et al., 2008]. Higher values a = 5/3 or 3/2 are expected for hydrodynamic or MHD inertial range turbulence, respectively. After 0135 UT B decreases, which corresponds to the closest approach to Venus, VEX is close to or below the induced magnetopause [Zhang et al., 2007]. [8] Between 0140 and 0205 UT high-frequency fluctuations are absent, only low-frequency wavy fluctuations are seen. These wavy structures might be associated with Kelvin-Helmholtz instability at the terminator ionopause or detached plasma clouds near the terminator, observed already during Pioneer Venus orbits [Brace et al., 1982]. During interval b the corresponding spectrum exhibits significant wave power only near 0.07 Hz ( 14 s) then the spectral power rapidly decreases with a scaling index a = 2.5 ± 0.2 (the second bottom subplot in Figure 1). Further downstream (after 0205 UT in Figure 1) broadband fluctuations occur again. The spectral indices within the intervals c and e are a = 1.5 ± 0.2 and 1.6 ± 0.2 respectively, indicating the presence of developed turbulence. In contrary, the interval d, in between c and e, shows again 1/f noise-like scaling behavior (the last three bottom subplots in Figure 1). [9] The physical difference between turbulence and noise is clear. Turbulence in the wake is a consequence of nonlinear multiscale interactions and it is strongly dissipative, heating the background plasma at the small scales. Noisy fluctuations, exhibiting 1/fa scaling behavior with a

around 1, may have multiple physical sources not connected with nonlinear interactions, typical for turbulence. 0 < a < 1 over higher frequencies (around 1Hz) can also be associated with the noise of the magnetometer [Vo¨ro¨s et al., 2004]. The low values of a can also indicate that the spacecraft is not in a physical region where strong nonlinear interactions and turbulence can exist, e.g., because of low plasma density or lack of plasma flows. In the following, the notation ‘‘1/f noise’’ refers to 1/fa noise with spectral scaling index in a range of a2 (0.6, 1.4) (find below the definition of the spectral index used in this paper). [10] In this paper we put the emphasis on statistical examination of the circumstances under which developed turbulence occurs in the post-terminator wake and magnetosheath regions.

3. Statistical Analysis of Magnetic Fluctuations [11] We investigated the time series of magnetic field strength statistically, obtained in the near-Venusian space during the first 20 days in May 2006. During 1 day one orbit is performed, therefore, during the 20 day interval we have observed twenty crossings. Because of the nonhomogeneity and dynamical nature of the near-Venus physical regions the occurrence and the length of at least quasistationary intervals slightly differs each day. We identified the approximate beginning and end of the steady intervals computing the scaling indices over timescales 2 – 30 s, using the wavelet method within sliding overlapping windows. After that, changing slightly the starting point and the length

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Figure 2. (top left) Magnetic field strength (B, gray line) and BX magnetic component (black line) on 19 May 2006; the horizontal lines show the intervals a – e depicted in Figure 1. (bottom left) Venus Express (VEX) crossings (thin black lines) of the Venusian plasma environment in Venus-Sun-Orbital (VSO) coordinates. The intervals a – e (black lines) are shown alongside the VEX trajectories. The filled circles show the bow shock, and the open circles show the magnetopause during multiple crossings (after Zhang et al. [2007]). The large triangles along the lowest trajectory correspond to the region of wavy structures and spectral scaling index a 2.5. The family of thick black lines along the trajectories (intervals c and e) correspond to turbulent regions with a 1.6. The gray ‘‘+’’ signs are intervals of 1/f noise (interval d). (right) Enlarged magnetosheath crossings in VSO coordinates. The same notations are used as in Figure 2, left. The bottom right subplot shows the approximate spatial size of the turbulent (intervals c and e) and noisy (interval d) regions in space. of turbulent/noisy data, optimized data intervals with smallest errors in a were found. We rejected time intervals where the error of the estimation of a was larger than ±0.3. The available steady intervals in the dayside magnetosheath are shorter, so we selected 9 min long intervals there. Further downstream 12 min long intervals were selected. Selecting longer data periods would significantly reduce the number of available intervals, while shorter data sets would decrease the statistical reliability of results. Data intervals with fluctuating magnetic field shorter than 12 min were not included into our analysis. Because of occasional data gaps or shortness of steady fluctuations in the time series, the number of events is not the same in different physical regions. There were crossings with no steady fluctuations along the VEX trajectory. There were also crossings where the time interval with steady fluctuations was longer than 24 min. [12] Because of data gaps or shortness of statistically stationary time series, only 17 out of 20 crossings are shown in Figure 2 (left bottom and right subplots). A cylindrical coordinatepsystem is used in Figure 2, where the events are shown in (Y2 + Z2) VSO versus X VSO (bottom left) or in VSO coordinate pairs (right subplots). VSO is the Venuscentered Venus-Sun-Orbital coordinate system, where X is in the direction of the Sun, Y opposite to the orbital

direction of Venus and Z perpendicular to the orbital plane, positive to ecliptic north. [13] The near-Venusian space is physically nonhomogeneous and the spatial and temporal variations cannot be straightforwardly separated from single-spacecraft measurements. Nevertheless, during the investigated time period in May 2006, the typical sequence of physical regions visited by VEX remained approximately the same as in Figure 1: crossing of the dayside magnetosheath (increasing B, interval a), low-frequency wavy structures after the terminator (interval b) and entering into the region of broadband fluctuations further downstream (intervals c – e). The intervals a –e were introduced for the event on 19 May 2006 (Figure 1, top, and Figure 2, top left). The wavy structures are not always present along the whole near-terminator region and wake. In the absence of the wavy structures only low-amplitude magnetometer noise is observed. Possibly, the occurrence/absence of wavy structures can be associated with the upstream conditions. For example, during times when the solar wind dynamic pressure is high the ionopause moves to low altitudes ( 250 km), and direct interactions between the ionosphere and the solar wind can occur (plasma-plasma interactions or solar wind electric field – ionospheric currents interactions). When the solar

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Figure 3. Histograms of the scaling indices in different regions of near-Venusian space (approximately obtained during the intervals a – e in Figure 2.). (top to bottom) Dayside magnetosheath (interval a); nearterminator and post-terminator wake (interval b); magnetosheath boundary and near bow shock region (intervals c – e); post-terminator tailward magnetosheath (interval d). These spatial regions are depicted more clearly in Figure 4. wind dynamic pressure is low the ionospheric width increases [Elphic et al., 1981] and the draped IMF above 300 km can stop the solar wind more efficiently. In this paper we investigate only the features of magnetic fluctuations, without considering the changes in the upstream conditions. The open triangles (Figure 2, bottom left) indicate the whole region (marks are only on the lowest trajectory) where the wavy structures appear during the first 20 days in May 2006. [14] Because of the similarity of crossings, the depicted intervals (lines) along the trajectories (Figure 2, bottom left), in terms of spectral properties, refer generally to similar physical regions along the VEX trajectory. For example, the thick black lines (Figure 2, bottom left) indicate turbulent processes identified through a spectral scaling index near a = 1.6. The location of turbulence in space approximately coincides with time intervals c and e. The time interval d coincides roughly with the position of gray ‘‘+’’ signs along the trajectory, where scaling indices corresponding to 1/fa noise were observed. For a better visibility, the trajectories together with turbulent and noisy time intervals (c, e and d) are depicted in the VSO coordinates (Figure 2, right subplots). From Figure 2 (bottom right) it is visible that the spatial regions exhibiting the same a along multiple trajectories are partially overlapping, indicating that these regions and the corresponding boundaries are moving or the fluctuations are patchy or intermittent. Moving boundaries can appear in connection with changing conditions in the solar wind. [15] Switching between temporal and spatial coordinates helps us to identify spatial regions with typical scalings. Occasionally we will use plural indicating the change between temporal and spatial coordinates. For example,

