Building the European Capacity in Operational Oceanography, Volume 69: Proceedings 3rd EuroGOOS Conference (Elsevier Oceanography Series)

EuroGOOS Publication No 19 BUILDING THE EUROPEAN CA PA CITY IN OPERA TiONA L OCEANOGRAPHY Elsevier Oceanography Seri...

Author: H. Dahlin | N.C. Flemming | K. Nittis | S.E. Petersson

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EuroGOOS Publication No 19

BUILDING THE EUROPEAN CA PA CITY IN OPERA TiONA L OCEANOGRAPHY

Elsevier Oceanography Series Series Editor: David Halpern (1993-) FURTHER TITLES IN THIS SERIES For more information see our website: http://www.elsevier.com/Iocate/eos

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J.C.J. Nihoul (Editor) Coupled Ocean-Atmosphere Models J.C.J. Nihoul (Editor) Small-Scale Turbulence and Mixing in the Ocean S.R. Massel Hydrodynamics of Coastal Zones V.C. Lakhan and A.S. Trenhaile (Editors) Applications in Ocean Modeling J. Dera (Editor) Marine Physics K. Takano (Editor) Oceanography of Asian Marginal Seas Tan Weiyan Shallow Water Hydrodynamics R.H. Charlier and J.R. Justus Ocean Energies, Environmental, Economic and Technological Aspects of Alternative Power Sources P.C. Chu and J.C. Gascard (Editors) Deep Convection and Deep Water Formation in the Oceans P.A. Pirazzoli, J. Pluet World Atlas of Holocene Sea-Level Changes T. Teramoto (Editor) Deep Ocean Circulation - Physical and Chemical Aspects B. Kjerfve (Editor) Coastal Lagoon Processes P. Malanotte-Rizzoli (Editor) Modern Approaches to Data Assimilation in Ocean Modeling J.H. Stel, H.W.A. Behrens, J.C. Borst, L.J. Droppert and J.P. van der Meulen (Editors) Operational Oceanography D. Halpern (Editor) Satellites, Oceanography and Society P. Boccotti Wave Mechanics for Ocean Engineering Richard E. Zeebe and Dieter Wolf-Gladrow CO2 in Seawater: Equilibrium, Kinetics, Isotopes N.C. Flemming (Editor-in-Chief) Operational Oceanography: Implementation at the European and Regional Scales V.C. Lakhan (Editor) Advances in Coastal Modeling G.J. Goni and P. Malanotte-Rizzoli (Editors) Interhemispheric Water Exchange in the Atlantic Ocean

Elsevier Oceanography Series, 69

BUILDING THE EUROPEAN CAPACITY IN OPERATIONAL OCEANOGRAPHY Proceedings of the Third International Conference on E u r o G O O S 3 - 6 D e c e m b e r 2002, Athens, Greece

Edited by H. D a h l i n , EuroGOOS Office, Norrkoping, Sweden N.C. F l e m m i n g , Southampton Oceanography Centre, U.K. K. Nittis, National Centre for Marine Research, Anavyssos, S.E. P e t e r s s o n , EuroGOOS Office, Norrkoping, Sweden

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2003

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Conference Organisers Scientific Steering Committee EuroGOOS Hans Dahlin DMI, Denmark Erik Buch SOC, UK Howard Cattle NERSC, Norway Ola M. Johannessen Ifremer, France Jacques Legrand ENEA, Italy Giuseppe Manzella Ifremer, France Philippe Marchand IMC/CNR, Italy Silvana Vallerga POL, UK David Prandle IOC GOOS Project Office, France Colin Summerhayes European Commission Alan Edwards University of Bologna, Italy Nadia Pinardi Ilkay Salihoglu Black Sea GOOS, METU-IMS, Turkey Kostas Nittis NCMR, Greece International Advisory Committee Dik Tromp EuroGOOS chair, Netherlands George Chronis President of NCMR, Greece RDANH, Denmark Charlotte Christensen M6t6o-France, France Franqois Gerard Hannu Gr6nvall FIMR, Finland met.no, Norway Bruce Hackett Bertil H~kansson SMHI, Sweden Dieter Kohnke BSH, Germany Wlodzimiwez Krzyminski IMWM, Poland Valery Martyschenko Roshydromet, Russia JP van der Meulen KNMI, Netherlands David Palmer EA, UK Bob Papenhuijzen RIKZ, Netherlands Nicole Papineau Mercator Ocean, France Gregorio Parrilla lEO, Spain Georges Pichot MUMM, Belgium Jan Piechura PAS, Poland Ignacio Rodriguez PE, Spain

vi

Conference Organisers

Howard Roe Harald Loeng Jan Stel Kazimierz Szefler Jon Turton Jonathan White

NERC, UK IMR, Norway NWO, Netherlands MIG, Poland Met Office, UK MI, Ireland

Organising Committee Christos Tziavos Kostas Nittis Hans Dahlin Alexander Carcantzos Theodoros Kardaras George Sakelaridis Christopher Koutitas Alex Lascaratos George Triantafyllou

Conference Chair, NCMR, Greece NCMR, Greece EuroGOOS Ministry of Merchant Marine, Greece Hydrographic Service, Greece National Meteorological Service, Greece University of Thessaloniki, Greece University of Athens, Greece IMBC, Greece

NCMR Executive Organising Group Chrysoula Diamanti Anastasios Papadopoulos Leonidas Perivoliotis Aris Prospathopoulos Takvor Soukisian Elina Tragou Vasilis Zervakis

Preface The EuroGOOS Conference is now established as a periodic event. The Third International Conference entitled "Building the European Capacity for Operational Oceanography" held in December 2002 in Athens followed the conferences in The Hague, 1996 and in Rome, 1999. The proceedings from all the conferences have been published in the Elsevier Oceanography Series. The Third Conference in Athens was attended by over 300 experts from 46 countries and had the goal of demonstrating current development in Operational Oceanography, showing the operational capabilities of newly developed science and technology, and obtaining user perspectives on existing and planned activities. The conference also hosted the first GOOS Regional Forum, the European Commission GMES Forum on the Role of Ocean Observing Systems and a European Commission Operational Forecasting Cluster meeting. The conference had been planned to be in phase with the launch of the 6th Framework Programme of European Community Research. The marine part of the programme was introduced at a special session. The first EuroGOOS Conference had the title "Operational Oceanographymthe challenge for European co-operation". The purpose of that conference was to discover if the EuroGOOS Strategy matched the priorities perceived by representatives of the European governments, the European Commission, European Agencies, Industry and the Scientific Community. The Strategy was published in 1996 and updated with a forward look in 1999. The presentations at the third conference substantially demonstrated the on-going implementation of the strategy and the plans. Work that had started through EuroGOOS task teams, working groups, workshops, and pilot projects were now partly funded through EU Framework Programme 5, FP5, projects, or proposed as projects under FP6, or implemented by EuroGOOS members at national and regional levels. A remaining challenge for European co-operation is now not only to develop and demonstrate science and technology at the front line of operational oceanography but also to build and manage a sustained system that supports the production of required services, including national commitments to intergovemmental agreements, from local to global scales. Research has a tradition of transboundary projects while in many countries the development of oceanographic services still struggle with co-ordination at national levels. The special sessions for the European initiative for the Global Monitoring for Environment and Security, GMES, and the IOC/GOOS Regional Forum opened new opportunities for pan-European co-operation and the establishment of a European Operational Oceanographic System. The EuroGOOS members today represent the majority of the experience and the available resources to run such a system and will participate in the continued planning. In the Global Ocean Observing System, GOOS, EuroGOOS already has the role of a Regional GOOS Alliance, and also has the goal to be one of the leading regions in the world. To be an active partner in the development of GMES will probably

viii

Preface

in the long run require a stronger constitution for EuroGOOS than the present association of agencies. When this book is published EuroGOOS will have started its tenth year. Regional groups and projects continue many of the initiated activities without direct steering from EuroGOOS and to some extent under new and stronger agreements. In its role of promoting and co-ordinating operational oceanography EuroGOOS looks forward. During the conference the EuroGOOS members had a session to discuss priorities for the near future. Still following the original strategy some main priorities crystallised from these discussions: 1. To advance and implement a marine monitoring and forecasting system using a European Centre(s) approach. This includes an operational forecasting suite from global to local, and connection to regional/national systems based on local and regional user requirements. 2. To co-ordinate the contribution from European countries to the global systems designed by GOOS and GCOS. 3. To promote and pull the necessary research and technical development needed for future operational oceanographic systems. Reading these conference proceedings you will discover that this is not exclusively a publication of scientific papers. Reviewers have expressed concern about the lack of new science in some of the papers. The EuroGOOS conferences mix science, engineering, plans and very practical applications. To implement and run monitoring systems and operational forecasting calls for experience rarely described in scientific papers but still essential knowledge for oceanographic research and the development of operational systems. Operational Oceanography is embracing a wide spectrum of expertise from basic science to local applications, which is also reflected in this book. Special thanks to the authors, the reviewers and my co-editors for their efforts on the contributions to this book. Hans Dahlin

Director, EuroGOOS

Table of Contents Conference Organisers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

v vii

Conference Opening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

Current D e v e l o p m e n t s in Operational O c e a n o g r a p h y . . . . . . . . . . . . . . . .

3

Global Ocean Observing Systems and the challenges of the 21st century . . . . . . . . . .

5

D. James Baker New European developments for Operational Oceanography . . . . . . . . . . . . . . . . . .

10

Jean-Frangois Minster The European contribution to GODAE

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

16

Mike Bell and Pierre Bahurel Regional Systems I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23

A possible migration from marine scientific research to operational oceanography in the context of the United Nations Convention on the Law of the Sea (UNCLOS) . . . . . 25

Peter Ryder Cyprus coastal ocean forecasting and observing system . . . . . . . . . . . . . . . . . . . . . .

36

George Zodiatis, Robin Lardner, Georgios Georgiou, Encho Demirov and Nadia Pinardi M A M A ~ T o w a r d s a new paradigm for ocean monitoring in the Mediterranean . . .

46

The MAMA Consortium Model-derived seasonal amounts of dust deposited on Mediterranean Sea and Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

57

G. Kallos, A. Papadopoulos, and P. Katsafados Evaluation of POSEIDON forecasts in the Aegean Sea for a three-year period . . . .

64

A. Papadopoulos, L. Perivoliotis, K. Nittis, and P. Katsafados Fluorescence lidars and their potentials for the remote sensing of the marine environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

71

Giovanna CecchL David LognolL Iacopo MochL Valentina Raimondi Long-term sustained observing system for climatic variability studies in the Mediterranean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

78

Alexander Theocharis and the CIESM initiative group Comparison of E C M W F operational surface meteorology and buoy observations in the Ligurian Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

R. Bozzano, A. Siccardi, M.E. Schiano, M. Borghini, and S. Castellari Impact of climate change on Adriatic Sea hydrology . . . . . . . . . . . . . . . . . . . . . . . . .

Alfred Frasheri, and Niko Pano

92

Temperature sampling strategies assessment in the Mediterranean Forecasting System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

97

Fabio Raicich Water masses and diagnostic circulation west of Sardinia from 23 March to 4 April 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

100

R. Sorgente, A. RibottL and L Puillat XBT observations in the Eastern Mediterranean: data analysis and assessment of numerical ocean forecasts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

105

Vassilis Zervakis, Kostas Nittis, Leonidas Perivoliotis, Christos Tziavos and George Papadoniou Marine meteorological and oceanographic services in the Hydrometcenter of Russia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

109

Z.K. A bousiarov, V.S. Krasjuk, E.S. Nesterov, S. T. Sokolov R e m o t e Sensing S y s t e m s

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

113

Measurement of wave groups using radar-image sequences . . . . . . . . . . . . . . . . . .

115

H. Dankert, J. Horstmann, H. Gfinther, and W. Rosenthal Synergy of remote sensing and numerical modelling for monitoring of suspended particulate matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

A. Pleskachevsky, J. Horstmann, G. Gayer, H. Giinther, and W. Rosenthal Monitoring precipitation using underwater acoustic remote sensing . . . . . . . . . . . .

128

T. H. Guymer, G. D. Quartly, K. G. Birch, J. M. Campbell, C. E. Jones and K. M. Shannon Marine SAR Analyses and Interpretation S y s t e m ~ M A R S A I S

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

135

Johnny Johannessen, Torill Hamre, Rene Garello, Roland Romeiser, Stefan Kern, Bertrand Chapron, Ian Robinson, Susanne Ufermann, Valerie Cummins, Niamh Connolly, Kostas Nittis, Leonidas Perivoliotis, and Dario Tarchi Study and monitoring of sea ice cover in the Caspian and Aral Seas from TOPEX/ POSEIDON microwave data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

141

Alexei V. Kouraev, Fabrice Papa, Petr I. Buharizin, Anny Cazenave, Jean-Francois Cretaux, Julia Dozortseva, and Frederique Remy Oceanpal: an instrument for remote sensing of the ocean and other water surfaces using GNSS reflections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

Giulio Ruffini, Marco Caparrini, Bertrand Chapron, Frangois Soulat, Olivier Germain and Leonardo Ruffini Monitoring of waves with X-band radar in the port of Sines . . . . . . . . . . . . . . . . . .

154

P. Izquierdo, C. Guedes Soares and J. B. Fontes Performance of the PISCES HF radar during the DEFRA trials . . . . . . . . . . . . . . .

161

Lucy R. Wyatt, J. Jim Green, Lesley A. Binks, Mike Moorhead, and Martin Holt Long-term changes in the Black Sea surface chlorophyll a according to in situ and modern satellite data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168

Oleg A. Yunev, Vadim Suetin, Vyacheslav Suslin, and Snejana Moncheva

The role of synergy in developing a Marine SAR Analysis and Interpretation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

174

Susanne Ufermann, Ian S. Robinson, and Johnny A. Johannessen Routine scatterometer winds for the Mediterranean . . . . . . . . . . . . . . . . . . . . . . . . .

180

A d Stoffelen Sea Surface Salinity mapping with SMOS space mission . . . . . . . . . . . . . . . . . . . .

186

Jordi Font, Gary Lagerloef Yann Kerr, Niels Skou, and Michael Berger Sea level prediction at the Portuguese coast based on model and remote sensed data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

190

C. Guedes Soares, Hafedh Hajji and P. Sebasti6o

Numerical Modelling and Data Assimilation

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

195

197 Mike Bell, Rosa Barciela, Adrian Hines, Matt Martin, Michael McCulloch, and David Storkey The Forecasting Ocean Assimilation Model (FOAM) system . . . . . . . . . . . . . . . . .

Coupled physical and biochemical data driven simulations of Black Sea in springsummer: real-time forecast and data assimilation . . . . . . . . . . . . . . . . . . . . . . . . . . 203

Sukru T. Besiktepe Data assimilation in an operational forecast system of the North Sea-Baltic Sea system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

211

J. V. T. Sorensen, H. Madsen, H. Madsen, H.R. Jensen, P.S. Rasch, A. C. Erichsen, and K. I. Dahl-Madsen Impact of the progress in operational oceanography on oil spill drift forecasting in the Mediterranean Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218

Pierre Daniel, Fabien Marty, and Patrick Josse The study of seasonal variability in the Adriatic Sea with the use of EOF analysis

222

A.Grezio, N. Pinardi, S. Sparnocchia, and M. Zavatarelli

Next Generation Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

227

AUVs: designing and operating next generation vehicles . . . . . . . . . . . . . . . . . . . .

229

Gwyn Griffiths and Ian Edwards Sustainability analysis in marine research, monitoring and forecasting systems . .. 237

Jun She

EC Operational Forecasting Workshop: Reports on EC Operational Forecasting Projects . . . . . . . . . . . . . . . . . .

243

The use of HF radar networks within operational forecasting systems of coastal regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

245

K.-Iu Gurgel, H.-H. Essen, and T. Schlick The DIADEM/TOPAZ monitoring and prediction system for the North A t l a n t i c . .

Laurent Bertino and Geir Evensen

251

xii G A V D O S : A satellite radar altimeter calibration and sea-level monitoring site on the island of Gavdos, Crete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258

S.P. Mertikas, E. C. Pavlis, P. G. Drakopoulos, K. Palamartchouk, and E. Koutroulis EDIOS: European Directory of the Initial Ocean Observing System . . . . . . . . . . . .

265

Jennifer Verduin and Johanne Fischer I O M A S A - - I n t e g r a t e d Observing and Modelling of the Arctic Surface and Atmosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

272

Georg Heygster, Soren Andersen, Nils Gustafsson, Klaus Kunzi, Thomas Landelius, Harald Schyberg, and Leif Toudal Marine EnviRonment and Security for the European Area, MERSEA Strand- 1 .. 279

J.A. Johannessen, P.-Y. Le Traon, I. Robinson, K. Nittis, M. Bell, N. Pinardi, P. Bahurel, and B. Furevik Integrated marine science in European shelf seas and adjacent waters . . . . . . . . . .

285

Jun She and Erik Buch E S O N E T - - E u r o p e a n Sea Floor Observatory Network . . . . . . . . . . . . . . . . . . . . . .

291

Imants G. Priede, Juergen Mienert, Roland Person, Tjeerd C. E. van Weering, Olaf Pfannkuche, Nick 0 'Neill, Anastasios Tselepides, Laurenz Thomsen, Paolo Favali, Francesco Gasparoni, Nevio Zitellini, Claude Millot, Hans W. Gerber, and Jorge Miguel Alberto de Miranda. In Situ M o n i t o r i n g

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

295

Use of a Ferry-Box system to look at shelf sea and ocean margin processes . . . . . .

297

D.J. Hydes, A. Yool, J.M. Campbell N.A. Crisp, J. Dodgson, B. Dupee, M. Edwards, S.E. Hartman, B.A. Kelly-Gerreyn, A.M. Lavin, C.M. Gonzrlez-Pola and P. Miller Monitoring the marine environment operational practices in Europe

...........

304

Jacques Legrand, Marta Alfonso, Roberto Bozzano, Gdrard Goasguen, Henrik Lindh, Alberto RibottL Ignacio Rodriguez, and Christos Tziavos Smartbuoy: A marine environmental monitoring buoy with a difference . . . . . . . .

311

D.K. Mills, R. W. P. M. Laane, J.M. Rees, M. Rutgers van der Loeff J.M. Suylen, D.J. Pearce, D.B. Sivyer, C. Heins, K. Platt and M. Rawlinson ARGOS capabilities for global ocean monitoring . . . . . . . . . . . . . . . . . . . . . . . . . .

317

Christian Ortega FerryBox systems for monitoring coastal waters . . . . . . . . . . . . . . . . . . . . . . . . . . .

325

Wilhelm Petersen, Michail Petschatnikov, Friedhelm Schroeder, and Franciscus Colijn Real-time oceanographic measurements using the M3A system . . . . . . . . . . . . . . .

334

I. Thanos, K. Nittis, and C. Tziavos E G O S - - E u r o p e a n Group on Ocean Stations providing real time buoy observations from data sparse areas of the North Atlantic Ocean and adjacent seas . . . . . . . . . . . . . . . 340

Volker Wagner, Anne A. Hageberg, and Christian Michelsen CORIOLIS, a French project for in situ operational oceanography . . . . . . . . . . . . . 345 S. Pouliquen, T. Carval, Y. Desaubies, L. Petit de la Villdon, G. Loaec, and L. Gourmelen

Xlll

ASSEM: Array of Sensors for long term SEabed Monitoring of geohazards . . . . .

349

J. Blandin, R. Person, J.M. Strout, P. Briole, V.Ballu, G. Etiope, M. Masson, S. Smolders, V. Lykousis, and G. Ferentinos Adaptive sampling for coastal environmental monitoring using a geo-referenced mobile instrument platform and correlative data visualisation . . . . . . . . . . . . . . . . . . . . . . . 353

T.O. Ojo, M. Sterling, J.S. Bonner, F.J. Kelly, C.A. Page, J. Perez and C. Fuller A comparison with the Argo observing s y s t e m m G y r o s c o p e 0302 cruise

.......

356

Gregorio Parrilla-Barrera, Manuel Vargas-Y6~ez, Pedro V~lez-Belchi, Alicia Lavin, COsar Gonz61ez-Pola, Eugenio Fraile, Alonso Hern6ndez-Guerra, Elena Tel and Daura Vega Coastal oceanographic station at the entrance of the Gulf of Trieste (Northern Adriatic) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

361

V. , D. Sonc and B. Petelin The NOR-50: a fast research vessel for operational oceanography . . . . . . . . . . . . .

366

Philippe Marchand and Jacques Servain Monte Carlo simulation of NaI(T1) gamma-spectra in sea water . . . . . . . . . . . . . . .

370

D.S. Vlachos and C. Tsabaris In situ calibration of biofouling-prone oceanographic sensors in the framework of the POSEIDON project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

373

Vassilis Zervakis, Eva Krasakopoulou, Georgia Assimakopoulou, Panagiotis Renieris, Dionysios Ballas, A ggelos Mallios and Emmanuel Papageorgiou Waves Monitoring and Forecasting

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

Wave and current forecasting along the Spanish Catalan coast . . . . . . . . . . . . . . . .

377 379

A. Sanchez-Arcilla, M. Espino, R. Bola~os, d. Gomez, G. dorda, S. Ponce de Leon, and A. Sairouni Progress in building a wave climate database along the French coasts through numerical hindcast simulations over a 20-years period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386

M. Benoit, D. Violeau, J-C. Fournier, J. L 'Her, and G. Goasguen Modelling of sea states sequence along a ship route using Markov theory . . . . . . .

392

Chrysoula Diamanti and Takvor Soukissian Real time monitoring of Spanish coastal waters: The deep water network . . . . . . .

398

E. Alvarez Fanjul, M. Alfonso, M.I. Ruiz, J. D. L6pez, and I. Rodriguez Adaptive neural network for wave forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

403

D.S. Vlachos and A. Papadopoulos User Perspectives

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

407

Demand side "pull" for EuroGOOS products: Identifying market and policy decisions impacted by new environmental information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409

Mary G. Altalo, Colin Summerhayes, Nicholas Flemming, and Patricio Bernal International public goods and operational oceanography . . . . . . . . . . . . . . . . . . . .

Martin Brown

422

xiv Global operational oceanography and the role of the Joint WMO/IOX Technical Commission for Oceanography and Marine Meteorology . . . . . . . . . . . . . . . . . . . .

430

Peter Dexter and Johannes Guddal The Global Ocean Observing System: design and implementation of the coastal module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

442

Thomas C. Malone High-resolution wind fields from synthetic aperture radars and numerical models for offshore wind farming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 450

S. Lehner, J. Horstmann, and C. Hasager E u r o G O O S T a s k Teams

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

Towards N O O S m T h e EuroGOOS NW Shelf Task Team 1996-2002 . . . . . . . . . .

459 461

Martin Holt (Chair, NOOS Steering Group) Present status of B O O S ~ B a l t i c Operational Oceanographic System . . . . . . . . . . .

466

BOOS Steering group Regional Systems 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

473

Review of the last 15 years with the Seawatch system . . . . . . . . . . . . . . . . . . . . . . .

475

Svein Erling Hansen Real-time forecast modelling for the NW European Shelf seas . . . . . . . . . . . . . . . .

484

Martin Holt, Zhihong Li, and Jeff Osborne Arctic climate c h a n g e m w i l l the ice disappear this century? . . . . . . . . . . . . . . . . . .

490

Ola M. Johannessen, Martin W. Miles, Anne-Metre Olsen, Lennart Bengtsson and Cathrine Myrmehl Approach to the operational Ocean Observing System in the Yellow Sea through ChinaKorea bi-lateral cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497

D. Y. Lee, G. K. Tan, C. S. Kim and J. E Han Operational products and services for the Belgian coastal waters . . . . . . . . . . . . . .

503

Virginie Pison and Jos~ Ozer Co-ordinating UK inputs to EuroGOOS and GOOS

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

510

M. J. Cowling and I. H. Townend 3D, EOF-based spatial analysis of Gyroscope observations in the North Atlantic Ocean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

513

S. Ruiz, D. Gomis and J. Font A unified model system for the Baltic Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

516

Lennart Funkquist Alg@ line--joint operational unattended phytoplankton monitoring in the Baltic Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

519

Lotta Ruokanen, Seppo Kaitala, Vivi Fleming, and Petri Maunula Pre-operational system for oil spill simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

P. Sebasti6o and C. Guedes Soares

523

XV

Coastal Systems

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

Monitoring the Norwegian Coastal Zone Environment (MONCOZE)

527

..........

529

d.A. dohannessen, B. Hackett, E. Svendsen, H. Soiland, G. Evensen, L.P. Roed, N. Winther, d. Albretsen, M. Skogen, L. Pettersson, D. Durand, and D. Obaton Sensing the coastal environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

535

d.S. Bonnet, F.d. Kelly, P.R. Michaud, C.A. Page, d. Perez, C. Fuller, T. Ojo, and M. Sterling The Bay of Biscay project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

542

Jean Boucher and Philippe Marchand The POL Coastal Observatory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

548

R. Proctor and M. d. Howarth Optical variability associated with phytoplankton dynamics in the Cretan Sea during 2000 and 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554

Panos Drakopoulos, George Petihakis, Vasilis Valavanis, Kostas Nittis, and George Triantafyllou Contemporary problems of navigation safety and sea pollution in the Georgian Exclusive Economic Zone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 562

George Metreveli andKakhaber Bilashvili Outfall of storm sewers in the s e a ~ a technical review . . . . . . . . . . . . . . . . . . . . . .

564

J. D. Demetriou Ferrybox and databuoy measurements of plankton blooms . . . . . . . . . . . . . . . . . . .

568

S.E. Hartman, D.J. Hydes, D.K. Mills, J. Waniek, and D. B. Sivyer Engineering-biological method for coastal protection . . . . . . . . . . . . . . . . . . . . . . .

574

Dimitar Parlichev and Georgi Parlichev Data-Products-Users

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

Defence-related applications for operational oceanography . . . . . . . . . . . . . . . . . . .

577

579

Didier dourdan and Christel Lucion The modelling system for simulation of the oil spills in the Black Sea . . . . . . . . . .

586

I. Brovchenko, A. Kuschan, V. Maderich, M. Shliakhtun, S. Yuschenko, and M. Zheleznyak Society and sustainable use of the Exclusive Economic Zones . . . . . . . . . . . . . . . .

592

Jan H. Stel Society and sustainable use of mineral resources in the Exclusive Economic Zones 598

Jan H. Stel, Tjerk Homminga and Henk van Muijen Applications and availability of ocean model products from the Met Office . . . . . .

605

Jon Turton CORIOLIS: Providing a data management infrastructure for operational oceanography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

611

L. Petit de la VilHon, Th. Carval, P. Blouch, E. Duporte and the CORIOLIS project group An approach to integration of oceanographic information production on the Web . 615

Evgeny Vyazilov, Nickolay Mikhalov, and Sergey Belov

xvi

IWICOS: Integrated Weather, Sea Ice and Ocean Service System . . . . . . . . . . . . .

