Advances in Geophysics: Satellite Oceanic Remote Sensing

ADVANCES IN GEOPHYSICS VOLUME 27 Contributors to This Volume JOHN c. ALISHOUSE J. R. APEL ROSWELLW. AUSTIN DONALDE...

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ADVANCES IN

GEOPHYSICS

VOLUME 27

Contributors to This Volume JOHN c. ALISHOUSE J. R. APEL ROSWELLW. AUSTIN DONALDE. BARRICK PETERG. BLACK JOHNT. BRUCKS W. J. CAMPBELL VINCENTJ. CARDONE D. J . CAVALIERI DENNISK . CLARK L. S. FEDOR R. CECILGENTRY P. GLOERSEN E I. GONZALEZ HOWARDR. GORDON CECILIAGIRZGRIFFITH JEFFREYD. HAWKINS NANCYJ. HOOPER

WARRENA. Hovis WILBERB. HUSTON R. MICHAELLAURS BELINDAJ. LIPA E. PAULMCCLAIN RONALDD. MCPHERSON N. M. MOGNARD BRUCEH. NEEDHAM JAMES OVERLAND S. PETEHERYCH JR. WILLARD J. PIERSON, DUNCANB. Ross C. L. RUFENACH I11 JOHNW. SHERMAN, C. T. Swim JOHNC. WILKERSON CHARLES S. YENTSCH Yu TSANN-WANG H. J. ZWALLY

Advances in

GEOPHYSICS VOLUME 27

Satellite Oceanic Remote Sensing Edited by

BARRY SALTZMAN Department of Geology and Geophysics Yale University New Haven, Connecticut

1985

Academic Press, Inc. (Harcourf Brace Jovanovich, Publishers)

Orlando San Diego New York London Toronto Montreal Sydney Tokyo

COPYRIGHT 8 1985, BY ACADEMIC PRESS, INC. ALL RIGHTS RESERVED. NO PART OF THIS PUBLICATION MAY BE REPRODUCED OR TRANSMITTED IN ANY FORM OR BY ANY MEANS,ELECTRONIC OR MECHANICAL, INCLUDING PHOTOCOPY, RECORDING, OR ANY INFORMATION STORAGE AND RETRIEVAL SYSTEM, WITHOUT PERMISSION IN WRITING FROM THE PUBLISHER.

ACADEMIC PRESS, INC. Orlando, Florida 32887

Unitcd Kingdom Edition published by ACADEMIC PRESS INC. (LONDON) LTD. 24-28 Oval Road, London NWI 7DX

LIBRARYOF CONGRESS CATALOG CARD NUMBER:52-12266 ISBN: 0-12-018827-9 PRINTED IN THE UNlTED STATES OF AMERICA 85868788

9 8 7 6 5 1 3 2 1

CONTENTS CONTRIBUTORS ............................................................................... FOREWORD .................................................................................... PREFACE .......................................................................................

ix

...

xiii

xv

1 . Introduction JOHN W. SHERMAN. Ill

:.

I . Purpose and Scope .................. ................................................... 2 . Historical Background .................................................................. 3 . Approach and Organization ............................................................ References ................................................................................ 2. The 1978 Oceanic Trilogy: Seasat. Nimbus-7. and TIROS-N

JOHN W. SHERMAN . I11 1. Introduction ............................................................. ........................... 2 . The Seasat Sensors and Results .... 3. The Nimbus-7 Sensors and Results ................................................... 4 . The TIROS-N Series and Results .. ........................... 5 . Summary .................................................................................. References ............................ ...........................

11

12 47 52 56 59

3 . Analysis and Interpretation of Altimeter Sea Echo DONALD E. BARRICK AND BELINDA J . LIPA 1 . Introduction .....................................

.......................... 2 . The Convolutional Representation of the Signal and Its Use ...................... 3. Model Fits of Recovered Sea Surface Probability Density ......................... 4 . The Study of Altimetric Biases Using Models ....................................... 5 . Electromagnetic Bias .................................................................... 6. A General, Improved Deconvolution Algorithm .................................... 7. Conclusions ............................................................................... Appendix ......... ................................................................... References .........................................................

V

61 62 68 73 86 93 96 97 99

vi

CONTENTS

4 . Oceanic Surface Winds

DUNCAN B . Ross. VINCENT J . CARDONE. JAMESOVERLAND. RONALD D . MCPHERSON . WILLARD J . RERSON. JR.,AND TSANN-WANG Yu 1 . Introduction ............................................................................... 2 . Mechanism for Measurement of Surface Wind Speed Using Microwave Systems ..................................................................... 3. The Marine Surface Boundary Layer ................................................. 4 . Development of a Model Function Relating SASS Data to Surface Wind Speed .................................................................. 5 . Global Data Assimilation Experiments ............................................... 6 . Conclusions ............................................................................... References ................................................................................

101 103 105 109 121 137 138

5 . Surface and Internal Ocean Wave Observations

c. L . RUFENACH.L . S . FEDOR. J . R . APEL.AND F . I . GONZALEZ 1. Introduction ............................................................................... 2 . Ocean Surface Waves ................................................................... 3. Ocean Internal Waves ................................................................... 4 . Summary and Conclusions ............................................................. References ................................................................................

141

142 176

192 193

6. Seasat Microwave Wind and Rain Observations in Severe Tropical and Midlatitude Marine Storms PETERG . BLACK.R . CECILGENTRY.VINCENT J . CARDONE. AND JEFFREY D . HAWKINS 1 . Introduction ............................................................................... 2 . The Nature of Severe Marine Storms ................................................. 3. Microwave Measurements in Severe Marine Storms ............................... 4 . Seasat Observations of Rain Rate and Microwave Attenuation in Tropical Cyclones ..................................................................... 5. Seasat Surface Wind Observations in Tropical Cyclones ........................... 6. Analysis of Individual Storms and Comparison of Seasat Data with “Surface-Truth’ Data .............................................................. 7 . Seasat Observations of Sea Surface Temperature Near Tropical Cyclones ....... 8. Application of Seasat Observations to Operational Marine Storm Forecasting Needs ....................................................................... 9. Conclusions ............................................................................... References ................................................................................

198 200 206 208

215 218 264 265 273 274

vii

CONTENTS

7. Sea Surface Temperature Determinations

JOHNc . ALISHOUSE AND E. PAUL MCCLAIN I. 2. 3. 4.

Introduction to Remote Sensing of Sea Surface Temperature ..................... Microwave Sensing of Sea Surface Temperature .......................... ................... Infrared Sensing of Sea Surface Temperature ..... Summary .............................. ...................................... References ........ ................................

279 28 1 286 292 294

8 . Ocean Color Measurements

HOWARD R. GORDON, ROSWELL W. AUSTIN, DENNIS K. CLARK, WARREN A. HOVIS.AND CHARLES s. YENTSCH

......................... ...................... 297 303 ............................. 2. The CZCS System ................. ................ 306 3. Response to Oceanic and Atmospheric Conditions ..... 1 . Introduction .......

4. Remote Sensing of the Phytoplankton Pigment 5. Remote Sensing of the Diffuse Attenuation Co ................... 6. Summary and Conclusions .......................... References .................. ................................

313 322 33 1 332

9. Observations of the Polar Regions from Satellites Using Active and Passive Microwave Techniques

C. T. SWIFT.W. J. CAMPBELL, D. J . CAVALIERI, L. S. FEDOR.P. GLOERSEN. S. PETEHERYCH. AND H. J. ZWALLY N. M. MOGNARD, I. 2. 3. 4. 5.

Introduction .......................... .................................. .................. Sea-Ice Observations by Seasat: A .................................... Sea-Ice Observations by Nimbus-7 ... Observations of Ocean Waves in the Antarctic ................. Seasat Altimeter Observations of Ice Sheets ........ ................... References .................................... .............................

336 337 361 373 379 390

10. Precipitation in Tropical Cyclones

CECILIA GIRZGRIFFITH AND L. S. FEDOR 1. Introduction ................................. .............. 2. Rain Rate Estimates from the VIRR ...................................... ................................... ..... 3. Future Studies ....... ................................ References ..................................

393 394 415 416

viii

CONTENTS

11. Living Marine Resources Applications

R . MICHAEL LAURSAND JOHN T. BRUCKS 419 1 . Introduction ............................................................................... 2 . Satellite Ocean Remote-Sensing Applications in Fisheries Research ............. 421 3 . Utilization of Satellite Data in Fisheries-Aids Products Distribution 443 to Fishermen .............................................................................. 450 References ................................................................................

Appendix A

.

instruments

A.1. Introduction ............................................................................ A.2. Radar Altimeter ....................................................................... A.3. Seasat Scatterometer System ........................................................ A.4. Scanning Multichannel Microwave Radiometer .................................. A S . Synthetic Aperture Radar ............................................................ A.6. Coastal Zone Color Scanner .........................................................

453 453

453 457 451 457

Appendix B. Seasat Validation Program JOHN C . WILKERSON

B .1 . B.2. B.3. B.4. B .5. B.6. B .I. B.8.

Introduction ............................................................................ Weather Conditions ................................................................... Plan of Operations (General) ........................................................ OSS Oceanographer .................................................................. Ocean Weather Station PAPA ....................................................... NOAA Data Buoys ................................................................... Aircraft ................................................................................. Total Data Sets ........................................................................

463 466 467 469 474 474 471 477

Appendix C . Data Availability BRUCEH . NEEDHAM C .I . Seasat Data Archive and Distribution .............................................. C.2. Nimbus-7 Coastal Zone Color Scanner (CZCS) .................................. C.3. Ordering Data .........................................................................

Appendix D. Glossary of Acronyms

...................

481 487 493 495

WILBERB . HUST~NAND NANCYJ . HOOPER

Appendix E. Glossary of Symbols

....................

499

INDEX ..........................................................................................

505

WILBER B. HUSTON

CONTRIBUTORS Numbers in parentheses indicate the pages on which the authors’ contributions begin.

JOHN C. ALISHOUSE, NOAAINESDISISEL, Washington, D.C. 20233 (279)

,J. R. APEL,Applied Physics Laboratory, The Johns Hopkins University,Laurel, Maryland 20707 (141)

ROSWELLW. AUSTIN,Visibility Laboratory, Scripps Institution of Oceanography, La Jolla, California 92093 (297) DONALDE. BARRICK, Ocean Surface Research, Boulder, Colorado 80303 (61) PETERG . BLACK,Atlantic Oceanographicand Meteorological Laboratory, Hurricane Research Division, Miami, Florida 33149 (197) JOHN T. BRUCKS,National Marine Fisheries Service, Fishery Engineering and Development Division, National Space Technology Laboratories, NSTL Station, Mississippi 39529 (419) W. J. CAMPBELL, United States Geological Survey, University of Puget Sound, Tacoma, Washington 98416 (335)

VINCENT J. CARDONE,Oceanweather, Inc., White Plains, New York (101, 197) D. J. CAVALIERI, National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, Maryland 20771 (335) DENNISK. CLARK,National Oceanic and AtmosphericAdministration, National Environmental Satellite, Data, and Information Service, Washington, D.C. 20233 (297) 1

L. S. FEDOR,National Oceanic and AtmosphericAdministration, WavePropagation Laboratory, Environmental Research Laboratories, Boulder, Colorado 80303 (141,335, 393) R . CECILGENTRY,Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29631 (197)

P. GLOERSEN, National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, Maryland 20771 (335) ’

F. I. GONZALEZ, National Oceanic and Atmospheric Administration, Pacific Marine Environmental Laboratory, Environmental Research Laboratories, Seattle, Washington 98115 (141)

ix

CONTRIBUTORS

X

HOWARDR. GORDON, Department of Physics, University of Miami, Coral Gables, Florida 33124 (297)

CECILIAGIRZ GRIFFITH,National Oceanic and Atmospheric Administration, Weather Research Program, Environmental Research Laboratories, Boulder, Colorado 80303 (393) JEFFREY D. HAWKINS ,* Atlantic Oceanographic and Meteorological Laboratory, Hurricane Research Division, Miami, Florida 33149 (197) NANCYJ. HOOPER,Metrics, Incorporated, Atlanta, Georgia 30339 (495)

WARRENA. HOVIS, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, Washington, D.C. 20233 (297) WILBER

B. HUSTON,OAO Corporation, Greenbelt, Maryland 20770 (495,499)

R. MICHAELLAURS,National Marine Fisheries Service, Southwest Fisheries Center, La Jolla, California 92037 (419) \

BELINDAJ.

LIPA,

Ocean Surface Research, Woodside, California 94062 (61)

E . PAULMCCLAIN, NOAAINESDISICESL, Washington, D.C. 20233 (279)

. RONALDD. MCPHERSON,

National Oceanic and Atmospheric Administration, National Meteorological Center, Washington, D. C. 20233 (101) N. M. MOGNARD, Groupe de Recherche de Geodesic Spatiales, Centre Nacional d’etudes Spatiales, Toulouse, Cedex, France 31055 (335) BRUCEH. NEEDHAM, NOAAINESDISINCDC, Satellite Data Services Division, Washington, D.C. 20233 (481)

JAMES OVERLAND, National Oceanic and Atmospheric Administration, Pacific Marine Environmental Laboratories, Seattle, Washington 98115 (101)

\

S . PETEHERYCH, Atmospheric Environment Service, Downsview, Ontario, Canada M3H 5T4 (335) \

WILLARD J. PIERSON, JR., CUNY Institute of Marine and Atmospheric Sciences, The City College of New York, New York, New York 10031 (101)

,DUNCANB. ROSS, National Oceanic and Atmospheric Administration, Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida 33149 (101)

*Present address: Naval Ocean Research and Development Activity (NORDA), Bay St. Louis, Mississippi.

CONTRlB UTORS

xi

C . L. RUFENACH,National Oceanic and Atmospheric Administration, Wave Propagation Laboratory, Environmental Research Laboratories, Boulder, Colorado 80303 (141)

JOHN W. SHERMAN, 111, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, Washington, D.C. 20233 (1, 11) C. T. SWIFT,Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, Massachusetts 01002 (335)

JOHN C. WILKERSON, NOAAINESSIOceanic Sciences Branch, Washington, D.C. 20233 (463) CHARLESS. YENTSCH,Bigelow Laboratory for Ocean Sciences, West Boothbay Harbor, Maine 04575 (297) TSANN-WANG Yu,National Oceanic and Atmospheric Administration, National Meteorological Center, Washington, D.C. 20233 (101)

H . J. ZWALLY,National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, Maryland 20771 (335)

This Page Intentionally Left Blank

FOREWORD With the rapid refinement of satellite remote sensing techniques over the last decade, we have witnessed a virtual step-function increase in the acquisition of oceanic data; it is not an overstatement to say that these new techniques have opened a new era in oceanographic research. In this special volume of Advances in Geophysics we are pleased to present a review of the development, and main results, of the pioneering studies conducted primarily by the National Oceanic and Atmospheric Administration (NOAA) following from the operation of three satellite systems launched by the National Aeronautics and Space Administration (NASA) during 1978: Seasat, Nimbus-7, and TIROS-N. It is clear that all aspects of oceanographic research, including the study of surface winds, waves, currents, thermal structure, ice coverage, and biota, have been advanced considerably by the unique new data sets generated. We hope that this review volume will provide a useful summary of the accomplishments and, as developmentsin this area proceed at their almost breathless current pace, that this review will remain as a landmark of the early breakthroughs. The editor and publisher are grateful to all of the authors for their contributions, and especially to John W. Sherman, 111, who was the driving force behind the volume and main coordinator of all of the contributions. One of the contributors to the volume communicated the following remarks concerning John Sherman: There are many individuals who no doubt are justly deserving of some sort of acknowledgement for contributions made to satellite-oceanography. But, in my opinion, there’s one who stands out-Jack Sherman. I believe the science of satellite-oceanography has made the progress it has largely because of Jack’s efforts. Jack has worked and fought tirelessly and unselfishly for more and better sensors, and for funding and research programs to ensure the development of satellite-oceanography; I would guess that virtually every U.S. scientist who has conducted research in satellite-oceanography during the past I % decades has at one time or another received funding as a direct or indirect result of Jack’s efforts.

It is a pleasure to acknowledge these efforts with the publication of this volume.

BARRYSALTZMAN

xiii


PREFACE Don’t try to describe the ocean if you’ve never seen it. . . . Jimmy Buffet

From ancient mariners to modern troubadours mankind has sought to describe the vast beauty, serenity, and awe of the sea. Many observations by early mariners were maintained orally in order to protect unique routes used for commerce. The spectrum of written descriptions ranges from chastushkas to highly mathematical papers. Over the years the methods of description have shifted from sight, sound, and smell, to lead lines and Nansen bottles, and on to current techniques of science. Each new method has provided another view of the sea. In 1785 marine chronometers were perfected to measure longitude as well as the traditional latitude. The addition of longitude greatly assisted navigation for commerce and allowed the birth of oceanology wherein measurements could be located in time and space. During the intervening 200-year period navigational improvements were extended, so that by 1985 satellite techniques provide global, three-dimensional navigational accuracies on the order of meters. In addition to contributions to navigation, earth-orbiting satellites have had major scientific success in communications and environmental observations; indeed, so successful that some satellites have become operational systems. Both research and operational environmental satellites have modified and extended the understanding and knowledge of many global phenomena, including concepts about the ocean. These satellite observations of the ocean describe, at best, the upper tens of meters of the sea surface. However, this is the most dynamic portion of the ocean requiring the greatest frequency of observations. For a large portion of the global oceans, seasonal effects do not occur below 200 meters. Thus oceanic remote sensing opportunities afforded by satellites to view the upper ocean are not competitive with those in situ systems used on ships and buoys, but free these in situ systems to describe other, deeper portions of the ocean. During this same 200-year period, commerce has become even more dependent on the seas for many types of resources, recreation, and transportation. Because of this importance, the 1984/1985 time period has been recognized by Presidential Proclamation as the Year of the Ocean. xv

xv i

PREFACE

Further, the Silver Anniversary of earth-observing satellites is April 1, 1985, when 25 years earlier the first Television and InfraRed Observation Satellite (TIROS-1) was launched by the National Aeronautics and Space Administration. In this evolutionary period for satellites, remote sensing techniques for oceanic observations developed as a significant scientific program. The purpose of this volume of Advances in Geophysics is to provide a summary of the results of this scientific achievement which provides yet another description of mankind’s view of the ocean. JOHN W. SHERMAN, I11

INTRODUCTION JOHNW. SHERMAN, I11 National Oceanic and Atmospheric Administration National Environmental Satellite. Data. and Information Service Washington, D. C .

1. Purpose and Scope . . . . . . . . . . . . . . . . . . . . . . . 2. Historical Background . . . . . . . . . . . . . . . . . . . . . . 3. Approach and Organization . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 8 9

1. PURPOSE AND SCOPE

During 1978 three satellite systems were launched by the National Aeronautics and Space Administration (NASA) that provided both a new view of the earth’s oceans and a revised picture of the nature and dynamics of the behavior of the ocean surface and its interaction with the atmosphere and land. The three 1978 satellite systems were Seasat, a California Institute of Technology’s Jet Propulsion Laboratory (JPL) development, launched June 26; and two satellites developed by NASA Goddard Space Flight Center (GSFC)-TIROS-N, launched October 13 to serve as the prototype third-generation operational satellite for NOAA, and Nimbus-7, launched October 24. These three satellites provided an ensemble of ocean-unique sensors which covered the microwave, infrared, and visible portions of the electromagnetic spectrum. The purpose of this volume of Advances in Geophysics is to summarize the results and accomplishments from these three satellites and to illustrate selected applications of the operational use of satellites for environmental monitoring of the global oceans. The summary is based on both the results of the authors and the efforts of many other researchers. The scope is limited to techniques for the remote observation of the ocean surface or near surface from satellites. 2. HISTORICAL BACKGROUND

While the oceans have been observed visually for winds, waves, color, and color change for centuries, it has been only within the last 100 years or so that quantitative measurements have been undertaken. The Beaufort scale for 1 ADVANCES IN GEOPHYSICS, VOLUME 2’1 ISBN 0-12-018827-9

2

JOHN W.SHERMAN, 111

winds originated in 1805, the Forel-Ule scale for color evolved with the Secchi disk between 1865 and 1900, and the Douglas scales for waves were first proposed in 1921 (Fairbridge, 1966). Many optical, infrared, and microwave systems were given impetus by the needs of World War 11. As an outgrowth for environmental applications, aircraft infrared measurements of sea surface temperature (SST) were begun in the early 1950s (Stommel et al., 1953), and microwave studies of the ocean surface also were begun in about the same time period (Skolnik, 1962). Even though aircraft remote-sensing applications to oceanology and marine research were still in their infancy in the early 1950s, the first known paper to discuss earth observations from satellites was published prior to the advent of earth-orbiting spacecraft (Stehling, 1953). The first earth-observing environmental satellite was NASA’s Television and InfraRed Observation Satellite (TIROS-1) launched for meteorological observations on April 1, 1960. The first satellite observations of ocean features began with the TIROS-2 detection of SST differences in 1960/1961. The first satellite oceanic experiment involved the TIROS-4 (launched February 8,1962) observation of sea ice and icebergs, and was designated the TIROS Ice Experiment (TIREX). During this same time period TIROS-8 (launched December 21, 1963) carried the first Automatic Picture Transmission (APT) system, an optical vidicon sensor which allowed real-time readout of local cloud features using relatively inexpensive shipboard receivers. This continuously transmitted picture permitted direct observation of marine weather in the vicinity of ships to support at-sea operations. With the potential of satellites to address synoptic oceanic problems illustrated by these early satellites,NASA sponsored a summer study in 1964 to consider the application of satellites to oceanic research and operational needs (Ewing, 1965). At that time, no satellite sensors had been specifically designed for oceanic measurements,so the study drew primarily on the stateof-the-art technology being used in airborne remote sensing of oceans and sea ice. The study was far-reaching in its view of potential uses and served as a long-term guide for NASA research and development, ranging from optical colorimeters for water mass detection and chlorophyll concentration to microwave altimeters for sea level, tsunamis, and sea state. Further reinforcement of satellite altimetry for studies of oceanic processes was made in a subsequent NASA-sponsored study at Williams College during 1969, now designated as the “Williamstown” study (Kaula, 1970). This 1969 study is regarded as providing the primary impetus (Barrick and Swift, 1980) for the altimeter systems flown on Skylab in 1973 and the third Geodynamic Experimental Ocean Satellite (GEOS-3), launched April 9, 1975. The Skylab altimeter measured the distance between the spacecraft and the surface to a precision of 1 to 2 m. The GEOS-3 altimeter was three to four times more

1. INTRODUCTION

3

accurate with precision of 30 to 40 cm. The GEOS-3 results are documented in a special issue of Marine Geodesy (Saxena, 1980). The early 1970s also provided the time in which temperature detection advanced to providing global SST maps with precision approaching 2"C, but more importantly, on a local and regional scale, provided details on temperature gradients not previously observed. Particularly, the Scanning Radiometer (SR) on the Improved TIROS Operational Satellite (ITOS), launched January 23, 1970, provided the first attempt at operational SST products. The development of polar-orbiting satellites was complemented by the development of earth-observing geostationary satellites. The visible and infrared scanners, first on the NASA Synchronous Meteorological Satellites (SMS-1, launched May 17,1974)and subsequently on the NOAA Geostationary Operational Environmental Satellites (GOES-1, launched November 16, 1976), proved invaluable in monitoring severe weather conditions over both land (tornadoes) and oceans (hurricanes and typhoons). Presently, the two GOES satellites cover the United States in the region approximately & 55" latitude and 20"W to 180"Wlongitude (nominally one satellite positioned at 75"W longitude and the other at 135"W), with each satellite providing coverage every 30 min of weather systems over and surrounding the United States. Through international cooperation with the USSR, Japan, and the European Space Agency (ESA), near-global coverage(to -C- 55" latitude) is now acquired using geostationary satellites. ESA uses Meteosat at O"E, Japan occupies a position at 14D"W, and the USSR is planning to operate at 70"E. The NOAA GOES satellite infrared systems have been used to demonstrate the dynamics of major ocean currents such as the Gulf Stream, but have also provided data on the propagation of equatorial long waves in the eastern equatorial Pacific (Legeckis et al., 1983). While such a wave mechanism had been postulated, it was the GOES system that first provided for the detection and quantification of the long-wave characteristics. Except for GEOS-3, no satellite sensors had been designed specifically for oceanic observations prior to 1978. During the early 1970s the capabilities for oceanological applications of microwave altimetry, scatterometry and radar, microwave and infrared radiometry, and optical-imaging spectrometers matured, along with satellite technology to provide for specific oceanic sensor design. Thus, the NASA-sponsored Seasat, Nimbus-7, and TIROS-N launches marked the beginning of a new era in oceanic observations,just over 100 years after the sailing of the HMS Challenger in 1872. It is anticipated that these satellites will mold the thinking about the dynamics of the global oceans just as the HMS Challenger revised the state of scientific knowledge about the earth's oceans.

4

JOHN W.SHERMAN, 111

The understanding of oceanic surface dynamics is tantamount to further improvement in life at sea and the efficient use of the world's oceans for transportation, energy, and food. Just as communication and navigation improvements have been made with satellites, global satellite-derived information on ocean surface winds, temperature, waves, currents, ice, and biological features has been determined feasible by Seasat, Nimbus-7, and TIROS-N. This volume of Advances in Geophysics focuses on the environmental information provided by these satellites. To borrow the thought of Dr. Gifford C. Ewing, who chaired the 1964 NASA summer study on oceanography from space, the length, breadth, and depth of the world's total ocean are approximately proportional to the length, breadth, and depth of a single page from an average newspaper. Satellites,as Dr. Ewing saw it, offered the opportunity for the first time to read the dynamics of global oceanic behavior. The summary of U.S. civil satellites is contained in Tables 1-111. The list is not all-inclusive but demonstrates a unique earth-monitoring capability. These satellites form the heritage and legacy for current and future marine satellites. The United States is now entering its third generation of earthobserving satellites, in terms of both time and technology. Not shown are the NASA Skylab and Space Transportation System, which have also contributed to oceanic remote sensing. TABLE I. CHRONOLOGY OF THE FIRST GENERATION OF U.S. CrviL ENVIRONMENTAL SATELLITES Satellite"

Purposeb

Launch date

Satellite"

Purposeb

Launch date

TIROS-I TIROS-I1 TIROS-I11 TIROS-IV TIROS-V TIROS-VI TIROS-VII TIROS-VIII Nimbus-1 TIROS-IX TIROS-X ESSA 1 ESSA 2

Research Research Research Research Research Research Research Research Research Research Research Operations Operations

04/01/60 11/23/60 07112/61 02/08/62 06119/62 09/18/62 06/19/63 12/21/63 08/28/64 01/22/65 07/02/65 02/03/66 02/28/66

Nim bus-2 ESSA 3 ATS-1 ESSA 4 ATS-2 ESSA 5 ATS-3 ESSA 6 ESSA 7 ESSA a ESSA 9 Nimbus-3 ATS-5

Research Operations Research Operations Research Operations Research Operations Operations Operations Operations Research Research

05/15/66 10/02/66 12/06/66 0 1/26/67 04/05/67 04/20/67 11/05/67

11/10/67 08/16/68 12115/68 02/26/69 04/14/69 08/ 12/69

" Acronyms: TIROS, Television and EnfraRed Observation Satellite; ESSA, Environmental Science Services Administration (NOAA predecessor); ATS, Applications Technology Satellite; Nimbus, not an acronym; derived from the Latin rainstorm or cloud. Research satellites are NASA and operational satellites are NOAA or its predecessor ESSA. All first-generation satellites were primarily for meteorology/weather-related observations using visible and IR imaging sensors.

'

TABLE 11. CHRONOLOGY AND APPLICATIONS OF TIE SECOND-GENERATION U.S. CIVIL ENVIRONMENTAL SATELLITES Orbital characteristics Satellite"

Primary applicationb

Launch date

Altitude (km)

Inclination (deg)

Primary sensorsc

ITOS-1 (TIROS-M) Nimbus-4

Met operational prototype Met research

01/23/10

1,456

04/08/70

1,240

99.9

NOAA-1 Landsat-1 NOAA-2 Nimbus-5

Met operations Land research Met operations Met research

12111/70 07 109/I2 10/15/12 12111/12

1,438 919 1,451 1,099

102 99.09 98.8 99.9

NOAA-3 SMS-1

Met operations Met operational prototype Met operations Land research Met operational prototype Sea surface topography research Met research

11/06/73 051 11/14

1,505 35.790

102 0.6

IDCS, THIR, IRIS, SIRS, SCR, FWS, IRLS, BVV APT, AVCS, SR, FPR RBV, MSS SR, VHRR, VTPR THIR. ESMR, ITPR, MWS, RDR, SCMR SR, VHRR, VTPR VISSR, SEM, DCDR

11/15/14 01122115 02/06/75

1,451 919 35,793

102 99.09 0.4

SR, VHRR, VTPR RBV, MSS VISSR, SEM, DCS

04/09/75

843

114.98

Radar altimeter

06/12/15

1,057

99.9

NOAA-4 Landsat-2 SMS-2 GEOS-3 Nimbus-6

102

APT, AVCS, SR, FPR

THIR, ESSMR, TWERLE, HIRS, SCAMS, ERB, TDRE (Continued)

TABLE11. (Conzinued) Orbital characteristics Satellite" NOAAJ GOES-1 GOES2 Landsat-3 HCMM GOES-3 GOES4

Primary applicationb Met operations Met operations Met operations Land research Land research Met operations Met operations

Launch date 07/29/76 10J 16/16 06/16/77 03/05/78 04/26/78 06/16/78 09/09/80

Altitude (km)

Inclination (deg)

1,511 35,788 35,900 919 600 35,900 35,900

102 0.8 0.5 99.09 100 0.5 0.5

Primary sensors' SR, VHRR, VTPR VISSR, DCS VISSR, DCS RBV, MSS HCMR VISSR, DCS VAS, SEM, DCS

a Satellite acronyms: ITOS, Improved TIROS Operational Satellite; NOAA, National Oceanic and Atmospheric Administration; SMS, Synchronous Meteorological Satellite;GEOS, Geodynamic Experimental Ocean Satellite;GOES, Geostationary Operational Environmental Satellite. As footnote b to Table I, but operational prototype satellites are NASA-sponsored systems built to NOAA requirements. Sensoracronyms: APT, Automatic picture Transmission; AVCS, Advanced Vidicon Camera System; BUV, Backscatter Ultraviolet Spectrometer; DCDR, Data Collection and Data Relay; DCS, Data Collection System; ERB, Earth Radiation Budget; ESMR, Electrically Scanning Microwave Radiometer; FPR, Flat Plate Radiometers; FWS, Filter Wedge Spectrometer; HIRS, High-resolution InfraRed Sounder; HCMR, Heat Capacity Mapping Radiometer; IDCS, Image Dissecter Camera System; IRIS, InfraRed Interferometer Spectrometer; IRLS, Interrogation, Recording, and Location System; ITPR, Infrared Temperature Profile Radiometer; MSS, MultiSpectral Scanner; MWS, Microwave Spectrometer; RBV, Return Beam Vidicon; RDR, Real-time Data Relay; SCAMS, Scanning Microwave Spectrometer; SCMR, Surface Composition Mapping Radiometer; SCR, Selective Chopper Radiometer; SIRS, Satellite InfraRed Spectrometer; SR, Scanning Radiometer; TDRE, Tracking and Data Relay Experiment; THIR, Temperature-Humidity Infrared Radiometer; TWERLE,Tropical Wind, Energy conversion,and Reference Level Experiment; VHRR, Very High Resolution Radiometer; VISSR, Visual and Infrared SpinScan Radiometer; VTPR, Vertical Temperature Profile Radiometer.

TABLE111. CHRONOLQGYAND APPLICATIONS OF THE THIRDGENERATION U.S. CIVIL ENVIRONMENTAL SATELLITES

Deactivation

Orbit Alt./Incl. (Wdeg)

10110178

800/108

Date Satellite Seasat

Launch 06/27/18

Sensor(s)" Altimeter

SMMR SASS VIRR SAR

4

TIROS-N

10/13/78

Nimbus-7

10/24/78

Landsat-4

07/16/82

02/17/81

8561102.3

AVHRR TOVS (three units: SSU, HIRS-2, and MSU), DSC, and SEM

955199.3

czcs

705.3198.2

SMMR LIMS, SAMS, SAM-1 1, SBV/TOMS, ERB, and THIR TM MSS

Landsat-5

03/01/84

7053198.2

TM, MSS

Marine application Sea surface topography, currents and circulation features, surface wind speed, significant wave height SST, surface wind speed, sea ice mapping, atmospheric H,O and precipitation Surface wind velocity Meteorological features, SST Wave spectra, sea ice dynamics, internal waves, current boundaries and eddy features SST, current boundaries, sea ice mapping Meteorological and weather operations Ocean color, color fronts, SST,chlorophyll concentration, diffuse attenuation coefficient. Same as Seasat Atmospheric research

Research for coastal mapping, shallow wave bathymetry, and coastal effluents Similar to TM but with less resolution and/or contrast (operational for land use) Duplicates Landsat-4 satellite

Acronyms: AVHRR, Advanced Very High Resolution Radiometer; CZCS, Coastal Zone Color Scanner; DCS, Data Collection System; ERB, Earth Radiation Budget (radiometer); HIRS/2, High-resolution InfraRed Sounder (second generation); LIMS, Limb Infrared Monitoring of the Stratosphere; MSS, MultiSpectral Scanner; MSU, Microwave Sounding Unit; SAMS, Stratospheric Aerosol Measurement Sensor; SAM-11, Stratospheric Aerosol Measurement (second generation); SAR, Synthetic Aperture Radar; SASS, Seasat-A Scatterometer System; SBV/TOMS, Solar and Backscatter ultraViolet/Total Ozone Mapping System; SEM, Space Environment Monitor; SMMR, Scanning Multichannel Microwave Radiometer; SSU, Stratospheric Sounding Unit; THIR, Temperature-Humidity Infrared Radiometer; TM, Thematic Mapper; TOVS, TIROS-N Operation Vertical Sounder (combination of SSU, HIRS-2, and MSU); VIRR, Visible and InfraRed Radiometer.

8

JOHN W.SHERMAN, I11

3. APPROACH AND ORGANIZATION With the exception of Chapters 2 and 3, the results and accomplishmentsof the three ocean-unique satellite systems are presented in this volume of Advances in Geophysics with regard to the observable rather than the specific sensor. An overview summary of the results of each satellite is contained in Chapter 2, followed by the nature of the interaction of microwave electromagnetic energy with the ocean surface in Chapter 3. (The needed infrared and optical-region interactions are discussed in the specific chapters on SST and ocean-color measurements.) Subsequently, the state of the art for observation of winds, waves, severe marine storm winds and waves, sea surface temperature, ocean color, cryosphere, precipitation, and living marine resources is reviewed in the remaining chapters. Supporting appendices are provided to give quick reference to’satellite details and data availability. Several general references are noted which complement and augment this volume of Advances in Geophysics. These references are listed below in chronological order. Science, Vol. 204, No. 4400, June 29, 1979. Contains eight papers on the initial assessment of Seasat performance. IEEE Journal of Oceanic Engineering, Vol. OE-5, No. 2, ISSN 03364-9059, April 1980. Provided a special issue on the Seasat sensors with emphasis on engineering aspects. Gower, J. F. R., ed., Oceanography from Space, COSPAR/SCOR/IUCRM Symposium (May 1980),Plenum Press, New York, 1981. Contains results from Seasat,Nimbus-7, and TIROSN. IEEE International Geoscience and Remote Sensing Symposium, June 8- 10,1981(IGARRS’ 81, IEEE Digest, IEEE Catalog No. 81CH1656-8). Reviews specific oceanic instruments in detail. Beal, R. C., DeLeonibus, P. S., and Katz, I., eds., Spaceborne Synthetic Aperture Radarfor Oceanography, The Johns Hopkins Oceanographic Studies, No. 7, The Johns Hopkins University Press, Baltimore, London, 1981. Provides a comprehensive summary of the Seasat radar system with detailed theoretical and experimental results. Bernstein, R. L., ed.,Journal of Geophysical Research, “Seasat Special Issue I: Geophysical Evaluation,” Vol. 87, No. C5, April 30, 1982. Provides an initial geophysical evaluation of Seasat, focused on the development and validation of algorithms for converting sensor signals to geophysical data. Kirwan, A. D., Ahrens, T. J., and Born, G. H., eds., Journal of Geophysical Research, “Seasat Special Issue 11: Scientific Results,” Vol. 88, No.C3, February 28, 1983. Devoted to the scientific results from the Seasat sensors and to the research achievements by many investigators in the field. Gordon, H. R. and Morel, A. Y.,“Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery, A Review,” Lecture Notes on Coastal and Estuarine Studies, Springer-Verlag, New York, 1983.

1. INTRODUCTION

9

REFERENCES Barrick, D. E., and Swift, C. T. (1980). The Seasat microwave instruments in historical perspective. IEEE J. Ocean Eng. OE-S,I4-19. Ewing, G. C., ed. (1965). “Oceanography from Space.” Woods Hole Oceanographic Institution, Ref. No. 65-10. Fairbridge, R. W. (1966). “Encyclopedia of Oceanography.” Reinhold, New York. Kaula, W. M., ed. (1970). “The Terrestrial Environment: Solid Earth and Ocean Physics.” MIT NASA CR-1579. Legeckis, R., Pichel, W., and Nesterczuk, G. (1983). Equatorial long waves in geostationary satellite observations and in a multi-channel sea surface temperature analysis, Bull. Am. Meteorol. SOC.64 (2), 133-139. Saxena, N. K., ed. (1980). Interaction of marine geodesy and ocean dynamics (special issue of Marine Geodesy).Int. Ocean Suru.. Mapping, Sensing 3 (1-4). Skolnik, M. I. (1962). “Introduction to Radar Systems.” McGraw-Hill,New York. Stehling, K. R. (1953). Earth scanning techniques for a small orbital rocket vehicle. SpaceFlight Problems.” Astronaut. Congr.. 4th. Zurich. Stommel, H.W., von Arx, W. S., Parson, D., and Richardson, W. S. (1953). Rapid aerial survey of Gulf Stream with camera and radiation thermometer. Science 117 (3049), 639-640.


THE 1978 OCEANIC TRILOGY: SEASAT, NIMBUS-7, AND TIROS-N JOHNW. SHERMAN, I11 National Oceanic and Atmospheric Administralion National Environmental Satellite, Data, and Information Service Washingfon, D.C.

1. Introduction. . . . . . . . . . . . . . . . . 2. The Seasat Sensors and Results . . . . . . . . . 2.1. SurfaceInformation . . . . . . . . . . . . 2.2. Preliminary Results. . . . . . . . . . . . . 3. The Nimbus-7 Sensors and Results . . . . . . . . 3.1. Surface Information . . . . . . . . . . . . 3.2. Preliminary Results. . . . . . . . . . . . . 4. The TIROS-N Series and Results . . . . . . . . 5. S u m m a r y . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . .

. . . . . . . . .

11

. . . . . . . . . . 12 . . . . . . . . . 14

. . . . . . . . .

15

. . . . . . . . . . . . . . . . . . .

47

. . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . .

50 50 52 56 59

1. INTRODUCTION

The United States, through the National Aeronautics and Space Administration (NASA), Jet Propulsion Laboratory (JPL), launched Seasat, the first ocean-dedicated satellite, on June 26,1978, local time (June 27 at 02: 12 GMT, day 178)from the Air Force Western Test Range (AFWTR)at Vandenberg Air Force Base, California. This research spacecraft carried five sensors on board the 2290-kg platform, operating in a 108" inclination circular orbit 800 km above the earth. Seasat circled the earth 14 times daily, covering 95% of the global ocean area every 36 hr. Data transmitted from five sensors on board included information on sea surface winds, waves, and temperatures; sea ice features; storm surge; ocean topography; characteristics of severe storms at sea; and atmospheric water vapor and precipitation. On October 10,1978,at about 03:12 GMT (day 283), a catastrophic failure occurred during revolution (Rev) 1503. The failure was apparently in the satellite's prime power electrical subsystem and resulted in no signals being received from Seasat after 04:08 GMT. All further efforts to make contact with the spacecraft were unsuccessful. NASA's Goddard Space Flight Center (GSFC) launched on October 24, 1978, at 08: 14 GMT (day 297) the Nimbus-7 satellite, which began operation on October 29 and has been in operation since that time. This launch also occurred from AFWTR. The nominal sun-synchronous orbit parameters for 11 ADVANCES IN GEOPHYSICS, VOLUME 27 ISBN 0-12-018827-9

12

JOHN W.SHERMAN,111

the 933-kg Nimbus-7 platform are an inclination of 99.2", altitude of 955 km, period of 104.16 min, and ascending node time of 1152. Successive orbits are displaced westward by 26.04" at the equator. There are 13.83 orbits day-' with the fourteenth orbit 4.56" west of the corresponding orbit of the previous day. After 6 days the satellite again passes over a specific equatorial site, but the track is 1.3" west of the previous 6-day earlier overpass. The third generation of operational polar-orbiting satellites began with TIROS-N, a 715-kg satellite (excluding propellants and motor assembly case) launched from AFWTR at 11:23 GMT on October 13,1978, and deactivated on February 17, 1981. The TIROS-N satellite served as the prototype operational satellite used in the National Oceanic and Atmospheric Administration (NOAA) series of polar orbiters. NASA funded TIROS-N development and launch and NOAA subsequently sponsored the remaining satellites in this series, which began with NOAA-6, launched from AFWTR on June 27, 1979. TIROS-N operated at an altitude of 856 km, inclination of 98.9", 102min orbit (14.2 orbits day-'), and with an ascending crossing time of 1500 local time. NOAA-6 operates with essentially identical characteristics except its altitude is 833 km and has a 07:30 local descending equatorial crossing time. NOAA-7, launched June 23,1981, replaced TIROS-N in the afternoon orbit to maintain a two-polar satellite coverage system. The orbits of NOAA6 and NOAA-7 each repeat approximately every 10 days, although each satellite views the entire earth at least once per day. It is noteworthy to indicate that the TIROS-N predecessor, the Improved TIROS Operational Satellite (ITOS),operated as the second-generationseries of NOAA operational polar-orbiting spacecraft from October 1972 until March 1979. This series provided the NOAA-1 through -5 satellites and operated as a single-satellitepolar-orbiting system at an altitude of about 1475 km and an orbital period of 115 min. The reduced altitude of the TIROS-N series required the use of a two-satellite series to provide comparable daily coverage. The three 1978 satellite systems formed a virtual trilogy of oceanic monitoring capability. This chapter summarizes the results achieved from Seasat, Nimbus-7, and TIROS-N as a function of the sensors rather than the geophysical observables as used in the remaining chapters.

2. THESEASATSENSORS AND REsut-rs The five Seasat instruments included a radar altimeter, a Seasat-A Scatterometer System (SASS), a Synthetic Aperture Radar (SAR), a Visible and InfraRed Radiometer (VIRR), and a Scanning Multichannel Microwave

2. SEASAT, NIMBUS-7, AND TIROS-N

13

Radiometer (SMMR).' These instruments are discussed in summary form in Appendix A and in the chapters relevant to the results of the specific geophysical measurements. A short-pulse (3-nsec) radar altimeter operating at 13.5 GHz was the first instrument selected for Seasat. Because the sensor looked only at nadir (its swath width was between 1.8 and 12 km, depending on sea state), the orbit of Seasat was tailored to support the geodetic requirements of the altimeter. Precision of the altimeter measurement was expected to be 10-cm RMS over seas with a significant wave height HI,, from calm to about 20 m. Precise spacecraft tracking was needed for geodesy, currents, and storm-surge analyses. Processing of the altimeter pulse yielded an estimate of HI,, to +0.5 m or lo%,whichever was larger, over seas from calm to 20 m. The SASS was an active microwave instrument that illuminated the sea surface with four fan-shaped beams (two orthogonal beams, each 500 km wide on each side of the Seasat groundtrack). The amount of energy returned depended on the capillary waves and, by inference, provided an estimate of sea surface wind magnitude and direction. The transmitted frequency was 14.6 GHz with the returned energy shifted slightly because of Doppler effects. Doppler shift processing established a spatial resolution of about 50 km over a region from 200 to 700 km on either side of the spacecraft. As a goal, surface winds were to be determined to L- 2 m sec-' or 10%in magnitude, whichever was greater, and & 20"in direction. The SASS was expected to measure, with less precision, winds out to 950 km from the spacecraft. The range of winds expected to be measured was from 3 to 25 m sec-'. The VIRR was identical to the NOAA-series Scanning Radiometer (SR) sensor except for modification because of orbital altitude differences and a digital telemetry down-link tailored for the signals associated with ocean surface phenomena as well as clouds. VIRR, on Seasat primarily for feature identification,operated with both a visible channel (490 to 940 nm), providing information on cloud conditions (day only), and a thermal infrared channel (10.5 to 12.5 pm), providing surface and cloud temperatures. Temperature sensitivity was expected to be 0.5"C. The L-band (1.275 GHz) SAR looked to the starboard side of Seasat and was centered 20" off nadir with a swath width of 100 km. The length of the SAR image track was determined by the receive-station-view duration, with about 4000 km being the maximum. Spatial resolution of 25 m needed for wave analyses generated a very high data rate so that on-board recording was not used. Only data within view of Merritt Island (Florida), Goldstone (California),and Fairbanks (Alaska) within the United States, and Shoecove, Seasat also carried a laser retroreflector and tracking beacons for precise orbit determination, but these are not discussed here.

14

JOHN W. SHERMAN, 111

Newfoundland (Canada) and Oakhanger (England) receiving stations were collected from SAR. The goal was to measure waves and wave spectra to ocean wavelengthsof 50 m or greater, with additional objectives to identifysea ice features; detect possible icebergs; identify wave-land interfaces; observe coastal and current-induced wave refraction, internal waves, and current boundaries; and penetrate to the ocean surface through major storms such as hurricanes. The SMMR sensor operated at frequencies of 6.63, 10.69, 18, 21, and 37 GHz. Dual polarization was available at all frequencies. Spatial resolution ranged from about 100 km at 6.63 GHz to 22 km at 37 GHz. Three primary classes of data obtained from SMMR were sea surface temperature (SST),sea ice mapping, and surface winds, Liquid water and water vapor were measurable as well and were used to formulate path length and attenuation corrections for the altimeter and SASS, respectively. SST was expected to be + 2 K, an important first step to determining SST under cloudy conditions. The magnitude of surface winds was expected to be measured to f 2 m sec-' or lo%, whichever was greater, from 7 to about 40 to 50 m sec-'. The Seasat SMMR conically scanned the starboard side of the spacecraft, aft viewing, with a constant 42" angle from nadir. The scan angle was from about 0 to 50" from the aft-starboard side, resulting in a swath width of about 600 km. The 42.0"angle from nadir resulted in a 48.8"angle of incidence at the surface because of the earth's curvature. The Seasat SMMR coverage was chosen for maximum overlap with SASS. The SMMR on Seasat was identical to the SMMR on Nimbus-7 except for a slight change in scan rate to accommodate the difference in altitude of the two satellites. Hence, SMMR is not discussed in Section 3 on Nimbus-7. Data from the microwave sensors, excluding SAR, were recorded on board and telemetered to one of the previously noted SAR receiving-station locations. Hence, global coverage was available for four of the five Seasat instruments. 2.1. Surface Information

Two major sea surface information activities were jointly planned by NASA and NOAA in the United States and the multinational Joint Air-Sea Interaction (JASIN) experiment. The Gulf of Alaska Seasat Experiment (GOASEX) was specifically dedicated to the early validation of Seasat data. The JASIN program, conducted in the eastern Atlantic near Scotland (August-September 1978),focused on the marine boundary layer and air-sea energy transfer. This Atlantic data set was used for independent validation of Seasat but is not considered in this overview chapter.

2. SEASAT, NIMBUS-7,AND TIROS-N

15

The initial validation of Seasat used the GOASEX surface data set (Born et al., 1979). This activity was planned and conducted by NOAA, including the Pacific Marine Environmental Laboratory (PMEL), the National Earth Satellite Service (NESS),2 the Atlantic Oceanographic and Meteorological Laboratory (AOML), the Wave Propagation Laboratory (WPL), and the National Data Buoy Office (NDBO). The principal oceanic research facility deployed during GOASEX was NOAA's Class 1 Research Vessel Oceanographer. The Canadian weather ships Quadra and Vancouver, alternating at Ocean Weather Station PAPA, also obtained special data at satellite overpass times. Major support was provided by NASA (JPL, Johnson Space Center, Ames Research Center, and Langley Research Center) and the U.S. Navy (Naval Research Laboratory). The GOASEX operating area is shown in Fig. 1. Participating aircraft included the NASA Ames Research Center CV-990 equipped with an airborne version of the SMMR,the NASA Johnson Space Center NC-130B with the Seasat underflight scatterometer, the Naval Research Laboratory RP-3A equipped with meteorological and microwave radiometer instrumentation, and a Canadian CV-580A aircraft carrying the Environmental Research Institute of Michigan's multifrequency synthetic aperture radar system. A typical flight track is shown in Fig. 2. This experiment was also supported by nine NOAA data buoys moored in the Gulf of Alaska. Selected research vessels from the U.S. Geological Survey and the University of Alaska made special weather observations during satellite overpasses of their positions. A very comprehensive data set was collected which corresponded to some 60 satellite overpasses, including more than a dozen SAR passes. An intensive, coordinated study of this data set was conducted in January 1979 and is the basis for much of the initial Seasat validation (Weissman, 1980). Appendix B presents additional details on the Seasat validation program.

2.2. Preliminary Results 2.2.1. Altimeter. Studies with the Seasat radar altimeter have been divided into three classes of activities in this chapter: those concerned with highly accurate altitude measurements, those with wave heights, and those with surface wind speeds. A fourth study of altimeter measurements of ice-sheet heights is contained in Chapter 9. Aside from the discussion in this chapter, the altimetric application to geodesy and circulation are not considered in this volume of Advances in Geophysics. The schematic of the'altimeter and its measurement geometry are Now the National Environmental Satellite, Data, and Information Service.

-@- OCEAN STATION PAPA @ OATAEUOY

...1

ye-CANADA -----_ UNITED SITE B 8 SEPT

.

170

1

I60

I

150

I

140

9 SEPT

- 26 SEPT I

130

STATES

SEATTLE

1

120

FIG.1. Ship track for the NOAA Research Vessel Oceanographer for the Gulf of Alaska Seasat Experiment (GOASEX)August 28-September 26,1978 (J. Wilkerson, Experiment Coordinator; R. Reed,R. V. Oceanographer Chief Scientist; and P. Deleonibus, Senior Oceanographer; all are members of NOAA).

I 55-

55

50-

sa

UNITED STATES

65

I

145

140

135

I30

I 25

120

FIG.2. Data flight lines of the NASA CV-990A on September 16, 1978, for orbit 1163. Time 05:33-08:42 GMT (J. Blue, JPL, Aircraft Coordinator; E. Peterson, NASA, Mission Manager; and T. Wilheit, NASA, Chief Scientist).

18

JOHN W. SHERMAN, I11

illustrated in Fig. 3 (after Tapley et al., 1982). The design requirements were such that over a significant wave height interval between 1 to 20 m, the altimeter was to satisfy the following criteria (Townsend, 1980): 1. At an output rate of one height measurement data point per second, the noise level of these data points should be such that 68% of the points lie within 10 ern of the fitted mean. 2. At an output rate of one measurement per second, the altimeter should provide a measurement of the of the ocean surface beneath the spacecraft.

SEASAT

CORRECTIONS

ALTIMETER

1?iI t

f

ORB> l

I

I I I

ATMOSPHERIC CORRECTIONS

h

Ih'

I

- -* I

OCEAN

7

SURFACE GEOPHYSICAL CORRECTIONS

I

I f -

a

LASER SITE

OCEAN SURFACE TOPOGRAPHY

# ,"I #.'

k BOTTOM TOPOGRAPHY \

\

FIG.3. Schematic of Seasat altimeter data collection modeling and tracking system. (After Tapley et al., 1982.)


19

The absolute accuracy of this measurement should be at least f 10% of or 0.5 m, whichever is greater. 3. The altimeter should provide information concerning the measurement of the normalized radar backscatter coefficient (ao)of the ocean surface beneath the spacecraft,i.e., normal incidence. When subjected to appropriate ground processing, this information should result in the measurement of a 0’ to an absolute accuracy of at least k 1.0 dB. In addition to the measurement of altitude, and ooby the altimeter, the orbit was required to comparable accuracy. The altimeter operated from July 3 until October 10, 1978, in two basic orbital modes. Equatorial spacing of 165 km was acquired for a complete global data set during the period July 7 to August 17 and was designated as the baseline orbit. By September 5 the orbit was changed to a 3-day repeat cycle with a 900-km equatorial spacing and remained in this orbit until failure on October 10. This latter orbit, designated as. the “frozen” orbit, was used to evaluate altimeter performance in which eight repeating passes (within about 1 km) were acquired. The prime goal of the altimeter was to provide improved global maps of the mean sea surface topography. An 18-day set (July 28 to August 15, 1978) of altimeter data was merged with the precise ephemerides of Seasat to obtain two forms of contour maps of the height of the mean sea surface above a reference ellipsoid (see Fig. 3). The approach is discussed in detail (Marsh and Martin, 1982) in combination with the Seasat ephemerides analyses (Lerch et al., 1982). The results are shown in Fig. 4,designated as the SS4 mean sea surface topography. The SS4 model uses the Lerch et al. (1982) ephemerides, which has an RMS radial accuracy of about 70 cm and is corrected for ocean tides and sea state bias effects. The number of data points contained in Fig. 4 is 692,860. Study of Fig. 4 reveals the major maxima in the northeast and southeast Atlantic, the southwest Pacific, and the Indian Ocean. Also the major minima are seen over the Puerto Rican Trench, off the coast of Baja California, and just off the southern tip of India. Island chains are readily evident. Figure 5 is a color-enhanced version of this same data set. Removal of geoidal data from the altimeter-measured altitude provides residual sea height. This height can be directly related to ocean current features as shown in Fig. 6 (Cheney, 1982), in which the Gulf Stream west wall was passed over twice for the descending revolution 558. In general, to achieve these detailed results a number of environmental and measurement errors had to be assessed, including topographically trapped waves and currents; local currents; wind setup; atmospheric water vapor, pressure, and tropospheric refractioneffects;tidal measurement uncertainty; shelf topographic effect;and leveling errors.

FIG.4. SS4 mean sea surface topograph based upon Seasat altimeter data. (After Marsh and Martin, 1982.) Also see Fig. 5. ~

~~

FIG.5. This topographical relief map of the world’s ocean surface was produced from Seasat altimeter data by the Jet PropuIsion Laboratory of the California Institute of Technology. The image is dominated by features indicative of sea-floor spreading, including midocean ridges, trenches, and seamount chains. Steep, small-scale features appear white. More quantitative data are shown in Fig. 4. (Courtesy of Dr. Michael Parke, JPL.)


21


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FIG.6. Seasat Rev 558 after geoid removal. The pass first intersected the Gulf Stream near Cape Hatteras and then recrossed the Gulf Stream near the Florida Straits. (After Cheney, 1982.)

Significant wave height and surface wind speed can be determined from analysis of the altimeter return pulse shape and the backscattering cross section, respectively. These mechanisms for deriving winds and waves from the nadir-view electromagnetic response to the ocean surface are discussed in Chapter 3, Interpretation of Instrument Observations. Two algorithms were used to process wave-height data: one predetermined on board using results from GEOS-3, and one using a surface-processed algorithm designated as the "Fedor" algorithm (Fedor and Brown, 1982). These results are considered in Chapter 5 on wave observations. Both algorithms provided excellent wave information. The Fedor results are shown in Fig. 7 where the altimeter data is compared to buoy data. At nadir the radar backscattering cross section oodecreases with increasing wind speed because winds are inferred by capillary wave scattering action. This nadir response is in contrast to the off-nadir behavior of with wind speed (see Chapters 3 and 4). Figure 8 compares the altimeter wind speed to buoy wind speed using the Brown algorithm (Fedor and Brown, 1982). Unlike the SASS, the altimeter does not measure wind direction. GO,

GO

2.2.2. Seasat-A Scatterometer System (SASS). The dual-polarization SASS, operating at a frequency of 14.599 GHz, provided a footprint on the surface of earth as shown in Fig. 9. An asymmetry in the actual footprint arose due to spacecraft motion and earth rotation. As noted earlier, maximum

22

JOHN W.SHERMAN, I11 6.0.

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Total iV Points = 51 Mean Din. = 0.065 m RMS Din. = 0 . W m Regression Cwff. = 0.902 Y-Intercept = 0.123 m CorrelationCorff. = 0.985 RMS Diff. from Regr. Line = 0.288

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sensitivity to surface winds occurred between about 200 and 700 km on either side of the spacecraft. Limited sensitivity existed at nadir (& 70km) and from 700 to 950 km (each side of the groundtrack). Several wind algorithms have been developed (Jones et al., 1982;Schroeder et al., 1982) requiring use of fore and aft antenna beams and dual polarization. This discussion does not compare the algorithms, only their geophysical results (Chapter 4 provides details). All appear to agree well in defining wind direction (subject to a 180"directional ambiguity)and to illustrate the need for individual spot reports for surface data. Figure 10, based on the City University of New York (CUNY) algorithm (Schroeder et al., 1982),illustrates this point and the nature of high-resolution wind fields derivable from SASS

23


14.0

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Total Points = 87 ~n D ~ I I=. 0.249 rn sec RMS Ditl = 1575 m sec-' Regression Coeff = 0.893 Y-Intercept = 0 806 m secCorrelation Cosfl. = 0.858

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I )

-

FIG.8. Scatterdiagram comparingaltimeter(Brown algorithm) wind speed with NDBO wind measurements. (After Fedor and Brown, 1982.)

and the variations and gradients that may exist in surface winds about a specific point. In this particular data set the winds appeared to maintain about the same direction with a gradient of the wind from about 2 to 5 m sec-' over a distance of about 350 km. For SASS comparisons, surface winds have been corrected to the 19.5-m anemometer height. A more general comparison of SASS data to surface data is shown in Fig. 11 (Schroeder et al., 1982). The surface data have been derived from the JASIN experiment and the CWK model is an algorithm which combined several earlier models. The error statistics developed early in the SASS analysis are shown in Table I, and most importantly, indicate the need for controlled surface data such as buoys (Jones et al., 1979). Note that the error statistics compared only to the buoys are within SASS specifications.

SIDE VIEW

INSTRUMENT CHARACTERISTICS 14.59927 GHz 100 W PEAK RF POWER ELECTRONIC SCAN (15 DOPPLER CEI.LS) ORTHOGONAL MEAS. (AZIMUTH) 4 ANTENNAS DUAL POLARIZATION 0.5" x 25" ANTENNA BEAM

ANTENNA ILLUMINATION PATTERN

ANTENNA BEAM NUMBER

HIGH WINDS ON1 HIGH AND LOW WINDS-

CE ROUGHNESS MEASUREMENT SWATH SAT TRACK

FIG.9. Plan view geometry of the Seasat scatterometersystem.

2. SEASAT, NIMBUS-7, AND TIROS-N 50'1

I

I

I

-

49'1

y

48°t.l -

'

1309

Wind speed

47%

25

1

m sec-'

I

I

I

229OE

23O0E

240°E

FIG. 10. Scatterometer wind field (Rev 1298)in the vicinity of Weather Station PAPA (Canada) using the CUNY algorithm.

Seasat made a number of passes over hurricanes and typhoons, and Chapter 6 discusses the results for severe marine storms. Figure 12 shows a visible spectrum image from the NOAA Geostationary Operational Environmental Satellite (GOES) which was taken over Hurrican Fico just before a Seasat pass on July 20. The Seasat subtrack is the dotted line in the image. The SASS-derived surface wind fields were believed to be the first time that synoptic-scale oceanic wind velocities had been obtained in a hurricane. In the 450-km SASS swath width, the winds were seen to range from about 2 m sec-' in the doldrums south of Fico to more than 25 m sec-' near the hurricane's center. Again, atmospheric corrections were not made, nor was this SASS data set processed at its full 50-km spatial resolution. Winds were averaged over a 1 x 1" grid, and were probably underestimated due to the spatial averaging. Details of other specific storms are documented (Cane and

24

6l

Slope = 1.0439 Intercept = 0.1071 Correlation = 0.91 14

3

0'

6

3

9

12

15

18

21

24

27

30

GROUND TRUTH WIND SPEED (m sec")

*.

/

0

0

144-

3 $

10872

-

t

//

/

All polarization pairs CWK algorithm

/

L'

Slope = 0.9530 Intercept = 10.2468 Correlation = 0.9847

, ~ * l " ' " l l l " ' " ' ' " l l ' ' l ' ' l l ' ' 0 36 72 108 144 180 218 252 288 324

360

GROUND TRUTH WIND DIRECTION (DEG)

FIG.11. Scatterometer versus surface data collected during the Joint Air-Sea Interaction experiment (JASIN). (a) Wind-speed comparisons; (b) wind-direction comparison. (After Schroeder et a/., 1982.) 26

TABLE I.