the notation ‘‘intervals c’’ means the set of all crossings in space near the time interval/space region c on 19 May 2006. [16] Figure 3 shows histograms with the number of events per unit interval of the scaling index a, estimated during the time periods a – e. In each subplot, the horizontal lines over the histograms represent the average of 95% confidence limits for the mean in each value of a. The largest average uncertainty corresponds to the dayside magnetosheath region, where the length of analyzed data was shorter. Figure 3 indicates that the specific scaling features characterize the statistical properties of different regions in space rather well. [17] During the magnetosheath periods (intervals a and d: top and bottom subplots in Figure 3) only two events display scaling indices >1.3, for the other events a 2 (0.6 1.25). This indicates that the fluctuations or waves present in the magnetosheath, except of a few cases, usually do not evolve to a fully developed turbulence state. The observed range of scaling indices suggests that the continuous part of the magnetosheath spectra is associated with 1/f noise rather than turbulence. Noisy fluctuations can be convected from the dayside magnetosheath (interval a) to the post-terminator magnetosheath downstream (interval d), where the spectral power is smaller, possibly the driving sources are more distant than at the dayside (compare the spectra corresponding to the intervals a and d in Figure 1). [18] In the post-terminator wake (intervals b in Figure 3) wavy structures dominate with periods from 5 to 50 s (similar to event b in Figure 1). Since toward higher frequencies the power rapidly decreases (a 2.5)-independent driving sources associated with broadband noise or nonlinear multiscale turbulence are absent. [19] Finally, a well discernible turbulence scaling index a 1.6 was observed during the intervals c and e (Figure 3,

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Figure 4. A cartoon showing the type of physical processes in the spatial regions of near-Venusian space; the magnetopause and the bow shock (dashed lines); a VEX crossing (solid black line); the optical shadow (shaded region); and draped IMF (thin black line). The time intervals/spatial regions a – e are marked with squares, triangles, circle, squares, and circle, respectively. third subplot from top). The events from both intervals are plotted together because the corresponding scaling indices are similar. [20] The cartoon in Figure 4 helps to interpret the occurrence of typical scaling regimes, summarizing the results of the statistical analysis along VEX trajectory in the cylindrical VSO coordinate system: [21] 1. 1/f noise appears under rather different conditions in the magnetosheath: in front of the planetary obstacle and downstream in the post-terminator magnetosheath (regions a, d: squares in Figure 4). Scaling indices which can be associated with turbulence occurred in 5% of cases (see Figure 3). [22] 2. Coherent wavy fluctuations occur between the dayside magnetopause and post-terminator boundary of the wake (largely within the optical shadow, region b: triangles in Figure 4), where noise and turbulence are absent (only magnetometer noise is present when the wave activity is absent). Closer to the terminator, the wavy structures can be associated with the Kelvin-Helmholtz instability occurring at the terminator ionopause, resulting probably in detached plasma clouds, first observed during the Pioneer Venus mission by Brace et al. [1982]. The PVO spacecraft observed filamentary structures (rays), density holes and radially aligned draped magnetic field lines in the near-planet nightside wake [Luhmann and Russell, 1983; Marubashi et al., 1985]. [23] 3. Developed turbulence is present at or near boundaries: at the wake/magnetopause/magnetosheath boundary and near the bow shock (regions c, e: circles in Figure 4).

4. Discrimination Between Boundary Turbulence and Magnetosheath Noise [24] Figure 3 shows that, in terms of spectral scaling characteristics, turbulence and noisy intervals are well

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separable. In the following we will further investigate some other differences between turbulence (intervals c and e) and noisy magnetosheath fluctuations (intervals d). We first recall some similarities between the observations obtained from previous missions and VEX near the regions with developed turbulence. [25] Early observations from Mariner 5 [Bridge et al., 1967] already showed the existence of a plasma boundary located at the inner edge of the post-terminator magnetosheath (VEX is crossing this region where the occurrence of turbulence is depicted by the bottom circle in Figure 4). During a flyby, Mariner 5 found the location of the boundary layer from 3.2 to 4.5 RV behind Venus and from 2 (magnetopause location) to 3.5 RV from the center of the wake. Within the boundary layer Mariner 5 observed a strongly fluctuating lower-intensity magnetic field, a decrease of the density and velocity, but an increase of the temperature. The magnetic field strength was larger when the spacecraft entered from the inner magnetosheath, across the magnetopause, to the wake. VEX observations show that the magnetopause location is rather variable over distances X VSO < 1 RV (open circles in Figure 2, bottom left). Therefore, multipoint turbulence fluctuations indicating the crossing of the magnetopause start in different distances from the center of the wake. For example, on 19 May 2006 (Figure 2, top left) the strongly fluctuating magnetic field strength reaches a local maximum at about 0205 UT, then it slowly decreases. It indicates that VEX is entering to the boundary layer from the wake (from intervals b to c) and at the boundary the character of fluctuations is suddenly changing, from scaling index a 2.5 to a 1.6 (see also Figure 1). [26] Another interesting feature includes the change of the sign of BX. During the interval c on 19 May it happens several times (Figure 2, top left). It can indicate that VEX is crossing the neutral sheet behind the planet. The VEX trajectory for this day (marked by an arrow in Figure 2, top right subplot) is roughly along Y VSO 0.9 RV. In fact the Venusian wake/magnetotail can be formed by flux tubes convected around or slipping over the planet and filled with the plasma from the dayside. At the same time, both ends of the flux tubes are comoving with the solar wind. In a simple case, the resulting magnetic field in the tail is stretched along the X axis, because the central part of the flux tubes is slowed down by the planet while both ends travel faster with the solar wind. Since the IMF is usually close to the ecliptic plane, the tail current sheet, in ideal case, should be formed by the stretched magnetic field mainly in the northsouth plane [Russell and Vaisberg, 1983]. Depending on the upstream IMF and its draping around the planet, the current sheet plane in the Venusian magnetotail can also rotate [Luhmann et al., 1991]. Since turbulence can drive local mixing and vorticity, the change of the magnetic field direction might be also associated with turbulence, not necessarily with neutral sheet crossing. For example, BX is also changing sign several times during the shock associated turbulence interval e (Figure 2, top right), but it has nothing common with a neutral sheet crossing. [27] The same magnetic field and plasma signatures of the inner magnetosheath boundary layer were observed by other missions, too. For example, Venera 10 observed the boundary layer tailward from the Mariner 5 flyby [Romanov