621

Stein Sandven, Torill Hamre, Robin Berglund, Jyrki Haajanen, Ari Seind, Morten Lind, Leif Toudal Petersen, Roberto Saldo, Halla Bjorg Baldursdottir and G. Hafsteinsson Digital, high resolution weather, sea ice and ocean information to the users at sea: the IWICOS demonstration during the Aranda expedition in the Fram Strait . . . . . . . . 627

Ari Seind, Robin Berglund, Jyrki Haajanen, Ville Kotovirta, Jenni Mansner, Renne Tergujeff Jouko Launiainen, Patrick Eriksson, Milla Johansson, Jouni Vainio, Morten Lind, and Hannu GrOnvall IWICOS architecturemsoftware architecture for marine GIS-data interoperability

633

Jyrki Haajanen, Robin Berglund, Ville Kotovirta, and Renne Tergujeff IWICOS m e t a d a t a m d e s c r i b i n g m e t - i c e - o c e a n information with metadata . . . . . .

636

Jyrki Haajanen, Morten Lind, and Leif Toudal Petersen Delivering near real-time met-ice-ocean observation and forecast d a t a m t h e 1WICOS

Faqade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

639

Ville Kotovirta, Robin Berglund, Jyrki Haajanen, Jenni Mansner, and Renne Tergujeff Interactive Internet coastal wave information production and retrieval system . . . .

642

K. C. Jun, D. Y. Lee, S.D. Kim and K. S. Park 2002: A Mediterranean and Black Sea database for operational ...................................................... 645

MEDAR/MEDATLAS

oceanography

M. Fichaut, M.-J. Garcia, A. Giorgetti, A. Iona, A. Kuznetsov, M. Rixen and the MEDAR Group POLIS: Poseidon On-Line Information System . . . . . . . . . . . . . . . . . . . . . . . . . . . .

649

D. S. Vlachos GMES

Marine

Forum

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

653

An introduction to the Global Monitoring for Environment and Security (GMES) initiative

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

655

Peter Ryder, Jan H. Stel, Alan Edwards, and Michel-Henri Cornaert Closure

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

Conference Valedictory Speech . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

667

669

Nicholas Flemming Index of Keywords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

671

Index of Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

675

List of Reviewers

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

681

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

687

List of Participants

Conference Opening

The conference was opened by words of welcome from: Dimitris Deniozos (GRST, Ministry of Development) Alan Edwards (European Commission) George Chronis (NCMR) Patricio Bernal (IOC) Hans Dahlin (EuroGOOS)

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Current Developments in Operational Oceanography

Jean-Francois Minster


Global Ocean Observing Systems and the challenges of the 21st century

D. James Baker

Chair, GOOS Steering Committee President, Academy of Natural Sciences, Philadelphia, USA Abstract The 21st century has brought a strong recognition of the increasing vulnerability of society to environmental change and an understanding of human impact on the environment. Key to this recognition and understanding are measures or indicators of change and impact and education of the public about the issues. These indicators, such as global surface temperature and concentration of carbon dioxide in the atmosphere or sea level rise and changing chemistry in the ocean, are widely used for policy decisions ranging from energy and pollution control to fisheries quota determinations. The indicators depend critically on the collection of calibrated and validated data. The data from operational ocean measurement systems such as the Global Ocean Observing System are a critical element in the establishment of such indicators. Thus it is essential that commitments are in place to maintain the flow of accurate data, and that the data and the indicators are available and in a form that is accessible to policy-makers. Public awareness of the issues is also critical, and must be enhanced. In this talk I will use examples from the development of national and international indicators to illustrate what needs to be done, and I will discuss public education in venues ranging from universities to museums.

1. Introduction I would like to thank EuroGOOS, the Intergovernmental Oceanographic Commission, and the European Commission for hosting this important meeting and for inviting me to speak today at this plenary session on current developments in operational oceanography. I am looking forward to Dr. Jean-Franqois Minster's remarks about implementation of European operational oceanography, and to those of M. Bell on the European contribution to the Global Ocean Data Assimilation Experiment. Like Dr. Minster, I am a scientist who has also had experience in managing a large operational agency. I hope that my remarks can help set the stage for the other talks in this session. It is clear to all that the 21 st century has brought a strong recognition of human impact on the environment and of the increasing vulnerability of society to environmental change. In the 20th century we saw for the first time the global impact of the collective actions of humans. In my tenure at the U.S. National Oceanic and Atmospheric Administration (NOAA), I worked closely with colleagues both inside and outside the agency to find ways to deal with these issues by promoting and funding observing systems. We found that there were three main issues that must be dealt with. These are:

Email: [email protected]

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1. recognition of a problem 2. adequate science to make the case for possible solutions 3. awareness and readiness of the public to accept action Long-term observations are critical for all three of these. Long-term observations allow us to identify a problem, provide the basis for scientific understanding, and allow us to predict future change. In this talk, I will discuss the use of indicators and public education as ways to address these issues.

2. Indicators Indicators come in many forms, but here we are talking about indicators of environmental change, indicators of systems at risk, and indicators of progress toward sustainability. All of these need a flow of accurate data, that data being put in a form that is accessible, and recognition and use of the information. I will discuss briefly the sustainability progress indicators, but will focus on indicators of environmental change. Perhaps the best example comes from the much-reproduced figure of carbon dioxide concentration changing in time. The time series of stratospheric ozone levels, carbon dioxide concentrations, and sea level rise have probably been shown millions of times, and are the 'poster children' of environmental change. All of us here expect that we will see many more such changes in the ocean. It cannot be emphasised too much that the public recognition of these issues is greatly enhanced by the long-term data sets that show the changes. The relevant question for us here at this GOOS conference is how can we identify, collect and present data relevant to ocean issues that will be equally dramatic to the three I just mentioned? The power of graphical change, or use of indicators, has been recognised by many groups that are looking at broader measures of environmental sustainability. I will reference just one internationalBthe work at the World Economic F o r u m - - a n d one national effort, the U.S. Heinz Center report. I recognise that this is only a small sample of the work that is going on. The World Economic Forum has developed the 2002 Environmental Sustainability Index. The Index tracks success for countries in five areas: health of environmental systems, reducing environmental stresses, reducing human vulnerability to environmental change, social and institutional capacity to deal with environmental issues, and global stewardship of natural resources and the environment. Sixty-eight underlying data sets are used to develop twenty indicators for each of the five core components. The higher a country' s score, the better positioned it is to maintain favourable environmental conditions into the future. The five highest ranking countries are Finland, Norway, Sweden, Canada, and Switzerland. But no country is above average in each of the twenty indicators, and no country can be said to be on a sustainable environmental path. One important conclusion from this study is that within income brackets, country results vary widely. It appears that environmental sustainability is therefore not a phenomenon that will emerge on its own from the economic development process, but rather requires focused attention on the part of governments, the private sector, communities, and individual citizens. The report is on the web at www.ciesin.columbia.edu/indicators/ESI.

D. James Baker

7

How can GOOS results be used to help develop these indicators? We must ensure that GOOS products that focus on measures of current conditions and pressures on these conditions are put into a format that can easily be used by the groups that develop indicators. Examples are the data on sea surface temperature that is combined with land surface temperature, sea level rise, the various data on E1 Nifio/La Nina forecasts, and global and regional climate change. The recent decision to make the Florida Keys in the United States an internationally recognised protected area was largely based on time series of data collected and analysed to show adverse impacts of shipping. There are many other groups doing global and international indicators, and I urge the GOOS community to make links. This will help us in the long run with recognition and funding. In the U.S., while I was the head of NOAA, I worked with Bill Merrell, also an oceanographer, and then head of the John Heinz Center for Science, Economics, and the Environment, to develop a report c a r d m o r set of indicators--on the state of the ecosystems in the U.S. That report--The State of the Nation's Ecosystems: The Indicators--was just issued, and is available on the web at www.heinzctr.org. One of the central themes of The Heinz Center report is the condition and use of coasts and oceans, with a focus on the Exclusive Economic Zone, but also with a recognition of the importance of the global ocean. Sixteen indicators were identified: three for shorelines including erosion and water quality, two for estuaries, including invasive species and animal health, three for coastal waters focused on oxygen and chlorophyll concentrations and contamination, seven for deeper waters, including habitat, harmful algal blooms, fish stocks and health, and one for the global ocean, sea surface temperature. You can argue with their list, but this is one that was developed not by ocean enthusiasts, but by practical decision-makers. Why is this list useful for a GOOS discussion? Because the Heinz Center found that most of the data needed to develop long term indicators was not available. Let me summarise what they found. Adequate data was available for only sea surface temperature, chlorophyll in open ocean, and fisheries stocks. Partial data was found for coastal vegetated wetlands, shoreline type, sediment contamination, and marine mammal health. But by far the longest list was the one that reported data not adequate for national reporting. This list includes coral reefs, shellfish beds, oxygen depletion, coastal erosion, near-shore chlorophyll, water quality, and harmful algal blooms. In some cases the data is so lacking that it is not even possible to sensibly develop an indicator. Together with similar lists developed by other countries, this information will be important guidance for the development of the Coastal Ocean Observations Module of GOOS. The coastal ocean community has identified the primacy of data management and products; the list of needed indicators from coastal states will help drive the development of those products. One important area where global and national come together is on sea level rise. The IPCC has done an excellent job of showing the impacts: both in terms of the long-term commitmentmsea level has a long lag t i m e m a n d in terms of the people who are affected.

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3. Public education The second part of my remarks is aimed at public education. We have seen a lack of public awareness of these issues, and we need new educational programs that show why these issues are important. My interest comes from my long-term involvement and from my new responsibilities as the head of natural history museum. At the Academy of Natural Sciences in Philadelphia, we have an active and vibrant water resources and estuarine research program. The program began more than fifty years ago with studies by Ruth Patrick who showed that ecosystem diversity was diminished by human impact, a good first use of indicators. The subsequent work has led to a wide set of programs in which we both lead and cooperate with other institutions nationally and internationally, for example, we are working with the country of Mongolia in developing training and education programs for management of water resources. This work helps lay the scientific basis for understanding the impact of a changing climate on water resources. The question that we and other museums around the country face is how to reflect the issues of the day in our exhibits. How can we convey the passion and excitement that drives scientists to new discoveries, the commitment of local communities to preserving and protecting their watersheds, estuaries, and coastal waters. How do we explore the full range of policy issues that must be part of the public consciousness if society is to solve the problems we face? We, along with other museums, are in the process of redefining our traditional role. Peter Drucker has said that the role of the non-profit is to change human behaviour. In particular, we want to see our museum as a place where exhibits and education can raise awareness of water issues and global climate change as we are discussing today. As we reinvent ourselves, we want to develop a closer and interactive relationship with our audiences and other stakeholders in order to stay relevant to provide the kinds of services that best serve the public interest. Museums are civic enterprises with substantial but unrealised potential. Museums offer a reflective s p a c e ~ a neutral, calm, and comfortable venue where the rules of engagement are grounded in respect for multiple viewpoints. They are a place where people can consider issues against the backdrop of their own history. The museum foundation in scholarship and research allows them to contribute to an informed debate. Yet do you know a museum that really tells about climate change and all its aspects; shows all sides of development and environment, or presents in a clear and compelling way how sustainable development can occur? Environment exhibits have been with us for more than two decades. But they are well behind what we really need to transform the ideas and conclusions from research into strong education programs for the public of all ages. And the new ideas about how communities and constituencies relate in coming together to solve environmental problems ~ such as the water issues we are discussing h e r e ~ a r e not well presented in museums today. We need your help. I would like to see the Academy and its sister academic institutions be places where the public is excited, can learn, and can discuss these important issues. I'd like our museums to be places where when coming out, people will say "that was

D. James Baker

9

interesting, I learned something new, and I realise that the environment is complex; there are many factors to be considered as we make decisions". We need exhibits that dramatically show the importance of oceans issues, why it is important to have national and international collaborations, and interactive programs of interest to all ages, including, for example, computer games of all types. And we will need collaboration with universities, who can take a similar role.

4. Conclusion To be relevant in the 21st century, the GOOS community must be part of developing new products with the recognition of the importance of environmental and sustainable development indicators. We will need new education programs in universities and museums and other public venues. And all of this will require international collaboration. In that way we can move to a properly educated public that is ready to take on new environmental responsibilities.

New European developments for Operational Oceanography Jean-Francois Minster Ifremer, France

1. The scheme of operational oceanography

Figure 1 Example of operational oceanography at work 1.1 The CNES/NASA Jason-1

JASON-1 was launched on December 7, 2001, on the same orbit as Topex/Poseidon. It carries an altimetry payload derived from Topex/Poseidon. The satellite mass is only 500 kg (over 2.5 T for Topex/Poseidon). The Jason-1 commissioning phase was successfully completed on March 4, 2002. Jason Operational Sensor Data Records are delivered within 3 hours. An international effort is being made in the high-precision satellite altimetry programme. Letters were exchanged by CNES and EUMETSAT with NASA and NOAA on the Ocean Surface Topography Mission in Kyoto, 7 November 2001. Agreement by EUMETSAT members was reached in June 2003. 1.2 CORIOLIS In the year 2001 there were data sets from XBT, CTD, and moorings providing global coverage with 165190 profiles (T, S). The CORIOLIS 2002 data set concerning the ARGO profilers is shown in Figure 2.


Jean-Frans

Minster

11

Figure 2 The CORIOLIS 2002 data set 1.3 European prediction systems A number of preoperational or operational current-prediction systems have been implemented in Europe, one of them being the MERCATOR project. The partners in this project are CNRS, METEO-FRANCE, CNES, SHOM, IFREMER, and IRD, and their goal is to progressively develop an operational capacity to analyse and predict the global ocean currents. This will be achieved by contributing to the development of an ocean and climate prediction system through assimilation of near-real time satellite and in s i t u data into an ocean model. Customers of the system will be researchers, Public service, civil security, defence, and commercial applications of oceanography. MERCATOR provides bulletins predicting the state of the ocean for the next week, including data on temperature and salinity at various depths, and sea surface height. 1.4 The next step

Existing preoperational systems need to be transformed into operational systems using: 9 Satellite recurrent systems 9

In situ

observation operational teams and tools

9 Permanent modelling and prediction system 9 Service to intermediate and end-users 1.5 Legislation

There is a lot of international legislation, treaties, and declarations on environmental protection, marine, transport, fisheries policies to take into account. These include KYOTO, UNFCCC, UNCLOS, AMAP, OSPARCOM, MARPOL, BARCELONE, and HELCOM.

12

New European developments for Operational Oceanography

1.6 Users There is a huge range of users interested in operational prediction systems, including: 9 European Environmental Agency (EEA) 9 Meteorological services 9 Coastal protection agencies 9 National and international environment administrators 9 Water basin authorities 9 Climate and environmental research organisations and communities

2. GMES: Global Monitoring for Environment and Security GMES is a joint initiative of the European Commission and ESA to provide a sound basis to European policies related to environment and security. GMES will provide Europe with an independent access to global information useful for example for international conventions such as the Kyoto protocol. GMES will also develop applications to global change, environmental security, natural hazards. GMES will function as an extended partnership for national space agencies (CNES, DLR, BNSC, ASI, etc.), industry and science. GMES will network the ESA Earth Watch GMES Services element (funded 83 M~, 2002-2006), the 5th FP call for proposals, and the 6th FP Integrated Projects and Networks of Excellence.

3. The MERSEA concept 3.1 General objectives MERSEA aims to produce, assess and deliver real time and continuous observations of the ocean three dimensional structure and associated biochemical components. MERSEA will also produce, assess and deliver in real time hindcast and forecasts of the three dimensional ocean variability at the highest resolution possible for the short time scales (a few weeks).

MERSEA will deliver a global scale operational hindcast, nowcast and forecast system. This will include support for shelf sea hindcast, nowcast and forecast system and interconnection with coastal zone operational hindcast, nowcast and forecast systems.

3.2 A "European Centre for Ocean Monitoring and Forecasting" (ECOMF) The ECOMF will be responsible for operating a global system, with a short to mediumterm (e.g. 1 month) prediction capacity at high resolution. The ECOMF should have strong research connections and build partnerships with national centres. Some European countries are already running national global ocean systems for practical or political reasons (e.g. defence needs). These national systems should enter into partnerships with the ECOMF, so as to share services and products for their mutual benefits. It is expected that several services currently operated under national systems will be transferred to the ECOMF.

Jean-Frans

Minster

13

Most practical issues and applications are regional and require very high resolution monitoring and modelling. Such systems can be best managed in a distributed manner. Regional "outcentres" can be run as integrated systems, carrying out observation, modelling and assimilation, real-time and off-line operation, validation, analysis, distribution of products and regional services. They will use ECOMF outputs as boundary conditions and will contribute to data acquisition and model development useful for ECOMF. Outcentres will generally develop in an open and competitive manner, but some can be part of an institutional network (e.g., the Mediterranean, Baltic, and Arctic seas). 3.3 MERSEA data processing modules MERSEA does not contribute to infrastructures (e.g. ships, satellites, computers), but will include modules necessary to ensure that ocean observations are adequately processed.

A number of processes are under consideration, including: 9 real-time, multi-satellite processing of altimetry data (e.g. DUACS) 9 real-time, multi-satellite processing of ocean colour data 9 specific processing for ocean forcings and SST These should be negotiated with meteorological data centres. 3.4 MERSEA in situ observations

A global in situ observation system is required, such as ARGO and time series observations. MERSEA will include the European contribution to this world scale system. MERSEA will aim at creating real-time access to environmental ocean monitoring data, and at implementing their assimilation into numerical models to improve the value of this environmental monitoring. 3.5 The Biogeochemistry component

There are a number of contrasting requirements. Global requirements include CO 2 fluxes (for climate change), and primary production. These are of interest to end-users and decision-makers. Regional requirements include trophic interactions to zooplankton and predators, and harmful algal blooms. These are interesting to intermediate and end-users such as fisheries, aquaculture and tourism. Local (coastal) requirements include complex ecosystems with benthic, pollutants and suspended matter. These are important to intermediate and end-users such as tourism managers and local policy-makers. This is a complex problem~preoperational systems are less developed and there is a lack of data. We need global primary production with ocean colour data assimilation. Models should have regional and local very high resolution with operational dispersion modelling, requiring preoperational complex systems. We are talking about an evolutionary system 3.6 ECOMF and research ECOMF must maintain connections with research such as:

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New European developments for Operational Oceanography

9 evolution of the system will benefit from open research and technological developments 9 evolution of requirements and the general mission of the centre will lead its team to define research and development questions to be addressed 9 research activity devoted to ECOMF outputs is an essential component of their validation, beyond the operational, in-house validation 9 research will be a customer of ECOMF services and products ECOMF will maintain in-house R&D capability, networked with external research teams from institutes and academic organisations. ECOMF needs manpower and funding capacity to address research issues for operational oceanography, a visitor programme, participation in projects, etc.

3.7 MERSEA interfaces MERSEA will have to establish interfaces with many other organisations, including: 9 marine science activities of relevance, including European networks of excellence and integrated projects 9 other operational systems, more particularly those dealing with meteorology, climate prediction, fisheries, and marine environment monitoring 9 national agencies and private companies involved in the development of operational oceanography, whether at global or regional scales (likely to become members or associates of ECOMF)

3.8 A European organisation for Operational Oceanography European Inter-Institute organisation Inter-institute MoU

Advantages

Disadvantages

9Weak commitment 9Low visibility for multi-year planning and operation

9Simplest and immediate 9Flexibility 9Lowest level of decision

European company

stakeholders (national Institutes, agencies) and partners (local authorities, companies)

Advantages

9Relatively simple 9Flexible (variable levels stakeholderspartners) 9Commitments by stakeholders 9Easy management of the program (resourcelimited) 9Accountability

Disadvantages

9Realistically based on existing Institutional resources 9Multiplicity of players

European IntergovernmentalAgency Advantages

9Highest level of commitment by a small number of players 9Long term stability 9Potentially high level of resource 9Accountability

Disadvantages

9Heavy to implement 9Lower flexibility

Jean-Frans

Minster

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3.9 The MERSEA IP General principles

MERSEA will build on incremental developments of ongoing science and technology. It will aim at establishing a European operational system. MERSEA will identify and help organise a set of European agencies to implement and fund a long term operational system as a component of a world-wide organisation by 2008. The MERSEA IP should have the capacity to adjust to new requirements, research results and technologies.

4. Conclusion Europe should build a global component of GOOS, which is the main goal of MERSEA IP. GMES is an opportunity to implement this European operational system for the Ocean, by 2008, relying on existing experience. The actual organisation still needs more detailed definitions concerning ECOMF, national centres and outcentres and an organisational model must be put together. Let us build it together

The European contribution to GODAE Mike Bell *l and Pierre Bahurel z

1Met Office, UK 2Mercator-Ocean, France Abstract The principal objective of the Global Ocean Data Assimilation Experiment (GODAE) is to demonstrate the feasibility and value of routine, real-time, high-resolution forecasts for the global ocean. The forecasts will be generated by the assimilation of data from a coherent network of measurements into physically based ocean models, and the main phase of the experiment will take place between 2003 and 2005. We summarise the rationale and scope of the experiment and highlight some of the main contributions being made by European collaborators.

Keywords: Operational oceanography, ocean forecasting, ocean data assimilation, ocean modelling, oceanographic services. 1. Introduction GODAE emerged during 1997 from discussions of the Ocean Observations Panel for Climate (00PC). Its main demonstration phase will take place over the next three years (2003-2005) and be followed by a two-year consolidation phase (2006-2007). Section 2 of this paper reviews the main objective of the experiment, its rationale, scope and organisation. Section 3 outlines the contributions to it from European partners for each of the functional components illustrated in Figure 1.

2. Overview of GODAE 2.1 Objectives, Vision & Rationale

The principal objective of GODAE was first expounded by Smith and Lefebvre (1997) as "a practical demonstration of real-time global ocean data assimilation providing regular, complete depictions of the ocean circulation, at high temporal and spatial resolution, and consistent with a suite of space and direct measurements and appropriate dynamical and physical constraints". This is a step towards the long-term vision of "a global system of observations, communications, modelling and assimilation that will deliver regular, comprehensive information on the state of the oceans in a way that will promote and engender wide utility and availability of this resource for maximum benefit to society" (IGST, 2000). The demonstration is intended to parallel and imitate the successful First GARP Global Experiment (FGGE) which demonstrated the viability of global atmospheric forecasts. The opportunity to make the demonstration has been created by the achievements of the scientific community in developing satellite-borne sensors (e.g. altimeters and scatter* Corresponding author, email: [email protected]

Mike Bell* a n d Pierre B a h u r e l

17

ometers), in situ instruments (e.g. on profiling floats and moored buoys), ocean models and assimilation techniques during the period of the World Ocean Circulation Experiment (Siedler et al., 2001). In order to achieve the long-term vision described above, a symbiotic relationship between the operational and scientific oceanographic communities is needed. The operational community will increasingly support an observing system that meets the needs of the scientific community (Koblinsky and Smith, 2001) and scientific researchers will make the scientific developments needed to improve the operational ocean forecasts. This opportunity is short-lived since continuation of the funding of the observational systems requires a demonstration of their value to support a transition from research and development to "operational" status. The timetable for GODAE was chosen to fit the window of opportunity, the main demonstration period having a good coverage of satellite data. This timetable has proven to be valuable to many groups as it has helped them to make proposals and developments with the confidence that other aspects of the system will be in place during this period.

2.2 Specific aims IGST (2002) lists specific aims, benefits and anticipated outcomes of GODAE. The specific aims, for example, are to: 1. Coordinate and foster a more efficient, responsive and sustainable system for data assembly, quality control and access 2. Improve public access to and awareness of the many marine services products, both operational and research, that are available 3. Foster the development of a shared "common" of ocean information and tools for the production of improved ocean products 4. Foster the production and analysis of improved ocean service products 5. Undertake experiments to assess the utility of various ocean data streams for different applications 6. Guide the evolution of a global ocean observing system to produce ocean service products that meet the needs of the GODAE sponsors. The concept of a shared "common" of ocean information and tools has been developed with the aim of improving the existing collaboration between groups and to improve the input of research and development groups into operational oceanography. It is based on the paradigms of open, readily accessible, routine, real-time data and products and open scientific investigation. For example we aim to distribute data using modem methods and formats, assemble coherent consistent data streams and documentation, make products widely available and invite feedback on their value, and make open and detailed intercomparison of products.

2.30rganisation and Scope " The primary focus in GODAE is on real-time provision of analyses and short-period forecasts (to less than 2 weeks ahead) of the physical properties (temperatures, salinities and currents) of the ocean with near-global coverage and sufficiently high resolution to resolve the ocean mesoscale. The primary focus also includes the development of links

18

The European contribution to GODAE

with service providers and evaluation of the skill and usefulness of the products. Coupled seasonal forecasts and forecasts for coastal waters are additional applications which GODAE aims to support. Several of the systems being developed for GODAE also make forecasts of sea-ice, phytoplankton and zooplankton. Two teams were formed in 1997 to promote a coherent demonstration. The International GODAE Scientific Steering Team (IGST) is responsible for GODAE, and for providing and evaluating its scientific and technical basis. The second team is a group of patrons, which focuses national participations in and co-ordinates resources for GODAE. The IGST has produced a strategic plan for the experiment (IGST, 2000) outlining its rationale and scope and a draft implementation plan (IGST, 2002). Figure 1 depicts the scope of GODAE in terms of the functional components that need to be in place to support a successful demonstration. These components extend from the measurement network to the production and delivery of products and services. The implementation plan uses this structure to summarise the technical details of the components that are in place. Section 3 follows a similar structure.

Figure 1 The functional components required for GODAE

3. European contribution GODAE involves partners from the USA, Europe, Japan and Australia. This section describes some of the contributions that are being made by the European partners focusing on the systems that are in place to support real-time delivery of products rather than the scientific contributions on which GODAE is being built. Papers from the recent GODAE symposium (e.g. Hankin, 2002) provide much more detailed information on

Mike Bell* and Pierre Bahurel

19

each of the topics. The MERSEA (Marine EnviRonment and Security for the European Area) consortium has proposed an integrated project, which, if funded, would greatly strengthen the co-ordination and collaboration of the main European groups contributing to GODAE. 3.1 Measurement networks The Argo project (Argo, 2001) to implement a global array of 3000 floats making profiles of temperature and salinity to 2000m is a joint pilot project of GODAE and CLIVAR. It was agreed by the first IGST meeting that this array is essential for a credible GODAE. The Argo project has proved to be a good example of the open data exchange policy advocated by GODAE. The European contribution is strong accounting for 193 of the 706 floats in the water by the end of March 2003.