STATISTICS FOR

(SASS-SwT

OBSERVATIONS) WIND SPEED AND W I N D

DIRECTION'

~~

Wind speed (m sec-')

Wind direction (deg)

Standard deviation of error

Mean errorb

Mean error

V+H

V

V+H

V

No. of samples

1.79

1.48

0.9

1.5

22, 14

2.33

V+ H

-

Standard deviation of error

V

V+H

V

0.36

16.2

18.6

I . N

Buoy Ship Known anemometer height Unknown anemometer height Beaufort

'From Jones et nl. (1 979). V, Vertical; H, horizontal.

3.12

2.44

9

- 1.39

3.0

2.69

24

3.34

22.92

2.11

2.66

30

7.6

16

17.9

28


FIG.12. NOAA GOES image of Hurricane Fico with scatterometer-derivedwinds superimposed for July 20, 1978.

2. SEASAT, NIMBUS-7, A N D TIROS-N

29

Cardone, 1981; Gonzalez et al., 1982)and a series of workshops conducted are summarized in Chapter 6. 2.2.3. Scanning Multichannel Microwave Radiometer ( S M M R ) . The Nimbus-7 and Seasat SMMR instruments, whose characteristics were described earlier, were identical in all respects except for slight differences in scan rates necessitated by the difference in the altitudes of the two spacecraft (955 km for Nimbus-7 and 800 km for Seasat). The SMMR performed well over open oceans (away from land contamination of the antenna sidelobes and radiofrequency interference) in the absence of rain contamination. SMMR was designed principally to measure SST and surface windspeed and to map sea ice. Additionally, SMMR provided atmospheric corrections to other instruments, for which the correctional data were quantified to provide quantitative data on atmospheric water content and precipitation. The SMMR provided the first microwave observations of SST, thus initiating the much needed observation of ocean surface temperature through clouds. The goal was to retrieve temperatures over 100-km areas to an absolute accuracy of +2"C. This goal was achieved for open-ocean measurements when sun glint and rain were avoided. Figure 13 (Lipes, 1980) summarizes the SST results from several surface sources, and Table I1 (Lipes, 1982)provides the statistical results. It is noted that the tropical Pacific results are ship injection temperature measurements and yield the poorest comparison. More detail on SST measurements by SMMR can be found in Chapter 7 and in Bernstein (1981). The SASS wind velocities were well calibrated, typically using surface data from near-shore locations. For SMMR open-ocean analysis of wind speed, the SASS data have been used for comparison (Lipes, 1982) as shown in Fig. 14, which also includes the statistics. The SMMR wind-speed goal of 2 m sec-' was achieved, assuming the reliability of SASS over the open ocean and subject to the same constraints on the measurement of SST (no sun glint or rain). Chapter 9 thoroughly reviews the performance of SMMR and its applications to sea ice mapping, dynamics, and edge observations. By comparison, the Seasat SMMR collected only a fraction of the data that the Nimbus-7 SMMR collected. The latter instrument has collected ice data on an alternate-day operation since it became operational shortly after launch in late October 1978. Several SMMR research products are being generated for the polar science community, in particular, sea ice concentrations and the mix of multiyear ice with first-year ice. Figure 15 (courtesy of P. Gloersen, NASA/GSFC) is an example of the multiyear ice fraction wherein the gray scale indicates the percentage of total ice cover which is multiyear sea ice. The total sea ice concentration during the same period for this region is an additional product