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Figure 5. (top to bottom) Comparison of 12 min averages of hbX2i, hbY2i, and hbZ2i with the observed spectral scaling indices a. The subscripts indicate VSO magnetic field components; and the horizontal lines correspond to averages of points (open and filled circles) over the noisy a2 (0.6 1.4) and turbulent a = 1.6 ± 0.2 scaling index ranges in each subplot. The turbulent intervals include boundary layer (filled circles) and near-shock (open circles) events. For each 12 min interval, bX2(t) + bY2(t) + bZ2(t) = 1. et al., 1978]. The near-terminator part of the boundary layer was investigated during the Pioneer Venus Orbiter mission. Perez-de-Tejada et al. [1991, 1993] have seen the boundary between the bow shock and magnetopause (they are using the term ionopause instead) in the vicinity of and downstream from the terminator. The boundary layer observed in different distances along the inner edge of magnetosheath confirms the persistent presence of this boundary as a rarefaction wave emerging from the terminator magnetopause and extending downstream. The high level of magnetic fluctuations and occurrence of magnetohydrodynamic waves near a quasi-parallel Venusian bow shock is wellknown from the PVO mission [Luhmann et al., 1983]. In this paper we emphasize the detection of intermittency near the Venusian bow shock and its differentiation from magnetosheath noisy fluctuations. Also, we investigate only the magnetic field signatures of the boundary layers. 4.1. Magnetic Field Orientation [28] First, we investigate the local magnetic conditions under which turbulence (or noise) can appear. For this purpose we calculate the instantaneous contribution of magnetic field VSO components to the total magnetic field: bX(t) = BX(t)/B(t), bY(t) = BY(t)/B(t) and bZ(t) = BZ(t)/B(t). In each time step t the sum b2X(t) + b2Y(t) + b2Z(t) = 1. Our goal is to compare the average magnetic field direction during turbulent (c, e) and noisy (d) intervals, when the scaling index is well defined (see Figure 2). Since a is a statistical descriptor over 12 min long intervals the mean values hb2Xi,

hb2Yi, and hb2Zi, respectively, were computed over the same time periods; their sum is again 1. Each average defines the relative contribution of a magnetic VSO component to the average magnetic strength during the considered intervals. [29] Figure 5 compares the local mean magnetic field components with the values of scaling indices estimated during the same 12 min long intervals (open and filled circles). The horizontal lines are averages of points over the noisy a < 1.4 and turbulent a = 1.6 ± 0.2 scaling index ranges. These distinct ranges of a correspond to the statistical results in Figure 3. The filled circles correspond to turbulent intervals within the magnetosheath boundary layer, while, over the same range of as, the open circles correspond to bow shock associated turbulent intervals. [30] The horizontal lines in Figure 5 show that, over the turbulence range, the average of hb2Xi and hb2Zi; is larger while the average of hb2Yi is smaller than over the noise range of scaling indices. Therefore, when turbulence is observed hb2Xi dominates over hb2Yi and the latter is still stronger than the increased hb2Zi. Magnetosheath boundary layer associated turbulence intervals (filled circles – intervals c in Figure 2) show even larger hb2Xi values than the nearshock turbulence values (open circles – intervals e in Figure 2, over the same a range). In accordance with our findings, the analysis of PVO magnetic, electric and plasma data has shown that, near the terminator region but within the magnetosheath boundary layer, the magnetic field is nearly aligned with the Sun-Venus line (X VSO direction). It was interpreted in terms of a friction-like viscous inter-

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Figure 6. Probability density functions (PDFs) constructed from magnetic time series (two-point differences defined through dB = B(t + t) B(t)) of (left) turbulent and (right) noisy intervals. Gaussian fits are shown as dashed gray lines. At small scales (e.g., t = 3 s) the PDF is a non-Gaussian for turbulent time series and Gaussian for noisy time series. At large scales (e.g., t = 30 s) the PDF is a Gaussian in both cases. The error bars represent 95% confidence limits for the mean in each point of dBT. action between the shocked solar wind and the ionospheric plasma over the magnetic polar regions where the draped interplanetary magnetic field lines slip over the planet [Perez-de-Tejada et al., 1993]. The viscous plasma-plasma interaction and frictional heating can explain the enhanced temperatures inside the magnetosheath boundary layer, observed tailward by Venera 10 [Romanov et al., 1978]. An alternative explanation is represented by local turbulent heating of the boundary layer plasma. We note that near the magnetopause, where VEX crosses the turbulent boundary layer, the draped interplanetary magnetic field lines are more stretched having a large X VSO component (Figure 4). Although the number of events is rather limited, the near-shock turbulent regions have smaller X VSO but larger perpendicular Y VSO and Z VSO magnetic components than the magnetosheath boundary layer turbulence (Figure 5). [31] Finally, noise (open circles – intervals d in Figure 2) is associated with a large hb2Yi, smaller hb2Xi; and almost negligible hb2Zi. Since the IMF is mostly in the X-Y plane, the large-scale magnetosheath magnetic field is not affected by the boundaries, but exhibiting only noisy broadband fluctuations and it has the strongest average components in the X-Y plane. [32] Let us investigate deeper now the intermittent nature of turbulence. Besides the characteristic scaling exponents (a 1.6) and the expected average magnetic field directions, intermittency represents another key feature of fully developed turbulence. Therefore, the occurrence of intermittency represents a further evidence that we are dealing

with a real turbulence, capable of heating the background plasma. 4.2. Turbulent Intermittency Versus Gaussianly Distributed Noise [33] Higher-order statistics is needed to fully describe the nature of nonlinear fluctuations. In turbulent, nonhomogeneous plasma flows the shape of the probability density functions (PDFs) is scale-dependent and peaked with long tails [e.g., Frisch, 1995]. Because of the shortness of time series during one crossing the shapes of non-Gaussian PDFs or their scale dependency cannot be evaluated [Vo¨ro¨s et al., 2008]. Instead, we construct PDFs from the 15 magnetic time series (realizations) of turbulence for which spectral scaling near a 1.6 was observed (events from both c and e intervals in Figure 2). Splitting the data into magnetosheath boundary layer and near-shock turbulence regions would not change the shape of turbulent PDFs significantly. PDFs corresponding to the noisy magnetosheath (intervals d in Figure 2) will be also reconstructed. [34] PDFs of two-point differences of magnetic field strength were estimated from dB = B(t + t) B(t), for t = 2. . .30 s. Figure 6 shows the PDFs for t = 3, 30 s only. Boundary turbulence associated PDFs are shown on the left, 1/f noise related PDFs on the right-hand side. The error bars represent 95% confidence limits for the mean in each point of dB. The dashed gray points are least squares Gaussian fits to the experimental two-point PDFs. The ð x mÞ2 , where m Gaussian PDF is given by p1ffiffiffiffiffiffiffi exp 2s2 s 2ðpÞ 2 is the mean and s is the variance. For the smaller timescale t = 3 s the tails of the experimental turbulent PDFs are

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Figure 7. The evolution of (K, top) kurtosis and (S, bottom) skewness with the timescale t, computed from two-point differences defined through dB = B(t + t) B(t)) of (left) turbulent and (right) noisy time intervals. K is peakedness of the PDF; and S is asymmetry around the mean, both relative to a Gaussian distribution, for which S = K = 0; The error bars represent 95% confidence limits for the mean in each value of t. higher than the Gaussian tails. The departure from the Gaussian indicates that nonhomogeneously distributed fluctuations become more probable as the scale decreases because of turbulent structures and long-range interactions [Leubner and Vo¨ ro¨ s, 2005]. Gradual decorrelation is obtained by enhancing the two-point separation scale and a Gaussian is approached for large enough t even in a turbulent field. This is because the typical correlations for turbulent structures are lost if the separation is large, and only Gaussianly distributed noise is observed. For t = 30 s, the PDF is a Gaussian in Figure 6, left. Noise shows Gaussianly distributed PDFs over both timescales. [35] Let us further investigate the shape of PDFs in terms of statistical moments. Using standard procedures [Press et al., 1992], the skewness (S) or the third moment, S ðt Þ ¼

N xj x 1 X N j¼1 s

3

ð1Þ

and the kurtosis (K) or the fourth moment, K ðt Þ ¼

N xj x 1 X N j¼1 s

4

3

ð2Þ

are computed for turbulent and noisy intervals, as above. Here xj = d B(tj, t), s is the standard deviation, x is the mean value of the elements and N is the number of the data points. In this way the dimensionless S(t) and K(t) characterize the scale and time evolution of the shape of a distribution (S is

asymmetry around the mean; K is peakedness or flatness; both relative to a Gaussian distribution, for which S = K = 0). K increases toward small scales in intermittent turbulence [Frisch, 1995]. [36] Figure 7 shows the timescale (t) evolution of kurtosis K(t) (top) and skewness S(t) (bottom) for turbulent (left) and noisy (right) intervals. The error bars represent 95% confidence limits for the mean in each value of t. As is expected from turbulent PDFs in Figure 6, K is increasing as t decreases, which proves that the underlying magnetic fluctuations are non-Gaussian, peaked and intermittent. K practically does not depend on t in the case of 1/f noise, indicating a Gaussianly distributed process. [37] The skewness remains close to zero (Figure 7, bottom) in both cases, showing symmetric distributions around the mean value.