The Ship-of-Opportunity-Program (SOOP), the Global Sea Level Observing System (GLOSS) and moorings such as the PIRATA array in the tropical Atlantic and the multiparameter moorings supported by the ANIMATE project will also contribute data to GODAE for assimilation or validation. The assimilation of altimeter data from JASON-1 and Envisat will be an essential element of GODAE. The French National Space Agency, CNES, and NASA jointly fund the high precision JASON-1 satellite (the follow-on to TOPEX-Poseidon) and Envisat has been developed and is operated by the European Space Agency (ESA). ESA is also providing the GOCE satellite, which will measure the Earth's geoid with high spatial resolution and accuracy. Europe has strongly supported the development of satellite surface wind stress and surface temperature data, which are also viewed as high priorities for GODAE. ESA's ERS satellites flew the AMI scatterometer and EuMetSat's MetOp satellite will fly the wide-swath ASCAT scatterometer. EuMetSat's MeteoSat Second Generation (MSG) will provide geostationary SST data and an AATSR instrument is on-board Envisat. 3.2 Data Assembly and Serving The SSALTO/DUACS system (www.aviso.oceanobs.com/duacs) run by CLS in Toulouse will provide surface height data for GODAE partners from all satellite altimeters close to real-time with accurate orbital and geophysical corrections. Similarly the CORIOLIS data centre (www.ifremer.fr/coriolis/cdc/default.htm) at Brest will collect and make available real-time in situ profile measurements.

The GODAE High Resolution Sea Surface Temperature Pilot Project has ambitious plans to produce high quality products from polar orbiting infra-red and microwave instruments and geostationary satellites (Donlon et al., 2002). ESA will support a GHRSST project office and EuMetSat are supporting the Ocean and Sea Ice Satellite Application Facility at Lannion, which already produces prototype SST products for GODAE. Several NWP centres, including ECMWF and the Met Office, are making their surface flux forecasts available for GODAE.

20

The European contribution to GODAE

3.3 Assimilation Systems The systems listed in Table 1 generate analyses and forecasts of the ocean on a regular weekly or daily basis. The model configurations (areas and resolutions) supported by the systems and the details of the formulation of the model and assimilation schemes evolve fairly rapidly. Section 7 of the GODAE implementation plan (IGST, 2002) provides a summary of up-to-date details of the plans for each of the GODAE partner' s systems.

Table 1 List of European ocean assimilation systems providing forecasts available on the web on a routine basis System FOAM (Met Office, UK) Mercator (France) TOPAZ (Norway et a/.) MFSPP (Italy et al.)

Web site displaying forecasts http://www.nerc-essc.ac.ulVlas http://www.mercat0r.com.fr/ http://topaz.nersc.no/ http://www.cineca.it/mfspp/

3.4 Product Servers

A number of methods for distributing products will be explored within GODAE. Most groups to facilitate access to real-time forecasts and on-line archives of analyses are installing flexible web-based tools, such as the Live Access Server (LAS) software. These tools enable users to generate their own graphics or select data for download. They allow users to perform aggregation and processing of model data fields at the storage site enabling the volumes of data transferred over the Internet to be greatly reduced. They also use "generic data models" which are compatible with many tools (e.g. Matlab, IDL and GRADS) and data formats (e.g. NetCDF and GRIB) making the formats in which data are stored "transparent" to the users (Hankin, 2002). These tools will facilitate the scientific intercomparisons of the forecasts produced by the different centres. 3.5 Development of Services

Several NWP centres have made global surface wave and/or storm surge forecasts in shallow waters for more than two decades. During the 1980s and early 1990s ocean forecast systems were primarily developed to support Naval operations. More recently forecasts of ocean currents at the mesoscale have become important to offshore oil and gas companies as drilling moves off the continental shelf. They are also needed to support search and rescue operations, prediction of the evolution of oil and chemical spills and prediction of enhanced waves generated by wave-current interactions. Systems for monitoring and forecasting of the northern European shelf seas intend to benefit from improved boundary information provided by GODAE partners and experimental coastal observatories will explore the value of boundary data for their operations. Monitoring and forecasting of the ecosystem in support of the assessment and management of fish stocks will also be explored. Workshops will be held as part of GODAE to bring together forecast and service providers and users to share experiences of best practice.

Mike Bell* and Pierre Bahurel

21

References Argo Science Team, 2001, Argo: The global array of profiling floats, pp 248-258 in Observing the oceans in the 21st century, Eds. Koblinsky, C.J. and N.R. Smith, GODAE Project Office, c/o Bureau of Meteorology, Melbourne, Australia. Donlon, C.J., The GODAE high resolution SST pilot project strategy and initial implementation plan. GODAE Project Office, c/o Bureau of Meteorology, Melbourne, Australia. Hankin S.J., M. Clancy, P. Cornillon, B. Doty, D. Glover, B. Domenico, 2002, Providing a service: ocean data systems for GODAE. Pp 69-76 in Proceedings of the International Symposium "En route to GODAE", 13-15 June, Biarritz, CNES. IGST, 2000, The Global Ocean Data Assimilation Experiment Strategic Plan. GODAE Report No. 6, December 2000, GODAE Project Office, c/o Bureau of Meteorology, Melbourne, Australia. IGST, 2002, GODAE Development and Implementation Plan. Draft l ~ M a y 2002. Available from http://www.bom.gov.au/GODAE/frames.html Koblinsky, C.J., and N.R. Smith, 2001, Observing the Oceans in the 21st century. GODAE Project Office, c/o Bureau of Meteorology, Melbourne, Australia, ISBN 0642 70618 2. Siedler, G., J. Church, and J. Gould, 2001, Ocean circulation and climate. Observing and modelling the global ocean. International Geophysics Series, 77, Academic Press, 715 pp. Smith N.R. and M. Lefebvre, 1997, The global ocean data assimilation experiment (GODAE). In Monitoring the oceans in the 2000s: an integrated approach. International symposium, Biarritz, 15-17 October, 1997.


Regional Systems I


A possible migration from marine scientific research to operational oceanography in the context of the United Nations Convention on the Law of the Sea (UNCLOS) Peter Ryder Environmental Information Services, UK Email" [email protected], co. uk

1. Introduction The implementation of any system capturing data intended to enable the provision of information services must consider at least four issues: 1. The need for particular information services 2. The current state of relevant scientific knowledge 3. The availability and associated costs of technology to capture and process data 4. The legal/regulatory context within which data collection and use is to take place. It is not necessary to review the second and third of these issues here for the EuroGOOS community, beyond reflecting that considerable progress has been made in recent years to increase our scientific understanding of the marine environment, to the point where very effective services are available in the domain where it is possible to predict the phase and amplitude of atmospheric disturbances that drive the oceansmcharacterised as the marine-state forecasting domain below. Some useful skill is beginning to be demonstrated at longer (seasonal to annual) time scales when there is strong coupling on these time scales between the atmosphere and oceans. Such couplings are strongest in the tropics but some effects are manifest at higher latitudes too. It remains the case however that the marine environment is seriously undersampled and because seawater is opaque to electromagnetic radiation, monitoring from space, which has proved particularly effective for monitoring the atmospheric and terrestrial domains, is limited to the surface layers of the oceans. Here remarkable achievements have been m a d e m i n measuring sea surface topography for example. The current boundaries of Marine Scientific Research (MSR) are largely defined by our difficulty in obtaining quantitative information about physical, chemical and biological processes below the surface layers of the ocean. The lack of ecosystem information on and near the continental shelf is particularly acute in this respect. We should also recall under issue (2) that the Exclusive Economic Zones (EEZs) and continental shelves of coastal States 1 contain 40% of the oceans. Flow in the oceans is strongly influenced by the bottom and lateral topography. By definition, much of that constraining topography is comprised of the continental shelves and margins and falls within EEZs rather than the High Seas therefore. Furthermore, because of the earth's 1 As defined within the UNCLOS

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A possible migration from marine scientific research to operational oceanography in the context of the United Nations Convention on the Law of the Sea (UNCLOS)

rotation, many o f the strongest near surface flows are located at the western boundaries.

Upwelling is also predominantly located at eastern boundaries and run-off from the land is an important source of nutrients and pollution. It would be impossible to conduct the MSR and monitoring that is required without access to those domains. This community is very aware that there has been substantial investment in marine environment measurement technologies, using both remote sensing and in situ methods. At present, the available inventory of observing systems has never been better or more capable. Autonomous monitoring methods are being deployed to reduce costs and adaptive 2 methods are beginning to be available to help overcome the undersampling problem. Nevertheless, it remains the case that the resources needed to carry out fundamental MSR in the real environment are very substantial. To obtain generally valid results, proper attention has to be paid to the prevailing conditions, which are normally quite uncontrolled. "Events" in the oceans are generally long-lived, so a sustained effort is necessary to capture information about and study them over (ideally) several cycles of variability. Such events are quite unconstrained by the boundaries specified in the UNCLOS. For this reason, on efficiency grounds and to spread the cost of the work, there is an increasing tendency for MSR to be conducted for a range of integrated purposes and on an international scale. There are considerable incentives to exploit technology and ingenuity to the full, to capture vital information that has been practically inaccessible in the past and to make cost savings. If there is to be trust that all partners will perform, there is also a need to ensure a comprehensive and inclusive approach to data capture, quality control, management and access to the resulting information. Happily there is growing confidence that large scale programmes involving scientists from a wide range of States, can be carried out successfully. Components of the WCRP such as TOGA, WOCE and CLIVAR and of the IGBP such as GLOBEC, JGOFS and LOICZ provide clear examples of this. The programmes of EuroGOOS provide an excellent example of such collaboration on the regional scale. There is also growing tendency for the monitoring, modelling and some elements of the data management structure required for operational information services to be set up initially within one or more MSR programmes. This reflects the experimental approach and learning process that is an essential feature of infrastructure building on a large scale. Furthermore many of the disciplines that are required in the provision of operational services are also those that are required in long-term research programmes. As a consequence the distinction between research and operations is inevitably blurred. Process research gives way to pilot experiments, feasibility studies, pre-operational and finally operational ocean observing systems. This is a very important progression which enables risks to be managed and the confidence of all those involved to be built.

2

In the sense that the data collection programme is adapted to what is measured, usually in near real time.

Peter Ryder

27

2. The need for information a b o u t the m a r i n e e n v i r o n m e n t Information about the marine environment is required for two broad, connected purposes; to assess the current and past state of the environment and to make predictions about its future state. There is a strong connectivity between the oceans and atmosphere and a practical limit to the predictability of the phases and amplitudes of individual meteorological events of 10 days or less. As a result it is useful to distinguish between predictions about the future of the marine environment on timescales less than and more than ~10 d a y s m w h i c h are characterised here as marine-state forecasting and ocean forecasting respectively.

2.1 Assessments Assessments of the current state of the marine environment are required primarily to inform the making of policies~particularly those relating to sustainable d e v e l o p m e n t ~ to monitor their effectiveness and to assist their efficient implementation. The drivers of change and the resulting impact of those pressures are normally the subject of monitoring and assessment. The aim is normally to identify or characterise particular problems, spatial variability and temporal trends with respect to earlier assessments. Many of the existing marine Conventions require regular assessments to be made of various forms of pollution, primarily to determine whether or not specific management actions are being effective. Attention is normally focused upon known or suspected "hot spots" and these are inevitably in or close to land, from where most pollution originates, and hence in territorial waters or EEZs. In the absence of agreed methods of valuing the environment it is difficult to place a social and economic value on such assessments but given the intergovernmental nature of the Conventions there can be little doubt that there is substantial political will behind their implementation. Because of their impact on policymaking it seems likely that the benefits of marine environmental assessment are to be measured in many ~B on a global basis. Other assessments are carried out to monitor the state of fish stocks for example. Ideally, the objective here is to manage such stocks to maximise yield whilst minimising environmental damage and the risk of over-fishing. The consequences of taking illfounded action can be profound in economic and social terms and in irreversible damage to ecosystems and biodiversity. The main gaps in knowledge for the conduct of assessments concern the physical properties and dynamics of the seas below the surface layers and the functioning of marine ecosystems. There are also profound gaps in understanding of how stresses on the environment generate particular outcomes or impacts. These are the subjects of current research.

2.2 Marine-state forecasting Marine-state forecasting is limited by the predictability of the atmosphere as noted above, but there are many important benefits to be realised from this capability. They appear most obviously in the form of increased safety of life and property subject to natural hazards such as flooding due to storm surges, erosion, saltwater intrusion and subsidence on land, and swell, waves and currents at sea. Services based on the

28


advection of chemical and biological anomalies, such as oil spills or harmful algal blooms are also in place and under development. Such forecasting also enables more efficient use of resources, in the form of capital-intensive assets, manpower, time and fuel. These services are provided to the shipping, offshore oil and gas and beach management industries. Invariably coupled atmosphere-ocean models produce the relevant predictions, and such models are also used to carry out hindcasts. The coupled models are driven by historical data sets of atmospheric and ocean data, where available, to create a climatology of the marine environment, from which statistical properties of state variables can be inferred. Such data are extremely valuable for the design of safe but economically optimised marine structures for example. Overall the annual benefits of forecasting and hindcasting seem likely to be measured in lOOs of ~M on a global basis. The main thrusts of research and development in this field are directed towards increasing the resolution of numerical models, to better capture the behaviour and role of time variable structures such as eddies and (eventually) to be able to incorporate biogeochemical processes, in nested or linked ecosystem models. 2.3 Ocean forecasting Ocean forecasting is the task of predicting the dynamic variability of the oceans on timescales longer than the predictability of individual atmospheric events. The products depend on internal physical processes in the ocean (e.g. those which control quasigeostrophic eddies, Kelvin and Rossby waves in the ocean). Some such products depend upon the prediction of ecosystems and the long-term fate of pollutants and therefore upon biological and chemical processes too. Such forecasting largely remains a research task in its own fight, and is a crucial component of climate research and prediction. There is no doubt that the oceans provide an important long-term boundary condition on climate. There is ample evidence that in certain parts of the world the oceans fluctuate in manner that implies a measure of predictability and, in the last few years, useful prediction of particular coupled modes of behaviour of the atmosphere and ocean has been achieved, when the amplitude of the fluctuation has been large. The E1 Nifio Southern Oscillation (ENSO) phenomenon in the tropical Pacific is the most well known of these. Ocean forecasting research will require substantially more physical data than is currently available. If phenomena such as the North Atlantic Oscillation and the various monsoons are to be described well enough to discover how ocean processes affect them, so that they can be forecast, those new data must be in long-term time series, extending over decades. For some purposes and in some places, very high-resolution contemporaneous physical, chemical and biological data (100m) will be required in order to determine how to identify critical regions and make affordable long-term observations in them. There is a growing appreciation that the collection/management of data and their assimilation by numerical models, which is the norm in marine-state forecasting, is also a very necessary and powerful way of maximising their value in ocean forecasting. Time pressures are somewhat less but there are many problems still to be solved in assembling multiple streams into quality controlled data sets, assimilating these into capable numerical models and delivering products and services to users. Available computing power is barely adequate and much of the work is being done through incremental

Peter Ryder

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enhancement of existing capabilities. The Global Ocean Data Assimilation Experiment (GODAE) project is an important international focus for this. Very substantial economic and social benefits would accrue if the skill of ocean forecasts, as defined here, and hence climate prediction, could be improved. The benefits would be realised primarily in the form of mitigating action taken in response to more skilful seasonal, interannual and longer period predictions of environmental properties. Many of the predictions would be in the form of predictions of weather phenomena-rainfall, temperature, etc.mbut they could include consequences such as fluvial flooding, storm surges, algal blooms and the like. The predictions would be in the form of a distortion of the "normal" climatology, expressed in probabilistic terms. The Kyoto Protocol to the United Nations Framework Convention on Climate Change (UNFCCC) calls for a different kind of mitigating action that seeks to reduce or prevent the predicted change in climate due to anthropogenic green house gas emission. The economic and social consequences of this are immense. Attempts to limit the future growth of atmospheric CO2 concentration, however modest, will involve major, and potentially costly, changes in energy and technology policy. The Second and Third Assessment Reports of Working Group 1 of the Intergovernmental Panel on Climate Change (IPCC) confirm confidence in their recommendations but also point to areas of uncertainty. These include the simulation of E1 Nifio, and monsoons. A particular need was identified for sustained, systematic observations, modelling and process studies. Overall the global benefits which would be realised by optimised decision making enabled by high quality ocean forecasting seem likely to be measured in 10s ~B.

3. The Regulation of Monitoring and Marine Scientific Research The Conference that prepared the UNCLOS was convened in 1973. It ended nine years later with adoption of the Convention in 1982. The UNCLOS came into force in November 1994, one year after reaching its 60th ratification accession. 9 The legal regime that it outlines marks a substantial shift from the view of the oceans, beyond narrow territorial seas, as free and open to all. Thus, Article 3 of the Convention provides for a territorial limit up to 12 nautical miles, measured from baselines defined in the Convention, subject to rights of innocent passage through straits required for international navigation. Responsibilities are also placed on those exercising those fights. 9 The Convention recognises and elaborates on the EEZ as a basic juridical zone in ocean law, albeit as a zone sui generis, being part neither of the territorial sea nor the high seas, and extending to a maximum distance of 200 nautical miles from the baselines used to measure the territorial sea. In the EEZ coastal States have sovereign rights over "living and non-living natural resources super adjacent to the sea bed and its sub soil" and limited jurisdictional capacity but not sovereignty. 9 As to living marine resources, the coastal State has the legal duty to ensure that EEZ resources are protected against over-exploitation and to promote their "optimum utilisation", for which purposes the coastal State is to employ the best available scientific evidence.

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9 Marine scientific activities in the EEZ are made subject to a "consent regime" that necessitates coastal State approval for research in the EEZ and on the continental shelf. However, researching States and the international organisations have rights too, and consent for scientific research on the shelf is normally not to be withheld except in "those specific areas which coastal states may at any time publicly designate as areas in which exploitation or detailed exploratory operations" are occurring or will occur "within a reasonable period of time". 9 From the point of view of this paper, the most important jurisdictions extend to "marine scientific research and the protection and preservation of the marine environment". Part XIII is concerned with marine scientific research in the various ocean spaces; in Part VII such research is explicitly recognised as a freedom to be enjoyed on the high seas. Part XIV is very pertinent in promoting the transfer of marine technology and capacity building by states and international organisations, including the IOC. The provisions of Parts XII, XIII and XIV are discussed further below. All of Part XII of the Convention is devoted to the protection of the marine environment. Article 192 stipulates that "States have the obligation to protect and preserve the marine environment" and to that end are, individually and jointly, regionally and globally, to take appropriate measures "to prevent reduce and control pollution of the marine environment from any source". This duty of care extends to the export of pollution damage to the high seas or zones where other states have sovereign rights. There are substantial provisions to deal with vessel-source pollution. The cause of protection is further advanced by legal obligations ranging from the collection of data and scientific study to the preparation of contingency plans and the harmonisation of national policies for the protection of the marine environment. Thus Article 200 places an obligation on States to "undertake programmes of scientific research and encourage the exchange of information and data acquired about pollution of the marine environment". Whilst Article 204 requires them "to observe, measure, evaluate and analyse, by recognised scientific methods, the risks or effects of pollution of the marine environment"man obligation which entails routine monitoring rather than fundamental research. Part XIII of the UNCLOS is concerned with MSR. However, there are few explicit statements about the nature of MSR in the text. 9 Article 243 encourages States and competent international organisations to cooperate "to create favourable conditions for the conduct of marine scientific research in the marine environment and to integrate the efforts of scientists in studying the essence of phenomena and processes occurring in the marine environment and the interrelations between them". 9 Article 246, paragraph (3) requires that consented research should be exclusively for peaceful purposes and in order to increase scientific knowledge of the marine environment for the benefit of all mankind. Paragraph (5) enables coastal States to choose to withhold consent for the conduct of MSR projects in their EEZ or on their continental shelves if the projects: -

are of direct significance for the exploration and exploitation of natural resources, whether living or non-living

Peter Ryder

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-

-

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involve drilling into the continental shelf, the use of explosives or the introduction of harmful substances into the marine environment involve the construction, operation or use of artificial islands and other (defined) structures (are not properly or not accurately defined, as specified in Article 248).

9 Article 247 outlines a procedure for obtaining the approval of coastal States for MSR conducted in their EEZs or continental shelves under the auspices of international organisations. However, Pugh (2001) points out that in the twenty years since the text of the UNCLOS was settled, MSR and the context in which it is applied has changed considerably. He notes that the procedures defined in the UNCLOS are heavily predicated on the use of research vessels as the platforms from which research will be conducted. Article 248, in particular, assumes the use of such platforms. Current research methods rely on the use of other platforms, including satellites, aircraft, ships in passage, autonomous vessels, buoys and floats etc. Simulation modelling has become a much more powerful research tool over this period too. 9 This all falls well short of a comprehensive definition of MSR. Indeed, article 251 of UNCLOS requires signatory States to "seek to promote through competent international organisations the establishment of general criteria and guidelines to assist States in ascertaining the nature and implications of marine scientific research". It appears that the drafters of UNCLOS were aware the MSR was likely to change, both in its objectives and methods of prosecution. Therefore they appealed to organisations such as IOC for ongoing guidance on the matter. Presumably it was expected that such forums would maintain relevance and enable practical compromises to be reached between the benefits of consenting to the pursuit of particular research objectives and the possible subordination of national interests in doing so. 9 In summary and in keeping with the spirit of UNCLOS, those wishing to conduct MSR in the EEZ of a coastal State might maximise the chances of obtaining consent if: -

That State was actively engaged in the planning for and implementation of the research and able to benefit from it

-

The programme of research had extensive international support and the underpinning science was widely accepted as well founded

-

The primary objective was to expand knowledge and/or human welfare

-

The results were to be published freely in the open literature

-

Those conducting the research had a good track record in such matters and hence the confidence of the coastal State.

Part XIV is concerned with the development and transfer of marine technology. Article 270 is particularly relevant in indicating how international co-operation is to facilitate this: "International co-operation for the development and transfer of marine technology shall be carried out, where feasible and appropriate, through existing bilateral, regional or multilateral programmes, and also through expanded and new programmes in order to facilitate marine scientific research, the transfer of marine technology, particularly in new fields, and appropriate international funding for ocean research and development."

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It is important to keep in mind that the UNCLOS was developed in the 70s, to solve problems that began to emerge in the period after the Second World War and that it effectively introduced a completely new legal regime. That regime has been used in the succeeding period to introduce a number of additional Conventions designed to deal with problems that are new or have become more pressing. Chapter 3.1 of IOC (1998) provides a description of Conventions that encourage or require signatories to monitor the environment for identified purposes. It is noteworthy that the although the UNCLOS sets out the fights and obligations of coastal States in many areas of human endeavour that entail the use of territorial waters, EEZs, continental shelves and the open oceans, it has nothing to say about the capture of data within these regimes that aid the production of weather forecasts and warnings and the preparation of climatological summaries. The Safety of Life at Sea Convention (SOLAS) and FCCC encourage the capture of such data, without specifying what is required and when. In practice such data include the measurement of sea surface temperature and the observation or measurement of sea state and the presence of ice, as well as atmospheric properties, such as pressure, wind speed and direction, air temperature, dew point, and various types of significant weather, in all the zones specified in the UNCLOS. The making and free and open exchange of such measurements and observations from States' land and marine territories, under the auspices of the WMO, predate the drafting and ratification of the UNCLOS, so it may be assumed that their exclusion is deliberate and no subsequent Convention or Protocol has sought to regulate the practice. One can only conjecture that, on the basis of long experience, coastal States have accepted that the many benefits of continuing such practices far outweigh the threat posed by uncontrolled access to meteorological information gained over their territories (land and sea) and EEZs. There is some evidence of this in an exchange of correspondence between the WMO and Ambassador A. Yankov, Chairman of the Third Committee of the 3rd UN Conference on the UNCLOS. WMO's concerns were set out in Resolution 16 (Cg-VIII), which expressed the hope that the emerging legal provisions relating to MSR "would not result in restrictions to operational meteorology and related oceanographic observational activities carried out in accordance with international programmes such as WWW and IGOSS". Ambassador Yankov responded on 25 August 1980 to the effect that, as chairman he was of the opinion that the provisions on MSR "would not create any difficulties and obstacles hindering adequate meteorological coverage from the ocean areas including areas within the EEZ carried out both in the framework of existing international programmes and by all vessels since such activities had already been recognised as routine observation and data collecting which was not covered by part XIII of the negotiating text and that they were in the common interest of all countries and had undoubted universal significance". This opinion was expressed both in the 46th meeting of the 3rd Committee and 134th Plenary meeting of the Third Conference, without dissent. The UNCLOS is also silent on the use of remote sensing techniques to monitor the marine environment and in conducting MSR. Such remote sensing is limited in its scope to the surface layers of the marine environment if the remote sensing employs electromagnetic radiation, but deep-water properties can be inferred remotely using sound

Peter Ryder

33

waves. Again such knowledge, if not the capabilities of the current technology, predates the UNCLOS, so one must assume that the silence is deliberate.