30


25

-

V

$ 20 -

15

-

SURFACE SST, OC

FIG.13. Seasat Scanning Multichannel Microwave Radiometer (SMMR) sea surface temperature comparison with surface observations. (After Lipes, 1980.) COMPARISONS WITH IMPROVED ALGORITHM' TABLE11. SEA SURFACE TEMPERATURE Comparison PAPA Buoy 46006 N W Pacific XBTs Rev 1223 Atlantic AH points with good surface truth Tropical Pacific All comparisons ~

~~~~~

li

From L i p s (1982).

Number and type of revolution

Number of points

20 Descending I Ascending

20

0.0

7

0.2

3 Descending, 4 ascending 1 Descending, 1 ascending

32 12

-0.3 0.6

0.9

14

0.4

0.8

85

0.1

0.8

16

0.4 0.1

1.3 0.9

4 Ascending

101

Bias

U

0.4 1.1

1.2

31


r-----N=250

BIAS = -0.6 m

sac-

u = 1.4 m sec-

0 0

5

10

15

20

'

1

25

SASS WIND, rn sac-'

FIG. 14. Open-ocean wind-scatter diagram comparing SMMR-derived winds with SASSderived winds in Rev 1135. (After Lipes, 1982.)

and, when multiplied by the multiyear ice fraction in Fig. 15, will provide the multiyear sea ice concentration (Cavalieri et al., 1981). The SMMR performance for atmospheric water content has been analyzed in terms of columnar water vapor and liquid water content and rain rate. Only columnar water vapor is illustrated here based on the frozen-orbit data sets which provided radiosonde observations (raobs) deployed from Funafuti, Majuro, Kwajalein, Wake Island, Truk, Ponape, and Ocean Station TANGO (Katsaros et al., 1981). The results are summarized in Table 111 (after Lipes, 1982) as a function of tropic and midlatitude sampling. The comparisons between the raobs and SMMR retrievals, which involved matchups to within 1O in latitude and longitude and no rain in the SMMR field of view, are shown in Fig. 16. In general, SMMR underestimated the water vapor in the tropics and slightly overestimated in the midlatitudes, but the deviation of SMMR


32

FIG.15. SMMR-derivedsea ice fraction(day 26-30,1979) wherein the gray scale indicates the percentage of total ice cover which is multiyear ice. (Courtesy of P. Gloersen, NASA/GSFC.)

TABLE 111. ~~~

~~

Data set (g cm- '1

PRECIPITABLE WATER COMPARISON' ~~

Bias (SMMR-raobs)

Standard deviation about bias

Mean

Standard deviation

5.0 5.0

0.8, 16%

0.8, 16%

-0.007, 0.14%

0.3, 6%

1.7 1.6

0.6,35% 0.6, 38%

-0.03,2%

0.16, 10%

~

Tropics, n = 30 Raobs SMMR Midlatitudes, n = 26 Raobs SMMR ~~~

~

~~

From Alishouse (1983).

-


33

raobs (g cm-* 1

FIG. 16. Comparison of SMMR columnar water-vapor retrievals with radiosonde observations (raobs) in the tropics and midlatitudes. (After Lipes, 1982.)

from the raobs is within the accepted error of the raobs themselves and the spatial variation of water vapor over 1" increments. 2.2.4. Visible and InfraRed Radiometer (VIRR). The VIRR was not designed as a primary sensor on Seasat, but rather as a supporting instrument to the microwave sensors (McClain and Marks, 1980). The VIRR provided more conventional visible reflectances and thermal infrared emissions from oceanic, coastal, and atmospheric features. These features aided interpretation of data from other sensors.

34


300-

-

-

-

-

*

295-

@

-

8

8

$

c

-> 02

TVIRR = 0.846TNOM+46

290-

r

= 0.84

R M S DIFF. = 1.7 K N = 139

-

-/ 285

285

I

I

1

)

1 290

'

1

1

I T

1 295

1 ~

1 ~

1

1 ~

1

300

1

I

1

1

*

FIG.17. NOAA VHRR temperature field estimatesversus Seasat VIRR temperatures. VHRR field for July 5-10,1978; VIRR temperatures for July 7, 1978. Region of data comparison is in Fig. 18. (After McClain and Marks, 1979.)

However, by using corrections to satellite brightness temperature for atmospheric water vapor, it was possible to compare (on a pixel-by-pixelbasis) the NOAA SSTs, as reported by buoys, to the VIRR-corrected temperatures. Figure 17 shows the results of this comparison. The linear regression correlation coefficient, r = 0.84, and the RMS difference of 1.7"C represented excellent agreement in view of uncertainty in the atmospheric correction and uncertainty and smoothness of the NOAA field. Figure 18 shows the VIRR thermal channel of the east coast of the United States acquired on July 7, 1978 (Rev 156, day 188). The gray scale was enhanced to improve contrast between warmer water (dark) and colder water (light). Clouds are also shown where warmer clouds (lower in altitude) were discriminated from the colder clouds (higher in altitude). These images permitted detailed definition of weather patterns through which microwave energy from other Seasat sensors penetrated.

305

FIG. 18. Seasat VIRR infrared image obtained July 7, 1978, used for day-night feature identification for other Seasat sensors. Approximate collection time 22:51 :OOGMT. (Courtesy of E. P. McClain and the JPL Image Processing Laboratory.)

36

JOHN W.SHERMAN, 111

a

Components

Image

Two-dimensional Transform

C

Image

Two-dimensional Transform

FIG.19. Diagrams of linear images and corresponding two-dimensional Fourier transforms. Note that the distribution in the two-dimensional Fourier transform is governed by the frequency and orientation of the spatial distribution of the images. Spacings of components in (b) and (d) are inversely proportional to spacings of lines in the images (a) and (c). The sizes of the dots indicate the amount of energy in the diffracted orders-smaller dots indicate smaller amounts of energy. (After Shuchman et al., 1979.)

2.2.5. Seasat Synthetic Aperture Radar ( S A R ) . The SAR was designed to image ocean wavelengths as short as 50 m. A principal goal was to derive wave spectra for ocean wavelengths between 50 and lo00 m. Other goals included studies of sea and lake ice, icebergs, internal waves, waves in hurricanes, oil spills, and other environmental features such as current boundaries and waverefraction effects. The methodology for deriving wave spectra using Fourier transform is shown schematically in Fig. 19 (after Shuchman et al., 1979). A portion of an SAR image from Hurricane Fico is shown in Fig. 20, which indicates the nature of a confused sea generated by hurricane-force winds. The area in Fig. 20 is about 5 x 5 km. The optical Fourier transform of Fig. 20 is shown in Fig. 21. The transform in Fig. 21 shows that while significant energy is oriented at angles about - 10" off the abscissa, some wave energy is available at nearly all angles. To illustrate the domain wave energy and wavelength, the spectrum in Fig. 21 is filtered to the principal angular component of waves. The result is shown in Fig. 22. The enhanced image of the dominant waves


37

FIG.20. An analog-processed Synthetic Aperture Radar (SAR) image of area 1 of Hurricane Fico covering about a 5 x 5 km segment. (Courtesyof W. Brown of JPL and D. Ross of NOAA.)

resulting from this filtering is shown in Fig. 23 wherein waves on the order of 160-m wavelength are most apparent, Details on the interaction and imaging of ocean waves by radar are contained in Chapter 5. A second major application of SAR was for high-resolution imagery of sea ice. Chapter 9 reviews the satellite observations of the polar regions. The untimely demise of Seasat prevented significant SAR temporal coverage through the winter season, as has been possible with the Nimbus-7 SMMR. Figure 24 is an SAR image of a portion of the Beaufort sea ice pack west of Banks Island, Canada (right portion of image). The region imaged covered an area about 30 x 120 km and was northeast of Alaska, some 800 km iqside the

38

JOHN W.SHERMAN, Ill

FIG.21. Optical Fourier transform of Fig. 20. Range dependence of mean and variance removed. Note that the optical transform produces two peaks for each dominant wavelength present. (Courtesy of W. Brown of JPLand D. Ross of NOAA.)

Arctic Circle. The image was acquired at very low visibility conditions (Rev 205, around 2:OO AM local time on July 22,1978) when visible imagery would have been difficult. The dark zone adjacent to Banks Island is an area of

shore-fast ice composed primarily of first-year sea ice, 1 to 2 m thick. Linear pressure ridges are seen within the shore-fast zone, and west (left) of this zone is a shore lead (open water). At the edge of the lead is a marginal ice zone composed of a mixture of open water and large and small rounded multiyear floes, typically 3 to 4 m thick. Some first-year ice is also present. Further west is the main polar pack, consisting of floes up to 20 km in extent, and


39

FIG.22. Optical Fourier transform in Fig. 21 limited to the dominant waves located about the -10" angle with respect to the abscissa.

surrounded by new leads. The very bright areas within the floes indicate intensive surface roughness. Additional SAR imagery of sea ice is provided in Chapter 9. The SAR demonstrated a consistent ability to image other features including the surface manifestations of internal waves, current and eddy boundaries, bathymetry-related surface features, ships and ship wakes, and oil slicks (Fu and HoIt, 1982). Figure 25 shows one of many SAR images of internal waves observed in the Gulf of California3 (as obtained on Rev 1355,

'

Internal waves cause a concentration of surface natural oils and materials whjch modify surface tension that alters the small wave structure and hence the radar backscattered energy.


40

FIG.23. Dominant wave system of area 1 in Hurricane Fico with waves on the order of 160-m wavelength. (Courtesy of W. Brown of JPL and D. Ross of NOAA.)

September 29, 1978). The bathymetric chart, as prepared by Fu and Holt, shows the locations of eight major internal wave groups, labeled A-H. Strong tidal currents of the twice-monthly spring tides (6-mtides in this region) interacting with the bottom topography are assumed to generate these internal wave features. If the M 2 tidal cycles (period 12.42 hr) sequentially generated the E,D, and C internal wave groups, then the extrapolated group speed is 1.2 m sec- The maximum wavelength of the internal waves shown in all groups is around 2 km. The shear and strain effects on the ocean surface because of currents also alters the radar backscattering of electromagnetic energy. Two examples associated with the currents system illustrate this effect, an effect which had

'.

FIG.24. SAR image of the Beaufort Sea area just west of Banks Island, Canada. The area is about 30 x 120 km acquired on July 22, 1978, at about 2:OO AM local time. Banks Island, right corner, is bound by shore-fast ice (black) with open water (about 20-25 km in extent) westward and then pack ice and leads composing the left portion of the image. (Courtesy of W. Brown of JPL and W.Campbell of USGS.)

-

ILLUMINATION

DIRECTION

"1'

-O

25 km

FIG.25. Signatures of internal waves have been acquired by SAR in many coastal regions. This Scene of the Gulf of California, with the bathymetric chart, illustrates the complexity of internal waves believed to be generated by spring tides. This image was obtained September 29, 1978, on Rev 1355 (Fu and Holt, 1982).


43

FIG.26. Gulf Stream western and eastern (lower left) boundaries obtained by SAR on August 31,1978, Rev 931 (Fu and Holt, 1982).

been underestimated prior to Seasat. First, both the west and east boundaries of the Gulf Stream appear to have been detected in Fig. 26, acquired August 31,1978, on Rev 931. This image is about 100 km wide and 400 km long with the tip of Cape Hatteras, North Carolina, showing in the top left portion. The deflection of the western boundary of the Gulf Stream is generally believed to be caused by the submarine ridge off Charleston, South Carolina (Legeckis, 1979). The eastward wall of the Gulf Stream is believed to be in the bottom right portion of the image as the diagonal feature because this feature corresponds to satellite IR imagery location of the Gulf Stream during this time period. Second, eddies of varying size from that of the warm and cold rings of the Gulf Stream (Lichy et al., 1981)-rings of the order of 100 to 200 km down to eddies of the order of 5 to 10 km-were imaged by SAR. The latter type of eddy structure is shown in the vortices in Fig. 27, an image that is about 100 x 150 km in extent. The location is just off the Florida coast with the Grand Bahama Island showing in the upper portion of the figure. From the orientation at the vortices, it appears that they are generated by a southeastward current along the westernmost edge of the Grand Bahama Island (Fu and Holt, 1982). The southeasterly flow apparently weakens, leading to a compression of the distance between the eddies. Three ships and their wakes are visible, two having passed through fourth and sixth eddies, respectively, from the eddy point of origin, and one approximately 7 or 8 km south of the island and approximately 20 km from the eastern portion of the island. Surface stress changes induced by current interaction with bottom features were frequently detected by SAR in coastal regions. These slight changes altered the backscattering of microwave energy to be within the sensitivity of the Seasat SAR. To illustrate the nature of this sensitivity, a region of highly variable bottom features,from depths of less than a meter to around 15m and then to 1500 m, is found off the coast of Andros Island and is known as the Tongue Of The Ocean (TOTO). A Landsat-1 image (185 x 185km) using the green channel (MSS 4 is 500-600 nm) from January 24, 1973, is shown in

FIG.27. SAR-imaged small-scale eddy structures south of Grand Bahama Island. Of a number of SAR Scenescollected over this region, this is the only one which showed these cyclonic vortices (Fu and Holt, 1982).


45

Fig. 28a and illustrates the TOTO region and the pattern of sandbars that run along the southern edge of the tongue-like feature which gives rise to the name “TOTO.” A few of these bars are exposed during low tide (total tidal range for the Bahama region is only 0.6 to 1.0 m), but most remain under water. Depth between the sandbars is typically from 5 to 15 m and the ocean water in the entire area is very clear. The 100 x 200-km SAR image is shown in Fig. 28b to approximately the same scale size but with a slight difference in orientation because of differences in the Landsat and Seasat orbits. The sandbar regions and the tongue feature are essentially identical but the mechanism by which the Landsat and SAR images are created are fundamentally different. Landsat-imaged differences are due to variations in the green response because of sunlight

FIG.28a. The Landsat green-channel image of the Tongue Of The Ocean (TOTO) acquired January 24,1973),shows both the deep-water tongue (about 1500 m deep) and the shallow-water sandbars (1 to 15 m deep).

46

JOHN W. SHERMAN. 111

FIG.28b. SAR-acquired image of TOT0 more than 5 years later than the image seen in Fig. 28a. The bathymetric features are believed to be imaged by the coupling of currents to bottom features which in turn affect the surface wave structure at wavelengths of 20-30 cm.


47

penetration and reflectance from the ocean bottom. The optical depth is tens of meters in the green region. In contrast, the same “optical” depth or skin depth for microwave energy at the SAR frequency (1.275 GHz or 23.5-cm wavelength) is only around 8 mm, and the SAR image must of necessity be created by a surface manifestation of a subsurface. phenomenon. The similarity of the sandbars between the two images obtained more than 5 years apart indicates the stability of the bottom features in the TOT0 region. Because the SAR is sensitive to slight changes in surface tension the potential to detect oil sheen/spills on the surface was anticipated. Damping of capillary waves in the presence of surface winds reduces the energy backscattered from oil sheen relative to surrounding areas. This is shown in Fig. 29 in a scene that occurred on October 3, 1978, in the Caribbean Sea at about 82”W, 20”N (Rev 1404). The area is about 25 x 50 km in this image. The history of the apparent petroleum productlresidue is unknown but its presence persisted for at least three additional days when the streaks were again imaged (Fu and Holt, 1982). The two ships visible also indicate the nature of the Doppler processing involved in imaging by synthetic aperture radars. Because the wakes are relatively stationary compared to the ship, a moving vessel with a radial component of velocity with respect to the radar appears to be displaced from its wake (Jain, 1978). If the ship velocity component is toward the radar, the ship appears shifted in the direction the radar is moving; this is reversed for ships moving away from the radar. Accurate measurement of the displacement of the ship from its wake, coupled with knowledge of the radar processing characteristics, allows ship velocity to be determined. 3. THENIMBUS-? SENSORS AND RESULTS The Nimbus-7 spacecraft carries sensors designed for studies related to atmospheric sciences and pollution and to oceanology, including sea and lake ice. These sensors include Earth Radiation Budget (ERB) radiometer, Limb Infrared Monitoring of the Stratosphere (LIMS) sensor, Stratospheric Aerosol Measurement (SAM 11) sensor, Solar and Backscatter ultraViolet/Total Ozone Mapping System (SBV/TOMS), Temperature-Humidity Infrared Radiometer (THIR), Scanning Multichannel Microwave Radiometer (SMMR), and Coastal Zone Color Scanner (CZCS). Only the last two instruments collect data directly related to oceanic monitoring. Because the SMMR is identical to the radiometer flown on Seasat, it is not reviewed in this section. The focus is on the application of a high-spectral-resolution visible region sensor, the CZCS, to oceanic sciences (see Chapter 8).

48


FIG.29. The effects of both ships and oil on the ocean surface are shown in this SAR image of October 3,1978, Rev 1404, off the southerncoast of Florida. Thedirectionof illuminationis from bottom to top and the SAR flight direction is from right to left. (Courtesy B. Holt, JPL.)


49

The CZCS began operation on October 29,1978,5 days after the launch of Nimbus-7, and has been in operation over U.S.coastal areas for the majority of the overpassingorbits since that time, and for many other parts of the world using on-board tape recorders. The CZCS is an image scanner which provides measurementsof the apparent radiance of the scene below the spacecraft in six coregistered spectral bands centered at 443, 520, 550, 670, and 750 nm, and 11.5 pm. Spectral bands of CZCS were chosen to correspond to strong absorption features of organic material in the ocean and to spectral regions where absorption is minimal. The 11.5-pm band provides registered, simultaneous measurements of equivalent blackbody temperature. CZCS scans across track with an instantaneous field of view of 0.05", or 825-m resolution near nadir at the surface, from a nominal altitude of 955 km. The active portion of the scan is 75" centered about the nadir trackline, corresponding to a swath width of 1469 km. Of this, the central 40" is utilized for most processing to reduce limb-brightening (path radiance change with scan angle) effects. The remainder of the image is used primarily for image location, where identifiable land masses are present in the scene, and for more qualitative analyses. Two minutes of along-track scanning are defined as an image for the purpose of data storage on computer-compatible tapes (CCTs). This corresponds to a surface displacementof 769.6 km. (Thus, the complete 2-min image is about 770 x 1470 km.) The information is slightly oversampled resulting in 9.7 x lo5pixels in each of the six bands for the central 40" portion of the image (or an area about 770 x 695 km). At 59" latitude and greater, successive orbits overlap (for the full 75" swath width). The scanner can be tilted k20" in 2" increments in the along-track direction to aid in reducing the effects of sun glint. The total power requirements of the spacecraft and the various sensors exceeded the available power provided. Because of this power budget, CZCS data are recorded or transmitted directly to the ground for an average total of 2 hr day-'. Two hours of data are equivalent to 32 x lo6km2, an area just under 10% of the entire global oceans and seas area. Stated in other terms, CZCS can collect data daily from an area about eight times the size of the Gulf of Mexico and Caribbean, or all global oceans and seas could be covered about three times a month. Data from the CZCS are processed into calibrated radiance for those bands sensing reflected sunlight and equivalent blackbody temperature for the infrared wavelength band. In addition, four derived products are produced from algorithms developed by the Nimbus Experiment Team (NET). They are K (the diffuse attenuation coefficient), chlorophyll pigment concentration, aerosol radiance (670 nm), and sea surface radiance (443nm). The algorithms are reviewed in Gordon et al. (1983), Clark (1981), and in Chapter 8 of this

50

JOHN W.SHERMAN, I11

book. The diffuse attenuation coefficient is dependent upon the total particulate matter concentration in the water, and the pigment concentration is principally an indication of chlorophyll a and its degration product, pheopigment a. All of these products, except SMMR data from Nimbus-7, are available through NOAA’s Satellite Data Services Division of NESDIS in both photographic and CCT format (see Appendix C). 3.1. Surface Information

Data were collected from a number of dedicated oceanic stations to permit quantitative analyses of the CZCS data set. The extent of these stations is shown in Fig. 30. Other experimentaldata sets were collected and analyzed by Scripps Institute of Oceanography, Bigelow Laboratories, University of Miami, University of Southern California, San Jose State, and Texas A & M. At the oceanic stations data typically collected were upwelled (nadir) spectral radiance, upwelled spectral irradiance, and downwelled spectral irradiance, all as a function of depth, using submersible spectroradiometers. Continuous monitoring of irradiance from the sun and sky was accomplished at the surface as a function of wavelength. Beam transmittance/volume attenuation coefficient provided a rapid means of assessing the vertical structure of the water column. Munsell color scale and Secchi disk completed the optical measurements made. The biological measurements included chlorophyll a observations by both fluorometer and acetone fixation, total suspended particulates (dry weight), and particulate size distributions (Coulter Counter). 3.2. Preliminary Results

Figures 31a-c show the radiance values in an image format at 443, 550, and 670 nm, respectively. For illustrative purposes of the CZCS algorithms, Wrigley (NASA/Ames)processed November 2,1978, data to derive the chlorophyll alpheopigment a ratio in the northeastern portion of the Gulf of Mexico. The CZCS algorithm for atmospheric correction is the key to successful application of this sensor. The two main assumptions made in this algorithm are that (1) Rayleigh scattering in the atmosphere is known, and (2) the reflection coefficient of the ocean surface at 670 nm is zero (total absorption). Using the first assumption, the Rayleigh scatter was subtracted from the radiance values shown in Figs. 31a-c. For the over-water portion of Fig. 31 with the Rayleigh term subtracted, the difference between the remaining radiance value at 670 nm is assumed to be because of aerosols. This residual radiance is then adjusted for aerosol wavelength dependency and

FIG.30. Location of oceanic stations for surface comparative data for Coastal Zone Color Scanner (CZCS)analyses. (Courtesy of D. Clark, NOAA.)

52

JOHN W.SHERMAN, 111

solar flux and ozone transmittance at the 443- and 550-nm wavelengths. The Rayleigh-corrected images, blue (a) and green (b), are then corrected for aerosol scattering to generate the images labeled (d) and (e) in Fig. 31. This is done on a pixel-by-pixel basis. Thus, images (d) and (e) are the equivalent of viewing the ocean surface with the atmosphere removed. The two atmospherically corrected images provide significantly improved contrast with regard to water features. The blue channel (d) emphasizes contrast in differing water masses based on pigments, and the green (e), which is relatively insensitive to chlorophyll alpheopigment a, emphasizes total suspended sediment. The variation in response is made possible only through the use of high-spectral-resolution (20 nm) channels of CZCS. The remaining two images in Fig. 31 (f and g) provide data on the total chlorophyll u/pheopigment a in the water column. Image (f) provides relative chlorophyll ulpheopigment a for high levels of concentration (the higher the concentration, the whiter). Image (g) is reversed to emphasize low levels of chlorophyll (the lower the concentration, the whiter). The methodology used to provide the chlorophyll u/pheopigment a concentrations is more fully discussed in Chapter 8. In essence, the data in (f) and (g)were derived from the corrected images in (d) and (e) using an equation of the form, Concentration = A(LA,/L&)B where Lis the corrected radiance term at wavelength I and A and B are constants. Analyses are also presented in Chapter 8 on the derived total suspended particulates from CZCS. 4. THETIROS-N SERIES AND RESULTS

The October 13, 1978, launch of TIROS-N began the third generation of civil satellites to be used operationally during most of the 1980s. TIROS-N was a NASA-sponsored satellite used as an operational prototype by NOAA. The follow-on satellites, NOAA-6, NOAA-7, and NOAA-8, are essentially identical spacecraft. The instrument payloads which have flown or are expected in future NOAA series include two versions of the Advanced Very High Resolution Radiometer (AVHRR/1 or 2), High resolution InfraRed Spectrometer (HIRS/2), Stratospheric Sounding Unit (SSU), Microwave Sounding Unit (MSU), Data Collection System4 (DCS), Solar and Backscattered UltraViolet/Total Ozone Mapping System (SBUV/TOMS), and the Earth Radiation Budget (ERB)radiometer. A Solar Environmental Monitor This system is furnished by the Centre National &Etudes Spatiales(CNES) of France and is designated as the “ARGOS Data Collection and Location System.”

FIG.31. Nimbus-7 CZCS imagery and derived products from orbit 130 in the northeastern portion of the Gulf of Mexico, November 2, 1978. (a) Blue band, 4.43 nm; (b) green band, 550 nm; (c) red band, 670 nm; (d)blue band, atmospheric correction using red-band data; (e) green band, atmospheric correction using red-band data; (f) chlorophyll analysis using 443/550algorithm: white indicates high chlorophyll concentration, dark indicates low chlorophyll concentration; (g) identical to (f ) but white indicates low chlorophyll concentration, dark indicates high chlorophyll concentration. (Courtesy of R.Wrigley, NASA.)

54

JOHN W.SHERMAN, 111 TABLE IV. SUMMARYOF TIROS-N AND NOAA-SERIES~ SENSORS

Satellite Instrument

TIROS-N

NOAA-6-7-C-D

NOAA-E/SARb

NOAA-F-G/SARb

~

AVHRR/l AVHRR/2 HIRS/2

ssu

MSU DCS SBUV ERBE

X

X

X X X

X X X X

X

X

NOAA-B failed to achieve orbit, thus NOAA-C became NOAA-7 and NOAA-E became NOAA-8. SAR, Search and rescue.

(SEM) completes the TIROS-N sensor complement. Table IV is a summary of the TIROS-N and NOAA-series instrumentation. The SSU, HIRS, and MSU instrument combination forms the TIROS-N Operational Vertical Sounder (TOVS) system and replaces the Vertical Temperature Profile Radiometer (VTPR) used on the NOAA-2 through -5 satellites (see Chapter 1). Also, the HIRS/2 is an adaption of the HIRS sensor flown on Nimbus-6. Further details on the TIROS-N series of satellites can be found in Yates (1981) and Schwalb (1982). The principal oceanic data derived from the TIROS-N satellite are from the AVHRR system. The other sensors provide indirect support to marine users through improved weather forecasts and storm warnings. The development of appropriate real-time algorithms has permitted the AVHRR to provide SST to an accuracy of 0.75"Cat resolutions as small as 1.1 km (Chapter 7 and McClain, 1981). The AVHRR characteristics delineated in Table V define the spectral coverage of the system. The change in Channel 1 between the prototype unit on TIROS-N and the NOAA series was done to eliminate overlap with Channel 2, thus providing improved areal extent of snow cover. A swath width of about 3000 km is achieved from the AVHRR with a 1.3 & 0.1 mr IFOV (1.1-km resolution near nadir) with all channels registered to 20.1 mr. The noise-equivalent temperature difference in Channels 3, 4, and 5 is designed to be at least 0.12 K for a 300 K scene. The equivalent to the APT used on earlier civil environmental satellites (Chapter 1) is derived from AVHRR and is continuously transmitted at a 4-km resolution for Channels 2 and 4. (For NOAA-7 this is 725 to 1100 nm and 10.3 pm to 11.3 pm, respectively.)

55

2. SEASAT, NIMBUS-7, A N D TIROS-N TABLEV. AVHRR SPECTRAL CHARACTERISTICS' _

_

_

_

~

~

~

~

AVHRRjl Channel no.

AVHRRJ2

TIROS-N

NOAA-A-D

NOAA-E-G

1

0.55-0.90

2 3 4 5

0.72-1.10 3.55-3.93 10.5-11.5 -

0.55-0.68 0.72-1.10 3.55-3.93 10.5-11.5 -

0.72- 1.10 3.55-3.93 10.3-1 1.3 11.5-12.5

0.58-0.68

Channel wavelengths in micrometers.

A multichannel sea surface temperature (MCSST) procedure using AVHRR data has been developed by NESDIS. Since becoming operational in November 1981, the technique has demonstrated superior results to the older Global Sea Surface Temperature Computation (GOSSTCOMP) procedure it replaced (1972-1981). Atmospheric water vapor is now corrected for on a pixel-by-pixel basis. Validations are being updated monthly using highquality drifting-buoy data scattered over the global oceans. Results from these drifter/MCSST matchups are shown in Fig. 32 (courtesy of Alan E. Strong, NOAA). Results have also been encouraging from comparisons of drifting-buoy SST measurements to the MCSST-analyzed field, produced on a weekly basis by NESDIS. Typical results of these comparisons are illustrated in Fig. 33, where eight buoys (denoted by x's with temperature) were reporting in February 1982

25

#

Matchups

Mean Bias

124

-0.o'C 0.6T

Scatter

0.6.C

RMSD

Night +-

-+

# 4

-

L

.

-5

Matchups 143 Mean Bias -0.4.C Scatter 0.6-c RMSD 0.8'C

0

5

10

15

20

25

30

MCSST ('C)

FIG.32. Scatter diagram for AVHRR-derived sea surface temperatures versus drifting-buoy surface temperature. (Courtesy A. Strong, NOAA.)

56


FIG.33. Multichannel sea surface temperature (MCSST) for the South Atlantic and southern Indian Ocean. (Courtesy W. Pichel, NOAA.)

from the South Atlantic and South Indian Ocean (Chapter 7). Only two buoys, one to the extreme west (32"W) and one to the extreme east (88"E), on this figure depart by 1" or more from the MCSST analysis. Two difficulties with the present AVHRR-derived SST measurements that the reader should understand in the routine use of these data are aerosol contamination and noise problems in the detectors. Such problems can arise in any such system and are cited here to illustrate the need to validate constantly the data. The aerosol contamination problem was highlighted dramatically by the April 1982 eruption of the Mexican volcano, El Chichon. Contamination of several degrees on the retrieved MCSST was typical during the summer/fall of 1982 from H,S04 aerosol attenuation between 10"N and 30"N latitude (El Chichon is at 17"24'N,93"W). Channel 3 AVHRR noise on NOAA-7 has diminished the number of MCSST retrievals beginning in late summer 1982. While the specificnoise problem has been identified and should be alleviated on future AVHRR systems, additional quality control must be exercised to maintain temperature accuracy and provide error limits. 5.

SUMMARY

The six new oceanic sensors outlined in this chapter all met or exceeded the original design specifications for geophysical data. Each has provided a unique view of the ocean surface or near-surface, and except for the altimeter

TABLE V1. GENERAL SUMMARY OF OCEAMC SENSQRRESULTS' Sensor type Multichannel radiometer Observable Altitude Geodesy Mean sea surface model Waves HI,, Wavelength Wave direction Internal Winds (surface) speed Direction Sea surface temperature Sea ice Location Type Edge

Altimeter

Scatterometer

Microwave

Infrared

Synthetic aperture radar

Ocean-color instrument

8 an (prec.) 70 cm (prec.)

10% or 0.5 m 12% 15"

Detected

2 m sec-'

1.3 m sec-

16" 1"C

Detected

Detected

N.Q. First year versus multiyear Detected

0.75"C

7km

0.5km

7km

0.5 km

(Continued)

TABUVI. (Continued)

Sensor type Multichannel radiometer Observable Ice sheets Height change Icebergs Ocean color Pigments Diffuse atten. coeff. Water mass detection Atmospheric water Columnar H 2 0 vapor Columnar H,O liquid Rain rate Circulation Current boundary Currents Eddies

Altimeter

Scatterometer

Microwave

Infrared

Synthetic aperture radar

(Very large)

N.Q.

Ocean-color instrument

1.6 m (prec.)

40% 40% N.Q. 10%or 0.2 g cn-’ N.Q. N.Q.

Detected Variability N.Q.

Detected

Detected

Detected

Warm eddies

Detected

Detected

a Unless otherwise noted, the values are the lu accuracies compared to in situ sources; where a percentage and a number are given, whichever is greater applies; N.Q. indicates not quantified; detected means the feature was observed but did not lend itself to further quantification.


59

and the AVHRR, each sensor has offered for the first time other methods for observing global oceans and ice dynamics. For the original design expectations quantitative measurements have been made. Table VI is a summary of results of these measurements. This table also includes observables which have not been quantified but still provide useful qualitative data on other ocean features. For example, the variability in ocean currents has been derived from the altimeter data, but surface quantification of total volume flow has not been done. Similarly, the SAR has detected numerous internal wave systems and permitted measurements on internal wave surface features, but quantification or limits have not been established. Other observables also have been noted in this manner. REFERENCES Alishouse, J. C. (1983). Total precipitable water and rainfall determinations from the Seasat scanning multichannel microwave radiometer. J. Geophys. Res. 88, 1929-1935. Bernstein, R. L. (1981). SMMR-derived sea surface temperature and temperature anomaly patterns in the mid-latitude Western Pacific. EOS 62,294. Born, G. H., Wilkerson, J. C., Sherman, J. W., and Lame, D. B. (1979). Seasat Gulf of Alaska workshop report, executive summary. Publ. No. 622- 101, Jet Propulsion Lab., Pasadena, Calif. Cane, M. A,, and Cardone, V. J. (1981). The potential impact of scatterometry on oceanography: A wave forecasting case. “Oceanography from Space” (J. F. R. Cower ed.), pp, 587-595. Plenum, New York. Cavalieri, D. J., Gloersen, P., and Campbell, W. J. (1981). Observation of sea ice properties with Vol. 1, pp. 69-78. the Nimbus-7 SMMR. IEEE Catalog No. 81CH1656-8, Cheney, R. E. (1982). Comparison data for Seasat altimetry in the Western North Atlantic. J. Geophys. Res. 87,3247-3253. Clark, D. K. (1981). Phytoplankton pigment algorithms for the Nimbus-7 CZCS. “Oceanography from Space” (J. F. R. Cower, ed.), pp. 227-237. Plenum, New York. Fedor, L. S., and Brown, G. S. (1982). Waveheight and wind speed measurements from Seasat radar altimeter. J. Geophys. Res. 87, 3254-3260. Fu, L., and Holt, B. (1982).Seasat viewsoceans and sea ice with synthetic aperture radar. Publ. No. 81-120, Jet Propulsion Lab., Pasadena, Calif. Gonzalez, F. I., Thompson, T. W., Brown, W. E., Jr., and Weissman, D. E. (1982). Seasat wind and wave observations of Northern Pacific Hurricane Iva, August 13,1978. J. Geophys. Res. 87, 3431-3438. Gordon, H. R., Clark, D. K., Brown, J. W., Brown, 0. T., Evans, R. H., and Broenkow, W. W. (1983). Phytoplankton pigment concentrations in the middle Atlantic Bight: Comparison of ship determinations and CZCS estimates. Appl. Opt. 22.20-36. Jain, A. (1978). Focusing effects in the synthetic aperture radar imaging of ocean waves. Appl. Phys. 15,323-333. Jones, W. L., Black, P. G., Boggs, D. M., Bracalente, E. M., Brown, R. A., Dome, G., Emst, J. A., Halberstam, J. M., Overland, J . E., Peteherych, S., Pierson, W. J., Wentz, F. J., Woiceshyn, P. M., and Wurtelle, M. G. (1979). Seasat scatterometer: Results of the Gulf of Alaska workshop. Science 204, 1413-1415.

60


Jones, W. L., Schroeder, L. C., Boggs, D. H., Bracalente, E. M., Brown, R. A., Dome, G. J., Pierson, W. J., and Wentz, F. J. (1982). The Seasat-A satellite scatterometer: The geophysical evaluation of remotely sensed wind vectors over the ocean. J. Geophys. Res. 87,3297-3317. Katsaros, K. B.,Taylor, P.K., Alishouse, J. C.,and Lipes, R. G.(1981). Quality of Seasat SMMR atmospheric water determinations. “Oceanography from Space” (J. F. R. Gower, ed.), pp. 691-706. Plenum, New York. Legeckis, R. V. (1979). Satellite observations of the influence of bottom topography on the seaward deflection of the Gulf Stream off Charleston, South Carolina. J . Geophys. Oceanogr. 9,403-497. Lerch, F. J., Marsh, J. G., Klosko, S. M., and Williamson, R. G. (1982). Gravity model improvement for Seasat. J. Geophys. Res. 87,3281-3296. Lichy,D. E., Mattie, M. G.,and Mancini, L. J.(1981). Trackingof a warm water ring. Spaceborne Synthetic Aperture Radar for Oceanography (R. C. Beal, P. S. Deleonibus, and I. Katz, eds.), pp. 171-182. Johns Hopkins Univ. Press, Baltimore. Lipes, R. G., ed. (1980). Seasat scanning multichannel microwave radiometer mini-workshop 111 report. Publ. No. 622-224, Jet Propulsion Lab., Pasadena, Calif. Lipes, R. G. (1982). Description of Seasat radiometer status and results. J. Geophys. Res. 87, 3385-3395. McClain, E. P. (198 1). Multiple atmospheric-window techniques for satellite-derived sea surface temperatures. “Oceanography from Space” (J. F. R.Gower, ed.), pp. 73-86. Plenum, New

York. McClain, E. P.,and Marks, R. A. (1979). Seasat visible and infrared radiometer. 204,1421-1424. Marsh, J. G., and Martin, T. V. (1982). The Seasat altimeter mean sea surface model. J. Geophys. Res. 87,3269-3280. Schroeder, L. C., Boggs, D. H., Dome, G., Halberstam, I. M., Jones, W. L., Pierson, W. J., and Wentz, F. J. (1982). The relationship between wind vector and normalized radar cross section used to derive SFasat-A satellite scatterometer winds. J. Geophys. Res. 87,3318-3336. Schwalb, A. (1982). Modified version of the TIROS-N/NOAA A-G satellite series (NOAA E-J)Advanced TIROS N (ATN). NOAA TM Memo. NESS 116, Washington, DC. Shuchman, R. A., Kasischke, E. S., Klooster, A., and Jackson, P. L. (1979). Seasat SAR coastal ocean wave analysis. Environmental Research Institute of Michigan Report 138600-2-F. Tapley, B. D., Born, G .H., and Parke, M. E. (1982). The Seasat Altimeter data and its assessment. J . Geophys. Res. 87,3179-3188. Townsend, W. F. (1980). An initial assessment of the performance achieved by the Seasat-1 radar altimeter. IEEE J. Oceanic. Eng. OE-5, 80-92. Weissman, D. E., Ed. (1980). I E E E J . Oceanic Eng. OE-5,71-76. Yates, H. (1981). The United States operational polar-orbiting satellite series, TIROS-N. Ado. Space Res. ( C O S P A R ) 1,57-71.

ANALYSIS AND INTERPRETATION OF ALTIMETER SEA ECHO J. LIPA DONALD E. BARRICK BELINDA Ocean Surface Research Boulder, Colorado

Ocean Surface Research Woorlside. California

1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . 2. TheConvolutional Representation of the Signal and Its Use . . . . . . . . 2.1. Derivation of Convolutional Form . . . . . . . . . . . . . . 2.2. Recovery of Joint Probability Density from Seasat Data . . . . . . . . 3. Model Fits of Recovered Sea Surface Probability Density . . . . . . . . . 4. The Study of Altimetric Biases Using Models . . . . . . . . . . . . . 4.1. Echo Model with Gaussian Beam/Pulse Shapes and Gram-Charlier Surface Probability Density. . . . . . . . . . . . . . . . . . . . . . 4.2. Semiempirical Seasat Model, Neglecting Pointing Error . . . . . . . . 4.3. Tracker-Bias Study Using the Semiempirical Model . . . . . . . . . 4.4. Antenna Pointing-Error Effects-Model for Echo Plateau . . . . . . . 4.5. Rain Effects on Altimeter Echo. . . . . . . . . . . . . . . . . 5. Electromagnetic Bias. . . . . . . . . . . . . . . . . . . . . . . 6. A General, Improved Deconvolution Algorithm . . . . . . . . . . . . 7. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . Appendix.. . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . .

.

61 62 62 65 68 73 73

. 75 . 78 . 79

.

83 86 93 96 97 99

1. INTRODUCTION

Short-pulse altimetry from space was first suggested in the mid-1960s in a study supported by NASA at Woods Hole, Massachusetts(Ewing, 1965). This study drew on the state of the art of airborne remote sensing as the basis for satellite techniques and applications. Microwave altimeters were proposed for the measurement of sea level, sea state, and tsunamis. Further development of specific sensors for oceanic physics evolved during what has become known as the “Wil1iamstown”study (held at Williams College 5 years after the Woods Hole gathering), Here a strong case was made for microwave satellite altimetry (Kaula, 1970) that provided impetus for the altimeters flown on Skylab and GEOS-3. In contrast with other microwave instruments (e.g., the scatterometer, radiometer, and synthetic aperture radar), the altimeter is supported by a mathematical model relating the echo to the sea surface interaction that is both noncontroversial and useful for designing algorithms to extract information. Since the backscatter seen by the altimeter in space is restricted to a fraction of a degree around the nadir position, the scattering mechanism is 61 ADVANCES IN GEOPHYSICS, VOLUME 27

Copyright @ 1985 by Academic Press, Inc. All rights of reproduction in any form reserved. ISBN 0-12-018827-9

62

DONALD E. BARRICK AND BELINDA J. LIPA

essentially optics-type reflection from thousands of specular points randomly distributed across the rough, moving sea surface. The mathematical model describing the altimetric echo waveform was derived by Barrick (1972a)using physical optics to represent the scatter from the rough surface. This model is a double integral, and can be written in a convolutional form. It shows that the sea surface response to a short pulse is a ramp starting at zero and rising sharply in time to a plateau, after which it falls off slowly. Sea surface mean position and wave-height information is contained in the ramp portion, called the leading edge. Wind-speed information is extracted from the backscatter signal intensity on the plateau. Models have been devised that are fitted to the leading edge directly to extract mean surface position and wave height, both on board the satellite [for Seasat, see MacArthur (1978) and Townsend (1980)], as well as on earth, post facto (Hayne, 1981). In this chapter is discussed the physics behind and use of Barrick’s model in information extraction and data interpretation. In particular, in Section 2 is presented the convolutional form of the model, demonstrating a much simpler but more illuminating method for its inversion than those used in the above references; it is based on deconvolution by straightforward fast Fourier transform (FFT)algorithms. In the subsequent section the recovered quantity is then interpreted, namely, the sea surface wave-height probability density function in terms of models that allow maximum-likelihood parameter extraction and uncertainty estimation. In Section 4 we develop and employ models to study various factors (both instrumental and near-surface effects) that bias or distort the altimeter echo, and use Seasat to demonstrate their application. In Section 5 we discuss the important and interesting phenomenon called electromagnetic bias, i.e., where the altimeter reckons the mean sea surface position to be, compared with its actual position (with all other errors/biases removed). Finally, in Section 6 a double-deconvolutional-based algorithm for altimeter echo analysis is discussed that can handle various antenna error and rain biases, is computationally efficient, and outputs parameter uncertainties along with the parameters themselves.

2. THECONVOLUTIONAL REPRESENTATION OF THE SIGNAL AND ITS USE 2.1. Derivation of Convolutional Form

Scatter from a Gaussian, random distribution of rough-surface specular points contained in a downward-propagating spherical altimeter pulse was derived by Barrick (1972a,b) using physical optics, and is repeated here. The result gives the average radar cross section for backscatter, polarized in the

63

3. ALTIMETER SEA ECHO

same sense as was transmitted, as a function of time t , while the altimeter receiver is responding to the interaction of its pulse with the rough sea or earth surface. For the coordinate geometry, see Fig. 1.

1

o ( t ) = 2 n ' a 2 1 ~ ( 0 ) 1 ' ~ ~ g ( ~ ) s e c -4m ~~ s i n(- ~tP [ jj ~( t ) d t

d4

(1)

where II/ is the angle at the antenna from nadir to a point ( on the ocean surface; 4 is the angle at earth center from the satellite to a point 5 on the ocean surface;&) is the two-way antenna gain pattern, normalized so that it is unity at its maximum (accounts for pointing error); P(x) is the effective pulse shape at the receiver output, normalized to unity at its maximum, versus spatial propagation distance x = c t / 2 ; a is the earth's radius; R(0) is the Fresnel reflection coefficient of sea surface at normal incidence; 6 is the angle between the local normal to the surface at [ and the satellite; and pj([) is the joint height-slope probability density function of the surface height i (positive upward) of the waves above a mean local surface, and wave slopes corresponding to specular angle 0. The above expression is thus far exact; the only approximations implied are those inherent in the specular-point explanation of scatter. For backscatter very near vertical, two decades of experimental data have shown the specular-point model to be totally adequate for the microwave radar echo. Operation from a satellite such as Seasat requires the use of a narrow-beam antenna, which restricts backscatter to a region very near nadir, i.e., backscatter near the normal or vertical to the mean surface. It is this condition that permits considerable simplification of Eq. (1) to obtain linearization and reduction to a convolutional form. We employ the parameters of Seasat to demonstrate this process. Although exaggerated in Fig. 1, theangles t+b, 4, and 8 are very small. The angle IC/ at Seasat's two-way antenna pattern half-power point is 1.6"/(2fi) 'Y 0.57". The last sampling gate at which data are taken

Satelliten

b Earth Center FIG.1. Coordinate geometry for satellite altimeter;( is height of surface point above the mean (spherical)earth/sea, and 0 is slope angle (from vertical) seen from altimeter to the mean sea.

64

-


and preserved on Seasat (i.e., gate 60)restricts $ even further; at its altitude of 800 km, $ at the last gate is 0.3”. Therefore, small-angle trigonometric simplification of the exact law of cosines (see Fig. 1) relating the height ( of the roughness above the mean spherical earth of radius a to time t and satellite height H,i.e.,

(C

+

=(H

+ Ct/2)2 + (a + H ) 2 - 2(H + ct/2)(a + H)cos JI

(2)

yields (=-4 2

+ H’($2/2)

(3)

where time t measured at the receiver is taken to be zero when the radar cell center intercepts the mean spherical earth. We also employ the fact that /h3. For


71

ocean waves, the fact that this quantity is nonzero and positive is explained by the pointiness near the crests and flatness near the troughs. A2h includes any shifts of the waveform from its “perfect” location (with gate 30.5 centered halfway up the echo leading edge), normally due to the on-board tracker bias. The quantity A,h would normally be included in the exponential [i.e., instead of we would have (5 - A2h)’3, but since Seasat error A,h is known to be small in terms of RMS wave height h, this quantity is brought outside by a Taylor series expansion about zero bias, retaining only the first and second terms in A2h. The resulting model, Eq. (6), is therefore linear in two unknown parameters, A , and A,, while it is nonlinear only in h. This fact will make leastsquares estimation of these three quantities very easy. Finally, we “normalize” the data, pjN(li), such that the area under it is unity [i.e., p j N ( l i )= p j ( c i ) / Z i p j ( l i ) ] .We then do a least-squares fit of measured data p j N ( l i )to by minimizing E i [ p j N ( l i )- j j j N ( l i ) 1 2 with respect to h, A , , the model pjN(li) and A,. Because the model Eq. (6) is linear in A , and A,, but nonlinear in h, Lipa and Barrick (1981) show that this leads to a simple one-dimensional grid search in h, requiring approximately N 2 steps to find the minimum, with a final interpolation between the three lowest points h j - , , h j , and h j + , , to give the RMS wave-height estimate h: There is another obvious way of determining A , that does not involve use of a model: direct calculation of the first moment from the retrieved probability density, i.e.,

cz

J --oo

I J-m

This would be identically equal to A,h if the data fit the model of Eq. ( 6 ) perfectly. Theoretically, one can also determine hZ in this way from the second moment and A , or 1, from the third moment. There is a problem with higher moments, however. The numerical integration to obtain the moment runs theoretically from - co to co;practically, one must truncate at some point. Because of noise and statistical fluctuation in the original data, the tails of the retrieved probability p j N ( ( )never become identically zero, but fluctuate about a low but constant “noise” level. Multiplication of these tails by c” enhances this noise for larger values of n. Therefore, one must truncate the numerical integration at some upper limit to keep this enhanced “tail noise” low. We have found, however, that for larger values of n (e.g., for moments equal to or greater than the second, where n 2 2), the result then becomes quite sensitive to the limit chosen. Hence, our experience with the Seasat data has shown that moments higher than the first cannot be computed stably, and the model-fitting procedure described above must be applied. In Table I we present values of mean sea-level difference (from gate 30.5) f, using both the first-moment and model-fit techniques (along with standard

+

72

DONALD E. BARRICK AND BELINDA J. LIPA TABLEI. PARAMETERS OF HEIGHT-SLOPE PROBABILITY DENSITIES FOR SEASAT ORBIT280 Mean sea level difference (an)

Time (GMT) (hr:min:secl

First moment

Model fit

RMS wave height (cm)

Height skewness parameter

Csec averages

14: 12:00-14:12:06 14:12:06-14:12:12 14:12:12-14:12:18 14: 12:18-14: 12:24

10.4 f 1 10.6 f 1 11.4 f 1 11.4 & 1

11.0 f 2 11.1 f 2 11.8 f 2 11.9 f 2

56.3 f 2 59.5 f 2 59.5 f 2 60.5 f 2

0.22 f 0.1 0.29 f 0.1 0.25 f 0.1 0.30 f 0.1

14:14:42-14:14:48 14:14:48-14:14:54 14: 1454-14: 15:OO 14: 15:00-14:15:06

35.4 f 7 37.4 f 6 38.2 f 6 35.9f 5

51f8 47 f 9 39 f 8 36 f 8

166 f 5 177 f 5 169 f 4 164 f 4

0.23 f 0.2 0.32 f 0.2 0.08 f 0.2 0.15 f 0.1

14:15:30-14:15:36 14:15:36-14:15:42 14:15:42-14:15:48 14:15:48-14:15:54

16.4 f 3 18.3 f 3’ 14.7 f 3 18.2 f 3

17 f 5 19 f 4 16 f 5 1854

112 f 3 110 f 3 110 f 3 100f3

0.03.* 0.2 0.13 f 0.2 0.01 f 0.2 0.21 f 0.2

14: 17:30-14: 17:36 14: 17:36-14: 17~42 14:17~42-14:17:48 14:17:48-1417:54

10.5 f 2 9.4 f 2 11.7 f 2 11.5 f 2

12 f 3 11 f 2 12 f 2 1352

65.5 f 2 64.5 f 2 64.5 f 2 62.4 f 2

0.26 f 0.1 0.20 f 0.1 0.20 f 0.1 0.45 f 0.1

Wsec averages 14:12:00-14:12:24 14:14:42-14:15:06 14:15~30-14:1554 14:17:30-14:17:54

10.8 f 0.4 41.6 f 2 17.0 f 0.7 11.1 f 0.4

11.4f 0.5 47.2 f 2 17.0 f 1 11.3 f 0.6

59.7 f 0.4 167 f 1 108 f 0.8 64.2 f 0.5

0.27 f 0.03 0.27 f 0.04 0.10 f 0.04 0.29 f 0.03

deviations or errors in these quantities); positive numbers mean the actual shift from gate 30.5 is upward, The third and fourth columns are RMS wave-height 6 and height skewness A,, retrieved using the model-fit method. In the top section of this table, each of these is done over 60 total wave forms, comprising 6 sec (-40-km path distance). The bottom section is for a 24-sec average. Table I1 shows comparisons of RMS wave height 6 from our method for Hurricane Fico (24-sec averages), compared with the on-board wave-height estimator, as well as algorithms of Fedor and Hayne. (Hayne’s data were available for only two of the four periods we analyzed.) These results are taken from Lipa and Barrick (1981). It is possible to linearize Eq. (6) completely by expanding about an initial estimate of wave height ho (obtained either from the on-board wave-height

73


TABLE 11. WAVE-HEIGHT COMPARISONS Time period 1412:00-14:12:24 14: 14:42-14 15~06 14: 15130-14:1554 14: 17:30-14: 1754

Present analysis (cm)

Fedor

(4

Hayne

(4

59.7 167 108 64.2

58.8 188 117 64.1

128 74 -

On board (cm)

-

60.5 206 116 63.7

estimator or from the leading-edge slope). This leads to an iterative search for the true value of h. However, we have found no savings in computer time over the one-dimensional grid search. In addition if ho is a poor estimate, the iterative method may converge to an incorrect value. Hence we do not recommend this method.

4.

THE

STUDY OF ALTIMETRIC BIASES USING MODELS

Thus far, in our convolutional representation for the altimeter echo waveform Eq.(4) we avoided using models for any of the three functions in the integrals that produce the echo: the antenna pattern function, the pulse-shape function, and the surface probability density function. Since the first two should be known from calibration tests of the instruments, the third is obtainable exactly using deconvolution. This method was then tested on Seasat data and the actual probability density was in fact accurately measured. Only in the last section, then, did we fit a model to the surfaceheight probability density in order to relate its most important descriptors (RMS wave height and wave-height skewness) to the same parameters obtained by other investigators. There are situations or reasons in which models for all three functions are illuminating. These have to do with how various departures of the system, propagation medium, or scattering process from the ideal affect the echo. Said another way, one can insert biases into the models one at a time and see how the system performs and/or how candidate algorithms will misinterpret the output. 4.1. Echo Model with Gaussian BeamlPulse Shapes and Gram-Charlier Surface Probability Density

Here we assume a Gaussian shape for the compressed altimeter pulse P(x), with z being the half-power width of the pulse. Likewise, we assume a Gaussian shape for the antenna beam near its boresight (i.e., the last range gate

74


taken on Seasat subtends 0.3"from boresight, while the two-way half-power antenna beamwidth extends 0.57" from boresight). At this time we assume the antenna points directly at nadir; pointing-error biases and models will be examined later. The total two-way half-power antenna beamwidth is $, (1.13" for Seasat), from which we define +b = I,b,,/,,/KiiZ and u b = H'I,bi/2. Finally, we define a joint height-slope probability density after the fashion of Eq.(6) that includes height skewness as P i ( < ) = P() qj(q)

(28)

being Fourier transforms of the other quantities defined similarly. Then, because of Eq. (4),the Fourier transform of the desired quantity, qj(q), is given by qj(q) = Ss(-q)/Q(qW(q)

(29)

Then the desired quantity is obtained by an inverse FFT, i.e.,

1

m

p i ( [ ) = F-'(qj(q)) = (1/2a)

qj(q)eic"q

(30)

-m

It is important to note that it is necessary to actually do FFTs only twice: once on a&) and once again on qj(q). The Fourier transform of P(u), namely Q(q), is done once for the pulse obtained from the calibrate mode and stored as a table. (The pulse shape does not appear to change over many orbits.) The Fourier transform of the antenna gain factor is known in closed form. Substituting Eq. (14) into Eq. (27),we find from integral tables that

As a check on Eq. (31), note that as ub+ 00 (i.e., the antenna becomes omnidirectional in its gain pattern), we obtain H ( q ) -+ l / i q , which is the Fourier transform for the unit step function. This is as it should be, as one sees from Eq. (14), where the unit step is all that is left in this limit. In other words, if the beamwidth is large enough, pointing error obviously does not matter. Therefore, a general, double-deconvolutional algorithm incorporating these effects and producing real-time uncertainties in desired output parameters is summarized here, based on the Seasat altimeter: 1. Renormalize each waveform (every 0.1 sec) to correct occasional high and low waveforms caused by AGC malfunction. This is done by dividing each waveform by the average energy in gates 45-60. 2. Average a desired, predetermined number of waveforms together. 3. Multiply all range-gate amplitudes by predetermined gain bias corrections.


95

4. Identify and separate the three characteristic portions of the waveform: pre-leading-edge noise, the leading edge, and the plateau. Store the gate positions representing the beginnings and ends of these three segments. This separation is easily done by noting the maximum slope for the three middle gates, and using a simple predetermined criterion for these gate positions based on this slope. 5. Fit a straight, horizontal line to the pre-leading-edge portion. Determine the mean and standard deviation of this constant. 6. Subtract the constant determined in (5) from each gate amplitude constituting the remaining two waveform regions. 7. Fit a linear regression line to the plateau segment. Determine from Eq. (17) the amplitude constant for the plateau, the pointing-angle error fl from the slope, and the standard deviations in these quantities. 8. Use the constant and /3 to do a first correction to the o0 value obtained from the AGC gain. Rain corrections, if applicable and desired, can then be applied to no. 9. Deconvolve the leading-edge waveform segment using Eqs. (27)-(31) to obtain the joint height-slope probability density for the sea. Lookup tables of the Fourier transform of the pulse (obtained from the internal calibration mode) are divided into the Fourier transform of the leading edge. Likewise, the Fourier transform of the antenna beam factor including the tilt /3 is simply calculated from Eq. (31) and divided into the leading-edge Fourier transform also. Then the inverse FFT of this quantity [i.e., Eq. (3011 gives the desired probability density. 10. Renormalize the probability density so that the area under it is unity; this is simply done by dividing the probability density at each gate position by the sum over all gate positions. 11. Using matrix covariance techniques employed in Lipa and Barrick (1981),determine uncertainties in the probability density at each gate position. 12. Fit the three-parameter model of Eq. (6) (linear in two parameters: height skewness and height bias) to the recovered probability density. Do a one-dimensional grid search to determine wave height. These techniques are tested and described in Section 3. 13. Use linear error propagation theory and covariance matrix methods (as done in Lipa and Barrick, 1981) to determine the statistical uncertainties in waveheight, skewness, and height bias. 14. Determine nadir wind speed from co using the best, empirically supported model function available at the time. This method has the following advantages over versions that attempt to fit a model with six parameters to the entire leading edge by least-squares methods (Hayne, 1981):

96


1. It provides the completejoint height probability density function for the sea surface, rather than a two-parameter description of it. This additional information will prove useful for future research and applications involving ocean surface processes. If the same two parameters are desired, they have been shown in Lipa and Barrick (1981) to be easily obtainable from this probability density function. 2. Because of the natural segmentation of the waveform into its three constituent regions, our method is much more efficient in terms of computer operations and time. 3. The method here, not involving multiparameter grid searches and/or matrix inversions, is’stable. If one attempts to get around a time-consuming, multidimensional grid search, the usual procedures are (a) to linearize the model about initial guesses for the several parameters and then solve the leastsquares problem by matrix inversion; (b) to “home in” on the minimum in an iterative fashion, starting with an initial guess and using a variation of a rootfinding scheme. One hopes these will converge to the solution. When noise is present, however, a poor initial guess can cause either no convergence, or worse yet, convergence to parameters that represent the least-squares solution for a local minimum rather than the global minimum. The present algorithm involves no initial guess or repeated iterations, and hence cannot be unstable. 4. The present approach uses standard matrix covariance and linear error propagation techniques to output statistical uncertainties (or confidence limits)for all derived quantities continuously; no other existing algorithm does this.

7. CONCLUSIONS Much is ,known about the interaction of the altimeter pulse with the ocean surface. Solutions based on specular-point scatter theory show that the echo waveform is a double convolution. This special form of a double-integral equation has been inverted-or deconvolved-using FFT methods, yielding the surface wave-height and slope probability density in a very efficient algorithm. A straightforward least-squares model fit to this probability density (nonlinear in only one parameter) then yields wave height, mean surface position, and wave-height skewness. Uncertainties in these three parameters are routinely provided by the algorithm. Tested with Seasat data for a pass over Hurricane Fico (orbit 280), these uncertainties for a 160-km section of path data are 0.7, 4, and 15% (RMS)of the mean values, respectively. Models for the sea surface probability density are used to study and interpret other biases quantitatively. For example, the Seasat height tracker


97

outputs a mean height position that is biased downward by an amount between 3.5 and 6.5% of significant wave height. This bias is sea-state dependent, but can be removed by an algorithm that fits a model to the inverted echo waveform. Both antenna beam pointing error and moderate rain within the radar cell can distort the echo severely, and if uncorrected will produce values for height and wind speed (deduced from the plateau backscatter cross section, a,) that are grossly in error. Pointing error is easily removed when it occurs by a double-deconvolutional algorithm described here; its magnitude is first measured by either (1) using an independent attitude sensor or (2) measuring the slope of the echo plateau. Rain distortion is not easily removed. The best hope appears to be to identify those situations when rain is present (either from independent measurements or from the distorted altimeter signal itself) and throw out those samples whose distortion exceeds a certain amount. Electromagnetic bias is a height error not easily removed. Although it varies with sea state, it is seen to depend significantly on other factors also. Quantitative estimates of these dependences from both theoretical and experimental investigations are as yet incomplete. Since altimeter-measured surface heights can be in error by as much as 15-25 cm because of EM bias, further investigations are necessary if accurate sea surface topography is to be realized from future altimeters. We present a brief outline of an efficient, alternative algorithm for the altimeter echo, different from those presently being employed. It incorporates most of the techniques studied in this manuscript for interpreting biases and the echo waveform statis‘tics. Furthermore, it can provide uncertainties in all of the extracted parameters-along with the parameters-so that the user can decide how or whether to apply each geophysical data record. APPENDIX A definitive series of experiments done over a decade ago resulted in the synthesis of a new model-called the JONSWAP spectrum-for the waveheight directional spectrum that supports the theoretical concepts of nonlinear energy transfer to the longer ocean waves developed by Hasselmann in the early 1960s. The first studies (Hasselmann et al., 1973, 1976)developed a parametric representation for the nondirectional temporal wave-height spectrum. The term “parametric” refers to the fact that this spectrum has the same shape regardless of the physical conditions producing it. For the user, only two parameters are needed to produce the final, absolute spectrum: the wind velocity ii = (u,0,) and the development factor v. The latter is shown experimentally to be a function of fetch (i.e., the distance over which the wind

98


has blown at a more or less constant velocity),and is known to depend on the duration (i.e., the time during which the wind has blown at a constant velocity). For “fully developed” seas (where the fetch and duration are very large), v z 0.14, whereas it increases to 0.25-0.4 for seas that are less than fully developed. Recently the nondirectional spectrum has been extended (Hasselmannet al., 1980), based on the same measurements, to include a directional factor, resulting in a complete model for the wave-height directional spectrum. The directional factor is parametric also, being a function of the same two parameters as the nondirectional factor. The spectrum peaks azimuthally, of course, along the wind direction 6,; it is narrowest in angular spread at frequencies or wavenumbers near the most energetic waves, and tends to isotropic in angle toward the shorter waves. Here, we put both the nondirectional and directional factors together, and convert to wavenumber k [k = (k, O)] rather than wave frequency f. The resulting model is then defined as S A ~= ) W, 6 ) = f ( k ) g ( k ,e)

(All

normalized such that

j:

h Z = c ( =~ ~k dk~

:j

do $(k, 6) = -z

f ( k ) k dk

where

j;Kg(k,6)d6= 1 The nondirectional spectral factor is given by

where rJ={

cra = 0.07

c,,= 0.09

for k I k , for k 2 k ,

y = 3.3 k,

= ( 2 ~ v ) ~ g / u=’ position of

spectrum maximum

a =0 . 0 3 2 5 ~ ~ ‘ ~

h Z N (u4/g2)(5.2 x 10-6v-10/3)= mean-square wave height u = wind speed (m sec- ’)

g

= 9.806 m sec-’ = gravitational constant

(A21


99

This factor peaks at wavenumber k,, and has the same equilibrium-range k-4 dependence as the Phillips and Pierson-Moskowitz models (corresponding to an f-’frequency dependence). The JONSWAP spectrum has a sharper peak than previous models, as represented by the second term in the exponential argument. By setting that second term to zero and letting a = 0.0081, one recovers the Pierson-Moskowitz (1964) model for fully developed seas that has been accepted over the previous decade. The directional factor is given by

s(k@

= C~/A(s)lIcosC(~ - ~,)/21IS

(A5)

where

s = 20(k/k,)”

for k > l.lk,,,

and = -2.33 - 1 . 4 5 ( 2 ~-~ 1.17)

while 2

s = 14(&)

for k c l.lk,,,

REFERENCES Barrick, D.E. (1968). Rough surface scattering based on the specular point theory, IEEE Trans. Antennas Propag. AP-16,449-454. Barrick, D.E. (1972a). Remote sensing of sea state by radar. In “Remote Sensing of the Troposphere” (V.E. Derr, ed.), Ch. 12. U. S. Govt. Printing Office, Washington, D.C. Barrick, D. E. (1972b). Determination of mean surface position and sea state from the radar return of a short-pulse satellite altimeter. In “Sea Surface Topography from Space” (J. R. Apel, ed.), Vol. I, Ch. 16. U.S.Govt. Printing Office, Washington, D.C. Barrick, D. E., and Weber, B.L. (1977). On the nonlinear theory for gravity waves on the ocean’s surface. Part 11: Interpretation and applications. J. Phys. Oceanogr. 7 , 11-21. Brandt, S.(1976). “Statistical and Computational Methods in Data Analysis.” North- Holland, Publ., Amsterdam. Brown, G. S. (1977). The average impulse response of a rough surface and its applications. IEEE Trans. Antennas Propag. AP-25,67-74. Brown, G. S. (1979). Estimation of surface wind speeds using satellite-borne radar measurements at normal incidence. J . Geophys. Res. 84(B8),3974-3978. Choy, L. W., Hammond, D. L., and Uliana, E. A. (1984). Electromagnetic bias of 10 GHz radar altimetric measurements of MSL. J . Mar. Geod. 8,296-312. Ewing, G. C., ed. (1965). Oceanography from space. Woods Hole Oceanographic Institute, Ref. NO. 65-10.

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Fedor, L. S.,and Barrick, D. E. (1978). Measurement of ocean wave heights with a satellite radar altimeter, EOS 59,843-847. Fedor, L. S., and Brown, G. S. (1982). Waveheight and windspeed measurements from the SEASAT radar altimeter. J. Geophys. Res. 87(C5), 3254-3260. Goldhirsh, J., and Walsh, E. J. (1982). Rain measurements from space using a modified SEASAT type radar altimeter. I E E E Trans. Antennas Propag. AP-30,726-733. Hasselmann, K., Barnett, T. P., Bouws, E., Carlson, H., Cartwright, D. E., Enke, K., Ewing, J. A., Gienapp, H., Hasselmann, D. E., Kruseman, P., Meerburg, A., Muller, P., Olbers, D. J,, Richter, K., Sell, W. and Walden, W. (1973). Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project. Deutsches Hydrographisches Institut, Hamburg. Hasselmann, K., Ross, D. B., Muller, P., and Sell,W. (1976). A parametric wave prediction model. J. Phys. Oceanogr. 6,200-228. Hasselmann, D. E., Dunckel, M., and Ewing, J. A. (1980). Directional wave spectra observed during JONSWAP 1973. J . Phys. Oceanogr. 10.1264-1280. Hayne, G. S. (1981). Radar altimeter waveform modeled parameter recovery. NASA Tech. Memo. No. 73294. NASA Wallops Flight Center. Hayne, G. S.,and Hancock, D. W., 111 (1982). Sea-state-related altitude errors in the SEASAT radar altimeter. J . Geophys. Res. 87(C5), 3227-3231. Jackson, F. C. (1979). The reflection of impulses from a nonlinear random sea. J. Geophys. Res. 84,4939-4943. Kaula, W. M. (1970). ‘“The Terrestrial Environment: Solid-Earth and Ocean Physics” (NASA Contractor Rep. No. 1579). MIT Press, Cambridge, Massachusetts. Lipa, B. J., and Barrick, D. E. (1981). Ocean surface height-slope probability density function from SEASAT altimeter echo. J . Geophys. Res. 86(Cll), 10921-10930. Longuet-Higgins, M. S. (1963). The effect of non-linearities on statistical distributions in the theory of sea waves. J. Fluid Mech. 17,459-480. MacArthur, J. L.(1978). SEASAT, a radar altimeter design description. Johns Hopkins Unio. Appl. Phys. Lab. Rep. SDO-5232. Pierson, W. J., and Moskowitz, L. (1964). A proposed spectral form for fully developed wind seas based on the similarity theory S.A. Kitaigorodsky. J . Geophys. Res. 69(24). Townsend, W. F. (1980). An initial assessment of the performance achieved by the SEASAT-1 radar altimeter, IEEE J . Oceanic Eng. OE-5,80-92. Walsh, E. J. (1979). Extraction of ocean waveheight and dominant wavelength from GEOS-3 data. J . Geophys. Res. 84,4003-4010. Walsh, E. J. (1981). Altimeter rain detection. NASA Tech. Memo 73291, NASA Wallops Flight Center. Walsh, E. J. Hancock, D. W., 111, Hines, D. W., and Kenney, E. J. (1984). Electromagnetic bias of 36 GHz radar altimetric measurements of MSL. J. Mar. Geod. 8,265-296. Weber, B. L., and Barrick, D. E. (1977),On the nonlinear theory for gravity waves on the ocean’s surface. Part I: Derivations. J . Phys. Oceanogr.7,3-10. Yaplee, B. S., Shapiro, S.,Hammond, D. L., Au, B. D., and Uliana, E. A. (1971). Nanosecond radar observations of the Ocean surface from a stable platform. IEEE Trans. Geosci. Electron. GE-9,170-174.

CHAFIZII4

OCEANIC SURFACE WINDS DUNCAN B. Ross

VINCENT J. CARDONE

National Oceanic and Atmospheric Administration Allanric Oceanographicand Meteorological Laboratory Miami, Florida

Oceanweather, Inc. White Plains, New York

RONALDD. MCPHERSON

JAMES OVERLAND National Oceanic and Atmospheric Administration Pacific Marine Environmental Laboratories Seattle. Washington

National Oceanic and Atmospheric Administration National Meteorological Center Washington, D.C.

WILLARD J. PIERSON, JR.

TSANN-WANG Yu

CUNY Institute of Marine and Atmospheric Sciences The City College of New York New York. New York

National Oceanic and Atmospheric Administration National Meteorological Center Washington, D.C.

1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . 101 2. Mechanism for Measurement of Surface Wind Speed Using Microwave Systems . . 103 3. The Marine Surface Boundary Layer . . . . . . . . . . . . . . . . . 105 3.1. The Surface Roughness and Drag Coefficient . . . . . . . . . . . . . 107 3.2. Stability Effects . . . . . . . . . . . . . . . . . . . . . . . 108 4. Development of a Model Function Relating SASS Data to Surface Wind Speed . . 109 4.1. The Comparison Data. . . . . . . . . . . . . . . . . . . . . 110 4.2. The Time-Averaging Problem . . . . . . . . . . . . . . . . . . 116 4.3. Definition of Surface Winds. . . . . . . . . . . . . . . . . . . 117 4.4. The Synoptic Scale . . . . . . . . . . . . . . . . . . . . . . 120 5. Global Data Assimilation Experiments . . . . . . . . . . . . . . . . 121 5.1. Assimilation System . . . . . . . . . . . . . . . . . . . . . 122 5.2. Preprocessing of the SASS Wind Data . . . . . . . . . . . . . . . 127 5.3. Assimilation Experiments. . . . . . . . . . . . . . . . . . . . 128 132 5.4. Forecast Experiments . . . . . . . . . . . . . . . . . . . . . 5.5. Summary . . . . . . . . . . . . . . . . . . . . . . . . . 135 6. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . 137 References . . . . . . . . . . . . . . . . . . . . . . . . . . 138

1. INTRODUCTION For the past century, man has resorted primarily to transient ship reports to obtain information on the global characteristics of the actual surface wind field. These reports have proved invaluable in developing a better understanding of the global ocean circulation. In recent years important new sources of in situ data have been instrumented buoys, both free floating and 101 ADVANCES IN GEOPHYSICS, VOLUME 21

Copyright 0 1985 by Academic Press, Inc. All rights of reproduction in any form n ~ e ~ e d . ISBN 0-1 2-018827-9

102

DUNCAN B. ROSS ET AL.

moored. The introduction of the higher quality data sources has not, however, materially effected the accuracy of global measurements due to the preponderance of ship reports which make up the data set. The Seasat experiment, however, has brought the space age to oceanography and demonstrated the tremendous potential of a satellite system to observe the global wind field frequently to scales as small as 50 km. The Seasat-A Scatterometer System (SASS) is an outgrowth of the Skylab program (Ross and Jones, 1978) and was designed to provide the required global measurements of surface wind speed and direction. Jones et al. (1978) and Schroeder et a!. (1982) describe in detail the instrument, the geophysical algorithm, and the results of two major experiments which provided validation data sets. Basically, radar energy backscattered from the ocean is a function of the wind speed and direction through the ability of the wind to roughen the scattering surface. The algorithm required to extract a valid wind speed is empirically based and also subject to additional constraints such as the effect of atmospheric stability on the ability of the wind to roughen the ocean surface. The Seasat-A Scanning Multifrequency Microwave Radiometer (SMMR) also provides an estimate of the surface wind through changes in the emissivity of the air/water interface due to the presence of foam and to the change in reflectivity of the surface due to roughening by the local wind [see Lipes et al. (1979) for a description of the instrument]. The SMMR observed upwelling microwave energy TBat five frequencies and at both horizontal and vertical polarization (6.6, 10.7, 18.0, 21.0, and 37.6 Hz). The higher frequencies are predominately influenced by atmospheric effects while the lower frequencies (6.6 and 10.7) are sensitive primarily to see surface temperature and surface roughness. By exploiting these variable sensitivities, atmospheric water vapor (clouds) and liquid water (rain) may be separated from ocean parameters. The Seasat-A altimeter elsewhere described in this volume also provides an estimate of surface wind speed. The altimeter measures the radar backscatter go at nadir incidence angle which is inversely proportional to surface roughness. Although containing no directional information, the altimetermeasured wind speed seems to be at least as accurate as SASS (Fedor and Brown, 1982). In this chapter we present a brief review of radar backscatter which provides a foundation to the satellite approach. We then consider the vicissitudes of the marine surface wind field and how it is affected by varying degrees of surface roughness and thermodynamic influences. Some history is provided regarding the development of algorithms and an evaluation is described of the effect on a numerical atmospheric circulation model of inclusion of satellite data in the standard data base.

4. OCEANIC SURFACE WINDS

103

2. MECHANISM FOR MEASUREMENT OF SURFACE WINDSPEEDUSING SYSTEMS MICROWAVE

Electromagnetic waves incident on the ocean surface at angles away from nadir are backscattered by resonant Bragg waves whose wavenumber K is 2 p sin 6, where p = 2n/A is the wavenumber of the incident wave of length 1,and 8 is the angle of incidence with respect to the vertical. Furthermore, the magnitude of the backscattered signal is proportional to the normalized radar cross section 0’’which is proportional to the amplitude of the resonant roughness elements. This resonant scattering was confirmed by Crombie (1955) as the dominant mechanism responsible by radar backscatter. Wright (1968) developed a composite theory which explains the effect of “tilting” of the roughness elements by long gravity waves. The net effect of tilt is to increase the backscatter for a given depression angle and is most pronounced for horizontal polarization. The wind-speed dependence of the amplitude of the Bragg waves was poorly understood at the time, however, and was not included in the theory. Pierson and Stacy (1973) were the first to offer a model of .the coupling between surface wind speed and the energy density of the short-wave structure. The model was later abandoned as an unnecessary step if one desires only wind speed from a measurement of radar 0’. Subsequent efforts, therefore, concentrated on the anisotropic nature of the radar return and its dependence on wind speed. The high radar signature of breaking waves when observed at low grazing angles had been known for many years, although the importance was not appreciated until high-resolution radar imagery of rough sea conditions became available [see Zhilko and Zagorodnikov (1975) and Alpers et al. (1981)l. The role of breaking waves, however, is not treated explicitly in existing geophysical algorithms. Rather, a model of the form oo = f ( 8 ,,y, V), where 8 is the incidence angle, x is the azimuth angle relative to the wind direction, and U is the 19-m wind speed (Schroeder et a!., 1982), is empirically tuned. Valenzuela et al. (1971) used the above Bragg theory to calculate the behavior of the high-frequency end of the wave spectrum and found good agreement with published data. Furthermore, their data also confirmed a dependency of the wave spectrum on wind speed for the higher wavenumbers. A difficulty with the results of Valenzuela was the apparent saturation or decreasing sensitivity, at rather low wind speeds of about 10-12 m sec-’. The experimental data of Valenzuela were for wavelengths of 3 cm (X band) and longer, Moore and Pierson (1971) suggested use of a higher radar frequency (Ku band) based upon their experimental work. Pierson and Stacy (1973) then proposed the high-frequency end of the wave spectrum to be wind-speed

104


dependent according to the region of the spectrum and the applicable restoring force (gravity or surface tension). Mitsuyasu and Honda (1974) presented data which confirmed a wind-speed dependency of the HF wave spectrum and the possibility of global wind speed and direction measurements from satellites was secure. It is clear that the small-scale roughness is responsible for the backscatter signal needed to extract a surface wind speed and direction by microwave techniques. It is not clear, however, exactly how the Ocean wave field responds to the surface wind speed. Snyder et al. (1981) and Dobson and Elliot (1978) studied the process of energy exchange between the wind and the wave field. They found the process to be proportional to the relative speed of the wind and the phase velocity of the waves. In their study, the process was referenced to the wind speed U as measured at an altitude of 5 m. Other studies use the wind friction velocity U, as a reference, where U t = C , U 2 and the drag coefficient C,depends upon the altitude at which the wind is measured. The relationship between the wind speed at some altitude and the actual momentum transfer is an area of active research and will be discussed in detail in Section 3. One mechanism which can significantlymodify o0for a given wind speed is the presence of ocean currents, The wind at any height U must be determined in a coordinate system moving with a current component, if present. Thus, U = U' - Us when U is the actual wind speed relative to a fixed coordinate system, U' is the wind observed by a ship, buoy, or satellite, and Us is the current vector in the direction of the wind. A complicating factor often associated with current boundaries is an abrupt change in sea surface temperature and hence stability of the atmosphere. Liu and Ross (1980) show the importance of atmospheric stability in wave growth. When the wind crosses a current boundary and encounters the opposing flow of a warmer sea surface,extremely dramatic roughness changes can occur (cf. Ross, 1981; Ross and Cardone, 1974). Stability and sea surface temperature effects are further examined by Liu and Timothy (1984) using SASS data and a large collection of ship reports. Surprisingly, he finds little stability dependence although the number of stable cases examined was very small and the wind speeds examined were moderate. We have briefly reviewed the mechanism involved in the backscatter of electromagnetic waves from the ocean surface that can be exploited by satellite to yield an estimate of the surface wind speed. The Seasat altimeter has been proved susceptible to rain attenuation, but useful, by Fedor and Brown (1982), Wentz et al. (1981), Mognard et al. (1981), Ross et al. (1981), and many others to observe surface winds on a global basis, including tropical hurricanes and winter cyclonesin the southern oceans. The Seasat SASS has produced global


105

wind speed and direction measurements in all types of weather situations. After correction for attenuation by clouds and light rain the SASS has proved capable of speed accuracies better than + 2 m sec-' and direction accuracies better than _+ 10"(Businger et al., 1980). The Seasat SAR has proved sensitive to local wind variations on scales of meters and has provided new insight into the submesoscale variability which can occur in squall lines (Ross, 1981; Gonzales et al., 1982). 3. THEMARINESURFACE BOUNDARYLAYER

Many concepts developed to describe flow over solid boundaries have been successfullyextended to the marine boundary layer. The sea surface, however, is basically different from a solid boundary in that the fluid interface cannot support a surface stress discontinuity. Moreover, the interface is characterized by traveling waves whose momentum is derived typically from the air flow above the surface. Nevertheless, the vast increase in our understanding of the processes governing wave generation and dissipation have provided a framework for application of many classical boundary layer concepts to the layer of air in contact with the sea. Consider first the flow over a horizontal plane surface with horizontal wind components ui(i,j = 1,2),and the vertical wind being w. The x axis is in the direction of the mean wind and the z axis directed opposite to the direction of gravity. The x2 axis is directed so that a right-handed coordinate system results. The horizontal components of the momentum equation can be written au, -+at

auiuJ ax,

au,w +-=

I ap

az

P axj

+ VV%,

where p is pressure, v is the kinematic viscosity, w is the vertical motion, and the continuity equation for a constant density fluid aui/ax,

+ aw/az = o

has been employed. In turbulent flows, the velocity and pressure can be decomposed into an average value and an instantaneous fluctuation as ui = ui

+ u;

w=W+w' p=P+p'

106


Substitution of Eq. (2) into Eq. (l), and averaging, yields

aq -+at

auluj auiq +-= ax,

a 1 ap --+ VVWi + -(-W) a2 p axi

ax,

(3)

Imposing the restrictions that the mean flow is steady and that all averaged quantities are functions of z only reduces Eq. (3) to

a

-ppuiw

az

- pv(dqp2) = 0

(4)

The quantity - pmrepresents the horizontal components of the Reynolds stress and implies that the total stress z =

+

- p u i ~ pv(aUi/az)

(5)

is constant with height. The assumptions leading to Eq. ( 5 ) are generally valid for a neutral boundary layer over land to heights of 20-200 m for typical stresses of 1-10 dynes cm-2 (Lumley and Panofsky, 1964). Except very near the surface, where viscous stress may be appreciable, the surface stress over land is supported entirely by the turbulent Reynolds stress, and a velocity scale

u:

= z/p

may be defined which alone characterizes the flow in the “constant stress” portion of a neutral boundary layer. The above analysis does not hold strictly for flow over waves since the air flow contains velocity fluctuations V, in fixed phase with respect to the water surface elevation. These fluctuations are more organized than turbulence and are associated with the wave-generation mechanisms of the type described originally by Miles (1957). The mean stress balance over the sea is then

(a/az)Vw + il,w + v(aQ/az)

=o

(6)

The total stress, which remains approximately constant with height in the surface layer, now includes a wave-induced Reynolds stress z, = -puiw

(7)

which is equivalent nearly to the mean flux of momentum to surface waves arising from pressure variations on the wave surface induced by the air flow (Phillips, 1977). The structure of the wind profile in the marine surface layer will depart from that characteristic of a land boundary layer if a large fraction of the stress is supported by momentum flux to the dominant waves on the sea surface. However, most recent estimates, derived in part from direct measurement of


107

pressure above waves (excluding the high-frequency ripples) suggest conservatively a value of about 0.2 for the ratio z,/o (Phillips, 1977). The friction velocity therefore characterizes the wind profile in the marine surface layer (except in a possible laminar sublayer in contact with the surface)and the wind speed in a neutral atmosphere is logarithmic a u p z = u,/kz

(8)

where k is the Karman constant (0.38-0.42). 3.I . The Surface Roughness and Drag Coeficient

The variation of wind speed with height in the surfacelayer is obtained from Eq. (8): U*/klog(z/zo) (9) where zo is the roughness parameter. The drag coefficient is defined through uz

=

?

= pc,u;

(10)

The relationship between C, and zo is therefore

c, = k2/(logz/z0)2

(11)

The variation of C, with sea state and/or wind speed has been debated for decades. By analogy, to flow over a smooth, flat plate it may be expected that for the case of light winds over a calm sea, the stress at the sea surface is supported by viscosity and the flow may be characterized as aerodynamically smooth. For such flow, z, and hence C, decrease with increasing wind speed (e.g., Kraus, 1967). With increasing roughness of the sea surface associated with increased amplitude of high-frequency gravity waves, an increasing proportion of the stress becomes attributable to form drag and momentum flux to the short waves and the flow may be expected to become aerodynamically rough. Charnock (1955) predicted that in fully rough flow z, should be proportional to U : : zo = a,Uig-' (12) It can be shown from Eqs. (9)-(11) (e.g., Wu, 1969) that for rough flows C, depends on the Froude number, U,/(gz)l'z, though most investigators have adopted a standard reference height of 10 m and simply relate C, to the 10-m wind speed. Open ocean determinations of C,, based upon direct and accurate measurementsof the Reynolds flux over a wide range of wind speedshave only recently been reported (Smith, 1980; Large and Pond, 1981). In both studies,

108


C,, was found to increase with wind speed above wind speeds of about 10 m sec-’ to the upper extent of the measurements (about 25 m sec-’). In light winds the variation with wind speed is not clear, and Large and Pond (1981) suggest a constant of 1.14 x for Clo in wind speeds less than 10 m sec-’ for neutral stability. At wind speeds greater than 10 m sec-’, Eq. (12) with a value of a, of 0.01 provides a reasonable fit to the data (Smith, 1980). The influenceof larger waves on Clo is not well understood. Sea state effects are implicit in the finding of Large and Pond that Ci0is larger on falling winds and smaller on rising winds than the steady state value, Smith, however, reported no discernible dependence of C,, on fetch in their data set. 3.2. Stability Efects

The similarity theory of Monin and Obukov has been shown to provide a reasonable framework for the description of the mean wind profile and turbulence properties of the marine surface layer in nonneutral stratification. According to this theory, the nondimensional wind shear 4, defined as

9,

= (kz/u&au/az)

(13)

is a function of the dimensionless ratio z / L where

H is the heat flux (positive from sea to air) and C, is the specific heat of air at constant pressure. A number of functions have been shown to provide a reasonable description of the variation of 9, with z / L . Cardone (1969) used the KEYPS function (Lumley and Panofsky, 1964) to describe unstable conditions and linear dependence for stable conditions. Most investigators have adopted the Businger-Dyer forms (Dyer, 1974): 0 < ZJL < 0.2 (1 - 1 6 ~ / L ) - ” ~ -1.0 < z/L < 0

(15)

The wind profile may be integrated (Paulson, 1970) to yield

uz = U*/kClog(z/zo) - Cpm(z/L)I

(16)

where Cp,(z/L) = - 5 z / L

0 < z / L < 0.2 (stable)

+ 4 / 2 1 + lnC(1 + x2)/2] tan-’ x + n/2 - 1.0 < z / L < O(unstab1e)

= 21n[(1 = -2

x = (1 - 1 6 ~ / L ) ” ~

(17)


109

Measurements of the drag coefficient at sea when stratified according to the stability (Deleonibus, 1971; Large and Pond, 1981; Smith, 1980) are not inconsistent with the dependence expected from Eq. (16), namely c z = k*/Clogz/z, -

4,(Z/~)lZ

(18) which implies that for a given anemometer-level wind speed, the surface stress will be higher for unstable conditions (positve heat flux, air colder than water) than for stable conditions (negative heat flux, air warmer than water). Several workers (e.g., Cardone, 1969)have used the above theory to develop iterative schemes for the calculation of the wind stress from routinely available marine surface measurements. The latter consist of a wind measurement at anemometer level [typically 10-50 m in elevation, though Ross and Cardone (1974) report surface-layer wind measurements derived from low-flying ( I 150 m) aircraft], and measurements of air temperature and humidity, and sea temperature. Cardone (1969) introduced the concept of the effective anemometer-level wind, which has been used extensively in the interpretation of remotely sensed winds. The preferred reference height is 19.5 m. The effective 19.5-m wind is simply the wind speed at 19.5-m height that would pertain in a neutral atmosphere for the same surface stress of a given stratification. Therefore, for unstable (stable) stratification, the effective wind would be greater (less) than the measured wind.

4. DEVELOPMENT. OF A MODELFUNCTION RELATING SASS DATATO SURFACEWINDSPEED Vector winds measured by a remote-sensing technique must be compared to vector winds measured by conventional means for two reasons: first, to develop the model function used to convert backscatter measurementsto wind speeds and directions (plus aliases); and second, once a model function is selected, to find out how good the winds computed from the backscatter are. If the winds measured by conventional means are in error, then the model function derived from them may be in error. The differences between the radar winds and the meteorological winds will be caused in part by the errors in the conventional measurements. As an extreme example, suppose that the winds measured by conventional means have RMS errors at all wind speeds of k3 m sec- in speed and k 20" in direction due to the combined effects of poor calibration, poor anemometer exposure, uncorrected variations in anemometer height, failure to correct for ship speed and direction properly, and an unrepresentative averaging time of 2 min. Wind speeds and directions obtained from the SASS at nearly the

110


same time and nearly the same place as conventionally measured wind speeds and directions would then differ in an RMS sense by & 3 m sec-’ and k20” from the conventional data. Those who do research in remote sensing frequently use the term “ground truth” for the measurements made by conventional means when they are compared to corresponding remotely sensed quantities. For measurements of the wind by conventional means, there is no “truth; there are only comparison data. These comparison data are of varying quality and accuracy, and, depending upon the source of the data, the RMS errors of the measurements vary. 4.1. The Comparison Data

A major effort of the Seasat program was to gather sets of high-quality comparison data to permit the assessment of the accuracy of the vector winds computed from the SASS backscatter measurements using a model function derived from pre-Seasat airborne measurements. The conventional measurements of wind speed and direction can be roughly ranked in order of increasing quality (or decreasing RMS variability). The poorest are ship reports based on Beaufort estimates. These are followed by reports from transient ships that have anemometers at known heights, analyzed fields based upon a mix of ship reports, and adjusted geostrophic winds. Better data are obtained from the National Data operational buoys, weather ships, and oceanographic ships making special observations. The highest quality data availablefor comparison to Seasat measurements were from the the Joint AirSea Interaction (JASIN) experiment obtained by a closely spaced network of cross-calibrated experimental buoys and ships in the North Atlantic. There were two major programs conducted to gather conventional meteorological comparison wind data during Seasat. One was the NOAA Gulf of Alaska experiment, which obtained operational data buoy reports every hour and also special observations from weather ship PAPA and an oceanographic research ship, the Oceanographer, as described by Wilkerson and McNutt (1979). The other took advantage of the JASIN experiment to ensure that Seasat obtained SASS measurementsin modes three and four over the area of concentrated wind measurements during that program. The primary objective of the measurement of winds at sea should be to obtain data that will define the synoptic-scale wind field in the planetary boundary layer. Each conventional measurement is meant to represent the movement of the air over an area of many thousands of square kilometers within a surface layer which varies from 500 m to 1 km. Even the most casual inspection of a recording of wind speed and direction by a ship at sea shows

111


that a 2-min average of the wind speed and direction is usually not representative of the longer term synoptic scale. A 20-min average would clearly be superior, but an even longer time average of the wind might often be better. Pierson (1983) has given results that indicate that a 1-hr average might be good for most synoptic-scale conditions and that the best averaging time will depend on the location of the measurement and the synoptic-scale pat tern. For comparison of Seasat SASS and SMMR winds with meteorological winds during the lifetime of Seasat, conventionally measured winds were obtained from analyzed fields weighted by ship reports, by data buoys each hour on the hour, and by PAPA and the Oceanographer at the time of the Seasat pass for the GOASEX program. The JASIN program provided the highest quality data set from the standpoint of consistency, but Weller at al. (1983) report a positive bias of about 10% in the buoy used as a standard. For the SASS, paired cells of backscatter measurements that usually did not fall one on top of the other were used to recover the winds from the backscatter data by means of the model function. The cells did not occur in the scanning pattern at locations that would contain the conventional measuring platform. Figures 1-6 are scatter plots of the intercomparison of Seasat data to windspeed and, in the case of SMMR, wind-friction velocity during the GOASEX experiment. The error statistics are tabulated in Tables I and 11. For the data buoys, the measurements by the buoy did not occur at the center position of the Seasat measurement or at the same time that the Seasat measurements were made. The PAPA and Oceanographer measurements also did not occur at the center position, although they were taken at approximately the same time. Because of the dense data network of JASIN, the satellite cells were quite a bit closer in both space and time to the various buoy and ship measurements that were obtained. Under such variable conditions, the diverse averaging times cause the winds to differ by various amounts in both speed and direction from a more stable longer average. For example, for two platforms, say 50 km apart, it would not TABLE 1. COMPARISON STATISTICS FOR GOASEX

(SASS-SUR)

Polarization

No. obs.

H

V

392 608

Wind speed (m see-') 0.2 1.7 0.2 1.8

H V

392 608

Wind direction (deg) 0.7 12.4 -0.4 11.7

RMS

Intercept

Slope

Correlation

-0.2

1.00 0.99

0.91 0.92

0.96 0.98

0.98 0.99

0.0

8.8 4.4

112


J 30 GROUND-TRUTH WIND SPEED (m see-' )

FIG.1. Wind speed from SASS versus surface observation for the GOASEX experiment. (From Jones et al., 1981.)

be expected that winds measured for only a few minutes at each location would be the same. Yet, they could both have been measured with quite small errors. There are consequently three different effects that cause the winds measured by the satellite, referred to some particular location on the sea surface, to differ from winds measured by buoys and ships at some other nearby sea surface location. These effects are (1) actual errors in the measurements of the winds by the satellite, (2) actual errors in the measurement of the winds by the anemometers, and (3) real differences between the two wind measurements that result from differing averaging times, the finite areas satnpled by the radar,

113

4. OCEANIC SURFACE WINDS 360

/*

288

..

* *

M = 0.9420

B = 13.4002

/. -' J

36c'

R

+ '

= 0.9909

**/

-9

0 6 ' 1

1

'

1

I

I

1

I

l

l

I

~

~

1

~

~

~

1~

lack of homogeneity within the footprint, and the lack of space-time coincidence of the two measurements. It is not an easy problem to separate the various contributions to the different winds from the actual errors of the satellite and anemometer measurements and to make these errors quantitative. It is also not an easy problem to find how the amount by which nearby measurements of the wind differ simply because they do differ and not because of an error in the measurement of either wind. A brief review, with a somewhat different emphasis, of the result of Pierson (1983) follows.

J

"

2!

/. ..... ..... . ..... ........ . ..:. ........ . . . . .....

/

/

a*.-

SASS WIND SPEED (m sec-I)

FIG.3. Wind speed from SASS versus surface comparison data during the JASIN experiment using the GOASEX tuned algorithm. (From Businger et al., 1980.)

I

0

R

I

I

1

144 216 SASS WINO DIRECTION, DEGREES

288

I

360

FIG.4. Wind direction versus surface comparison data for vertical polarization using JASIN data. (From Businger et al., 1980.)

115


u a

0.12

-

0.10

-

0.08

-

0.06

-

E>

z5

t y p i c a l error bar for points

v1

0.04 -

0 w

3

0.02

U X

-

. ..... ... .. n .we..

-

0.00

0.12

.

0

.

Do..

0 .

H.

.OD.*

0

I 40

20

-0.02

. .

Y.W.. YI

0..

0 0

I

I 60

I 100

80

1 120

i

4-

t y p l c a l error bar for points

1

m 0.08

. a.

0.06 a oo

0

ma

a 0

a

m

a

e

a

a a

a

.

O 0.02 * O I

0.00,

0 .

a

01

20

I

I

I

I

I

40

60

80

100

120

116


TABLE 11. WIND-SPEED ~ M P A R I S O N STATISTICS FOR JASIN" Incidence angle bin (deg) 20-25

25-30

30-35

No. obs. (SASS-SUR) RMS

22 -0.7 2.1

35 -0.4 2.0

H polarization 29 21 -0.2 0.0 1.4 1.3

No. obs. (SASS-SUR)

22 -0.7 2.0

21 -0.7 1.6

V polarization 31 20 -0.2 0.8 1.4 1.8

RMS a

35-40

40-45

45-50

50-55

23 2.3 2.5

80

27 2.6 2.7

42

86

0.1

0.3

1.2

1.o

59 1.3 2.0

49 0.6 1.5

14 1.1 1.5

1.5 2.0

55-60

Values expressed as meters per second.

4.2. The Time-Averaging Problem

Averages of the wind speed and direction for different averaging times can produce different averages. A running boxcar average of a function of time can be thought of as a filter that removes the higher frequencies from a time history. It acts on the variance spectrum of the time history as a filter of the form [sin(~rfT/nfT)]~,where T is the averaging time. Figure 7 shows a spectrum of the wind speed computed from a 5-day, 16-hr, 32-min sample of consecutive 1-min averages. It is plotted as nS(n) versus log n so as to be area preserving. The spectrum extends to 30 cycles per hour, which is the Nyquist frequency. Beyond that value, the spectrurp is based on data sampled five times per second. There is a relatively flat part over the range from 2 hr or so, to 2 min, which is called the mesoscale valley. The time history can be thought of as a series of points 1 min apart for the wind. If a running 20-min average of this time history were made, and if the spectrum of the resulting series of points, still 1 rnin apart, were computed, the spectrum would be close to zero to the right of the arrow marked 20 min. A running 60-min average would produce a spectrum close to zero to the right of the 1-hr arrow. If the original 1-min values were plotted, as well as the 20-min values (or perhaps 19 or 21) as well as the 60-min values for each minute for the original time history, there would be three curves, each different. The variance of the point-by-point differences between the 20-min values and the 1-min values would be given by the area under the spectrum from 20 to 2 min and by similar computations for 60 and 2 min and 60 and 20 min. If there had been another anemometer, say 20 km away, making the same measurements as a function of time, and if the data were processed in the same way, the spectrum would be very similar to the one in Fig. 7. The 1- or 2-hr


117

2 3 hr

l o g , o f (cycles hr")

FIG.7. Compositespectrum of the wind for an average wind speed of 6.6m sec-'. One-minute averages over a period of 5 days, 16 hr, 5 and 32 min were used for the low frequency of the spectrum. The HF region was obtained by averaging spectra with a maximum sample rate S (Hz). (Courtesy of Dr. Mark Dovelan.)

running averages of the winds would be similar, but the minute-by-minute details would be different. The 20-min averages, even at the same time, would not be equal. For this example the variance of the differencesbetween a 1-hr average and a 1-min average would be given approximately by the area under the spectrum between zero on the log scale and the end of the plotted points. A 20-min average versus a 1-hr average would involve the area between the two arrows. The variability of the mesoscale spectrum has been modeled as a function of the synoptic-scale wind speed and atmospheric stability so as to try to account for the effect of time- and space-varying averaging times. The results that follow are a simplified discussion of this model. 4.3. Definition of Surface Winds

Consider a vector wind chosen as the closest of the SASS solutions to a nearby vector wind measured by an anemometer. The wind measured by the SASS will be designated as V, (for radar) and the meteorologically measured

118


wind will be designated as V, (for meteorological). For a set of such vector measurements, the root-mean-square difference (RMSD) will be defined using the squares of’the magnitudes of the vector differences as found in Eq. (19). The differenceis partially the result of various errors and partially the result of actual differences.

Though simply written, Eq. (19) is presently too difficult to treat. AIternatively, the vector winds can be expressed in polar coordinates as VR, XR and k,ZM (20) The root-mean-square difference between the magnitudes of the two winds can be found as

A similar equation for direction is

For the area surrounding the two measurements, there is the concept of a correctly measured synoptic-scalevector wind that, if used for an initial value update in a numerical weather prediction model, would be the best possible and most representative value to use. An anemometer-averaged wind for 1 hr might come very close to being this best possible wind. Let this wind be V, (for synoptic), which can be resolved into a speed V, and a direction. For this discussion, only the variability of the magnitude of the vector wind will be treated. It will be assumed that the various quantities that are involved are more or less normally distributed random variables so that for illustrative purposes they can be modeled by Monte Carlo simulations as the standard deviations of various terms times numbers picked at random from a normal population with a zero mean and a unit variance. The wind speed measured by the anemometer will differ from the synopticscale wind speed that would have been the desired measurement because of a too brief averagingtime. This is an effect of mesoscale turbulence in the winds and will be designated by SDMA so that V, = V,

+ t,(SDMA)

(23) where SDMA is a function of the averagingtime and the speed of the synopticscale wind. This measurement could be obtained by a perfectly calibrated anemometer. There would be no real error in it.

119


The anemometer could be incorrectly exposed or incorrectly calibrated and there could have been an actual error in the measurement so that the meteorological wind becomes V, = V,

+ t,(SDMA) + t,(SDME)

(24)

where SDME is the actual error of the anemometer. The area-averaged winds over the two cells scanned by the radar are different from the wind in Eq. (23) because the equivalent averaging time, using Taylor’s hypothesis, is different; the area of the ocean sampled is not at the anemometer location and the mesoscale turbulence over the area is different. The magnitude of the wind measured by the radar would be V,

=

V, + t,(SDMR)

(25)

where SDMR represents the effect of mesoscale turbulence in causing the wind that the radar measured to be actually different from the wind that would represent the synoptic scale. The value of SDMR is wind-speed dependent and a function of the size and orientation, relative to the wind direction, of the radar cells. These are two identifiable sources of error for the backscatter measurements. These are discussed at length in other reports, especially those associated with the design stages of the SASS on Seasat. The radar wind then becomes V, = V,

+ t,(SDMR) + t4(SDRE)

(26)

where SDRE is the standard deviation of the radar error. It depends in a complex way on wind speed, wind direction, and incidence angle. When Eq. (21) is evaluated by means of Eqs. (24) and (26), the result, for a sample of comparisons of radar and meteorological winds, is RMSD,

= [(l/m)(K

+ t,(SDMR) + t,(SDRE)

- t,(SDMA)

- t2(SDME))2]”2

- Vs (27)

The mesoscale effects enter unless the wind is measured at exactly the same time as the radar measurement over an area exactly centered on the anemometer (and even then there is a mismatch). All of the difference terms are functions of a large number of different effects such as for one term or another, wind speed, air-sea temperature difference, anemometer averaging times, the SASS cell sizes and the incidence angles. Equation (21)can be evaluated simply from pairs of measurements. Results for the JASIN experiment are given in tabular form by Schroeder et al. (1982), stratified by wind-speed ranges and incidence-angle ranges.

120


If tl through t4 are randomly varying and independent, Eq. (27) reduces to RMSD,

+ [(l/m)((SDMR)2 + (SDRE)’ + (SDMA)2 + (SDME)2)]’’2

(28)

For JASIN vertical polarization measurements, RMSD2 was equal to 1.23 m sec-’, and for horizontal polarization it was equal to 1.39 m sec-*. The study by Pierson (1983) attempted to evaluate these four terms. It shows, if the model used is anywhere near correct, that SDMR and SDMA make very important contributions to the sum, which are increasingly important with increasing wind speed. The actual radar error for the actual wind over the cell (or cells) actually sampled by the radar is the t4 term in Eq. (26). Theoretical calculations and Monte Carlo simulations both indicate that it is very small. The values of 1.23 and 1.39 m sec-’ may be a very large upper bound on the actual accuracy of the SASS. Similar results can be obtained for the wind direction xR, and the radar measurement can be described by XR

= xs

+ t,(SDMR*) + t,(SDRE*)

(29)

4.4. The Synoptic Scale The surface measurements by Seasat SASS were extremely dense, and 10 or 20 of them can be averaged (perferably vectorally) to obtain a synoptic-scale wind at a grid point of a numerical model. The result would be 1

V = -C(& m

+ t,i(SDMR) + t,i(SDRE))

and

where m is the number of observations. In such a representation, the values of t j i and tSi could be weakly correlated because of the larger wavelengths in the mesoscale turbulence. If this complication is ignored, the average value of the wind speed and direction is the synoptic-scale wind speed and direction. The variances are Var(v- V,) = (l/m)((SDMR)2 + (SDRE)’)

(32)

and Var

(x- xs)= (l/m)((SDMR*)’ + (SDRE*)’)

(33) For 16 winds clustered around a grid point, conservatively the values of 1.23 and 1.39 would reduce to 0.31 and 0.35. Values such as these for defining the


121

synoptic-scale wind in the planetary boundary layer should make an important contribution to the initial value specification of a numerical weather-forecasting model if assimilated properly in a model that takes full advantage of the high quality of the data. 5. GLOBAL DATA ASSIMrLATIOW EXPERIMENTS Recently, Cane et al. (1981) discussed sensitivity and potential ,impact of marine surface winds on numerical weather prediction. Their study is based on a set of simulated ocean surface winds and used the general circulation model of Goddard Laboratory for Atmospheric Sciences. The purpose of this section is to discuss results of a global data assimilation experiment using 2 days (July 16, OOZ-July 18, OOZ, 1978) of real global scatterometer winds from Seasat-A. These experiments were designed to give a preliminary assessment of the impact of SASS winds on the operational numerical weather analysis and forecasting system used at the National Meteorological Center (NMC). This material is substantially the same as that of Yu and McPherson (1984).

The scatterometer on board Seasat-A provided fine-resolutionwind vectors (about 100-km spatial resolution) over the world's oceans for approximately 3 months in 1978. These winds have directional ambiguities, as previously discussed, with each SASS wind containing up to four directions. Only one is the correct wind direction; the rest are so-called aliases. It is necessary to remove these aliases before the SASS wind data can be usable. For this purpose, Yu and McPherson (1979) have designed an objective alias-removal scheme that uses conventional data and forecasts to help determine the correct wind direction. Then, the dealiased SASS winds are subjected to manual editing to correct those winds which still do not appear consistent with other information. A brief description on the alias-removal scheme is given in Section 5.2. After this preprocessing, the SASS wind data were used in two ways. First, the SASS winds were introduced into the surface-pressure analyses as demonstrated by Yu and McPherson (1979). Second, and perhaps more important, the SASS winds were used directly in the lower-tropospheric wind analysis. This was accomplished through the statistical-interpolationanalysis scheme incorporated in NMC's Global Data Assimilation System, used in this study. To assess the impact of SASS wind data, two parallel experiments were run, one with SASS winds (SASS mode), the other without (NOSASS mode). For the SASS mode, global SASS wind data were assimilated at 6-hr intervals, together with other data sources conventionally available at NMC. For the

122

DUNCAN B. ROSS E T AL.

NOSASS mode, an identical assimilation procedure was conducted, except that SASS data were excluded. Results of these two experiments after 48 hr of assimilation are discussed in Section 5.3 At the end of the assimilation period, a 72-hr forecast was made from each of the SASS and NOSASS analyses. Results of the forecast comparison are discussed in Section 5.4. 5.1. Assimilation System

It has been learned through considerable experience that the impact of a given type of data on a numerical weather-prediction system cannot be judged without considering the characteristics of the assimilation system itself (Tracton et al., 1981). That is, some assimilation systems are more amenable to improvement by the addition of the data being tested than are others. We therefore think it important to describe the structure and behavior of the system used in the experiments reported here, especially noting those aspects of particular relevance to the assimilation of the SASS winds. The assimilation system is the NMC Global Data Assimilation System in the configuration used operationally in January 1981. It is essentially the system described by McPherson et al. (1979), based on a statisicalinterpolation objective-analysis method developed by Bergman (1979)following the work of Gandin (1963), Schlatter (1975), and Rutherford (1976). First implemented in NMC operations in September 1978 (after the 2-day period of Seasat data used in this experiment), it has since undergone a series of evolutionary changes; the most important of these are described by Kistler and Parrish (1 982). In the configuration used in the experiments described here, the system consists of a primitive-equation prediction model (Sela, 1980)that is updated each 6 hr using statistical interpolation of available data. The prediction model uses spectral representation in terms of spherical harmonic functions to calculate quantities involving derivatives, but physical processes such as precipitation are parameterized on a network of grid points. Ordinary finite difference approximations are used in the vertical. Horizontal resolution in terms of spectral representation is 30 waves with rhomboidal truncation; the physical grid has 96 equally spaced points around latitude circles, and there are 76 points between poles. These are not quite equally spaced but constitute the so-called “Gaussian” grid, with points at the modes of the spherical barmonic functions. On average, the spacing between points meridionally is about 3.75“ latitude. Twelve layers are used to represent the vertical structure of the atmosphere, and the vertical coordinate in any column of air is pressure, normalized by the


123

pressure at the base of the column; i.e., the surface pressure. This sigma coordinate,' first introduced by Phillips (1957), is commonly used in numerical weather prediction and has the advantage of following the contours of the earth's surface. This is of no special benefit in the present experiment, of course, since the SASS winds are introduced only over the oceans. The vertical structure of the model is depicted in Fig. 8. Note especially the finer structure in the lower troposphere. The prediction model includes parameterization of some physical processes, such as precipitation, surface friction, and sensible and latent heat exchange; it does not attempt to parameterize others, such as radiative

-

SPECTRAL 12 LAYER (

12bar) t

0

50

0

50

@

50

@

50

50

DO

00 50

30 DO

0 0 .............. @ 0

50 !50 75

125

00 00

00 M)

DO 00

@.............J5.0 ................................................. 0 150 .................. 125 ................................. 73

I

FIG.8. Vertical structure of the model. From the Greek symbol almost universally used in the coordinate definition: c = P/P,,,.

124


processes. At the time this experiment was conducted, climatological sea surface temperature fields, rather than current analyzed fields, specified the lower thermal boundary conditions over oceanic areas. Each 6 hr, the forward integration of the prediction model is interrupted, and timely observations are used to correct or update the model’s representation of the atmosphere, where this is necessary. Data are stratified into 6-hr time blocks, and are treated as synoptic at the central time of the block; thus, the data base available for each update may contain data as much as 3 hr off time. For small-scale local applications, such an approximation would not be reasonable, but for the global, large-scale application addressed here, the error introduced is not as serious. The updating is done by first determining the error in the predicted fields of the model’s history variables (eastward and northward wind components, temperature, surface pressure, and specifichumidity) at the locations of each observation by subtracting the interpolated, predicted value from the observed value. These differences are then interpolated back to the physical grid points of the prediction model, forming corrections which are then added to the original prediction fields. The interpolation of the observed-minuspredicted differences from the locations of the data to the model grid points is accomplished by the statistical procedure frequently referred to as “optimum interpolation.” This is a method which minimizes, in principal, the meansquare error of interpolation. In practice, approximations necessitated by operational considerations result in some deviation from strict statistical optimality; hence, we use the term “statistical interpolation” to describe the procedure. Statistical interpolation has been adopted in operational numerical weather-prediction centers in several countries. One of the principal motivations for this popularity is that the statistical method provides a systematic framework for blending together observations from different sources, and importantly, with different error characteristics. Given the wide variety of data sources in today’s global data base, this capability is essential. Table 111 presents a summary of the RMS observational errors associated with various types of data used in this experiment. All else being equal, the weight assigned each observation is inversely proportional to its assumed error level: the greater the error, the less weight accorded in the analysis. The error of the SASS wind is assumed to be 5 m sec-’, about the same as low-level, cloud-motion winds and surface ship wind observations,the other sources of wind observations in the lower marine troposphere. This is higher than errors obtained by direct comparison with ship and buoy data in the GOASEX and JASIN programs and was chosen in part because of a suspected greater uncertainty in wind direction and speed in the gridded global data set. In any event, the assigned error levels mean that

125


TABLE 111. ROOT-MEAN-SQUARE OBSERVATIONAL ERRORS OF TEMPERATURES AND WINDSUSEDIN THE GLOBAL DATAASSIMILATION EXPERIMENTS Data types

Temperatures ("C)

Winds (m sec-')

Radiosonde Aircraft U.S. cloud-tracked winds Low level High level Japanese cloud-tracked winds Low level High level Surface ships Scatterometerwinds ASDAR (AIRCAR) Upper air bogus

0.8

1.5 2.0

1.o

4.2 1.5

6.I 13.0 5.0 5.0

2.0 1.o

the SASS data compete with the other two data types on an approximately equal footing. The fact that the statistical interpolation is done to the grid points of the prediction model's physical grid, including the midpoints of the 12 vertical levels, requires that a certain sequence of events be followed in the updating process. Because the vertical coordinate c i s a function of an updated variable (surface pressure), that variable must be updated first. The predicted surface pressure is updated by a two-dimensional version of the statisical-interpolationprocedure, using observed station pressures from land stations, ships, and buoys. Wind observations from ships and buoys are also allowed to influence the surface-pressure update poleward of about 15" latitude through a geostrophic relationship between the wind and the gradient of pressure. SASS winds (in the SASS mode) were also used in this way in the experiment reported here. Once the surface pressure has been corrected, the vertical coordinate is adjusted, and the updates of the wind, temperature, and humidity in each of the 12 layers begin. Here, three-dimensional statistical interpolation is used: that is, an observation at a given pressure may not only influence the update in its own layer, but also layers above and below. In particular, the SASS winds typically influence the lowest two layers significantly, and the next two to a much smaller degree. Figure 9 depicts the influence of a single wind observation at 1OOOmbaron the analysis at higher levels. The illustration may be interpreted as stating that a 10-m sec- change in the u component at 1000 mbar from a single observation implies a change of 6 m sec-' at 700 mbar,

'

126


100

i50 ,200 250

P

,300 (mbar)

400 500 -700

450

0 0.2

0.4 0.6 0.8

~1000 1.0

CORRELATION FIG.9. Modeled correlation function for forecast wind errors as a function of pressure.

about 2.5 m sec-' at 500 mbar, and very small changes at higher levels. This influence function is a part of the three-dimensional statistical interpolation representing the covariance of a prediction error in wind at one level with that at another level. For details, the reader is encouraged to consult Bergman ( 1979).

Thus, the SASS wind data are used in two ways. First, they are used to influence the surface-pressure update. This is likely most important in connection with remote temperature sounding data, which require an accurate reference level for maximum usefulness. Second, the SASS winds directly affect the wind update in the lower troposphere, through a moderately deep layer. Here is probably the greatest potential for beneficial impact. Following the completion of the statistical-interpolation part of the update, the corrected fields are adjusted by a nonlinear normal mode initialization procedure (Ballish, 1980) based on a method suggested by Machenhauer (1977). Initialization modified both mass and motion fields to resolve any imbalances arising from the updating process. The initialized fields serve as initial conditions for the next 6-hr forecast of the spectral prediction model.


127

5.2. Preprocessing of the SASS Wind Data In this section,we discuss the alias-removal scheme used to obtain the most nearly correct direction from the SASS data. The scheme is an iterative method consisting of five passes: 1. First pass. This step resolves the directional ambiguity in areas of uniform wind direction. Abundant examples of uniform wind fields are observed over the trade-wind regimes in the tropics. Here we define uniform winds as having directions in the same quadrant within a specified area. During this pass, the scheme locates the areas of uniform winds. Then, NMC's operational 1000-mbar wind analysis, which has been derived from a forecast plus conventional data, is used to select correct wind directions over a 5" x 5" longitude-latitude grid box. Vectors thus resolved are then separated from the remaining ambiguous winds. 2. Second pass. The SASS wind data are searched for unambiguous winds. If the unambiguous-wind distribution from the SASS data contains at least three vectors in the four quadrants of the 5" x 5" longtitude-latitude box, these winds are used to resolve ambiguous vectors in the box. These resolved winds are then used for the next pass. It turns out that this was a superfluous step; no unambiguous winds were found in the 2-day data set. 3. nird pass. Statistical interpolation of the winds resolved in the previous passes is used to select the most probable direction to the location of each twofold (i.e., one report contains two pairs of wind speeds and wind directions) ambiguous wind. Vectors thus resolved are added to the data base for the next pass. 4. Fourth puss. Same as the third pass except that threefold ambiguous winds are resolved. 5 . Fifth pass. Same as the fourth pass except that fourfold ambiguous winds are resolved.

Before processing actual SASS wind data, the alias-removal scheme was tested on a set of simulated data generated by the Langley Research Center (Jones et a!., 1978). The advantage of using the simulated case is that we can compare the final dealiased wind field with the starting wind field; thereby we may evaluate the performance of our alias-removal scheme. In general, it suffices to say that from the test experiment we found that the dealiased wind field agreed well with the surface-pressure pattern except over the areas where the pressure gradient was large. Within these areas, the resolved wind directions frequently did not agree well with the surface-pressure pattern; these constituted about 10 to 15% of total winds.

128


To address these remaining ambiguous winds, manual editing was performed on the residual 10- 15%. The information on which the manual editing was based consisted of satellite imagery, low-level, cloud-tracked winds from satellites, and the conventional surface-pressure analyses. Therefore, it is likely that over the areas where, for example, satellite low-level, cloud-tracked winds are available, the SASS wind data will tend to be redundant, and their impact on the analysis Iess evident. This will be discussed further in Section 5.3. 5.3. Assimilation Experiments In the lower atmosphere at 1000mbar, after 48 hr of data assimilation,large differencesbetween SASS and NOSASS wind and height analyses are found in the Southern Hemisphere. Differences in height analyses exceeding 100 m are seen over the Southern Hemispheric oceans (see Fig. 10). For example, height differences as large as 140 m are seen over the Indian Ocean near 260"W and Longitudes (WEST)

0 20

40 I

I c

u)

Q

0

a

.= c

0

20

60

FIG.10. (a) The 1000-mbar height differences and (b) vector-wind differences between SASS and NOSASS in the Southern Hemisphere after 48-hr assimilation valid at OOZ, July 18, 1978. The height unit is in meters and vector winds are represented by a full bar (10 m sec-') and a half bar ( 5 m sec-I). The map covers the area from 350"W to 235"Wand 0" to 60%


129

40"s (Fig.lOa), and over the west coast of South America near 80"Wand 45"s (Fig. 1la). Over those areas where analyzed height differences are large, there are correspondingly large differences in vector-wind analyses exceeding 20 m sec-' (see Figs. 10b and 1lb). Moreover, these differences are well organized and consistent with the height analysis differences. In the higher atmosphere at the 250-mbar level, large differences between SASS and NOSASS wind and height analyses are found as well in the Southern Hemisphere (see Figs. 12 and 13). This demonstrates that the influence of the scatterometer wind data has spread through a deep layer of the atmosphere as a result of the data assimilation process. Although some of the features which appear at the 1000-mbar level are well preserved at the 250mbar level, most of the features become less organized in the higher troposphere. Note also that percentage of height and wind differences in the higher troposphere are not as large as those near the surface. In the Northern Hemisphere,differences in geopotential and wind analyses between SASS and NOSASS experiments are rather small; differences in Longitudes ( W E S T )

20

40

60

FIG.11. Same as Fig. 10except showing the area from 11YW to 15"W.

130

DUNCAN B. ROSS ET AL. Longitudes

(WEST)

0

20

-

40

x

I3 0

- 60

r n

FIG.12. Same as Fig. 10 except for 250-mbar height (a) and vector-wind differences between SASS and NOSASS.

geopotential are in general less than 20 m and in wind speed less than 5 m sec- near the surface of the earth as well as in the higher atmosphere. This is due to the fact that it is summer in the Northern Hemisphere and lower winds prevail and, also, there are abundant other sources of data such as ship reports, buoy reports, and low-level, cloud-tracked winds (see Table IV for a comparison of the conventional NMC data sources between the Northern and Southern Hemispheres at a synoptic time). As a consequence, the impact of SASS winds on global-scale atmospheric analyses is not so great as in the Southern Hemisphere, where conventional data are sparse and a winter season prevails. It should also be pointed out that over the areas where low-level, cioudtracked winds from satellites are available, the impact of SASS winds on the analysis of wind and height becomes less significant due to overlapping observations. This is illustrated in Fig. 14. Note that in the areas bounded by 115"W,70"W,WS, and 30"s there are abundant low-level, cloud-tracked winds and the differences between SASS and NOSASS wind and height analyses were very small (see Fig. 11). As mentioned earlier, this is due to the

'

131

4. OCEANIC SURFACE WINDS Longitudes

(WEST)

0

20

40 I

I I-

3

0

- 60. v)

40

60

FIG.13. Same as Fig. 10 except showing the area from 115"W to 1S"W.

TABLE Iv. NUMBER OF DATAREPORTS USED IN WIND ANALYSES OF ASSIMILATION EXPERIMENTS'

THE GLOBAL DATA

~~~~~

Data types Radiosonde Aircraft Satellite cloudtracked winds Surface land reports Surface ship and buoy reports Scatterometer wind

Level (mbar)

Northern Hemisphere

Southern Hemisphere

Tropics (20"N-20°S)

850

250 lo00

66 1 256 137

133 16 156

-b -b

lo00

3221

195

1267

lo00

406

23

252

lo00

353

716

652

For 122, July 17,1978. Data counts already included the tropical region in each hemisphere.