5. Discussion and Conclusions [38] In this paper the unique data from the VEX spacecraft were used to investigate magnetic fluctuation statistics from Venusian magnetosheath and wake regions. The interaction of the solar wind with the planet drives magnetic fluctuations exhibiting different scaling regimes in different regions of near-Venusian space. To identify spectral scaling ranges and indices, we used a wavelet technique, successfully applied for studying the continuous spectra in the Earth’s plasma sheet turbulence [Vo¨ro¨s et al., 2004]. The technique and the data intervals were not optimized for finding waves (peaks in power spectra).

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[39] Three types of scaling were observed. Inside the dayside/tailward magnetosheath, far from boundaries, 1/f noise is present in the prevailing majority of cases indicating the contribution of multiple independent driving sources. It also means, that this type of spectral scaling is not formed by an isolated single physical process. In other words, there might exist multiple sources of fluctuations, but no one of them is close enough in space to dominate in the spectral power. This changes when VEX enters to a region where multiple sources are missing, or where a single physical process dominates with a scaling feature other than that of the noise. In fact, these are distinct regions of nearVenusian space where the ‘‘solar wind – planetary obstacle’’ interactions are enhanced or the multiple sources of noise are shielded. [40] The interaction is enhanced at the terminator ionopause, probably due to the Kelvin-Helmholtz instability, at the magnetosheath boundary layer due to the magnetosheath boundary shear flows and near the quasi-parallel bow shock. The near-planet wake represents a region which is shielded from the plasma of solar wind origin. Filamentary structures, detached plasma clouds, depleted density holes and radially aligned draped magnetic field lines were observed by PVO spacecraft in the near-planet nightside wake [Luhmann and Russell, 1983; Marubashi et al., 1985]. The magnetosheath flow is expected to converge into the wake only near 5 RV behind the planet [Intriligator et al., 1979]. The observed wavy structures near the terminator and in the nightside near-planet wake can be associated with the detached coherent structures or holes. The occurrence/ absence of these structures can be controlled by the direct interaction between the solar wind and ionosphere, e.g., by the high/low solar wind dynamic pressure. In our interpretation, the spectral index a 2.5 indicates, that the coherent wavy structures represent the dominating physical process in this region. Because of the shielding of the near-planet wake, turbulence or noise are absent in this region. Because of the converging flows the shielding can disappear at distances close to or larger than X VSO 5 RV, where the character of fluctuations would change. [41] The magnetosheath regions with distinct scaling indices (turbulence or noise) partially overlap (see Figure 2). This can be explained through a movement of boundaries under the influence of changing upstream IMF conditions. Spatial intermittency, typical for turbulence with scale-dependent non-Gaussian distributions, can lead also to interwoven scaling structures. The turbulent regions are formed near the ‘‘supersonic solar wind flow – planetary obstacle’’ boundaries in the presence of draped IMF. The outer boundary is the bow shock, where the solar wind slows down and heated for the first time. Here, the local turbulence is associated with the quasi-parallel shock geometry. Second time the solar wind flow decelerates at the inner magnetosheath producing a velocity shear near the magnetosheath boundary layer. The near-terminator ionosphere/magnetopause and its interaction with the solar wind plays an important role. The rarefaction wave (the boundary) and the observed plasma conditions in the tailward boundary layer can emerge from the magnetopause near the terminator and extend downstream [Perez-de-Tejada et al., 1991]. Local heating of the plasma within the boundary layers is also possible through the shear flow associated

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turbulence. The spatial size of boundary layer turbulence (see Figure 2) is roughly between 0.5 and 1 RV, shorter turbulent intervals were observed near the quasi-parallel bow shock. The width of the noisy magnetosheath in between the turbulent boundaries is of the same order. The estimation of the spatial sizes of these regions is rather rough. The data are available only from single point measurements (the boundaries can move during the measurements) and the 12 min long data intervals represent a pure resolution. The estimation of scaling indices within shorter time intervals, however, was not possible due to the large statistical errors in these cases. Multipoint Cluster observations show, that turbulence downstream of the quasiparallel terrestrial bow shock is intermittent and that the level of intermittency increases over the spacecraft separation, reaching larger values than 8000 km [Yordanova et al., 2008]. Our results show, that the spatial scale of intermittent turbulence is less than 1 RV near the Venusian bow shock. In between the near-shock region and the magnetosheath boundary layer fluctuations are noisy (in Figure 4, squares between triangles). [42] It was shown by Perez-de-Tejada et al. [1993] that the magnetic field is nearly aligned with the Sun-Venus line (X VSO direction) within the near-terminator magnetosheath boundary layer. We found a similar alignment within the boundary layer at a distance of X VSO 2.2 RV (Figures 2 and 5). It indicates, that the magnetic field geometry detected during the viscous plasma-plasma interactions near the terminator ionosphere is conserved and observed further downstream along the VEX trajectory, where the draped magnetic field is stretched (Figure 4). [43] Data intervals that were found to be turbulent or noisy in the spectral analysis were further investigated using the two-point (time delayed) probability density functions (PDFs). Over large two-point separations (e.g., t = 30 s), both turbulent and noisy PDFs are well fitted by the Gaussian distributions, indicating the occurrence of uncorrelated fluctuations. Over small separations (e.g., t = 3 s), large deviations from the Gaussian distribution are observed only for turbulent intervals, noise remains Gaussianly distributed (Figure 6). The scale dependency of kurtosis (Figure 7) shows that turbulent structures are intermittently distributed, noise is more homogeneous. Skewness remains close to zero in both cases which corresponds to symmetrical distributions. A simultaneous increase of S and K toward small scales would indicate that the multiscale fluctuations in turbulence might be affected by strong large-scale gradients or close boundaries [Vo¨ro¨s et al., 2007]. Therefore, S(t) 0 for t 2 (2 – 30) s is a signature of intermittency, not affected by boundaries along the VEX trajectory over the scale of seconds. However, asymmetries or nonzero skewness in magnetic field statistics can appear below the 1 s timescale. Anyhow, besides the expected spectral index (a 1.6), the observation of intermittency represents a further evidence for the occurrence of real turbulence in the near-Venusian space. The key feature of turbulence is its strong dissipative nature and a capability for the local heating of plasma. Multiscale turbulence can channel the large-scale energy of the flow to kinetic scales, where dissipation processes are strong. We will investigate this point in a different paper.

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[44] Acknowledgments. The work of Z.V. and M.P.L. was supported by the Austrian Wissenschaftsfonds under grant P20131-N16.