4. A possible way ahead MSR and operational information services that are and will continue to emerge from it will be planned and implemented by national agencies but will certainly be coordinated in international forums. The international and territorial dimensions of much of this MSR dictate that some of these forums must be intergovernmental. Assessments are invariably carried out under the auspices of intergovernmental Conventions by national agencies. Issues of quality assurance, methodologies to be used, partition, in time and space, and coordination of the tasks of the Contracting Parties are determined and agreed under the auspices of the appropriate Commission. Access arrangements are dealt with as one of these issues. These arrangements appear to work well, once they are set up and there is confidence in them. In the marine-state forecasting domain, IOC and WMO, within the Joint Commission for Oceanography and Marine Meteorology (JCOMM) - - all of which are intergovernmental - - will coordinate and manage jointly the requirements for and means of supply of in situ data, working through national operational agencies. Requirements for space-based data will be coordinated through the mechanisms set up by WMO and CEOS. The satellite operators responsible for operational satellite programmes, such as NOAA and EUMETSAT, and those responsible for research satellites, such as NASA, NASDA, ESA, will meet these to the extent possible. JCOMM will also coordinate data management and the supply of required services, covering for example prediction of wind waves and storm surges, sea ice, and marine pollution emergency response. Because the primary requirement for these data is operational, they are collected and exchanged routinely in a manner similar to their meteorological counterparts. Accordingly, it is suggested here that such operational data could be treated in an analogous manner, falling outside the consent provisions of the UNCLOS. It is beyond the scope of this present paper to judge whether a specific protocol is needed but in any event, it would need to be developed in an appropriate intergovernmental forum. In meteorology, there is a long history of practical and material assistance being provided to developing countries, by developed countries, as a quidpro quo for access to data captured on their territories for use in WMO programmesmin particular in the World Weather Watch. Such assistance takes the form of observing systems, telecommunication capacity and facilities to receive regional observations, satellite data and forecast products created in the developed countries. Training programmes and fellowships are also provided to assist in the interpretation and use of the data and products in the creation of services. It is suggested that this practice could be adopted as the norm in the creation and use of assessments and operational marine-state forecasting products, including hindcasts. In the ocean forecasting domain, it is reasonable to expect JCOMM to be advised of the emerging research requirements for data and to coordinate actions to meet them, again through national agencies. In this, JCOMM will require scientific advice. The Coastal Ocean Observations Panel and Ocean Observations Panel for Climate have been set up

34


to provide that. Implementation is likely to be coordinated in forums at regional level, such as those provided by EuroGOOS, or within specific international research programmes such as those of the WCRP, IGBP, GODAE and ASOF etc. Research agencies usually implement them. From the foregoing it is seen that large and multi-national scale efforts will be needed to study detailed physical, chemical or biological oceanographic processes or to test new observing systems, whether for systematic, long-term observation for climate or for ecosystem research purposes, or to study marine pollution--in keeping with Articles 200 and 204. It is therefore suggested here that any MSR to be carried out in territorial waters, EEZs, or on continental shelves should seek the required consents at this integrated regional or global programme level, under the terms of the UNCLOS and the auspices of the IOC or equivalent international organisations. This would have significant implications, as follows: 9 Each programme for consideration under this regime would have to be sanctioned or adopted as an official programme by an appropriate UN organisation such as IOC and developed and approved in consultation with expert bodies of that organisation. The plans for each programme would specify in necessary detail the general nature of access and activities to be conducted within the EEZs of coastal states and specify critical requirements of access. They would also describe the contingencies (such as uncertainties in timing, adaptive deployment and redeployment) that would have to be allowed for in the experimental nature of the programme. 9 Coastal States would then be requested to give their overall consent under Article 247 provided that activity is within the approved programme and as many as possible of the descriptions called for by Article 248 are adapted as necessary and made available to the coastal States concerned. The elaboration of practical procedures for such authorisation will require careful consideration of national interests and the public good benefits likely to accrue from the programme. 9 There will also be a need for confidence building, particularly in the early use of the procedures. It does seem likely that a two stage process will be required: Firstly the development and approval of the essential features of the programme and secondly the monitoring of its implementation using IOC mechanisms. There are signs that this approach is acceptable. For example, IOC has adopted Resolution X X - 6 which requires that "the concerned coastal States must be informed in advance through appropriate channels, of all profiling floats which might drift into waters under their jurisdiction, indicating the exact location of such deployments". To that end, an International Argo Information Centre, now known as J C O M M O P S has been established, to inform national focal points about float deployments in the Argo pilot project, how to track float positions, and how to access float data. Each such programme should have capacity building and the delivery of emerging products generated by the programme built into i t - - n o t as an optional extra or a separate programme, that might or might not have adequate funding to fulfil its mandate. If MSR programmes are authorised in this way, there will be every opportunity for an easy migration from research to operations as States develop confidence in the research

Peter Ryder

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results, the way that the programme has been conducted, the individuals concerned and the benefits from implementing operational systems based upon the research.

Acknowledgements This paper was prepared under IOC Contract SC-298.004.02. The advice of Professor J. D. Woods CBE, Professor of Oceanography, Imperial College, London, UK, Dr Ralph Rayner, Managing Director, Fugro GEOS, UK and Dr Angus McEwan, Bureau of Meteorology, Tasmania, Australia and the financial support of MAMAmMediterranean network to Assess and upgrade the Monitoring and forecasting Activity in the basin (EVR1-CT-2001-20010) are gratefully acknowledged.

References Pugh, D., 2001, Towards an Implementation of Article 251 of UNCLOS, I O C / A B E LOSI/7, Paris, June 2001 IOC, 1998, The GOOS 1998, Paris 168pp

Cyprus coastal ocean forecasting and observing

system

George Zodiatis* 1,2, Robin Lardner 1'2, Georgios Georgiou 2, Encho Demirov 3 and Nadia Pinardi 3

! Oceanography Centre, Department of Fisheries & Marine Research, Cyprus 2Computational Oceanography Group, University of Cyprus, Cyprus 3Istituto Nazionale di Geofisica e Vulcanologia, Italy Abstract A complete operational oceanographic forecasting and observing system has been developed in Cyprus, coveting the coastal and open deep sea areas around Cyprus and the Levantine Basin, and has been operational since early 2002. The system is called CYCOFOS--Cyprus Coastal Ocean Forecasting and Observing Systemmand integrates the main features, which are required in GOOS, EuroGOOS and MedGOOS design. CYCOFOS is a result of several years of oceanographic research activities carried out in the framework of EU projects such as the MFSPP, MAMA and MedGLOSS. The CYCOFOS at present consists of several modules that provide regular NRT oceanographic information, both to local and sub-regional end-users in the Levantine Basin.

1. Introduction The sustainable development of the coastal and offshore sea regions of the Mediterranean and the marine economic activities depends crucially on the scientific knowledge of the marine system variability, particularly on our capability to monitor and forecast at the relevant space and NRT (near real time) scales. The challenge of sustainable development of coastal and ocean related economic activities has been addressed in several international fora. In the Agenda 21 of the United Nations Conference on Environment and Development in 1992, the establishment of a Global Ocean Observing System (GOOS) is addressed. The design, promotion and implementation of GOOS world-wide was given to the Intergovernmental Oceanographic Commission (IOC) of UNESCO in 1992. Marine monitoring and forecasting systems on global, regional (European) and local scales will play key roles in balancing the relationship between development and the environment. The development of an operational oceanographic monitoring and forecasting system will certainly support better management of the marine environment, reducing environmental problems that arise from the various economic activities in the marine sector. The R&D of these systems will enable a continued sustainable improvement mitigating the effects of disasters and will benefit the economy, particularly that of the marine sector. The GOOS consists of the following main operational modules: a) a network of remote sensing in situ and satellite oceanographic systems to monitor the marine environment, b) an integrated set of various oceanographic models that will provide NRT coastal and * Corresponding author, email: [email protected]

George Zodiatis*, Robin Lardner, Georgios Georgiou, Encho Demirov and Nadia Pinardi

37

ocean forecasts, and c) a data network that connects the monitoring systems and the models, and provides updated information to oceanographic databases and to end-users. Following GOOS, the EuroGOOS and the MedGOOS initiatives were established respectively in 1994 and 1999. The EuroGOOS supports the objectives of GOOS at the local and mainly the regional (European) level. In EuroGOOS there is a strong emphasis on the development and application of new and existing technology, which will allow more efficient use of the forecasting, observing and other related tools, with minimum cost and human resources (EuroGOOS, 1997). Similarly, the objectives of MedGOOS are to link existing operational systems in the Mediterranean and to extend the area of operational oceanographic systems to the whole region. MedGOOS modules will be based on principles similar to the EuroGOOS ones. The development of a regional operational forecasting and observing system for the Mediterranean will contribute to the benefit of local users in all aspects of the marine sector (IOC, 1998). In Cyprus, the main oceanographic institution of the country, the Cyprus Oceanographic Centre of the Department of Fisheries and Marine Research (DFMR), undersigned the founding MoU for its participation in the MedGOOS co-operation framework and has also recently applied for EuroGOOS membership. Moreover, along with the Computational Oceanography Group of the University of Cyprus, DFMR has participated actively in numerous EU-funded research projects, jointly with other EU and Mediterranean partners, promoting operational oceanography mainly in the Mediterranean area.

2. Discussion The promotion of the GOOS, EuroGOOS and MedGOOS, in Cyprus and elsewhere, requires the establishment of an infrastructure for operational oceanography, participation in European and international activities for the development of common methodologies and tools to be used and applied by all the regional partners, and, finally, the development of derived applications to assist decision-makers as well as end-users. With regards to the infrastructure, the institutions developing/applying the scientific modules for the operational oceanography in Cyprus are the Oceanography Centre at the DFMR, and the Computational Oceanography Group of the University of Cyprus. It is worth mentioning that the DFMR is a member of IOC, CIESM, UNEP, ESEAS and a founding member of MedGOOS. At present, the existing oceanographic infrastructure related to coastal and open deep sea monitoring and forecasting activities in Cyprus consists of: 1. the CYBO (Cyprus Basin Oceanography) long-term monitoring of coastal and deep sea areas of Cyprus and SE Levantine basins. The CYBO contributes to the updating of the Mediterranean database, particularly for the Levantine Basin, as was done for the new MEDATLAS oceanographic database 2002 2. the Cyprus MedGLOSS coastal stations for long term monitoring on sea level and water temperature, as part of the MedGLOSS and ESEAS networks 3. the CYCOFOS HRPT ground receiving station, capable of providing regular remote sensing SST at any part of the Eastern Mediterranean Sea,

38

Cyprus coastal ocean forecasting and observing system

4. the CYCOM & CYWAM high resolution flow and offshore wave forecast models in the Levantine 5. the MEDSLIK & MEDPOL oil spill and pollutant-dispersion models for the Levantine Basin. The development and the promotion of the operational coastal/ocean monitoring and forecasting activities in the Mediterranean and European seas is carried out in the framework of several EU-funded research projects, which include: 1. MFSPPmMediterranean Forecasting System Pilot Project, a EuroGOOS activity aiming, among other objectives, at demonstrating the feasibility of establishing an operational ocean forecasting and observing system in the region (Pinardi et al., 1999) 2. MAMAmMediterranean network to Assess and upgrade Monitoring and forecasts Activities in the region, a MedGOOS activity aiming, among other objectives, at establishing a network for preparatory design of an initial observing and forecasting system including all Mediterranean countries (MAMA group, 2002) 3. MFSTEP--Mediterranean Forecasting System Towards Environmental Predictions, a EuroGOOS activity aiming, among other objectives, at the further development of operational forecasting and observing system in the Mediterranean, following the MFSPP, and, in addition, at demonstrating the usefulness and the benefits of the operational oceanographic products to end-users via certain derived applications 4. MERSEA strand 1--Marine Environment and Security for the European Area, a GMES activity, whose main objectives are to integrate existing satellite observations with data from in situ monitoring networks through ocean modelling and data assimilation systems, and to deliver information products needed by users concerned with European marine environment and security policies 5. E S E A S - R I ~ E u r o p e a n sea level service research infrastructure, an ESEAS activity also including the MedGLOSS activities whose main objectives are to support the ESEAS research infrastructure and to facilitate pan-European coordination, and the upgrading and standardisation of the network of observing sites in the European sea areas. Within the framework of the above EU research projects promoting operational oceanography, the CYCOFOS, an operational Cyprus Coastal Ocean Forecasting and Observing System, has been developed for the sea areas around Cyprus and the Levantine Basin, Eastern Mediterranean. The CYCOFOS at present provides NRT (near real time) operational forecasts of sea currents, water temperature, salinity, sea level, significant wave height and direction, as well as NRT operational in situ observations of sea water temperature, sea level, atmospheric pressure, and remote satellite SST. In addition, the CYCOFOS provides to the system's end-users with the MFSPP/ECMWF offshore wind fields in the Levantine Basin. The first module of the CYCOFOS system has been providing operationally in situ data since September 2001, while operational ocean forecasts have been provided on the system web page since March 2002. In the near future, NRT in situ data will be provided by the MedGOOS-3 Ocean Observatory, as well as forecasts from specific environmental applications.


39

At present the CYCOFOS forecasting system consists of the following modules: 9 data acquisition from the MFSPP system and pre-processing of the initial and boundary data for the flow and wave models 9 the CYCOM model (an adaptation of the POM - - Princeton Ocean Model) used for high resolution hindcasts and forecasts of the flow 9 the CYWAM model (an adaptation of the W A M m w a v e model) used for coarse- and fine-grid wave forecasts 9 the VIOD interfaces for the visualisation of the model's products. At present, the CYCOFOS in situ and remote sensing monitoring systems consist of the following parts: 9 the MedGLOSS coastal station at Paphos for near-shore in situ observations 9 the CYCOFOS remote sensing HRPT ground receiving station 9 the MedGOOS-3 Ocean Observatory in the open deep Levantine Basin (this is actually under preparation for deployment). 2.1 MFS Cyprus Near Real Time ocean forecasts

The CYCOM--Cyprus Coastal Ocean Model (Zodiatis et al., 2002a,b) is a version of the POM (Blumberg and Mellor, 1987) that has been used in the MFSPP project for climatological and operational coastal and regional flow simulations. The CYCOM model is a high resolution flow model and has been upgraded to operational status since early March 2002. The main characteristics of the CYCOM model are: non linear equation of momentum, sigma co-ordinate system, time splitting with externalbarotropic model following the CFL conditions and internal-baroclinic mode with longer time step, Cartesian co-ordinates, an Arakawa C-grid for the flow and Arakawa A-grid for the scalar fields, Smagorinsky horizontal eddy viscosity and a Mellor-Yamada vertical eddy viscosity using a 2nd order turbulence closure sub-model.

Figure 1 MFSPP model domains (left) and CYCOFOS-CYCOM fine model domain (right) The CYCOM model with two open boundaries (Figure 1, fight) is nested operationally into the coarse grid of the MFSPP-OGCM Mediterranean model (Figure 1, left). In CYCOFOS, the data for the initialisation and the boundary conditions, both lateral and atmospheric forcing (ECMWF), are downloaded weekly from the MFSPP operational system. The CYCOM uses the atmospheric forcing provided by the MFSPP-OGCM.

40


The latter is based on the 6 hourly ECMWF analysis and forecast provided by M6t6oFrance (MF) of parameters such as: air and dew point temperature, mean sea level pressure, clouds, 10m winds. The air-sea physics used to compute the MFSPP-OGCM ocean boundary conditions are the surface solar radiation, net longwave flux, sensible and latent heat flux, Hellerman and Rosensenstein wind stress, water flux and include relaxation to M e d - 6 monthly mean climatology. The initial and boundary data used in CYCOM include the assimilation of weekly SST and SSH from satellites, also provided by the MFSPP system. The CYCOFOS flow model provides daily forecasts of currents, sea temperature, salinity and sea level, once a week for the forthcoming week. Within the frame of the Mediterranean Forecasting System Towards Environmental Predictions (MFSTEP), the flow forecasting system of the CYCOFOS will be upgraded and its resolution will be increased from 3 km to 1.5km, providing more detailed information, of particular value near to the coast. 2.2 Cyprus Offshore wave forecasts in the Levantine Basin

The CYCOFOS use a WAM model (WAMDI group, 1988) for offshore wave forecasts in the Levantine Basin. The CYCOFOS WAM model was upgraded to operational status in August 2002. The fine resolution Levantine WAM model (Figure 2, right) is nested entirely in a coarse Mediterranean WAM model (Figure 2, left). The Levantine WAM model provides high resolution forecasts of the significant wave height and direction. The CYCOFOS WAM models use the 6-hourly ECMWF wind forecasts obtained by the MFSPP system. The WAM wave model is a 3rd generation wave model, which solves the wave transport equation explicitly without any presumptions on the shape of the wave spectrum. The equation describes the variation of the wave spectrum in space and time due to the advection of energy and local interactions. The wave spectrum is locally modified by the input of energy from the wind, the redistribution of energy due to non-linear interactions and energy dissipation due to wave breaking and bottom friction. The advective term is integrated with a first order upwind scheme. The source function is integrated with an implicit scheme that allows an integration time step greater than the dynamic adjustment time of the highest frequencies in the model prognostic range.

Figure 2 CYCOFOS-CYWAM coarse model domain (left) and fine model domain (right)


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3. MedGLOSS Cyprus Stations 3.1 The MedGLOSS Paphos station

Within the framework of MedGLOSS (Mediterranean network of global sea level observing system) a sea level station was set up late September 2001 at Paphos harbour, on the western coast of Cyprus, with the aim of promoting systematic sea level measurements in the Mediterranean. The primary purpose is long term monitoring of sea level rise caused by melting of polar ice and upper ocean thermal expansion as a result of global warming. The Cyprus MedGLOSS stations will contribute to the needs for setting up platforms for operational monitoring in the Mediterranean Sea. In addition, the Paphos MedGLOSS station contributes to the ESEAS network and to the EU ESEASRI project. The equipment of the station consists of sea level, water temperature and atmospheric sensors, a GPS and a PC. The data are transmitted every hour from the side of the Paphos MedGLOSS station to the O C - D F M R facilities for further data processing and interpretation. The equipment of the Paphos MedGLOSS station was donated by the CIESM and the tuning and the installation of the equipment by IOLR, which is coordinating the MedGLOSS activities. Expansion of the Cyprus MedGLOSS stations in the near future will include similar stations in the south and on the east coast of Cyprus. 3.2 The MedGOOS-3 Ocean Observatory

As part of the EU MAMA/MedGOOS project and to promote the open deep-sea operational in situ data collection and transmission in the Levantine Basin, the MedGOOS-3 Ocean Observatory is under preparation for deployment in the Eastern Mediterranean, off the southern coast of Cyprus. The MedGOOS-3 Ocean Observatory is scheduled for deployment jointly with Harris MCS (Maritime Communication Services), USA, who owns this ocean observatory system. It is worth mentioning that a similar Ocean Observatory, the MedGOOS-1, has already been deployed in the Western Mediterranean, off the coast of Sardinia jointly by International Marine Centre (IMC) and Harris MCS. The sampling strategy of the MedGOOS-3 Ocean Observatory includes data on sea water temperatures, salinity, pressure, oxygen, currents, as well air temperature, wind speed and direction. The MedGOOS-3 sampling rate is set to 10 minutes and transmits to the CYCOFOS site every 1 hour, through a satellite communication system.

4. CYCOFOS Ocean remote sensing Since the end of 2001, the CYCOFOS HRPT ground receiving station has been providing regular (almost daily depending on the cloud cover) remote sensing SST images of the Levantine Basin. A SmartTech HRPT, Professional Researcher model is operated by the CYCOFOS team, capable of coveting quite well in one single capture, depending on the satellite's orbit, the entire Eastern Mediterranean and the Black sea 2 3 times per day, with a spatial resolution of about 1km. The limitation of resources and the need to minimise the demand for data processing, in view of the operational demands, the CYCOFOS module is at present set up to provide SST images only for the entire Levantine Basin.

42


SmarTrack software for stand-alone data reception is used, while for the processing of the raw IR data an integrated software package specifically developed by the CYCOFOS collaborators is in use for auto-mode rectification (geometric correction) of the images and the computations of the SST. The computation of the SST in the CYCOFOS remote sensing module, is based on the algorithms recommended by NOAA, using IR channels 4 and 5. The CYCOFOS remote sensing system is set up to receive data only from night or early morning satellite passages. This strategy for capturing IR images is necessary in order to avoid the hot spots that usually appear during daily SST images of the Levantine Basin, most of the year. As part of the EU MAMA/MedGOOS project the CYCOFOS was assigned to provide remote sensing SST images in the Levantine Eastern Mediterranean through the system's web page, while a similar remote sensing system from Spain was assigned to provide SST for the Western Mediterranean. 4.1 End-users derived applications The environmental issues in the Levantine Basin, Eastern Mediterranean are connected to marine pollution and the eutrophication and other algae-growth related phenomena. On the other hand the commercial activities in the Levantine Basin are increasing, the most important being the growth in oil transfer, exploration and production, pelagic fisheries, shipping and yachting and particularly coastal tourism.

In order to give a basis for any user derived application that tries to manage either the exploitation or the protection of the marine environment, it is necessary to offer an efficient and quality controlled estimate of marine state variables. The recommended procedure for responding, for example, to marine pollution incidents, that will assist the local and regional decision-makers to take the appropriate actions, includes the application of operational models in order to provide predictions of the behaviour and movement of the harmful substances. This pre-requirement for an operational response improves the predictions of the sea water characteristics. In a similar way the same information is needed for many others marine activities. Thus the user community interested in ocean forecasting is connected to the exploitation of resources and the protection of the marine environment. The exchange of the information derived from the operational forecasts, both within the scientific community and the end-users, plays a substantial role in response to certain marine environmental situations. In view of the above, the usage of the Visual Interface of Oceanographic Data (VIOD) tool and exportation of the CYCOFOS operational forecasts to the system's web page operationally supports the derived end-users applications in the Levantine Basin. Additional components of CYCOFOS may also be considered in the MEDSLIK oil spill model and the MEDPOL general dispersion model that were developed especially for end-user-derived applications, employing the MFS, MERSEA and CYCOFOS products, to assist the end-users and the decision-makers in the Levantine Basin.


43

4.2 MEDSLIK oil spill model

The MEDSLIK oil spill model was first developed in pre-operational mode in 1997 (Lardner et al., 1998) to assist the objectives of the EU LIFE project "Subregional contingency Plan for Preparedness and response to Major Pollution Incidents in the Eastern Mediterranean-Levantine". The MEDSLIK algorithms are based on an earlier version of the OILPOL model (A1 Rabeh et al., 1995) that was used for oil predictions during the Gulf War in 1991. MEDSLIK is a 3D oil spill model designed to predict the transport, fate and weathering of an oil spill in the Levantine Basin, developed by the CYCOFOS team and which has now been coupled to the MFSPP and CYCOFOS NRT operational forecasts, (respectively used the MFSPP-OGCM and CYCOM/POM flow models). The MEDSLIK input requirements are the forecasting products from MFSPP Mediterranean basin scale model and CYCOFOS nested high resolution coastal ocean model in the NE Levantine basin, the atmospheric field from ECMWF used in MFSPP, as well in CYCOFOS. MEDSLIK incorporates REMPEC' s list of over 200 oils together with their physical parameters. Coarse and fine resolution bathymetry and coastline are used respectively for the Levantine Basin and of the NE Levantine. 4.3 CYCOFOS potential end-users The potential end-users of the operational CYCOFOS's products are:

1. the National and Subregional contingency plan for preparedness and response to major pollution incidents in the Levantine between Cyprus, Israel and Egypt, in cases of oil spill emergency in the open sea 2. the Cyprus search and rescue centre, Port Authorities, marine police 3. the local and offshore consortiums from the fisheries sector 4. the fish farmers from the marine aquaculture industry 5. desalination plants, telecommunications cable laying, oil & gas industry, and environmental agencies from the coastal and open sea engineering sector 6. shipping companies and yachting clubs from the navigation safety sector 7. tourism organisations, media, etc, from the coastal tourism industry 8. international organisations, research centres, etc. 5. C o n c l u s i o n s

The development and the operation of the CYCOFOS certainly contribute to the promotion of EuroGOOS and MedGOOS initiatives in the Mediterranean, following the objectives of the EU research projects MFSPP, MFSTEP, MAMA, MERSEA, as well as those of ESEAS, ESEAS-RI and MedGLOSS. At present the NRT operational forecasting & observational products of the CYCOFOS are available to end-users at: www.ucy.ac.cy/cyocean Further developments of the CYCOFOS include end-users applications using the CYCOFOS, MFS and MERSEA products, further downscaling of the prognostics models and the expansion of the ocean/coastal NRT observations. At this stage

44


CYCOFOS supports a wide local and regional user community, in activities related to oil-spill pollution, pelagic fisheries, search and rescue operations, navigation safety, marine aquaculture, coastal management, tourist industry, etc.

Acknowledgements The development of the CYCOFOS modules has been partially carried out in the framework of several European Union research projects and other relevant international activities: MFSPPwMediterranean Forecasting System Pilot Project, M F S T E P ~ Mediterranean Forecasting System Toward Environmental Predictions, both EuroGOOS activities, MAMA~Mediterranean network to Access and upgrade Monitoring and forecasting Activities in the region, a MedGOOS activity, MERSEA-strand 1~ M a r i n e Environment and Security in the European Areas, and MedGLOSS~Mediterranean Global Ocean Level Services. We acknowledge the support of the European Commission's Marine Science and Technology Programme (MAST IV), contract MAS3-CT98-0171 of the European Commission's Programme Energy, Environment and Sustainable Development, contracts EVR1-CT-2001-20010, E V K 3 - C T - 2 0 0 2 0089, and EVK3-CT-2002-00075, the CIESMwCommission for the Scientific Exploration of the Mediterranean Sea for the donation of the equipment for the Cyprus MedGLOOS station and of the Harrist-Maritime Communication Services for the so far preparations concerning the MedGOOS-3 Ocean Observatory. We are also grateful to Dr. Dov Rosen, coordinator of MedGLOSS, for his valuable support and all CYCOFOS collaborators for their contributions to the system' s modules.

References AI Rabeh, A.H., R.W. Lardner, N. Gunny and M. Hossain, 1995, OILPOLwAn oil fate and transport model for the Arabian Gulf. Proc. Fourth Saudi Eng. Conf. (King Abdul-Aziz Univ Press, Jeddah) V, pp. 415-427. Blumberg, A.F., and G. L. Mellor, 1987, A description of a three-dimensional coastal ocean circulation model, in Three-Dimensional Coastal Ocean Circulation Models, Coastal Estuarine Sci., vol. 4, edited by N.S.Heaps, pp. 1-16, AGU, Washington, D.C. EuroGOOS, 1997, The strategy for EuroGOOS. EuroGOOS Publ. 1,132 pp. MAMA group, 2002, MAMAntowards a new paradigm for ocean monitoring in the Mediterranean. Proceedings of 3rd EuroGOOS Conference (this publication), p. 46. IOC, 1998, Workshop on GOOS capacity building for the Mediterranean region, Valletta, Malta, 26-29 November 1997. IOC Workshop Report No. 140, 18pp. plus annexes. Lardner, R.W., G. Zodiatis L. Loizides and A. Demetropoulos, 1998, An Operational Oil Spill Model in the Levantine Basin, International Symposium on Marine Pollution, Monaco, 5 - 9 October. Pinardi N. et al., 1999, The Mediterranean ocean forecasting system: the first phase of implementation, Proceedings published in OCEANOBS 99 Conference. WAMDI Group, 1988, The WAM model--a third generation ocean wave prediction model, Journal of Physical Oceanography 18: 1775-1810.


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Zodiatis, G., R. Lardner, A. Lascaratos, G. Georgiou, G. Korres and M. Syrimis, 2002a, High resolution nested model for the Cyprus and NE Levantine Basins, Eastern Mediterranean Sea: Implementation and Climatological Runs, Annales Geophysicae, 20, 1-16. Zodiatis, G., R. Lardner, A. Lascaratos, G. Georgiou, G. Korres and M. Syrimis, 2002b, Mediterranean Forecasting System: Submodel for the Cyprus and NE Levantine Basins, Rapp. Comm. Int. mer Medit., 36, 90.