-b

132


404

2dN

0 '

?dS

46s FIG.14. Analysis at 850mbar for OOZ, July 18,1978. The plotted wind vectors are thelow-level, cloud-tracked winds from satellites.

fact that the SASS wind data must be manually edited based on information from satellite imageries and cloud-tracked winds. Therefore, SASS wind data thus edited contain essentially the same information as cloud-tracked winds. 5.4. Forecast Experiments

The height and vector-wind 72-hr forecast differencesvalid at OOZ, July 21, 1978, from the SASS and NOSASS analyses are shown for the Southern Hemisphere at the 1000- (Figs. 15 and 16) and 250-mbar (Figs. 17 and 18) levels. We note that large differences in the initial analyses at 1000 mbar lead to large differences in the forecasts in the Southern Hemisphere (Fig. 15). These forecast differences are well distributedin the vertical, as can be seen from Figs. 17 and 18 for a higher level (250mbar) of the atmosphere. Note that a large vector-wind difference of about 30-35 m sec-l in magnitude was observed over the Indian Ocean at 250 mbar. A subjective comparison with satellite cloud imagery and streamline analyses suggests that, in general, the forecast from the analysis with SASS winds are an improvement over that from the analysis without SASS winds.

Longitudes (WEST)

340

320

300

280

260

240

FIG.15. (a) The 1000-mbar height differences and (b) vector-wind differences between SASS and NOSASS in the Southern Hemisphere after 72 hr of forecast valid at OOZ, July 21,1978. The height unit is meters, vector winds are represented by a full bar (10 m sec-') and a half bar (5 m o",and 60"s. sec-I). The map covers the area bounded by 350"W,235"W, Longitudes ( W E S T )

FIG.16. Same as Fig. 15 except showing the area between 115"Wand 15"W.

FIG. 17. Same as Fig. 15 except for 250-mbar height differences (a) and vector-wind differences (b) between SASS and NOSASS.


135

In contrast to the initial smaller differences shown in Figs. 12 and 13 at 250 mbar, 72-hr forecast differencesare very large at 250 mbar in vector winds (see Figs. 17 and 18). Moreover, several distinctly well-organized features which are present at 250 mbar are not as clearly depicted near the surface at 1000 mbar. In the Northern Hemisphere, 72-hr forecast differences in heights and winds between SASS and NOASS are very small. To objectively assess the impact of SASS winds on forecasts, we computed root-mean-square absolute vector-wind error for six radiosonde networks in the Southern Hemisphere. These radiosonde networks include (1) Southern Hemisphere, 31 stations; (2) South Africa, 20 stations; (3) South America, 30 stations;(4) Australia, 24 stations; (5) South Pacific, 22 stations; and (6)isolated stations in the Southern Hemisphere. Table V shows the calculated RMS absolute vector-wind errors for four radiosonde networks. From Table V we can see that in general, from 1000 up to 100 mbar, the 24- and 48-hr forecasts with SASS winds in the initial analysis are better than the forecasts generated from the initial analysis without SASS winds. However, for the 72-hr forecast, we notice that forecasts for the SASS case are slightly worse than those for the NOSASS case in the lower atmosphere (ie.,. 850 and 500 mbar), but slightly better at higher levels (ie., 250 and 100 mbar). Based on these statistics, one may conclude in general that forecasts are slighlty better for the SASS case than for the NOSASS case. 5.5. Summary

In the Southern Hemisphere, differences in wind and height analyses between SASS and NQSASS are quite large and well organized after 48 hr of assimilation. Height differences are as large as 140 m and wind differences as large as 15-20 m sec-'. These differences are not only in the lower atmosphere where SASS winds are injected (e.g., 1000 mbar), but also in the higher troposphere (e.g., 250 mbar), although the percentage of changes in the higher troposphere is smaller than that near the surface. These differences in the Southern Hemisphere in the initial analysis of height and wind fields between SASS and NOSASS in turn lead to significant differences in forecasts of wind and height fields through 72 hr. In the Northern Hemisphere, the differences in wind and height analyses between SASS and NOSASS are much smaller. This is due to the fact that in the Northern Hemisphere summer prevails with a relatively low average wind speed and there are many other sources of meteorological data to compete with SASS wind data. The fact that there is little difference between SASS and NOSASS initial analyses in the Northern Hemisphere suggests that SASS

136

DUNCAN B. ROSS ET AL. TABLE V. RMS ABSOLUTE VECTOR-WIND ERROR'

Forecast

Pressure level(mbar)

SASS

NOSASS

Southern Hemisphere, 31 stations 24 hr

48 hr

72 hr

850 500 250 100 850 500 250 100 850 500 250

100

9.9 10.5 18.6 14.1 9.3 9.0 16.0 12.0 8.3 13.7 18.8 9.7

10.0 10.7 19.1 14.4 10.2 9.8 18.0 11.6 8.0 12.5 16.3 10.1

Australia, 24 stations 24 hr

48 hr

72 hr

a

850 500 250 100 850 500 250 100 850 500 250 100

7.8 8.0 17.3 11.0 6.5 8.8 12.0 9.3 9.2 12.7 19.1 11.6

7.9 8.7 17.4 11.1 7.2 9.7 13.2 10.7 9.4 10.7 16.2 14.0

SASS

NOSASS

South Africa, 20 stations 5.4 6.1 15.6 17.0 6.4 5.3 16.6 12.5 7.9 8.8 16.7 7.4

6.6 6.9 16.2 17.8 8.3 5.4 17.1 11.8 6.5 7.5 15.8 8.8

South Pacific, 22 stations 6.7 7.9 14.4 10.7 6.4 8.7 11.3

9.0 7.9 9.5 10.4 8.o

7.2 8.7 15.0 10.8 6.6 9.0 12.4 9.6 7.7 9.4 10.8 8.2

SASS

NOSASS

South America, 30 stations 11.2 13.5 17.2 9.0 7.5 12.1 16.5 7.6 6.6 18.6 20.6 9.0

10.8 12.8 17.8 8.8 7.3 11.8 18.2 6.6 7.1 15.4 21.4 9.2

Southern Hemisphere, isolated island stations 6.3 9.4 11.5 5.2 7.4 13.0 8.7 3.4

9.2 7.5 12.9 -

10.4 10.6 12.5 9.5 12.5 15.2 14.4 5.7 8.2 9.4 7.3 -

Values expressed as meters per second.

winds are at least as useful as ship winds, buoy reports, and low-level, cloudtracked winds using the 5-m sec-' error assumption. Decreasing this tolerance should lead to increased differences. This also suggests that we may have confidence in the adequacy of the SASS winds for contributing to the atmospheric initial states in the Southern Hemisphere. Subjective evaluations by comparing the forecasts with satellite imagery and low-level, cloud-tracked winds indicate that with SASS winds, the lowpressure centers are more intense. Some smaller features appear in the


137

forecast generated from the SASS analysis, which though not clearly verified by large-scale analyses, are present in the cloud imagery as well. This suggests that SASS winds provide information on subsynoptic-scalefeatures. Objective evaluations by comparing the 24- and 48-hr forecasts with the radiosonde network in the Southern Hemisphere indicate that short-range forecasts generated with SASS winds are slightly better than the forecasts generated without SASS as initial analyses. This conclusion is not true, however, for the 72-hr forecast comparison. This assessment, based on only one case, must be labeled preliminary. A longer assimilation sequence and more forecasts are clearly necessary. 6 . CONCLUSIONS

The Seasat experience has proved the potential of a space platform to obtain global measurements of the surface wind speed and direction to an accuracy of better than k2.0 m sec-' and +20" in direction. While the comparison data sets have shown the magnitude of the error to be closer to or even less than 1 m sec-l, the data were predominantly of low-to-moderate winds under neutral-to-unstable atmospheric conditions. However, in many applications such as global ocean circulation studies, such low-to-moderate winds are the major driving force. Thus, a potential limitation with respect to extreme winds or highly stable atmospheres is relatively unimportant. Global atmospheric-modeling studies of the impact of Seasat winds have been limited in many respects by the models themselves in that they are not geared to assimilation of highly accurate and closely spaced global data sets. Furthermore, verification of model forecasts is limited by the relatively few essentially oceanic sites required to collect data sets uncontaminated by land proximity. In preparation for the launch of new satellite programs, the Seasat experience suggests that considerable emphasis should be placed on the development of global atmospheric circulation models which have the grid spacing and nonsynoptic assimilation routines appropriate for such highquality global data sets. Additional work is needed in verificationof the data under extreme situations such as in winter storms contaning high winds and moderate-to-heavy rainfall. The effect of a stable atmosphere needs to be examined further in view of the lack of sensitivity in the results of Liu and Timothy (1984),in conflict with the results of Liu and Ross (1980), who found that application of a boundary layer model to adjust the winds to a neutral or unstable equivalent nevertheless underestimated the strong effect of stability in reducing the growth rate of wind-generated waves.

138


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Jones, W. L., Wentz, F. J., and Schroeder, L. C. (1978). Algorithm for inferring wind stress from Seasat-A. J. Space Rockets 15,368-374. Jones, W. L., Black, P. G., Bogs, D. M., Bracellente, E. M., Brown, R. A., Dome, G., Ernst, J. A., Halberstam, I. M., Overland, J. E., Peteherych, S., Pierson, W. J., Wentz, F. J., Waiceshyn, P. M., and Wurtele, M. G. (1979). Seasat-A scatterometer: Results of the Gulf of Alaska workshop. Science 204, 1413-1415. Jones, W. L.,Wentz. F. J., and Schroeder, L. C. (1981). Microwave scatterometer measurements of oceanic wind vector. in “Oceanography from Space,” pp. 553-563. Plenum, New York. Kistler, R. E., and Parrish, D. F. (1982). Evolution of the NMC‘s data assimilation system: September 1978-January 1982. In preparation. Kraus, E. B. (1967). The aerodynamic roughness of the sea surface. J. Atmos. Sci. 23,443-445. Large, W. G., and Pound, S. (1981). Open ocean momentum flux measurements in moderate to strong winds. J. Phys. Oceanogr. 11,324-336.


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Lipes, R. G., Berstein, R. L., Cardone, V.J., Katsaros, K. B., Njoku, E. G., Riley, A. L., ROSS,D. B., Swift, C. T.,and Wentz, F. J. (1979). Seasat scanning multichannel microwave radiometer: Results of the Gulf of Alaska Workshop. Science 204,1415-1417. Liu, P. C., and Ross, D. B. (1980). Airborne measurements of wave growth for stable and unstable atmospheres in Lake Michigan. J. Phys. Oceanogr. 10, 1842-1853. Liu, P. C., and Timothy, W. (1984). The effects of the variations in sea surface temperature and atmospheric stability in the estimation of average windpseed by Seasat-SASS. J. Phys. Oceanogr. 14(2), 392-401. Lumley, J. L., and Panofsky, H. A. (1964). “The Structure of Atmospheric Turbulence.” Wiley (Interscience), New York. Machenhauer, B. (1977). On the dynamics of gravity oscillations in a shallow water model with application to normal mode initialization. Beitr. Phys. Atmos. 50,253-271. McPherson, R. D., Bergman, K. H., Kistler, R. E., Rasch, G. E., and Gordon, D. S. (1979). The NMC operational global data assimilation system. Mon. Weather Rev. 107, 1445-1461. Miles, J. W. (1957). On ihe generation of surface waves by shear Boys. J. Fluid Mech. 3,185-204. Mognard, N. M., Campbell, W. J., Cheney, R. E., Marsh, J. G., and Ross, D. B.(1981). Southern ocean waves and winds derived from SEASAT altimeter measurements. Proc. IVCRMIAMS Wave Dyn. Symp., Miami. Moore, R. K., and Pierson, W. J. (1971). Worldwide oceanic wind and wave predictions using a satellite radar-radiometer. J . Hydron. 5(2), 52-60. Paulson, C. A. (1970). Themathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J. Appl. Meteorol. 9,857-861. Phillips, N. A. (1957). A coordinate system having some special advantage for numerical forecasting. J. Meteor. 14,184-185. Phillips, 0.M. (1977). “The Dynamics of the Upper Ocean,” 2nd Ed. Cambridge Univ. Press, London. Pierson, W. J. (1983). The measurement of the synoptic scale wind and the ocean. J. Geophys. Res. 88, 1683-1708; also to be published as a LARC. Pierson, W. J., and Stacy, R. A. (1973). The elevation, slope, and curvature of a wind roughened sea surface. NASA Contr. Rep. 2247. NASA Langley Research Center, Va. Pierson, W. J., Peteherych, S., and Wilkerson, J. C. (1980). The winds of the comparison data set for the Seasat Gulf of Alaska Experiment. IEEE J. Ocean Engr. OE-5 (2). Ross, D. B. (1981). The windspeed dependancy of ocean microwave backscatter. In “Spaceborne Synthetic Aperture Radar for Oceanography” (R. C. Beal, P. S. DeLeonibus, and I. Katz, (eds.), pp. 75-86. J o l p Hopkins Univ. Press, Baltimore, MD. Ross, D. B., and Cardone, V. J. (1974). Observations of oceanic whitecaps and their relation to remote measurements of surface windspeed. J. Geophys. Res. 79,444-452. Ross, D. B., Lawson, L. M., and McLeish, W. (1981). Comparison of Hurricane Fico winds and waves from numerical models with observations from SEASAT-A. Proc. IUCRMIAMS Wave Dyn. Symp. Miami. Rutherford, I. D. (1976). An operational three-dimensional multivariate statistical objective analysis scheme. GARP Rep. No. 11, pp. 98-121. Proc. JOC Study Group Go@ FourDimensional Data Assimilation, Paris. Schlatter, T. W. (1975). Some experiments with a multivariate statistical objective analysis scheme. Mon. Weather Rev. 103,246-257. Schroeder, L. C., Boggs, D. H., Dome, G., Halberstam, I. M., Jones, W. L., Pierson, W. J., and Wentz, F. J. (1982). The relationship between wind vector and normalized radar cross section used to derive Seasat-A satellite scatterometer winds. J. Geophys. Res. 87,3318-3336. Sela, J. G. (1980): Spectral modeling at the National Meteorological Center. Mon. Weather Rev. 108,17279- 1292.

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Smith, S. D. (1980). Wind stress and heat flux over the ocean in gale force winds. J. Phys. Oceanogr. 10,709-726. Snyder, R. L., Dobson, F. W., Elliott, J. A,, and Long, R. B. (1981). Array measurements of atmospheric pressure fluctuations above surface gravity waves. J . Fluid Mech. 102, 1-59. Tracton, M. S.,Desmarais, A. J., van Haaren, R. J., and McPherson, R. D. (1981).On the system dependency of satellite sounding impact-comments on recent impact test results. Mon. Weather Rev. 109, 197-200. Valenzuela, G. R., Laing, M. B., and Daley, J. C. (1971). Ocean spectra for the high frequency waves as determined from Airborne Radar Measurements. J. Mar. Res. 29,6944. Weller, R. R., Rayne, R. E.,Large, W. G., and Zenk, W. (1983). Wind measured from an array of oceanographic moorings and from F/S METEOR during JASIN, 1978. J . Geophys. Res. 88(C14),9689-9705. Wentz, F. J., Christensen, E. J., and Richardson, K. A. (1981). Dependence of sea-surface microwave emissivity on friction velocity as derived from SMMR/SASS. In “Oceanography from Space,” pp. 741-750. Plenum, New York. Wilkerson, J. C., and McNutt, S . L. (1979).Seasat Gulf of Alaska Experiment surface truth data inventory. JPL Document No. 622-99,15 Feb. 1979. Wright, J. W. (1968).A new model for sea clutter. IEEE Trans. Antennas Propag. AP-16,217-223. Wy J. (1969). Froude number scaling of wind-stress coefficients. J. Atmos. Sci. 26,408-413. Yu, T . W., and McPherson, R. D. (1979).Surface pressure analysis using scatterometer-derived wind data from the Seasat-A satellite. Preprint paper, Cunf Numer. Weather Predict., 4 t h Oct. 29-Nov. I , Silver Spring, M D pp. 351-355. Yu,T. W., and McPherson, R. D. (1984).Global data assimilation experiments with scatterometer winds from Seasat-A. Mon. Mather Rev. 112,368-376.

SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS C. L. RUFENACH AND L. S . FEDOR National Oceanic and Atmospheric Administration Wave Propagation Laboratory Environmental Research Laboratories Boulder. Colorado

J. R. APEL Applied Physics Laboratory The Johns Hopkins University Laurel, Maryland

F. 1. GONZALEZ National Oceanic and Atmospheric Administration Par& Marine Environmental Laboratory Environmental Research Laboratories Seattle. Washington

1. Introduction. . . . . . . . . . . 2. Ocean Surface Waves. . . . . . . . 2.1. Physical Characteristics . . , . , 2.2. Altimetry . . . . . . . . . . 2.3. Synthetic Aperture Radar. . . . . 3. Ocean Internal Waves . . . . . . . 3.1. Observations . . . . . . . . . 3.2. Ancillary OceanographicInformation 3.3. Interpretation. . . . . . . . . 3.4. Theoretical Considerations . , . . 3.5. Amplitude Estimates . . . . . . 4. Summary and Conclusions. . . . . . . References . . . . . . . . . . .

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1. INTRODUCTION

Two microwave radars, a radio altimeter and a synthetic aperture radar (SAR), were flown on board Seasat. The usefulness of these radars for ocean wave studies depends on an understanding of the interaction of these radar signals with both the short (capillary and gravity) and long ocean surface waves. The modulation in the SAR signal due to the long waves can be detected as an intensity modulation in the imagery whereas the stretching of the leading edge of the altimeter radar pulses is directly related to roughness of the surface or equivalently the ocean wave height. A theoretical description of this pulse stretching is not given here since it is well understood and has been given by others (e.g., Brown, 1977; Rufenach and Alpers, 1978). However, the interaction of SAR signals with the ocean surface is an area of active research where our understanding is still incomplete. Indeed, ad141 ADVANCES IN GEOPHYSICS.VOLUME 27

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ditional experimental and theoretical work is required to consistently infer the djrectional wave spectrum from SAR. High-frequency internal waves are frequently observed in the stratified ocean near the continental shelf. These waves, which occur at a stratified boundary in the water column, modify the capillary and short gravity waves at the ocean surface. These changes in the short-wave field are visible in the SAR imagery since these surface manifestations of the internal waves change the radar cross-sectional modulation. This chapter is separated into two primary areas of discussion: In Section 2 ocean surface wave measurementsare described and in Section 3 internal wave measurements are described. Section 2 is separated into a description of the physical characteristics of the surface waves, altimetric measurements of wave height, and SAR theory and measurements. Described in Section 3 are the inferred internal wave results using SAR. 2. OCEAN SURFACE WAVES

2.1. Physical Characteristics

Ocean surface waves exist over a wide range of heights (centimetersto tens of meters) and wavelengths (hundreds of meters to millimeters). These water waves are governed by Navier-Stokes differential equations for incompressible fluids, which express the balance between inertial, convective, and restoring forces of the wave motion. The restoring force for waves with wavelengths longer than 1.7 cm is the gravitational acceleration g and such waves are called gravity waves, The restoring force for waves with wavelengths shorter than 1.7 cm is surface tension and these waves are called capillary waves. The sea surface is specified by a three-dimensional spatial and temporal displacement field ((x, y, t). Because of the nature of this field, it must be described by a random process that requires statistical analysis. The random field is usually considered locally homogeneous and stationary, which means that ensemble averaging is independent of (x, y , t ) over the area of interest. Therefore, the surface displacementcan be specified in terms of the directional wave-height spectrum X(k, o),which is a function of both the wavenumber k = ( k x , k,,) and radian frequency w = 2nf. The directional spectrum is the Fourier transform of the covariance of the sea surface displacement [ from its mean level at two points in space and time.

JJJ

X(k,w)=(2a)-3

([(x,t)&x

+ r , t +~))exp[-i(k.r -oz)]drdz

(1)

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS

143

where x = (x, y ) and.the angle brackets represent an ensemble average. The spectrum X(k, W) is never actually measured in practice since simultaneous averages of estimates in time and space would be required. The spectrum given by Eq. (1) is normalized such that the mean-square surface height (c’) is

r or

where $(k) and S(o) are the two-dimensional spatial and one-dimensional temporal spectra, respectively, given by

$(k) = (24-l

I

X(k, W) d o

and

fs

S ( o ) = ( 2 ~ ) - ~X(k,w)dk

Ocean surface waves are considered here to be the superposition of many individual waves, which, in general, may include a wide range of wavelengths L = 2n/k and periods T = 2401 traveling in a wide range of directions 4 = arctan(k,/k,). Furthermore, gravity waves with small slopes in deep water are governed by a dispersion relationship with an intrinsic frequency o moving with the underlying deep water,

,/m

(6)

o=&i

where k = and g is the acceleration of gravity. Indeed, the waves are dispersive: the longer wavelength components travel faster than the Nonlinear, selfshorter ones with a phase velocity = o / k = interaction effects modify Eq. (6) when the wave amplitude c0 increases, i.e., l o k 4 1. Most notable, harmonic wave components are generated and there is a fractional increase of i(l0k)’ in the phase velocity. The one-dimensional temporal spectrum, Eq. (5), is usually measured with wave staffs and accelerometers on buoys or ships (time series of wave height sampled at a single point). This spectrum is dependent on the wind speed, duration, and fetch (distance over which the wind blows). Suppose that a uniform stationary wind is blowing constantly and perpendicular from a long, straight shoreline. Then a wave spectrum that grows in height and wavelength

c

fl.

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develops along the fetch, according to Hasselmann et al. (1976):

where f, is the spectral peak frequency,a is the Phillips constant, y is the peakenhancement factor, and Q is the peak width (more precisely Q was separated by Hasselmann et al. for f 5 f, with oa and ob given by the left and right spectral widths, respectively). Equation (7) simplifies to the PiersonMoskowitz spectral shape for fully developed seas when y = 1. It has been shown by Kitaigorodskii (1973)that all wave field parameters, when nondimensionalized in terms of g and U,. should be functions of only the nondimensional fetch parameter 5 = g x / U 2 where x is the fetch and U is the wind speed referred to 10 m above mean sea level. The nondimensional peak frequency v = L'f,/g and Phillips constant a weie determined empirically over the nondimensional fetch range lo-' < 5 < lo4 relevant for both wave tanks (lo-' < t c 10') and open ocean (10' < 5 < lo4).

The shape parameters y, oa,and bb exhibited scatter, but no significant mean dependence on fetch; y = 3.3, aa = 0.07,and q,= 0.09. Although no detailed empirical dependence between v and duration has yet been established,it is clear that ultimately a dependence of v on both fetch and duration is involved. Indeed, a nondimensional duration parameter, gr'/ U, where z' is the duration, can be defined. At high wind speeds, fetch and duration are rarely sufficient to allow full development of the wave spectrum. The parameter v has been experimentally determined to approach 0.13 for fully developed seas ( 5 w lo4) whereas v > 0.13for less than fully developed seas. For some applications, the mean-square displacement (5') of Eq. (3) or equivalently the significant wave height, SWH, is of interest. The expression is SWH(m) = 4((52))1/2 = 9.4 x 10-4v-'0/6U2 (msec-')

(10)

Wave height for a fully developed sea based on v = 0.13, as given by Hasselmann et al. (1976)and as adjusted by Pierson (1977),is SWH(m) = 2.6 x 10-'U2

(msec-')

(1 1)


145

The above relationships do not consider swell propagating from distant sources. Furthermore, Hasselmann et al. (1980) have shown a detailed directional dependence, which may be useful for certain applications. The two-dimensional spectrum describes the direction of energy flow, Eq. (4), and is usually measured with a pitch-and-roll buoy, an array of pressure sensors, or an SAR. Pitch/roll (P/R) buoy measurements are carried out in the frequency domain, whereas SAR measurements are carried out in the wavenumber domain. The transformation from the wavenumber domain to the frequency domain is given by changing to polar coordinates $(k)dk,dk,

= $,(k,#)kdkd4 = S(o)dw

(12)

The one-dimensional temporal spectrum is then related to the twodimensional spatial spectrum based on Eq. (12) by s(w) = k(dk/do)$k(k, 4 )d4

(13)

It is sometimes reasonable to separate the directional dependence into $k(k, 4 ) = @O(k)f($) where [02nf(4)db = 1 Then S(w) = (k/P#o(k) where 5 is the group velocity [ x $ ( g / k ) for deep water]. The use of satellite altimeter signals scattered from the rough ocean surface is a well-established method of measuring the mean-square displacement of the surface (Brown, 1977). Stretching on the leading edge of the transmitted radar pulse is used to infer the mean-square value, which is usually reported in terms of the significant wave height. Recently, a new method of extracting more detailed ocean wave information has been developed. The surfaceheight probability function p ( [ ) appears in the integrand of the expression for the leading edge of the radar altimeter signal. This integral equation is easily inverted, therefore, to give the probability p ( r ) . The second moment of p ( [ ) , i.e.,

(C2>

=

rzP(c)dr

(14)

is the familiar mean-square displacement defined in Eqs. (2) and (3) and related to significant wave height in Eq. (10). Although the ocean surface often appears to have a Gaussian probability density to lowest order, there are situations that depart significantly from Gaussian. Breaking waves have a bimodal distribution, with height peaks that are harmonically related. Large

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swell observed over a small area will produce a probability density with two peaks at c0 = +a, where a is the amplitude of the sinusoidal swell. The skewnessof the surface height probability, directly inferred from the altimeter, will convey more information about the waves than merely the wave height. The SAR capability for measuring the directional wave spectrum quantitatively, Eq. (4), is not as well understood as the altimeter wave-height capability. However, the limited available SAR measurements give accurate estimates of dominant wavelength and direction when observed in the imagery. Directional wave information is not extractable from the other instruments on board Seasat. 2.2. Altimetry 2.2.1. Background. The Seasat radar altimeter is the third in a series of spaceborne radar altimeters dedicated to oceanographic remote sensing. The basic measurementsare the height of the radar above the mean surface and the shape and amplitude of the mean return-power waveform. From these radar measurements and appropriate models for the interaction of the radar signal with the surface, one can extract certain surface and near-surface characteristics such as wind speed and SWH, which are very useful in describing the state of the surface. Although the altimeter designs of Seasat and the earlier altimeters differ somewhat, the basic functions of these radars remain the same. The Seasat system has a shorter pulse width and a higher pulse repetition frequency (Townsend, 1980), which lead to more accurate surface measurements. In addition, the Seasat radar signal processor was designed to provide an on-board estimate of the significant wave height of the ocean waves with only minor postcalibration corrections (Townsend, 1980). This feature is very important because of the goal to be able eventually to extract ocean surface parameters on board in near real time, and therefore eliminate or minimize ground processing of the raw radar data. The purpose of this chapter is to report on a study of the accuracy of ocean surface significant wave height derived from the radar altimeter data and appropriate scattering models. An accuracy study, of course, implies that there is an independent source to compare experimental measurements with and that the accuracy of the independent sources is known. In the past these independent sources have been hindcast estimates based upon numerical models, ship reports, and buoy measurements. Numerical model hindcast estimates and ship-reported measurements were very useful in initial comparison studies. Buoy measurements are considered to be the best source of comparative data. In this study we will consider only buoys as an independent source of surface measurements.


147

2.2.2. Comparison Considerations. A buoy measurement of wind speed and wave height comprises a point measurement in space and an average over time. The altimeter measurements are essentially averages over space at a specific point in time. That is, although the altimeter data may be averaged over many kilometers of along-track distance, the time required to do this may be less than a second. What this means is that comparisons between the altimeter and buoy data have meaning only if the measurements are nearly coincident in time and space or if the surface is homogeneous with respect to wave height or wind speed over the difference in time and space between the buoy and altimeter measurements. If the altimeter and buoy measurements are required to be exactly coincident in time and space, the common data set becomes extremely small because the altimeter’s groundtrack seldom passes directly over a buoy just when the buoy is reporting a measurement. To increase the common data set to a meaningful number, it is necessary to consider data sets that are not exactly coincident in time and space. Based upon similar wind and wave studies using GEOS-3 satellitemeasurements,the following time/space “window” was used in this study. In regard to space, the point of closest approach (PCA) between the altimeter groundtrack and the buoy location was required to be less than 80 km; for time, the difference between the PCA altimeter measurement and the buoy measurements was required to be less than 1.5 hr. As a result of this time/space window, the two data sets may differbecause of temporal and spatial inhomogeneity in the surface statistics. It is impossible to correct for this problem. There were cases in the data set in which the altimeter-derived wave height showed an along-track gradient in the vicinity of the buoy. There were also a few instances in which the wave height was changing with time as the altimeter groundtrack passed the PCA. However, these cases were not numerous, and furthermore there was no clear pattern to the difference between the two measurement sets. Consequently, although there may be some errors resulting from the noncoincidencein space or time, they are not considered to be a significant error source. With future spaceborne altimeters, this error source can be investigated in much more depth. The buoys used in this comparison (Fig. 1) comprised the NOAA buoy network located along the East, West, and Gulf Coasts of the United States. In addition, limited data were also obtained from Canadian Ocean Station PAPA located at 50”N and 150”W. For reference, the NOAA buoy networks consist of six buoys along the East Coast (Fig. la), three along the Gulf Coast (Fig. lb), and nine along the West Coast (Fig. lc). 2.2.3. Wave-Height Comparisons. The primary effect of ocean waves on the transmitted pulse of a radar altimeter is to stretch the leading edge of this

o

I

0

8

!

SB a

Ki


149

pulse. The slope of the leading edge responds inversely to the height of the waves: the smaller the slope, the higher the waves. As in a previous study (Fedor et al., 1979), we will consider the SWH to be four times the RMS height of the ocean waves. Earliest efforts to validate the capability of the Seasat radar altimeter onboard microprocessor to measure SWH employed a number of wave-measuring sensors including computer hindcast charts of SWH contours routinely distributed by the U.S. Navy Fleet Numerical Oceanographic Central, buoys, aircraft underflights, and intersecting arcs with the GEOS-3 radar altimeter (Tapley et al., 1979). East of these data sets has its own bias and quality of measurement. The most numerous comparisons with in situ measurements are provided by the buoys (along with instruments deployed at Ocean Station PAPA). The altimeter data were also processed using an algorithm developed by one of the authors (Fedor et al., 1979) as an aid in resolving differences between the buoy and on-board processed estimates on SWH. The Seasat preflight specifications for measuring SWH were accuracies within k0.5 m or 10% of SWH, whichever was larger (Townsend, 1980). A fundamental difference between buoy-measured wave heights and satellite altimeter measurements is that the measurements of the former are averaged temporally while the latter are averaged spatially along the satellite subtrack. To provide comparability between the two sets of measurements the altimeter results represent a spatial average over approximately 50 km along-track distance. A scatter diagram comparing the buoy measurements against the results of the on-board processor estimate of SWH is given in Fig. 2. A clear bias is evident for SWH > 2.0 m. It appears that the on-board processor overestimates SWH by 0.5 m in this range. For SWH < 2.0 m there is some scatter, but the values are well within the preflight specifications. The correlation coefficient between the two sets of data is very high, p = 0.945. Despite the obvious bias, the mean and RMS differences between two sets of data are very small, 28 and 38 cm, respectively. A similar diagram in Fig. 3 compares the buoy measurements with the radar altimeter measurements using the Fedor algorithm. A strong bias is no longer evident. The mean and RMS differences are well within the specifications for the measurement: 6 and 29 cm, respectively. Again the correlation coefficientis high, p = 0.965. These results show that the bias is introduced by the on-board processor and is not in the buoy measurements or raw altimeter waveform. The SWH data from the buoys were obtained from the NOAA National Data Buoy Office (NDBO), Bay St. Louis, Mississippi. These data represent standard output products from NDBO. The Ocean Station PAPA wave-rider buoy data (Earle, 1981) were specially processed for the Gulf of Alaska Experiment (GOASEX) (Wilkerson et al., 1979). Figure 4 shows individual

150

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2 m_

C. L. RUFENACH ET AL.

5.0

-

4.0

-

Total # Points = 51 Mean Diff. = 0.279 m RMS Diff. = 0.377 rn Regression Coeff. = 0.838 Y-Intercept = 0.065 rn Correlation Coeff. = 0.945 RMS Diff. from Regr. Line = 0.329 rn

’

/ -/

3.0-

,/ / O /

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0 PAPA

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SWH (On-board Algorithm) (m)

FIG.2. Scatter diagram comparing significant wave-height estimates from the NOAA buoy network and Ocean Station PAPA with estimates from the Seasat altimeter on-board processor (51 observations).

scatter diagrams comparing S WH measurements from three NOAA buoys and Ocean Station PAPA with the altimeter estimate of SWH determined by the Fedor algorithm. The results for the PAPA comparison (Fig. 4a) cover the range of SWH from approximately 1 to 5.5 m. The mean differenceis 7 cm and the RMS difference is 10 cm, both well within the Seasat altimeter specification. No trends or biases are evident in the comparison. The correlation coefficient is 0.998, The comparison of altimeter estimates with NOAA buoy 41001 (Fig. 4b) shows a definite trend, i.e., the buoy underestimates the SWH.

1

6.0

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS 6.

1

I

Total #I Points = 51 Mean Diff. = 0.065 m RMS Diff. = 0.286 rn Regression Coeff. = 0.902 Y-Intercept = 0.123 rn Correlation Coeff. = 0.965 RMS Diff. from Aegr. Line = 0.266 m

5

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FIG.3. Scatter diagram comparing significant wave-height estimates from the NOAA buoy network and Ocean Station PAPA with the Seasat altimeter estimates determined by using the Fedor algorithm (51 observations).

Note that the RMS difference from the regression line is 13 cm compared with the RMS differenceof 29 cm. On the other hand, the comparison for N O A A buoy 44004 (Fig. 4c) does not show a trend. The mean difference between the altimeter estimates and the buoy estimates of SWH is - 7 cm. This is the same order of magnitude as the PAPA comparisons. The scatter is slightly larger for buoy 44004 with an RMS difference of 16 an. Finally, the comparison for N O A A buoy 46005 again shows a trend (Fig. 4d); there is small scatter about the regression line and an RMS difference of 12 cm. These comparisons

152


6.0

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60

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Total U Points = 11 Mean Dill. = 0.066 m RMS Dill. = 0.098 m Regression Coell. = 0.976 Y-Intercept = 0.002 m Correlation Coell. = 0.998 RMS Dilf. from Regr. Line = 0.092 m /

/ / / / P /

-6

z

8

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Total # Points = 13 Mean Difl = 0 196 m RMS Dill = 0 287 m o- Regression Coell = 0 650 Y-Intercept = 0 444 m Correlation Coell = 0 967 4 0- RMS Difl from

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Total U Points = 11 Mean Difl = -0 073m Dill = 0 164 m o- RMS Regression Coell = 0920 Y-Intercept = 0 209 rn Correlation Coell = 0 977 4 0 - RMS Dill from Regr Line = 0 153 m

’/

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2.0 3.0 4.0 5.0 SWH (Fedor Algorithm) (m)

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Total U Points = 8 Mean Dill. = 0.251 m RMS Difl. = 0.263 IT1 5,0- Regression COeff. = 0.713 Y-Intercept = 0.328 m Correlation Coefl. = 0.979 4.0 - RMS Regr. Dill. Line from = 0.121 m

/ // / // / /

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3.0

4.0

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SWH (Fedor Algorithm)

(m)

FIG.4. Scatter diagrams comparing significant wave-height estimates from individual buoys with estimates from the Seasat altimeter determined by using the Fedor algorithm: (a) Ocean Station PAPA (11 observations), (b) NOAA buoy 41001 (13 observations), (c) NOAA buoy 44004 (1 1 observations),(d) NOAA buoy 46005 (8 observations).

appear to show definite differences between the various buoys. The difficulty, of course, is an insufficient number of data samples. The scatter is small enough about the regression lines to indicate that the altimeter is able to detect definite differences between the buoys. This poses the interesting possibility in the future of using radar altimeters to calibrate new or refurbished buoys. 2.2.4. Summary. The Seasat altimeter has been shown to be capable of measuring SWH with a precision that exceeds the prelaunch specifications

6.0


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of f0.5 m or 10% of SWH, whichever is greater. It is recommended that investigators requesting altimeter SWH data from the NOAA Environmental and Data Information Service reduce the data by 0.5 m for SWH greater than 2.0 m. 2.3. Synthetic Aperture Radar 2.3.1. Background. Of the various microwave sensors, SAR is generally considered to contain the greatest amount of ocean surface wave information. SAR has the potential of measuring ocean wavelength, wave direction, and wave height. However, the transfer function between the radar image and the ocean wave field is not, in general, a single one-to-one mapping. Indeed, on occasion, ocean waves are not imaged at all by SAR, implying that no wave information is available. The lack of wave patterns in the radar imagery can be attributed to two effects: first, the contrast, or equivalently, the intensity modulation in the image could be smaller than the system noise level; second, if the transfer function is highly nonlinear a gray image is formed. These limiting cases are dependent on the radar and ocean wave parameters, but typically, the first case occurs for smaller wave heights and longer wavelengths, whereas the second occurs for higher wave heights and shorter wavelengths. SAR is a phase-coherent radar that utilizes the Doppler shift, or equivalently its phase history produced by the uniform platform velocity, to locate targets in the flight direction. If the targets move during the time interval required to form the phase history, then its history is modified and the target locations differ from the ones expected for stationary targets. Suppose that an ensemble of targets are moving uniformly in the flight direction; then there is no relative position error between the targets. However, suppose that targets exist that are spatially separated in the flight direction with different radial velocity components. Then the radar senses differential Doppler histories in the flight direction which are encountered when the orbital motion of the long ocean waves travel in the flight direction. The wavelike patterns observed in SAR imagery can be caused by two mechanisms: (1) the usual, “real” cross-section modulation Acr due to tilt and hydrodynamic modulation by the long ocean waves; and (2) an “artificial” cross-section modulation due to ocean wave motion-orbital velocity and acceleration of the long waves. Furthermore, the importance of motion could be dominated by the stochastic character of wind waves. Random drifting scatterers riding on the long waves can cause a dispersion of the radial velocities of these scatters, which can degrade the image formation process. A measure of this randomness of the scene is the scene coherence time. No conclusive measurements are currently available that establish the dominant

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mechanism. However, ocean wave motion is most important for long ocean waves traveling along the direction of radar platform motion, while real crossscatterers riding on the long waves can cause a dispersion of the radial platform motion. Synthetic aperture radar itself has an inherent integration time required to resolve targets along the flight direction (azimuth resolution). This integration time can be modified during processing; however, the azimuth resolution is also modified. In other words, the integration time, or equivalently the time interval that a target is illuminated by the radar beam, is longest if the full azimuth bandwidth is utilized in processing. Alternately, if only a fraction of the total available bandwidth is used, then the resolution is degraded, or equivalently a shorter effective integration time, However, for this case, only a fraction of the total available bandwidth is available to form the image, implying that several fractional bandwidth subimages can be incoherently averaged over a period equal to the full integration time needed to form the total image. This type of processing is called mixed integration or multiplelook processing, which always leads to a degraded resolution over the onelook (full-bandwidth)processing. Indeed, if the full bandwidth is utilized in image formation of a stationary scene, then multiple-look processing can be used to decrease the noisy background (speckle)in the image without loss of scene detail, provided the scene detail is not undersampled. However, for a moving ocean surface, the optimum processing is more complicated. The effect of moving point targets on SAR is well understood and has been investigated over the last 10 years (e.g., Raney, 1971; Shuchman et al., 1981). However, the extension of these point-target results to imaging of ocean waves (distributed scene) is complicated since not only must the results be extended from a point target to imaging of a distributed scene, but the ocean motion, which includes the scene coherence and long-wave parameters (orbital motion and phase velocity), must be considered. The interaction of the SAR with these ocean wave motions has been investigated by numerous authors: e.g., Larson et al. (1976), Alpers and Rufenach (1979), Shuchman et al. (1978), Jain (1978), Tomiyasu (1978), Rufenach and Alpers (1981), Valenzuela (1980), Harger (1980), Alpers et al. (1981) Raney (1980), Hasselmann (1980), and Shuchman (1980). However, the radar interaction mechanisms due to these motions are sometimes interpreted differently by different authors or their underlying assumptions may differ on occasion. For example, Valenzuela (1980), Jain (198 l), and Shuchman and Zelenka (1978) concluded that the influenceof the long-wave phase velocity on the image formation is important whereas others such as Alpers et al. (1981) argued that this velocity is unimportant or second order. The relative importance of the randomness of the ocean wave scene compared to the systematic orbital motion as the dominant mechanism for image degradation is also interpreted differently.


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Raney (1980) investigated how a scene with a random fade is imaged by SAR, deriving expressions for azimuth resolution in terms of scene coherence. Rufenach and Alpers (198 1 ) derived an expression dependent on both scene coherence and orbital motion. They argued that the orbital motion dominates and that the relevant scene coherence time is on the order of 1.0 sec in contrast to 10 to 100 msec suggested by Raney. Furthermore, Swift and Wilson (1979) and Alpers and Rufenach (1979) have suggested that the secondharmonic component of the ocean waves may be present in the imagery under certain conditions. The limited available imagery has not been interpreted in terms of this second-harmonic component. The real cross-sectional modulation is maximum for the long ocean waves traveling in the range direction, in contrast to the maximum sensitivity for ocean surface motions, which occurs for motion along the direction of flight, the cross-range direction. A quantitative two-dimensional description of the cross-sectionalmodulation including both tilt and hydrodynamics is not well developed. Some experiments specifically designed to measure the modulation transfer functions on the open ocean have been performed (e.g., Alpers and Jones, 1978; Wright er al., 1980). These experiments show that the dominant scattering mechanism is two-scale Bragg scattering as originally described by Wright (1968) and Bass et al. (1968). This model is based on a surface wave field divided into two regions of different wavelength scales: short waves represented by the Bragg resonant condition; and long waves represented locally by tangent surface segments called facets of dimension D, which are small compared with the long wavelength but large compared with the short wavelength. Therefore,the Bragg scattering theory can be applied in the local reference system of moving tilted facets. This two-scale model describes cross-sectionalmodulation caused by tilt and hydrodynamic effects. The tilt modulation is due to changes in the local incidence angle induced by the slope of the long ocean waves. The hydrodynamic modulation is due to hydrodynamic interaction between the short Bragg scattering waves and the long waves, which results in an asymmetrical distribution of the short waves relative to the crests of the long-wave field (e.g., Keller and Wright, 1975). 2.3.2. Theory 2.3.2.1. ArtiJcial modulation. Suppose that a radar platform is moving along the x direction at a range r with a velocity V. Furthermore, assume that a single point target located at xo, yo in a horizontal plane is riding on a monochromatic long ocean wave with wavenumber k = (kx,k,,), angular frequency w, and wave amplitude with the wave field traveling at an angle with respect to the flight direction. An artist’s conception of the relevant geometry is given in Fig. 5, including the angle of incidence 8. The form of the

co

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C. L.RUFENACH ET AL. z

I

b Serest

Satellite

z

, ~

/

SAR Antenna /

/

/

R

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Phase Histories of Points A & 0 7 -

\

a

FIG.5 . Geometry for observation of the ocean surface by Seasat synthetic aperture radar. In (a) the geometry for Seasat SAR observations of the ocean surface is defined in the x y plane; in particular, the observations are of long gravity waves at a point (x,y) traveling at an angle Q, with respect to the SAR flight path and having a wave vector k, where k = 2n/L and L is the ocean wavelength. In (b) note that SAR image resolution along the y or range direction is obtained by a short radar pulse length. In (c) resolution along the x or azimuth direction is obtained by observing the different phase histories of points A and B.

point-target intensity response at the image plane ix(x,xo) along the flight direction, following Rufenach and Alpers (1981), is i,(x,xo)a =-T:~erp[-'I

(15)

V ur

where x and xo are the azimuthal positions of the target in the image plane and on the surface, respectively. T, = IZr/(2Vpa)is the integration time, pa = v/Bais the theoretical azimuthal resolution for a stationary target, Bais the azimuthal bandwidth, and

is the resolution of a point target, due to harmonic motion of the long ocean waves. The radial components of orbital velocity and acceleration are u, and a,, respectively, and 1 is the radar wavelength. The radar transmits pulses as it moves along the x direction. These pulses travel down to the surface point target and return to the radar receiver. The pulses are modulated with a linear


157

frequency called a chirp modulation, which can be represented by a quadratic variation of phase; the 'radar geometry causes a natural quadratic phase variation in the azimuth or along-track direction. Therefore, the surface is scanned in the range direction (r) at one-half the speed of light and in the azimuth direction (x) at a much slower platform velocity I/. If the same procedure is followed as in developing Eq. (1 5 ) and if coupling between the two orthogonal intensities is neglected (Hasselmann, 1980), the point-target intensity response ir(r,ro) along the range direction is where r and ro are the range positions of the target in the image plane and on the surface, respectively. The transmitted pulse width is T,, p; = c/B, is the one-way range resolution, c is the velocity of light, and B, is the radar system bandwidth. For the SAR flown on board the Seasat satellite, Y 2 7 km sec-', Ba 2 1kHz, pa 2 6 m,and pr = 2pi = 2c/B, (Jordan, 1980). Note that pr is the slant range resolution and pa is the one-look resolution or equivalently the fullbandwidth resolution. The conversion from slant range r to ground range y is p,, = pr/sin 8, where the angle of incidence 8 E 20" for Seasat. The smearing or degradation in azimuthal resolution caused by ocean wave motion, as given by Eq. (16),has been extended to include the influence of the number of multiple looks N, and the scene coherence time z, (Rufenach and Alpers, 1981):

Suppose that the scene is coherent, Tx/z,= 0, and that the orbital acceleration is negligibly small, (n/AN)T:ar >C

This simplification of Eqs. (46)-(48) is in agreement with Alpers and Hasselmann (1978) and Alpers et al. (1981). However, Eq. (49) gives a maximum modulation of 4.5, whereas the measurements show modulations of 5 to 20. These measurements are much larger than expected based on Eq. (47). Therefore, the theory needs to be reexamined with the hope of bringing it into better agreement with the larger measured values. One possibility is to include a wind-dependent factor for the spectral density at the shorter gravity waves while another is to reexamine the tilt-modulation theory in more detail. Available measurements show a wind dependence for these short gravity waves (Wright and Keller, 1971).

2.3.3 Wave Measurements, Spaceborne SAR has provided quantitative information on dominant wavelength and direction of the long-surface-wave field. However, before this imagery can be fully exploited to provide routine synoptic information on the long-wave field, both the conditions under which the wave field causes wave-like patterns to form in the imagery must be quantitatively defined, and if the wave spectra are required, then the conditions when the transfer function between the ocean wave field and the image intensity is linear must be specified. In addition, an optimum SAR system and processor should be investigated for future satellite systems. This section describes field observations of ocean surface waves by Seasat and airborne SAR, with emphasis on the satellite measurements. The ocean surface wave information extracted from SAR is conveniently separated into three physical quantities: the dominant ocean wavelength, wave direction, and the directional wave spectra. First, the accuracy of SAR-derived dominant wavelength and direction estimates must be considered, given the existence of dominant ocean wave signatures in a SAR image. Second, the dependence of the detectability of these dominant wave systems on the scene parameters needs to be defined. The scene parameters are the oceanographic, meteorological, radar, and geometric parameters that characterize a particular measurement. Third, a

166


description of the transfer function relating the ocean wave directional spectrum to a SAR image intensity spectrum is needed. The first two categories are discussed in Sections 2.3.3.1 and 2.3.3.2, and the third in Section 2.3.3.3. These three categories are, of course, related. In fact, it is clear that precise knowledge of the transfer function could yield answers to the first two categories. Nonetheless, this artificial separation has proven useful. 2.3.3.1. Dominant wavelength and direction. Straightforward comparisons of in situ ocean measurements and radar measurements are reported here from three major field experiments: GOASEX (the Gulf of Alaska Seasat Experiment), JASIN (the Joint Air-Sea Interaction experiment), and Duck-X (an experiment performed near Duck, North Carolina). These comparisons have been summarized by Vesecky and Stewart (1982)in plots presented in Figs. 8 and 9. Details of the data reduction and analysis for each of the three individual data sets are given by Gonzalez et al. (1981), Vesecky and Stewart (1982),and Beal(l981). Each data point is the resuit of two different procedures. The first procedure, used to obtain in situ estimates,involved computing a wavelength by inserting the observed peak frequency of the ocean wave spectra into the

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400

0

0 GOASEX 0 Duck - X 0 JASIN

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300 -

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FIG.8. Comparison of dominant ocean wavelength as estimated by Seasat SAR and by surface P/R buoys. The average percentage difference between the SAR and insitu estimates is f 12%.(From Vesecky and Stewart, 1982.)


100"

2000

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Direction by Surface Observation (degrees true) FIG.9. Comparison of dominant ocean wave direction as estimated by Seasat SAR and by surface P/R buoys. The SAR estimates show no significant bias with respect to the surface estimates, and the mean difference between the two measurements is f 15". (From Vesecky and Stewart, 1982.)

appropriate ocean surface gravity-wave dispersion relation. Most of these in situ observations were made with wave-rider buoys, which provided only onedimensional energy spectra; only a few estimates of the directional energy distribution were available from P/R buoys. This explains the fewer number of data points plotted in Fig. 9. The second procedure involved taking a twodimensional optical Fourier transform (OFT) or digital Fourier transform (DFT) of a portion of SAR imagery corresponding to a 10- to 25-km2 area centered at or near the position of the in situ wave measurements. The wavelength and direction associated with each local peak in the resulting image intensity spectra were then assumed to be the dominant wave systems present and were paired with the corresponding surface observations to provide each data point in Figs. 8 and 9. Examples of such a Seasat ocean wave scene, an OFT and DFT of that scene, and a P/R buoy record taken coincident with the SAR measurements are presented in Fig. 10. The limited available measurements show that the SAR-provided dominant wavelength and direction estimates yield more detailed information than the corresponding in situ estimates. There are several aspects of the data taken in tota that support this view. The agreement between the limited available SAR and in situ measurements is within the accuracy of the surface observations: the average percentage difference between estimates is +12%, and the mean difference in wave

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plr, BUOY Rev I126 I3 Sept 1978

1730 GMT Significant wave height 2 7 rn Wind speed 3 8 m h c Wind direction 2 4 4 '

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FIG.10. (a)The 15 x 15-km SAR sceneacquired on September 13,1978, during GOASEX,and optically processed by the Jet Propulsion Laboratory. (b) Optical (OFT) and (c) digital (DFT) Fourier transforms of the SAR scene in (a). In each representation. distance from the center is proportional to wavenumber k = 2n/L. The two local peaks evident in each transform represent two dominant wave systems characterized by similar wavenumbers (approximately 0.0225 and 0.0250),but whose direction differs by about 20". (d) P/R buoy spectral estimates S(k) computed from data acquired coincident with the SAR imagery of (a). Note that, in contrast to the Fourier transforms, only one dominant wave system is indicated at wavenumber k z 0.024 rad m-'. The original data provides spectral estimatesin terms of the temporal wave frequency;these data have been transformed to wavenumber space using the Jacobian appropriate to the linear surface gravity wave dispersion relationship. The wavenumbers in (b),(c),and (d) are plotted in terms of spatial frequency K = l/L.

direction estimates is f 15%. Recent simulated SAR wave spectra suggest that the dominant ocean wavelength will be shifted (biased) toward longer wavelengths over a certain range of ocean/radar parameters (Alpers, 1983). This range corresponds to a nonlinear transfer function, which requires a


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Monte Carlo method to obtain the SAR spectra. Figure 8 tends to support this bias toward longer wavelengths for part of the SAR-inferred wavelengths. Additional analysis of measurements are required to see if this bias may be more significant on occasion. Biases in the wave direction should also be present since only the azimuthal SAR wavelength is biased. Finally, there are strong indications that SAR gives greater detail than the P/R buoys. Two examples in point are presented in Figs. 10 and 11. In each case, the sea state was evidently characterized by two dominant wave systems somewhat similar in wavenumber and direction. The SAR detected both systems, which the P/R buoy was unable to resolve. It is conceivable that the indications of a multiple wave system in the cases are not real, and are instead an artifact of the SAR system. This does not seem likely, however, since there is good agreement between a number of SAR and in situ observations of single dominant wave systems that were taken under similar conditions. The accuracies claimed for SAR assume that the scene parameters are favorable, in the sense that wave signatures are, in fact, present in the imagery. The important question of what constitutes favorable scene parameters for detectability is discussed below. 2.3.3.2. Detectability. A number of scene parameters may affect the detectability of a dominant-wave system: among them are oceanographic and meteorological parameters (wavelength, significant wave height, wind speed

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Wave Directional Distribution JASlN FIA Area 4 Aug 1978 0600 UT Buoy

SAR Image. Rev 547

Atlantis II Pitch/Roll Buoy

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FIG.11. Comparison of the ocean wave directional distribution derived from P/R buoy measurements and the image intensity spectrum obtained as the result of performing the digital Fourier transform of a Seasat SAR image. The distribution refers to ocean wavelengths near the dominant wavelength of about 170 m. (From Vesecky and Stewart, 1982.)

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and direction, atmospheric stability);radar parameters (frequency,resolution, integration time); and geometric parameters (range, incidence angle, and radar-wave and radar-wind angles). For this reason a relevant theory and the associated dependences are required for a quantitative understanding of the image formation process. A good measure of the quality of SAR wave imagery is the fractional modulation of the backscattered intensity. An example which illustrates the different image formation processes is the complex ocean wave field present during the GOASEX experiment on September 26, 1978. Three separate identifiable wave systems traveling in different directions with different wavelengths (nominally 250, 130 and 20 m) were directed by surface measurements (Gonzelez et al., 1981). An analysis of these surface measurements indicates approximately equal energies in each system, SWH z 0.6 m, since the total SWH z 1.1 m. The longer wavelength (250-m) system was not detected in the satellite or aircraft imagery whereas the shorter wavelength (130 m) system was detected by both the satellite and aircraft radars. Furthermore, the shortest wavelength (20-m) system was detected by the aircraft radar whose resolution is pa = 3 m, whereas the satellite radar whose resolution was paN= 25 m did not detect this sytem as expected because of resolution limitations. The detectability is dependent on both the real and artificial modulation with significant azimuthal degradation occurring when ocean wave motions are important. These resolution limitations are not easily obtained when the wave motion is important since a closed form expression is unavailable. The modulation and hence the degradation is given by substituting Eqs. (15)-( 18) into Eq. (20). The degradation is most important for azimuth traveling waves (4 = 00). This case is illustrated in Rufenach and Alpers (1981) for scene parameters similar to the Seasat (L-band) radar with a range of ocean wave lengths and wave heights. For the complex wave field observed on September 26 c0 is approximately 0.21 m, based on narrow wave systems. Therefore, a small azimuthal modulation is expected for the 250-m system and a larger modulation is expected for the 130-m system based on extrapolating the results of Rufenach and Alpers. Furthermore, a significant degradation at I = 0.25 m is expected for the 20-m system. The real modulation expressions [see Eq. (29)] show increased detectability as the long-wave slope increases. The wave systems on September 26 were traveling primarily in the range direction (@ > 45") for both the Seasat and aircraft radar imagery selected here (r/V ratios within a factor of 3). The important result is that the 250-m wave system was not detected by either radar. This nondetection is interpreted as insufficientwave slope for real modulation detection whereas the 130-m wave system is interpreted as sufficient wave slope for detection. An example which illustrates the importance of ocean motion effects on detectability is the case of azimuth-traveling ocean waves. Indeed, theory and


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measurements suggest that relatively low wave heights or wave slopes are required to detect these waves. Therefore, it is significant that the Seasat analysis detected only two azimuthal wave systems generated by distant storms that were hundreds of kilometers away from the measurements and resulted in nearly monochromatic ocean wave swell. Thus, one of these wave systems, approximately 240 m in length, traveling in azimuth and apparently generated about 2 days earlier by Hurricane Gilma, was detected off the coast of Baja California, after traveling some 2000 km across the northeast Pacific (Fig. 12). In the second case, Gonzalez et al. (1981) reported that a 500-m azimuth-traveling wave, evidently generated in the Southern Ocean (Fig. 13), was detected in the northeast Pacific Ocean. These nearly monochromatic wave systems evidently experienced less smearing due to nonlinear mapping than the wider spectral wave systems usually associated with wind-driven wave fields. 125" I

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FIG.12. Seasat SAR detection of azimuth traveling waves generated by Hurricane Gilma approximately 2 days earlier at a point about 2000 km away. L = 240 m and @ 2 0.

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FIG.13. Digital Fourier transform of SAR scene acquired August 13,1978, on Seasat Rev 674 at a point in the northeast Pacific approximately 17"N and 115"W. The azimuth orientation is straight down in this presentation, and the spectral peak at k = 1/2nL = 6.4 x radm-' or k = 1/L = 0.002 m-', corrpponding to a 500-m-long azimuth-traveling wave believed to have originated in the South Pacific. The radial axis is in units of.h-(m-').

A dual-frequency SAR (X and L band) was mounted on the Canadian CV580 aircraft and flown in ihe Puget Sound region during the GOASEX experiment (September 1978). This SAR observed ocean waves traveling in the azimuth direction on flight lines (1) and (4) as illustrated in Fig. 14. The

GOASEX and Seasat results illustrate that azimuth waves can be detected by both aircraft and satellites. 2.3.3.3. Directional wave spectrum. A quantitative description of ocean surface wave fields is needed for verifying and/or forecasting input to marine wave models (Earle, 1981). Ideally, the two-dimensional wave spectrum with fine resolution is desired. In practice, few measurements of the directional


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FIG.14. CV-580 aircraft and satellite SAR wave measurements during September 23, 1978; GOASEX Rev 1269. Airborne observations were made with six different viewing angles as illustrated. @,isthe true azimuth heading and @A is the long-wave azimuth heading relative to the aircraft direction (see Fig. 5). From P/R bouy: L = 151 m; CP = 93". Seasat data, optical Fourier transform; CV-580 data, digita1 Fourier transform.

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properties of the waves are available in the region of interest and at the time desired, Therefore an operational imaging radar which consistently images the long ocean waves would be useful since at least the dominant wavelength and direction could be extracted. Eventually, when the nature of the transfer function is better understood and optimum radar systems are developed for wave imaging, it may be possible to provide the directional wave spectrum. A spaceborne SAR could provide global directional information as well as measurements useful in determining the spatial evolution of the wave field (Beal, 1981;Shuchman and Kasischke, 1981). This type of measurement could lead to a better understanding of wave dynamics including interaction with current boundaries boundaries and bathymetry effects. To extract directional wave spectra using SAR one must correct for radar system distortions including the effect of the finite resolution of stationary scatterers as well as the nonlinear transformation between the ocean wave field and the intensity modulation in the image. Past oceanographic experiments have demonstrated that SAR can provide information on the general shape of the directional wave spectrum. Furthermore, wave measurements can be obtained under a variety of wave conditions including hostile environments such as hurricanes (Elachi et al. 1977). Monaldo and Beal (1981) have corrected the intensity wave spectra using Seasat imagery. This finite resolution is caused by a faster spectral decrease than expected based on an infinitesimally small resolution at the high wavenumbers. In particular, if the azimuthal antenna beamwidth is approximated by a uniform rectangular be’am,then the point target response is of the form sin x’/x’, where the width of the spectrum is a measure of the resolution and the shape is a measure of the correction in the wave spectrum. Monaldo and Beal(l981) and Beal(l981) show that this correction is of a Gaussian form with width k, 12 ~ / 6 0m-l. Unfortunately, once the correction has been performed, the intensity spectral estimates may show azimuthal wavenumber smearing directly related to the local sea state. In other words, the corrected spectra show azimuthal smearing that is most pronounced at the high wind speeds, consistent with the theory given by Eq. (18),provided the high winds imply larger long waves and orbital motions. Therefore, if the intensity spectral estimates are used to extract qualitative information about the ocean wave spectrum, the high-frequency portion of this intensity spectrum, say wavelengths shorter than 100 m, would underestimate the ocean wave spectral estimates because of the radar’s finite resolution (Beal, 1981). Spectral analyses of wavelike patterns in SAR imagery have been performed and compared with P/R buoy measurements and other surface measurements in or near the area of radar measurements. McLeish et al. (1980) and Vesecky and Stewart (1982)provide two of the more comprehensive comparisons that


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have been undertaken. McLeish et al. showed a radar directional spectrum that was narrower than the fitted model based on the P/R buoy estimates. It was also noted that the radar system directional resolution was higher than the P/R buoy resolution. Wave-height spectra obtained by a laser profile meter exhibit spectral shapes similar to the radar-extracted intensity spectra on most occasions. However, some significant discrepancies are present on occasion. Vesecky and Stewart suggest that the local wind has little effect on the wave formation process in the imagery except for long waves traveling in regions of very low surface winds. They claim that the Bragg (short) waves may not scatter sufficient energy at these low wind speeds, say 2 m sec-' or less. Furthermore Vecesky and Stewart have suggested the intensity spectra more closely approximate the wave-height spectra than the wave-slope spectra, after including a correction for finite resolution. 2.3.4. Conclusions. Among the various microwave radars developed to measure ocean waves from either aircraft or satellites, synthetic aperture radar imagery is considered to contain the greatest amount of information. When ocean wavelike patterns are observed in the imagery they are in agreement with surface measurements of dominant wavelength and direction. The ocean surface waves are observed more often when they are traveling perpendicular to the flight direction (in the range direction). Imagery of ocean waves is smeared or degraded in the flight direction because of the coherent integration time (- 1.0 sec) required to form the image. For this situation, wave motion causes the modulation transfer function to become nonlinear unless orbital acceleration and velocity are negligibly small. This nonlinearity imposes a fundamental limitation on extracting the directional wave spectrum from SAR. Linearity is required for interpreting the intensity (radar) modulation in terms of the directional wave spectrum. The intensity modulation is caused by two mechanisms: first, artificial modulation due to the orbital motions described above; and second, the real cross-sectional modulation due to the geometric tilting and hydrodynamic straining of the Bragg scatterers riding on the long ocean waves. Artificial modulation is dominant for the long ocean waves traveling along the flight direction, whereas the real modulation dominates for waves traveling cross-flight (in the range direction). The modulation-transfer function is likely to be nonlinear except for a narrow cone of long-wave directions centered on the range direction. If possible, future SAR systems should be designed in an attempt to ensure linearity and detection of the long waves. This means a radar with the shortest possible radar wavelength to minimize the degraded azimuth resolution caused by orbital acceleration.