References Abry, P., P. Flandrin, M. S. Taqqu, and D. Veitch (2000), Wavelets for the analysis, estimation and synthesis of scaling data, in Self-Similar Network Traffic and Performance Evaluation, edited by K. Park and W. Willinger p. 39, Wiley-Intersci., Hoboken, N. J. Amerstorfer, U. V., N. V. Erkaev, D. Langmayr, and H. K. Biernat (2007), On Kelvin-Helmholtz instability due to the solar wind interaction with unmagnetized planets, Planet. Space Sci., 55, 1811 – 1816, doi:10.1016/ j.pss.2007.01.015. Bauer, S. J., L. H. Brace, D. M. Hunten, D. S. Intriligator, W. C. Knudsen, A. F. Nagy, C. T. Russell, F. L. Scarf, and J. H. Wolfe (1977), The Venus ionosphere and solar wind interaction, Space Sci. Rev., 20, 413 – 430. Biernat, H. K., N. V. Erkaev, U. V. Amerstorfer, T. Penz, and H. I. M. Lichtenegger (2007), Solar wind flow past Venus and its implications for the occurrence of the Kelvin-Helmholtz instability, Planet. Space Sci., 55, 1793 – 1803, doi:10.1016/j.pss.2007.01.015. Brace, L. H., R. F. Theis, and W. R. Hoegy (1982), Plasma clouds above the ionopause of Venus and their implications, Planet. Space Sci., 30, 29 – 37. Bridge, H. S., A. J. Lazarus, C. W. Snyder, E. J. Smith, L. Davis Jr., P. J. Coleman Jr., and D. E. Jones (1967), Mariner V: Plasma and magnetic fields observed near Venus, Science, 158, 1669 – 1673. Elphic, R. C., C. T. Russell, J. G. Luhmann, F. L. Scarf, and L. H. Brace (1981), The Venus ionopause current sheet: Thickness, length, scale and controlling factors, J. Geophys. Res., 86, 11,430 – 11,438. Frisch, U. (1995), Turbulence, the Legacy of A.N. Kolmogorov, 296 pp. Cambridge Univ. Press, New York. Intriligator, D. S., H. R. Collard, J. D. Mihalov, R. C. Whitten, and J. H. Wolfe (1979), Electron observations and ion flow from the Pioneer Venus Orbiter Plasma Analyzer Experiment, Science, 205, 116 – 119. Leubner, M. P., and Z. Vo¨ro¨s (2005), A nonextensive entropy approach to solar wind intermittency, Astrophys. J., 618, 547 – 555. Luhmann, J. G., and C. T. Russell (1983), Magnetic fields in the ionospheric holes of Venus: Evidence for an intrinsic field?, Geophys. Res. Lett., 10, 409 – 412. Luhmann, J. G., M. Tatrallyay, C. T. Russell, and D. Winterhalter (1983), Magnetic field fluctuations in the Venus magnetosheath, Geophys. Res. Lett., 10, 655 – 658. Luhmann, J. G., C. T. Russell, K. Schwingenschuh, and Y. Yeroshenko (1991), A comparison of induced magnetotails of planetary bodies: Venus, Mars and Titan, J. Geophys. Res., 96, 11,199 – 11,208. Marubashi, K., J. M. Grebowsky, H. A. Taylor, J. G. Luhmann, and C. T. Russell (1985), Ionosheath plasma flow in the wake of Venus and the formation of ionospheric holes, J. Geophys. Res., 90, 1385 – 1398. Pe´rez-de-Tejada, H., D. S. Intriligator, and R. J. Strangeway (1991), Steadystate plasma transition in the Venus ionosheath, Geophys. Res. Lett., 18, 131 – 134. Pe´rez-de-Tejada, H., D. S. Intriligator, and R. J. Strangeway (1993), Magnetic field properties of the intermediate transition of the Venus ionosheath, Geophys. Res. Lett., 20, 991 – 994. Press, W. H., B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling (1992), Numerical Recipes in C: The Art of Scientific Computing, 994 pp., Cambridge Univ. Press.

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Romanov, S., V. Smirnov, and O. Vaisberg (1978), Interaction of the solar wind with Venus, Kosmich. Issled., 16, 746. Russell, C. T. (1992), The Pioneer Venus mission, in Venus and Mars: Atmospheres, Ionospheres and Solar Wind Interactions, Geophys Monogr. Ser., vol. 66, edited by J. G. Luhmann, M. Tatrallyay, and R. O. Repin, pp. 225 – 236, AGU, Washington, D. C Russell, C. T., and O. Vaisberg (1983), The interaction of the solar wind with Venus, in Venus, edited by D. M. Hunton et al., pp. 873 – 940, Univ. of Ariz. Press, Tucson. Spreiter, J. R., and S. S. Stahara (1992), Computer modeling of solar wind interaction with Venus and Mars, in Venus and Mars: Atmospheres, Ionospheres and Solar Wind Interactions, Geophys Monogr. Ser., vol. 66, edited by J. G. Luhmann, M. Tatrallyay, and R. O. Repin pp. 345 – 383, AGU, Washington, D. C Terada, N., S. Machida, and H. Shinagawa (2002), Global hybrid simulation of the Kelvin-Helmholtz instability at the Venus ionopause, J. Geophys. Res., 107(A12), 1471, doi:10.1029/2001JA009224. Volwerk, M., T. L. Zhang, M. Delva, Z. Vo¨ro¨s, W. Baumjohann, and K.-H. Glassmeier (2008a), First identification of mirror mode waves in Venus’ magnetosheath?, Geophys. Res. Lett., 35, L12204, doi:10.1029/ 2008GL033621. Volwerk, M., T. L. Zhang, M. Delva, Z. Vo¨ro¨s, W. Baumjohann, and K.-H. Glassmeier (2008b), Mirror-mode-like structures in Venus’ induced magnetosphere, J. Geophys. Res., 113, E00B16, doi:10.1029/2008JE003154. Vo¨ro¨s, Z., et al. (2004), Magnetic turbulence in the plasma sheet, J. Geophys. Res., 109, A11215, doi:10.1029/2004JA010404. Vo¨ro¨s, Z., W. Baumjohann, R. Nakamura, A. Runov, M. Volwerk, T. Takada, E. A. Lucek, and H. Re`me (2007), Spatial structure of plasma flow associated turbulence in the Earth’s plasma sheet, Ann. Geophys., 25, 13 – 17. Vo¨ro¨s, Z., T. L. Zhang, M. P. Leubner, M. Volwerk, M. Delva, W. Baumjohann, and K. Kudela (2008), Magnetic fluctuations and turbulence in the Venus magnetosheath and wake, Geophys. Res. Lett., 35, L11102, doi:10.1029/2008GL033879. Wolff, R., B. Goldstein, and C. Yeates (1980), The onset and development of Kelvin-Helmholtz instability at the Venus ionopause, J. Geophys. Res., 85, 7697 – 7707. Yordanova, E., A. Vaivads, M. Andr, S. C. Buchert, and Z. Vo¨ro¨s (2008), Magnetosheath plasma turbulence and its spatiotemporal evolution as observed by the Cluster spacecraft Phys, Rev. Lett., 100, 205003-1 – 205003-4. Zhang, T. L., et al. (2006), Magnetic field investigation of the Venus plasma environment: Expected new results from Venus Express, Planet. Space Sci., 54, 1336 – 1343. Zhang, T. L., et al. (2007), Little or no solar wind enters Venus’ atmosphere at solar minimum, Nature, 450, 654 – 656, doi:10.1038/nature06026. W. Baumjohann, M. Delva, M. Volwerk, and T. L. Zhang, Space Research Institute, Austrian Academy of Sciences, Schmiedlstrasse 6, A-8042 Graz, Austria. M. P. Leubner and Z. Vo¨ro¨s, Institute of Astro- and Particle Physics, University of Innsbruck, Technikerstrasse 25, A-6020 Innsbruck, Austria. ([email protected])

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Venus Express observations of an atypically distant bow shock during the passage of an interplanetary coronal mass ejection T. L. Zhang,1,2 S. Pope,3 M. Balikhin,3 C. T. Russell,4 L. K. Jian,4 M. Volwerk,1 M. Delva,1 W. Baumjohann,1 C. Wang,2 J. B. Cao,2 M. Gedalin,5 K.-H. Glassmeier,6 and K. Kudela7 Received 11 March 2008; revised 4 July 2008; accepted 4 August 2008; published 22 October 2008.