M A M A - - T o w a r d s a new paradigm for ocean monitoring in the Mediterranean The MAMA Consortium: S. Vallerga (main coordinator and MedGOOS Chair) l, A. Drago (Assistant Coordinator and MedGOOS Executive Secretary) z, T. Aarup, A. Abdelbaki, A. Abuissa, H. Awad, M.B. Awad, C. Beken, S. Besiktepe, A.F. Boargob, G. Brundrit, M. Capari, A. Carlier, B. Cermelj, G. Casazza, F.S. Civili, Y. Cohen, C. Tziavos, H. Dahlin, M. Dalla Costa, P. Drakopoulos, N.C. Flemming, J. Font, G. Fusco, I. Gertman, G. Georgiou, A. Harzallah, G. Herrouin, A. Ibrahim, N. Kabbara, Z. KIjajic, H. Kouyoumjian, J. Legrand, J.L. Lopez-Jurado, P. Magni, A. Mahmoud AI-Sheikh, C. Maillard, V. Malacic, G.M.R. Manzella, P. Marchand, M. Morovic, P. Pissierssens, N. Pinardi, K. Nittis, D.S. Rosen, C. Summerhayes, A. Ribotti, G. Reed, A. Selenica, I. Salihoglu, C. Sammari, D. Sauzade, C. Silvestri, M. Snoussi, R. Sorgente, C. Tziavos, G. Umgiesser, M. Vargas, B. Vucijak, J. Woods, M. Zavatarelli, G. Zodiatis

1Consiglio Nazionale delle Ricerche & International Marine Centre, Italy 2IOI-Malta Operational Centre, University of Malta Abstract Sustainable development requires the intelligent management of the marine environment, to protect the marine ecosystem, minimise the impacts of climate change and anthropogenic influences, and provide benefits for a wide range of users. Routine ocean monitoring and forecasting based on sound science, long term and adaptive monitoring, and co-operation between nations, is the main tool for such a management. The assets and needs of all countries have to be identified, as well as the constraints impeding data exchange and marine observations in the EEZs. The challenge is to build a monitoring system based on up-to-date science and technology, and adapted to the specificity of the basin for the benefit of different users in all Mediterranean countries. It is necessary to involve all riparian countries in the process of building the Mediterranean monitoring system. A strengthened and dedicated link between the scientific community and the public authorities is moreover necessary to provide a sound scientific background for policy decisions based on environmental monitoring. Building on these concepts the Mediterranean network to Assess and upgrade the Monitoring and forecasting Activity in the region (MAMA), funded under the EESD Programme of the 5th FP, and involving partners from all the Mediterranean countries, aims to establish the multi-national network that will prepare the institutional linkages and regional platform for such an integrated and sustained monitoring system in the region. The project builds on the trans-national pooling of scientific and technological resources and provides a concerted basin-scale effort towards the planning and design of the initial ocean observing system in the Mediterranean. The system-wide approach of MAMA is expected to trigger an enhanced motivation on the relevance of systematic * Corresponding author, email: [email protected]

S. Vallerga, A. Drago and the MAMA Consortium

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marine observations for the sustainable and shared use of the marine resources of the Mediterranean Sea. These catalytic ingredients constitute the thrust of MAMA and an enabling asset to the future projection into long-term commitments at governmental level.

Keywords: Operational oceanography, building, awareness, inventories

Mediterranean,

networking,

capacity

1. Preamble The improved understanding and trend prediction of the marine ecosystem, for the benefit of the economy of coastal States and the protection of public health, are longstanding issues in the Mediterranean. Indeed the combined effects of global climate change and human alterations of the environment are already pronounced in many coastal waters of the global ocean, including to no lesser extent the Mediterranean Sea. Yet the maritime sector contributes up to 5% of the GNP of the riparian countries, and this is expected to increase in the future. The increasing range of maritime transport, offshore industries, tourism and human activities in the coastal area are exacerbating pressures on the marine environment, raising even more the need to adequately manage its resources. Routine and long term monitoring of the ocean and coastal seas, the forecasting of the state of the sea based on sound science, reliable assessments, and the efficient co-operation between nations, constitute the main management tools. In addition, the role of the oceans on the functioning of the global climate system is now well recognised. The mutual influence and interaction between the ocean and the atmosphere is even more intense in a land-locked sea like the Mediterranean. Meteorologists use atmospheric observations from a network of land and ocean surface measurements to produce three- to five-day weather forecasts to anticipate the impact of storms, warm periods and other day-to-day events. Reliable longer term climate predictions covering the broader patterns of the weather over seasons and years, require additional and improved observations within the upper layer of the oceans. An improved understanding and functioning of climate variability relies on systematic observations of both the ocean and atmosphere, with concomitant improved predictions of both. Sustained, integrated and routine data sets of the ocean and coastal seas will also target multiple users. The advent of multi-disciplinary, spatially widespread, long term data sets is expected to trigger an unprecedented leap in the economic value of ocean data. This will bring about a radical transformation in our perception of managing marine resources, and provide benefits to many sectors in industry and services such as marine transportation, safety and public health. National ocean monitoring and forecasting programmes already exist in some countries on the northern perimeter of the Mediterranean, but efforts are not co-ordinated. In other cases, national capabilities, including human skills and available technology, are not sufficient, and/or not well integrated to provide a comprehensive approach. Data on a regional scale are not brought together over large expanses of the basin, especially along the southern and eastern perimeter. Data are often taken from 'platforms of convenience' and at locations that may not be the best ones from an oceanographic point of view.

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MAMA--Towards a new paradigm for ocean monitoring in the Mediterranean

On the other hand, coastal seas are affected by ocean and atmospheric processes occurring on a regional or global scale, and dictate the need for a regional approach to observations and to the absolute necessity of data sharing. This is most evident in the Mediterranean Sea, characterised by generally narrow shelf areas, where large-scale oceanic structures predominantly force the coastal ecosystem (Pinardi and Flemming, 1998a). Moreover ocean nowcast/forecasts require observations of sufficient duration, spatial extent and resolution as well as real-time data telemetry, assimilation into models and analysis. This is the way in which the monitoring of the open ocean areas and coastal seas will provide a comprehensive means for assessing the health of the marine environment, understanding ocean variability and its reaction to external changes, for improving their sustained exploitation and mitigating their threat through a suite of new marine services and applications.

2. The a n s w e r is M e d G O O S The sustainable management of the seas and oceans was called for by Agenda 21 (Agenda 21, 1992) and resulted in the launching of the Global Ocean Observing System (GOOS), to be planned and implemented regionally. The European component of GOOS was established in 1994 by 14 European agencies, reaching 30 in 2001. The EuroGOOS Association developed its strategy, science plan, technological surveys, and implementation plans in the European seas (Pinardi and Flemming, 1998a; Woods et al., 1996a; Woods et al., 1997b; Bosman et al., 1998; Prandle and Flemming, 1997; Buch and Dahlin, 2000). The EuroGOOS Mediterranean Task Team developed the science base for an ocean forecasting system in the region, through EC funded projects. The institutional framework for the Mediterranean Global Ocean Observing System (MedGOOS) was established under the auspices of the UNESCO/lntergovernmental Oceanographic Commission (IOC) in November 1997 (IOC, 1998), during the Workshop on GOOS Capacity Building for the Mediterranean Region in Malta. The mission of MedGOOS is to respond to the priority demands outlined above by facilitating the development of an operational ocean observing and forecasting system at a regional and coastal scale to the benefit of a wide group of users in the region. The MedGOOS strategy was endorsed by over thirty national institutions, interagency and intergovernmental organisations, from almost all the Mediterranean countries, at a dedicated meeting held in Rabat, Morocco in 1999. To date the MedGOOS association binds together 18 marine institutions from 15 European and Mediterranean Partner countries, namely Morocco, Egypt, Israel, Cyprus, Turkey, Greece, Albania, Serbia and Montenegro, Slovenia, Croatia, Bosnia and Herzegovina, Malta, Italy, France and Spain. MedGOOS focuses on the regional priorities for operational ocean forecasting and marine meteorology, addresses the related economic and social implications, and guides and assists the riparian states to the harmonious implementation of the Mediterranean ocean observing and forecasting system built on existing elements and based on principles of co-development, co-ownership and sharing of benefits. Furthermore MedGOOS promotes the upgrading of national systems to a uniform level of expertise and infrastructure, and stimulates the necessary pre-operational R&D to ensure that GOOS is fully effective when it is fully established in the region.


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In terms of what concerns the scientific and technological development in the field of operational oceanography, there have also been outstanding achievements in the Mediterranean. GODAR (UNESCO/IOC, 1995a) and MEDAR/MEDATLAS (UNESCO, 1997b; Maillard et al., 1999) have located and collated the major data holdings. The establishment of NODCs and the development of an adequate data management structure in the region (including handling of NRT data) are identified as a basic next step forward. The Mediterranean Forecasting System Pilot Project (MFSPP) has implemented the first phase of the EuroGOOS Mediterranean Forecasting System Science Plan and is the only experience in the region of NRT data exchange, between participating institutes, at time scales of hours (buoy data), days (XBT data) or weeks (remote sensing SST and SSH data). This experience has demonstrated the feasibility of such a system but, at the same time, revealed that major technological and scientific developments are still required to apply operational forecasting in the coastal areas, even if the major tools are ready to be assembled (Pinardi et al., 2001c; Pinardi et al., 2003d). The Mediterranean ocean Forecasting System: Towards Environmental Predictions (MFSTEP) is an RTD project currently running under the 5th Framework Programme of the EU as a follow-up to MFSPP. It seeks to consolidate and further develop the Mediterranean ocean forecasting system by enhancing the hydrodynamics predictive component, through the application of advanced technologies for improved multi-parametric monitoring in operational mode, and by implementing marine ecosystem predictions. All these projects involve the participation of Institutions of some of the South and Eastern Mediterranean countries, thus offering a first important North-South enhanced research cooperation in marine science.

3. MAMA, a MedGOOS project The MFSPP experience has started to build the S&T base required to develop and implement an operational ocean observing and forecasting system in the Mediterranean. While the methodological approach to extend environmental predictions into the coastal areas is unfolding, the needs dictated by the variability of the marine ecosystem and the demands of the potential end-users are shaping the very challenging requisites that an effective and useful ocean/coastal monitoring and predictive system must have. It is becoming more apparent that the transit to the implementation phase necessitates a parallel effort to broaden the participation to all the riparian countries, to build a case for operational oceanography in the region, and to set up an organisational framework that supports the establishment of an ocean observing and forecasting system on the basis of trans-national cooperation and with an assurance to its sustained functioning in the future. An ongoing 3-year project entitled "The Mediterranean network to Assess and upgrade the Monitoring and forecasting Activity in the region (MAMA)", funded by the 5th EU Framework Programme and involving for the first time all the Mediterranean countries, is intended to resolve these deficiencies. MAMA brings together a consortium of major marine institutions aiming to put in place the institutional networking and enhance the necessary infrastructure for a future integrated and sustained observing and forecasting system in the Mediterranean.

50

MAMAmTowards a new paradigm for ocean monitoring in the Mediterranean

Figure 1 MAMA consortium with a representation from all the Mediterranean countries MAMA is a direct outcome of the MedGOOS strategy which advocates an implementation process that centres upon long-term partnerships, capacity building to favour direct participation, the creation of awareness on the far-reaching benefits, an open approach to all stakeholders with demonstration applications, and the design of a system that addresses the specific needs of the region. This project will enable MedGOOS to make the first concrete steps and be in a position to provide guidance to the Mediterranean states, stimulating the necessary awareness, invoking capacity building and preoperational R&D to ensure that MedGOOS is fully effective when it eventually attains maturity, hopefully in ten to twenty years time. MAMA focuses on the trans-national pooling of scientific and technological resources in the basin, through the sharing of experiences and the transfer of expertise, to bring capacities in operational oceanography at comparable levels, and provide an integrated effort towards the planning and design of the initial ocean observing and forecasting system in the Mediterranean. Furthermore, MAMA will interact with end-users, stakeholders and relevant international organisations, work in the whole basin to trigger local awareness on the benefits of operational oceanography and ocean forecasting with dissemination of results and demonstration products, and build momentum towards long term commitments by governments. MAMA is contributing to the initial phase of the EC-ESA Global Monitoring for Environment and Security (GMES) initiative, with: 9 an inventory on existing monitoring activities 9 the design of an initial observing system for the coastal area 9 reports on the present monitoring capabilities and on the limitation of data flow MAMA will be implemented through the planned activities, divided into 9 workpackages, with a strong emphasis on assessing current capacities, cooperation, networking and awareness. A high level advisory board is providing on-line monitoring on the quality of the work and with suggestions for future development.


51

4. Update on the main M A M A activities After the first year of activities, several key activities have already been initiated. The network is working in harmony and following anticipated schedules, already a good result given the geo-political complexity of the partnership.

4.1 Assessing the present situation A key activity in MAMA is the assessment of the present Mediterranean capability in terms of infrastructures, programmes, human resources, and funding for pre-operational oceanography. This exercise is being undertaken by a process of national consultation with public authorities responsible in marine affairs, marine research institutions and marine service providers in general. The information is being collected by means of a survey conducted by an online Questionnaire on Marine Monitoring Activities in the Mediterranean covering a comprehensive overview on the availability of technological infrastructures and equipment; human resources and funding capability; and existing national/international initiatives related to operational oceanography in the region. Country profiles are also being prepared to give an overview o n : 9 the operations of institutes/agencies/organisations dealing with the monitoring, assessment and forecasting of the state of the ocean and coastal areas 9 the national structure for the support and running of marine monitoring and research activities 9 the key public administration/authorities responsible for marine affairs, and for environmental policy formulation and implementation 9 the relevance of the maritime sector in the economic activities of each country; 9 implications for MedGOOS in the optimal design and implementation of operational forecasting for maximal benefits to the coastal states. The information will serve to build a regional database on the current arrangements and facilities for pre-operational ocean monitoring and forecasting, and will provide the basis for an assessment on the needs and potentials for operational oceanography in the region. The MeDir directory consisting of an online searchable database of marine scientists and professionals working in the Mediterranean region has already been established by the MedGOOS Secretariat in collaboration with the IODE Secretariat, and can be found at http://ioc2.unesco.org/medir.

4.2 National Awareness Meetings The consultation process will run in parallel and make use of the national awareness meetings programmed in each country, mainly during the second year of the project. These meetings will address a full hierarchy of stakeholders aiming to gain societal support by increasing public recognition and appreciation of the need for operational oceanography. They will also provide an opportunity for direct consultation, to identify national needs for capacity building, infrastructure and local organisational frameworks. Moreover, the meetings will aim to fuel the creation of strong linkages between the scientific community involved in preparing the basis of operational oceanography, and the policy community committed to securing a sustainable society. This should serve as a

52


catalyst to establish national commitments in favour of MedGOOS, possibly through the creation of National GOOS Planning Committees, involving data and potential service providers, end-users and potential beneficiaries, including entities involved in policymaking, environmental management and the marine industry. The MAMA W W W The first version of the MAMA website (http://www.ifremer.fr/mama) has been developed using state-of-the-art technology and on the basis of a dynamic content management system with a multiple author environment, providing a community portal to partners. The MAMA website supports special facilities including a calendar, built-in search engine, forum discussions and a dedicated area reserved for exchanges between the MAMA partners. The website will evolve throughout the project, and use facilities to make it auto-sustaining, requiting little maintenance effort after full development, and compatible to future enhancements and extensions. Besides providing a tool for easy and fast communication amongst the partners, the MAMA WWW is being developed to serve as a showcase for operational oceanography in the region and to promote awareness by means of dedicated Partner Pages focusing on each country, and giving the MAMA WWW a regional dimension and scope. 4.3

4.4 Capacity building activities The capacity building element of MAMA is mainly conducted by means of a visiting scientist exchange scheme. While contributing to strengthen the network, the trainingon-the-job experiences offered within the scheme enable the development of skills required to participate in the GOOS. In the first year of activity a number of host centres have provided several training opportunities. Some training visits were also performed outside the MAMA network, at selected centres for technology transfer. Four high-resolution circulation models are being implemented in key shelf/coastal areas on the southern and eastern Mediterranean shores not yet covered by previous numerical modelling efforts. These models are being developed by the four MAMA partner institutes in Morocco, Algeria, Tunisia and Lebanon with the assistance of the reference modelling institutions UNIBO-CIRSA (University of Bologna) and IMC (Intemational Marine Centre--Sardinia). The models are nested to the basin-wide Mediterranean General Circulation Model developed at the Mediterranean Forecasting Centre (UNIBO-CIRSA). These model implementations serve as a very effective transfer of modelling experiences to new partners in key countries. 4.5 Designing the initial observing system MAMA is also targeting to contribute to the design of the initial observing system in the region. A report on "Monitoring Strategies for Operational Oceanography" has prepared the background for methodologies to define strategies for routine ocean observing systems that optimally describe the state of marine ecosystems in the three biogeographical areas: the open ocean, the coastal ocean and inshore areas. The attention is purely devoted to GOOS objectives: to provide data on an operational basis for nowcasting/forecasting. The report develops tools and concepts for the design of an operational observing system to repeatedly assess and anticipate changes in the status of


53

marine ecosystems on national to global scales, by extracting the most important temporal and spatial scales from data and with well-defined optimal sampling strategies. A number of currently available NRT remote sensing products are being assembled to provide to MAMA partners a general compendium of applications using a range of satellite observations, at different levels of processing, accuracy, spatial resolution, spatial and temporal coverage, NRT delay and platforms. Products developed by some partners such as the Cyprus Coastal Ocean Forecasting and Observing System (Zodiatis et al., 2002) (CYCOFOSmwww.ucy.ac.cy/cyocean/) and the new satellite receiving station installed at the Institut de Ci~ncies del Mar (CSIC, Barcelona, Spain) will be included. MAMA is also raising awareness of the development and use of biological indicators of ocean health as an aid to existing observing systems in the Mediterranean for a sustainable use of the coastal zone. The aim is to combine data sets of environmental and biological variables to identify consistent patterns of association and select primary response parameters that could serve as indicators, or "warning signals", of related adverse environmental conditions leading to stress in the benthos. 4.6 M A M A - N e t

MAMA-Net is preparing to establish a one-stop showcase of operational ocean data and information. The objective of this activity is to initiate a prototype data and information exchange system to support exchange of operational data/metadata between agencies, and provide access to operational prototype products for MAMA partners. The current networking capabilities of the partners have been assessed and the identification of the networking requirements for MAMA-Net are being defined. This will lead to the definition of parameters and products to be exchanged through MAMA-Net, together with common indexes and standard protocols for exchange. The publication of operational products on MAMA-Net will include the development of software tools for preprocessing of data and transformation to GTS format. The selection of appropriate encoding / compressing methods for forecasting model outputs is also being considered. The list of products (data or metadata) to be distributed by project partners has already been partially prepared and includes real time data from the M3A buoy and Poseidon networks and related meteorological--wavemcirculation products; temperature (XBT) data from Ships of Opportunity; Sea Level Data in NRT, R/S data and hydrological data. 4.7 MDIM Workshop

Preparations are also underway for a "Marine Data and Information Management" (MDIM) workshop, scheduled within the programme of the 4th MAMA general meeting in Rome next June. The workshop aims to address the steps needed for an improved data and management system with enhanced efficiency, adapted to handle data and information in operational mode, with full geographical coverage of the basin, and aiming towards adding value to ocean data in response to the needs of users. 4.8 MAMA demonstrators

MAMA is also establishing open and constructive links with the end-user community to identify their needs and priorities. The aim is to prove the usefulness of operational

54


oceanography through pilot demonstration applications and tools. The two main streams of work are to develop: 9 a web-based system providing guidance and information on protection from coastal erosion and on Integrated Coastal Zone Management (ICZM) in general. The Coastal Erosion Protection and ICZM Guidance Demonstrator (CEROSPIG), aims to provide information on coastal erosion problems, and to develop a capacity for an integrated Coastal Zone Management based on forecasts of the coastal environment 9 a user-friendly interface and provision of tools (software) for viewing and using forecast and ecosystem results. The transformation of data into usable products is still an area to be fully explored and enhanced. Current products are heavily based on physical measurements in the coastal seas and the upper ocean. This task addresses the need to improve and ease the capacity of analysing, merging and using observed and modelled data for the management of the marine resources at short term. The capacity to analyse merged data sets (in situ, satellite and modelled) and extract the major information for practical applications is being investigated by a dedicated pilot exercise for the coastal zone. In situ and satellite data will be merged to provide information on the status and trends of the coastal marine environment. These products are intended to provide information in the form of a water quality index based on temperature, salinity, oxygen, nutrients concentration. It also aims to provide an example of 'near real time' information to ocean managers and the public in general.

5. Conclusion An integrated and sustained ocean monitoring system needs an enduring collaboration among the neighbouring countries and a strong will to share efforts, resources and knowledge. An early dialogue is crucial, and the involvement of all the Mediterranean countries is vital, for the long-term success of MedGOOS. The regional dimension of the MedGOOS Association will ensure that this collaborative venture will bring benefits and opportunities equally to all the riparian coastal peoples. MAMA, a MedGOOS project involving a partnership from all the Mediterranean countries, is building on this thrust and applies the concept of shared development and co-ownership. This will lead to the establishment of the multi-national network to prepare the institutional linkages and regional platform for the implementation of GOOS in the Mediterranean. MAMA takes advantage of the scientific and technological base achieved in Europe, the enlargement process in the European Union, and the Mediterranean policy of the Union, all favourable conditions for a concerted basin-wide effort to form a strong, common research infrastructure for operational oceanography in the region. The system-wide approach of MAMA, involving all stakeholders and using national ramifications, is expected to trigger consensus and action for systematic long marine observations for the sustainable and shared use of the resources of the Mediterranean Sea. The constraints impeding data exchange and marine observations in the EEZ will be addressed, within the principles of UNCLOS.


55

These catalytic ingredients constitute the thrust of MAMA and an enabling asset to the future projection into long term commitments at governmental level. This unprecedented effort will put the region at the forefront of operational oceanography and prepare the ground for the region to take full advantage of the emerging opportunities in environmental monitoring and large marine integrated projects in Europe, with the Mediterranean being conceived as a unique test base for implementation.

Acknowledgements MAMA--Mediterranean network to Assess and upgrade the Monitoring and forecasting Activity in the region~is a thematic network funded under the EC Programme Environment and Sustainable Development, 5th FP (contract EVR1-CT-2001-20010).

References Agenda 21, 1992, United Nations UNCED Document A/CONF. 151/4 (Parts I and II). Bosman, J., N.C. Flemming, N. Holden, and K. Taylor, 1998, The EuroGOOS Marine Technology Survey. EuroGOOS Publication N. 4, Southampton Oceanography Centre, Southampton UK. ISBN 0-904175-29-4, 47pp. Buch, E. and H. Dahlin, 2000, BOOS planmBaltic Operational Oceanographic System 1999-2003. EuroGOOS Publication N.14, Southampton Oceanography Centre, Southampton UK. ISBN 0-904175-41-3.51 pp. IOC, 1998, Workshop on GOOS capacity building for the Mediterranean region, Valletta, Malta, November 26-29 1997. IOC Workshop Report No. 140, 18pp. plus annexes. Maillard C., M.-J. Garcia, B. Manca, E. Th. Balopoulos, J.-M. Beckers, S. Brenner, I. Oliounine, N. Pinardi, G. Manzella, H. Dooley, N. Mikhailov, A. Suvorov, G. Kortchev, H. Yuce, A. Orbi, R. Sermoud, A. Drago, G. Zodiatis, S. E1-Agamy and S. Lakkis, 1999, Rescuing oceanographic data and strengthening the Mediterranean data management structure: the MEDAR/MEDATLAS concerted action (MAS3CT98-0174/IC20-CT98-0103), Proceedings International Conference on Oceanography of the Eastern Mediterranean and Black Sea, Athens, Greece, 23-26 February, European Commission, Research in enclosed seas seriesm8, EUR 19302, p. 443-444. Pinardi, N. and N.C. Flemming, 1998a, Mediterranean Forecasting System report. EuroGOOS Publication N.11, Southampton Oceanography Centre, Southampton UK. ISBN 0-904175-35-9.30 pp. Pinardi N., D. Antoine, M. Babin, J. Baretta, S. Bassini, S. Brenner, M. Crepon, A. Cruzado, P. Dandin, P. De May, A. Drago, G. Evensen, M. Gacic, G.P. Gasparini, W. Hamza, A. Lascaratos, P.Y. Le Traon, M.A. Garcia Lopez, C. Maillard, G.M.R. Manzella, C. Millot, F. Raicich, O. Raillard, P.C. Reid, R. Sorgente, I. Thanos, G. Triantafyllou, C. Tziavos and G. Zodiatis, 1999b, The Mediterranean ocean forecasting system: the first phase of implementation, Proceedings published in OCEANOBS 99 Conference. Pinardi N., F. Auclair, C. Cesarini, E. Demirov, S. Fonda-Umani, M. Giani, G. Montanari, P. Oddo, M. Tonani and M. Zavatarelli, 2001c, Toward marine environmental predictions in the Mediterranean Sea coastal areas: a monitoring approach, In:

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Ocean Forecasting: Conceptual basis and applications, Ed. Pinardi N. and Woods J., Springer-Verlag. Pinardi N., I. Allen, P. de Mey, G. Korres, A. Lascaratos, P.Y. Le Traon, C. Maillard and C. Tziavos, 2003d, The Mediterranean Ocean Forecasting System: First phase of implementation (1998-2001), Annales Geophysicae, 25, 1-18. Prandle, D. and N.C. Flemming, 1997, The Science base of EuroGOOS. EuroGOOS Publication N.6, Southampton Oceanography Centre, Southampton UK. ISBN 090417530-8.36 pp. UNESCO/IOC, 1995a, IOC-ICSU-CEC Regional Workshop for Member States of the MediterraneanmGODAR-IV, 25-28 April 1995, Malta, Workshop Report No.110, pp43. UNESCO, 1997b, IOC/INF-1073 Summary Report of the MEDAR/MEDATLAS Meeting (21-23 May 1997, Istanbul, Turkey). Woods, J. D., H. Dahlin, L. Droppert, M. Glass, S. Vallerga, and N.C. Flemming, 1996a, The Strategy for EuroGOOS. EuroGOOS Publication N.1, Southampton Oceanography Centre, Southampton UK. ISBN 0-904175-22-7. 132pp plus annexes. Woods, J. D., H. Dahlin, L. Droppert, M. Glass, S. Vallerga, and N.C. Flemming, 1997b, The EuroGOOS Plan. EuroGOOS Publication N.3, Southampton Oceanography Centre, Southampton UK. ISBN 0-904175-26-X. 28pp. Zodiatis G., R. Lardner, E. Demirov, G. Georgiou and N. Pinardi, 2003, The Cyprus coastal ocean forecasting system, Proceedings of 3rd EuroGOOS Conference, p. 36.

Model-derived seasonal amounts of dust deposited on Mediterranean S e a and Europe G. Kallos*l, A. Papadopoulos 2, and P. Katsafados l 1Department of Applied Physics, University of Athens, Greece 2Institute of Oceanography, National Centre for Marine Research, Greece Abstract The dust amounts deposited on the surface depends critically upon the seasonal variability of the dust cycle in the atmosphere. Analyses of ground- and satellite-based observations can lead to useful results in relation to the seasonal variability of dust deposition. However, to gain a feeling of the magnitude and the geographical distribution of the dust deposition on ground surfaces and on coastal and open seas, the use of a credible numerical model is considered essential. In this study, using the SKIRON/Eta weather forecasting system, a database of seasonal amounts of dust deposited on Mediterranean Sea and Europe has been created.

K e y w o r d s : SKIRON system, dust cycle, dry and wet dust deposition, Mediterranean Sea.