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L. RUFENACH ET AL.

3. OCEAN INTERNAL WAVES 3.1. Observations

Internal gravity waves are frequently observed in stratified waters on the continental shelf as highly coherent groups having well-defined but variable wavelengths and generally propagating shoreward. It is well known that there are often surface signatures accompanying the underlying oscillations which take the form of regions of either enhanced or reduced small-scale roughness, or both, depending upon the amplitude and phase of the waves. Such surface signatures have been observed with a variety of remote sensors, including photographs and scanner images on satellites, as well as synthetic aperture radar images made from aircraft (Apel et al., 1975a,b, 1976; Elachi and Apel, 1976). The Seasat spacecraft, launched in June 1978, was equipped with a SAR having a wavelength of 25 cm, a mean incidence angle of 23.4, and a spatial resolution of 25 m. This instrument was originally intended to provide directional spectra of long-length surface gravity waves, especially under storm conditions. However, SAR also images many features on the sea surface that modulate the short-wavelength (-30 cm) ocean roughness, since waves of this length are effectively Bragg scatterers under the existing radar illumination conditions. It was therefore expected that the radar would image surface manifestations of internal waves (Brown et al., 1975). Such has apparently proved to be the case. Figure 15a shows a map of the Baja California region in the vicinity of Bahia San Juanico on the Pacific side of the peninsula. Figure 15b illustrates an optically processed SAR image of that region made during pass 150 on July 7,1978, at approximately 04:OO local time. The general low reflectance of the sea surface indicates that very light wind conditions existed during this predawn pass. The image reveals a considerable number of periodic striations on the water surface. Superimposed on the map of Fig. 15a is a line-drawing interpretation of the features on Fig. 15b that displays the periodic nature of both the striations within the packets and the intervals between packets. Visible are several wave groups located landward of the 200-m isobath, oriented approximately parallel to the depth contours. These features are interpreted as being due to underlying quasiperiodic internal wave fields. An analysis of the packets visible in the SAR image yields the following characteristics, which are deduced by direct measurement of the image in Fig. 15b: 1. The waves occur in well-defined packets or groups, typically four in

5. SURFACE AND INTERNAL OCEAN WAVE OBSERVATIONS 26'

I

I

0

177

112"

1

\

10 20 30 40 50

/ 25.

1

113"

FIG.15a. Map and line-drawinginterpretation of several packets of internal waves in Bahia San Juanico off Baja California,as observed on July 7, 1978, with the Seasat SAR. Packets are labeled lS, 2 s,...; lN, 2N,....

number, propagating shoreward and separated by an average interpacket distance D = 18.7 km, with a range of 15 to 23 km. 2. The crests are generally oriented along isobaths and have an average leading-crest length in a packet, C = 55 km, with a range of 36 to 76 km. 3. Within a given packet, the crest lengths are longest at the front of the wave group and shortest at the rear. 4. For the packets that are visible, the individual wavelengths I average 390 m in length, with a range of 200 to 1600 m. The waves exhibit a general decrease in wavelength from the front to the rear of the packet. This feature is illustrated on Fig. 16 for the four groups located along the south shore of Bahia San Juanico and numbered sequentially from west to east as 1s to 4s. 5. For the packets, the average angular spread of propagation direction A8 is 12",with a range of 7 to 20". The individual packet characteristics may be represented to a reasonable extent by a spectral analysis of the surface signature of a given packet. Figure 17 illustrates an enlargement of a portion of the well-defined packet 2s located near Santo Domingo del Pacifico. This group is propagating toward shore

FIG.15b. SAR image for a portion of the region shown in Fig. 15% illustrating quasiperiodic variations in surface radar reflectivit) associated with underlying internal waves.


179

.

\ Y

&

400

P,

.s T

O

O

1 2 3 4 1 2 3 D I S T A N C E FROM FRONT O F W A V E PACKET s (km)

FIG.16. Variationsin wavelengthsof internal waves from front to rear of four packets located along the southeastern portion of Bahia San Juanico. Packets are numbered sequentially from west (seaward)to east as shown in Fig. 15a. A monotonic decrease in wavelength is a ubiquitous feature of nonlinear internal waves.

at an angle of about 78" true (approximately ENE). The image shown is 14.54 km on a side and has a fundamental spatial resolution of 25 m. The two largest visible waves have lengths of 1000 to 1600 m, while the trailing waves are near 400 m in length. This information is summarized succinctly in the two-dimensional Fourier transform' of the image sector in Fig. 17, as shown in Fig. 18a. The concentrations of image intensity, i.e., radar backscatter, are located at angles of approximately 78 and 258"T, with full widths at half-maximum, (A& 2 12". The five dominant wavenumbers are at 0.35,0.71, 1.2, 1.8, and 2.6 cyc km-', coinciding with the wavelengths apparent in the visual image (the peak at 0.35 cyc km-' may well be an artifact). Figure 18b shows the angular distribution of the two-dimensional transform, taken along a constant wavenumber of 1.19 f 0.10 cyc km-'; Fig. 18c gives the wavenumber distribution obtained from a scan at a constant angle of 78 & 5". The latter clearly shows the multiple-peaked wave number distribution dominated by peaks at the five wavenumbers mentioned above. Although this spectral analysis strictly represents the spatial variation of only backscattered radar power, it has been shown by Ape1 and co-workers

' The fundamental pixel size used is 28.2 m, and the image and transform arrays have 512 pixels on a side.

180


FIG.17 Enlargement of packet 2s seen in Fig. 15a, showing details of a portion of the packet. The section shown is approximately 14.54 km on a side and contains 2 waves having lengths of about 1400m, followed by approximately 10waves with lengths less than 500 m. Dark stripes are regions of increased surface roughness overlying internal wave troughs, followed by light regions caused by smoother-than-average surface wave fields.

that the surface roughness overlying coherent internal waves bears a definite phase relationship to the underlying internal wave field (Apel and Holbrook, 1980). In particular, the rough surface regions appear to be phase-locked to the downgoing or trough phases of the internal oscillations; these are followed by an anomalously smooth surface region immediately behind the rough portion. Indications of this rough-to-smooth transition are clearly visible in the long waves of Fig. 17 (Elachi and Apel, 1976). Thus the patterns of surface roughness and smoothness serve to delineate the underlying internal wave fields, at least insofar as the fundamental internal spatial frequencies are concerned, and a spectral analysis of the surface signatures such as presented here may reasonably be taken as representing the dominant internal wavenumbers and directions.


181

Nor1 h

FIG.18a. Two-dimensional digital Fourier transform of the SAR image of packet 2s (Fig. 17), showing spatial frequencies of surface roughness variations as a function of east (k,) and north (k,) components of the wave vector. The three largest maxima correspond to the variable lengths of waves seen in Fig. 17. This spectrum is related to the distribution of dominant wave vectors in the underlying internal waves.

3.2. Ancillary Oceanographic Information Other properties of the ocean in this region that are pertinent to internal wave generation are the vertical density distribution, the characteristics of tidal currents, and the presence of bathymetric features that protrude into the thermocline. Figure 19 shows vertical profiles of density anomaly, a, = ( p - 1) x lo’, and Brunt-Vaisala frequency N , where

N 2= -(g/P)(aP/W and where p is the density and g = 9.8 m sec-’. The profiles were calculated from historical data for July 1978, in the region of interest (Bell et al., 1974). A shallow mixed layer of 20 to 30 m in depth and a maximum buoyancy frequency Nm8,/2n of approximately 8.3 cyc hr-l are apparent. Tidal height data for July 1978 are shown in Fig. 20, for the tide station at Bahia Magdalena. The tidal heights show mixed semidiurnal and diurnal tides with a range approaching 1.4 m. On continental shelf waters, such amplitudes may generate tidal currents in excess of 1 knot; indeed, tidal current tables give ebb and flood speeds of 0.50 and 0.65 m sec-’, respectively, on July 7.

1.50

>-

I

k

1

1

= 1.19 f .ICyC km-'

In

I\

z

: W

1.00

I(e)

2 l-

0.50

a

J

w

f

a

o.oo--d---

1 ' L---

1

3.0

t v)

: 2

z

2.0

I (Ikl)

; a

1.0

J

w

IL

Ox)

2

4

8

6

2

WAVENUMBER

12

10

14

(cycles km-'1

FIG.18band c. (b) Angular distribution of surface roughness variations obtained by evaluating Fig. 18a along a constant wavenumber. The narrowness of the angular spread indicates a nearuniform direction of propagation for this section of the packet. (c) Wavenumber distributions of surface roughness variations obtained by evaluating Fig. 18a along a constant angle. The peaks correspond to the dominant wavenumbers visible in Fig. 17.

0

1

1

1

'

1

'

"

\

\

$

1

-.

50-

91

d 4

100-

X

klu

150-

200

I

I

I

FIG.19. Historical density anomaly and Brunt-Vaisala frequency during July for the region of Fig. 15. A shallow mixed layer and highly stable water characterize the upper ocean.


183

Y

i=

T I M E t (day)

FIG.20. Tidal heights for early July 1978, for the region of Fig. 15. Associated ebb and flood current speeds are near 0.50 to 0.65 m sec-', respectively.

Bathymetric charts for the region of the Seasat image illustrate a broad continental shelf of under 200 m in depth, extending some 80 km to sea. A sequence of offshorebanks whose depths are as shallow as 15 m lie beyond the shelf break, and their positions with respect to the internal wave packets suggest that these play some role in the wave-generation process (Halpern, 1971; Haury et al., 1979; Farmer and Smith, 1978). 3.3. Interpretation

To explain the characteristicsof the surface signatures,it is assumed that the waves are generated on a semidiurnal basis by tidal flow over the offshore banks, or along the shelf break near 200 m, through one of two possible mechanisms. The first, shear flow instability, invokes the existence of a strongly baroclinic tidal flow in the vicinity of the banks or the shelf for a few hours near the peak tidal flow onshore, during which time an internal wave packet is generated and propagates shoreward (Proni et al., 1978). This process is repeated approximately every 12; or 25 hr, depending on the semidiurnal or diurnal nature of the tides. The linear theory for shear flow instability shows reasonably good agreement with experiment for internal waves at the continental shelf edge south of Long Island (Tsai and Apel, 1979). The second mechanism is similar to the first in terms of the tidal periodicity of the packets, except that it assumes the wave packet is generated during offshore flow as a lee wave consisting of a few to several oscillations. The condition for generation is that the flow speed over the bank should exceed the intrinsic speed of propagation for internal waves. The wave packet may be linear or nonlinear at its point of formation, depending on the Froude number of the flow (Maxworthy, 1979). Either of these two mechanisms may result in a series of nonlinear, coherent internal wave packets, which then propagate shoreward as radiation fields, to be absorbed via breaking and turbulence in water whose depth approaches the mixed-layer depth. The problem at hand is to deduce something of the

184


generation and propagation characteristics of the individual packets from the SAR images through the application of a simple hydrodynamic theory. 3.4. Theoretical Considerations

The propagation of weakly nonlinear internal waves in a stratified medium is known to be governed by the Korteweg-deVries equation or its generalizations (Keulegan, 1953; Benjamin, 1966,1967; Ono, 1975; Kubota et al., 1978). For a two-dimensional ( x , z ) wave field (with z downward), the displacement q(x,z, t ) within the thermocline may be written as ~ ( xz,, t ) = W ( z ) A ( x t, )

(50)

where A ( x , t ) is the separation function for the wave amplitude given as a departure from an undisturbed streamline, and W(z) is the vertical eigenfunction or structure function, valid in this depth regime for either the linear or nonlinear problem. For waves with long horizontal scales L,and small but finite amplitudes such that H / L and A / H are both small compared to unity, the double amplitude A(x, t ) satisfies the Korteweg-deVries equation, A,

+ co(A, + yA,,, + otAA,) = 0

(51)

where co is the phase (and group) speed of infinitesimal-amplitudewaves at zero wavenumber and x, z, and t subscripts refer to partial derivatives. The environmental parameters y and a are interpreted below. In the absence of a mean current, the structure function W(z)satisfies the eigenvalue equation,

W,,,+ k:"2(z)/o,z

- 11 W,= 0

where k, = 2 ~ / 1 ,is the linear wave number, w, is the radian frequency for mode n, and N ( z ) is the Brunt-Vaisala frequency as defined previously. The boundary conditions for a rigid lid model have W(0)= W ( H )= 0, where H is the depth of water. Solutions to Eq. (52) yield both eigenfunctions W,(az)and eigenvalues w,(k), which define families of the dispersion relation o = o,(k), with n = 1, 2,.. .. Figure 21 shows examples of the two lowest-order eigenfunctions obtained using the Brunt-Vaisala profile of Fig. 19 and evaluated at a wavenumber k1/2x = 2.6 cyc km-'. Figure 22 illustrates the linear phase and group speeds, cpl= w/k and cgI= dw/ak, respectively. Note that for the lowest mode, both speeds are asymptotic to co = 0.716 msec-' as k + 0. Henceforth only this lowest-order mode will be considered, and the modal subscript n will be suppressed. Figure 23 illustrates the dispersion relation. The waves observed in Fig. 15, whose average wavelength is


185

0 NORMALIZED EIGENFUNCTIONS, W ( o z )

FIG. 21. Vertical eigenfunctions for the two lowest-order modes, evaluated at a linear wavenumber k,/2n = 2.6 cyc km-'.

FIG.22. Linear phase and group speeds for the two lowest-order modes, n calculated from the density profile of Fig. 19. I

7

-Nrnox 8-

I

1

I

=

1 and n = 2, as

_i

-

2

-2 0

6-

t N

3

4-

0 >-

z

5

2-

L

O

ea

1

I

0

2

4

I

I

6

8

1

i0

HORIZONTAL WAVENUMBER k,/Er(cyc

km-')

FIG.23. Dispersion relation for the two lowest-order modes. The short waves in Fig. 15 correspond to wavenumbers near 2.6 cyc km-'.

186


approximately 390 m, have k1/2n = 2.6 cyc km-’, cpl= 0.52, cgl = 0.27 msec-’, and a period T = 2n/w = 0.21 hr. Returning to Eq. (51), the environmental parameters a and y are functions of the density profile and eigenfunctions and are given by

where the expressions on the right-hand side are the parameters evaluated for a two-layer model of upper and lower depths h, and h 2 , respectively, and are included for illustrative purposes only. To interpret Eqs. (53)-(59, the nonlinear dispersion equation that is equivalent to Eq. (51) is used. Differentiate between the linear phase and group speeds cpl= o/kl and csl = aw/dk,, respectively, and their nonlinear duals cpand cg. Then for periodic (but not necessarily sinusoidal) oscillations, the nonlinear speeds may be obtained directly from the Korteweg-deVries equation using arguments found in Whitham (1974):

o / k , = cp = co(l - ykf = cpl

c%o/dk,

+ 2CoaA/3

= cg = co(l - 3yk; = cgl

+ 2aA/3)

+ 2coaA/3

(56)

+ 2aA/3) (57)

The quantities cpl= co(l - yk:) and cgl= co(l - 3ykf) are long-wavelength quadratic approximations to the full linear dispersion relation (Fig. 22). Thus, from Eq. (56) the coefficient y gives the wavenumber y-1/2 for which the quadratic approximation, 1 - yk:, to the full dispersion term vanishes. For the case at hand, numerical integration gives y = 1860 m2,or 1/2 ~ y = ” 3.69 ~ cyc km-’. These quadratic approximations are shown for the lowest-order mode in Fig. 22; the approximations are poor beyond k1/2n E 3 cyc km-’ for the phase speed and 1 cyc km-’ for the group speed. The nonlinear parameter a measures the degree of amplitude “cohesion” brought about by the tendency for a finite-amplitude wave to travel faster, behaving as if it were propagating on a thermocline that is deepened by the wave’s own amplitude. For the present example, it has the numerical value of 1.25 x m-’, or a-’ = 80 m. From Eq. (56), a wave of double amplitude


187

80 m would have more than 13times the phase speed of an infinitesimal wave; an internal wave with A of order 80 m would thus be strongly nonlinear in this geographical region. Very large waves that have the speed increment, 2c0aA/3, of order 0.40 m sec-', have been directly observed with current meters in the Sulu Sea in the Philippines (Ape1and Holbrook, 1980; Holbrook et al., 1980). There are several steady-state solutions known for the internal KortewegdeVries equation (Keulegan, 1953; Benjamin, 1966, 1967; Ono, 1975; Joseph, 1977; Whitham, 1974). The best known is the long-wavelength soliton solution, which, for water deep compared with the thermocline depth but still shallow, has the simple form A(x,t ) = - A , sechz[2(x - c,t)/L]

(58)

describing a single bell-shaped thermocline depression moving to the right at constant speed cp,with the parameter L characterizing the width of the pulse. In the absence of dissipation and horizontal divergence, the solitary wave propagates indefinitely without change of shape or amplitude. A second steady-state solution incorporates the periodic cnoidal function, cn,(c$), one of the Jacobian elliptic functions (Abramowitz and Stegun, 1964), and is given by A(x, t ) = A, - A,~n;[1/2(k,x - ot)]

(59)

This solution also moves without change of shape but is characterized by an infinite number of down-going troughs of double amplitude - A , , with the requirement for zero mean displacement to be determined by the constant A,, which is generally much smaller than A , . The cnoidal wave is a nonlinear generalization of the cosine, to which it reduces when the nonlinear parameter mz + 0. It has a so-called stretched wavelength A, defined by , I= A(m2) = 4K(m2)/kl 2 271/kl

(60) where K(m2)is the complete elliptic integral of the first kind with argument mz, and where 0 I mz I1. As before, k, = 271/A, is the linear wavenumber. As the degree of nonlinearity increases, the wavelength A increases from its smallamplitude value of 271/kl until, with complete nonlinearity, mz + 1 and A + 00, leaving only an isolated pulse as the solution. In this limit, cnrn(4) m-i sech(4)

and the solitary wave is recovered. The asymptotic approach to the soliton solution allows one to relate the small-amplitude wavenumber k, to the soliton width parameter L. Clearly kl = 4/L. Thus the cnoidal solution to Eq. (51) is a generalization of the solitary wave, encompassing it at one limit and the ordinary cosinusoidal solution at the other limit of infinitesimal amplitude.


188

This lower limit also allows one to discuss groups or packets of cnoids in exactly the same fashion as for cosines. The nonlinear parameter m2 is given by (Whitham, 1974) m 2 = aA/3yk: = (c0aA/3)/(c0- cpl)

(61)

which is the ratio of the nonlinear speed increment to the dispersive linear phase speed decrement. The invariance of the steady-state solutions results from a balance between ordinary linear dispersion on one hand, and amplitude cohesion on the other. While longer waves travel faster, so do larger ones. The wavelength and amplitude variations in the observed internal wave packets therefore reflect the tendency for the longest and largest Fourier components to move out faster than the shorter, smaller ones. This suggests that an approximate model function to describe the observed internal wave fields can be constructed from cnoidal solutions to the Korteweg-deVries equation, without asserting that the model constitutes a rigorous solution to the governingequations. Parameters of the model can be fitted to the observations and information thereby deduced. The model function advanced is given by q(x, y, z, t ) = 0, =

s

- c,t < 0

-Ao(s - c,O%(x,

(62)

Y )W(az)

x cnf [1/2(k, s - wt

+ &)I,

s - C,t

>0

This function describes a cnoidal wave packet propagating along a horizontal arc s = s(x, y) in the xy plane. The modulation envelope, - Ao(s - c,t) of the oscillations, which determinesthe packet amplitude, shape, and dimensions,is downgoing and commences at s - c,t = 0. The curvature of the packet isophase fronts in the horizontal plane is described by the function R,(x, y) and may be empirically determined directly from the SAR imagery. W(az)is the vertical eigenfunction with a characteristic scale of 1/a. The nonlinear parameter m2,as given by Eq. (61), is a function of both amplitude and the linear phase speed decrement, and connects the envelope function A. , the phase speed, and the stretched wavelength through Eqs. (56), (60),and (61). It is by these relationships that the variable wavelength is introduced into the model function. Phase $, adjusts the onset of the oscillation to some convenient value and is taken as $,, = 2K(rn2). Equation (62) is not an exact solution to the Korteweg-deVries equation due to the inclusion of the amplitude modulation function but is in error only by a factor of order Ao/T’ compared with 2A0/?jwhere Tpis the duration of the packet, of order 2 hr. In general, the ratio exceeds 20 and hence the error is small, The function models the salient features of continental shelf internal waves as seen at a large number of sites around the world.


189

3.5. Amplitude Estimates

It is in principle possible to estimate the amplitude of internal waves from satellite observations of the surface signatures by at least two methods. The first makes use of the nonlinear group speed increment mentioned above in conjunction with measurements of interpacket separation to deduce the amplitude from Eq. (57). It will be called the time-of-flight method. The second makes use of the observed variation of wavelengths within a given packet, invoking properties of the cnoidal solution to the Korteweg-deVries equation to deduce the degree of nonlinearity and from this, amplitude. This will be called the variation-of-wavelength method. The first measures average speed and amplitude between any two packets separated by a tidal cycle in time and the attendant propagation distance in space; the second measures the local, near-instantaneous properties of a single packet. Such deductions are not expected to be highly accurate, of course, but they can provide factor-oftwo estimates if the nonlinear effects are appreciable. These concepts have been applied to the SAR imagery of Figs. 15b and 17a, and the results are interpreted in terms of the cnoidal model function and its auxiliaries, Eqs. (56), (57), and (60)-(62). 3.5.1. Time-of -Flight Method. The quantity determined here from the SAR imagery is the interpacket separation D, which together with the assumed semidiurnal tidal period z = 12.42 hr yields an average nonlinear group speed (c,). In this case, the average separation D between centroids of the offshore packets is 22 km, which gives (c,) = 0.49 m sec-'. By integrating Eq. (57) over a tidal period, the double amplitude (assumed constant) is obtained

from which one may in principle obtain the packet-average amplitude, A. For the case at hand, however, the characteristics of the bathymetry and density distribution mitigate against the application of the method, mainly because the nonlinear speed increment vanishes in some region between the packets near the 200-m isobath and those packets inshore, near H = 100 m. This can be understood from Eq. (53), where the two-layer model predicts a = 0 when hl = h2. The equivalent two-layer depth is approximately 75 m (Fig. 21), which implies a = 0 near the 150-m isobath. Solitary waves go through some sort of an ill-understood transition at such a point, but presumably propagate at near-linear speeds in the vicinity of the transition. The theoretical linear group speed for these packets, when calculated as an intensity-weighted average over the dominant spectral components, has a

190


value cg, = 0.49 m sec-' which is the same as the average nonlinear group speed derived from the image, within the errors associated with the method. Thus the nonlinear increment in this case is too small to be deduced from Eq. (63),and the time-of-flight fails. 3.5.2. Variation-ofi Wavelength Method. The starting point for the second method is Fig. 16, from which we have extracted an averaged and smoothed wavelength variation, A = A(s), for the three southernmost packets, 1s to 3S, which are in moderately deep water. The origin for s is taken ahead of the lead wave at one-half its length. Table I lists these wavelengths, which range from 700 m at the leading edge to 272 m at the trailing edge. From this, the smallamplitude asymptotic wavelength is obtained by extrapolation as A1 = 270 m. The standard deviation of A for the data set is approximately f50 m. Thus the wavelength stretch is A(350)/1, = 2.59 = 2K(0.995)/n. From Eq. (60) one calculates K ( m 2 )and hence m'(s); from Eq. (61)and the data for Fig. 22 comes A&). As can be seen from Table I, the estimated nonlinearity and amplitude range from 0.995 and 30 m at the leading edge, to 0.03 and 3 m at the trailing edge. The values at the approximate packet centroid are 0.63 and 50 m. The considerable surface roughness implied by the intensity variations in the SAR image lends credence to such large amplitudes. It is likely, however, that these

TABLE I. SMOOTHEDNONLINEAR WAVE-PACKET PARAMETERS'

350 1050 1540 1950 2320 2660 2980 3285 3575 3860 4135 m

700 490 410 370 340 320 305 290 285 275 212 270

0.625 0.559 0.518 0.493 0.47 1 0.456 0.443 0.430 0.425 0.415 0.413 0.41 1

0.995 0.943 0.848 0.747 0.626 0.509 0.395 0.252 0.199 0.070 0.030 0.000

30 50 56 56 51 44 36 27 19 7 3 0

Parameters: s, distance from front of packet; I , local smoothed wavelength; cp,, linear phase speed; m', argument of complete elliptic integral; A,, amplitude of nonlinear internal wave envelope.

I

-

I

I

1

I

1

I

1

I

I

I

I

1

I

I

1

1

1

1

-

-10-

E

-40-

-

-50-

-

I

5

-20;

+* -30-

z

W V

a J

a

e -60

I

I

I

I

I

I

I

1

I

I

1

I

I

I

FIG. 24. Calculatd vertical displacements from a damped cnoidal internal wave model using numerical values obtained from the data of Figs. 15 and 16. The model incorporates the decrease in wavelengths apparent in Fig. 16 as well as the nonlinear increase in phase and group velocity deduced from Fig. 15.

192


are overestimates because spreading and dissipation of wave energy are obviously occurring in the fields visible on Fig. 15b. The data of Table I have been used to calculate the spatially varying vertical displacement according to the model function, Eq. (62), and the results are shown in Fig. 24. Several features are worth mentioning. First, the monotone wavelength variation of the wave train is clear, being incorporated into the cnoidal function via the specification of the nonlinearity. Second, the halfwidths of the troughs are constant, in essential agreement with observations elsewhere (Ape1et al., 1975a;Holbrook et al., 1980).Third, the leading wave at s = 350m, while the most nonlinear, is not the largest but has something of the form of a precursor, a phenomenon seen frequently in such packets; this is due to its high speed and long length. Fourth, the oscillations in the remaining main body of the packet are essentially rank ordered, just as the soliton evolution theory prescribes. However, while solitons, in order to be considered as such, must be separated from their neighbors by something in excess of s z 3L, the cnoidal function is not so restricted. 4. SUMMARY AND CONCLUSIONS The Seasat SAR has returned imagery of continental shelf internal waves that appear to be generated by semidiurnal tidal flow over bathymetric features. The nonlinear, packetlike characteristics of the waves are similar to those observed in the New York Bight and the Sulu Sea. By using measurements of the spatial properties of the wave patterns in conjunction with a cnoidal wave model, it is possible to obtain reasonable estimates of wave amplitude and degree of nonlinearity. It should be emphasized, however, that this model is not viewed as a rigorous theory but rather a convenient and simple way of interpreting the data and connecting them to analytical expressions. More sophisticated interpretations of the remotely sensed data may lead to a greater understanding of the physics of internal waves. Such interpretations must proceed through a more rigorous theoretical model than the one used here, presumably fashioned along the lines of inverse scattering theory (Osborne and Burch, 1980) or solutions to an integro-differential equation of the type advanced by Kubota et al. (1978). This work is in progress for the Sulu Sea data. ACKNOWLEDGMENTS

This research was supported by the Seasat Project within NOAA and partially by the NASA Ocean Processes Branch under Work Order W-15,084.


193

REFERENCES

Abramowitz, M., and Stegun, I. A. (1964).“Handbook of Mathematical Functions.” U.S. Govt. Printing Office, Washington, D.C. Alpers, W. (1983). Monte Carlo simulations for studying the relationship between Ocean wave and synthetic aperture radar image spectra. J. Geophys. Res. 88,1745-1759. Alpers, W., and Hasselmann, K. (1978).The two-frequency microwave technique>formeasuring ocean wave spectra from an airplane or satellite. Bound. Layer Meteorol. 13,215-230. Alpers, W., and Jones, W. L. (1978).The modulation of the radar backscattering cross section by long ocean waves. Proc. Int. Symp. Remote Sensing Environ. 12th, Manila pp. 1597-1607. Alpers, W. R., and Rufenach, C. L.(1979). The effect of orbital motions on synthetic aperture radar imagery of ocean waves. IEEE Trans. Antennas Propag. AP-27,685-690. Alpers, W.R., Ross, R. B., and Rufenach, C. L. (1981).On the detectability of ocean surface waves by real and synthetic aperture radar. J. Geophys Res. 86,6481-6498. Apel, J. R., and Holbrook, J. R. (1980). The Sulu Sea internal soliton experiment, Part A: Background and overview. EOS 61,1009. Apel, J. R., Byrne, H. M., Proni, J. R., and Charnel], R. L. (1975a).Observations of oceanic internal and surface waves from the earth resources technology satellite. J. Geophys. Res. 80, 865881. Apel, J. R., Proni, J. R., Byrne, H.M., and Sellers, R. L.(1975b). Near-simultaneous observations of intermittent internal waves on the continental shelf from ship and spacecraft. Geophys. Res. Lett. 2, 128-131. Apel, J. R., Byrne, H.M., Proni, J. R., and Sellers, R. L. (1976).A study of oceanic internal waves using satellite imagery and ship data. Remote Sens. Enoiron. 5, 125. Bahar, E. (1981). Full-wave solution for the depolarization of scattered radiation fields by rough surfaces of arbitrary slope. IEEE Trans. Antennas Propag. AP-29,443-454. Bass, F. G., Fuks, I. M., Kalmykov, A. I., Ostrovsky, I. E., and Rosenberg, A. D. (1968).Very high frequency radiowave scattering by a disturbed sea surface. IEEE Trans. Antennas Propug. AP-16,554-568. Beal, R. C. (1981).Spatial evolution of Ocean wave spectra. In “Spaceborne Synthetic Aperture Radar for Oceanography”(R. C. Beal et al., eds.), pp. 110-127. Johns Hopkins Univ. Press, Baltimore, Md. Bel1,T. H.,Mays A. B., and deWitt, W. P. (1974). Upper ocean stability: A compilation of density and Brunt-Vais&lBfrequency distributions for the upper 500 m of the world oceans. Vols. I and 11, NRL Report 7799,Naval Research Laboratory, Washington, D. C. Benjamin, T. B. (1966).Internal waves of finite amplitude and permanent form. J. Fluid Mech. 25, 241-270. Benjamin, T . B. (1967). Internal waves of permanent form in fluids of great depth. J . Fluid Mech. 29,559-592. Brown, G. S.(1977).The average impulse response of a rough surface and its applications. IEEE Trans. Antennas Propag. AP-25,67-74. Brown, G. S. (1978). Backscattering from a Gaussian-distributed perfectly conducting rough surface. IEEE Trans. Antennas Propag. AP-M,472-482. Brown, W.E., Jr., Elachi, C., and Thompson, T. W. (1975). Radar imaging of ocean surface patterns. J. Geophys. Res. 81,2657-2667. Earle, M. D. (1981). Problems in ocean wave hindcasting, In “Spaceborne Synthetic Aperture Radar for Oceanography” (R. C. Beal et al., eds.), pp. 98-109. Johns Hopkins Univ. Press, Baltimore, Md. Elachi, C., and Apel, J. R. (1976). Internal wave observations made with an airborne synthetic aperture imaging radar. Geophys. Res. Lett. 3,647-650.

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Elachi, C. E., and Brown, W. E. (1977). Models of radar imaging of the ocean surface waves. IEEE Trans. Antennas Propag. AP-25,84-95. Elachi, C., Thompson, T. W., and King, D. (1977).Ocean wave patterns under hurricane Gloria: Observations with an airborne synthetic aperture radar. Science 198,609-610. Farmer, D., and Smith, J. D. (1978).Nonlinear internal waves in a fjord, In “Hydrodynamics of Estuaries and Fjords” (J. Nihoul, ed.). Elsevier, Amsterdam. Fedor, L.S., Godbey,T. W., Gower, J. F. R., Guptill, R., Hayne, G. S., Rufenach, C. L.,and Walsh, E. J. (1979). Satellite altimeter measurements of sea state--an algorithm comparison. J . Geophys. Res. 84(B8), 3991-4002. Gonzalez, F. I., Shuchman, R. A., Ross, D. B., Rufenach, C. L., and Gower, L. F. R. (1981). Synthetic aperture radar wave observations during GOASEX. In “Oceanography from Space” (J. F. R. Gower, ed.), pp. 459-467. Plenum, New York. Halpern, D. (1971). Semidiurnal internal tides in Massachusetts Bay. J. Geophys. Res. 76,6573. Harger, R. 0.(1980). The side-looking radar image of time-invariant scenes. Radio Sci. 15, 749-765. Hasselmann, D. E., Dunckel, M., and Ewing, J. A. (1980). Directional wave spectra observed during JONSWAP 1973. J. Phys. Oceanogr. 10,1264-1280. Hasselmann, K. (1980). A simple algorithm for the direct extraction of the two-dimensional surface image spectrum from the return signal of a synthetic aperture radar. Int. J. Remote Sens. 1,219-240. Hasselmann,K., Ross,D. B., Miiller, P., and Sell, W.(1976). A parametric wave prediction model. J. Phys. Oceanogr. 6,200-228. Haury, L. R., Briscoe, M. G., and Om, M. H.(1979). Tidally generated internal wave packets in Massachusetts Bay. Nature (London) 278,312-317. Holbrook, J. R., Apel, J. R., and Tsai, J. J. (1980).The Sulu Sea Internal Soliton Experiment, Part B: Observations of large-amplitude nonlinear waves. EOS 61, 1009. Jain, A. (1978).Focusing effectsin synthetic aperture radar imaging of ocean waves. Appl. Phys. 15,323-333. Jain, A. (1981). SAR imaging of ocean waves: Theory. IEEE J. Ocean Eng. OE-6,130-139. Jordan, 0.(1980).The SEASAT-A synthetic aperture radar system. IEEE J. Ocean Eng. OE-5, 154- 164. Joseph, R. I. (1977).Solitary waves in a finite depth fluid. J . Phys. A 10, L225-L227. Keller, W.C.,and Wright, J. W. (1975). Microwave scattering and straining of wind generated waves. Radio Sci. 10,139-147. Keulegan, G. H. (1953).Characteristics of internal solitary waves. J. Res. NBS 51, 133-140. Kitaigorodskii, S. A. (1973). The physics of air-sea interaction (translated from Russian). U.S. Dept. of Commerce, National Tech. Info. Service, TT-72-50062. Kubota, T., KO,D. R. S., and Dobbs,.I (1978). Propagation of weakly nonlinear internal waves in a stratifiedfluid of finitedepth, J. Hydronaut. 12,157-168. Larson, T. R.,Moskowitz, L.I., and Wright, J. W. (1976). A note on SAR imagery of the ocean. IEEE Trans. Antennas Propag. AP-24,393-394. McLeish, W., Ross, D., Shuchman, R. A,, Teleki, P. G., Hsiao, S.V., Shemdin,0.H., and Brown, W. E. (1980). Synthetic aperture radar imaging of ocean waves: Comparison with wave measurements. J. Geophys. Res. 85,5003-SO1 1. Maxworthy,T.(1979). A note on the internal solitary waves produced by tidal flow over a threedimensional ridge. J. Geophys. Res. 84,338-346. Monaldo, F. M., and Beal, R.C. (1981).The effects of high sea states on the SEASAT SAR ocean wave spectra. Proc. IUCRM Symp. Wave Dyn. Radio Probing Ocean Surf. (in press). Ono, H. (1975).Algebraic solitary waves in stratified fluids. J. Phys. SOC.Jpn. 39, 1082-1091. Osborne, A. R., and Burch, T. L.,(1980). Internal solitons in the Andaman Sea. Science 208, 451-460.


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Phillips, 0. M. (1981). The structure of short gravity waves on the ocean surface. In “Spaceborne Synthetic Aperture Radar for Oceanography” (R. C. Beal et al., eds.), pp. 24-31. Johns Hopkins Univ. Press, Baltimore, Md. Pierson, W. J. (1977). Comments on a parametric wave prediction model. J. Phys. Oceanogr. 7 , 127-137. Proni, J. R., Apel, J. R.,Byrne, H. M., Sellers, R. L., and Newman, F. C. (1978). Oceanic internal waves from ship, aircraft and spacecraft: A report on the New York-to-Bermuda Sensing Experiment. NOAA Atlantic Oceanographic and Meteorological Laboratories. U.S. Govt. Printing office, USGPO 1978-796-417-1 16-10. Raney, R. K. (1971). Synthetic aperture imaging radar and moving targets. IEEE Trans. Aerosp. Electron. Syst. AES7,499-505. Raney, R. K. (1980). SAR processing of partially coherent phenomena. I n t . J. Remote Sens. 1, 29-51. Rice, S. 0. (1951). Reflection of electromagnetic waves from slightly rough surfaces. Commun. Pure Appl. Math. 4, 351-378. Rufenach, C. L., and Alpers, W. (1978). Measurement of ocean waveheights using GEOS-3 altimeter. J . Geophys. Res. 63, 501 1-5018. Rufenach, C. L., and Alpers, W. (1981). Imaging ocean waves by synthetic aperture radars with long integration times. IEEE Trans. Antennas Propag. AP-29,422-428. Shuchman, R. A. (1980). Processing synthetic aperture radar data of ocean waves. In “Oceanography from Space” (J. Gower, ed.), pp. 477-496. Plenum, New York. Shuchman, R. A., and Kasischke, E. S. (1981). Refraction of coastal ocean waves. In “Spaceborne Synthetic Aperture Radar for Oceanography” (R. C. Beal et al., eds.) pp. 128-135. Johns Hopkins Univ. Press, Baltimore, Md. Shuchman, R. A., and Zelenka, J. S. (1978). Processing of ocean wave data from a synthetic aperture radar. Bound. Layer Meteorol. 13, 181-202. Shuchman, R. A., Kasischke, E. S., and Klooster, A. (1978). Synthetic aperture radar ocean wave studies (Final Rep. 131700-3-F). Environ. Res. Inst. of Mich., Ann Arbor. Shuchman, R.A., Ma!Tett, A. L., and Klooster, A. (1981). Static modeling of SAR imaged ocean scene. IEEE J. Oceanic Eng. OE-6,41-99. Swift, C. T., and Wilson, L. R. (1979). Synthetic aperture radar imaging of ocean waves. IEEE Trans. Antennas Propag. AP-27,725-729. Tapley, 8. D., Diamante, J. M., Douglas, B. C., Goad, C. C., Kolenkiewicz, R., Marsh, J. G., Martin, C. F., Smith, S. L., 111, Townsend, W. F., Whitehead, J. A., Byrne, H. M., Fedor, L. S., Hammond, D. C., and Mognard, N. M. (1979). SEASAT altimeter calibration: initial results. Science 240,1410-1412. Tomiyasu, K. (1978). Tutorial review of synthetic aperture radar (SAR) with application to imaging of the ocean surface. Proc. IEEE 66 (5), 570-583. Townsend, W. F. (1980). An initial assessment of the performance achieved by the SEASAT-I radar altimeter. IEEE J . Oceanic Eng. OE-5(2),80-92. Tsai, J. M., and Apel, J. R. (1979). Tidally-induced shear-flow instability as a source of internal waves on the continental shelf. NOAA Pacific Marine Environmental Laboratory, Contribution No. 415. Valenzuela, G. R. (1968). Scattering of electromagnetic waves from a tilted slightly rough surface. Radio Sci. 3, 1057-1066. Valenzuela, G. R. (1980). An asymptotic formulation of SAR images of the dynamical ocean surface. Radio Sci. 15, 105-1 14. Vesecky, J. F., and Stewart, R. H. (1982). The observation of ocean surface phenomena using imagery from the SEASAT synthetic aperture radar. J. Geophys. Res. 87,3397-3430. Whitham, G. B. (1974). “Linear and Nonlinear Waves.” Wiley, New York. Wilkerson, J. C., Brown, R. A., Cardone, V. J., Coons, R. E., Loomis, A. A., Overland, J. E.,

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Peteherych, S., Pierson, W. J., Woiceshyn, P. M., and Wurtele, M. G. (1979). Surface observations for the evaluation of geophysical measurements from SEASAT. Science 204, 1408-1410. Wright, J. W. (1968). A new model for sea clutter. IEEE Trans. Antennas Propag. AP-16, 217-223. Wright, J. W., and Keller, W. C. (1971). Doppler spectra in microwave scattering from wind waves. Phys. Fluids 14,466-474. Wright, J. W.,Plant, W. J., Keller, W. C., and Jones, W. L. (1980). Ocean wave-radar modulation transfer functions from the West Coast Experiment. J . Geophys. Res. 85,4957-4966.

CIIAFTER 6

SEASAT MICROWAVE WIND AND RAIN OBSERVATIONS IN SEVERE TROPICAL AND MIDLATITUDE MARINE STORMS PETERG . BLACK

R . CECIL GENTRY

Atlantic Oceanographic and Meteorological Laboratory Hurrirane Research Division Miami Florida

Department of Physics and Astronomy Clemson University Clemson South Carolina

.

. VINCENTJ . CARDONE

JEFFREY D. HAWKINS’

Oreanweather. Inc . White Plains . New York

Atlantic Oreanographir and Meteorological Laboratory Hurricane Research Divison Miami. Florida

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 2. The Nature of Severe Marine Storms . . . . . . . . . . . . . . . . . 2.1. Tropical-Cyclone Structure . . . . . . . . . . . . . . . . . . . 2 2. Severe Midlatitude Marine Storms . . . . . . . . . . . . . . . . 3. Microwave Measurements in Severe Marine Storms . . . . . . . . . . . . 3.1. Remote Sensing of Tropical-Cyclone Cloud and Precipitation Patterns . . . . 3.2. Microwave Remote Sensing of Ocean Surface Winds . . . . . . . . . . 4. Seasat Observations of Rain Rate and Microwave Attenuation in Tropical Cyclones . . . . . . . . . . . . . . . . . . . . . . . 5 . Seasat Surface Wind Observations in Tropical Cyclones . . . . . . . . . . . 5.1. Aircraft Data . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Cloud-Motion Winds . . . . . . . . . . . . . . . . . . . . . 5.3. SASS Winds and Methods of Comparison . . . . . . . . . . . . . . 5.4. SASS Alias Removal in Tropical Cyclones . . . . . . . . . . . . . . 6. Analysis of Individual Storms and Comparison of Seasat Data with “Surface-Truth’’Data . . . . . . . . . . . . . . . . . . . . . 6.1. Hurricane Fico. July 1978 . . . . . . . . . . . . . . . . . . . 6.2. Hurricane Ella. September 1978 . . . . . . . . . . . . . . . . . 6.3. Hurricane Greta, September 1978 . . . . . . . . . . . . . . . . . 6.4. Selected Typhoons . . . . . . . . . . . . . . . . . . . . . . 6.5. Hurricane Ella Model Fields . . . . . . . . . . . . . . . . . . 6.6. QEIIStorm . . . . . . . . . . . . . . . . . . . . . . . . 6.7. Discussion of Errors in Wind Measurement . . . . . . . . . . . . . I. Seasat Observations of Sea Surface Temperature Near Tropical Cyclones . . . . 8. Application of Seasat Observations to Operational Marine Storm Forecasting Needs . . . . . . . . . . . . . . . . . . . . . 8.1, Tropical-Cyclone Forecast Applications . . . . . . . . . . . . . . . 8.2. Extratropical-Cyclone Forecast Applications . . . . . . . . . . . . .

198 200 200 204 206 206 207 208 215 216 217 217 218 218 218 229 238 246 248 249 258 264 265 265 270

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Present address: Naval Ocean Research and Development Activity (NORDA). Bay St.Louis. Mississippi. 197 ADVANCES IN GEOPHYSICS.VOLUME 27

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Copyright @ 1985 by Academic Press Inc. All rights of reproduction in any form reserved. ISBN 0-12-018827.9

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9. Conclusions. References

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1. INTRODUCTION This overview presents initial results of studies concerning Seasat measurements in and around tropical and severe midlatitude cyclones over the open ocean and provides an assessment of their accuracy and usefulness. Sensors flown on Seasat provided complementary measurements of surface wind speed and direction, rainfall rate, significant wave height and wave length, and sea surface temperature. These measurements were made with the Seasat-A Satellite Scatterometer (SASS), the Scanning Multichannel Microwave Radiometer (SMMR), the Seasat altimeter, and the Seasat Synthetic Aperture Radar (SAR). This is the first time that such a sophisticated array of microwave instruments has been used to study tropical cyclones. Up to now, polar-orbiting and geostationary satellites carrying visible and infrared radiometers have provided data for tropical cyclone studies. Primarily, these satellites have been used to assess storm intensity and characteristics of the inflow and outflow layers by means of cloud morphology and cloud tracking, respectively. Seasat, however, has opened new horizons for satellite-based measurements of winds and precipitation in tropical cyclones. The potential for using these data in hurricane forecasts and warnings is far reaching. Some possible studies are (1) determination of the radius of gale-force winds, (2) measurement of initial wind and precipitation fields for input to dynamic prediction models, (3) measurement of initial wind fields for wave and storm surge prediction models, and (4) estimation of storm rainfall for storm-related flood and inundation forecasting. For the case of the severe midlatitude marine storm, these data are also important in composing timely and accurate warnings of position as well as wind and wave distributions to aid shipping interests. In addition, Seasat can provide real data inputs to regional and global circulation models. This capability is especially important for the Southern Hemisphere in rapidly developing situations where very little data exist. A tropical cyclone is a warm-core, low-pressure system that develops over the warm tropical ocean in conjunction with an upper level anticyclone(highpressure system). In its mature state, the tropical cyclone has intense winds that blow counterclockwise in the Northern Hemisphere and clockwise in the Southern Hemisphere around its clear, calm “eye” region. A tropical cyclone derives its energy from warm, moist air flowing into it at low levels. This

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energy is released in towering convective clouds concentrated in the storm's eyewall, a nearly circular ring of clouds near the center, and in outer rainbands. If maximum winds, located close to the center, exceed 33 m sec- ', the cyclone is called a hurricane, typhoon, or cyclone,depending on the area of the world. If maximum winds are 17.5-32 m sec-', the system is called a tropical storm. Weaker systems are called tropical depressions or disturbances. Midlatitude marine storms, on the other hand, are low-pressure systems of larger scale than tropical cyclones (diameter usually 1000 km or more) and develop along the boundaries between warm and cold air masses in conjunction with an upper-level trough in the westerly jet stream. The boundaries between the warm and cold air masses are swept ahead of the midlatitude system in the form of fronts, which may generate smaller scale squall systems. Generally, the regions of strong winds and heavy rain are spread out over a larger area than in tropical cyclones. The high winds, seas, and precipitation, as well as the large gradients of these parameters, make the tropical cyclone the severest test of Seasat's capabilities. This study summarizes the limits of these measurements. The objectives of this study of Seasat measurements in storms were to determine the following parameters: 1. Biases and root-mean-square (RMS) errors of individual SASS and SMMR wind measurements when compared with individual high-quality surface wind observations from aircraft, ships, and buoys, with special emphasis on high-wind areas ( > 15 m sec - '). 2. Biases and RMS errors of SASS and SMMR wind fields when compared with subjective analyses of the storm wind fields. 3. Magnitude of attenuation corrections derived from SMMR data applied to SASS measurements and to evaluate the SASS performance in measurement of high winds through heavy precipitation. 4. Capability of SMMR to measure sea surface temperature (SST)in storm areas by comparing individual surface observations and SST fields with SMMR estimates.

Other studies have attempted to determine additional parameters: 5. Bias and RMS error in significant wave-height observations by altimeter in storm areas. 6. Ability of SAR to image the dominant waves generated by hurricane winds.

The second set of objectives to be accomplished, once the first had been achieved, were as follows:

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1. Determine the capability of Seasat to define storm center locations to within 60 km or less and to define the extent of gale-force and hurricane-force winds for storm warnings. 2. Demonstrate the usefulness of Seasat-derived wind fields and fields of latent heat release in the initializing of storm forecast models.

During its lifetime, Seasat acquired data over most of the Northern Hemisphere tropical cyclones in 1978. It completed’ 126 passes over 21 hurricanes and typhoons and 179 passes over 20 tropical storms. In addition, there were 64 passes over the tropical depression stages of these storms. About 75 of the passes were selected for processing after surveying the availability of surface truth data. About 10 of these have been selected for detailed analysis and will be discussed in this overview. Since Seasat’s lifetime spanned the Northern Hemisphere summer months, only one severe midlatitude storm occurred for which high-quality surface truth data were available. Data for this case were processed and results compared with the tropical storm cases. A large data set of virtually untouched data remains available for researchers interested in exploiting the use of microwave remote sensing in tropical cyclones, as well as in other midlatitude marine storms, especially in the Southern Hemisphere. In Section 2 of this review, general tropical-storm structure is discussed. The progress, to date, with microwave remote sensing of tropical storms is reviewed in Section 3. In the sections that follow the accuracy is assessed of derived wind, precipitation, and sea surface temperature fields in and around tropical cyclones. In a final section initial efforts to demonstrate the usefulness of Seasat data for operational applications, such as storm warning and forecasting, are reviewed.

2. THE NATURE OF SEVERE MARINESTORMS 2.1. Tropical-Cyclone Structure

Hurricanes have been called “the greatest storms on earth” (Gentry, 1970) and, over the years, considerable effort has been devoted to the study of their dynamics and to the development of models for forecasting their intensity and tracks. As a result, much attention has been focused on the hurricane boundary layer, because it is there that convective processes are fueled that drive the hurricane’s circulation through the transport of heat and moisture from the ocean. Furthermore, it is the winds at the surface that cause the


20 1

damage at landfall (either directly or indirectly by the storm surge). Unfortunately, most critical hurricane forecasts have to be made while the storm is still at sea and usually well removed from the observational networks. In situ measurements of the surface winds under such conditions currently are not practical on a large scale. Therefore, a means of obtaining measurements of these surface winds is of paramount interest to researchers and forecasters. The hurricane is the most destructive of natural phenomena, While tornadoes have stronger winds, those in hurricanes last longer and affect much larger areas. In addition, hurricanes can develop over all tropical oceans of the world, except the South Atlantic. In the United States alone, over 17,000 deaths have been caused by hurricanes since 1900 (Dunn and Miller, 1964), and in many areas the number is much higher. In the United States, the average annual damage from hurricanes was $745 million during 1971-1980, according to records at the National Hurricane Center as published in the annual hurricane summary articles (e.g., Lawrence and Pelissier, 1981). Although other marine storms are not so dramatic, they are more frequent, affect more and larger areas, and cause great losses to shipping and along exposed coasts. Figure 1, an airborne radar presentation of Hurricane Anita (1977), illustrates some distinctive features of a hurricane. The darkest shadings, which represent heavy concentrations of water drops, are areas where the rainfall is heaviest. The open circular area in the center is the eye of the storm. There, the winds are light and variable, cloudiness is much less than in the surrounding areas, and there is little or no rain. Immediately outside the eye, however, is the eyewall with its massive clouds, which frequently extend from about 150 m above the surface upward 9-15 km or more. In most mature hurricanes, the strongest winds and the most intense rain are associated with the eyewall. The spiral bands in Fig. 1, called rainbands, are typical of most hurricanes. Like the winds, they tend to rotate around the storm, although much less rapidly. The rainfall, squalls, wind speeds, and gustiness are much greater in and beneath a rainband than in the areas between bands. Wind speeds along the bands frequently vary by 8 m sec-' in 15 km and the variation is even greater perpendicular to the bands (Gentry, 1964; Willoughby et al., 1982; Jorgensen, 1984). The variation of wind speed with radial distance in a hurricane is highly variable. Figure 2a-g shows some examples of the typical range in radial profiles of tangential wind in hurricanes. Small, intense hurricanes can exhibit profiles, like Hurricane Allen on August 8 in Fig. 2a, where the peak winds occur only a few kilometers from the center, exhibiting horizontal shears of

202


1-50 k m - q

I

I

I

a *

I

FIG.1. Radar map of Hurricane Anita, September 1,1977,06:27GMT,from lower fuselage Cband weather radar on board NOAA WP-3D Orion aircraft flown at 700-mbar pressure altitude. Contour intervals in decibels are, in ascending order, 25 (thick solid line), 32, 39, 46, and 53 (solid shading). These contours represent rainfall rates of 1, 3, 10, 35, and 120 mm hr-', respectively.

about 60 m sec-' per 10 km. Other storms can exhibit secondary wind maxima at various radii from the center (Willoughby et aZ.,1982),as typified by Hurricanes Allen on August 5 and 9 and David on August 30 in Fig. 2b-d, respectively. Figure 2e represents the more typical profile for a mature hurricane, exhibiting peak winds at 25 km. Figure 2f and g illustrates profiles that are typical of large storms. For these cases, maximum wind radii ranging from 50 to 150 km have been measured. Thus, storm sizes, and the associated horizontal wind shears, can vary greatly from storm to storm, with the entire circulation of a small-sizestorm out to the radius of gale-forcewinds being contained within the clear eye of a large-size storm, such as Hurricane Ginger in Fig. 2g. The expanse covered by hurricanes also varies greatly. If the horizontal scale is defined by the area of subnormal surface pressure, its diameter is frequently about 1000 km,but individual storms may differ by a factor of 2 or 3. Maximum winds measured in a few hurricanes have exceeded 80 m sec-'.

6. SEASAT WINDIRAIN OBSERVATIONS 8o

203

r

ALLEN 8 AUG 80 600 mbar

20

It

ALLEN 9 AUG. 80

700 mbar

60

,

GINGER 28 SEP. 71 850 mbar

'0

20

40

60

80

100

RADIUS (km)

120

140

160

FIG.2. Typical wind profiles in mature hurricanes.

The average radial extent of hurricane-force winds is typically about 100 km, but gale-force winds (17 m sec-l or more) may extend 500 km from the center (Dunn and Miller, 1964). Rainfall can be extremely heavy. Dunn and Miller (1964) report one storm near Miami, Florida, that dropped 3.4 cm of rain in 10min. This is an unusual rate of rainfall, but the flood potential of a tropical cyclone can be very great. Simpson and Riehl (1981) write that inside the 200-km radius about the hurricane center, computed rainfall from a moderate storm in one day has been shown equal to the average annual discharge of the Colorado River at its point of largest flow. Just as with the winds, however, rainfall does not occur uniformly over the entire area of the storm. Even in the inner 200-kmradius,

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the percentage of the area covered with active radar echoes is quite small, typically < 10% (Malkus et al., 1961; Jorgenson, 1984). Tropical cyclones form over tropical waters where the sea surface temperature is 26°C or higher (Palmen, 1948), and they occur most frequently during the warmest four months of the year. Tropical storm formation usually proceeds from a preexisting tropical disturbance that consists of organized cloud and wind patterns. However, only about 10% of all tropical disturbances reach storm intensity, in spite of the superficially similar synoptic conditions associated with each disturbance. Thus, the forecaster has the extremely difficult problem of detecting in advance the disturbances that will develop (Simpson, 1971). This difficulty, again, emphasizes the need for wind measurements, including those near the surface. The average life span of hurricanes is about 1 week, but individual storms may survive indefinitely (longer than 4 weeks), if they remain over warm tropical water. The primary reason for the short average life is the tendency for tropical cyclones to either strike land or move under the influence of the large-scale midlatitude circulation systems and into the cooler, more hostile, environment of middle latitudes. 2.2. Severe Midlatitude Marine Storms

Whereas the tropical cyclone obtains its energy from the warm moist air flowing into it at low levels, the other type of intense marine storm derives its energy either from the surrounding air currents or from contrasting air masses with different temperatures flowing into the storm. As a result, the maximum winds are rarely much above 40 m sec- the gradients in wind speed are much less than in tropical cyclones, and the rainfall is usually not as concentrated. The temperatures in the center of the storm are frequently colder than the average environmental temperatures. Usually the distribution of winds and rain are more asymmetrical than in tropical cyclones. On the other hand, the expanse of the storm may be two or three times as great as in a hurricane. The storm that buffeted the oceanliner Queen Elizabeth I1 (QE I1 storm) is typical of intense storms. Its structure is discussed in Section 6.6. The nature of the origin and movement of extratropical cyclones has been a subject of intense interest since the eighteenth century. The principal tracks and mean frequencies of cyclones and preferred areas of cyclogenesis have been well documented for the Northern Hemisphere (Klein, 1957). Much less is known about Southern Hemisphere cyclones because of the scarcity of observations. Indeed, a preliminary analysis of the impact of data from drifting buoys deployed in the Southern Ocean during the First GARP Global Experiment (FGGE), on operational surface weather analysis, suggests that

',


205

the mean frequency and intensity of cyclones there are much greater than had been thought (Guymer and Le Marshall, 1981). In high and midlatitudes, severe extratropical massive storms (hereafter referred to as SEMS) extract a steady toll of life and property. Recently, a SEMS crossed the path of 302 boats participating in the FASTNET Race in the Irish Sea (Rice, 1979). Fifteen sailors died in the heavy seas, 5 boats sank, and 18 were abandoned. Coastal residents are also susceptibleto rising water caused by SEMS. For example, in 1953 a storm surge associated with a storm in the North Sea flooded parts of the Netherlands and drowned 2000 people. In most coastal regions outside the tropics, design criteria for port and harbor installations and offshore structures on the continental shelf are determined by severe surface winds, wave heights, and storm surges associated with SEMS. Recently, there has been renewed interest in SEMS. First, it is becoming apparent that advanced numerical weather prediction techniques have met with only limited success on SEMS compared with other weather systems(e.g., Leary, 1971; Bosart, 1981). Second, it appears that most of the Northern Hemisphere’s deep cyclones, which, in their aggregate, define those major features of the general circulation known as the Aleutian and Icelandic Lows, begin their life cycle as explosively developing extratropical cyclones. SEMS, which deepen at a rate of at least 1 mbar hr-’ for 24 hr, have been termed “bombs” by Sanders and Gyakum (1980). Finally, the physical mechanism responsible for the formation and intensification of SEMS appears to be rather more complicated than the classical view, which states that extratropical cyclogenesis is a manifestation of a wavelike baroclinic instability on the tropospheric thermal gradient associated with the polar front. The climatology of “bombs” has been studied by Sanders and Gyakum (1980), who find a preference for their formation on the western sides of the Atlantic and Pacific Oceans, near or just north of the Gulf stream and Kuroshio currents (Fig. 3), respectively. Gyakum (1981) studied the nature and evolution of a particularly severe bomb which occurred in the North Atlantic during the Seasat mission. He found the system to possess several features of intense hurricanes, such as a deepeningrate of over 60 mbar in a 24hr period, hurricane-force surface wind speeds, a clear eye surrounded by intense convection, and a deep tropospheric warm core. He concluded that, while baroclinic forcing provided an initial mechanism for formation, bulk heating of the troposphere caused by cumulus convection was a significant factor in the intensification. This finding has important implications for the design of numerical weather prediction and general circulation models. It also indicates a potential for improvement in marine forecasts from operational, remotely sensed, surface marine wind data (Cane and Cardone, 1981). These considerations are discussed in Section 8.2.

206

PETER G.BLACK ET AL.

FIG. 3. Distribution of "bomb" events during three winter seasons. Isopleths represent smoothed bomb frequencies in units of number per 5" latitude/longitude square.

3. MICROWAVE MEASUREMENTS IN SEVERE MARINE STORMS 3.1. Remote Sensing of Tropical-Cyclone Cloud and Precipitation Patterns

Measurements of various tropical storm parameters have been obtained from satellites through several methodologies. Most widely used and accepted is the Dvorak (1975) method to infer storm intensity and center locations from visible and infrared cloud morphology from the Geosynchronous Operational Environmental Satellite (GOES) and polar-orbiting satellites. Kidder et al. (1978), Grody et al. (1979), and Rosenkranz et al. (1978) have attempted to use the Scanning Microwave Sounder (SCAMS) on Nimbus to infer storm intensity through measurement of the upper-level warm-temperature anomaly over the storm core. Alder and Rodgers (1977) and Allison et al. (1974) have attempted to use the Electrically Scanning

FIG. 3. Distribution of "bomb" events during three winter seasons. Isopleths represent smoothed bomb frequencies in units of number per 5" latitude/longitude square.