[1] On 10–11 September 2006 the Venus Express magnetometer detected a very

strong Interplanetary Coronal Mass Ejection (ICME) event with an average field about 2 times higher than that of a typical ICME at 0.72 AU. While the effective obstacle to the solar wind is compressed to a smaller dimension during this ICME event, the bow shock is located far upstream of its nominal location. The observed shocks are weak and appear very dynamic. The location of the shock crossing can be found all along the Venus Express trajectory, which has an apocenter of 12 RV. We attribute the atypical distant bow shock location as an effect of the extremely low Mach number during the ICME. Citation: Zhang, T. L., et al. (2008), Venus Express observations of an atypically distant bow shock during the passage of an interplanetary coronal mass ejection, J. Geophys. Res., 113, E00B12, doi:10.1029/2008JE003128.

1. Introduction [2] Understanding the response of the planetary environment to extreme solar conditions is crucial not only in space weather studies but also in reconstructing the evolution of planetary atmospheres. Recent studies suggest that extreme solar events might play an important role in the evolution of Venus and Mars atmospheres and their water loss processes [Kulikov et al., 2006; Luhmann et al., 2007; Terada et al., 2008]. In searching for clues to the nature and strength of atmospheric evolution, one has to develop models to simulate the solar wind interaction in ancient times. These models of necessity must be tested under present solar wind conditions. Since extreme solar events could provide conditions which might more resemble the ancient solar wind, it is important to study and gather information on these events which presently rarely occur. [3] Here we report observations of such an extreme solar event encountered by Venus Express. While the event itself is worth studying from several different aspects, we limit our effort in this paper to the atypical distant bow shock location. It is well known that the standoff distance of a 1 Space Research Institute, Austrian Academy of Sciences, Graz, Austria. 2 State Key Laboratory of Space Weather, Chinese Academy of Sciences, Beijing, China. 3 Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK. 4 Institute of Geophysics and Planetary Physics, University of California, Los Angeles, California, USA. 5 Department of Physics, Ben Gurion University, Beer-Sheva, Israel. 6 Institut fu¨r Geophysik und Extraterrestrische Physik, Technische Universita¨t Braunschweig, Braunschweig, Germany. 7 Institute of Experimental Physics, Slovak Academy of Sciences, Kosice, Slovakia.


shock in front of an obstacle depends on the size and the shape of the obstacle and the Mach number. In the case of Venus which has no intrinsic magnetic field, the obstacle is formed by the highly conducting ionosphere plus magnetic barrier, which is more stable to the solar wind dynamic pressure variations than the Earth’s magnetosphere. In fact, the location of the Venus bow shock does not vary with solar wind pressure [Zhang et al., 2004]. [ 4 ] Many previous researchers have examined the response of the Venus bow shock position to various external solar wind conditions [e.g., Slavin et al., 1980; Tatrallyay et al., 1983; Russell et al., 1988; Zhang et al., 1990]. It was found that Venus bow shock is controlled by solar EUV flux, solar wind Mach number, and interplanetary magnetic field (IMF) orientation. However, the data for the above studies are mainly from normal solar conditions. For example, no extremely low Mach number cases were included in these studies. [5] Russell and Zhang [1992] first presented two cases of an atypical distant Venus bow shock which were found at 12 RV above terminator. This motivated the modification of the theoretical models for bow shock [e.g., Farris and Russell, 1994; Verigin et al., 2004]. Several cases of distant bow shocks have also been observed at Mars and Earth [Slavin et al., 1994; Fairfield et al., 2001]. Nevertheless, the catalog of the low Mach number bow shock cases is still very small, making it difficult to choose between the existing bow shock models. In this paper we present Venus Express observations of unusual distant bow shock during an Interplanetary Coronal Mass Ejection (ICME) passage. We find that the dynamics of the shock location is controlled by magnetosonic Mach number.

2. Observations [6] Venus Express spacecraft has a polar orbit of 90° inclination, a low periapsis altitude of 250– 350 km at 80°N,

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Figure 1. Time series of interplanetary magnetic field behavior during an Interplanetary Coronal Mass Ejection (ICME) passage. The darker shade is the sheath, and the lighter shade is the flux rope of ICME. and a 24-h period orbit [Titov et al., 2006]. The magnetometer aboard measures the magnetic field continuously along the orbit in various modes [Zhang et al., 2006, 2007]. The data used in this study are at 1 Hz resolution in Venus Solar Orbital (VSO) coordinates where the x axis points from Venus to the Sun, the y axis is opposite to the Venus orbital motion, and the z axis is northward. [7] Figure 1 shows the magnetic field data on 10– 11 September 2006. During the 2 days, Venus Express observed an ICME with a leading shock at 1413 UT on 10 September and a 4-h sheath region thereafter. There was a well-defined left-handed flux rope (1817 UT 10 September-1640 UT 11 September). Within the flux rope, Bx stayed close to zero; By rotated from westward to eastward, while Bz rotated from northward to southward and then remained southward for about 19 h. [8] During a typical orbit, the duration between inbound and outbound bow shock crossing is about 2 h and multiple crossings rarely occur because of the favorable orbital geometry. [9] In Figure 2, we plot only the total field magnitude for part of the flux rope to illustrate the multiple shock crossings during this event. The first inbound bow shock was encountered at 2209 UT on 10 September 2006, and the last outbound shock was encountered at 1220 UT on 11 September 2006. Essentially, the shock crossing is rather ‘‘randomly’’ distributed along the Venus Express orbital trajectory. Altogether, we observe 18 shock crossings. In Table 1 we list for each shock crossing the timing, solar zenith angle (SZA), the shock distance, the upstream magnetic field magnitude Bu, and the shock strength Bd/Bu which is the ratio between upstream and down field magnitudes. We use the coplanarity assumption to determine the shock normal and to obtain qBn angle which is the angle between the shock normal and the upstream magnetic field. The magnetosonic Mach number listed in Table 1 is determined from the shock strength and qBn based on the Rankine-Hugoniot equations by assuming the plasma is isotropic and Maxwellian upstream and downstream, b as 1,

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and the ratio of specific heats of the plasma as 5/3 [Kivelson and Russell, 1995]. [10] Figure 3 displays nine distant bow shocks observed on 10– 11 September. Figure 3a, 3d, and 3g display the magnitude of magnetic field as measured by Venus Express magnetometer for three relatively oblique shocks (qBn angle around 50°) with their Mach number increasing from top to bottom. The shocks in Figures 3b, 3e, and 3h have a qBn angle of around 60° with Mach number increasing from 1.05 to 1.20. Figures 3c, 3f, and 3i display crossings of almost perpendicular shocks with qBn angle around 80° and mach number increasing from 1.18 to 1.40. All nine shocks display a front profile typical for subcritical shocks as the change in field magnitude that takes place in the ramp is 0.15– 0.70 Bu. All of the shocks except that in Figure 3d exhibit a whistler wave precursor typical of dispersive shocks, together with oscillations downstream of the ramp indicating the kinematic nature of ion relaxation [Balikhin et al., 2008]. Typical crossings of interplanetary shocks with such a high relative spacecraft shock speed do not allow the observation of the structure of the shock front and whistler wave precursor. On the contrary, all nine plots displayed in Figure 3 show well defined precursors providing another argument in favor of bow shock crossings as opposed to interplanetary shocks. [11] Figure 4 shows the locations of the shock crossings during this ICME passage. Also depicted is the average shock position model derived from the initial Venus Express observations at solar minimum [Zhang et al., 2008a]. As shown in Figure 4, on 10– 11 September 2006, Venus’ bow shock expanded far upstream and its extension was only limited by the Venus Express apocenter distance of 12 RV. It is notable that scaling this distance to the Earth’s magnetospheric obstacle, this would be equivalent to finding the bow shock at a distance of 180 RE above the terminator. Thus all the unusual distant bow shock cases summarized by Fairfield et al. [2001] are far less distant compared to the case presented here. In fact, such a very atypical distant bow shock as recorded by Venus Express on 10– 11 September, occurred only twice, namely, on 6 July 1981 and 4 August 1987, both at Venus [Russell and Zhang, 1992].