1. Introduction Mineral dust, produced by wind erosion over arid and semi-arid areas of North Africa, may be transported away to the Middle East, Mediterranean, Europe, even into and across the Atlantic Ocean (Kallos et al., 2002). This material transported away from its origin is considered as an important climate and environment modifier. Through the long-range dust transport, important nutrients are transported from their sources to other regions and may significantly modify the biogeochemistry of these marine and terrestrial ecosystems. For example, the deposition of the North African dust material on the Mediterranean Sea provides important nutrients, such as nitrogen species, phosphorus and iron, which may enhance the marine productivity. As has been found from satellite observations and ground-based measurements, there is a large seasonal variability of the dust mobilisation that depends on the source characteristics as well as the global atmospheric circulation (Ozsoy et al., 2001). In this study, we illustrate and briefly describe a database of model-derived seasonal amounts of dust deposited on Mediterranean waters and European land. The SKIRON/ Eta weather forecasting system coupled with a dust cycle model is used (Kallos et al., 1997). The dust modules incorporate the state-of-the-art parametrisations of all the major phases of the atmospheric dust life such as production, diffusion, advection and removal related to particle size distribution (Nickovic et al., 2001). The SKIRON/Eta system has been in operational use since 1998 providing 72-hour forecasts for the Mediterranean region. The results are available from the Internet site http://forecast.uoa.gr. The daily operation of the system gives a unique opportunity for its potential development. The * Corresponding author, email: [email protected]

58

Model-derived seasonal amounts of dust deposited on Mediterranean Sea and Europe

latest modifications concerning the definition of the dust sources, and the dust production mechanism, enhance the forecast skill of the system to predict with a satisfactory accuracy the dust cycle in the atmosphere. Since dust is removed from the atmosphere due to mechanisms such as gravitational settling and turbulent mixing and/or precipitation rates, we estimated dry and wet dust deposition. An attempt was made to provide spatiotemporal distribution of the dust deposited over the Mediterranean waters and Europe (dry and wet deposition) by utilising this unique database of the SKIRON/Eta outputs during 2000 and 2001.

2. Derivation of seasonal amounts of dust deposited on the Mediterranean basin and Europe In order to derive the seasonal amounts of North African dust mass deposited on Mediterranean basin and Europe, the SKIRON/Eta system was integrated over the domain coveting North Africa, the Mediterranean and a large part of Europe and the Middle East as illustrated in Figure 1. In the horizontal, a grid increment of 0.24 degrees was applied. At each model grid point, the dry and wet dust deposition was estimated, assuming them as an average of the sub-area around each one. The coverage of each subarea depends on the horizontal grid increment, which in our case is of 0.24x0.24 degree 2. Utilising the first 24-hour model output of each daily simulation, we calculated the geographical distribution of the total dust mass deposited on surface for each month from January 2000 to December 2001 (24-month period). Since the model has the capability to calculate dry and wet deposition separately it was possible to derive the geographical distribution of these two components of the total dust deposition, for each month of the two-year period.

Figure 1 Model domain. W, Cs, Cn, E and Eu denote the West, Central-Southern Part, CentralNorthern Part, East Mediterranean sub-basins and the European part, respectively, over which the model-derived dust deposition are calculated.

G. Kallos*, A. Papadopoulos, and P. Katsafados

59

The annual geographical distribution of the dust mass deposited on surface (in units of gm -2) for the years 2000 and 2001 is illustrated in Figure 2 and Figure 3, respectively. From this distribution we note that during 2000 much more dust mass has been deposited over Europe, with its total annual peak located in Italy, while in 2001 the dust mass deposited over Europe was less with its peaks shifted towards east. This high variability in dust deposition from year to year suggests we should be careful when estimating average annual depositions in specific locations and also the need for long time series (for several years) when we derive average amounts of deposited dust. Just one strong dust event can significantly change the local deposited quantities on a monthly and annual base. In order to check the amount of North African dust mass deposited over the Mediterranean Sea and the amount that crosses it and is then deposited over European land some additional calculations were made. More specifically, the model domain was divided in sub-regions. Then, the deposited (dry, wet and total) quantities over these regions were estimated. These sub-regions are called Europe (Eu), West Mediterranean (W), East Mediterranean (E), Central Mediterranean North (Cn) and Central Mediterranean South (Cs) and are indicated in Figure 1.

Figure 2 Annual dust deposition in g m-2 for 2000. Total (top), dry (bottom left) and wet (bottom right).

60


Figure 3 Annual dust deposition in g m-2 for 2001. Total (top), dry (bottom left) and wet (bottom fight). In Figure 4, the monthly amounts of dust deposited on the Mediterranean Sea and Europe are displayed for the years 2000 and 2001, respectively. As can be seen, the higher deposition occurs during the spring months followed by the autumn. The dry deposition is higher over the Mediterranean Sea while the wet dominates over the European continent. Obviously, this is due to the amounts of rain over Europe and the Mediterranean Sea. In these calculations we considered the dust amounts deposited only on the land grid points for Europe while only the sea grid points are counted in the case of the Mediterranean Sea and its four sub-basins. The number of grid points used for each subarea is shown in Table 1. As we see, there are different numbers of grid points at each sub-region and this must be taken into the account when we try to interpret these amounts and figures. Table 1 Number of grid points used for each sub-area in the calculations of dust amounts deposited on the Mediterranean Sea and Europe Mediterranean Europe

Whole

Land grid points

5783

.

Sea grid points

-

4344

Central South

West .

. 1105

. 694

Central North

East

1000

1545

.


61

Figure 4 Monthly amounts of dust deposited (total, dry and wet) on Mediterranean Sea and Europe for 2000 (left) and 2001 (fight).

Figure 5 Monthly amounts of dust deposited (total, dry and wet) on four basins of the Mediterranean Sea for 2000 (left) and 2001 (right). In order to elucidate the effects of dust inputs to the Mediterranean waters in various regions, the dust deposition was also calculated over the four sub-basins shown in Figure 1, denoting West, Central southern part, Central northern part and East Mediterranean Sea, for the two-year period. Central Mediterranean has been divided in southem and northern parts because the deposition patterns are different. The deposited amounts on each sub-region are illustrated in Figure 5, for the years 2000 and 2001, respectively. As is shown, the Eastern part of Mediterranean receives a high amount of dust through wet

62


deposition. This is due to the fact that the Middle East region receives most of its rain from the lows in Cyprus and secondarily from the lows moving along the coast of North Africa. These systems favour the dust production over East Saharan and then the transport towards East Mediterranean and Middle East. Utilising the estimated monthly dust deposition, the total annual amount of dust deposited on the Mediterranean Sea and Europe has been calculated. These quantities are tabulated in Table 2. As we see, the annual deposition of dust over the Mediterranean waters and European land is much higher for 2000 than for 2001. Table 2 Total annual dust deposition (in 103 tons) on the Mediterranean Sea and Europe year

Europe

Mediterranean sea

total

dry

wet

total

dry

wet

2000

3914

936

2978

3962

1541

2421

2001

2909

725

2184

2500

1255

1245

According to the Guerzoni et al. (1999) the atmospheric dust mass deposited on Mediterranean region, is estimated approximately to 40x106 tons. The model-derived values presented above seem to be an order of magnitude less. But, Guerzoni et al. methodology is characterised by low spatial representation (measurements from 9 coastal sites were used) and there is no clear discrimination between the long-range transported Saharan dust and the locally produced dust due to soil erosion and human activities. In this version of SKIRON/Eta one average dust particle diameter of 2 ~m (i.e. PM2. 5) is used. This may lead to an underestimation of the total dust mass deposited mainly near the North African Coast because larger particles can be deposited there, but this should not be an important bias for the rest of Mediterranean Sea and Europe. Only in some few extreme dust events larger dust particles may be deposited over these areas. In order to take into account the larger particles, the dust module of SKIRON/Eta has been enhanced handling four size particles with diameter of 1.5, 12, 36 and 76 microns. A systematic intercomparison between this new version with the one used in the present work is still under way within the frame of the ADIOS project. The first results of the intercomparison verified the above. 3. C o n c l u d i n g

remarks

The dust cycle in the atmosphere is considered as important due to several implications such as in climate, urban air quality, ecosystems, regional/mesoscale weather and rain. With the aid of the SKIRON/Eta modelling system, which is able to accurately simulate weather conditions and the desert dust uptake-transport-deposition cycle, a database of model-derived seasonal amounts of dust deposited on Mediterranean Sea and Europe has been created. It is shown that the amount of Saharan dust deposited on the Mediterranean waters or over the European land exhibits significant seasonal and inter-annual variability. The strength and the frequency of occurrence of the Saharan dust episodes define the annual deposition amounts and patterns to a high degree, alternating the mean annual


63

values. This leads to the fact that long-term modelling and measurement data are essential in understanding the spatiotemporal distribution of the deposited dust.

Acknowledgements This study was performed with the support from the ADIOS project funded by the EU (EVK3-2000-00604).

References Guerzoni, S., R. Chester, F. Dulac, B. Herut, M.-D. Loye-Pilot, C. Measures, C. Migon, E. Molinaroli, C. Moulin, P. Rossini, C. Saydam, A. Soudine, and P. Ziveri, 1999, The role of atmosphere deposition in the biogeochemistry of the Mediterranean Sea, Progress in Oceanography, 44, 147-190. Kallos, G., S. Nickovic, A. Papadopoulos, D. Jovic, O. Kakaliagou, N. Misirlis, L. Boukas, N. Mimikou, G. Sakellaridis, J. Papageorgiou, E. Anadranistakis, and M. Manousakis, 1997, The regional weather forecasting system SKIRON: An overview, Proceedings of the International Symposium on Regional Weather Prediction on Parallel Computer Environments, 15-17 October 1997, Athens, Greece. Kallos, G., S. Nickovic, A. Papadopoulos, and P. Katsafados, 2002, Transport of Saharan dust towards the USA: Model simulation, Journal of Geophysical Research, (submitted). Nickovic, S., G. Kallos, A. Papadopoulos, and O. Kakaliagou, 2001, A model for prediction of desert dust cycle in the atmosphere, Journal of Geophysical Research, 106, 18113-18129. Ozsoy, E., N. Kubilay, S. Nickovic, and C. Moulin, 2001, A hemisphere dust storm affecting the Atlantic and Mediterranean in April 1994: Analyses, modeling, groundbased measurements and satellite observations, Journal of Geophysical Research, 106, 18439-18460.

Evaluation of POSEIDON forecasts in the Aegean Sea for a three-year period A. Papadopoulos*l, L. Perivoliotis l, K. Nittis l, and P. Katsafados 2

linstitute of Oceanography, National Centrefor Marine Research, Greece 2Department of Applied Physics, University of Athens, Greece Abstract In this study, the POSEIDON forecasts in the Aegean Sea are evaluated against surface observations retrieved from the POSEIDON buoy network over a three-year period. The estimated fields of the wind speed and direction, the air temperature, the mean sea level pressure, the wave height and direction, the sea surface temperature and the current speed and direction are verified using the point measurements of the buoys. The evaluation system consists of various statistical schemes depending on the nature (scalar or vectorial) of the verified fields. Despite the fact that the buoys are located close to the coastline, where topography often modifies the general conditions creating local effects, the numerical models present quite satisfactory forecasting skill.

Keywords: Operational oceanography, Aegean Sea, POSEIDON system, forecast

skill score.

1. Introduction Accurate prediction of the sea state has long been recognised as a need, but only during the last years has it become a subject of scientific research. Operational Oceanography nowadays is considered as a necessity, since it can provide the means to achieve solutions of societal, economic, environmental and scientific problems related to the coastal environment. The Greek National Centre for Marine Research (NCMR), as a member of GOOS and EuroGOOS, contributes in the field of Operational Oceanography with the implementation of an integrated monitoring and forecasting system, the POSEIDON system.

2. The POSEIDON system The POSEIDON system is an operational monitoring, forecasting and information system for the marine environmental conditions of the Aegean Sea. It has been fully operational since October 1999, providing daily 72-hour weather, sea-state and ocean circulation forecasts. The monitoring system of the POSEIDON consists of 11 Seawatch type buoys, equipped with meteorological, oceanographic and environmental sensors. The data are recorded on a 3-hour basis and are transmitted to the NCMR Operational Centre through satellite (Inmarsat-C) and GSM telecommunication systems. NCMR also possesses 10 Smart800 type buoys for measuring the directional wave spectrum.

* Corresponding author, email: [email protected]

A. Papadopoulos*, L. Perivoliotis, K. Nittis, and P. Katsafados

65

To satisfy forecasting requirements a fully operational modelling system has been developed consisting of: 9 a weather forecasting system based on the SKIRON/Eta system (Kallos et al., 1997; Papadopoulos et al., 2002) 9 two offshore wave models, the 3rd generation WAM-cycle 4 (Komen et al., 1994) and the 2nd generation DAUT (Christopoulos, 1997) 9 an ocean hydrodynamic model based on the Princeton Ocean Model (POM) applied to the Aegean Sea (Blumberg and Mellor, 1987; Korres et al., 2002) Apart from these models for specific requests or emergency situations, a near shore wave model (based on SWAN model) and a buoyant pollutant transport model are also available. 2.1 The operational use of the POSEIDON forecasting system

In order to downscale the weather conditions, the weather forecasting system daily performs two simulations using the one-way nesting technique. Two different model configurations are applied, the COARSE with a 0.25 ~ and the FINE with a 0.10 ~ grid increment for the domains A and B (Figure 1), respectively. Utilising the nesting technique, the FINE model defines its initial and boundary conditions using the COARSE output at 24 standard pressure levels in hourly frequency, while the COARSE uses the NCEP global data with 1.25 ~ resolution at 10 standard pressure levels every 6 hours. A detailed description of the SKIRON/Eta model can be found in Nickovic et al. (1997). Using the nesting technique the WAM model is applied in two domains, the Mediterranean domain (C) and the Aegean domain (D) with 0.25 ~ and 0.05 ~ grid increment, respectively (Figure 1). In addition, the DAUT model performs simulations in the Aegean domain with two resolutions, 0.10 ~ and 0.05 ~ The two models, WAM and DAUT, are currently under assessment. Both models have a one-way coupling with the weather forecasting system, using the predicted surface winds as driving factors. The POM version model is also applied in two different domains (E and D in Figure 1), with grid increment of 0.10 ~ (EMED) and 0.05 ~ (Aegean), using the same nesting philosophy. The EMED model provides hourly boundary conditions to the Aegean model. The two configurations of the ocean hydrodynamic model are also one-way coupled to the corresponding weather model. The coupling is obtained through the surface fluxes of momentum and heat as well as precipitation and radiation (short wave and long wave). To satisfy the operational and uninterrupted use of the POSEIDON forecasting system, several procedures have been developed. Starting with downloading the necessary data for the initial and boundary conditions from National Centre for Environmental Prediction (NCEP), the COARSE weather model starts its simulation while the FINE starts its own taking advantage of the 8 CPU of the ORIGIN-2000. In sequence, the wave and the hydrodynamic models are driven by the automatic weather procedure. The model forecasts with the on-line measured data being uploaded daily to the POSEIDON web page (www.poseidon.ncmr.gr).

66

Evaluation of POSEIDON forecasts in the Aegean Sea for a three-year period

Figure 1 Model domains

3. Evaluation methodology The evaluation methodology of the POSEIDON forecasts is based on the point-to-point comparison between model estimations and observations. The model values are not extracted from the nearest grid point but are interpolated from the four grid points surrounding the observation site using the simple bilinear interpolation expression: 4

Zwk M=

k=l

4 E

' Wk

Wk-

1 2

(1)

Fk

k=l

where M k are the four model values and r k is the distance from the k model grid point to the observation. The weighting factor wk is defined in such a way that the nearest points have the most influence. Using this expression for the wave and ocean parameters, the land grid points are ignored. For the sampling period, which is more than three years (from September 1999 to October 2002), approximately 40000 pairs of model and observed values are available for evaluating the models' performance. The parameters that are evaluated are the wind speed and direction, the air temperature, the mean sea level pressure (MSL), the wave height and direction, the sea surface temperature (SST) and the current speed and direction. The evaluation scheme consists of various statistical tests. Four basic statistical measures suggested by Wilks (1995)mmean error (bias), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (r)--are calculated. In addition the


67

absolute skill score S C O R E a is used to assess the forecast performance. Using this measure the forecast accuracy of the value M against observation 0 is computed by:

SCORE.

= 100.

/ '2 1-~-

i=1

M'-o;~

(2)

For direction a different approach is considered, based on correctly counting the model direction if it ranges within 22.5 ~ from the observed value. So, the directional forecast skill score is computing by: D

E N

(3)

where E is the number of correct estimations and N the total number of observations.

4. The P O S E I D O N forecast skill score A first effort to evaluate the model results is to plot them together with the corresponding observations. The point-to-point comparison between model values and observations faces difficulties arising from problems such as: 9 the imposed phenomena caused by sea/land interface, since for security reasons most of the buoys are deployed close to coastline 9 the expected underestimation of the resultant vector after the interpolation procedure of a vectorial parameter 9 the discrepancy between the sampling recording period of the buoy (average of the last 10 min) and the output of the model (at the time step) Nevertheless the comparison of the model and observed time-series show quite satisfactory accuracy, especially for the MSL and air temperature. To investigate these first indications the distribution of the statistical criteria for the whole simulation period has been computed at nine sites in the Aegean Sea where almost three years of records are available. At these nine sites, the statistical analysis for MSL and air temperature shows an overall forecasting skill score close to 100%. There is a slight underestimation of the wind speed while the forecast for the wind direction is quite satisfactory. In order to perform a more accurate evaluation, a combination of different statistical measures such as bias, RMSE and MAE should be also taken into account. Since the absolute skill scores are normalized values, a quality evaluation based only on these scores could be insufficient, especially for MSL (with typical values of 1000hPa even a large declination of 5 hPa should keep the score at high level). The overall results of the statistical analysis are summarised in Table 1.

Evaluation of POSEIDON forecasts in the Aegean Sea for a three-year period

68

Table 1 Statistical evaluation of MSL, air temperature and wind speed and direction SCOREa

D

bias

RMSE

MAE

r

0.35

1.20

0.92

0.98

99.9

-

Air temperature (~

-0.08

1.83

1.31

0.96

97.3

-

Wind speed (ms -1)

-0.58

2.33

1.58

0.66

75.8

-

Wind direction (degrees)

.

MSL (hPa)

.

.

.

.

80.7

Although the wave models' integration uses the full wave energy spectrum, for output the results are summarised. Significant wave height, direction and period are calculated at each model grid point. The same statistical analysis as for the weather parameters, has also been performed for the significant wave height and direction. The results of this analysis show a significant underestimation. With the scatter diagrams (Figure 2) it is more pronounced that the WAM model shows an underestimation increasing at higher wave heights while the DAUT shows a uniformly distributed underestimation over the whole range of wave height records.

Figure 2 Comparison of WAM (left) and DAUT (fight) significant wave height to observed significant wave height in the Aegean Sea. Since WAM and DAUT model use the wind input from the SKIRON/Eta model, the model values were divided into three regimes according to buoy records in order to investigate in more detail the performance of the models. The same statistical analysis has been performed for the three different regimes and in Table 2 the regimes, the number of valid pairs of model and observed values, and the forecast skill score for the winds and the waves are reported. We can see that WAM has a constant forecast skill score close to 60%, meaning that the same percentage of the underestimation appears through the whole range of wave height, while the DAUT shows better skill in the more intense events but quite poor in the lower wave height.


69

Table 2 Statistical evaluation of SKIRON/Eta winds and WAM and DAUT waves in three regimes SKIRON/ETA winds ms -1

(M,O)

bias

RMSE

SCOREa

D

Ws 2.0 mm) create larger bubbles, A large bubble is generated in the process of closure of the splash crater, producing a turbulent jet directed downwards. These bubbles encompass a wide range of sizes, and so generate a wide range of frequencies, covering the range 3 to 25 kHz, but with roughly half the bubbles resonating between 3 and 7 kHz. 4. Large drops also create a number of smaller drops on impact ('splash products') and these too can create bubbles emitting mainly between 10 and 20 kHz. Measurements in the field have also shown the dependence of the sound of rain as a function of frequency (Scrimger et al., 1989 off the west coast of Canada, and Nystuen et al., 1993 in the Gulf of Mexico). The acoustic intensities observed between 4 and 20 kHz all show a significant correlation (usually at least 0.8) with rain rate, the only exception being for the higher frequencies (above 13 kHz) at wind speeds in excess of 10 ms -1, where attenuation by the ambient bubble layer reduces the correlation. Our approach has been to use fixed mooring systems for hydrophones which are capable of both real-time transmission via communication satellites and on-board logging of the full dataset.

3. Experimental details A sequence of measurement campaigns has been designed to develop more robust, longterm deployments, with the aim of obtaining data from the open ocean under different wind/rain environments. All the trials used Acoustic Rain Gauges (ARG) based on a design produced by Metocean Ltd. of Nova Scotia. The equipment consisted of a surface unit containing

130

Monitoring precipitation using underwater acoustic remote sensing

batteries, circuit boards for signal processing, logger and an aerial for satellite relay via ARGOS, and a hydrophone suspended on a 50m cable below the surface unit. The depth of the hydrophone could be adjusted by coiling some of this cable. 3.1 Loeb Etive Two acoustic sensors and conventional meteorological instruments were deployed in Loch Etive in Scotland (see Figure 1 for location). This particular site was chosen because it affords a deep saline environment, and because of the availability of the services of a local mussel farmer with a boat suitable for lifting small moorings. Loch Etive is covered by the UK rain radar network. Trials were conducted from AugustDecember 1999, May-July 2000 and October 2000-March 2001. Care was taken to minimise locally generated noise by suspending the hydrophone from a surface float rather than attaching it directly to the mooring line.

Figure 1 Locations of Loch Etive (left) and Aberporth (fight) deployments. The grid added in each shows the 5 km x 5 km resolution of the rain radar product used in validation Comparisons with other data Figure 2 provides a qualitative assessment of ARG performance over a few hour period by comparing the acoustic classification category derived from the onboard algorithm with activations of a nearby tipping bucket (0.1mm bucket used). During heavy rain acoustic signals are reliably associated with tips of the bucket, whereas the bucket appears a less reliable indicator of drizzle.

Figure 2 Comparison of ARG classification parameter with tipping bucket occurrences

T.H. Guymer*, G.D. Quartly, K.G. Birch, J.M. Campbell, C.E. Jones and K.M. Shannon

131

Figure 3 Rain-rate comparison between ARG, tipping bucket and rain radar The rain rate can be calculated according to an algorithm developed by Nystuen et al. (1993). In a quantitative comparison (Figure 3), the ARG is seen to compare well both with the tipping bucket and the rain radar for rain rates up to 7 mmhr -1. Agreement between two acoustic systems Some of the errors in an acoustic inversion may be due to inaccuracies in measurement technique and sampling, whilst others will be due to processes other than wind and rain modulating or adding to the underwater sound levels. The availability of two ARGs ~100m apart not only provided redundancy in the event of instrument failure but also the opportunity to examine the repeatability of acoustic measurements, for each frequency we compared the instantaneous (1.5-minute average) intensities observed by the two buoy systems. The degree of mismatch between the two can be expressed as the standard deviation of their difference. The topmost line in Figure 4 shows this mismatch to be ~4dB at low frequencies, increasing up to 10dB at the highest frequency. Averaging the data in consecutive groups of nine (13.5-minute averages) reduces the mismatch by about a third, whereas it would be reduced by about two thirds if the bias between one sensor and another was totally uncorrelated over timescales greater than 1.5 minutes. This implies that these differences have some component coherent over longer time periods. For comparison, Figure 4 also shows the temporal variability for each ARG i.e. the r.m.s, difference between consecutive 1.5-minute records at one location. For both buoys, this is 2 - 3 dB over most of the range. These results show that the spatial variation in the acoustic field can be quite significant in sheltered locations like Loch Etive. 10 9 8 m

7

>

6

- KEY - - 1.5-min sep n ( A R G #1) 1.5-min sep n ( A R G #2) . . . . . . . . . S i m u l t a n e o u s (1.5 m i n ) .....

"'"'"'"'"1 ,,,,,'"

,,'"

S i m u l t a n e o u s ( 13.5 m i n )

s"

," 99

."

.~~

~"

S

S

"o9 5

-6

., . . . . i , , "

N4

~

3 2 1 0.5

1

2

5

10

Frequency (kHz)

20

50

Figure 4 Standard deviation of differences due to separation in space and time

132


3.2 Aberporth The second deployment was in November 2001 near Aberporth, South Wales (Figure 1). In contrast to Loch Etive this is several kilometres away from land with a good fetch to the SW, the direction from which the prevailing winds blow. Observations from the meteorological station at Aberporth were supplemented by two additional rain g a u g e s - a tipping bucket and an optical device. Again, two ARGs were deployed close together in a region lying within the coverage of a rain radar.

Figure 5 Upper panels: Time series of acoustic intensity in different frequency channels for two ARGs. Lower panel: Rain rates from three other data sources.

Figure 6 (a) Acoustic spectrum observed at Loch Etive showing definition of drizzle peak, ~14" The solid line shows the actual spectrum, the dashed one is a straight line fitted through frequencies 1-10kHz, and ~14 is the perceived enhancement at 14kHz. (b) Magnitude of ~14 as a function of wind speed. The grey symbols are for Loch Etive data when no rain is present, and the black crosses are rain-free data from Aberporth, with the solid line showing the mean relationship of these rain-free data. The black dots represent occasions when the rain radar confirm that it is raining.

T.H. Guymer*, G.D. Quartly, K.G. Birch, J.M. Campbell, C.E. Jones and K.M. Shannon

133

Figure 5 is an example of data obtained during one 24-hour period from the various systems and clearly shows the sensitivity of the acoustic method to low precipitation rates (drizzle was reported). The tipping bucket values agree well with the rain radar though there is some difference in timing which may reflect the fact that the radar is averaging over a 5 k m x 5 k m box. The optical gauge estimate is lower than the others. Drizzle manifests itself in both ARG records as higher intensities around 14 kHz. Further analysis of the 'drizzle peak' was carried out on data from both Loch Etive and Aberporth. Figure 6 shows how the magnitude of this peak varies with wind speed.

3.3 Scotian Shelf For the third deployment (July 2002) we participated in a Canadian acoustic experiment off the Nova Scotian shelf. This had the advantage that a number of different underwater acoustic measurements were made, together with supporting meteorological data. Unfortunately, one of the ARGs failed shortly after deployment due to water ingress but the other appears to have worked satisfactorily. Processing and analysis is in progress; Figure 7 is an early example of temporal variations at five selected frequencies. The large increase in power at Day 150.35 corresponds to an independently observed rain event.