207

Microwave Radiometer (ESMR) to infer rainfall rates in typhoons. More recently, use of passive microwave radiometry to infer tropical cyclone rainfall has been summarized by Rodgers and Adler (1981). However, the most widely used and accepted technique is to infer rainfall rates from infrared cloud-top temperatures using methods such as that developed by Griffith et al. (1978). One of the most promising approaches to the precipitation problem has been described by Jung (1980). He used infrared-derived cloud-top temperatures over several typhoons to determine the cloud height. Using this with simultaneously observed microwave brightness temperatures from Nimbus-5 and -6, he derived rainfall rates. Several sensitivity tests to different drop size distributions and errors in cloud-top temperatures were used to derive error estimates for the calculations. 3.2. Microwave Remote Sensing of Ocean Surface Winds The use of passive and active microwave techniques for remote sensing of ocean surface winds is well documented (Barrick and Swift, 1980). For airborne passive observations, several authors (Ross et al., 1970; Nordberg et al., 1971; Ross and Cardone, 1974) have shown that the ocean brightness temperature is strongly correlated with wind speed through the creation of roughness, foam, and breaking waves (whitecaps). Still others (Guinard et a!., 1971; Krishen, 1971; Jones ec al., 1977) have shown that radar backscatter from the sea can be used to measure wind speed (through the proportionality of the Bragg off-nadir radar return to the wind-dependent spectrum of water waves a few centimeters long) and wind direction (through the anisotropic scattering characteristic of the sea surface). The first airborne passive and active observations of a hurricane were conducted during Hurricane Ava (Ross et al., 1974). However, in this case, the L-band radar (1.3 GHz) observations were used only to generate wave images. This type of observation was later quantified in 1976 (Weissmanet al., 1979) demonstrating that L-band radar cross sections obtained in Hurricane Gloria exhibited a weak dependence on wind speed. The concept of combining active and passive microwave observations was first implemented using a 13.0-GHz system (S-193 RADSCAT) on the NASA Skylab. During this experiment, the first such hurricane measurements were reported (Ross et al.. 1974; Cardone et al., 1976). The latter report also documented results for tropical storm Christine, which clearly demonstrated the ability to sense wind speed in hurricanes with a microwave scatterometer after regions of heavy rain were eliminated using the radiometer. This same concept was incorporated in the design of Seasat, although with the

208

PETER G . BLACK ET AL.

considerably coarser spatial resolution of 50 km for Seasat in contrast to 20 km for Skylab. A microwave altimeter was used in the GEOS-C experiment to measure winds up to 25 m sec-', as well as rain rates and significant wave height with a nadir-pointing, K-band instrument (Brown, 1979; Fedor et al., 1979). The method was refined using Seasat data (Fedor and Brown, 1982). Rufenach and Alpers (1978) employed these methods to measure wind and waves in tropical cyclones, based upon measurements made in Hurricane Caroline (1975). 4. SEASAT OBSERVATIONS OF RAINRATEAND MICROWAVE ATTENUATION IN TROPICAL CYCLONES

Rainfall was one of the most critical observations made by Seasat in tropical cyclones, SASS radar backscatter measurements were corrected for the effect of attenuation from rain to improve the accuracy of the winds measured in rain areas, Corrections were effective for cell area average rain rates of up to 10 mm hr-l. This observation is also intrinsically important for the specification of rainfall in tropical cyclones. The method of SASS attenuation correction adopted for production runs over tropical cyclones was developed at Kansas University (Moore et al., 1982). This method computed a 13.7-GHz attenuation value for SASS from SMMR measurements at 18 GHz as compared with the alternate Wentz algorithm, which used the 10 SMMR channels. The SMMR measurements were made only on the right side of the spacecraft,which prevented the use of a large data set. S M M R attenuation values were computed based upon average brightness temperatures measured over a 54-km2grid box. These values were interpolated to SASS cell locations. A comparison of attenuation values computed over Hurricanes Fico and Ella, with and without information from the 37-GHz channels, is shown in Fig. 4A and B. A slight improvement in the algorithm was realized when 37-GHz data were eliminated because they saturated at high rain rates and contaminated the calculations. Rainfall rates were computed using several techniques, two of which have been compared for Hurricanes Fico and Ella. In addition, the SMMR-derived rain rates have been compared with NOAA airborne radar-derived rain rates for Hurricane Ella. The techniques evaluated were developed by Wentz (1981b) and Chester (1981). The Wentz algorithm derives rain rates in two ways. First, information from all frequencies are used to compute an average attenuation over the 50-km2 SMMR grid. This average is then converted to a rain rate. The second method is to use only the 37-GHz brightness temperature and to derive an attenuation from a model-function-derived

209


100

1

1

I

I

1

1

I

I

94

88 i3 ; 82 L

ln ln

o-----

76

WENTZ f

-.-.-.

70

rJ

z

s 64

-----..............

(A1 1 Frequencies)

WENT22 (All but 3 7 G H t ) KANSAS1 (18 and 37GHt) KANSAS2 (18GHz)

1

OVERLAP

b-

58 52

A 4

100 94

88 i3 ;82 k

fln 76 5 70 ln z

2 +

1

2

3

1

I

I

1

4

5

ACROSS-TRACK

1

I

6

7

8

9

10

11

I

1

I

I

I

1

S M M R GRID 3 CELL

1

-

-

58 64

52

I

1

1

2

1

1

4

5

ACROSS-TRACK

I

6

I

7

SMMR GRID 3 CELL

1

1

1

1

8

9

10

It

FIG.4. Comparison of four methods of computing SASS attenuation from SMMR brightness temperatures for Hurricanes Fico (A) and Ella (B).

power law. Then a rain rate is calculated for each 37-GHz footprint, which has a resolution of 18 x 28 km. These steps are followed to maximize SMMR resolution. In contrast, the Chester algorithm uses a simple linear least-squares regression to derive rain rates from 37-GHz brightness temperatures. In the case of Hurricane Ella, it was possible to derive a distribution of rainfall rates from calibrated airborne C-band radar, which was flown on

1

210

PETER G. BLACK ET AL.

board the NOAA WP-3D research aircraft. Actually, two radars were used for the composite,which added to the credibility of the results. Figure 5 shows the resulting composite. Comparing this with Wentz's 37-GHz-derived rain rates for individual SMMR cells, shown in Fig. 6, one sees a remarkable degree of comparison. In fact, this technique is the only one capable of resolving rainband structure. The values saturate at 7-8 rnrn hr-', however. Figure 7 shows the results from the Chester algorithm averaged to SMMR grid 4, which has a resolution of about 30 km. One sees, however, that this is not sufficient to resolve rainband scale events. The W P 3 D radar rain rates were used to help tune this algorithm. The values above 8 mm hr- are somewhat artificial, in fact, because the corresponding brightness temperatures have very little variation with rainfall at those relatively high rain rates. Thus, the large

32

31

30

,-,

29 0

FIG.5. Radar composite of HurricaneElla, September 1,1978,13:20to 14:30GMTfromlower fuselage and nose C-band weather radars on board NOAA WP-3D Orion aircraft flown at a 150to 350-m altitude.

21 1


73

72

71

t

33

33

32

32

31

31

30

30

29 74

29 73

72

71

I

FIG.6. Rain rate in millimeters per hour derived, according to the Wentz algorithm, from “ungridded” 37-GHz brightness temperatures measured by the SMMR as it passed over Hurricane Ella during Rev 952 on September 1, 1978, 13:30 GMT.

gradients at the periphery of the rain pattern seem artificial. Both the Wentz and Chester techniques, however, do resolve the location of the eye very well. The Chester algorithm generally derives rain rates that are lower than the Wentz algorithm, except for a few very high points. This difference occurs because the 37-GHz brightness temperatures saturate at high rates and yield values that appear to increase rapidly above background values. For comparison, Fig. 8 shows the results from the original Wentz algorithm for SMMR grid 3. This analysis corresponds to the type of pattern that is used to derive the attenuation values applied to the SASS. It is not difficult to see why some problem is encountered in the attenuation correction procedure, since the details of the banded structure and the resulting variability in the attenuation corrections have been smoothed out. However, the average rain rates computed by the Wentz algorithm agree very well with the WP-3D radar rain rates averaged to the 50-km SMMR cell. These comparisons are shown in Fig. 9.

212

PETER G. BLACK ET AL. I

33

32

31

30

29

72

71

70

FIG.7. Rain rates in millimeters per hour derived according to the Chester algorithm, using 37-GHz SMMR data averaged to grid 4 (about 30 km2)for Hurricane Ella, Rev 952, on September 1, 1978, 13:30 GMT.

Similar analyses for Rev 331, Hurricane Fico, are shown in Figs. 10 and 11 for the Wentz algorithm, only. Again, one can see the high degree of averaging that occurs with the 50-km resolution in Fig. 11, totally obscuring the rainband features, which show up well in the ungridded data (Fig. 10). In the high-rain-rate areas, more realistic rainfall rates result when the 50-km resolution and the lower frequency information are used. However, near the eyewall, lack of resolution results in large underestimates of the rainfall. In this case, the 37-GHz, high-spatial-resolution techniques yield more realistic rain rates. Griffith has recently applied her satellite rain estimation technique (Griffith et al., 1978) with IR data to GOES and Seasat VIRR data coincident with the microwave observations. This technique has been recently applied at the NOAA, NWS National Hurricane Center, Miami (on an experimental basis), to operational rainfall estimation for landfalling hurricanes (B. Jarvinen,

33

1 33

32

32

31

31

30

30

29 I

72

71

29 1

FIG.8. Rain rates in millimeters per hour derived according to the Wentz algorithm, using 37and 18-GHz SMMR data averaged to grid 3 (about 50 km2) for Hurricane Ella, Rev 952, on September 1, 1978, 13:30 GMT. The storm center is indicated by an X.

FIG.9. Comparison of W P 3 D airborne radar measurement of rain rate, averaged over grid 3 boxes with Wentzalgorithm results for grid 3. Hurricane Ella, September 1,1978, Rev 952; 50-km cell.

214

PETER G. BLACK E T A L . 153”

1

I

3”

I

I 0

153’

1

FIG.10. Rain rate in millimeters per hour derived, according to the Wentz algorithm, from “ungridded” 37-GHz brightness temperatures measured by the SMMR as it passed over Hurricane Fico during Rev 331 on July 20, 1978,04:30GMT.

personal communication, 1982). Comparisons between simultaneous GOES and VIRR images for Hurricane Fico, Rev 280, were made in an effort to verify that the technique would work equally well with VIRR data as with the GOES data. An excellent comparison resulted. Therefore, this work has continued to derive VIRR rain rates over several typhoons and tropical storms. Derivation of attenuation values for use with both the SASS and the altimeter is accomplished from the rain values. Further evaluation of this technique is in progress. The satellite IR-derived rainfall rates will be compared with airborne radar rates for the Hurricane Ella case, as well as for two other hurricane cases. This technique offers the potential to estimate SASS attenuation values for the left-sideportions of the SASS swath not observed by the SMMR. Thus, we can use a large amount of typhoon and hurricane data that had been discarded earlier due to lack of an attenuation correction method.

215


'B 18"

0

lFlC0 R e v 331

I

1

I

153'

151'

14

FIG.1 1 . Rain rates in millimeters per hour derived according to the Wentz algorithm,usin 37and 18-GHz SMMR data averaged to grid 3 for Hurricane Fico, Rev 331, on July 20, 78, 04:30 GMT. The centers of the 50-km2grid boxes are indicated by solid dots and the storm center is indicated by a hurricane symbol.

5.

SEASAT SURFACE WIND OBSERVATIONS IN TROPICAL CYCLONES

Present in situ methods of deriving surface winds in tropical storms often involve techniques of limited accuracy. For example, surface winds derived from operational aircraft reconnaissance flights are frequently based on Beaufort state-of-the-sea relationships. However, these relationships are questionable in hurricane-force winds because they lack documentation. Further, the sustained flight-level wind at a 3300-m altitude is often used to approximate the peak gust near the surface. In addition, minimum surface pressures have been used to infer maximum surface winds (Atkinson and Holiday, 1977). In fact, to date, the only reliable method of obtaining the mean surface wind speed has been through low-level penetration of the

216


hurricane with aircraft that are equipped with inertial navigation systems. Then, flight-level wind measurements have been extrapolated to the surface using a boundary-layer model. The use of microwave remote-sensing methods from satellites offers the opportunity to measure surface winds without the need for dangerous, in situ measurements. Three hurricanes were selected for detailed analysis to evaluate the accuracy of Seasat-derived wind data. These were Hurricanes Fico in the eastern North Pacific, Ella in the western North Atlantic, and Greta in the western Caribbean Sea. The former was selected because of the availability of Air Force reconnaissance data in the inner core during Rev 331. The latter two were selected because of the simultaneous availability of Hurricane Research Division (formerly National Hurricane Research Laboratory) research flight data during Revs 952,966, and 988 for Ella, and Revs 1175,1182, and 1196 for Greta. Surface truth data from ships and from GOES cloud motions were also composited with respect to the storm center. The latter were available mainly outside the central dense overcast of the storm, i.e., beyond about 200 km, whereas the aircraft data were available in the inner core within 150 km of the center. Therefore, the three data sets were complementary in that they defined a wind field over the entire storm.

5.1. Aircraft Data The Air Force data in Fico were available only at the 700-mbar altitude. It has been established by others that for mature, steady-state hurricanes, there is relatively little wind shear with height between the boundary layer and about 400 mbar (LaSeur and Hawkins, 1963; Hawkins and Rubsam, 1968; Hawkins and Imbembo, 1976). Therefore, the Air Force 700-mbar winds were assumed to be representative of top of the boundary-layer winds. Powell(1980) has recently shown, based on his boundary-layer model and the work of Bates (1977), that the 19.5-m-level wind is about 20% lower than that at the top of the planetary boundary layer (PBL). Therefore, a factor of 0.8 times the flight-level wind was used to derive the neutral stability, 19.5-m wind, which is the assumed level for the SASS winds. Furthermore, the Cardone boundary-layer model was run for Fico using the aircraft-measured maximum wind, radius of maximum wind, and the prevailing synoptic-scale pressure gradient. The resulting surface wind directions were used as guidance for the analysis, with heaviest weighting going to ship reports. A 0.9 correction was applied to the GOES-derived cloud motions. The rationale for using this 0.9 factor is explained in Hawkins and Black (1982). For the Ella and Greta cases, the aircraft observations were made near cloud base, very close to the top of the PBL. The boundary-layer model


217

(Powell, 1980) was used to reduce these Inertial Navigation System (INS)derived flight-level winds to the surface. Detailed sea surface temperature fields were derived for the stability analysis from airborne expendable bathythermographs (AXBT) and airborne infrared radiation thermometer data. The reduced aircraft data were supplemented with cloud motions and ship winds at the larger radii.

5.2. Cloud-Motion Wnds Tracking of clouds using images from geostationary satellites to derive free air winds has been commonplace for many years. Problems arise, however, in obtaining such winds near hurricanes, because many of the clouds best suited for tracers at low levels do not persist or maintain their identity for even 30 min. When they do, they are obscured by high-level clouds much of the time, even though they may be viewed briefly through breaks in the clouds. To overcome these difficulties, experiments at the Goddard Space Flight Center sponsored by the National Aeronautics and Space Administration (NASA) and the National Earth Satellite Service (NESS) of NOAA have shown it feasible to obtain many low-level winds near hurricanes by tracking cloud motions if the imagery is taken at short intervals of 7.5 min or less (Rodgers et al., 1979). Hasler et al. (1977) had reported that cumulus cloud motions approximate, within amean vector difference of 1.3 m sec-', the speed and direction of the ambient flow at the cloud-base level compared with aircraft observations in the trade-winds regime over ocean areas. The cloud-tracking technique was applied for Hurricanes Ella and Greta and many winds were obtained at about the same time as the respective Seasat overpasses.

5.3. SASS Winds and Methods of Comparison The SASS algorithm used in this study selects backscatter observations from paired forward- and aft-looking antennas such that the individual observations are less than 50 km apart. The wind solution derived from the model function is that located at the midpoint between the paired observations. An explanation of the SASS algorithm is given in Chapters 3 and 4,when interpretation of instrument observations and ocean surface wind observations, respectively, are discussed. The surface truth winds are derived in several ways. First, and simplest, the wind at the midpoint between the cell pairs is interpolated from an analysis and compared with the SASS wind. The second method involves area

218


averaging the wind over each cell footprint that is used in the pairing. Then the two area-weighted surface truth winds are averaged. A third technique was used with the Greta data. Here, analyses were made of the wind speeds derived from the SASS measurements. The surface truth wind was then compared with the wind speed for the corresponding position interpolated from the SASS analysis. Results from all three comparisons are presented. It should be mentioned in passing that the SASS algorithm can also average the backscatter observations using the bin method. This involves averaging all the fore-beam backscatter observations and all the aft-beam backscatter observations within a bin of a prescribed size, such as 0.5 or 1” latitude on a side. The resulting averages are then used to compute the wind located at the center of the bin. This method was not used in tropical cyclone work because of the resolution that would have to be sacrificed to obtain a more stable backscatter measurement. 5.4. SASS Alias Removal in Tropical Cyclones

The SASS algorithm does not return a unique wind-vector solution. Two to four aliases result from only two independent backscatter measurements. The unwanted aliases were eliminated subjectively by selecting the direction closest to the tangent to a circle centered at the known storm location. The alias with relative inflow was selected when two aliases were close to the tangent direction. At distances greater than 200 km, the alias most consistent with the surface truth wind direction was chosen. A method for objective alias removal in tropical storms must await further research. OF INDIVIDUAL STORMS AND COMPARISON OF SEASAT DATA 6. ANALYSIS WITH “SURFACE TRUTH”DATA

6.1. Hurricane Fico, July 1978

Hurricane Fico developed from a tropical disturbance that was about 830 km south southeast of Acapulco, Mexico, on July 7,1978. While moving in a general westerly direction it gradually intensified and became a hurricane at 13.6”N and 111.4”W on July 10. It continued moving westward, alternately intensifying and weakening. By July 17, Fico had attained maximum winds of 59 m sec-’. On the 20th, it was about 410 km southeast of Hawaii when Seasat passed above. At about the same time, the Air Force reconnaissance aircraft made measurements at the 700-mbar level. By the next day, Fico’s track began to curve northwestward and the storm started weakening.

219


The surface truth data selected and processed as described in Section 5 are plotted in Fig. 12. Streamlines and isotachs analyzed to fit these data are shown in Fig. 13. For comparison with these data, the four aliases of the SASS wind solution are shown in Fig. 14. Figure 15 shows the selected “best” alias plotted in meteorological convention,’ and Fig. 16 shows the analyzed SASS streamline and isotachs. The surface truth and SASS streamline analyses for Fico (Figs. 13 and 15) are in generally good agreement. The one problem that emerges, however, is the existence of the artificially sharp wind shifts due north of the center in the SASS analysis (Fig. 15). These shifts appear to be a model

153’

151”

FIG.12. Distribution of surface truth data used for comparison with SASS data from Seasat Rev 331 for Hurricane Fico. Aircraft and ship wind data were composited over a 12-hr period. Two hurricane reconnaissance flights occurring between 0O:OOand 12:oO GMT, July 20, 1978, were used. Triangles with no wind barbs indicate that an aircraft wind-speed measurement was reported, but no direction was available. Cloud motions were centered at 04:OOGMT and based on If-hr motion vectors.

’ One crossbar is 5 m sec-’, one-half crossbar is 2.5 m sec-I, and a penant is 25 m sec-’

220


FIG.13. Isotach and streamline analyses for the 19.5-mlevel in Hurricane Fico, derived from the data in Fig. 12, representative of conditions for 04:30GMT,July 20,1978.

function deficiency and arise when the number of aliases returned from the wind solution changes from two or three to four. Further discussion of this is in Section 6.7.3. The surface truth and SASS-derived isotach fields also are in good agreement, up to wind speeds of 20 m sec- '. Above that the high-wind areas are clearly very much underestimated. The low estimates are due to coarseness of cell resolution and underestimation of the attenuation corrections caused by heavy precipitation. Table I summarizes the statistics for the comparison of surface truth fields with SASS wind fields for Fico. The table summarizesthe statistics for regions with and without attenuation corrections (AC) for intervening precipitation and water vapor. These statistics are presented in plotted form in Figs. 17a and 17b. For the precipitation-free regions, the RMS errors are about 2.0 m


22 1

FIG.14. SASS wind aliases for Hurricane Fico, Rev 331,04:30 GMT July 20,1978. Numbers indicate the average wind speed from the four aliases at each observation point.

TABLE I . STATISTICAL SUMMARY OF ERRORS FOR COMPARISON OF SASS WINDS WITH SURFACE TRUTH WINDSFOR HURRICANE Fico, REV33 1

No precipitation

No. of points E" speed RMS speed SD speed E" direction RMS direction SD direction

Precipitation

All data

No AC

AC

No AC

AC

No AC

AC

21 1 1.5 1.9 1.1 2.1 13.2 13.0

21 1 1.8 2.0 1.o 2.1 13.2 13.1

64

- 5.6 7.1 5.3 1.o 14.1 14.1

64 -3.1 5.2 4.2 0.5 14.0 14.1

215 -0.1 4.1 4.1 1.9 13.4 13.3

215 0.6 3.1 3.0 1.8 13.4 13.3

E = mean error(SASS- surface truth) = bias.

222


,

18'

16

FlCO R e v 331

14'

FIG.15. SASS wind aliases for Rev 331 that were closest in direction to the observed surface truth wind direction.

sec- for speed and 13" for direction, with standard deviation (SD)of i-1.0 m sec- and & 13.1'", respectively. The speed bias is significant at 1.8 m sec- for Fico. In the areas affected by precipitation, the attenuation correction reduces the R M S speed errors by about 30%, while leaving the direction errors unchanged. Even so, the speed errors are about double the errors for precipitation-free regions, exhibiting a larger negative bias of - 3.1 m sec-'. This bias is evident from the scatter plots in which the precipitationcontaminated points are seen to consist mainly of high-wind points. Other studies (Jones et al., 1982)have shown that SASS tends to underestimate high winds. Underestimated attenuation and poor spatial resolution appear to have further compounded the problem in this case. The directional errors for the precipitation areas also increase somewhat over the precipitation-free areas, ranging from 14 to 24". The statistics for all data considered together show little bias. This characteristic,however, is due to a positive bias, at winds

'


223

FIG. 16. Isotach and streamline analyses for Hurricane Fico based on SASS wind aliases for Rev 331 that were closest in direction to the surface truth analysis.

below 18 m sec-', of about 2 m sec-', and a negative bias, at higher wind speeds, of about 3 m sec-'. The RMS errors in speed and direction average out to about 3 m sec-' and 13". In the preceding comparisons, the surface truth winds have been interpolated to the point of the SASS wind solution. We now examine the case where cell area averages are computed. The cell pattern for Fico is shown in Fig. 18, with every other row of cells depicted. To determine if wind-speed statistics could be improved by improving the resolution, wind directions estimated from the SASS dealiased wind field were input to the algorithm as known quantities and wind speeds for each cell were computed. Surface truth winds averaged over the cell footprint were then compared with forwardlooking and aft-looking cells. The latter were larger for Fico, a descending orbit. The results are presented in Table I1 and as scatter plots in Figs. 19a and 19b for the fore and aft beams in Fico. The same tendency to overestimate

224

PETER G . BLACK ET A L .

L

I'

REV 331 v - POL

, # I

/

M

= 0.6771 = 0.3132

B = 5.3938 R

. ==

QUALITY 1 QUALITY 2

FIG. 17a. Scatter plot for Rev 331 over Hurricane Fico of surface truth wind speeds interpolatedto SASS wind locations versus SASS wind speeds. SASS observationswere obtained in the vertical polarization mode. Pluses indicate regions of cold cloud-top temperatures and large attenuation corrections. Circles indicate rain-free areas. The correlation coefficient is 0.91. TABLE11. ERRORSTATISTICS FOR FORWARD AND AFTBEAMS FOR Fico, REV 331 Aft beam

Forward beam

No. - points E RMS SD

No P

P

All

No P

P

All

119 2.4 2.9 1.5

52 -0.3 2.4 2.4

171 1.6 2.7 2.2

15 2.1 2.5 1.4

33 -0.7 1.9 1.8

108 1.2 2.3 2.0

225


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g- -216

5c -u

g1w0

-

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fl

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= 1.0tIlll

B = -3.6Y37 R = 0.9362 o.

r-"

T GROUND TRUTH WIND DIRECTION

I

=

QUALITY 1 QUALITY 2

lDEGI

FIG. 17b. Scatter plot for Rev 331 over Hurricane Fico of surface truth wind directions interpolated to SASS locations versus SASS wind directions.

wind speeds below 18 m sec- is seen in the scatter plots, as was the case for the original SASS-derived winds. This bias is tabulated as a function of wind speed in Table 111, averaged for both fore and aft beams. The primary result shown in this comparison is a reduction of the high-wind bias error in precipitation regions by a factor of 2 compared to the comparisons in Table I and Fig. 17a. In fact, the errors in the precipitation regions tend to be somewhat less than in the precipitation-freezones. This result suggests that a large portion of the high-wind errors are due to resolution problems. Limited comparisons of the nadir cell wind speeds with surface truth wind speeds have been made. Preliminary results for Fico and Greta indicate very large errors exist in the SASS algorithm for the nadir cells. Errors of 40-50% were found in both the Fico and Greta data. A sample comparison of shipderived surface winds in Fico with the nadir SASS winds is given in Fig. 20.


9.

-

u-

10-

17

-

-

1.

I-

U-

ll

-

' l r -

FIG.18. SASS antenna beam pattern showingall Doppler cell locationsfor every other row of cells for the forward- (foreshortened)and aft-lookingbeams as the Seasat subsatellitetrack passed east of Hurricane Fico, Rev 331,July 20,1978. The region of overlapping cell locations from the fore and aft beams is the region where SASS wind-vector solutions can be obtained.

227

6. SEASAT WIND/RAIN OBSERVATIONS 40

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36

40

FIG. 19a. Scatter plot of high-resolution SASS-derived wind speeds versus surface truth windfield-derived speeds at individual forward-looking cell locations for Hurricane Fico, Rev 331. Speeds were derived from measured radar cross sections at the cell location given the SASSderived wind direction. TABLE111. FOREAND AFTBEAMBIAS ERRORAS A FUNCTION OF WINDSPEEO FOR Fico, REV331 B(m sec-')

Speed range (m sec-')

2.1 1.4 - 1.3

< 15 15-20 20-25 z 25

- 3.0

Wentz (1981a) has recently used his SMMR algorithm (Wentz, 1983) to compute wind speed from the 10.7-GHz brightness temperatures. His initial results for Fico are shown in Fig. 21 from data averaged over the 90-km resolution SMMR grid 2. The agreement between the SMMR winds and the area-averaged surface truth (over the 90-km SMMR cell) agree to within 3 m

228

PETER G . BLACK ET AL. 40

I

1

1

I

1

I

I

1

c

3 6 r

32

t

:I-@-,

l2

I

I

16

20

1 4

'0

12

8

32

FIELD WINDS FIG.19b. Same as Fig. 19a, except for aft-looking cells.

32 30

I

I

I

100

200

300

I

I

I

I

I

I

I

400

500

600

700

800

900

1000

:t "0

20.5.N

ALONG-TRACK DISTANCE ( k m l

12.0'N

FIG.20. Comparison of SASS winds derived along the subsatellite track for Rev 331 at nadir incidence with surface truth winds based on merchant-ship reports.

229


8.2

8.8

0-7

i' looNR.2~-yq-j--~,s R FICO e v 331 1 -

-

8.8

7.9

8.5

7.4

7.2

6.2

3.5

I

1

I

4.1

I

I

I

I

I

I

FIG.21. Wind field derived from SMMR for Hurricane Fico, Rev 331, July 20, 1978.

'

sec- for Fico. In the region away from the storm, good agreement is found in the shape and location of the isotachs. SMMR's basic disadvantage is the large cell resolution (90 km) at the 10.7-GHz frequency, where the brightness temperature sensitivity to wind speed is strongest. 6.2. Hurricane Ella, September 1978

Hurricane Ella formed over the Atlantic Ocean and had moved to a point about 500 km southeast of Cape Hatteras, North Carolina, at the time of the Rev 952, September 1, 1978. It had intensified rapidly and had maximum

230


winds of 55 m sec-'. The strong winds were concentrated within 100 km of Ella's center. The intense gradients of the wind speed made accurate measurements difficult for the SASS instrument with its relatively low resolution. Northeast of the center, radially outward from the eyewall, the wind speed changed by 33 m s e t - ' in 55 km. 6.2.1. Comparisons with Surface Truth Fields Derived from Ship and Aircraft Data. As discussed in Section 5, the surface truth data for this storm came from aircraft measurements, low-level clouds, and surface ships. This time, aircraft data were obtained by the NOAA-RFC research aircraft at 170to 540-m altitudes. The surface truth winds are plotted in Fig. 22a, and

7'2"

-

FIG.22a. Surface truth wind data for the 20-m level in Hurricane Ella, September 1,1978. The dots represent measurements by NOAA-RFC research aircraft, the triangles are measurements by Air Force reconnaissance aircraft, and the circles are cloud motion measurements based on short-interval (7.5 min) GOES images processed on the NASA Goddard AOIPS facility.


23 1

analyses of these data are in Fig. 22b. The SASS winds, including the aliases, are shown in Fig. 23. The best-fit aliases in meteorological format are shown in Fig. 24, and analyzed fields are in Fig. 25. The SASS streamlines are in good qualitative agreement with the surface truth streamlines, except that, as in Hurricane Fico, there are artificially sharp wind shifts that seem to be a function of the number of aliases. See Section 6.7.3 for further discussion. There is excellent agreement, as there was in Fico, between the isotach fields for wind speeds up to 20 m sec-'. Above that, the high-wind areas are very much underestimated. In the case of Ella, much of this is due to cell resolution being too coarse. Underestimation of the attenuation corrections probably also contributes. Table IV summarizes the statistics for the comparisons of the surface truth fields with SASS wind fieldsfor Ella for regions with and without precipitation and with and without attenuation corrections for intervening precipitation and 72'

7 0"

14" -

/

32" 7

\

30°\

28" -

FIG.22b. Streamline and isotach analyses of data in Fig. 22a.

232


FIG.23. SASS wind aliases for Hurricane Ella, Rev 952, 13:OO GMT, September 1, 1978. Numbers indicate the average wind speed from the four aliases at each observation point.

Iv. STATISTICAL SUMMARY OF ERRORS FOR COMPARISON OF SASS WINDS TABLE WITHSURFACE TRUTHWINDSFOR HURRICANE ELLA,REV952 ~~

No precipitation

No. of points

E speed RMSspeed

SD speed

E direction RMS direction SD direction

Precipitation

All data

No AC

AC

No AC

AC

No AC

AC

185 -0.2 1.7 1.7 - 3.5 18.7 18.4

185 -0.1 1.7 1.7 - 3.5 18.7 18.4

112 -0.3 3.3 3.3

112 0.3 2.9 2.9 0.9 24.1 24.2

297 -0.2 2.5 2.5 -1.6 20.1 20.1

297 0.0 2.2 2.2 - 1.9 20.9 0.9

1.5 22.3 22.3

233


FIG.24. SASS wind aliases for Rev 952 that were closest in direction to the observed surface truth wind direction.

water vapor. The statistics are also presented in plotted form in Figs. 26a and 26b. The RMS errors for the precipitation-free region are 1.7 m sec-' for speed and 19" for direction. The speed bias is near zero. In the areas affected by precipitation, the attenuation correction reduces the RMS speed errors by about 15%, leaving the direction errors relatively unchanged. Even so, the speed errors are nearly double the errors for precipitation-free regions; i.e., about 2.9 m sec-'. This doubling is also evident in the scatter plots in which the precipitation-contaminated points are seen to consist mainly of high wind points. The strong gradients of wind speed in Ella also occur mostly in the areas of high winds. The RMS directional errors increase to 24". The statistics for all data considered together show little bias. This, however, is due to a positive bias at winds below 18 m sec-' of about 2 m sec- and a negative

'

234


52"

30"

28"

I

ELLA R e v 1952 /

1

1

I

28

700

FIG.25. Streamline and isotach analysesfor Hurricane Ella based on SASS wind aliasesfor Rev 952 that were closest in direction to the surface truth analysis.

bias at higher wind speeds, as was the case with Hurricane Fico. The RMS errors average out to 2.2 m sec-' and 21". This also is considered excellent. 6.2.2. Comparison between SASS Winds and Cloud Motions. The winds from tracking low-level clouds and from the SASS data for Ella on September 1,1978,were available for larger areas than shown in Figs. 22a and 22b. SASS winds and cloud motions were compared in the following way. Isotachs were drawn subjectivelyto provide a best fit to the SASS wind speeds and also to the cloud-derived wind speeds. There was relatively little random scatter in the SASS wind speeds because the mean of the absolute values of the differences between individual and analyzed values was only 0.6 m sec-' with a standard deviation of 0.7 m sec-'. The GOES wind speeds were reduced by 10% to make them more nearly approximate the winds at the 19.5-m level and were then compared with the

235


REV 952 v - POL fl

B R

.

0.8932

= 1.3536 = 0.8791 z

QURLITY 1 QURLITY 2

FIG.26a. Scatter plot for Rev 952 over Hurricane Ella of surface truth wind speeds interpolated to SASS wind locations versus SASS wind speeds. SASS observations were obtained in the vertical polarization mode. Pluses indicate regions of heavy precipitation based on airborne weather radar. Circles indicate rain-free areas. The correlation coefficient is 0.88.

SASS wind speeds, as explained in Hawkins and Black (1982). The comparisons were made between values at grid points of the two analyzed isotach fields. A summary of the differences for various portions of the storm is given in Table V where is the mean of the absolute values of the differences, and E is the mean of the differences. The correlation coefficient between SASS and GOES wind speeds was 0.64 for 99 pairs of values interpolated at grid points from analyses in all quadrants. Isogons were also analyzed subjectively for both the SASS and GOES wind directions. For the SASS winds, the alias at each point was selected

REV 952

v - POL ll

B R

a +

= 0.3009 14.4262 = 0.9648 =

QUALITY 1 QUALITY 2

FIG. 26b. Scatter plot for Rev 952 over Hurricane Ella of surface truth wind directions interpolated to SASS wind locations versus SASS wind directions.

TABLE v. DIFFERENCES BETWEEN SASS AND Northwest quad ~

X 0.9 GOES WIND SPEEDS*

Northeast quad

Southern quad

~~

I E(m ~ sec-') E (m sec-') Percentage of cases where SASS is greater Range of SASS speeds (m sec-') Range of GOES speeds (m sec-I) a

1.8 (0.5)b

1.4 (1.0)

1.8 (1.1)

+ 1.8 (0.5)

+ 1.3 (1.2)

+ 1.4 (1.6)

100

92

82

4-12

4-13

4-15

6-10

6-1 1

5-12

Positive values of E mean SASS winds are greater. Values in parentheses represent the SD.

237


subjectively after we considered which of the two to four aliases seemed reasonable under the existing hurricane conditions. When there was still doubt about which to select, the alias that most nearly agreed with the appropriately placed surface truth data was selected. Once the aliases were selected, comparisons were made between the analyzed values of the SASS and GOES isogons (lines of constant wind direction). The average difference between the values of the isogons interpolated at grid points for the SASS and GOES fields from the smoothed analyses was 17.5"with a SD of 13.6". Table VI lists values by quadrants. The correlation coefficient for 109 pairs of values interpolated at grid points from the isogon analyses in all quadrants was 0.81. If the wind direction differences between GOES and SASS are interpreted as real differences as a function of height, the change between the wind directions at the GOES level (cloud base or about 500 m) and the surface (SASS level, 19.5 m) is such that the angle of inflow decreases with increasing height in all quadrants, except the northwest. In that area, the angle of inflow increases with height. In most cases, one would expect the angle of inflow in a hurricane to decrease slightly with height in the subcloud layer. Hurricanes for which detailed flight data have been available show that some of the quadrants have outflow even at low levels, especially at radii greater than 100 km. However, it is surprising to find as much outflow in the northwest quadrant as suggested by the SASS winds. Of course, a different alias for the points in the northwest quadrant can be selected to obtain indications of inflow from the SASS data. However, the inflow angles are so large as to be unreasonable, and the differences from the GOES data are much larger. Furthermore, for both the aircraft and cloud motion winds, the major area of inflow was to the south of Ella with the angle of inflow being relatively small in the northwest quadrant. Figure 27 shows the wind speeds computed by Wentz (1981a) using his SMMR algorithm (Wentz, 1983) for wind speeds from the 10.7-GHZ brightness temperatures for Hurricane Ella. The agreement with surface truth was worse than for Hurricane Fico. SMMR speeds are too low by nearly 10 m sec-' near the center. TABLE VI. MEANDIFFERENCES j? BETWEEN DIRECTIONS OF SASS AND GOES WINDS' Northwest quad

Northeast quad

Southern quad

25" (20")

16" (10')

~~

-

-10"

E

a

(14")b

Positive value means winds veer with height. Standard deviation is given in parentheses.

238


35"N

30°N

25ON

FIG.21.

6.3. Hurricane Greta, September 1978 Hurricane Greta, September 13-20, 1978, offered a good opportunity for comparing the SASS winds with winds measured by low-flying hurricane research aircraft. Five Seasat revolutions were close enough to the storm for the scatterometer to make pertinent measurements and two aircraft flights were made by the NOAA aircraft to obtain wind measurements for comparison. The Seasat revolution times of closest approach to Greta are listed in Fig. 28 along with the times of the two research flights and a graph of the variation with time of the minimum surface pressures (a measure of intensity) in Hurricane Greta. Greta developed from a tropical wave which moved into the

239

6. SEASAT WIND/RAIN OBSERVATIONS I I

780918 FLIGHT # H

I

I

I

I

I

I160 I

16/00

1

1

I182

I175 I 16/12

I

17/00

1

I

17/12

1

I 1

I189 I 18/00

DATE /TIME (GMTI

I

1 1

I

I196 SASS REVOLUTION I 18/12

1

19/06

I 19/12

FIG.28. Minimum central pressure as a function of time for Hurricane Greta, September 16-18, 1978. Times of NOAA-RFC research flights and Seasat passes are indicated.

Caribbean on September 13 and caused winds at Barbados that gusted to 22 m sec-'. It was named at 12:OO GMT, September 14, while just north of The Netherlands Antilles, and reached hurricane intensity at 12:OO GMT September 16 while south of Jamaica. The storm moved in a general westerly direction across the Caribbean. Rev 1160 was about 20 hr before the first aircraft flight, but the other four revolutions were either during or between the flights. The hurricane attained its minimum central pressure of 947 mbar (59 m sec- maximum winds) early on September 18, as reported by an Air Force reconnaissance plane. 6.3.1. Comparisons with Aircraji Surface Truth Data. The areas for which data are available from SASS and from the aircraft are indicated in Fig. 29. In each case, the area has been outlined relative to the position of Hurricane Greta's center at the time the respective data were obtained. Figure 29 shows that fairly complete coverage of the storm was achieved by both Seasat and by the research aircraft. Caution needs to be exercised in this analysis, however, because the hurricane was changing in intensity with time, as shown in Fig. 28, and the time between Revs 1160 and 1196 is about 60 hr. Furthermore, about 30 hr elapsed between the two aircraft flights. The variations of intensity with time were used to interpret the differences that are found between the various wind sets. The analyses of the surface truth data were composed of winds that were from low-flying research aircraft and reduced to the 19.5-m level, x 0.9 cloud motions, and surface-ship winds. The distributions of these surface truth data are shown in Fig. 30. Streamlines and isotachs of the composited data are shown in Fig. 31. The SASS data are shown in Fig. 32, plotted in

240

PETER G . BLACK ET AL. TRACK

............. -------

FLIGHT #

780916 780918

FIG.29. Location of areas of SASS wind solutions and aircraft flight tracks relative to the center of Hurricane Greta, September 16-18, 1978.

meteorological convention, and the analyzed streamlinesand isotachs for this SASS data composite are shown in Fig. 33. All flight-level aircraft winds were reduced to the 19.5-m level with the Powell (1980) boundary-layer model, as was done for the Ella case. Comparisons between winds are made in three ways. First, 66 of the aircraft wind speeds, interpolated to grid points from the analyses in Fig. 30, are compared with interpolated values from the SASS wind analysis for the same grid point locations relative to the hurricane center. Efforts were made to compare aircraft and SASS data that most nearly agreed in time. Therefore, when SASS passes overlapped in the composite, the pass closest in time to the aircraft data was chosen for the composite field. The SASS and aircraft winds for Greta are plotted in a scatter diagram and are shown in Fig. 34. For these 66 pairs of data, the standard deviation of the differences between respective pairs was 3.9 m sec-* and the RMS was 3.8 m sec- l , The correlation coefficient between the aircraft and SASS winds was 0.86. For the pairs of values where the aircraft wind speed was < 31 m sec-', the average error was 0.5 m sec-' and the RMS was 3.0 m sec-'. The SASS winds were slightly stronger in the areas of weak winds and the aircraft winds were stronger in the areas of strong winds.


24 1

FIG.30. Composite of research flight data from September 17 and 18,1978, cloud motions and merchant-ship observations for Hurricane Greta. Winds are representative of the 20-m level.

A further comparison was made between the aircraft wind speeds and the SASS wind speeds. An aircraft wind speed every 2 min of flight in or near the

hurricane was compared with the interpolated value of the SASS wind speed for the position of the aircraft. The mean differences, standard deviations of the differences, and the R M S errors are tabulated in Table VII for all cases where the aircraft wind speed was 20 m sec-'

20

V 2 15 rn sec-'

45

V > lorn sec-'

137

-3.21 4.39 -1.44 3.33 -0.91 2.80

2.32 28.63 2.84 25.75 1.05 20.51

249


301

/

/

25

0 W W

% 0 I

I5-

I v)

s lo5-

O/

1

I

I

5

I0

I5

I 20

I

I

25

30

MODEL WIND INTERPOLATED TO CELL MATCH LOCATION (rn sec-’ )

FIG.36. Scatter plot of SASS wind speed versus Cardone boundary-layer model wind for Hurricane Ella, interpolated to SASS wind solution location for Rev 952, September 1, 1978.

errors for the storm, as a whole, are 2.4 m sec- with direction RMS errors amounting to about 25”. These errors are similar to those derived from in situ surface truth and suggest that a hurricane PBL model could be used to simulate surface wind fields in tropical storms and could be used with SASS observations for determining a detailed wind field over the entire storm. This procedure would be especially useful if only part of the storm were observed with SASS. Perhaps an iterative technique of fitting model wind fields to SASS wind fields could be derived to match the SASS observations with the initial guesses of maximum wind and radius of maximum wind. 6.6. Q E I 1 Storm A small, low-pressure system moved off the New Jersey coast on September

8,1978, and deepened explosively in the next 36 hr, causing the loss of a fishing trawler off the Grand Banks on September 9 and damage to the oceanliner Queen Elizabeth I1 (QE 11) 2 days later. Despite the network of NOAA data buoys and the relatively high density of transient ship reports off the U S . East

250

PETER G . BLACK ET A t .

25

-

-pro€

1

n w w

%

I5-

n

s P

v)

2

10-

5-

ELLA Rev 9 5 2

/ /

/

//

/

I

1

I

I

I

I

MODEL FOOTPRINT-AVERAGED WIND SPEED (rn sec-' )

FIG.37. Scatter plot of SASS wind speed versus Cardone boundary-layer model wind for Hurricane Ella, area averaged over SASS cell location for cell pairs used in the wind solution.

Coast, operational weather analyses and forecasts grossly underestimated the rate of deepening in this period. Operational surface wave analyses and forecasts were too low by a factor of 2-4 (Cane and Cardone, 1981). The QE I1 storm is an example of a bomb. Gyakum (1981) has performed an intensive study of the dynamics of the storm. Cane and Cardone (1981) studied the potential impact of Seasat scatterometer-derived winds on improved specification and prediction of the intense surface winds and sea states associated with this storm. The QE I1 storm was well observed by Seasat (Figs. 38a-38e). Particularly interesting data were returned from Rev 1066 near 12:OO GMT on September 9, 1981, where the left-side SASS scan viewed the early stages of the storm's development. Rev 1080, about 24 hr later, viewed the storm near maximum intensity. Rev 1093and 1094,24hr after Rev 1080,viewed most of the storm's circulation, including areas of high surface winds through a cloud-free atmosphere. Figure 39 compares the time history of maximum surface wind and minimum central pressure in the storm derived by postanalysis of Seasat and ship-log data, and the history indicated in operational NOAA analyses. The failure of the conventional real-time data base to resolve the storm

FIG.38a. Surface pressure analyses for time periods when SASS data were obtained over the QE I1 storm. 12:OO GMT, September 1978 map tirne-16:22 GMT SASS overpass time.

FIG.38b. Surface pressure analyses for time periods when SASS data were obtained over the Q E I1 storm. 00:00GMT, September 10, 1978, map time-04:22 GMT SASS overpass time.

FIG.38c. Surface pressure analyses for time periods when SASS data were obtained over the QE I1 storm. 12:OO GMT, September 10,1978, map time-14:Ol GMT, SASS overpass time.

FIG.38d. Surface pressure analyses for time periods when SASS data were obtained over the QE I1 storm. WOO GMT, September 11,1978, map time-0359 SASS overpass time. 252

FIG.38e. Surface pressure analyses for time periods when SASS data were obtained over the QE I1 storm. 12:00GMT, September 11, 1978, map time-16:31 GMT,SASS overpass time.

FIG.39. Time change of minimum central pressure and maximum wind for the QE I1 storm. 253

254


intensity led to poor initial-state specifications for numerical weather prediction. The strongest winds measured by SASS in the QE I1 storm were in Rev 1080, near 12:OO GMT on September 10, 1978. Figure 40 is a reanalysis of the surface pressure field for that time. The ship Euroliner (plotted near 43"N, SOOW) precisely fixed the location of the storm center and its central pressure. The SASS winds resolved the storm center as an area of wind speeds of 10-12 m sec-' with winds of 25-30 m sec-' (effective 19.5-mwind speeds) surrounding the center out to radial distances of about 220 km. The surface wind field in Rev 1093/1094, derived by detailed postanalysis of conventional data, was used to evaluate the performance of SASS geophysical evaluation algorithms (Boggs, 1981) in high winds. Figure 41 shows the location of the swaths, as well as the surface streamline and isotach analysis. A comparison of SASS wind speeds (from the SASS-1 model function) and wind speeds reported by ships (including the QE 11) within the fields of view of 1093/1094 is shown in Fig. 42. The differences seen are comparable to the errors characteristic of the ship data (Cardone et al., 1980). Comparisons of

FIG.40. Postanalysis of pressure field for the QE I1 storm for 12:OOGMT, September 10,1978, with location of SASS swath.

6. SEASAT WINDiRAIN OBSERVATIONS

255

GREENLAND,

FIG.41. Streamline and isotach analyses based upon SASS winds from revolutions for the QE I1 Atlantic storm on September 1 1 , 1978. Boundaries of wind solutions from these revolutions are shown by the dashed lines.

SASS and field estimates of the surface wind speed and direction are shown in Fig. 43a and b, respectively. These comparisons suggest that SASS has achieved measurement accuracies of at least f2 m sec- in speed and f20" in direction in winds as strong as 25 m sec- For this case, SASS seems to underestimate surface winds up to 20 m sec-', in contrast to the hurricane cases where it appeared that SASS overestimated winds in this range of speeds. SMMR wind data for the QE I1 revolutions have also been studied extensively by the SMMR Evaluation Task Group. Figure 44,from Wentz et al. (1982), is a comparison of collocated SMMR and SASS wind measurements for the QE I1 revolutions. The SMMR brightness temper-

'.

256


mi

SPOT REPORT COMPARISON REVS 1093/1094 v-POL SEPT 11,197e 0 BEAUFORT ESTIMATE (CORRECTED) 0 ANEMOMETER (ADJUSTED TO 19.5W

l4

I

OE 2

SASS ALGORITHM T (SASS-SPOT1 -1.58

b

--I .56

0 22

240

3t

/

OL

/

::

I:

15

Ilg

GROUND TRUTH WIND SPEED

I:

4:

$7

3

(m sec-'1

FIG. 42. Scatter plot of SASS wind speeds from Revs 1093 and 1094 versus individual merchant-ship wind-speed reports within the area of the QE I1 storm, September 11, 1978. Corrected Beaufort estimates, according to Cardone er ul. (1980), and anemometer-measured winds reduced to the 19.5-m level are compared.

atures are known to be contaminated by proximity to land and rain, and in daytime revolutions, by the contribution of reflected solar microwave radiation (sun glint). The data in Fig. 44 exclude cells at which the SMMR algorithm specified rain and at which the sun angle is < 15" for SMMR wind speeds that are < 15 m sec-'. Otherwise, sun angles < 10" are excluded. There is more recent evidence that SMMR wind estimates can be degraded for sun angles as large as 25" (Wentz, 1981b). Nevertheless, the SMMR winds in the QE 11 revolutions display sensitivity to at least 25 m sec-' and suggest that, at least for SMMR retrievals away from land or rain and not affected by sun glint, wind-speed measurement accuracy of f2 m sec-' is achievable with a passive microwave system.

257


--

24

'$21

-E

18

c3 W

w

a I5 n z -

= 12 UJ v) U

m 9

M = 0.8096 B = 1.5744 R = 0.8618 0 = QUALITY 1 + = QUALITY 2

6

3

o l 0

/

I

3

I

I

6

9

I 12

I 15

I

1 21

18

GROUND TRUTH WIND SPEED

1

24 fm sec-' I

1

I

27

30

324 288 I

-

252

0

g216 0 k-

E

I80

0 0

2 144 v,

2

108

M = 0.9566

72

36

'0

36

72

I08

144

180

216

252

288

324

360

GROUND TRUTH WIND DIRECTION (DEGI

FIG.43. Scatter plot of SASS wind speed from Rev 1093 (a) and direction (b) versus surface truth values for the QE I1 storm. The pluses are points where heavy cloud cover was observed from satellite cloud photographs.

258

PETER G.BLACK ET AL.

SASS WIND SPEED (m sec-1)

FIG.44. Scatter plot of SMMR wind speed versus SASS wind speed for Revs 1066,1074,1080, and 1094 over the QE I1 storm. [From Wentz et al. (1982).]

6.7. Discussion of Errors in Wind Measurement

The wind speeds and directions measured by Seasat compare quite well with the data assembled for surface truth, as shown in the error tables prepared for the various storms. There are some problems with both, although most are not too serious. 6.7.1. Accuracy of Surface Truth Data. The winds that were measured by aircraft and used in this study were all obtained with the aid of highly calibrated inertial navigation systems. The spot winds should have mean errors < 1 m sec- *. The wind directions,as long as the speeds are > 8 m sec- ', should have mean errors < lo". There are other problems however, which arise before these winds can be used for comparison. First, winds are measured at flight level and must be reduced to the 19.5-m level. While the best means available were used for accomplishing this reduction, undoubtedly some small errors were introduced. A more serious problem arises from the lack of coincidence in time. The Seasat made all of its measurements for a storm in about 3 min. The aircraft spent 3 or 4 hr making measurements for part of the same area. SASS winds are equivalent to long-term averages in time (perhaps 10-60 min) and about 30 km in space. By contrast, the aircraft winds are representative of speeds averaged over a period of 30 sec, or about 3 km in space. Furthermore, if an aircraft measures an extreme wind 1 hr after the Seasat overflight, that extreme wind may not have existed when the satellite was passing.


259

The cloud motion winds also have errors. In general, the cloud motion winds were obtained by averaging the cloud movement over about 30 min, with that 30 min coinciding very closely to the time of the Seasat overpass, so the time differences are not as critical as for the aircraft data. There always remains, however, the question of how closely low-level cloud motions correspond to the winds at the top of the boundary layer. Limited comparisons have been made with aircraft data in other hurricanes. These studies have indicated that the cloud-motion wind speeds agree with aircraft measurements that were made within a few hours of the time that the clouds were tracked to within about 2.5 m sec-' (Rodgers et al., 1979).There have not been sufficient cases to fully support this conclusion. The other surface truth data used here are from surface ships and buoys. There were very few buoy reports available. The surface-ship winds taken from merchant ships have well-known deficiencies. In general, not much emphasis was put on those reports in the hurricane comparisons reported in this study. Because of all the possible deficiencies in the surface truth data, especially those concerning time differences, it is remarkable that. the mean differences between surface truth and Seasat data were so small. 6.7.2. Accuracy of SASS Wind Speeds. In general, the SASS-measured speeds are higher than the surface truth speeds when the wind speed is less than 15 m sec- I. However, SASS underestimates aircraft-measured speeds when the wind speed is greater than 25 m sec-'. Exceptions to the last statement were the data from Rev 1182 over Hurricane Greta using HH or VH/HV mixtures of polarity. There, the SASS speed measurements were in reasonable agreement with surface truth for all speeds up to about 30 m sec-', although there was still a tendency for them to be high in the low ranges and low at speeds greater than 20 m secThe differences in the wind speeds measured by the various mixtures of polarization require further comment. Comparisons were made using only data where the three polarization mixtures (HH, VH or HV, and VV) were used to derive winds near the same location relative to the storm center. The wind-speed differences are greater for the higher wind speeds within 220 km of the hurricane center and especially for areas of high incidence angle. A schematic plot of the rows of SASS wind solutions that were parallel to the subpoint track for Rev 1182 is shown in Fig. 45. The hurricane center was between bands a and b and just north of 15" latitude. The differences between the mixtures of polarity were summarized for various areas and the results are shown in Table XJII. There were no data for the VV mixture in bands a or b; hence the comparisons are limited to the other bands. Table XI11 suggests that differences in wind-speed values for the various combinations of polarization were functions of wind speed and incidence

'.

260


FIG.45. Schematic representation of SASS data stratified according to increasing incidence angle bands from a to f for Rev 1182 over Hurricane Greta, September 17,1978. TABLEXIII. MEANWIND-SPEEDDIFFERENCES FOR MIXTURFS OF POLARIZATION FOR HURRICANE GRETA HH

- VV

Zone

(msec-')

Band c Band d Band e Band f Latitude belt 21-25" Latitude belt 17-21" Latitude belt' 13-17" Latitude belt 9-13" Area of 220-km radius centered on hurricane

0.9 0.4 1.8 4.7

(HV or VH) - VV (m set-')

0.1

0.6 0.2 1.1 3.1 0.0

1 .o

0.7

4.0

2.4

1.o

0.9

3.7

2.2

Hurricane center was in this belt.

angle. With the addition of similar data for Humcane Ella from Revs 966 and 988, these polarization differences were further investigated. An analysis of the wind speeds was prepared for each revolution for the V H (or HV) values since these values were more uniformly distributed over the entire storm area than were the HH and VV values. Differenceswere then calculated between the VH and each of the HH and VV values and plotted as a function of incidence angle and wind speed in Figs. 46 and 47, respectively. To facilitate drawing isolines of equal differences for these graphs, mean values were calculated for the differences for various incidence angle and wind speed intervals. These averages are plotted in Fig. 46 (representing 307 comparisons)and in Fig. 47 (representing41 1comparisons)and for HH versus V H and V H versus VV, respectively. Both Figs. 46 and 47 show that the differences are relatively small (less than 1m sec- l ) when the wind speed is less than 15 m sec- I . At wind speeds greater than 15m sec-', the differences vary rapidly with wind speed and less rapidly with the incidence angle. Since the

6. SEASAT WIND/RAIN OBSERVATIONS 55

-

50

-

g45

-

-

26 1

0

I

2

3

4

REVS 966 988

5

W

e 2 l

z40-

3 '

0

z

W

Q

u 35

-

=I1

-0.1 -0.1 -0.3

0

0.5

-3.y 2

-0.1 -0.1 - I 1.6 -0.3\-1.5

0

25

-1

I

,

Iw

5

10

I5

1

0.4

, 20

b

I

I

25

30

, 35

WIND SPEED (m sec-' ) HH POLARITY

FIG.46. SASS wind-speed differences (m sec- I) at a given location for HH polarization minus HV or VH polarization as a function of incidence angle and SASS HH polarization wind speed. Average values are plotted based on data from Revs 966 (Ella),988 (Ella), and 1182 (Greta).

FIG.47. Same as Fig. 46, except for HV or VH polarization minus VV polarization.

262


wind-speed data used in developing the algorithms were mostly less than 15 m sec-', this may explain why the algorithms do not handle well the problem of the mixed polarizations at high wind speed (Boggs, 1982). Comparisons of the research aircraft data with the HH, HV or VH, and VV polarization data were also made. Table XIV shows an example of these comparisons for Rev 988 over Hurricane Ella. These comparisons show that the HH winds are high and that the HV and VV values are slightly low. Typical maximum HH winds for Greta and Ella were 35 m sec-', while typical maximum VV winds were 25 m sec-'. Furthermore, the RMS values for HV are larger than for HH or VV. These comparisons suggest that a problem exists with the algorithm for deriving winds using horizontal polarization or a combination of horizontal and vertical polarization, especially at high wind speeds. It is, therefore, suggested that only vertical polarization values be used for severe storm analyses. 6.7.3. Accuracy of Wind Directions. Once an alias was selected for the wind direction, the SASS directions agreed quite well with those of surface truth. In most cases, when there was a bias, the SASS directions indicated more inflow than the directions measured at the top of the boundary layer. This agrees with boundary-layer theory and other observations for hurricanes. Selecting the proper alias does, at the present, require knowledge from other sources. In the case of a hurricane, the assumption of cyclonic flow around a known center location is generally sufficient for this purpose. It has already been mentioned (Section 6.1) that there is a problem with artificially sharp wind shifts due north of the center in Hurricane Fico and in the region northeast, northwest, southwest, and southeast of the center of Hurricane Ella. These shifts appear to be a model function deficiency and arise when the number of aliases returned from the wind solution changes from two or three to four. This occurs when the wind is changing direction relative to the antenna beam-pointing angle. In other words, when one antenna is looking directly upwind or downwind and the other is looking crosswind, a two- or three-solution case arises. As the wind shifts away from TABLE XIV. COMPARISON OF SEASAT AND AIRCRAFT WINDS MR HURRICANE ELLA,SEPTEMBER 3 AND 4,1978"

Polarity

HH VH(HV)

vv

Number of data points

Mean difference AC - Seasat (m sec-I)

RMS (msec-')

11 27 (13) 23 (18)

-3.1 1.4 (4.6) 1.1 (3.4)

6.0 (7.0) 5.8 (4.7)

4.4

'Numbers in parenthesesare for wind speeds >25 m sec-'.

263

6. SEASAT WINDiRAIN OBSERVATIONS

this situation, a four-solution case arises, with the need to select one of four aliases. A wind direction jump results. Since the beams are oriented SW-NE and NW-SE, the two- or three-solution case arises for SE, NE, NW, and SW wind directions. This situation could probably be minimized by some smoothing of the wind field. However, caution must be used in calculating vorticity, divergence, or wind stress curl, as artificial maxima and minima in the calculated fields will result. To examine these artificially sharp wind shifts, Table XV was prepared in which the errors were stratified according to whether the antenna orientation was upwind-crosswind, downwind-crosswind, or straddling the wind direction, either in an upwind-downwind orientation (perpendicular to the subpoint track), or in a crosswind orientation (generally parallel to the subpoint track). These statistics show that, in general, the speed errors are constant with orientation. However, the RMS directional errors for the upwind-crosswind and downwind-crosswind orientations tend to be larger than those for the straddle orientations. This is probably because most of the up- and downwind situations are two- and three-alias cases, whereas the straddle situations are four-alias cases. A smooth transition does not occur from the two- to three-alias case to the four-alias case. Furthermore, it was found that in all cases in Hurricanes Ella (Rev 952) and Greta (Rev 1182) when only two aliases were selected by the algorithms, the wind directions for these aliases were within 5" of the values 117, 293, 26, or 206". These values are the antenna viewing azimuths for an ascending orbit at 15-35" latitude. Furthermore, for the more common TABLE XV. VARIATION OF SASS ERRORS WITH ANTENNA AZIMUTH ANGLES TO THEWINDDIRECTION RELATIVE Up-Cross

Fico E speed RMS 8 direction RMS

Straddle up

NoP"

P

NoP

5-20b

>15

5-20

1.7

P >15

Straddle cross

Down-Cross

NoP

P

NoP

P

5-20

>15

5-20

>15

2.0 6.9 15.3

-3.8 5.4 3.8 14.3

1.8 2.1 12.4 17.8

0.9 1.7 10.7 12.5

2.0 2.2 0.3 9.1

-4.2 5.2 -1.4 7.9

1.5 1.8 -4.0 14.6

-3.3 5.6 -1.3 16.7

-0.6 1.8 2.9 20.3

1.0 2.0 5.2 24.7

-0.8 1.8 16.6 25.3

-0.3 2.4 8.8 18.1

0.2

0.0 3.5 2.0 16.6

-0.1 1.5 -5.1 8.2

0.2 3.8 -13.9 34.4

Ella

C, speed RMS 8 direction RMS a

No P, No precipitation;P, precipitation. Wind speed range.