3. Discussion [12] As mentioned in the introduction, it was found that Venus bow shock is controlled by EUV flux, solar wind Mach number and IMF orientation. The effect of the EUV flux is mainly due to its control of the obstacle size, i.e., the upper atmosphere neutral and ionospheric ions scale

Figure 2. Total magnitude of the magnetic field observed during ICME with 18 multiple bow shock crossings indicated by the short vertical lines.

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Table 1. Parameters of Distant Bow Shock Crossings on 10 – 11 September 2006 Day

Shock Crossing Time (UT)

SZAa (deg)

Shock Distance (RV)

2208:57 2211:19 2214:47 2219:57 2221:49 2301:03 2303:31 0422:07 0434:12 0443:49 0921:19 0927:50 0944:22 0957:17 1158:19 1210:05 1216:18 1220:24

112 113 113 113 113 116 116 40 45 49 81 82 82 83 88 89 89 89

9.43 9.40 9.35 9.28 9.25 8.64 8.59 2.80 3.28 3.64 9.72 9.81 10.01 10.16 11.27 11.35 11.39 11.41

10 September 2006

11 September 2006

Shock Normal 0.49, 0.15, 0.28, 0.32, 0.49, 0.28, 0.81, 0.04, 0.46, 0.33, 0.60, 0.20, 0.73, 0.18, 0.80, 0.16, 0.95, 0.26, 0.63, 0.77, 0.79, 0.12, 0.10, 0.94, 0.81, 0.27, 0.65, 0.61, 0.67, 0.02, 0.71, 0.07, 0.87, 0.06, 0.91, 0.41,

0.86 0.91 0.83 0.58 0.82 0.78 0.66 0.57 0.17 0.13 0.60 0.31 0.52 0.45 0.74 0.71 0.48 0.04

Bu (nT)

qBn (deg)

Bd/Bu

Magnetosonic Mach Number

31.49 31.97 32.15 31.99 31.69 32.24 32.28 32.97 31.54 32.06 33.47 32.98 32.93 31.47 28.85 28.26 28.46 27.55

64 81 71 81 69 80 78 72 79 52 49 62 61 52 45 53 59 60

1.29 1.27 1.26 1.34 1.30 1.31 1.30 1.52 1.35 1.33 1.10 1.10 1.09 1.12 1.08 1.09 1.07 1.10

1.20 1.18 1.19 1.23 1.21 1.20 1.22 1.40 1.24 1.28 1.06 1.08 1.05 1.07 1.07 1.06 1.05 1.05

a

SZA, solar zenith angle.

heights. While EUV variation is mainly responsible for the Venus bow shock solar cycle variation [cf. Russell et al., 1988], it probably has lesser effect on the daily shock location variation. Examining the magnetic field data on 10– 11 September 2006, we find that the effective obstacle,

the magnetopause of the induced magnetosphere, is located at 391 km altitude with a SZA of 71°, which is much lower than its averaged location of 738 km [Zhang et al., 2008b]. This is probably due to the enhancement of solar wind dynamic pressure within the ICME. Typically, the maxi-

Figure 3. Nine distant bow shock crossings grouped in columns according to qBn angle and sorted in rows according to Mach number. 3 of 5

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Figure 4. Venus Express trajectories during 10– 11 September with mean bow shock from Zhang et al. [2008a]. Small dots display the distant bow shock events during ICME passage. Short lines are the shock normal determined from coplanarity. mum solar wind dynamic pressure within ICMEs is about 20.7 nPa which is about 3 times higher than average solar wind condition at solar minimum [Jian et al., 2007]. [13] Thus, the dynamic of the bow shock location in this event can only be explained by low Mach number and IMF orientation. Russell and Zhang [1992] found that when the Mach number approaches unity, the bow shock could be very dynamic. The models by Farris and Russell [1994] and Verigin et al. [2004] both indicate that the bow shock location is a very sensitive function of Mach number at low Mach numbers. [14] Another indirect Mach number effect is the dependence of the bow shock on IMF orientation. Strictly speaking, Mach number effect includes the IMF orientation since the IMF orientation comes into play when one considers the magnetosonic Mach number. However, in common practice, one use the perpendicular magnetosonic Mach number in the solar wind to refer as the Mach number and calculates only the nose location. The field direction effect is omitted from the Mach number effect as are effects due to the flaring angle. Since qBn, the angle between the shock normal and the upstream magnetic field, varies along the surface of the bow shock, the magnetosonic Mach number varies. This qBn effect can move the quasi-parallel

shock to be closer to the obstacle than the quasi-perpendicular shock as observed by Zhang et al. [1991]. The resulting qBn effect on the bow shock location was determined to be 0.1 RV. This is a rather small value. However, it is worth to point out that the 0.1 RV qBn effect was determined for normal solar wind conditions with an average Mach number of 5. This should not be true when the Mach number is very low. [15] To combine the IMF orientation effect into the Mach number effect, we determine the real magnetosonic Mach number using the shock strength and qBn based on the Rankine-Hugoniot equations in this study. The Mach number moderately varies between 1.05 and 1.4; thus, the shock remains in the subcritical regime. The extreme low Mach number is responsible for both the unusually distant and dynamics of the shock location during the ICME passage. [16] While ICMEs at Venus are often observed, extremely distant bow shock observations are very rare. Since the beginning of Venus Express magnetic field measurements in April 2006, many ICMEs were identified [Luhmann et al., 2007], but distant bow shocks were only identified during the ICME presented here. Recently, Jian et al. [2007] surveyed PVO data during 1979–1988, and found 124 ICMEs. However, distant bow shock crossings were only reported

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for the two events of 6 July 1981 and 4 August 1987 by Russell and Zhang [1992], who also surveyed all the high field data. [17] It is interesting to examine why the majority of ICMEs do not move the bow shock to an atypical distant location; that is, it is interesting to compare the properties of 10– 11 September ICME case to typical ICME properties. Figure 1 shows the whole ICME lasted about 26.45 h, with a maximum magnetic field (Bmax) of 35 nT. This is near values for an average ICME at 0.72 AU, which has a median duration of 23.2 h and a Bmax of 26 nT [Jian et al., 2007]. In addition, the ratio of sheath duration to the whole ICME duration is about 15%, close to the median value (16%) of ICMEs at 0.72 AU. [18] The only notable difference of this ICMEs from the average ICMEs is the average magnetic field magnitude. As we can see in Figure 1, the ICME field magnitude is rather steady and stays above 30 nT most of the time within the flux rope. This is about 2 times higher than the average field value of 16 nT within a typical ICME [Forsyth et al., 2006]. Reexmaining the distant bow shock events from Russell and Zhang [1992], we find that those events also occur when the magnetic field magnitude is about 30 –40 nT. The magnetosonic Mach number consists of both sonic and Alfvenic Mach numbers. Since the Alfvenic Mach is inversely proportional to magnetic field magnitude, a twofold increases of the field can lead to a factor of 2 decrease of the Alfvenic Mach number. In this ICME event, the persistent abnormal enhanced magnetic field within flux rope might be the reason of low Mach number which resulted in the rare distant bow shock observations. [19] In summary, Venus plasma environment provides a better natural laboratory to study the fundamental plasma physics since the obstacle to solar wind is rather stable and less variable in comparison with the Earth’s magnetosphere. This is particularly true during the ICME passages when the solar wind dynamic pressure is about 3 times higher so that the ionosphere is magnetized and the obstacle is less compressive. In addition, the slowly varying IMF direction, strength and other solar wind parameters provide a quasisteady background condition, making it easier to distinguish between spatial and temporal variations. Finally, such extreme solar events provide rare vehicles for testing simulation and modeling to understand planetary atmosphere evolution. [20] Acknowledgments. The work at UCLA was supported by NASA under research grant NNG06GC62G. The work in China was supported by NNSFC grants 40621003, 40628003, and by 973 Program 2006CB806305; M.G. was partially supported by ISF grant 275/07; The work in Slovakia was supported by Slovak Research and Development Agency under the contract APVV-51-053805.