Figure 7 Example of data from ARG during rain event in Scotian Sea experiment

4. Conclusions and future plans Results obtained to date are summarised in Figure 8 which shows general agreement with results in the literature~an increase in acoustic intensity with wind speed across all frequencies, a peak at 14kHz due to drizzle and a reduction in slope of the spectrum in moderate to heavy rain. Good agreement has been found with other rain measurements but more work is needed to develop algorithms based on open ocean conditions. Some data have been obtained from acoustic systems situated only a short distance apart. In order to distinguish between instrumental and sampling differences further emphasis on sensor calibration is needed. Further deployments are planned for Plymouth (SW England) and Galway (Ireland).

134


Figure 8 Solid lines show mean acoustic spectra from Loch Etive trial for rain-free conditions (various wind speeds ranging from 1 to 10ms-l). The characteristically-different spectra for drizzle and heavy rain are shown by the dotted and dash-dotted lines. At the 2nd EuroGOOS Conference Guymer et al. (2002) reported results of a new technique for obtaining rainfall climatologies from radar altimetry. By positioning ARGs at the cross-overs of altimeter tracks it should be possible to validate instantaneous satellite estimates as well as the space-time averages used in the climatology. The ARGs can also be used for comparison with other satellite estimates such as the Tropical Rainfall Measurement Mission and, in the future, the proposed Global Precipitation Mission which will cover a larger range of latitudes.

References Guymer, T.H., P.G. Challenor, P. Cipollini, D. Cromwell, G.D. Quartly, M.A. Srokosz, and P.D. Cotton, 2002. Multi-sensor satellite monitoring of ocean climate, Operational Oceanography, Implementation at the European and Regional Scales, Proceedings of the second international Conference on EuroGOOS 11-13 March 1999, Rome, Italy, edited by N.C. Flemming et al., Elsevier Science Ltd. (Elsevier Oceanography Series, 66). ISBN: 0-444-50391-9. Nystuen, J.A., C.C. McGlothin and M.S. Cook, 1993, The underwater sound generated by heavy rainfall. Joumal of the Acoustical Society of America, 93, 3169-3177, (1993). Quartly, G.D., T.H. Guymer, and K.G. Birch, 2002a, Measuring rainfall at sea: Part I, In situ sensors, Weather, 57, 315-320 Quartly, G.D., T.H. Guymer, and M.A. Srokosz, 2002b, Measuring rainfall at sea: Part II, Spaceborne sensors, Weather, 57, 363-366 Scrimger, J.A., D.J. Evans and W. Yee, 1989, Underwater noise due to rain: Open ocean measurements. Journal of the Acoustical Society of America, 85,726-731.

Marine SAR Analyses and Interpretation System MARSAIS Johnny Johannessen *l, Torill Hamre l, Rene Garelio 2, Roland Romeiser 3, Stefan Kern 3, Bertrand Chapron 4, Ian Robinson 5, Susanne Ufermann 5, Valerie Cummins b, Niamh Connolly b, Kostas Nittis 7, Leonidas Perivoliotis 7, and Dario Tarchi 8

!Nansen Environmental and Remote Sensing Center, Bergen, Norway 2Ecole Nationale Superieure des Telecommunications de Bretagne, Brest, France 3Institute of Oceanography, University of Hamburg, Hamburg, Germany 4Ifremer, Brest, France 5Southampton Oceanography Centre, University of Southampton, Southampton, UK 6University of Cork, Cork, Ireland 7National Centre for Marine Research, Athens, Greece 8Joint Research Centre, Ispra, Italy Abstract There is a growing need for better monitoring and managing of the marine coastal zone environment. The parameters and features most frequently required for their relevance and importance include 9 surface waves and high-resolution wind fields 9 surface current strength and variability 9 identification and location of pollutant material including toxic algae bloom and oil spill Remote sensing observations play a valuable role in this context. Mature algorithms and synthetic aperture radar (SAR) imaging models are integrated and supplemented by complementary thermal and visible sensor data. This paper outlines how a system like MARSAIS can guide and help non-experts in exploring SAR imaging data for coastal ocean monitoring.

1. Introduction The project Marine SAR Analyses and Interpretation System (MARSAIS) for application to the coastal zone is aimed at developing a tool for the exploitation of Earth Observation data that allows better provision of scientific results into new or existing applications. MARSAIS takes stock of current analysis and interpretation capabilities for SAR image data, allows for a more user-friendly exploitation of the large volume of existing remote sensing data in Europe, and systematically prepares for a new era of utilization of imaging radar system. The availability of spaceborne SAR data for more than a decade has provided regular and global observation of ocean wave spectra (Heimbach et al., 1998). Furthermore, its capabilities to detect and locate oil spills, bathymetric features in shallow water, and * Corresponding author, email: [email protected]

136

Marine SAR Analyses and Interpretation SystemmMARSAIS

ships has also led to systematic use of SAR images in operational surveillance associated with marine coastal pollution control, bottom mapping, and fisheries (Espedal et al., 1998; Hesselmans et al., 1997). Recently, a new emerging application has been developed for the monitoring of atmospheric boundary layer processes and meso-scale coastal wind fields (Chapron et al., 1995; Korsbakken et al., 1998; Pichel and Beal, 2000). With the expected capability to further advance the regular application of the SAR data for upper ocean current feature monitoring, it is evident that the SAR will play a vital role in marine coastal ocean monitoring and prediction systems (Pichel and Beal, 2000; Johannessen et al., 1996; Lyzenga, 1998). This paper outlines how a system like MARSAIS can guide and help non-experts in exploring SAR imaging data for coastal ocean monitoring, and some examples of Earth Observation (EO) data and products derived from these are shown. The MARSAIS project has from the beginning interacted closely with end-users and feedback obtained has been used to update and redefine the desired content and functionality of the MARSAIS System.

2. The MARSAIS concept The Marine SAR Analyses and Interpretation System is composed of a three main subsystems, illustrated in Figure 1: 9 the MARS AIS database 9 the MARS AIS toolkit 9 the MARS AIS prototype

Figure 1 Main subsystems of the MARSAIS system. The MARSAIS database will be a consistent and user-friendly database of multi-satellite and multi-sensor data, coupled with dedicated in situ data fields from European shelf seas and the Mediterranean Sea. This database will be accompanied by a set of standard image processing techniques. The MARSAIS toolkit will contain a suite of tools for SAR analysis and interpretation, enabling estimation of geophysical parameters such as: 9 near surface wind field (wind speed, direction) maps and high-resolution wave field (amplitude, wavelength, direction) maps 9 current features, including surface current gradients, fronts and internal waves 9 slicks, including detection and classification (natural film, oil spill, and seepage)

Johnny Johannessen et al.

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In addition, the toolkit will contain algorithms for synergetic use of SAR combined with optical and infrared data (Ufermann et al., 2003). The MARSAIS prototype will have a web-based user interface, enabling access through standard web browsers without additional plug-ins. The prototype will demonstrate different types of products generated by the toolkit, and enable users to evaluate the capabilities of the SAR ocean interaction models and algorithms to deliver useful products for specific application in the coastal zone. The prototype will also include background information on both input and validation data used, and a general introduction to remote sensing for marine applications. Product examples are linked with associated support material, making it easy for non-experts to learn about the capabilities of SAR and EO data in ocean coastal monitoring. A first version of the web-based prototype has been developed, providing sample EO products and associated background information (Figure 2). Content has been organised hierarchically, with all main themes or product types (near surface wind field, wave spectra, current features, slicks) available from a menu that is included on every page. Within each theme, general information is presented first, and links to supplementary material given as needed.

Figure 2 Sample screen shot from the first version of the MARSAIS prototype identifying a current front outside the mouth of Rhine, developed by tidal current-river outflow interaction.

3. User aspects The MARSAIS project has from the beginning interacted closely with end-users in coastal zone management and other marine application domains. Continuous contact has been maintained through direct contact, e.g. phone calls or by dedicated meetings with individual organisations and workshops for large groups of end-users. An extensive Context of Use (CoU) Survey has been conducted; focusing on identifying specific user

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Marine SAR Analyses and Interpretation SystemmMARSAIS

requirements for uptake and use of SAR and SAR derived products in the target user communities. Nearly five hundred people were contacted in the framework of the CoU Survey. Ninetyfive completed questionnaires were returned, corresponding to an overall response rate of just below 20%. An initial preview of the results was undertaken in November 2002. It showed a wide geographic spread of interest in MARSAIS among end-users. The majority of respondents so far have been from organisations concerned with environmental preservation/protection or research, although different patterns can be seen within individual countries. A detailed analysis of the feedback from the CoU Survey is currently being conducted, and the results will be used to improve the MARSAIS Prototype in the last year of the project.

4. Product examples Mature algorithms and synthetic aperture radar (SAR) imaging models are integrated in the MARSAIS Toolkit, and supplemented by complementary thermal and visible sensor data. Algorithms and models included in the toolkit are described in e.g. Garello and Maitre, 2001. Figure 3 to Figure 6 show examples of products generated with these algorithms and models for the North Sea and the Mediterranean Sea. More information on MARSAIS products can be found on the following web pages: 9 http://marsais.ucc.ie 9 http://marsais.ucc.ie/frameset/fset approach.htm 9 http://www.poseidon.ncmr, gr/marsais/ 9 http://marsais.enst-bretagne, fr

Figure 3 ERS SAR image (left) with waves, and derived wave spectra (fight).

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Figure 4 ERS SAR image (left) with wind streaks, and estimated wind field (fight) (Portabella et al., 2002).

Figure 5 ERS SAR image (left) with internal waves, and estimated current speed across the train of internal waves (fight).

Figure 6 ERS SAR image (left) with slick, and computer-extracted slick object (fight).

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Marine SAR Analyses and Interpretation System--MARSAIS

5. Summary This paper has described the MARSAIS system and the prototyping activities in the MARSAIS project. As of December 2002, the database subsystem is almost completed, and a set of algorithms and models are available and undergoing final validation. A first version of the web-based prototype has been developed and tested. Through a user survey, contact has been made (with responses) with more than 90 users, of which about half has used the MARSAIS prototype and provided feedback. The MARSAIS subsystems will be enhanced and finalised by the end of 2003. With the coming of simultaneous flight operations of three calibrated, spaceborne SARs (Envisat, Radarsat-2, Japanese ALOS starting from 2002), employing wide swath (~500 km) technology, multi-frequency and multi-polarisation SAR application will be on the threshold of a new era, presenting novel and unique opportunities for the international marine coastal ocean user community. As such the MARSAIS prototype is also timely and relevant for the implementation of the Global Monitoring for Environment and Security (GMES).

Acknowledgements The MARSAIS project is supported by the European Commission through contract no. EVG 1-CT-2000-00029, and their support is gratefully acknowledged.

References Chapron, B., T. Efouhaily, and V. Kerbaol, 1995, Calibration and validation of ERS wave mode products, I Document DRO/OS/95-02. Espedal, H., O.M. Johannessen, J.A. Johannessen, E. Dano, D. Lyzenga, and J.C. Knulst, 1998, COASTWATCH '95, A tandem ERS-1/2 SAR detection experiment of natural film on the ocean surface, J. Geophys. Res., 103, C11, pp.24969-24982. Garello, R. and H. Maitre, 2001, Remote sensing from satellites, special issue of Annals of Telecommunications, 56, 11-12. Heimbach, P., S. Hasselmann, and K. Hasselmann, 1998, Statistical analysis and interpretation of WAM model data with global ERS-1 SAR wave mode spectral retrievals over 3 years, Advances in Oceanography and Sea Ice Research using ERS Observations, Journal of Geophysical Research-Ocean, 103, C4, pp. 7931-7979. Hesselmans, G., C. Calkoen, and H. Wensink, 1997, Mapping of Seabed topography to and from SAR, In Proceedings Third ERS Symposium, Space at the service of our Environment, ESA Publication Division, ESA SP-414, pp. 1055-1058. Korsbakken, E., J.A. Johannessen and O.M. Johannessen, 1998, Coastal wind field retrievals from ERS SAR images. J. Geophys. Res.,103, C4, pp. 7857-7875. Pichel, W.G. and R.C. Beal (guest eds.), 2000, Coastal and Marine Applications of Wide Swath SAR, Johns Hopkins APL Technical Digest, 21, 1. Portabella, M., A. Stoffelen, J.A. Johannessen, J. Geophys. Res., 107, C8, 2002. Ufermann, S., I.S. Robinson, and J.A. Johannessen, 2003, The Role of Synergy in Developing a Marine SAR Analysis and Interpretation System, Proceedings of 3rd EuroGOOS Conference (this publication), p. 174.

Study and monitoring of sea ice cover in the Caspian and Aral Seas from TOPEX/POSEIDON microwave data Alexei V. Kouraev* 1,2,3, Fabrice Papal, Petr I. Buharizin 4, Anny Cazenave 1, JeanFrancois Cretaux 1, Julia Dozortseva 5, and Frederique Remy 1 d'Etudes en GOophysique et Oc~anographie Spatiales (LEGOS)/CNRSCNES, Toulouse, France 2Moscow State University, Faculty of Geography, Moscow, Russia 3Nansen International and Remote Sensing Centre (NIERSC), St. Petersburg, Russia 4Water Problems Institute (WPI) of Russian Academy of Sciences, Astrakhan, Russia 5Hydrometeorological Centre of the Caspian Fleet, Russia ! Laboratoire

Abstract The paper discusses recent variations in sea ice cover in the Caspian and Aral seas. Analysis is done using synergy of data from active (radar altimeter) and passive (radiometer) microwave nadir-looking instruments onboard the TOPEX/Poseidon satellite for 1992-2002. The results show significant spatial and temporal variability of ice conditions. There is a significant decrease of both duration of ice period and ice presence for 1998-2002. The obtained time series of sea ice presence are unique, as the existing data on sea ice in these two seas since mid-1980s is fragmentary and mostly unpublished.

Keywords: Sea ice, Caspian Sea, Aral Sea, TOPEX/Poseidon, active and passive microwave data. 1. Introduction Every year the Caspian and Aral seas are covered by ice for several months. Ice presence affects various activities in this region (such as navigation and fisheries), and endangers engineering constructions on the coast, as well as on the shelf, such as the oil rigs that were recently installed in the Northern Caspian by Russia and Kazakhstan. Ice conditions are characterised by significant interannual fluctuations, which reflect meteorological conditions in this region, and therefore the data on ice cover variability in these seas may serve as an early indicator of the large-scale climate change. Studies of the sea ice in the Caspian sea started as early as the second part of the 19th century. Initially the observations were only made at the coastal stations, but from 1927 they were complemented by aerial surveys (Table 1). In the beginning aerial surveys were irregular and mostly oriented to the needs of sea hunting and fisheries. The situation changed during World War II, when the number and quality of ice surveys significantly increased. Later on ice observations became part of the national programme for hydrometeorological monitoring and were performed on a regular basis. Since the 1980s due to economical problems the number of aerial surveys have sharply decreased * Corresponding author, email: [email protected]

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and currently they are carried out mostly on an opportunity basis. Satellite information for studies of ice cover in the Caspian has been was used since the late 1970s, mainly in the visible and infra-red range (Krasnozhon and Lyubomirova, 1987; Buharizin et al., 1992). However, due to frequent presence of cloud cover in the winter over the Caspian sea this information is often fragmentary and up until now not used routinely. A total of 282 ice charts have been created based on satellite data for 1976-2002. Table 1 Number of ice charts based on aerial surveys for the Northern Caspian sea from 1927 to 2002 1927- 1930- 1934- 1941- 1945- 1960- 1979- 1988- 19931930 1934 1941 1945 1960 1979 1988 1993 2002

Period

Total

Number of winters

3

4

8

4

15

19

9

5

9

76

Number of ice charts

19

0

103

210

457

390

80

25

6

1290

In the Aral sea regular ice observations in the open sea started first at the coastal stations (in 1941). From 1950 they were complemented by aerial ice surveys performed on a regular basis. From then until 1985, the total number of aerial surveys amounted to 241. As for the Caspian sea, in the 1980s the frequency of aerial surveys in the Aral sea dramatically decreased. At the moment continuous time series of various ice cover parameters for the Caspian and Aral seas exist only until 1984-1985 and most of the existing literature refer to the data obtained during that period (Kosarev, 1975; Bortnik et al., 1990; Terziev et al., 1992; Kosarev and Yablonskaya, 1994). For later periods, materials that reside in local archives are much less regular, in a heterogeneous form (data from aerial surveys, satellite imagery, ship observations etc.), exist on different media and are not available to the general public. There are some recent initiatives to compile these data into a comprehensive "Atlas of ice features for the region of Northern Caspian and lower Volga" (Buharizin and Sharomov, 2002), but this work is still at an early stage. Continuation of times series of ice cover parameters will be of importance for various purposes--from practical application for ship routing, fisheries and protection of industrial infrastructure, to studies of ice variability and change, as well as forcing and verification of general circulation models. The satellite-derived continuous time series of ice cover parameters that are discussed here, bring the evidence on recent changes in the ice conditions and are probably the first attempt to fill this important information gap. 2. D a t a

and

methods

We studied ice cover in the Caspian and Aral seas using data from the TOPEX/Poseidon satellite for 1992-2002. Data for five satellite passes covering the northern part of the Caspian sea, where ice cover is annually present during the winter, and two passes for the Aral sea were selected. The spatial resolution along the track is about 6km, with a temporal resolution of 10 days. TOPEX/Poseidon has two main nadir-looking instruments--a dual-frequency radar altimeter (5 and 13.6GHz) and a passive microwave radiometer operating at 18, 23 and 37 GHz. We have found that the combination of active and passive microwave measurements may be successfully used for the

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studies of the ice cover, for example to discriminate open water and ice, as well as estimate ice extent and roughness (for details see Kouraev et al., 2002; Kouraev et al., 2003). 3. R e s u l t s

Applying the proposed method for TOPEX/Poseidon data we obtained time series of ice cover presence for each chosen pass. By averaging these data in time and space, various parameters characterising ice conditions in these two seas have been obtained. For the Northern Caspian, the location of passes made it possible to assess regional variability in three parts of the sea--western, central and eastern. Analysis of time series of ice extent and duration of ice period show pronounced regional, seasonal and interannual variability. The duration of the ice period varies from year to year. In the Aral sea during the last four winters, the end of the ice period occurred in January-February and not in MarchApril, as it was before. This shift for the Aral sea resulted in significant decrease of the duration of ice p e r i o d n f r o m 80-140 to 3 0 - 5 0 days. For the eastern part of the Northern Caspian the duration of the ice season appears to be more stable, ranging from 84 to 140 days. In western and central parts a marked decrease in the duration of the ice period is also observed: from 80-100 to 6 0 - 8 0 and even 40 days in the western part and from 100-140 to 3 0 - 7 0 days in the central part. This points to significant changes in climate conditions during 1998-2002. The presence of ice cover (in % of total number of TOPEX/Poseidon observations for November-April) observations (Table 2) was calculated for the Northern Caspian and Aral seas. It should be noted, that these numbers only represent ice conditions for specific regions covered by the selected satellite passes. For the first 6 - 7 years mild and more severe winters alternate. However, this pattern changes abruptly in the last 3 - 4 winters, when a sharp decrease of ice presence took placemfrom 30-60% to 10-35% in the Northern Caspian and from 30-80% to 10-25% in the Aral sea. This corresponds very well with the decrease of duration of ice presence already noted in 1998-2002. Table 2 Ice presence (%) in the Aral and Caspian seas 1992/ 93

19931 94

Northern Caspian

37

Aral sea

31

Winter

19941 95

1995/ 96

1996/ 97

1997/ 98

61

33

82

49

1998/ 99

19991 00

43

33

68

36

20001 01

2001/ 02

45

29

65

46

13

20

30

11

24

19

The dramatic reduction of ice extent in 1998-2001 also severely affects living conditions of the unique mammal in the seamthe Caspian seal (Pusa Caspica), listed as vulnerable on the IUCN (International Union for Conservation of Nature and Natural Resources) Red List of Threatened Species (http://www.redlist.org). In the early 20th century its population was about 1000000 specimens, at the end of 1960s it was 500000, while now it is estimated to be only 415000-435000 (Buharizin and Khuras'kin, 2001). From January to April seals gather on the sea ice in the Northern Caspian sea, where they pup, nurse, mate and moult. The lack of stable ice cover forces seal females to pup on the

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Study and monitoring of sea ice cover in the Caspian and Aral Seas from TOPEX/ POSEIDON microwave data

unstable pieces of broken ice instead of large ice fields, and later on stipulate low efficiency of pup nursing. Under such conditions another important stage of a pups life--moultingmalso takes place not on the ice, but in crowded and hard conditions on small islands. The resulting weakening of the immune system may have been seriously aggravated in 2000 by the spread of viruses leading to mass mortality of seals, when the canine distemper virus affected a large part of seal population. The first dead seals were found in 20-26 April 2000 in the western part of the Northern Caspian and then the virus quickly spread over almost the entire aquatory. The total number of seals that died during the epidemic is estimated to be between 20000 and 30000.

4. Conclusion The synergy of simultaneous observations by active and passive sensors from nadirlooking TOPEX/Poseidon satellite looks promising for studies of sea ice cover. The benefits are high spatial (along the track) and temporal resolution, independence of weather conditions for measurements, and robustness of discrimination between open water and ice. One of the drawbacks is the relatively coarse coverage of T O P E X Poseidon passes, limiting study to specific regions. This situation may be improved by using additional data sets from other satellites with similar set of sensors, such as Jason1, ERS-1 and -2, and ENVISAT. Another important source of information is passive microwave data from imaging radiometers (SMMR and SSM/I). Currently we are continuing our research by analysing more than twenty years of data from these instruments, that will help to further validate sea ice parameters derived from TOPEX/Poseidon. These additional series of data will fill the gap in the available information on ice cover variability between the end of aerial surveys and the start of TOPEX/Poseidon observations.

Acknowledgements The research was supported by the French Ministry of Research through a post-doctoral fellowship for Alexei Kouraev in 2001/2002.

References Bortnik, V.N., and S.P. Chistyayeva (eds.), 1990, Hydrometeorology and hydrochemistry of seas. Vol. VIImAral sea. Leningrad: Gidrometeoizdat (in Russian). Buharizin P.I., and L.S. Khuras'kin, 2001, Klimaticheskiy usloviya, sposobstvuyushiye vozniknoveniyu prichin massovoy gibeli tyuleney na Severnom Kaspii vesnoy 2000 goda (Climatic conditions, favouring creation of conditions for mass fatality of seals in the Northern Caspian in spring 2000). Proceedings of the interregional scientific and practical conference "Scientific research for solution of social and economical problems of the Astrakhan region", Astrakhan, 5 - 6 June, 2001. Buharizin, P.I., M.F. Vasyanin, and L.A. Kalinichenko, 1992, A method for short-term forecast of pack ice boundary in Northern Caspian. Meteorology and Hydrology, 4, 74-81 (in Russian). Buharizin, P.I., and V. Yu. Sharomov, 2002, Development of a prototype of an atlas of ice conditions and ice formations in the Northern Caspian and in the lower Volga.

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Paper presented at the scientific and practical meeting "Hydrometeorological providing of economic activity in the Arctic and ice-covered seas", St. Petersburg, 27-29 March 2002 (in Russian). Kouraev, A.V., F. Papa, P.I. Buharizin, A. Cazenave, J.-F. Cretaux, and F. Remy, 2002, Study of ice cover in the Northern Caspian sea using active and passive satellite microwave data. In: "Modem Problems of Oceanology of the Shelf Seas of Russia", Abstracts of International Conference (Rostov-on-Don, 13-15 June 2002), Murmansk, p. 132-133. Kouraev, A.V., F. Papa, P.I. Buharizin, A. Cazenave, J.-F. Cretaux, and F. Remy, 2003, in press: Ice cover variability in the Caspian and Aral seas from active and passive satellite microwave data. Polar Research. Kosarev, A.N. 1975: Hydrology of the Caspian and Aral seas. Moscow: Moscow University Publishing (in Russian). Kosarev, A.N. and E.A. Yablonskaya, 1994, The Caspian sea. The Hague: SPB Academic publishing. Krasnozhon, G.F. and K.S. Lyubomirova, 1987, Study of ice cover in Northern Caspian from meteorological satellites. Issledovaniye Zemli iz kosmosa (Study of Earth from Space), 5, 14-17 (in Russian). Terziev, F.S., A.N. Kosarev, and A.A. Kerimov, (eds.) 1992, Hydrometeorology and hydrochemistry of seas. Vol. VImCaspian sea, Issue l mHydrometeorological conditions. St. Petersburg: Gidrometeoizdat (in Russian).

Oceanpal" an instrument for remote sensing of the ocean and other water surfaces using GNSS reflections Giulio Ruffini* l, Marco Caparrini 1, Bertrand Chapron 2, Franqois Soulat 1, Olivier Germain 1 and Leonardo Ruffini 1

1Starlab, Edifici de l'Observatori Fabra, Spain, http://starlab, es 2Ifremer, Centre de Brest, France, http://www.ifremer.fr

Abstract This paper describes Oceanpal, an inexpensive, all-weather, passive instrument concept for remote sensing of the ocean and other water surfaces. Oceanpal is based on the use of reflected signals emitted from GNSS, and as such it is well grounded on the growing, long term GNSS infrastructure. As seen from the instrument, several GNSS emitters are simultaneously in view at any given time, providing separated multiple scattering points with different geometries. Reflected signals are affected by surface "roughness" and motion (i.e. sea state, orbital motion, and currents), mean surface height and dielectric properties (i.e. salinity and pollution). Oceanpal is envisaged to act as an accurate, "dry" tide gauge/surface monitoring system as a part of a future distributed ocean remote sensing network concept.

Keywords: GNSS-R, altimetry, remote sensing, GPS, Galileo 1. Introduction Two GNSS constellations are presently operational, the Global Positioning System (GPS), owned by the United States, and the Russian GLObal NAvigation Satellite System (GLONASS). In the next few years, the European Satellite Navigation System (Galileo) will be deployed. By the time Galileo becomes operational in 2008, more than 50 GNSS satellites will be emitting L-band spread spectrum signals with a well characterised structure, and they will remain in operation for at least a few decades. These signals can be used within GCOS/GOOS. The immediate objective of the Starlab Oceanpal project is the development of technologies for operational in situ or lowaltitude water surface monitoring using GNSS reflections, a passive, all weather radar technology of great potential. Oceanpal is an offspring of technology developed within several ESA/ESTEC projects targeted on the exploitation of GNSS Reflections from spacemsuch as the ESA/ESTEC projects OPPSCAT, OPPSCAT 2 (Speculometry), Paris-Alpha, Paris-Beta, ParisGamma (altimetry) and GIOS-1 (focusing on ionospheric monitoring)man example of bistatic (passive) radar (Cantafio, 1989). Although our focus here is on low altitude applications, it is worthwhile explaining in more detail the rationale for spaceborne deployment: an important aspect of the G N S S - R concept is the synergy between space


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and ground monitoring using the same technology and the same signal infrastructure, which will ensure homogeneity in the measurements.