1.7 -11.5 17.4

264


wind-vector solutions resulting in three or four aliases, the average direction of two of the wind aliases separated by less than 45" in direction corresponded to the above antenna viewing azimuths. Therefore, wind directions along the antenna orientation appear to be be preferentially selected. This may explain why wind direction errors will be preferentially larger for wind directions oriented at 45" to the antenna orientation. In the case of the Greta data, an analysis was made of the differences between aliases selected from two-alias reports and the aliases selected from four-alias reports at nearby points (within 55 km). The mean difference in direction was 25". 7. SEASAT OBSERVATIONS OF SEASURFACE TEMPERATURE NEAR TROPICAL CYLONES

Sea surface temperature patterns have been examined over the North Atlantic before, during, and after the passage of Hurricane Ella through the area using the Wentz SMMR algorithm. This algorithm relies mainly on the 6-GHz brightness temperatures. This frequency has a very coarse resolution, on the surface, of about 150 km. This channel also suffers from large antenna sidelobes, which prevent meaningful measurements from being made close to land. Very large land brightness temperatures contaminate the measurements over the ocean from the main lobe. It was found that when the main footprint is closer than 300 km from land in the cross-track direction or 600 km in the along-track direction, contamination occurs. Signal contamination also results from reflected microwave energy in sun glint areas (Wentz, 1981b). To avoid this problem, only night-time passes were used in the study. Orbits used for the study were Revs 902,945,952, and 988, all of which passed over the same geographical area. Surface truth sea surface temperatures were composited from ship data, AXBT data taken during 3 days of flights in the vicinity of Ella, and Polymode Experiment XBT data from ships in the area. Analyses were made corresponding to within 12 hr of the Seasat overpass time. Sea surface temperatures were then integrated over the 150-km footprint of SMMR grid 1. Figure 48 shows the alignment of swaths 1-4 over the area of interest, with SMMR-derived SST using the Wentz algorithm. The Ella storm track is superimposed. Figure 49 shows the surface truth sea surface temperature analysis for the area corresponding to Rev 945. Figure 50 shows the comparison of surface truth SSTs (indicated by triangles) and SMMR-derived SSTs. The solid symbols represent points that are contaminated by land. These comparisons, plus those from the other revolutions, support Wentz's error analysis over the North Pacific, indicating RMS errors on the order of f0.8"C.-


265 38

36

14

12

10

8

!6

FIG.48. Sea surface temperature measurements and SMMR cell locations based on 6-GHz brightness-temperature measurements for Rev 945, in the vicinity of Hurricane Ella, September 1, 1978.02:15 GMT.

8. APPLICATION OF SEASAT OBSERVATIONS TO OPERATIONAL MARINE STORMFORECASTING NEEDS

8.1. Tropical-Cyclone Forecast Applications Hawkins and Black (1982) have recently shown that Seasat measurements can be used to identify the gale-force wind radii in tropical cyclones as well as the radius of 25 m sec- wind. Every hurricane warning contains an estimate

'

266


30

L3

'26

36

a ~

34

.27-

32

30

-

28

.2B

'29

1

ELLA

-

30 26

'30 SEA SURFACE TEMPERATURE I*CI 12 GMT TBOBJt TO 12 GMT 780901

-

30

/ 16

?4

I2

?O

68

I

I

66

64

FIG. 49. Composite SST distribution for the 24-hr period 12:OO GMT, August 31, to 12:00GMT,September 1, centered on the time of Rev 945. The Hurricane Ella track and position at 0O:OOGMT, September 1, is shown with the position of the Gulf Stream (dash-dot line) and a cold eddy (dashed line) determined from Polymode XBT survey and a prior 5-day mean SST analysis.

of the extent of gale-force (17.5 m sec-') and hurricane-force (33 m sec-') wind for the four quadrants of the storm. In addition, typhoon warnings also contain an estimate of the radius of 26-m sec-' winds, since the probability of occurrence of winds above this threshold requires that certain actions be taken. It would, therefore, be of very great benefit if some observational data on these radii could be provided. Frequently, the gale-force radius is beyond the maximum radius for which normal reconnaissance is flown. In addition,

26

6. SEASAT WINDDAIN OBSERVATIONS

267

REV 945 CELL # 1

26 25

24

1

30

2

3

4

5

6

7

8

9

4

=,-[I CELL#2

0

1

10 W I-

1 I1

1

1

1

1

1

12

13

14

15

16

30FCELL K 3

% 29

FIG.50. SMMR-derived SST plotted as a function of cell number from north (left) to south (right) and for swaths 1 (top) to 4 (bottom). Uncontaminated values are indicated by an open circle, land-contaminated values from sidelobes by a solid circle, and precipitation-contaminated values from sidelobes (primarily in the vicinity of Ella) by solid squares. Area-averaged surface truth SSTs are indicated by the inverted triangle.

Seasat-derived radii would have the advantage of being actual surface measurements, not extrapolations from a higher aircraft altitude. Hawkins and Black compared SASS-derived gale-force wind radii with advisories issued by the main storm-advisory centers for the Atlantic, eastern Pacific and western Pacific regions. Nine revolutions were used for the East Pacific, eight for the Atlantic, and four for the West Pacific. This was especially worthwhile, since statistics for SASS winds at near gale force were

268


nearly unbiased and showed RMS speed and direction errors of 1.5 m sec-' and 20", respectively. Table XVI summarizes the mean differences between SASS and advisory gale-force wind radii, Advisory radii are overestimated, in general, by 30 to 50%. In the Atlantic, the advisory radii in the NE and NW quadrants are overestimated, while in the SE and SW quadrants the advisory radii are slightly underestimated on the average, based on this limited data set. This difference is probably due to intentional overwarning in the forward quadrants closest to land. The average overestimate is about 100 km, and the R M S is about 60 km. The Seasat scatterometer-derived radii for the extent of gale-force winds near tropical cyclones revealed several flaws in forecast advisory values. The radii of gale-force winds determined by SASS were consistently less than forecast. Advisory values exceeded those of SASS in 72% (56 out of 68) of the individual quadrant by quadrant comparisons presented in this study. Only 1 out of 31 cases in the East and Central Pacific indicated a forecast radius smaller than that independently observed by Seasat. These 31 cases include a mixture of advisories made with and without reconnaissance data. Thus, the advisories were conservative overestimates caused by high-quality surface wind data being unavailable to the forecaster in a real-time mode. The SASS measurements often detected large asymmetries in the distribution of the gale-force wind field. They were in direct contrast to forecasts specifyingone value to delineate the radius of gale force winds in all quadrants. This is clearly evident for the East and Central Pacific cases involving Hurricane Fico. Forecasters typically try to avoid this problem by using several rules of thumb to help specify wind field asymmetries. Westwardmoving storms are usually assigned semicircular asymmetries, with the larger radius to the right of the storm track (i.e., for Fico, 2Wkm northern

TABLE XVI. ADVISORY MINUSSASS GALE-FORCE WINDRADII(km)'

Quadrant

East Pacific

PI Atlantic

IEI West Pacific

PI a

NE

SE

sw

NW

109 (69) 105 55 (70) 74 -3 (110) 75

147 (106)

154(100) 160 - 15 (66) 47 - 12 (125) 100

100 (73) 100 -67 (107) 105 7 (34) 25

110

- 14 (64) 42

- 10 (26) 55

Mean and standard deviation (in parentheses) and average error, IEl.


269

semicircle- 125-km southern semicircle). Prevailing synoptic patterns may suggest a slightly rotated pattern, such as 200-km northeast semicircle and 125 km elsewhere. These semicircular advisories represent the best guess possible without the benefit of low-level reconnaissance data. This study indicates that the technique is far superior to the circular methods. But SASS observations also reveal that substantial errors can occur in forecasts specifying asymmetries. Several conclusions can be drawn about forecast errors for storms in different ocean regions. In general, Atlantic and western Pacific errors were relatively small and probably resulted from the availability of reconnaissance data and/or proximity to land. Eastern and Central Pacific comparisons for Fico were degraded when reconnaissance data became available. This occurred when the 700-mbar reconnaissance-derived values were increased by over a factor of 1.5-2.0 to represent the surface extent of gale winds as the storm neared Hawaii. A more correct method of reduction would have yielded values very close to those independently sampled by SASS. Mean error statistics for each quadrant are influenced strongly by individual errors because of the limited number of cases. Some tentative conclusions can be reached by taking these statistics into account. Forecasts delineating semicircularasymmetriesusually have more trouble with winds in the northern sector of the storm. In the majority of cases, the error is greater in the northeast quadrant (typically area of highest winds). Forecasts utilizing circular symmetries routinely produced very large errors in the southern quadrants. The conservativenature of these forecasts is at fault. The forecast must be applicable to the quadrant of the storm containing the largest radius of gale winds. The accuracy of this value in other quadrants suffered appreciably. Seasat surface winds could have provided tropical-cyclone forecasters around the world with an invaluable forecast aid. This would have been especially true for storms off Australia, India, and the East Pacific where reconnaissance data are nonexistent, or minimal at best. This “proof of concept” oceanographic satellite has shown that active microwave radars can detect gale winds created by tropical systems. Since SASS can work in the severe meteorologicalconditions associated with these storms, the technology has been validated. An operational scatterometer with three antennas per side to eliminate wind-direction aliases would benefit a much larger user community than tropical meteorologists, alone. It is, thus, recommended that a polar-orbiting scatterometer be launched as soon as feasible, possibly on a modified NOAA TIROS-N type meteorological satellite. Chang (1981) has recently conducted numerical studies to investigate whether SASS data could be used to initialize the new generation of hurricane dynamical prediction models. He studied cases using only initial data

270


as well as initial data followed by periodic update of the model at selected time steps. The impact of the satellite-sensed winds on the intensity forecasts of tropical cyclones is evaluated by a simulation study with an axisymmetric numerical model. The parameterized physics in the forecast model are deliberately made different from those in the model that generates the observations. Model-generated “observations” are assimilated into forecasts by 12-hr dynamic initialization. A series of 24-hr forecasts with and without assimilation of satellite-sensed winds are conducted and compared with the “observations.” Results indicate that assimilation with marine surface (or low-level) wind alone does not improve intensity forecasts appreciably, that a strong relaxation coefficient in the initialization scheme causes model rejection of the assimilation, and that an attenuating relaxation coefficient is recommended. However, when wind observations at the outflow level are included in the assimilation, forecasts improve substantially. The best forecasts are achieved when observations over the entire lower troposphere are assimilated. Additional experiments indicate the errors in the satellite observation contaminate the forecast. But the assimilation of inflow and outflow winds still improves the intensity forecast if the satellite observation errors are less than, or about the same magnitude as, those in the initial wind field. A more limited experiment was also conducted by Fiorino and Warner (1981) in which they initialized a 12-hr forecast for Hurricane Eloise (1975) with surface wind fields derived from composited ship reports. The fields were used as initial conditions for the forecast in a manner similar to the way Seasat winds could be used. The winds were used to diagnose a surface pressure field. However, results were inconclusive, since only a 12-hr integration was performed, during which no improvement over conventional methods resulted. Neumann and Pelissier (1981) have shown that dynamical-forecast models show relative improvements only for forecast periods greater than 36 hr. 8.2. Extratropical-Cyclone Forecast Applications

The early stages of the QE I1 storm provide a striking example of the potential impact of Seasat data on improved weather and state-of-sea forecasts. Cane and Cardone (1981) show how the satellite data could have detected the explosive development of the storm about 24 hr before it was seen in real-time conventional data. Early detection would contribute to forecast improvements as follows: First, marine forecasters and ship routers would be given early warning of the severity of developing severe marine storms and

6. SEASAT WlNDiRAIN OBSERVATIONS

27 1

nowcasts and short-range forecasts would improve. Second, numerical weather prediction models would be provided with initial-state specifications during the deepening stage. While numerical models still do not simulate well the role of convective-scale processes in the development of severe storms, frequent updating of model initial states with satellite data should lead to improved weather forecasts in the 1- to 5-day time frame. Improved weather forecasts would lead naturally to improvements in seastate forecasts. Cane and Cardone (1981) have reported a series of simulation experiments specificallydesigned to assess the potential impact of marine surface wind data on numerical weather prediction. The experiments were performed with the analysis scheme and general circulation model of the NASA Goddard Laboratory for Atmospheric Sciences. Care was taken to duplicate the spatial coverage and error characteristics of conventional surface, radiosonde, ship, and aircraft reports. These observations, suitably degraded to account for instrument and sampling errors, were used in an anal ysis-forecast cycle that resembles those used by major meteorological centers. A series of five 72-hr forecasts were made using the analyzed fields as initial conditions. The forecast error growth was found to be similar to that in operational numerical forecasts. Further experiments simulated the time-continuous assimilation of remotely sensed marine surface wind and temperature sounding data, in addition to the conventional data. The wind data were fabricated directly for model grid points that were intercepted by a Seasat scatterometer swath and were assimilated into the lowest active level (945 mbar) of the model with a localized successive correction method (SCM). The temperature sounding experiment assimilated error-free data that were fabricated along the actual Nimbus orbits. Forecasts were made from the resulting analysis fields and the impact of the simulated satellite data assessed by comparing these forecast errors with those of the control forecasts. The results of these experimentsfor the lowest layer wind and for the surface pressure are shown in Figs. 51 and 52, respectively. There are notable impacts in the extratropical Northern Hemisphere, particularly in the data-sparse North Pacific and in North America, downstream from the Pacific. The assimilation of error-free sounder data (again by the SCM) gave impacts comparable with the wind data, suggesting that surface wind data alone may be as valuable as temperature soundings for numerical weather prediction. Figure 52 also shows that the effect of nominal SASS errors ( k2 m sec-' in magnitude, &20° in direction) on the impacts derived from wind data were negligible (at least insofar as these errors are uncorrelated). Available Seasat results indicate that the operational SASS errors are of this magnitude. Examination of the individual cases shows that the impacts result from

272


r

0

r

N. AMERICA

I

DAYS

2

3

0

EURASIA

1

DAYS

2

3

FIG.51. Forecast errors out to 72 hr in the Goddard general circulationmodel for six regions of the world using simulated SASS surface winds as initial data. Data are integrated using a successive correction method (SCM).Forecasts were compared with the control (C) using conventional data and with simulated error-free remote temperature sounding data (T).

improvement in the forecast of specific features rather than a uniform global improvement. These types of forecast impact tests, utilizing real Seasat data, have begun at several centers and preliminary results are expected soon. Several studies have already been completed that show that Seasat wind data when combined with conventional ship data (Yu and McPherson, 1979) or satellite cloud-tracked winds (Endlich et al., 1981) can provide improved surface pressure field specifications in data-sparse marine regions. The spectacular improvements in Southern Hemisphere numerical weather predictions observed during FGGE (Guymer and LeMarshall, 1979; Leslie et al.. 1981) have been attributed, mainly, to improved initial surface pressure fields made possible by drifting buoy data. This suggests that significant improvements will also

273

6. SEASAT WIND/RAIN OBSERVATIONS 5 r

--..- -.*..--o..-

CONTROL

-Q- PERFECT WINOS [SCMI WINDS (SCMI WITH NOMINAL SEASAT-1 ERRORS PERFECT TEMPERATURES

4-

L 1 2 3

OO

,.a.

c,

*..*a

P OO

oar

U i 2 3 oar

FIG.52. Same as Fig. 51, except for sea-level pressure errors for North America and the North Pacific, only.

be derived with the assimilations of surface wind (and derived surface pressure fields), based upon real Seasat data sets that were obtained in Northern Hemisphere basins. 9. CONCLUSIONS Comparisons of the SASS-derived winds for Hurricanes Fico, Ella, and Greta, and the QE I1 case, show that the wind speeds and directions are accurate to within the design specifications as long as the wind speeds are less than about 26 m sec-'. That is, the wind speeds are accurate to within 2 m sec-' or lo%, whichever is larger, and the directions are accurate to within 20". The cases reported herein, where the errors were larger, can be explained as caused by inadequate computation of the attenuation, poor sensor resolution relative to observed gradients, or lack of time correspondence with surface truth data. In the case of Hurricane Greta data, there were suggestions that SASS might measure winds relatively well up to 30 m sec-l, In general, SASS tends to measure light winds too high and intense winds too low. The few comparisons for winds in hurricanes derived from measurements by SMMR revealed errors which exceeded SASS. Selecting the proper alias for the wind direction requires knowledge that is unavailable in the SASS data. There are still some problems with abrupt shifts in the wind directions that are related to changes in the number of aliases. Another problem arises because the SASS wind speed is a function of the polarization used to make the backscatter measurement. This problem seems

274


to be most serious with the higher incidence angles and the higher wind speeds. In spite of all the reservations expressed above, the data seem accurate enough to be very useful in severe marine storm forecasting and research.

ACKNOWLEDGMENTS This work was sponsored jointly by NOAA/NESS, Oceanic Sciences Branch; by NOAA, Atlantic Oceanographic and Meteorological Laboratory, Hurricane Research Division; by NASA, Langley Research Center; and by NASA, Jet Propulsion Laboratory. The authors gratefully acknowlege this support. This work would not have been possible without the pioneering algorithm development by Dr. Dale Boggs, Jet Propulsion Lab; Dr. Frank Wentz, Remote Sensing Systems: and Mr. Emidio Bracalente, Langley Research Center. Much of the work reported here was done in collaboration with Dr. W. Linwood Jones, Mr. L. C. Schroeder, and Mr. Willian Grantham, Langley Research Center. Program support has been provided by Mr. John W. Sherman, 111, NOAA/NESS/OSB, and by Dr. George Borne, Dr. David Lame, and Dr. James Dunne, Jet Propulsion Lab. The help of Mr. John Wilkerson, NOAA/NESS/OSB, in coordinating many of the activities and materials, especially the Seasat Storms Workshop, that resulted in this summary is gratefully acknowledged. The cooperation of Mr. Edward Rodgers, NASA/Goddard Space Flight Center, and Mr. Joe Steranka, GE-MATSCO, in producing GOES cloud-motions data is also sincerely appreciated. The results of this study are also due in no small part to the vision of Dr. John Apel, Johns Hopkins University, Dr. Willard Pierson, City University of New York, and Dr. Richard Moore, University of Kansas, who were instrumental in making Seasat possible. The assistance of Claire Black and Angel Tillman in typing the manuscript is gratefully acknowledged, as is the assistance of Constance Arnhols for editorial work, Dale Martin in drafting the figures, and Tom Tatnall in photographing the figures.

REFERENCES Adler, R. F., and Rodgers, E. B. (1977). Satellite observed latent heat release in a tropical cyclone. Mon. Weather Rev. 105,956-963. Allison, J. F.,Rodgers, E. B., Wilheit, T. T., and Fett, R. W. (1974). Tropical cyclone rainfall as measured by the Nimbus 5 electrically scanning microwave radiometer. Bull. Am. Meteorol. SOC.55,1074-1089.

Atkinson, G. D., and Holliday, C. R. (1977). Tropical cyclone minimum sea level pressure/ maximum sustained wind relationship for the western North Pacific. Mon. Weather Rev. 105,421-427. Barrick, D. E., and Swift, C. J. (1980). The Seasat microwave instruments in historical perspective. IEEE J. Oceanic Eng. 5,74-79. Bates, J. (1977). Vertical shear of the horizontal wind speed in tropical cyclones. NOAA Tech. Memo. ERL WMPO-39, US.Dept. of Commerce. Boggs, D. H. (1981). The Seasat scatterometer model function: The genesis of SASS 1. Publ. No. 622-230, Jet Propulsion Laboratory, California Inst. Tech., Pasadena, Calif. Boggs, D. H. (1982). Seasat geophysical data record (GDR) users handbook-scatterometer. Publ. No. 622-232, Jet Propulsion Laboratory, California Inst. Tech., Pasadena, Calif.


275

Bosart, L. F. (1981). The President’s Day snowstorm of 18-19 February, 1979: A subsynopticscale event. Mon. Weather Rev. 109, 1542-1566. Brown, G. (1979).Estimation of surface wind speeds using satellite-borne radar measurements at normal incidence. J . Geophys. Res. 84,3974-3978. Cane, M. A,, and Cardone, V. J. (1981). The potential impact of scatterometry on oceanography: A wave forecasting case. I n “Oceanography From Space”(J. F. R.Gower, ed.),pp. 587-595, Plenum, New York. Cardone, V. J., Young, J. D., Pierson, W. J., Moore, R. K.,Greenwood, J. A.,Greenwood, C., Fung, A. K.,Salfi, R., Chan, H. L., Aforani, M., and Komen, M. (1976). The measurement of the winds near the ocean surface with a radiometer-scatterometer on Skylab. Final Report, Contract No. NAS-9-13642, SUNY Inst. Marine and Atmos. Sci., City College, New York. Cardone, V. J., Broccoli, A. J., Greenwood, C. V., and Greenwood, J. A. (1980). Error characteristics of extratropical storm wind fields specified from historical data. J . Petrol. Tech. May, 872-880. Chang, S. W. (1981). The impact of satellite sensed winds on intensity forecasts of tropical cyclones. Mon. Weather Rev. 109, 539-553. Chester, T. (1981).SMMR rain rate algorithm modifications. SMMR Mini-Workshop IV,Ch. 10. Publ. No. 622-234, Jet Propulsion Lab., Pasadena, Calif. Dunn, G. E., and Miller, B. I. (1964). “Atlantic Hurricanes.” Louisiana State Univ. Press, Baton Rouge, La. Dvorak, V. R. (1975). Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Weather Rev. 103,420-430. Endlich, R. M., Wolf, D. E., Carlson, C. T., and Maresca, J. W., Jr. (1981). Oceanic wind and balanced pressure-height fields derived from satellite measurements. Mon. Weather Rev. 109,2009-2016. Fedor, L. S., and Brown, G. S.(1982). Wave height and wind speed measurements from the Seasat radar altimeter. J. Geophys. Res. 87, 3254-3260. Fedor, L. S., Godbey, L. W., Gower, J. F. R.,Guptil, R., Hayne, G. S.,Rufenach, C. L., and Walsh, E. J. (1979). Satellite altimeter measurements of sea state.-an algorithm comparison. J. Geophys. Res. 84,3991-4002. Fiorino, M., and Warner, F. L. (1981). Incorporating surface winds and rainfall rates into the initialization of a mesoscale hurricane model. Mon. Weather Rev. 109, 1914-1929. Gentry, R. C. (1964). A study of hurricane rainbands. NHRP Report No. 69, U S . Dept. of Commerce, Washington, D.C. Gentry, R. C. (1970). Modifying the greatest storm on earth-the hurricane. Underwater Sci. Tech. J . Dec., 204-214. Grifith, C. G., Woodley, W. L., Grube, P. G., Martin, D. W., Stout, J., and Sikdar, D. N. (1978). Rain estimation from geosynchronous satellite imagery-visible and infrared. Mon. Weather Rev. 106,1153-1171. Grody, N. C., Hayden, C. M., Shen, W. C. C., Rosenkranz, P. W., and Staelin, D. H. (1979). Typhoon June winds estimated from scanning microwave spectrometer measurements at 55.45 GHz. J . Geophys. Res. 84,3689-3695. Guinard, N. W., Ransom, J. L., Jr., and Daley, J. C. (1971). Variation of the NRCS of the sea with increasing roughness. J . Geophys. Res. 7 6 , 1525. Guymer, L. B., and LeMarshall, J. F. (1981). Impact of FGGE buoy data on Southern Hemisphere Analyses. Bull. Am. Meteorol. Soc. 62, 38-47. Gyakum, J. R. (1981). On the nature of an explosively developing cyclone in the Northern Hemisphere extratropical atmosphere. Ph.D thesis, Dept. of Meteorology, MIT, Cambridge, Mass.

276


Hasler, A. F., Shenk, W. E., and Skillman, W. (1977). Wind estimation from cloud motion: Phase 1,2 and 3 of an in-situ aircraft verification experiment,J . Appl. Meteorol. 18,812-815. Hawkins, H. F., and Imbembo, S. M. (1976). The structure of a small, intense Hurricane-Inez, 1966. Mon. Weather Rev. 104,418-442. Hawkins, H. F., and Rubsam, D. F. (1968). Hurricane Hilda, 1964 11: Structure and budgets of the hurricane on October 1,1964. Mon. Weather Reo. 96,617-636. Hawkins, J. D, and Black, P. G. (1982). Seasat scatterometerdetection of gale force winds near tropical cyclones. J. Geophys. Res. 88, 1674-1682. Jones, W. L.,Schroeder L. C., and Mitchell,J. L. (1977). Aircraft measurementsof the microwave scattering signature of the ocean. IEEE Trans. Antennas Propag. 25,52-61. Jones, W.L., Schroeder, L. C., Boggs, D., Bracalente, E. M., Brown. R. A., Dome, G., Pierson, W. J., and Wentz, F. J. (1982). The Seasat-A satellite scatterometer: The geophysical evaluation of remotely sensed wind vectors over the ocean. J. Geophys. Res. 87, 3291-3317.

Jorgensen, D. (1984). Mesoscale and convective-scale characteristics of mature hurricanes. I: General observations by research aircraft. J. Atmos. Sci. 41, 1268-1285. Jung, H. J. (1980). The determinationof rainfall rates from satellite measurementsof the thermal microwave emission. Contrib. Atmos. Phys. 3,366-388. Kidder, S.Q., Gray, W. M.,and Vonder Haar, T.H. (1978). Estimating tropical cyclone central pressure and outer winds from satellite microwave data. Mon. Weather Reu. 106,1458-1464. Klein, W.H.(1957). Principal tracks and mean frequencies of cyclones and cyclones in the Northern Hemisphere. Res. Paper No. 40.U.S. Dept. of Commerce,Washington, D.C. Krishen, K. (1971). Correlation of radar backscattering cross sections with ocean wave height and wind velocity. J. Geophys. Res. 76,6528-6539. LaSeur, N. E., and Hawkins, H. F. (1963). An analysis of Hurricane Cleo (1958) based on data from research reconnaissance aircraft. Mon. Weather Reu. 91,694-709. Lawrence, M. B. (1979). Atlantic hurricane season of 1978. Mon. Weather Rev. 107,447-491. Lawrence, M. B., and Pelissier, J. M. (1981). Atlantic hurricane season of 1980. Mon. Weather Reo. 109,201-216. Leary, C. (1971). Systematic errors in operational National Meteorological Center primitiveequation surface prognoses. Mon. Weather Reo. 99,409-413. Leslie, L. M., Mills, G.A., and Gauntlett, D. H. (1981). Recent improvement in limited area numerical prognosis accuracy in Australia. Q. J . R. Meteorol. Soc. 107,629-642. Malkus, J. S.,Ronne, C., and Chafee, M. (1961). Cloud patterns in Hurricane Daisy, 1968. Tellus 13,8-30.

Moore, R. K.,Birrer, I. J., Bracalente,E. M., Donne, G.J., and Wentz, F. J. (1982). Evaluation of atmospheric attenuation from SMMR brightness temperature for the Seasat satellite scatterometer. J. Geophys. Res. 87,3337-3354. Neumann, C. J., and Pelissier, J. M. (1981). Models for the prediction of tropical cyclone motion over the North Atlantic: An operational evaluation. Mon. Weather Rev. 109,522-538. Nordberg, W., Canaway, J., Ross, D. B., and Wilheit, F. (1971). Measurements of microwave emission from a foam covered, wind driven sea. J. A t h s . Sci. 28,429-435. Palmkn, E. (1948). On the formation and structure of tropical cyclones. Geophysica 3,26-38. Powell, M. D. (1980). Evaluations of diagnostic marine boundary layer models applied to hurricanes. Mon. Weather Rev. 108,757-766. Rice, R. B. (1979). Tracking a killer storm. SAiL 10, 106-107. Rodgers, E. B., and R. F. Adler (1981). Tropical cyclone rainfall characteristicsas determined from a satellite passive microwave radiometer. Mon. Weather R w . 109,506-521. Rodgers, E. B., Gentry, R. C.,Shenk, W., and Oliver,V.(1979). The benefits of using short interval satellite images to derive winds for tropical cyclones. Mon. Weather Rev. 107,575-584.


277

Rosenkranz, P. W., Staelin, D. H., and Grody, N. C. (1978). Typhoon June (1975) viewed by scanning microwave spectrometer. J . Geophys. Res. 83, 1857-1868. Ross, D. B., and Cardone, V. (1974). Observation of oceanic whitecaps and their relation to remote measurements of surface wind speed. J . Geophys. Res. 79,444-452. Ross, D. B., Conaway J., and Cardone, V. J. (1970). Laser and microwave observations of sea surface conditions for fetch limited 17 to 25 m s - winds. I E E Trans. Geosci. Electron. 8,326. Ross, D. B., Au, B., Brown, W., and McFadden, J. (1974). A remote sensing study of Pacific Hurricane Ava. Proc. Int. Symp. Remote Sens. Environ., 9th. Ann Arbor, Mich. pp. 163-180. Rufenach, C. F., and Alpers, W. P. (1978). Measurement of ocean wave heights using the Geos-3 altimeter. J . Geophys. Res. 83,501 1-5018. Sanders F., and Gyakum, J. R. (1980). Synoptic-dynamic climatology of the bomb. Mon. Weather Rev. 108, 1589- 1606. Simpson, R. H. (1971). Thedecision process in hurricane forecasting. NOAA Tech. Memo, NWS SR-53. Simpson, R. H., and Riehl, H. (1981). “The Hurricane and Its Impact.” Louisiana State Univ. Press, Baton Rouge, La. Weissman, D. E., King, D. B., and Thompson, L. W. (1979). Relationship between hurricane surface winds and L-band radar backscatter from the sea surface. J. A p p l . Meteorol. 18, 1023-1034. Wentz, F. J. (1981a). The effect of atmospheric attenuation on microwave scatterometer measurements. Remote Sensing Systems, Tech. Rep. No. 091781. Wentz, F. J. (1981b). The effect of sea surface sun glitter on microwave radiometer measurements. Remote Sensing Systems, Tech. Rep. prepared for Jet Propulsion Laboratory. Wentz, F. J. (1983). Model function for ocean microwave brightness temperatures. J . Geophys. Res. 88,1892- 1908. Wentz, F. J., Cardone, V.J., and Fedor, L. S. (1982). Intercomparison of wind speeds inferred by the SASS, altimeter and SMMR. J . Geophys. Rex 87,3378-3384. Willoughby, H. E., Clos, J. A,, and Shoreibah, M. G. (1982). Concentric eyewalls, secondary wind maxima and the evolution of the hurricane vortex. J. Atmos. Sci. 39,395-41 1. Yu, T., and McPherson, R.D. (1979). Surface pressure analysis using scatterometer derived wind data from the Seasat-A satellite, Preprints, Conf Am. Meteorol. Soc. Numer. Weather Predict., 4th. Silver Spring, Md., pp. 351-355.

’


CHAPTER

7

SEASURFACETEMPERATURE DETERMINATIONS JOHN C . ALISHOUSEE. PAULMCCLAIN NOAAINESDISISEL Washington, D.C .

NOAA/hrESDfS/CESL Washington, D. C.

I . Introduction to Remote Sensing of SeaSurfaceTemperature . 1.1. Radiative Transfer . . . . . . . . . . . . . . . 1.2. Transmissivity and Emissivity Considerations. . . . . 2. Microwave Sensing of Sea Surface Temperature . . . . . 2.1. Background . . . . . . . . . . . . . . . . . 2.2. SMMRResults . . . . . . . . . . . . . . . . 3. Infrared Sensing of Sea Surface Temperature . . . . . . 3.1. Background and Previous Results. . . . . . . . . 3.2. AVHRRResults. . . . . . . . . . . . . . . . 4. Summary. . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . .

. . . . . . .

. . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . .

. . . . . . .

. . . .

279 279 280 281 281 282 286 286 287 292 294

1 . INTRODUCTION TO REMOTE SENSING OF SEA SURFACE TEMPERATURE

1.1. Radiative Transfer The propagation of radiation through a nonscattering medium in thermodynamic equilibrium such as the earth’s atmosphere is described by the equation of radiative transfer (Chandrasekhar, 1960):

dJ(v, 6 ) = { - Z(v, 6 )

+ B[v, T ( Z ) ] } K ( vZ)p(Z)sec , 8 dZ

(1)

where I(v, 6)is the intensity of the radiation at frequency v and angle 6, B(v, T ) is the Planck function = (2hv3/c2)(l/[exp(hv/kT) - l]}, k is Boltzmann’s constant, c is the velocity of light, K ( v ,Z) is the absorption coefficient, p ( 2 ) is the density of the medium, and 2 is the vertical distance above the earth’s surface. The radiative transfer equation (RTE)is often written in integral form

where T is the transmittance and appropriate boundary conditions have been applied. The variable of integration has been changed from distance 2 to pressure P. 279 ADVANCES IN GEOPHYSICS,VOLUME 27

ISBN 0-12-018827-9

280

JOHN C. ALISHOUSE A N D E. PAUL McCLAIN

z(v, 0, Po) is the transmittance from the earth's surface (surface pressure, Po) to the top of the atmosphere at frequency v and direction 6. Radiation emitted by the surface is absorbed by the atmosphere and some radiation is reemitted but from a region of the atmosphere that has a temperature different from the surface. The absorption and reemission are described by the integral in Eq. (2). If the emissions from the surface deviate significantly from that of an ideal blackbody, then a quantity called the emissivity must be introduced. Thus the RTE becomes

where I , is the downward component of radiation. Strictly speaking the reflective term in the RTE should be written as an integral over all possible incidence angles. An ideal blackbody, by definition, emits radiation that is described by the Planck function. Many actual bodies emit radiation that does not follow this function. Thus one can define the emittance or emissivity of a body as E(V,

6 ) = I(v,

WW, T )

(4)

where 0 c E 5 1. Note that emittance has been written as a function of frequency and angle. 1.2. Transmissivity and Emissivity Considerations

In the infrared window regions of the spectrum centered at 3.7, 11, and 12 ,urn, emittances tend to be near unity and relatively independent of sea state and salinity. In the spectral interval 3.6-13 pm the emittance of sea water ranges from >0.99 to 0.97 (Hobson and Williams, 1971). The major limitations in deriving SSTs from satellite infrared observations are correcting for atmospheric attenuation by water vapor and accounting for clouds. Low, uniform clouds, which are often at temperatures close to the SST,are particularly troublesome. Thin clouds or partial filling of the field of view by clouds can adversely impact infrared SST determinations. The transmittance at 11 pm may vary from 0.4 to 0.9, depending on the amount of water vapor, while the transmittance at 3.8 pm may vary from 0.7 to 0.9 (Weinreb and Hill, 1980). In the microwave region of the spectrum the emissivity is much less and is a function of sea state. At 5 GHz and vertical incidence the emissivity of sea water can vary from 0.36 for a calm sea to 0.44 for a 20-m sec-' wind, and the

28 1

7. SEA SURFACE TEMPERATURE DETERMINATIONS

transmittance of the atmosphere varies only between 0.99 for a clear atmosphere and 0.98 for a thick, but nonprecipitating, cloud (Porter and Wentz, 1971). The major limitations in deriving SST from satellite microwave radiometric observations are rain, variable surface emissivity,and land effects including man-made radiofrequency interference.

2. MICROWAVE SENSING OF SEASURFACE TEMPERATURE 2.1. Background In the portion of the microwave spectrum used for SST determinations a useful approximation to the Planck function can be developed by assuming hv/kT 12 mm hr-l, respectively. In storms with maximum inferred rain rates greater than 17 mm hr-' (Table IV),

408

CECILIA GIRZ GRIFFITH AND L. S. FEDOR

/

POINTS. 398 M E A N DIFF.= 0.U30 RMS 0IFF.- U.681 R E G R E S S I O N COEFF.= 0.199 Y - I N T E R C E P T = 2.667 C B R R E L A T I O N COEFF.= 0.212 R M S OIFF. FROH REGR. L I N E = 3.522 TOTRL

~t

/ / /

b

c0

4.000

6.000

12.000

V I R F i RFlIN RRTE FROM CiRaIT a 2 8 0

16.000

20.0(

(mmhr-1)

FIG.6. A correlation of the Fico rain rates inferred from Seasat Rev 280 and from Seasat Rev 251. Both data sets were binned to 0.26" x 0.26" grid squares. The eye positions were then collocated.

these two contour levels represent 8 to 16 mm hr-I and >16 mm hr-l. These cases are noted in the appropriate figure captions. 2.3.2.2.Hurricane Fico. Hurricane Fico was viewed by Seasat on five passes that were virtually directly over the eye. In the first pass (day 193, July 12), Fico had just attained hurricane status at 1290 GMT and was located off the west coast of Mexico. The hurricane had peak winds of 115 knots in the eastern Pacific. By July 18 (day 199) Fico was in the central Pacific with 80-knot winds (Shaw, 1979). Fico traveled steadily westward

10. PRECIPITATION IN TROPICAL CYCLONES

TOTAl

I

POINTS=

H E R N DIFF.= RWS n 1 F F . I

409

/

Y13

/

0.808 3.910

R€GRESSION COEFr.=

/

0.533

Y - I N l E R C E P T - 0.933 CORRELATION COEFF..

4.000

/

0.508

12.000

8.OOU

V I F l R R R l N R A T E (mmhr-1)

-

16. D U O

20.00[

SEASRT O R B I T a?80

FIG.7. A correlation of the Fico rain rates inferred from Seasat Rev 280 and from Seasat Rev 331. Both data sets were binned to 0.26" x 0.26" grid squares. The eye positions were then colfocated.

and at the time of the last pass (day 201, July 20) was almost due south of the island of Hawaii. Figures 8-1 1 are the chronologically ordered plots of the VIRR-inferred rain rates for four passes of Fico. Rainbands and the eye can be seen in these plots. There is a major rainband to the east and south of Fico, for example, that appears in alternate plots. The highest rainfalls have been inferred in the central dense overcast region and are usually close to the eye of the storm.

TABLEIV. STATISTICS FOR SEASAT RAINFALL DATA

Day

Time GMT)

Rev

Area (105km2)

Volume (109m3hr-')

D (mmhr-')

max RR (mm hr-')

min T ("C)

Fico Fico Fico Fico Fico

193 194 195 197 20 1

12:56:16 01:21:58 13:34:13 1 4 11:40 04:26:00

222 229 251 280 331

5.1 1 12.32 4.47 6.70 6.03

1.05 2.44 1.03 1.34 1.16

2.06 1.98 2.3 1 2.00 1.93

15.47 18.34 13.82 13.61 15.65

- 92 - 88 -84 -80 - 83

Agnes Agnes Agnes

207 207 210

11:21:27 22:26:01 22:35:09

42 1 428 47 1

26.36 17.03 12.42

5.16 3.73 2.41

2.20 2.19 1.94

2 1.08 19.00 20.5 1

-93 -93 -86

Carmen Carmen

226 226

11:30:08 22:40:05

693 700

6.22 2.87

1.09 0.55

1.75 1.91

16.69 14.16

-73 -81

Storm


41 1 18.5N

..

1

.p'

r ,

I-

6. EN 126.3W

I10.5W

FIG.8. A plot of Seasat VIRR-inferred rain rates (mmhr-l] for Hurricane Fico on Rev 222. July 12, 1978. See text for contour intervals. Z0.6N

I

.

Y.

4.011

126. OW

101.9w

FIG.9. A plot of Seasat VIRR-inferred rain rates (mmhr-') for Hurricane Fico on Rev 229, July 13, 1978. The maximum contour is > 16 mm hr-'.

Three of the Seasat passes (Revs 222, 251, and 280) were early morning views, shortly after 05:OO local mean solar time. The remaining two passes were evening views at 17:30(Rev 229) and 18:30 (Rev 331) local time. With the exception of Rev 331 (in which Fico's behavior seems to be anomalous), these storms appear to exhibit the variation in storm area

-

-

412

CECILIA GIRZ GRIFFITH AND L. S. FEDOR 17.8N

I

1 133. BW

9. ON

118.2W

FIG.10. A plot of Seasat VIRR-inferred rain rates (mm hr-') for Hurricane. Fico on Rev 251, July 14, 1978. 20.1N

V

z=p

0 '

'

5.3N

I

142. SW

123

FIG.11. A plot of Seasat VIRR-inferred rain rates (mmhr-') for Hurricane Fico on Rev 280, July 16,1978.

documented by Browner et al. (1977). In a study of 16 days of eight Atlantic storms, Browner et al. found that the cloud area (defined by the -20°C isotherm) of hurricanes and tropical storms shows a diurnal oscillation. The data were composited by local mean solar time and, in the mean, storm area was maximum at 17:OO local time and minimum at 04:OO local time. The same phase of the diurnal oscillation is evident not only in Fico's area but also in the volumes, area-average depths, and maximum rain rates listed in Table IV, with

413


the exclusion of the Rev 331 data. The inferred volumetric output and areaaverage depths would be expected to show a diurnal oscillation, because each is a direct function of cloud area. The diurnal oscillation is apparent in comparing the early morning figures (Figs. 8,10,11) with one of the evening figures (Fig. 9). In the early morning figure Fico covers a much smaller area than in the evening figure. Although a minimum of 12-hourly imagery is required to document a diurnal variation, data from the first four Fico passes suggest its existence in the central Pacific tropical cyclone as well as in the Atlantic tropical storm. 2.3.2.2. Severe tropical storm Agnes. In the three Agnes passes, the center of this severe tropical storm was in the South China Sea off the coast of China. On July 26, 1978 (day 207), Agnes was about 60 nautical miles southeast of Hong Kong and moving westward. However, by July 29 (day 210) Agnes was approximately 150 nautical miles southwest of Hong Kong and moving eastward. The presence of Typhoon Wendy off Shanghai (1000 miles away) produced the Fujiwara effect,wherein two tropical cyclones move around and toward each other. From July 26 to July 30, Agnes’ track traced an elliptical, counterclockwise loop in the South China Sea, causing more than 500 mm of rain to fall in 8 days at Hong Kong (Bell, 1979). In the plot of rainfall for Rev 428 on July 26 (Fig. 12) the storm’s cyclonic organization can be seen, but 3 days later (Fig. 13) the structure is not as apparent, despite a lower central pressure at this time. Although there does appear to be a central 27.3N

0 c

7.111

I

101.5E

127.-

FIG.12. A plot of Seasat VIRR-inferred rain rates(mm hr- l ) for severe tropical storm Agnes on Rev 428, July 26, 1978. The maximum contour is > 16 mm hr-’.

414

CECILIA GlRZ GRIFFITH AND L. S. FEDOR

I

1

1 4.9N

FIG.13. A plot of Seasat VIRR-inferredrain rates (mmhr- ’) for severe tropical storm Agnes on Rev 471, July 29, 1978. The maximum contour is > 16 mm hr-’.

dense overcast region, there are no well-organized rainbands and the convection appears to consist of numerous cumulonimbi. On the average, Agnes is two to three times larger than Fico and Carmen for the data of this study. This is reflected in the rain volumes (Table IV), although the area-average depths of Fico, Agnes, and Carmen are comparable. Agnes’ minimum temperatures are the coldest of the three cyclones and the maximum rain rates are consequently greater. Bell (1979) remarks that because of an abnormally warm upper troposphere that limited the depth of the storm’s convection, rain rates in Agnes were never very intense and less than 10 mm hr-’ at the Royal Observatory in Hong Kong during most of the period between July 26 and 30. Presumably Bell is referring to rainfall rates determined from a time-integrated gage measurement rather than an instantaneous rainfall rate determined from radar. If so, Bell’s observation is not strictly comparable to the VIRR-inferred rain rates, because of the spatial and temporal differences of the two samples. However, Figs. 12 and 13 indicate that near and over land the maximum rain rates are relatively low, generally being less than 8 mm hr-l. 2.3.2.3. Typhoon Carmen. Carmen was a small typhoon that caused widespread flooding and considerable damage during its lifetime (Staff, Joint Typhoon Warning Center, 1979). In the two Seasat passes (Revs 693 and 700) on August 14,Typhoon Carmen was observed to be smaller, drier, and warmer than Hurricane Fico or the severe tropical storm Agnes (Table IV). The compact, symmetrical shape of Carmen is apparent in the rainfall plots of Figs. 14 and 15.


415 33.3N

4

'. 28 m/sec 0 CELL GRID SPACING-50 km x 50 km i 2 mlsec OR 10%. WHICHEVER IS GREATER 0

0

TECHN IC A L CHARACTER ISTI CS

DOPPLER CELLS EARTH COVERAGE DOPPLER CELLS (TYPICAL EACH ANTENNA FOOTPRINT) SATELLITE GROUND TRACK AFT BEAM

BACKSCATTER1 RECEIVED SIGNAL: BACKSCATTER, dB RECEIVED SIGNAL, dBm

0

*EXTRAPOLATED FROM i o w E R WIND SPEED DATA

FREQUENCY-14,59927 GHz BANDWIDTH-+500 kHr 0 TRANSMIT TlMElTOTAL TIME-0.2 0 PULSE WIDTH-4.8 mtec 0 PEAK TRANSMITTED POWER-110 W 0 PRF-34PULSESltec 0 AVERAGE TRANSMITTED POWER-20 W 0 AVERAGE RAW POWER-80 W REGULATED -85 W UNREGULATED 0 RECEIVER NOISE TEMP 1250 K 0 GAIN CONTROL-AUTOMATIC 0 ANTENNA PEAK GAIN-32.5 dB 0 ANTENNA POLARIZATION-HORIZONTAL/VERTlCAL DATA RATE-540 bps 0 0

750 km NEAR NADIR SURFACE MEASUREMENT SWATH

FIG.A-3. SASS technical summary.

APPENDIX A. INSTRUMENTS

457

A.4. SCANNING MULTICHANNEL MICROWAVE RADIOMETER The Scanning Multichannel Microwave Radiometer was a dual-polarized radiometer that measured microwave radiation at five frequencies:6.63,10.69, 18,21, and 37 GHz. This instrument generated a conical scan to the right of the subsatellite track at an incident angle of about 50". It recorded data in a swath 659 km wide for a series of elliptically shaped cells of varying sizes, depending on frequency as shown in Fig. A-1. The major axis of these elliptical cells varied from 121 km at 6.63 GHz, to 21 km at 37 GHz. The use of five frequencies permitted the radiometer to serve as an intermediate-to-high-speed wind-field anemometer (no wind direction), to measure sea surface temperature, to estimate corrections for atmospheric water vapor and liquid water, and to monitor sea-ice conditions. Additional characteristics are given in Fig. A-4. AS. SYNTHETIC APERTURE RADAR The synthetic aperture radar was an L-band 1.27-GHz radar with excellent cloud- and rain-penetration capability. The antenna illuminated a swath 100 km wide to the right of the flight track. The design provided spatial resolution of 25 m in both range and azimuth. Figure A-5 shows the principles of surface-element resolution. Range resolution was determined by the effective radar pulse length; azimuthal or cross-range resolution was determined by the antenna beamwidth. The image was formed from two bands of information. Range information was derived from the roundtrip travel time of radar echoes from the surface target. Azimuth information was composed of the Doppler shift history in the reflected signal from the surface in the direction of spacecraft motion. This information formed lines of constant Doppler shift in the azimuth or crossrange direction. The intersection of the two bands of information yielded the surface elements shown. Prime applications of the data are for wave directional spectra,coastal wave refraction analysis, and sea- and lake-ice dynamics. Because of the very high data rate (120 x lo6 b sec-'), there was no on-board recording of data. As a consequence, earth coverage was limited to swaths approximately 4000 km long in regions adjacent to the five ground receiving stations. A.6. COASTAL ZONECOLORSCANNER

The CZCS is an image scanner with six coregistered bands spectrally centered at 443, 520, 550,670, 750, and 1150 nm (Fig. A-6). The instrument utilizes a fully rotating scanner which scans across track at a rate of

458


PERFORMANCE OCEAN SURFACE WIND SPEED FROM 7 TO 5 0 * 2 m/SeC OR *lo%, WHICHEVER IS GREATER 0

OCEAN SURFACE TEMPERATURE TO WITHIN t2"C ABSOLUTE AND tO.5"C RELATIVE WIND AND TEMPERATURE RESOLUTION-121 km ICE FIELD MAPS. RESOLUTION-21 k m MEASUREMENT OF INTEGRATED ATMOSPHERIC WATER VAPOR AND LIQUID MATTER I N A COLUMN ALONG THE SIGNAL VECTOR MEASUREMENT OF RAIN DROP SIZE AND DISTRIBUTION IN A COLUMN ALONG THE SIGNAL VECTOR

TECHNICAL CHARACTER ISTICS CLOCK INPUTS-1 Hz, 10 kHz, 1.6 MHz, SATELLITE TIME ENG AND SCI DATA RATE-2 kbps WEIGHT-53 kg PRIME POWER-61 W (AVERAGE) FREQUENCY, GHr 0 ANTENNA DIAMETER, rn ANTENNA BEAMWIDTH. HALF-POWER, deg POLARIZATION FOOTPRINT MAJOR AXIS) ,km DIMENSIONS MINOR AXIS F U L L SWATH ANGLE, deg SWATH ARC WIDTH, km INCIDENCE ANGLE OF BEAM CENTER AT SURFACE, deg ORBITAL ALTITUDE, k m RF BANDWIDTH. MHz 0 DISSIPATIVE LOSSES: ORTHOMODE TRANSDUCER dB WAVEGUIDES SWITCHES AND ISOLATOR TOTAL DISSIPATIVE LOSSES, dB NOISE FIGURE (MIXER + IF AMPI, DSB.dB REFERRED TO PORT DsB' K OF MODULATOR PREDETECTION BANDWIDTH, MHz INTEGRATION TIME CONSTANT, milliseconds TEMPERATURE RESOLUTION, K ( l o ) (300 K TARGET) ABSOLUTE TEMPERATURE ACCURACY, K ( l o ) DYNAMIC TEMPERATURE RANGE,K 0

(

),

6.63

-

10.69

18 21 0.79 2.6 1.6 1.4 D U A L LINEAR 74 44 38 49 29 25 50

-

4

4.2

121

79

-~

~

V

"

"

37

0.8

21 14

* -

-

48.8-

+

794250-

0.55 0.25 0.6 1.4 4

0.37 0.34 0.6 1.21 4

0.52 0.25 0.6 1.37 5

1.03 0.2 0.7 1.93 5

0.3 0.2 0.7 1.2 5

490

490

692

703

728

100 62 0.89

62 1.01

-

t

--

126 0.51 2-

62 0.72

FIG.A-4. SMMR technical summary.

10-330

30 1.23


PERFORMANCE RADAR IMAGES AND INTENSITY SPECTRA OF WAVES I N DEEP OCEAN AND NEAR COASTS RADAR IMAGES OF SEA ICE AND FRESHWATER ICE 0 RADAR IMAGES OF LAND AND SNOW COVER 100-krn SWATH WIDTH, 4000km SWATH LENGTH I N l b r n i n PASS FOUR INDEPENDENT CELL MEASUREMENTS (4 LOOKS1 0 25 x 25 m CELL RESOLUTION (4 LOOKS) 0 . 5 . ~INTEGRATION ~ TIME PER CELL MEASUREMENT (PER LOOK) CELL SNR OF > dB OVER 100-km SWATH (4 LOOKS)

RESOLUTION CELLS FROM NADIR

SAT FLIGHT

230 km

-

1

330krn

I

1

1

j

I I

I

+1OOkm SWATH

d/

25 rn x 25 m R ESOLUTlON CELLS

I I

!

23 deg 4000 km 17 deg PER FROM NADIR 1Min PASS

TECH NIC A L CHAR A C T E R IST ICS CENTER FREQUENCY-1274.8 GHZ BANDWIDTH-19 MHz TRANSMIT TlMElTOTAL TIME = 0.35 PULSE WIDTH-33.8 psec CHIRP RATE-0.562 MHz/wec PULSE COMPRESSION RATIO (TIME BANDWIDTH PRODUCTM42 EFFECTIVE PULSE WIDTH-53 nsec PEAK TRANSMITTED POWER-1125 W NOM 0 PRF's-1464, 1540, 1580, 1647 Pulses/sec AVERAGE TRANSMITTED POWER-55 W

FIG.A-5. SAR technical summary.

459

I

I

Performance Parameters

Center Wavelength ( h Nanometera) Spectral Bandwidth ( A h Nanometers)

Channe 1s

1

2

3

4

5

6

443 (blue)

520 (green)

550 (yellow)

670 (red)

750 ( f a r red)

1150 (infrared)

433

- 453

510

- 530

Instantaneous Field of View (IFOV)

I

- 560

660

- 680

700

- 800

1050

- 1250

0.865 x 0.865 Milliradians (0.825 x 0.825 km a t sea l e v e l )

I

Coregistration a t NADIR

540

0.15 Milliradians

Accuracy of Viewing Posit ion Informat ion a t NADIR

2.0 Milliradians

~~

Signal- to-Noise Ratio (min) a t Radiance Input -2 N (mW c m Ster

m)

I

Consecutive Scan Overlap

I

Modulation Transfer Function (MTF)

> 150

> 140

> 125

> 100

at 5.41

at 3.50

at 2.86

at 1.34

100 at 10.8

25%

1 a t 150 km t a r g e t s i z e , 0.35 min. a t 0.825 km t a r g e t s i z e

FIG.A-6. CZCS technical summary.

NETD of 0.220 K at 270 K


46 1

8.0808 rev sec-'. The instantaneous field of view (IFOV) is 0.05",equating to a sea-level square of 825 m on a side from the nominal orbital altitude of 955 km (Fig. A-7). The active portion of the scan is 78.7", which produces a cross-track swath of 1659 km. The scan rate and IFOV size are such that each swath overlaps the preceding swath by about 25%. The scanner mirror is capable of being tilted forward or backward k20" line of sight about the spacecraft pitch axis in 2.0" increments. This movement is commandable and is used to avoid sun glint while taking advantage of maximum solar elevation angles.

0 = 1.374

rad (78.72')

a = 0 . 8 6 5 ~ 1 0 - ' rad ( 0.0496') d = 0.825 km orbital altitude = 955 km

D = 1659 km FIG.A-7. CZCS geometry.


APPENDIX B. SEASAT VALIDATION PROGRAM JOHNC. WILKERSON NOA A / NESSIOceanic Sciences Branch Washington, D.C.

B.l. B.2. B.3. 8.4. B.5. B.6. B.I. B.8.

Introduction . . . . . . . WeatherConditions . . . . Plan of Operations (General) . OSSOceanographer . . . . Ocean Weather Station PAPA. NOAADataBuoys . . . . Aircraft . . . . . . . . . Total Data Sets . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

463 466 467 469 414 414 411 411

B. 1. INTRODUCTION An initial and critical part of the Seasat proof-of-concept mission was a major NASA/NOAA field experiment in the Gulf of Alaska. The experiment was designed to collect in situ physical oceanographic and meteorological data for verification and evaluation of Seasat sensor performance. The Gulf of Alaska Experiment was conducted during the period of August 28 to September 26,1978, and was carried out from ships, aircraft, and buoys. The experiment was conducted under the control of a coordinator based at an experiment control center at the NOAA Pacific Marine Environmental Laboratory (PMEL), Seattle, Washington. After September 14, the lead ship, the NOAA Survey Vessel OSS Oceanographer, was joined by four research aircraft (two NASA, one Navy, and one Canadian), which made underflights coinciding with the passage of the satelliteover the ship. The aircraft collected remotely sensed data simultaneously from airborne sensors corresponding to those carried aboard the satellite. Nine NOAA data buoys moored in the Gulf of Alaska also supported the experiment, as did two Canadian weather ships, Quadra and Vancouver, stationed alternately at Ocean Station PAPA. Participating times of ships, buoys, and aircraft used in this experiment are given in Fig. B-1. Key personnel are identified in Table B-I. 463 ADVANCES IN GEOPHYSICS,VOLUME 21 ISBN 012418827-9

1

1

1

I

I

1

I

1

1

-

1

1

1

I

I ~

G W A 3 NVIOVNV3 V t d U AAVN

OWA3 VSVN

M L I N VSVM

EMJI

DaoJI 1w9*

m*

cm*

MMS*

EOOS? LWSb IWWSAOflE VdVd N O I l V l S NV330 Y3RdVYOONV330

52

97.

8%

011 692 11

DZ

12

CZ

1% 192 OM

bS2 CM

992 592

192

I2

02

91

01

652 157.

Ll

LFZ 952

FI

91

DI

Cl

I

TABLE B-I. KEYEXPERIMENT PERSONNEL Experiment Control Center Experiment Coordinator Aircraft Coordinator PMEL Coordinators

J. Wilkerson J. Blue M. Bvrne F. Gonzalez L. McNutt R. Anderson R. Ankney

NOAAINESS JPL NOAA/PMEL NOAA~PMEL NOAA/PMEL NOAA/NWS NOAA/PMC

R. Reed P. De Leonibus R. Berles M. Grigsby R. Lindsey

NOAA/PMEL NOAA/NESS NOAA/AOML NOAAjPMEL Contract employee

NASA NC-130B Mission Manager Chief Scientist

J. Lindeman L. Jones

NASA/JSC NASA/LRC

NASA CV-990A Mission Manager Chief Scientist

E. Peterson T. Wilheit

NASAIARC NASA/GSFC

Staff Meteorologist Communications Specialist OSS Oceanographer & Chief Scientist u n Senior Oceanographer Electrical Engineer Physical Scientist Physical Science Technician

Navy RP-3A Mission Manager Chief Scientist

J. Hollinger J. Hollinger

NRL NRL

Canadian CV-580 Mission Manager

R. Lowry

Chief Scientist

J. Gower

INTERA Ottawa, Ontario DFE Victoria, B.C.

Ocean Station PAPA Seasat Liaison Officer CCGC Quadra CCGC Vancouver National Data Buoy Office Seasat Coordinator Buoy Data Processing

A. Gibb J. Scarlett R. Weber

AES, Vancouver, B.C. CCG, Victoria, B.C. CCG, Victoria, B.C.

E. Kerut R. Erickson A. Johnson

NOAA/NDBO NOAA/NDBO NOAA/NDBO

466

JOHN C. WILKERSON

B .2. WEATHER CONDITIONS Weather conditions during the experiment period, August 29-September 9, were characterized by generally good weather. Winds were from calm to moderate and did not exceed 20 knots for sustained periods. Seas were light to moderate, with very few prolonged periods of overcast. Much stronger winds from the southeast or west were encountered from September 10 to September 26. These winds were accompanied by heavy seas and swell. The most severe weather was from September 10 to 14, with winds approaching 40knots and seas 15 to 20 feet. Conditions moderated somewhat from September 15 to 18, but from September 18 to 21, they worsened again, and winds frequently exceeded 30 knots. From September 22 to 26, conditions

WIND SPfED,

kmtr

I1

5 1-

3 2 -

--

I

0 5 4 -

- - L f 29

31

I

4UtUIl

I

30

2

1

4

5

6

7

8

?

I0

I!

I1

I1

I4

15

16

I7

I8

IV

20

21

SIPIfHlfR

FIG.B-2. Time histories of observations from OSS Oceanographer.

-

22

23

24

15

26

27

APPENDIX B. SEASAT VALIDATION PROGRAM

467

W I N D SEED. k M n I 5

f-

DEW MINI.

Y

I5

r

10

-

S-

W I N D WAVES,

rn

SWELL. m

FIG.B-3. Time histories of observations from weather ships at Ocean Station PAPA.

were appreciably less severe. During this period, winds were light to moderate and seas were low. Figures B-2 and B-3 show the time histories of weather observations from the Oceanographer and the weather ships at Ocean Station PAPA taken daily at 00:00Z, 06:OOZ, 12:00Z, and 18:00Z, and illustrate general weather conditions.

B.3. PLAN OF OPERATIONS (GENERAL) The Gulf of Alaska Experiment consisted of three phases. During phase 1, the Oceanographer proceeded to Ocean Station PAPA where an intership calibration of instruments was carried out with the Vancouver. During phase

-@- O C E A N S T A T I O N PAPA 0

P::

?

. .

S E C T I O N 1\

\

0

I

‘‘a/

1W T ~ \

._.

DATABUOY

I I I

SEPT

I

\

I

\ \ \

I

\+*--: I--______ 5

31 AUG

I

+r

SITE B 8 SEPT

0

*-zz=-s--

SITE A 9 SEPT

SITE C\

- 26 SEPT 0

FIG.B-4. Operating area of the Gulf of Alaska Experiment.

CANADA


469

2, the Oceanographer took two conductivity/temperature/depth (CTD) sections to define the general circulation in the Gulf of Alaska while collecting the appropriate in situ oceanographic and meteorological data for satellite sensor validation at relevant overpass times. During phase 3, the Oceanographer occupied sites near 49"N which marked the daily intersections of satellite orbits during the 3-day repeat cycle. At these sites, the Oceanographer took in situ data coincident with the overpass of the satellite for that day. During this phase, the aircraft also conducted overflights of the ship simultaneously with the passage of the satellite over these sites. Figure B-4 shows the general plan of operations.

B.4. OSS OCEANOGRAPHER A partial listing of in situ data taken from the OSS Oceanographer during phase 2 of the experiment is presented in Tables B-I1 and B-111. The locations are shown in Figs. B-5and B-6. The types of observation taken during other phases of the experiment are listed in Table B-IV.