References Balikhin, M. A., T. L. Zhang, M. Gedalin, N. Y. Ganushkina, and S. A. Pope (2008), Venus Express observes a new type of shock with pure kinematic relaxation, Geophys. Res. Lett., 35, L01103, doi:10.1029/ 2007GL032495. Fairfield, D. H., I. H. Cairns, M. D. Desch, A. Szabo, A. J. Lazarus, and M. R. Aellig (2001), The location of low Mach number bow shocks at Earth, J. Geophys. Res., 106, 25,361 – 25,376, doi:10.1029/ 2000JA000252. Farris, M. H., and C. T. Russell (1994), Determining the standoff distance of the bow shock: Mach number dependence and use of models, J. Geophys. Res., 99, 17,681 – 17,689, doi:10.1029/94JA01020.

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Forsyth, R. J., et al. (2006), ICMEs in the inner heliosphere: Origin, evolution and propagation effects, Space Sci. Rev., 123, 383 – 416, doi:10.1007/ s11214-006-9022-0. Jian, L. K., C. T. Russell, J. G. Luhmann, R. M. Skoug, and J. T. Steinberg (2007), Stream interactions and interplanetary coronal mass ejections at 0.72 AU, Sol. Phys., 249, 85 – 101. Kivelson, M. G., and C. T. Russell (Eds.) (1995), Introduction to Space Physics, 568 pp., Cambridge Univ. Press, Cambridge, U. K. Kulikov, Y. N., H. Lammer, H. I. M. Lichtenegger, N. Terada, I. Ribas, H. K. Biernat, C. Kolb, E. F. Guinan, S. Barabash, and R. Lundin (2006), Atmospheric and water loss from early Venus, Planet. Space Sci., 54, 1425 – 1444, doi:10.1016/j.pss.2006.04.021. Luhmann, J. G., W. T. Kasprzak, and C. T. Russell (2007), Space weather at Venus and its potential consequences for atmosphere evolution, J. Geophys. Res., 112, E04S10, doi:10.1029/2006JE002820. Russell, C. T., and T. L. Zhang (1992), Unusually distant bow shock encounters at Venus, Geophys. Res. Lett., 19, 833 – 836, doi:10.1029/ 92GL00634. Russell, C. T., E. Chou, J. G. Luhmann, P. Gazis, L. H. Brace, and W. R. Hoegy (1988), Solar and interplanetary control of the location of the Venus bow shock, J. Geophys. Res., 93, 5461 – 5469, doi:10.1029/ JA093iA06p05461. Slavin, J. A., R. C. Elphic, C. T. Russell, F. L. Scarf, J. H. Wolfe, J. D. Mihalov, D. S. Intriligator, L. H. Brace, H. A. Taylor Jr., and R. E. Daniell Jr. (1980), The solar wind interaction with Venus: Pioneer Venus observations of bow shock location and structure, J. Geophys. Res., 85, 7625 – 7641, doi:10.1029/JA085iA13p07625. Slavin, J. A., et al. (1994), The solar wind interaction with Mars: Phobos-2 bow shock observations on 24 March 1989, in COSPAR Colloquia, vol. 4, edited by T. I. Gombosi, pp. 279 – 283, Pergamon, Amsterdam. Tatrallyay, M., C. T. Russell, J. D. Mihalov, and A. Barnes (1983), Factors controlling the location of the Venus bow shock, J. Geophys. Res., 88, 5613 – 5621, doi:10.1029/JA088iA07p05613. Terada, N., et al. (2008), The role of the induced magnetic field and ion loss of atmosphere and water from early Mars under extreme solar wind and XUV conditions, Astrobiology, in press. Titov, D. V., et al. (2006), Venus Express science planning, Planet. Space Sci., 54, 1279 – 1297, doi:10.1016/j.pss.2006.04.017. Verigin, M. I., et al. (2004), Unusually distant bow shock encounters at Mars: Analysis of March 24, 1989 event, Space Sci. Rev., 111, 233 – 243, doi:10.1023/B:SPAC.0000032713.86796.d1. Zhang, T. L., J. G. Luhmann, and C. T. Russell (1990), The solar cycle dependence of the location and shape of the Venus bow shock, J. Geophys. Res., 95, 14,961 – 14,967, doi:10.1029/JA095iA09p14961. Zhang, T. L., K. Schwingenschuh, C. T. Russell, and J. G. Luhmann (1991), Asymmetries in the location of the Venus and Mars bow shock, Geophys. Res. Lett., 18, 127 – 129, doi:10.1029/90GL02723. Zhang, T. L., K. K. Khurana, C. T. Russell, M. G. Kivelson, R. Nakamura, and W. Baumjohann (2004), On the Venus bow shock compressibility, Adv. Space Res., 33, 1920 – 1923, doi:10.1016/j.asr.2003.05.038. Zhang, T. L., et al. (2006), Magnetic field investigation of the Venus plasma environment: Expected new results, Planet. Space Sci., 54, 1336 – 1343, doi:10.1016/j.pss.2006.04.018. Zhang, T. L., et al. (2007), Little or no solar wind enters Venus’ atmosphere at solar minimum, Nature, 450, 654 – 656, doi:10.1038/nature06026. Zhang, T. L., et al. (2008a), Initial Venus Express magnetic field observations of the Venus bow shock location at solar minimum, Planet. Space Sci., 56, 785 – 789, doi:10.1016/j.pss.2007.09.012. Zhang, T. L., et al. (2008b), Initial Venus Express magnetic field observations of the magnetic barrier at solar minimum, Planet. Space Sci., 56, 790 – 795, doi:10.1016/j.pss.2007.10.013. M. Balikhin and S. Pope, Department of Automatic Control and Systems Engineering, University of Sheffield, Western Bank, Sheffield S10 2TN, UK. W. Baumjohann, M. Delva, M. Volwerk and T. L. Zhang, Space Research Institute, Austrian Academy of Sciences, Schmiedlstrasse 6, A-8042 Graz, Austria. J. B. Cao and C. Wang, State Key Laboratory of Space Weather, Chinese Academy of Sciences, 52 Sanlihe Road, Beijing 100864, China. M. Gedalin, Department of Physics, Ben Gurion University, Beer-Sheva 84105, Israel. K.-H. Glassmeier, Institut fu¨r Geophysik und Extraterrestrische Physik, Technische Universita¨t Braunschweig, Pockelsstrasse 14, D-38106 Braunschweig, Germany. L. K. Jian and C. T. Russell, Institute of Geophysics and Planetary Physics, University of California, Los Angeles, CA 90095, USA. K. Kudela, Institute of Experimental Physics, Slovak Academy of Sciences, Watsonova 47, 040 01 Kosice, Slovakia.

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The transit of Venus

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The Wizard of Venus

The Forgery of Venus

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