TABLE B-11. CTD LOCATIONS:SECTION 1

Statioil

Latitude ("N)

Longitude

Depth (m)

54"13' 54"33'

150"18' 150"53' 151"28' 152"OO' 152"13' 152"24 152"36 152"41' 152"W 152"57' 152"os 153"ll' 153"16 153"22' 153"29 153"36 153"42' 153"48'

1501 1497 1500 1502 1500 1490 1502 1502 1504 1500 1497 1497 1502 95 1 355 319 214 79

1 2 3 4 5 6 7 8 9 10 11 12

55" 13' 55"19' 55"25' W32' 55"38' 55"43'

13

55"54

14 15 16 17 18

56"W 56"W 56"08' 56"1@ 56"18'

54"W

55"W

55"48' 55"52'

("w

TABLE 9-111. CTD LOCATIONS: SECTION 2 Latitude

Longitude

Station

(ON)

("W

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

5448 55"28' 55"58' 56" 13' 56"32' 56"51' 57"07' 57"28' 57"34' 5T40 57"45'

147"ll' 145"W 144"33'

57"53'

58"W 58"05' 58"Il' 58"14' 58"20' 56"17'

1WW 143"13' 142"32' 141"47' 141"W 140"43' 140"29' 14W16 1WOI' 13Y44' 139"28' 139"lI' 13YW 138"54' 139"18'

Depth (m)

Station

1504

31 38 39 40 41 42a 43 44 45 46 41 48 49 50 51 52 53 54

1496 1487 1484 1502 1500 1494 1499 1498 I500 1500 1499 1498 1498 1500 219 104

1502

Latitude ("N)

Longitude

Depth

("W)

(m)

56"OI'

140"02' 140"W 141"48' 142"35' 143"OI' 141"41' 141"44 141"43' 141"44' I41"42' 14l"42' 141"43' 141"42' 141"44' 141"44' 141'38' 141"W 141"44'

1497

55"37' 55"16 54"59 54"w

48"42' 48"45' 48"36 48-36' 48"42' 48"41' 48"W 48"42' 48"43' 48"34 48"42' 48"41' 48"42'

~~

Stations 42-54 were taken during the occupation of site 9. I so

I 50"

IS0

I SO0

FIG.9-5. CTD Station: section 1.

470

1501

1501 1501

1502 1500 1500 1500 1500

1500 1500 1500 1500 1500 1500 1500

1500 1500

150"

145'

1 40°

150"

145'

140'

1 35O

135" _.

FIG.B-6. CTD Station: section 2.

TABLE B-IV. OBSERVAT-ION PLANFOR OSS OCEANOGRAPHER Data type

Measurement accuracy

Temperature, salinity, and depth Expandable bathythermograph

*0.002"c, *O.Ol%

Sea surface temperature

f0.2"C

Sea surface salinity

f0.02%

*4 m

fO.TC, k 5 m

Wind speed and direction Wind-velocity GATE boom Air-temperature GATE boom Wet-bulb-temperature GATE boom Air-pressureGATE boom

+O.l"C k0.2 mbar

Recorded output 1-m averages of T, S,E, and D Temperature vs depth at almost any interval chosen Temperature vs time; various averaging schemes possible Salinity vs time; various averaging schemes possible Speed and direction vs time/2-min or 10-min averages spectra U and V means every 1.07 min Temperature mean every 1.07 min Temperature mean every 1.07 min Pressure mean every 1.07 min

Processing schedule

Responsible individual

1 January

R. Reed

Available

M. Byrne

PMEL PMEL Available

M. Byrne PMEL

Available

M. Byrne PMEL

15 March

M. Byrne PMEL

1 May

M. Reynolds

1 May 1 May

1 May

PMEL M. Reynolds PMEL M. Reynolds PMEL M. Reynolds PMEL

Sea surface temperature and salinity (bucket) Sea surface temperature (infrared) Rainfall amount rain gage

5 W

+0.2"C f0.02% +O.O8"C

f 10%

Insolation

*2%

Positions

kO.1 km

Drift buoys-Nimbus tracked Upper-air temperature and humidity Radiosondes Surface wave height and direction/wave-rider and pitch and roll buoy Radarscope photograph

+ 1 km

+ 1°C f 10% + 20 mbar 3-5% H,,3 3-5 direction

List of values for each overpass List of values for each overpass Amounts for various intervals usually weekly, but could be daily Daily totals Insolation vs time average possible List of positions a t various sites Daily speed and direction Pa,T,q with height

Available

Autospectra directional

Available

Available Available

R. Reed PMEL R. Reed PM EL R. Reed PMEL

Available

R. Reed PMEL

Available

Operations Officer Oceanographer R. Reed PMEL M. Reynolds PMEL

Available Available

D. Ross AOML

Photo print

Available

M. Byrne PMEL

474

JOHN C. WILKERSON

B.5. OCEAN WEATHER STATION PAPA Ocean weather station ships Vancouver and Quadra, of the Canadian Coast Guard, occupied Ocean Station PAPA (50"N,145"W) during the experiment period. In addition to the special weather observations, both ships monitored a wave-rider buoy, anchored at 50"N,144"58'W, and took continuous 20-min wind records with strip chart recorders at satellite overpass times. The ship's schedule was as follows:

Ship

On-station period

Vancouver Quadra

30 July to 10 Sept 10 Sept to 22 Oct

8.6. NOAA DATABUOYS The NOAA Data Buoys in operation in the Gulf of Alaska during AugustSeptember 1978 are shown in Table B-V. Locations of National Data Buoy Office (NDBO) buoys in the Gulf of Alaska are given in Fig. B-7. From August 28 to September 1, all buoys with the exception of Buoy 46003 were on a 3-hr synoptic reporting period. On September 1, NDBO switched all buoys from 3-hr to hourly reporting for the duration of the experiment. Typical measurement capabilities of the buoys are listed in Table B-VI.

TABLEB-V. NOAA DATABUOYS Buoy designation

Latitude ("N)

Longitude

("W

TYPe

46001 46002 46003" 46004 46005 46006 46007 46008 46009

5600' 42"30

148'90' 13000' 156"00' 136"00' 131"00' I38"oo' 152"42' 151"42' 146"48'

10-m discus 10-m discus 10-m discus 12-m discus 12-m discus 10-m discus 6-m boat 6-m boat 6-m boat

a

52"W 51"00'

46"w 41"OO'

5Y12 57"W W12'

This buoy deployed September 18, 1978.

4

TABLEB-VI. MEASUREMENT CAPABILITIES OF 10-m Drscus BUOYS" Parameter

Sensor type

Wind speedb Wind directionb

Vane-directed impeller Vane and gimbaled digital magnetic compass Platinum resistance vane, oriented Variable capacitance

Air temperature Barometric pressure Significant wave height Wave period Wave spectra

Surface water

Level (m)

Sampling interval

Range

Averaging period (min)

Measurement accuracy

+ 0.4 m sec-

0 to 65 m sec-' 0 to 360"

1/8sec 1/8sec

8.5 8.5

9

- 15 to

1/8 sec

8.5

- 0.2"C

9

900 to 1050 mbar

1/8sec

8.5

k0.6 mbar

Accelerometer

0

Oto 100m

Continuous

32

+0.3 m

Accelerometer Accelerometer (12-channel WSA)

0 0

4 to 25 sec 0.05 to 0.33 Hz 0.05-0.1 Af = 0.01 0.1-0.16 Af = 0.02 0.16-0.28 Af = 0.03 0.28-0.33 Af = 0.05 - 5 to 35°C

Continuous Continuous

32 32

f0.5 sec

1/8 sec

8.5

Platinum resistance

10 10

-1

40°C

+lo

+

d

+0.2"C

Buoys 46001,46002,46003,and 46006.

* Instantaneous wind direction and speed are translated into components, north and east. speed and direction. Total network system accuracy. *15%.

The averaged components are transformed into

477


B.7. AIRCRAFT Four aircraft participated in the Gulf of Alaska Experiment: the NASA NC-130B and CV-990A, the Navy RP-3A, and the Canadian CV-580. Plots of typical flights are shown in Figs. B-8-B-10.

DATASETS B.8. TOTAL The total number of data sets collected from ships and buoys during the GOASEX period are listed in Table B-VII. TABLE B-VII. TOTALBUOY DATASETSDURlNG GOASEX" Buoy no./ship

SAR

SASS

SMMR

ALTb

46001 46002 46003 46004 46005 46006 46007

1 20

3 20 4 9 20 20

0 0 0

46008

6

46009 R/V Oceanographer CCGC Quadra' CCGC Vancouvelf

9

27

5 40 16 15 40 29 79 39 58 51 31 27

180

430

Totals

4

3 10 8 10

51 31

25

9 18 51 31

0

0 0 0

0

27

0 0 0 0

231

0

~

Aircraft data sets not included. the subsatellite track failed to pass directly over buoys during the frozen 3-day repeat cycle of GOASEX, some passes were within 80 km of the buoys. These, together with other passes occurring outside the GOASEX period, were used in the validation of significant wave-height measurements from the altimeter. ' I n addition, the Canadian coast Guard cutters Quadra and Vancouver took special Seasat data sets twice each day during the periods July 17-August 17 and Sept 27-October 9, while on station at 0s PAPA.

* Although

FIG.B-8. Data flight of NC-130B on September 17, 1978, for orbit 1183. Time 15:17-19:38 GMT.

FIG.B-9. Data flight of CV-580 on September 22,1978,for orbit 1255. Time 17:24-21:57 GMT.

10'

10'

IP"

P 00 0

10

180

30'

30' 117'

30'

I1ba

30

12.5"

30

114'

30'

113'

FIG.B-10.Data flight of CV-580on September 23, 1978, for orbit 1269. Time 16:24-20:16 GMT.

30'

APPENDIX C. DATA AVAILABILITY BRUCEH. NEEDHAM NOAAI NESDlS~N~DC~SatelliIe Data Services Division Washington. D.C.

C.l. Seasat Data Archive and Distribution . . . . . . . . . . . . . . . . 481 C.l.l. General Remarks . . . . . . . . . . . . . . . . . . . . . 481 C.1.2. Chronology of Receipt of Data . . . . . . . . . . . . . . . . 482 C.1.3. Seasat Data Utilization . . . . . . . . . . . . . . . . . . . 487 C.2. Nimbus-7 Coastal Zone Color Scanner (CZCS) . . . . . . . . . . . . . 487 C.2.1. General Remarks and Background . . . . . . . . . . . . . . . 487 C.2.2. Contents of Data Archive . . . . . . . . . . . . . . . . . . 491 C.3. OrderingData . . . . . . . . . . . . . . . . . . . . . . . . 493

c.1. SEASAT DATAARCHIVEAND DISTRIBUTION C . l . l . General Remarks and Background

The Satellite Data Services Division (SDSD) of the National Climatic Data Center (NCDC), part of NOAA's National Environmental Satellite, Data, and Information Service (NESDIS), has been closely involved in the planning for the acquisition, archival, and distribution of Seasat data since the early stages of the project. SDSD has provided input to and maintained close liaison with the producers of the data at the Jet Propulsion Laboratory, and has worked with other members of the project during the Announcement of Opportunity process and internal NOAA investigations. Such'inputs have included suggestions on data formats, estimations of quantities of data requested by users (and associated costs for such data), reports to the project and JPL on users' interest'in data, definition of the zones for the Interim Geophysical Data Record (IGDR) products, identification of highest priority areas and dates to be processed, and many others. Services provided by the SDSD for the project have included the safe archival of all Seasat film and digital tapes; the production of inventory lists of data on archive;illustrations of geographic coverage by each sensor; provision of applicabledocumentation, user guides, and other publications to users; and development of software to extract specific geographic areas from the global archive tapes. SDSD personnel within the Data Services Branch have worked closely with users to help define the best data to suit their needs and to help identify the optimal numbers and least costly products to fill their 48 1 ADVANCES IN GEOPHYSICS,VOLUME 21 ISBN 0-12-018827-9

482

BRUCE H.NEEDHAM

requests; numerous hours have been spent laboring over a light table to find specific areas or features on SAR imagery to be sent to users. In this appendix the goal is first to summarize the chronology of the acquisition of all Seasat data currently on archive, with descriptions of the different types of data available, and, second, to provide a summary of how the data have been accessed and utilized by the end users. C.1.2. Chronology of Receipt of Data C.1.2.1. Interim Geophysical Data Records (IGDRs), SDSD received its first Seasat data product in late December 1978 with the altimeter IGDR “Starter Set” contained on one 9/800 bpi tape. Identified users were sent a letter in January 1979 informing them of the availability of this data set. Six users responded requesting copies of this data set. Additional IGDR data were received commencing in April 1979 and ending in May 1980. These included ALT, SASS, and SMMR IGDRs. A summary of these data is given in Table C-I. The IGDR data sets described above and in Table C-I were preliminary data sets meant for initial testing by Seasat experimenters. The geophysical parameters were not accurate, as compared to the Geophysical Data Records (GDRs). All users should thus order Seasat data from the GDRs and not the IGDRs. C.1.2.2. Sensor Data Records (SDRs). Commencing in May 1979, JPL proceeded to send the entire set of ALT SDRs to SDSD. The entire set of 1124 ALT SDRs are currently on archive. C.1.2.3. GeophysicalData Record (GDRs). Altimeter (ALT). Commencing in June 1979,SDSD started to receive the ALT GDR data tapes. The final set of ALT GDR tapes was received in December 1980. The ALT GDR data set comprises two types of data. One set contains the GEOPHYSICAL FILE ONLY and has 6 days of continuous, global ALT data on each tape. A total of 14 computer-compatible tapes (CCTs) are archived. The second set of ALT GDRs contains the SENSOR FILE ONLY and has 3 days of continuous, global ALT data on each tape. A total of 26 tapes are archived. A more complex description of these “Files” for the ALT and other sensors is shown in Table C-I1 and a summary of all the GDR tapes is shown in Table C-111, Scatterometer (SASS). SASS GDRs started arriving at SDSD commencing in May 1979 and ended in June 1981. The SASS GDR data set consists of three types: 1. Geophysical and sensor files: 6 hr/tape, total of 381 tapes.

IGDR TABLE C-I. SEASAT Data-set name

Dates of data (1978)

A L T 26 day A L T 12-day global A L T global weather A L T global SDR Zone 11

9/13-1019 7128-818 9/15-9116 7117- 10110 9/13-1019

Philippines Sicilian

9/25-9130 9128-29,1013-4

AND

SDR DATAON ARCHIVE AT SDSD Geographic area

Zones 1-6,8,9, and 12 Global (land and water) Global Global Zone 11 (10"-20'",

110"-130"E)

(30"-45"N, 10"-20"E)

Hurricane Fico

I / 12-7/20

Zones 1 and 9 extended

9/ 13- 1019

Hurricane Fico, Pacific Ocean Zones 1 and 9 extended

JASIN

7/15-9115

(53"-65"N, 5"-20"W)

SASS 21 day SASS 2-day global

9/13-1019 7/ 16-71 I 7

SASS 9-day global Zones 8,9, and 10 SASS 1.9, and extensions SASS Sicily SMMR miniworkshop

917- 15 9113- 1019 8119-916 8116-916 7110- 1019

Zones 1-5 Global (land data excluded) Global (land and water) Zones 8,9, and 10 Zones 1.9, and extensions Sicilian area 31 Revs, GOASEX, Grand Banks

Sensor ALT ALT ALT ALT ALT SASS ALT SASS ALT SASS ALT SASS ALT SASS ALT SASS

VlRR SASS SASS SASS SASS SASS SASS

SMMR

No. of CCTs 6 4 1 1124 1

3 1 1

1 1

1 1 1 7 2 4 (Imagery only) 21 8 36 14 4 2 1

TABLE C-11. SWAT FINALGEOPHYSICAL DATAREcoRo CONTENT DEFINITION Sensor File and record type

P

Geophysical Basic geophysical record

ALT One point Time/lat./long. Fully corrected h, and tidal height

Steric anomalies Corrected SS height above ellipsoid Atm. press. effect Ionosphere correction Wet dry tropo. corr. Surface pressure Supplemental geophysical record

SASS Mean tirne/lat./long. Wind-stress magnitude and u Wind-stress direction and u Mag/dir correlation 120 Wind-vector solutions

with aliases (Ambiguity in direction will not be removed)

Time/lat./long. Fully corrected backscatter coefficient (a") Individual SMMR channel temps. (int. over area for each of 15 cells/fan beam 1.89 sec

SMMR Mean time/lat./long SST, SSW, rain rate Atm. liq. water/ Water Vapor Path-length corr. 600 x 600-km area !90 sec of data

VIRR

Sensor Basic sensor record

Supplemental sensor record

One point Time/lat./long. Instrument corr. h Fully corr. IfLI3

Time Ah 1 min Calibration mode

Time/lat./long. Instr. corr. back. coeff. (a") Corner lat./long. S/N ratio a' corrections 1.89 sec Time 7.6-min. cal. sequence Noise Calibration mode

Mean time/lat./long. Individual channel brightness temps. and T , Same area as above

Start time of scan line Lat./long. of start-middle-end VIS brightness/IR temp. One scan line- 1.25 sec

Start/time/nadir lat./long. Cone-clock angle Indiv. chan. hot cold cal. mean and a Ant. temp. Footprint lat./long. for 30 footprint locations One 4.096-sec scan line Calibration data

Start time Nadir lat./long. Altitude IRjVIS HOT/COLD Cal. Calibration mode

486

BRUCE H. NEEDHAM TABLE C-111. INVENTORYOF SEASAT GDR DATAON ARCHIVE

Files

Sensor ALT

Geophysical

ALT

Sensor

SASS SASS SASS

Geophysical and sensor Geophysical Basic geophysical record only Geophysical and sensor Geophysical

SMMR SMMR

Dates of data

Storage type

717-7117,7124-8/28, 9/1,9/6,9/7,9/10, 9/13,9/15-10/10 7/7-71 17,7/24-8/28, 911,916,917,9110, 9/ 13,9/15-10/10 717-10/10 717-10/10 717-10/10

6 days global/tape

14

3 days global/tape

26

6 hr globalltape 24 hr global/tape 48 hr global/tape

381 96 48

717-10/10 717-10/10

6 hr global/tape 4 days globalltape

381 24

No. of CCTs

2. Geophysical file only: 24 hr/tape, total of 96 tapes. 3. Basic geophysical record only: 48 hrltape, total of 48 tapes. Microwave Radiometer (SMMR). SMMR GDRs started to arrive at SDSD in July 1981 and ended in September 1981. The SMMR GDR data set consists of two types of data: 1. Geophysical and sensor files: 6 hr/tape, total of 381 tapes. 2. Geophysical file only: 4 days/tape, total of 24 tapes.

Synthetic Aperture Radar ( S A R I . SAR optically processed 70-mm negative reels started to arrive at SDSD in February 1979 and continued through September 1981. A total of 518 reels have been received. The reels, each containing four swaths, vary in length from a minimum of 1 min to a maximum of 11 min, 45 sec. Most fall into the 2- to 5-min range, and all or portions of 353 of the 480 orbits have been processed (73.5%), including some from the United Kingdom and Canadian ground stations. A complete list of all 70-mm negatives on archive is available from SDSD. SAR digitally processed imagery in the form of 9/1600 bpi tapes and 4“ x 5“ or 8” x 8” negatives and prints started to arrive at SDSD in December 1979 and continue to be received up to the present. As of August 30,1982, a total of 332 scenes measuring approximately 100 x 100 km each have been archived. A complete inventory of all SAR digitally processed data on archive is available from SDSD. Visible and infrared radiometer (VIRR) photographic negatives are also archived at SDSD in limited numbers. These data are primarily over the JASIN experiment area and over the time periods from July 15 through August 27,1978.

APPENDIX C. DATA AVAILABILITY

487

C.1.3. Seasat Data Utilization

A study was conducted by SDSD to produce an itemized breakdown, by sensor type, describing how many orders were received for each sensor or data type, the products produced and delivered, and total costs recovered for each fiscal year, plus information showing which classes of users requested each data type. This itemized breakdown is summarized in Tables C-IV and C-V. In summary, as of August 31, 1982, SDSD received 267 orders, provided 3191 photographic products, and recovered $122,522,for a total of 750 orders, 4909 products, and $247,522. The majority of the requests came from U.S. universities (18%); the second highest category was the combination of foreign governments and foreign organizations (1573, the third highest category was consultants (1573, and the fourth highest was the U.S. Federal Government [NOAA, 10%; DOD, 2%; other Federal Government, 11% (see Table C-VI)]. The largest number of orders was for SAR film products, resulting in 63% of the total orders and nearly half the total dollar amount recovered ($122,522,or 49%). The second largest volume data type requested was the SASS, accounting for 11% of the orders and $62,800(25% of dollar amount). SAR CCTs were a close third with 11% of the orders and $13,534 (5% of the revenue), and the ALT CCTs were fourth with 10% of the orders and $15,084 (6% of the revenue). SMMR CCTs were lowest in number of requests with only 4% of the total orders, but produced $34,008 in revenue (nearly 14%). There were 82 orders placed in fiscal year 1979 (FY79), resulting in 507 products and revenue of $36,741. In FY80,196 orders were placed, resulting in 1005 products and revenue of $52,110. In FY81, 277 orders were placed, resulting in 1725 products and revenue of $79,426. In FY82 (as of August 1982), 195 orders were placed, resulting in 1672 products and revenue of $79,245. Of notable interest is the response for data from foreign (non-U.S.) concerns, amounting to 15% of the total orders. The largest in this category is Canada, with Japan second. c.2. NIMBUS-7 COASTAL ZONECOLOR SCANNER ( c z c s ) C.2.1. General Remarks and Background

The SDSD archives data only from the Coastal Zone Color Scanner (CZCS) from Nimbus-7. SDSD started to receive the CZCS data in late 1980 and continues to receive large volumes daily from NASA’s Goddard Space Flight Center.

TABLE C-IV. SEASAT DATAUTILIZATION SUMMARY FY79, FY80, FY8 1, FY82" Sensor

Product

Format

ALT ALT ALT SASS SMMR SAR SAR

IGDR SDR GDR GDR GDR Image Image

Mag tape Mag tape Mag tape Mag tape Mag tape Mag tape Photos

No. of orders

7

Total

User class breakdown

01 Individual 02 Attorney 03 Insurance adj. 04 Univ.Research 05 Non-univ. research 06 Engineer/contractor 07 Consulting meteorologst 08 Other consultants

ALT

SASS

SMMR

SS recovered

No. of tapes/film

6 61 81 21 85 483

11 tapes I tapes 169 tapes 914 tapes 451 tapes 160 tapes 3191 images

750

4909 products

1152 432 13,500 62,800 34,008 13,534 122,096

$241,522

SAR film

Total orders

2

20

22

3

129 53 25

18 8 4 1 14

20 6

I

11

7

9 6

86 31 19

1

3

8

I

14

12

101

1

% of total

SARCCT

1

09 Manufacturers 10 Utilities 11 Transportation 12 Agriculture 13 Other business 14 NOAA 15 DOD 16 Member of congress 17 Other fed. govt. 18 State & local govt. 19 Foreign govt. 20 Foreign organization 21 Newsmedia 22 Education Total

11 2

9

1

5

16

1

10 11

17 15

3

73

79

6

8

33

41

6

1 4 9 1

6 45 37 9

7 49 70 12

1 7 10 2

10 1 5

42 4 3 32

75 5 42 65

11 1 6 9

1

81

441

1 700

1 100

Tape summary-267 tape orders, 1718 tapes produced, $94,826 recovered. Imagery summary-483 images produced, $122,522 recovered. Totals-750 orders, 4909 products, $247,522 recovered.

imagery orders, 3,191

490

BRUCE H.NEEDHAM TABLE c-V. DATAUTILIZATION BY FY

Sensor

Product

Format

No. of orders

No. of products

%% recovered

FY19


IGDR GDR SDR GDR GDR Image Image





2 10 1 11 0 0 58 82

2 32 2 119 0 0 352 507

120 1,950 120 5,400 0 0 29,151 $36,741

4 25 4 165 2 36 769 1005

240 1,530 240 9,560 120 2,153 38,267 $52,110

FY80 4 13 4 22 2 19 132 196 FY81




1 19 1 34 4 45 173 277

11

50 1 506 1 89 1067 1725

792 4,504 72 36,992 60 8,807 28,199 $79,426

FY82 ALT ALT ALT SASS SMMR SAR SA R



0 19 0 14 21 21 120 195

0 124 448 35 1003 1672

0 5,5 16 0 10,848 33,828 2,574 26,479 $19,245

Totals (FY79-82)

7 50

4909

$247,522

0

62

49 1

APPENDIX C. DATA AVAILABILITY TABLEC-VI. DATAUTILIZATION BY USERCLASSEACH FY-NUMBER OF ORDERS User class breakdown

FY79

01 Individual 02 Attorney 03 Insurance adj. 04 Univ. research 05 Non-univ. research 06 Engineer/contractor 07 Consulting meteorologist 08 Other consultants 09 Manufacturers 10 Utilities 11 Transportation 12 Agriculture 13 Other business 14 NOAA 15 DOD 16 Member of congress 17 Other fed. govt. 18 State& local govt. 19 Foreign govt. 20 Foreign organization 21 News media 22 Education Total

1

6

9

19 7 2 1 13 2

44

40

18

10 2 19 8

19 13 0 37 18

19 3

1 22 8

5 17 23

7 0 0 8

16 0 10 12

82

176

FYSO

FY81

FY82

Totalno.

%of total

6

22

3

26 9 0 0 33 12

149 53 25 3 102 40

18 8 4 1

1

8 18 13

7 66 22 13

1 9 7 2

26 4 13 33

26 1 18 16

75 5 41 64

11

257

1 188

703

1

15

6

1

6 10 1 100%

C.2.2. Contents of Data Archive

The SDSD CZCS archive consists of photographic negatives (25 x 25 cm) and digital tapes (9/1600) for both the “Level I” (calibrated radiances) and “Level 11” (derived geophysical parameters) products. These are described in more detail as follows: Level I. (1) Photographic product-Six full swath images approximately 35 x 70 mm, each containing 2 min of data on a single 25 x 25-cm archived negative. Black and white transparency, negative, or print. Band 1 through 5 Rayleigh corrected. Band 6 equivalent blackbody temperature (normally not available in NH winter months). (2) Magnetic-tape product-(calibrated radiance tape) (CRT); nine-track, 1600 bpi CCT, 6 min of data maximum (three 2-min scenes). All data on photo product, no Rayleigh correction.

Level 11. (1) Photographic product-2 min of data containing four full swath images twice the size of Level I imagery; reproduced as two 25 x 25-cm archived negatives. Pigment concentration; diffuse attenuation coefficient; upwelled radiance, 443 nm; aerosol path radiance, 670 nm. (2) Magnetic tape product-CRCST; nine-track, 1600bpi CCT, 2 min of ~, L W ~ ,Pigment ~. concentration; diffuse attenuation data per CCT. LwdA3.L W , ~ LW,,~, coefficient; temperature.

120

120

El-W

120

60

W O E

60

120

E k s o W

120

120

60

W O E

60

120

El-W

120

FIG.C-1. Sample page from NASA CZCS monthly catalog, December 30,1980.

APPENDIX C. DATA AVAILABILITY

493

The SDSD maintains a computerized listing of all CZCS data on archive (Level I and Level I1 tapes and imagery), and will provide specific searches to users free of charge for small geographic areas. In addition, the SDSD archives and will distribute free of charge to users copies of the NASA CZCS monthly catalog (see Fig. C-1), which depicts the passes scheduled for processing. As of August 30, 1982, the SDSD had on archive a total of over 14,000 Level I scenes on tape and film, and nearly 250 Level 11 scenes. C.3. ORDERING DATA

Seasat and Nimbus-7 CZCS catalogs may be requested and data may be ordered by contacting: NOAA/NESDIS/NCDC Satellite Data Services Division Room 100, World Weather Building Washington, D.C. 20233 Telephone:Commercial-(301) 763-81 1 1 FTS-(301) 763-8111


APPENDIX D. GLOSSARY OF ACRONYMS WILBER B. HUSTON NANCY J. HOOPER 0.40 Corporafion Greenbelt, Maryland

Metrics, Incorporated Atlanta, Georgia

GLOSSARY OF ACRONYMS AFWTR AGC AGU ALT ASA AOML APT ARC ATS AVHRR AXBT BESEX CCT CNES CRCST CRT

czcs

DC DFT Duck-X EM ERL ESA ESMR ETG FGGE FFT FNOC

Air Force Western Test Range Automatic gain control American Geophysical Union (Seasat) a1timeter Applied Science Associates Atlantic Oceanographic and Meteorological Laboratory Automatic Picture Transmission (system) Ames Research Center Applications Technology Satellite Advanced Very High Resolution Radiometer Airborne expendable bathythermograph Bering Sea Experiment Computer-compatible tape Centre Nationale d’ Etudes Spatiale CZCS tape designator Calibrated radiance tape Coastal Zone Color Scanner Digital count Digital Fourier transform Seasat correlative data experiment conducted near Duck, North Carolina Electromagnetic bias Environmental Research Laboratories European Space Agency Electrically Scanning Microwave Radiometer Evaluation task group First GARP Global Experiment Fast Fourier transform Fleet Numerical Oceanographic Central (formerly FNWC, Fleet Numerical Weather Central) 495

ADVANCES IN GEOPHYSICS.VOLUME 21


496

WILBER B. HUSTON AND NANCY J. HOOPER

FOV FY GARP GDR GEM GEOS GMT GOASEX GOES GSFC HF HIRS HRIR IGDR INS IR ITOS JASIN JONSWAP JPL JTWC LMST MANICE MCSST MY NASA NCDC NDBO NESDIS

Field of view First year (sea ice) Global Atmospheric Research Project Geophysical data record Goddard earth model Geodynamic Experimental Ocean Satellite Greenwich mean time Gulf of Alaska Seasat Experiment Geostationary Operational Environmental Satellite Goddard Space Flight Center High frequency High-Resolution Infrared Spectrometer High-Resolution Infrared Radiometer Interim geophysical data record Inertial navigation system Infrared Improved TIROS Operational Satellite Joint Air-Sea Interaction experiment Joint North Sea Wave Analysis Project Jet Propulsion Laboratory Joint Typhoon Warning Center Local mean solar time Manual of Sea Ice Reporting Multichannel Sea Surface Temperature Multiyear (sea ice) National Aeronautics and Space Administration National Climatic Data Center National Data Buoy Office National Environmental Satellite, Data, and Information Service National Earth Satellite Service Nimbus Experiment Team National Hurricane Center National Hurricane Research Laboratory National Meteorological Center National Marine Fisheries Service National Oceanic and Atmospheric Administration Naval Ocean Research and Development Agency Designation of a numerical forecast experiment in which SASS-derived winds were excluded for comparison with forecasts in which they were used Naval Research Laboratory

NESS NET NHL NHRL NMC NMFS NOAA NORDA NOSASS NRL

APPENDIX D. GLOSSARY OF ACRONYMS

OFT PBL PGS-S4 PMEL PPI RA RFI RMS RTE SAR SASS

SCM SDSD SEMS SLAR SMMR

SMO SMS SR SSH SST SWH THIR TIREX TIROS TOT0 VIRR VIS WPL XBT

Optical Fourier transform Planetary boundary layer Preliminary gravity solution-Seasat version 4 Pacific Marine Environmental Laboratory (Radar) plan position indicator Radar altimeter Radiofrequency interference Root mean square Radiative transfer equation Synthetic Aperture Radar Seasat A Scatterometer System Successive correction method Satellite Data Services Division Severe extratropical massive storms Side-Looking Airborne Radar Scanning Multichannel Microwave Radiometer Surface meteorological observations Synchronous Meteorological Satellite Scanning Radiometer Sea surface height Sea surface temperature Significant wave height Temperature-Humidity Infrared Radiometer TIROS Ice Experiment Television and Infrared Observation Satellite Tongue of the Ocean Visible and Infrared Radiometer Visible Wave Propagation Laboratory Expendable bathythermograph

497


APPENDIX E. GLOSSARY OF SYMBOLS WILBER B. HUSTON 'OAO Corporalion Greenbelt, Maryland

GLOSSARY OF SYMBOLS Radius of earth; absorption coefficient Fraction of storm covered by the kth temperature Component of long ocean wave orbital acceleration along the radial direction Maximum radial acceleration due to the long wave Area of a storm as defined by the -20°C isotherm Backscattering coefficient Empirical weighting function of the (4)pixel Empirical weighting coefficient corresponding to the kth temperature Planck blackbody radiation function Azimuthal radar bandwidth Radar system bandwidth Velocity of light; phase speed of the short waves Actual group speed, nonlinear waves Small-amplitude, linear internal wave group speed Shallow-water phase (and group) velocity Cross-correlation function Ice concentration; pigment concentration; wave velocity (C = w/k = velocity bunching parameter Drag coefficient Specific heat of air at constant pressure Water depth Decibel Interpacket distance; difference Mean of a set of differences Absolute value of a difference Rainfall rate in the (ij) pixel of a grid Total energy of a wave field Mean of differences Mean of absolute value of differences Systematic component of radial ephemeris error Electromagnetic bias Coriolis parameter (f = - 2Rsin A) at latitude A Spectral peak frequency Fraction of ice which is multiyear; earth crust flexural rigidity Solar irradiance at wavelength 1, Acceleration due to gravity

a):

499 ADVANCES IN GEOPHYSICS,VOLUME 21


500

WILBER B. HUSTON Area (km') of the (ij)pixel of a grid Two-way antenna gain pattein, normalized so that it is unity at its maximum (accounts for pointing error)

B(J5m)

Antenna pattern RMS wave height; altitude; satellite elevation above the sea surface; radial distance of the satellite from the instantaneous sea surface N + (height of surface above ellipsoid) Satellite height; heat flux H(l + H/a), "extended" satellite height H/(l + H/a), reduced satellite height Significant wave height Height of a rain cell Hertz Point target intensity response along slant range direction Point target intensity response along flight direction Image intensity caused by moving ocean waves Zero-order modified Bessel function of the first kind with argument z Downward component of radiation Rainfall rate for tropical storms (set equal to 1.2 mm hr-') Index for each temperature increment in the IR data equal to or greater than -20°C; ocean wavenumber (2nlA)); diffuse attenuation coefficient (CZCS) Wavenumber of the long ocean waves Vector wavenumber = (k,,k,) Radar wavenumber (241) Linear wavenumber (2n/A) One-way signal attenuation rate due to rain Absorption coefficient Kilometer Kelvin; Karman constant; diffuse attenuation coefficient Upwelled spectral radiance Diffuse attenuation coefficient for pure sea water Corrected radiance; leading crest length of an internal wave Radiance contribution due to aerosol scattering Radiance contribution due to Rayleigh scattering Radiance received at sensor Water-leaving radiance In a surface wave field, wavelengths of the short and long waves Argument of the complete elliptic integral of the first kind; modulation level Number of points Number of pairs Geoid elevation relative to the ellipsoid; number of multiple looks; gate numbers; Brunt-Vaisala frequency; geoid height Pre-leading-edge average noise level Degree and order of spherical harmonic expansion Pressure Denotes surface Joint height-slope probability density function of surface height (positive upward) of the waves above a mean local surface, and wave s l o p corresponding to specular angle 8. Legendre polynomial of the first kind

APPENDIX E. GLOSSARY OF SYMBOLS

PCA r

501

One-way range resolution Effective pulse shape at receiver output, normalized to unity at its maximum, vs spatial propagation distance, x = 4 2 Point of closest approach Geocentric satellite position; linear-regression coefficient; correlation coefficient; radar slant range Mean earth equatorial radius Geocentric radius of reference ellipsoid, rainfall rate, reflectance Equatorial radius of earth Volumetric output of rain Fresnel reflection coefficient of sea surface at normal incidence Fresnel reflection coefficient for smooth sea water Standard error of estimate Total ocean wave slope RMS ocean wave slopes along any two orthogonal axes tangent to the mean sphere Cross spectrum One-dimensional ocean wave-height spectrum Sea surface height Significant wave height Epoch time; echo time; earth crust thickness Temperature; coherent integration time; period (2xlw); period (T = l/w) Brightness temperature Downward component of radiation Physical temperature of ice Transmitted pulse width Swell period (sec) Physical temperature of surface Integration time Ocean temperature field (scalar) Wind speed; a variable of integration a24'/2H" Horizontal component of surface water velocity Row Ocean current field (vector) Wind speed at reference height of 10 m above sea level Ocean current vector in the direction of the wind Wind-friction velocity Unit-step function Radar platform velocity; velocity Complex surface current vector Eckman transport vector Group velocity ( s ; f m f o r deep water) Meteorologically measured wind Radar-measured wind Synoptic wind Earth gravitational potential Vertical-wind velocity Vertical velocity; white-noise power Wind speed at 10 m height Vertical-structure function Distance traversed by radar pulse; fetch Radar wind direction

502

WILBER B. HUSTON X.

Synoptic wind directipn Three-dimensional directional wave spectrum Radar-reflectivity factor; altitude Slope of the water surface relative to the marine geoid; Phillips constant Speed-correction factor Attitude relative to nadir, pointing error Peak-enhancernent factor Delta function; relative phase of a point target Radial ephemeris error Spacing between observations Emissivity Nondimensional fetch parameter; height of the sea; wave surface height above a mean local surface (positive upward); total instantaneous sea surface height referenced to geoid; [, I, Wave amplitude Time-average sea surface Time-varying surface topography Three-dimensional spatial and temporal displacement wave field specifying the sea surface Long-wave amplitude, wavelength and direction referred to azimuth Wave amplitude, vertical displacement Angle of incidence (20" for Seasat SAR); latitude; wave direction (0 = arctan ky/k,); angle of radiation Local angle of incidence Wave-propagation direction relative to flight direction Nondimensional wind shear Dominant wave direction Partitioning wavenumber Ocean wavelength; elastic modulus Radar wavelength Wave-height skewness coefficient Skewness coefficient between wave height and wave slope squared Vertical diffusion coet€lcient; elastic modulus Relaxation-time constant Component of orbital velocity along the radial direction Nondimensional peak frequency; kinematic viscosity; frequency of radiation Random component of radial ephemeris error Density Theoretical azimuthal resolution for a stationary target Resolution of a point target degraded due to motion of the long ocean waves Slant range resolution Coherence power spectrum Correlation coefficient of wave slopes Atmospheric density as function of altitude Peak width Amplitude of the radar return; normalized radar backscatter coefficient; nadir backscattering cross section Average backscattering cross section per unit area at normal incidence Backscattering cross section per unit area of ocean surface at normal or vertical incidence

+

00

a"(0")

APPENDIX E. GLOSSARY OF SYMBOLS

503

The amount (in space) by which a pulse is stretched by scattering from waves of RMS height h Normalized radar cross section Density anomaly Average radar cross section for a short-pulse altimeter as function of time Total wind stress; atmospheric transmittance; temporal half-power width of a Gaussian pulse Complex surface wind-stress vector Zonal component of T Wave-induced Reynolds stress Angle at earth center from satellite to point [ on ocean surface; longitude Angle between direction of travel of ocean wave field and flight direction Nondimensional wind shear Error function of argument y Angle at antenna from nadir to point [ on ocean surface Two-dimensional wave-height spectrum as function of wavenumber Frequency (o= 1/T) Frequency of long ocean waves Dispersion relation Rotation speed of the earth Ensemble average


INDEX

A Absorption coefficient, 308 Acronyms, glossary of, 495-497 Advanced Very High Resolution Radiometer, 53,54,286 characteristics, 54-55,292 in fisheries research, 437-440 SST and, 287-294 tuna research, 424-427 Aerosol radiance, 49 Aircraft in Gulf of Alaska experiment, 478-480 Hurricane Greta data, 239-244 rainfall data, 209-211, 213 wind speed data, 216-217 Albacore tuna distribution, 424-427, 428, 437 ALT, see Seasat radar altimeter Altimeter echo waveform, see Echo model Altimetric biases, study of, using models, 73-87 Altitude, ocean, 57 Amplitude of internal waves, 189-191 time-of-flight estimation, 189- 190 variation-of-wavelength estimation, 190- 191 Antarctica, 372, 373-387 Antenna pointing error, 80-83 Arctic Basin, 371, 372 Atlantic Ocean, 427, 429 Atmosphere correction algorithm, 50, 53 Atmospheric water content, 58 AVHRR data, 55 SMMR data, 14, 31, 32, 33 ATS satellites, 4 Attenuation coefficient, 310 AVHRR, see Advanced Very High Resolution Radiometer

B Backscattering coefficient, 308 Barrick model, see Echo model Beaufort Sea, 337-367 Benguela Current, 440-442 505

Bio-optical algorithm. 310 application of, 317-322 development of, 313-317 Bluefin tuna distribution, 427 Bombs, 205, 206,249-257 Brightness temperature, 281,282,352-353 Brunt-VGsala frequency, 181, 182. 184 Buoy, NOAA, location and characteristics, 474-476 C

Chester algorithm, 209, 210, 211, 212 Chlorophyll pigment concentration, see Pigment concentration Circulation, ocean, 58 Clear water radiance, 312 Cloud-motion winds, 217, 230, 234-237, 244-246,259 Cloud pattern. cyclone, remote sensing of, 206-207 Cloud temperature, VIRR data, 34, 35 Cnoidal internal wave model, 187-188, 191-192 Coastal Zone Color Scanner, 48 applications to fisheries uses, 421-429, 436-442 atmospheric correction algorithm, 50, 53 calibration, 305-306 characteristics, 49, 303-306, 457, 460,461 data characteristics, 487, 491-493 optical system, 304 response to oceanic and atmospheric conditions, 306-312 Conductivitylternperaturddepth section, 469-470 Constituent concentrations, 308 Cross-sectional modulation artificial, 155-160 real, 160-165 CTD section, see Conductivity/temperature/&pth section Current interaction, SAR data, 40,43

506

INDEX

Cyclone, tropical, see also Hurricane characteristicsof, 198- 199 forecasting, 265-270 precipitation in, 393-417 CZCS. see Coastal Zone Color Scanner

Fisheries research, satellite applications to, 421-442 Forecast from SASS data, 132-135 of tropical storms, 200, 205, 265-270 Fresnel reflectance, 307, 310, 359

D Diffuse attenuation coefficient, 49, 50 remote sensing of, 322-331 Digital count, 396,403, 404 Directional wave-height spectmm, 142- 143 Drag coefficient, 107-108, 109 Duck-X experiment, 166 Duration parameter, nondimensional, 144

E Earth Radiation Budget radiometer, 48, 53 Echo analysis convolutional, see Echo model double-deconvolution algorithm, 93-96 Echo model convolutional form derivation, 62-65 model tits, 68-73 with Gaussian beadpulse shapes and GramCharlier surface probability density, 73-75 rain effects, 83-87 Echo plateau model, 80-83 Eddy structures, SAR data, 4 3 , 4 4 4 6 , 48 Electrically Scanning Microwave Radiometer, 206-207,336,368 Electromagnetic bias, 87-93 Emissivity, 280-281 Engruulis mordax, see Northern anchovy ERB radiometer, see Earth Radiation Budget radiometer ESMR, see Electrically Scanning Microwave Radiometer ESSA satellites, 4 Eucaryotes, 301

F Feature identification, VIRR data, 34, 35 Fedor algorithm results, 21, 22 Fetch parameter, nondimensional, 144 Fisheries-aids products, from satellite data, 443-450

G Geodesy, 57 Global data assimilation experiments, 121-137 assimilation system, 122- 127 Global Sea Surface Temperature Computation, 55 GOASEX, see Gulf of Alaska Seasat Experiment GOES satellites, 3, 5, 6,427,428 hurricane wind speed data, 230, 235-237, 244-246 rainfall data, 212, 214, 393-405 sea-ice observations, 339, 343 GOSSTCOMP, see Global Sea Surface Temperature Computation Gradient ratio, spectral, 370 Gram-Charlier surface probability density, 70-71,73-75,88-89 Greenland, 371, 377-389 Griffith-Woodley satellite rain estimation technique, 394-397 Gulf of Alaska Seasat Experiment, 15, 16, 17. 111-114, 149, 166, 168, 170, 172, 173, 283,463,467-469 Gulf of Mexico, 426427,428,429-436,437 Gulf Stream, SAR data, 40.43

H HCMM satellite, 6 High Resolution Infrared Sounder, 286, 287 High resolution InfraRed Spectrometer, 53, 54 HIRS, see High Resolution Infrared Sounder HIRS/Z, see High resolution InfraRed Spectrometer Hunicane, see also Cyclone, tropical defined, ly9 ocean wind measurements, 207-208 rainbands in, 201, 202 structure, 200-204 wind profiles in, 201-202,203 Hurricane Allen, 201-202.203 Hurricane Anita, 201 Hurricane Ava, 207

INDEX Hurricane boundary layer, 200 Hurricane Caroline, 208 Hurricane David, 202, 203 Hurricane Ella model wind fields, 248-249 SASS data, 229-237.260-26d SMMR and aircraft data, 208, 209, 210, 21 I , 212, 213, 237-238, 264-267 Hurricane Eloise, 270 Hurricane Fico, 483 ALT data, 68, 69. 71-73, 76-78, 80, 81, 96 SAR data, 36-39 SASS data, 25, 28. 218-227. 268-269 SMMR data. 208. 212, 214, 227-229 VlRR rainfall data, 401-413 Hurricane Gilma, 171 Hurricane Ginger, 203 Hurricane Gloria, 207 Hurricane Greta, 216, 225 aircraft data, 239-244 cloud-motion wind data, 244-246 SASS data, 238-246,259-264 Hurricane Keny, 203 Hydrodynamic modulation, 164- 165 1 Ice edge, Seasat observations, 365-366 Ice sheet, Seasat observations of, 377-389 Ice-sheet tracking algorithm, 379-387 Infrared interferometric spectrometer, 289-290 Intensity, 156-160 Internal waves, see Ocean internal waves IRIS, see Infrared interferometric spectrometer Irradiance reflectance, 307 ITOS satellites, 3, 5 , 12

J JASIN experiment. I 11, 114, 119- 120. 166 JONSWAP spectrum, 91-92,97-99

K K , see Diffuse attenuation coefficient K algorithm

application of, 327-331 development of, 324-327 Korteweg-deVries equation, 184

507

L Landsat satellites, 5, 6, 7 Larval transport mechanisms, modeling of, 429-435 Limb Infrared Monitoring of the Stratosphere sensor, 48 LlMS sensor, see Limb Infrared Monitoring of the Stratosphere sensor Loop Current, 426-427,428

M Marine surface boundary layer, 105- 109 Marine surface temperature, see Sea surface temperature Marine surface wind definition of, 117- 120 microwave remote sensing of, 207-208 Marine surface wind direction SASS data. 13, 22-29. 121-137 sources of error, 262-264 Marine surface wind field factors affecting. 105-109 SASS data, 23-25 Marine surface wind speed. 57 altimeter results, 21, 23 in Antarctic, 373-377 comparison data, 110- I15 measurement using microwave systems, 102- 105 RMS observational errors, 125 SASS data, 13, 22-23. 25, 26.27.31 assimilation experiments, 128- 132 forecast experiments, 132- 135 model function for, 109-121 preprxessing of, 127-128 SMMR data, 14,29, 31 sources of error, 258-262 stability effects, 108-109 time-averaging problem, 116- I17 in tropical storms, 215-264 aircraft data, 216-217 cloud-motion data, 217 SASS data, 217-264 MCSST; see Sea surface temperature, AVHRR data Menhaden, 436 Microwave polarization, 369 Microwave Sounding Unit, 53. 54 Microwave spectral gradient ratio, 369-370

508

INDEX

Midlatitude marine storm, 199, 204-205 forecasting, 270-273 microwave measurements in, 206-208 smcture, 249-257 MSU. see Microwave Sounding Unit Multiyear sea-ice fraction, 370, 372

N

Peak frequency, nondimensional, 144 Peak frequency, spectral, 144 Penetration depth, 310 Pheopigment, 50, 309 Phillips constant, 144 Photosynthesis, 299-301 Phytoplankton, see also Pigment concentration ocean color and, 297-303 Pigment concentration, 49, 50, 52-53, 309-310, 437-438, 440, see also Biooptical algorithm remote sensing of, 3.13-322 Planck function, 279, 280, 281 Polarization, 370 Polar regions, Seasat observations, 337-367, 373-389

Nimbus-7, 1,7, 11-12,487,491-492 results, 50-53 sea-ice observations, 367-373 sensors on, 7, 48-50 surface information, 50, 51 Nimbus satellites, 4, 7, 286, 289 NMC Global Data Assimilation System, 122- 127 NOAA satellites, 5,6,53-54,286,287,288,339, Q 343, see also GOES satellites Northern anchovy, spawning data, 421-424, Queen Elizabeth I1 storm, 249-257, 270 4394,441,442 North Pacific Ocean, 424-426.437-439 R NOSASS data, 121-122, 128-136 Radar altimeter, see Seasat radar altimeter Radiance 0 clear water, 312 Ocean color sensor, 306-312 causes of, 297-299 upwelled spectral, 313 measurements of, 299-333 water-leaving, 307-309, 312, 313, 315 use in fisheries research, 436-442 Radiative transfer equation, 279-280 Ocean color boundary charts, 448-450 Rainband, 201 Ocean frontal analysis charts, 443-446 Rainfall Ocean internal waves, 39,42, 176-191 effects on altimeter echo, 83-87 Ocean Station PAPA, 474 in hurricane, 203-204,393-417 Ocean surface, quantitative measures for, 1-2 in tropical cyclones Ocean surface waves, 142- 175, see also SignifiSASS data, 208-209 cant wave height; Wave direction SMMR data, 208-215 ALT data, 146-153 Rainfall pattern, cyclone, remote sensing of, one-dimensional temporal spectrum. 143- 144 206-207 physical characteristics, 142- 146 Rain rate estimate from VIRR SAR data, 153-175 limitations, 398-400 two-dimensional spatial spectrum, 143, 145 results, 400-415 wave-height directional spectrum, model for, technique, 394-397 97-99 Rayleigh-Jeans approximation, 281 Ocean wavelength, SAR data, 36-37,40, 143 Residual sea height, 20, 21 Oil slicks, SAR data, 47, 48 Roughness parameter, 107 OSS Oceanographer, 469-473 RTE, see Radiative transfer equation

P Peak-enhancement factor, 144

S S- 193 RADSCAT, 207

509

INDEX SAM I1 sensor, see Stratospheric Aerosol Measurement sensor S A R , see Synthetic Aperture Radar SASS, see Seasat-A ScatterometerSystem Satellite, civil first generation, 4 historical review of, 1-7 second generation, 5-6 third generation, 7 Satellite Data Distribution System, 447-448 Satellite infrared thermal imagery, use in fisheries research, 421-429 SBVlTOMS, see Solar and Backscatter ultraViolet/Total Ozone Mapping System SCAMS, see Scanning Microwave Sounder Scanning Microwave Sounder, 206 Scanning Multichannel Microwave Radiometer advantages of, 367 characteristics, 14, 102,292,352,457,458 data characteristics, 483, 484-485, 486 ice-edge observations, 365-366 QE I1 StOITTI, 255-256, 258 results, 29-33 sea-ice algorithm, 369-372 sea-ice observations, 339, 344, 345, 351-357.366-373 sea surface temperatures and, 264-265. 266, 267, 282-286, 292-294 tropical cyclone rainfall data, 208-215 wind friction velocity data, 115 SCDP, see Seasat Commercial Demonstration Program SCM, see Successive correction method SDDS, see Satellite Data Distribution System SDME,119- 120 SDMR, 119- 120 SDRE,119-120 Sea ice, observation of meteorological and surface, 339-351 by Nimbus-7, 367-373 by SAR, 37-39.41 by Seasat, 331-367, 377-389 Sea-ice algorithm for SMMR, 369-372 Sea-ice concentration, 370-3’12 Sea-ice forecast chart, 447 Sea-ice mapping, 14,29, 31, 32,58 Sea-ice temperature, 372 Seasat automatic gain control system, 66 chronology and applications, 1,7, 1I comparison to GOES data, 400-406

data archive and distribution, 481-487 data utilization, 487-491 extratropical cyclone forecasting, 270-273 observations in storms, objectives, 199 operating area and flight tracks, 15-17 polar observations, 337-367, 373-389 sensors on, 12-14 surface information, 14-15 tropical storm forecasting and, 265-270 Seasat-A Scatterometer System, see also specific hurricanes

advantages of, 367 characteristics, 13, 22, 24, 102, 105, 351, 453, 454,456 data characteristics, 22-29,482-486 larval transport study, 429-435 model function for deriving surface wind speed, 109-121 observation geometry, 24,454 QE I1 Storm, 249-258 sea-ice observations, 344, 345, 351-357 for storm forecasting, 132-135, 265-273 tropical cyclone rainfall data, 208-209 tropical storm wind data, 217-218, 249-258 wind speed data, 102, 105 accuracy of, 259-262 assimilation experiments, 128- 132 preprocessing of, 127-128 Seasat Commercial DemonstrationProgram, 447 Seasat model, semiempirical neglecting pointing error, 75-78 tracker-bias study, 78-80 Seasat radar altimeter, see a h Echo model characteristics, 13, 15, 18, 20, 102, 104-105, ,146,453,454,455 data biases, 73-93 data characteristics, 482-486 echo analysis, using convolutional form of signal, 62-68 using deconvolution algorithm, 93-96 electromagnetic bias, 87-93 ice-sheet observations, 377-389 observation geometry, 18, 63, 85,454 pointing error effects, 80-83 rain effects on echo of, 83-87 results, 15-21 sea-ice observations, 357-364 Seasat orbit 1453 data, 359-362 Seasat orbit 1482 data, 362-364 tracker bias, 78-80

5 10

INDEX

Seasat radar altimeter (continued) wave-height determination accuracy study, 146-153 in Antarctic, 373-377 wind speed data, 104-105, 373-377 Seasat satellites, 7 Seasat Validation Program, 15, 463-486 data collection, 469-480 personnel, 465 plan of operations, 467-469 ships, buoys, and aircraft in, 464 weather conditions, 466-467 Sea surface probability density Gram-Charlier correction, 70-71, 73-75, 88-89 Lipa-BarricK algorithms, 66-68.94-95 model fits of, 68-73 recovery from Seasat data, 65-68 Sea surface radiance, 49 Sea surface temperature, 57 AVHRR data (MCSST), 54-56, 288, 290-292 infrared sensing of, 286-292 microwave sensing of, 281-286 multichannel, 54-56, 288, 290-292 near tropical cyclones, 264-265, 266, 267 radiative transfer and, 279-280 remote sensing of, 279-281 RMS observational errors, 125 sensors of, 286 SMMR data, 14, 29, 30, 264-265, 266, 267 VIRR data, 34 Sea surface topography, based o n Seasat altimeter data, 19,20 Sea surface wind direction, see Marine surface wind direction Sea surface wind field, see Marine surface wind field Sea surface wind speed, see Marine surface wind speed Sea water, emissivity, 280-281 SEH, 144 SEM, see Solar Environmental Monitor SEMS, see Midlatitude marine storm Sensor radiance, 306-312 Shrimp, penaeid, 429-436 Side-looking airborne radar, 339 Sigma coordinate, 63-65, 123 Significant wave height, 57, 144 ALT data, 13, 18, 21, 22 accuracy study, 146-153

in Antarctic, 373-377 in Beaufort Sea, 357-364 buoy data, 147-153 comparison study, 146-153 SLAR, see Side-looking airborne radar SMDA, 118-120 SMMR, see Scanning Multichannel Microwave Radiometer SMS satellites, 3, 5 Solar and Backscatter ultraVioletlTotal Ozone Mapping System, 48, 53 Solar Environmental Monitor, 53, 54 Solar irradiance, 310 Southern California Bight, 421-424, 439-440, 441,442 Southern Ocean, 373-381 Spectral gradient ratio, 370 SST, see Sea surface temperature SSU, see Stratospheric Sounding Unit Stability effects, 108-109 Stratospheric Aerosol Measurement sensor, 48 Stratospheric Sounding Unit, 53, 54 Stress, 106-107 Successive correction method, 271, 272 Surface-layer transport, 429-436 Surface probability density, see Sea surface probability density Surface roughness, 107-108 Surface temperature, see Sea surface temperature Surface truth winds accuracy of, 258-259 application of, 218-264 derivation of, 217-218 Surface wind direction, see Marine surface wind direction Surface wind field, see Marine surface wind field Surface wind speed, see Marine surface wind speed SWH,see Significant wave height Swordfish, Atlantic, 427-429 Symbols, glossary of, 499-503 Synoptic scale, 120-121 Synthetic Aperture Kadar characteristics, 13-14, 105, 141-142, 153155, 156, 157, 176, 457. 459 data characteristics, 486 internal wave measurements, 176-191 observation geometry, 155-156,454 ocean internal wave data, 176-191

51 1

INDEX Synthetic Aperture Radar (conrinued) results, 36-48 sea-ice observations, 339-351, 366-367 superiority of, 366-367 surface wave measurements, 165-175 SWH data artificial cross-section modulation, 155I60 hydrodynamic modulation, 164-165 real cross-section modulation, 160-165 tilt modulation, 161-164 theory, 153-165 wave measurements, 165- 175

T Temperature, see Sea surface temperature Temperature-Humidity Infrared Radiometer, 48 Thermal boundary charts, 443-446 THIR, see Temperature-Humidity Infrared Radiometer Thunnus alalunga, see Albacore tuna Thunnus fhynnus thynnus, see Bluefin tuna Tilt modulation, 161-164 Time-averaging, 116-117 TIROS-N, 286, 287 chronology, 1, 7, 12 sensors on, 7, 53-54 TIROS satellites, 2-3, 4, 7 Tongue Of The Ocean, 43,45-46 Tracker bias, 78-80 Transmissivity, 280-281 Tropical storm, see also Cyclone, tropical; Hurricane definition, 199 Tropical storm Agnes, 410, 413-414 Tropical storm Christine, 207 ' h a , see Albacore tuna; Bluefin tuna Typhoon definition, 199 wind speed analyses, 246-248 Typhoon Carmen, 414-415 Typhoon Wendy, 413

U Upwelled spectral radiance, 313

V Velocity bunching, 158-160 Vertical Temperature Profile Radiometer, 287 VIRR, see Visible and InfraRed Radiometer Visible and InfraRed Radiometer characteristics, 13 data characteristics, 483, 485, 486 rainfall data, 33-35, 212, 214 Visible and Infrared Spin Scan Radiometer, rain fite estimates from, 394-415 VISSR. see Visible and Infrared Spin Scan Radiometer VTPR, see Vertical Temperature Profile Radiometer

W Water-leaving radiance, 307-309, 312, 313, 315 Wave direction, see also Ocean surface waves detectability, 169-172 dominant, 166-169 two-dimensional wave spectrum, 172-175 Wave height, see Significant wave height Wavelength (ocean surface wave), 36-37, 40, 143 detectability, 169-172 dominant, 165-169 Wavenumber, 142 Weighting coefficient (in rainfall calculation), 396-397 Wentz algorithm, 208-209, 210, 211, 212, 213, 214, 215, 264 Wind direction, see Marine surface wind direction Wind field, see Marine surface wind field Wind shear, nondimensional, 108 Wind speed, see Marine surface wind speed Wind-wave height, 375

51 1

INDEX Synthetic Aperture Radar (conrinued) This Page Intentionally Left Blank results, 36-48 sea-ice observations, 339-351, 366-367 superiority of, 366-367 surface wave measurements, 165-175 SWH data artificial cross-section modulation, 155I60 hydrodynamic modulation, 164-165 real cross-section modulation, 160-165 tilt modulation, 161-164 theory, 153-165 wave measurements, 165- 175

T Temperature, see Sea surface temperature Temperature-Humidity Infrared Radiometer, 48 Thermal boundary charts, 443-446 THIR, see Temperature-Humidity Infrared Radiometer Thunnus alalunga, see Albacore tuna Thunnus fhynnus thynnus, see Bluefin tuna Tilt modulation, 161-164 Time-averaging, 116-117 TIROS-N, 286, 287 chronology, 1, 7, 12 sensors on, 7, 53-54 TIROS satellites, 2-3, 4, 7 Tongue Of The Ocean, 43,45-46 Tracker bias, 78-80 Transmissivity, 280-281 Tropical storm, see also Cyclone, tropical; Hurricane definition, 199 Tropical storm Agnes, 410, 413-414 Tropical storm Christine, 207 ' h a , see Albacore tuna; Bluefin tuna Typhoon definition, 199 wind speed analyses, 246-248 Typhoon Carmen, 414-415 Typhoon Wendy, 413

U Upwelled spectral radiance, 313

V Velocity bunching, 158-160 Vertical Temperature Profile Radiometer, 287 VIRR, see Visible and InfraRed Radiometer Visible and InfraRed Radiometer characteristics, 13 data characteristics, 483, 485, 486 rainfall data, 33-35, 212, 214 Visible and Infrared Spin Scan Radiometer, rain fite estimates from, 394-415 VISSR. see Visible and Infrared Spin Scan Radiometer VTPR, see Vertical Temperature Profile Radiometer

W Water-leaving radiance, 307-309, 312, 313, 315 Wave direction, see also Ocean surface waves detectability, 169-172 dominant, 166-169 two-dimensional wave spectrum, 172-175 Wave height, see Significant wave height Wavelength (ocean surface wave), 36-37, 40, 143 detectability, 169-172 dominant, 165-169 Wavenumber, 142 Weighting coefficient (in rainfall calculation), 396-397 Wentz algorithm, 208-209, 210, 211, 212, 213, 214, 215, 264 Wind direction, see Marine surface wind direction Wind field, see Marine surface wind field Wind shear, nondimensional, 108 Wind speed, see Marine surface wind speed Wind-wave height, 375

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