2. G N S S - R in space: the Petrel Earth Explorer concept In the future, the artificial separation between geophysical "layers" (ocean, troposphere, stratosphere, etc.) will disappear, and future Earth system models will need to reflect the fundamental role of atmosphere-ocean coupling. The sea surface provides the oceanatmosphere link, regulating momentum, energy and gas exchange, and several fundamental ocean circulation features are directly related to wind-wave induced turbulent transports in the oceanic mixed layer. In particular, eddies are fundamental to understanding mixing, heat transport and feedback to general circulation, as well as transport of nutrients, chemicals and biota for biochemical processes, and therefore also important to climate research. At the atmosphere-ocean boundary, many temporal and spatial scales play an important role: from the molecular to the synoptic level, from seconds to aeons. For this reason, observing this surface appropriately is an important challenge for global observation systems, which will require high resolution, wide swaths, frequent revisits and long-term stability (Le Traon et al., 2002). All of these are part of the G N S S - R concept. The ocean-atmosphere interface is characterised (to the lowest statistical order) by the geophysical variables of local mean sea level (h), significant wave height (swh) and directional mean square slope (dmss). Mesoscale measurements of sea surface dmss are an important missing element from the global climate and ocean observation systems, and are needed to understand and quantify the atmosphere-ocean flux of energy, momentum and gas. Moreover, since ocean forcing is a strongly intermittent (in both space and time) non-linear phenomenon, frequent space-time collocated mesoscale measurements of h and dmss are also needed. The scientific objectives of a spaceborne G N S S - R mission (the Petrel concept, recently submitted to the Earth Explorer ESA programme) therefore provide the medium and long-term components for physical climate observation (theme 2 of the Earth Explorer programme) with a focus on providing a key element for the study of atmosphere-ocean coupling. The elementary geophysical products provided by such a mission are indeed mesoscale collocated altimetric and sea surface directional mean square slope measurements. These measurements are also of great interest for the observation of surface winds, mean sea surface, sea-ice, ionospheric electron content and salinity. Results from recent ESA studies and experiments (see the acknowledgements at the end for details) and also from work in the US indicate that G N S S - R data can provide sufficient information to resolve mesoscale features in the ocean, as well as collocated directional mean square slope measurements (for further information consult the references). Figure 1 shows a schematic rendition of a spaceborne G N S S - R mission, as well as an illustration showing multiple (GPS) reflections available to a ground receiver during a 24-hour period. Note the multi-static character of the technique: a single passive instrument can provide a large swath, thanks to the availability of multiple emitters. As recent ESA studies indicate, a single G N S S - R Low Earth Orbiter can provide spatiotemporal samplings of < 100km resolution with < 10 days revisit time with an equivalent

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Oceanpal: an instrument for remote sensing of the ocean and other water surfaces using GNSS reflections

altimetric precision of 1000 measurements) in deep areas (> 1000m) of the open Black Sea for the period 1964 to 1996. Data were derived mainly from the database prepared within the framework of the NATO TU Black Sea Project. Satellite (SeaWiFS) data were received from the Distributed Active Archive Center at Goddard Space Flight Center (DAAC GSFC). In the present investigation, daily Level 2 GAC data, i.e. chl a concentration calculated by use of standard NASA algorithm * Corresponding author, email: [email protected]

Oleg A. Yunev*, Vadim Suetin, Vyacheslav Suslin, and Snejana Moncheva

169

(O'Reilly et al., 1998), and its average half a month data, as well as normalised waterleaving radiances at the detector wavelengths: 490, 510, and 555nm, were used.

3. R e s u l t s and d i s c u s s i o n 3.1 Preceding steps for in situ chl a analysis Before analysis of in situ chl a long-term changes, essential preceding steps were executed: 1. evaluation of spatial-temporal changes in different sub-regions and the entire open Black Sea by use of standard parameters of variation 2. pre-testing the long-term changes of in situ chl a by use of mean annual concentrations 3. study of in situ chl a seasonal dynamics for different interannual periods. These analyses revealed that: 9 there were no statistically significant differences between sub-regions, as well as the entire open sea 9 there was appreciably lower spatial variability for the entire open Black Sea compared to long-term and seasonal variability 9 there were 3 interannual periods with different chl a means within the entire open Black Sea the seasonal dynamics of chl a fitted a bi-modal curve with a main winter-spring maximum in January-March and a less marked peak in November; also, there was a warm quasi-stationary interval (approximately, May to September) with low chl a concentrations These results enabled us, firstly, to consider the entire open Black Sea as a single water mass for the analysis of long-term and seasonal variability in chl a, and secondly led us to analyse the long-term trends of in situ chl a within the whole open sea, particularly for the different periods of the year.

3.2 In situ chl a long-term variability Analysis of long-term variability of different periods of the year revealed that in general, there were no regular trends in chl a levels during the cold season. In comparison, chl a data for the quasi-stationary interval (approximately, from May to September) revealed a clear long-term trend (Figure 1). Following a steady increase in 1988-1992 there was an abrupt decrease in summer chl a in 1993 down to approximately 0.26mgm -3, followed by a small negative trend during 1993 to 1996.

Long-term changes in the Black Sea surface chlorophyll a according to in situ and modern satellite data

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The barest necessity in analysis of modern satellite data was dictated firstly by the absence of regular scientific cruises in the Black Sea after 1995/1996. Monitoring of interannual and seasonal chl a changes in the Black Sea were therefore continued by using Level-2 SeaWiFS chl a, derived by standard NASA algorithm (O'Reilly et al., 1998) using averaged half-monthly data. Analysis of satellite data started with verification of the daily data for two situations: (a) and (b) (Figure 2). The result of this comparison did not reveal a statistically significant relationship between in situ and satellite (Level-2 SeaWiFS) data in the open Black Sea between 1997 and 2002. (a) the same day measured B

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171

peak in November, as well as the warm quasi-stationary monthly interval with low chl a concentrations during 1997-2002, i.e. the main patterns of chl a seasonal dynamics revealed for the in situ data. The above verification and comparison of main patterns in seasonality for in situ and satellite data, indicate that Level-2 SeaWiFS data for the open Black Sea were not indicative of real chl a concentrations, and that, for the Black Sea (Case-2 waters), there was a necessity to develop local algorithm to accurately transform SeaWiFS optical data to comparable in situ values. Jun e~

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Horizontal continuous line agrees with summer concentrations. Months of annual maxima are also shown. 3.4 MHI a l g o r i t h m s a t e l l i t e d a t a

Kopelevich's model (1983) was used to separate absorption of phytoplankton pigments and coloured organic matter ('yellow substance') from daily Level-2 SeaWiFS data (normalised water-leaving radiance at the detector wavelengths: 490, 510 and 555nm). This resulted in a suitable algorithm (MHI algorithm) to transform satellite data into chl a levels for the case of the Black Sea waters, i. e. Case-2 waters which differ from Case1 waters of Atlantic Ocean. Methodical details and a description of the algorithm will soon be available in periodicals. Here we show preliminary results from application of the first version of MHI algorithm with short comments. 1. Figure 4 shows the model verification. There is a statistically significant relationship between in situ chl a and MHI algorithm satellite data within the open Black Sea. 2. Patterns of seasonal and interannual changes are shown in Figure 5. In general, there is a main winter-spring maximum and a less marked peak in autumn, as well as the warm quasi-stationary monthly interval (approximately M a y - S e p t e m b e r ) with low chl a concentrations during all period 1997-2002. 3. Comparison of seasonal and interannual changes within the different sub-regions is shown in Figure 6. There are some differences in patterns of interannual and seasonal changes in different sub-regions, but they are not differences in principal features.

172

Long-term changes in the Black Sea surface chlorophyll a according to in situ and modern satellite data

4. Comparison of M H I algorithm satellite and SeaWiFS data for Sub-region # 1 in Figure 7. The two curves are very different. (a) the same day measured

(b) the day ___aday measured

Figure 4 Comparison of in situ and MHI algorithm satellite chl a. Equivalence line y = x is plotted. Correlation: (a) [MHI algorithm] = l .41[in situ], r2=0.87; (b) [MHI algorithm]=l.14 [in situ], r2=0.71

Figure 5 Patterns in seasonality and interannual changes of MHI algorithm satellite chl a. during 1997-2002. Horizontal continuous line agrees with summer concentrations. Months of annual maxima are also shown.

Figure 6 Patterns in seasonality and interannual changes of MHI algorithm satellite chl a during 1997-2002 in two different regions of the open Black Sea.


173

Figure 7 Comparison of patterns in seasonality and interannual changes of MHI algorithm and Level-2 SeaWiFS satellite chl a during 1997-2002 in Sub-regions # 1 of the open Black Sea.

4. Conclusions 1. There was a good correspondence between in situ and MHI algorithm satellite chlorophyll a values, between the main patterns in interannual and seasonal variability for values of chl a from estimates in situ and according to the MHI algorithm. 2. In general, there were no differences in seasonal and interannual patterns of chlorophyll a concentrations during 1997-2002 derived from MHI algorithm satellite data, and during 1993-1996, derived from in situ data.

Acknowledgements The authors would like to thank the SeaWiFS Project (Code 970.2) and the Distributed active Archive Center (Code 902) at the Goddard Space Flight Center, Greenbelt, MD 20771, for the production and distribution of these data, respectively. These activities are sponsored by NASA's Mission to Planet Earth Program. Some of the data used in this paper originates from the database prepared within the framework of the NATO TU Black Sea Project.

References Finenko, Z.Z., 2001, Primary production of the Black Sea: ecological and physiological characteristics of a phytoplankton. Sea Ecology, vol. 57, pp. 60-67 (in Russian). Kopelevich, O.V., 1983, Low-parametric model of seawater optical properties. In Ocean Optics, I: Physical Ocean Optics, (A.S. Monin, ed.). Nauka, Moscow, pp. 208-234 (in Russian). Nezlin N.P., 2000, Remote-sensing studies of seasonal variations of surface chlorophylla concentration in the Black Sea. In: Satellites, Oceanography and Society (ed. D. Halpen), Elsevier Science B. vol., pp. 257-271. Nezlin, N.P., 2001, Unusual phytoplankton bloom in the Black Sea during 1998-1999: analysis of remotely sensed data. Oceanology, 41, N3, pp. 394-399 (in Russian). O'Reilly, J.E., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L. Carder, S.A. Garver, M. Kahru, and C. McClain, 1998, Ocean color chlorophyll algorithms for SeaWiFS. J. of Geophysical Research, vol. 103, pp. 24937-24953.

The role of synergy in developing a Marine SAR Analysis and Interpretation System Susanne Ufermann *1, lan S. Robinson 1, and Johnny A. Johannessen 2

1School of Ocean and Earth Science, Southampton Oceanography Centre, UK 2Nansen Environmental Research Center, Norway

Abstract The European FP5 project "Marine SAR Analysis and Interpretation System for application to the coastal zones" (MARSAIS) is aimed at developing a tool for the exploitation of Earth Observation data and a better provision of scientific results into new or existing applications. Based on integrated use of mature algorithms and synthetic aperture radar (SAR) imaging models, the implementation of a MARSAIS prototype will concentrate predominantly on improving the exploitability of SAR data by the nonexpert user. In this study we demonstrate how synergy between the SAR images and data from thermal and visible sensors facilitates and contributes to SAR image interpretation in the coastal zone and how this will be applied within the framework of MARSAIS.

Keywords: Remote sensing, synthetic aperture radar, synergy, coastal processes 1. Introduction Numerous oceanic and atmospheric processes are known to modulate the sea surface roughness and thus become visible in synthetic aperture radar (SAR) data. This effect has been studied for decades and is widely documented in the literature for atmospheric convection rolls (e.g. Fu and Holt, 1982; Alpers and Brtimmer, 1994), atmospheric gravity waves (e.g. Thomson et al., 1992; Alpers and Stilke, 1996), atmospheric convection cells (e.g. Mitnik, 1992; Ufermann and Romeiser, 1999b), oceanic fronts and eddies (e.g. Johannessen et al., 1996; Askari et al., 1997; Ufermann and Romeiser, 1999a) and intemal waves (e.g. Apel and Gonzalez, 1983; da Silva, 1998). SAR is a well suited instrument for coastal monitoring since 1. it is independent of cloud coverage and illumination 2. it provides data at resolutions on the order of tens of metres. Modern sensors such as the Advanced Synthetic Aperture Radar (ASAR) aboard the satellite ENVISAT, launched in March 2002, have the potential to operate at swath widths of up to 400km. Nonetheless, the interpretation of SAR data still remains a complex task compared to data from temperature or colour sensors which are often more easily accessible. The following section gives a brief overview of the MARSAIS project aimed at the effective exploitation of SAR images, both alone and in combination with data from other sensors. Another paper focusing on MARSAIS is presented by Johannessen et al.


Susanne Ufermann*, lan S. Robinson, and Johnny A. Johannessen

175

(2003). Section 3 then uses an example to illustrate the concept of synergy between SAR and other remote sensing data.

2. The Marine SAR Analysis and Interpretation System~MARSAIS The project "Marine SAR Analysis and Interpretation System" (MARSAIS) is aimed at enhancing the exploitation of SAR images by making the data more widely accessible to non-expert end-users such as harbour authorities, coastal management and protection organisations and offshore oil and gas industries. By incorporating state-of-the-art algorithms for the retrieval of near-surface wind field, sea state and surface currents, along with procedures for the detection and monitoring of surface slicks, into a single prototype system for the interpretation of SAR images we are providing a generic tool for the non-expert user. In addition to the information that can be extracted from SAR images alone, we also facilitate the benefit of synoptic studies with data from other satellite sensors, mainly operating in the thermal infrared or the optical frequency domain such as the Advanced Very High Resolution Radiometer (AVHRR) or the Seaviewing Wide Field-of-view Sensor (SeaWiFS).

Figure 1 Temporal and spatial characteristics of several thermal infrared, optical and SAR sensors The combination of data from a SAR with data from these sensors is particularly promising for a number of reasons: 1. The temporal and spatial resolution of both types of sensor complement each other; SAR offers high spatial resolution at a low sampling rate; revisit times range between 3-5 days for wide-swath SARs such as RADARSAT and ASAR up to 35 days for ERS-1/2 SAR. AVHRR and SeaWiFS provide daily global coverage at a lower spatial resolution of 1.1km. The complementary sampling characteristics of the different types of sensors are illustrated in Figure 1. 2. The physical properties which are monitored by the sensors differ, but can show some relationship depending on physical processes in the area. For example, a

176

The role of synergy in developing a Marine SAR Analysis and Interpretation System

temperature front imaged by AVHRR can also become visible in a SAR image due to the wave-current interaction modulating the sea surface roughness at the front. 3. SAR images alone can be difficult to interpret unambiguously in which case SST and/or colour data can help to clarify the origin of the radar signatures. In the context of the MARSAIS project, these complementary properties of the different types of data will be used to illustrate the potential of synergy. Didactic information on the different types of data and their availability along with case studies of several oceanic phenomena will be included in the final toolkit. The following example demonstrates how data from a S AR, a thermal sensor and a buoy can be used to extract additional information from satellite data.

An example: Synergy in the North Aegean Sea The Aegean Sea was chosen as a region of interest for the MARSAIS project because of the availability of in situ data from a buoy network (Nittis et al., 2002) and generally favourable weather conditions. The latter is an important criterion for the use of data from thermal infrared and optical sensors since, unlike SAR, both types of data can only monitor the sea surface under cloud-free conditions. Data from the buoy network include current speed, water temperature and conductivity at 3m depth, and wind speed and direction, atmospheric pressure and air temperature at 3 m above the air-sea interface. Both oceanic and atmospheric parameters are measured every 3 hours and averaged over a 10 minute interval to reduce high-frequency noise (Nittis et al., 2002). Figure 2a shows a ERS-2 SAR image acquired in the northern Aegean Sea on May 31, 2000, at 14:36 UTC. The dark areas in the image correspond to regions where the smallscale sea surface roughness detected by a S AR sensor is not sufficiently pronounced to produce a detectable signal. Some SAR signatures of natural surface films can be observed towards the bottom right part of the image. Overall, the SAR image does not exhibit very strong signatures and would be hard to interpret further by itself. Additional data available for synergy studies of the SAR image in Figure 2a include an AVHRR derived sea surface temperature (SST) image, shown in Figure 2b which was acquired on the same day with a six hour time delay compared to the SAR image (20:36 UTC). Furthermore, buoy data are available from a buoy located approximately 20km off shore in a north-westerly direction from the island seen in the bottom right comer of the S AR image. The AVHRR derived SST image shown in Figure 2b shows a pronounced thermal front extending across the lower part of the region, separating the colder (and usually less saline) Black Sea waters from the warmer waters of the Aegean Sea. The location of the signatures of natural surface films observed in the SAR image corresponds to the position of this front. This indicates that the accumulation of surface film material is caused by a convergence in the surface currents of the two water masses. Combining SAR data and information on the wind direction at the buoy, the wind speed across the region shown in the SAR image can be derived with the help of the scatterometer model CMOD4 (Stoffelen and Anderson, 1993). Such a map of derived wind speeds is shown in Figure 2c. The production of this map is based on the assumption that the wind direction is uniform across the region which might not necessarily be the case.


177

For the purpose of quantitative investigations in regions where the wind direction is likely to vary over the studied region, a more complex model could be used that allows the retrieval of wind speed and direction from a SAR image (Horstmann et al., 2000; Monaldo et al., 2001).

Figure 2 Satellite images and derived products for a 100x 100 km2 region in the North Aegean Sea on May 31, 2000. (a) ERS-2 SAR image acquired at 20:36 UTC (b) AVHRR derived SST image acquired at 14:36 UTC (c) map of wind speed calculated from (a) using the model CMOD4 (d) map of heat flux calculated using (b) and (c). The combined data from all sources of information can now be used to calculate another physical quantity which usually can be measured only locally in situ, the sensible heat flux Qs between ocean and atmosphere:

Q,. = PaCpCHU( Y,. - Ta)

(1)

where Pa and cp are the density and specific heat of air respectively, c H is the Stanton number (Smith, 1980), u is the wind speed derived from the SAR image, and T s and T a are SST from AVHRR and air temperature from buoy measurements. Under the assumption that the air temperature is constant across the region a map of sensible heat flux between ocean and atmosphere can be derived as shown in Figure 2d.

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The role of synergy in developing a Marine SAR Analysis and Interpretation System

The assumptions on which this example is based (uniform wind direction and air temperature) mean that errors in the quantitative retrieval of wind speed and sensitive heat flux are likely to increase with distance from the location of the buoy. These errors can be minimised by employing a more sophisticated wind retrieval model and air temperature values from an atmospheric circulation model. However, the example demonstrates the concept of deriving new parameters with the help of synergy between data from different remote sensors and in situ measurements, extending observational capacity beyond those properties that are directly measured by the satellite sensors. Heat flux is an important parameter, it plays a vital role in air-sea interaction processes and is a critical parameter for coupled ocean-atmosphere models.

3. Summary and Conclusions The MARSAIS project is aimed at enhancing the exploitation of existing SAR data by a wide group of non-expert end-users. For this purpose, state-of-the-art algorithms for the analysis and interpretation of SAR data are combined with software tools, didactic information and case studies on synergy between data from SAR and other remote sensors. Thermal infrared and optical sensors are particularly well suited for such synergy studies since their data possesses complementary spatial and temporal characteristics such as resolution, repeat cycle and coverage to those of SAR images. With the help of an example from the northern Aegean Sea we have demonstrated some of the potential benefits of synergy. It was shown that SAR signatures could be interpreted in context with observed temperature distributions from an AVHRR image. The wind speed was derived from a combination of the SAR image and buoy data. Finally, sensible heat flux between the ocean and atmosphere was derived from buoy data jointly with the AVHRR and SAR images. So far, studies of such parameters that are not measured by satellites directly have been dependent on and limited by in situ point measurements. The improved future availability of synergy data from satellites such as ENVISAT will give rise to new techniques and insights obtained via the combination of different satellite and in situ types of data.

Acknowledgements SeaWiFS and AVHRR images were received by the Dundee Satellite Receiving Station and processed by the Remote Sensing Data Analysis Service (RSDAS) at Plymouth Marine Laboratory. Data courtesy of the NASA SeaWiFS project and Orbital Science Corporation. This work was supported by the European Commission, DG XII, as a part of the 5th Framework Programme, contract EVG1-CT-2000-00029 (MARSAIS).

References Alpers, W., and B. Brtimmer, 1994, Atmospheric boundary layer rolls observed by the synthetic aperture radar aboard the ERS-1 satellite, J. Geophys. Res., 99, 1261312621. Alpers, W., and G. Stilke, 1996, Observation of a nonlinear wave disturbance in the marine atmosphere by the synthetic aperture radar aboard the ERS 1 satellite, J. Geophys. Res., 101, 6513-6525.


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Apel, J.R., and F.L. Gonzalez, 1983, Nonlinear features of internal waves off Baja California as observed from Seasat imaging radar, J. Geophys. Res., 88, 4459-4466. Da Silva, J.C.B., S.A. Ermakov, I.S. Robinson, D.R.G. Jeans, and S.V. Kijashko, 1998, Role of surface films in ERS SAR signatures of internal waves on the shelf, 1. Shortperiod internal waves, J. Geophys. Res., 103, 8009-8031. Fu, L.L., and B. Holt, 1982, Seasat views oceans and sea ice with synthetic aperture radar, JPL Publ., 81-120, 200pp. Horstmann, J., W. Koch, S. Lehner, and R. Tomboe, 2000, Wind retrieval over the ocean using synthetic aperture radar with C-band HH polarization, IEEE Geosci. Remote Sens., 38, 2122-2131. Johannessen, J.A., R. Garello, B. Chapron, R. Romeiser, P. Pavlakis, I. Robinson, N. Connolly, K. Nittis, T. Hamre, S. Ufermann, W. Alpers, H. Espedal, B. Furevik, V. Cummins and D. Tarchi, 2001, Marine SAR analysis and interpretation systemm MARSAIS, Annales des T616communications, no. 11/12, 655-660. Johannessen J., T. Hamre, R. Garello, R. Romeiser, S. Kern, B. Chapron, I. Robinson, S. Ufermann, V. Cummins, N. Connolly, K. Nittis, L. Perivoliotis, and D. Tarchi, 2003, Marine SAR Analyses and Interpretation SystemmMARSAIS, Proceedings of 3rd EuroGOOS Conference (this publication), page 135. Johannessen, J.A., R.A. Shuchman, G. Digranes, D.R. Lyzenga, C. Wackerman, O.M. Johannessen, P.W. Vachon, 1996, Coastal ocean fronts and eddies imaged with ERS1 synthetic aperture radar, J. Geophys. Res., 101,6651-6667. Thomson, R.E., P.W. Vachon, and G.A. Borstad, 1992, Airborne synthetic aperture radar imagery of atmospheric gravity waves, J.Geophys. Res., 97, 14249-14257. Monaldo, F.M., D.R. Thompson, R.C. Beal, W.G. Pichel and P. Clemente-Colon, 2001, Comparison of SAR-derived wind speed with model predictions and ocean buoy measurements, IEEE Trans. Geosci. Remote Sens., 39, 2587-2600. Nittis, K., V. Zervakis, E. Papageorgiou, and L. Perivoliotis, 2002, Atmospheric and oceanic observations from the Poseidon buoy network: initial results, The Global Atmosphere and Ocean System, vol. 8, no. 2-3, pp.87-99. Smith, S., 1980, Wind stress and heat flux over the ocean in gale force winds, J. Phys. Oceanogr., 10, 709-726. Stoffelen, E., and D.L.T. Anderson, 1993, ERS-1 scatterometer data characteristics and wind retrieval skill, in Proc. First ERS- 1 Symposium, vol. 1, 41-48, European Space Agency publication SP-359, Paris, France. Ufermann, S., I.S. Robinson and J.C.B. da Silva, 2001, Synergy between synthetic aperture radar and other sensors for the remote sensing of the ocean, Annales des T616communications, no. 11/12, 672-681. Ufermann, S., and R. Romeiser, 1999, A new interpretation of multifrequency/multipolarization radar signatures of the Gulf Stream front, J. Geophys. Res., 104, 2569725705. Ufermann, S., and R. Romeiser, 1999, Numerical study on signatures of atmospheric convective cells in radar images of the ocean, J. Geophys. Res., 104, 25707-25719.

Routine scatterometer winds for the Mediterranean Ad Stoffelen

KNMI, The Netherlands

Abstract KNMI participates in the EUMETSAT Satellite Application Facilities (SAF) and in this context routinely produces freely-available scatterometer wind products over the globe; see http://www.knmi.nl/scatterometer/for further details. Example products are shown that focus on the Mediterranean area, but also on more general properties of the SeaWinds on QuikScat products produced at KNMI. Aspects such as near real-time delivery, spatial detail, and wind coverage twice daily are elaborated. Besides routine production, the SAFs foresee in state-of-the-art research and development activities, free software products, product monitoring, and user services. Opportunities like EuroGOOS 2002 are ideal to convey our services to, and interact with, a wide potential user group.

Keywords: Wind product, SeaWinds, ASCAT, near-real time, scatterometer, quality control, monitoring 1. Introduction ERS scatterometer winds have proven to be very useful for the analysing and forecasting of dynamic weather (Isaksen and Stoffelen, 2000). Increased coverage, such as from tandem ERS-1 and ERS-2 measurements, clearly improves the usefulness in extreme events (e.g. Stoffelen and Beukering, 1998). Improved coverage from the Ku-band SeaWinds scatterometers has thus great potential (Atlas and Hoffman, 2000) for meteorological or oceanographic use. After the development of improved data characterisation and assimilation procedures, operational use of SeaWinds at KNMI (HiRLAM, 2002), ECMWF (ECMWF, 2002), and UK Met Office for Numerical Weather Prediction (NWP) is a fact, while shift meteorologists are using the data for nowcasting. Moreover, wind conditions drive the ocean circulation that in turn plays a major role in the climate system and in ocean life (e.g. fisheries). Both scatterometer research and development, and routine processing and monitoring are funded by EUMETSAT through the Satellite Application Facilities (SAFs; EUMETSAT, 2002). More specifically, the Royal Netherlands Meteorological Institute (KNMI) participates in the Ocean and Sea Ice (OSI) SAF, the Climate Monitoring (CM) S AF, and the NWP SAF for these purposes. In the context of these SAFs KNMI provides software and data with 9 Tailor-made SeaWinds quality control (QC) in order to avoid unrepresentative wind data (e.g. rain contaminated or sea state, see Portabella and Stoffelen, 2002) 9 Genetic scatterometer backscatter data inversion Procedure to average backscatter measurements in a resolution cell of varying size, in order to provide spatially representative and accurate winds for NWP models Email: [email protected]

Ad Stoffelen

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9 Generic scatterometer cost function to cope with all kinds of scatterometer data 9 Routine processing and monitoring of wind and, in the near future, surface stress 9 Web-based product presentation, and distribution by FTP 9 Web-based monitoring reports in the near future. SAF activity is currently mainly focused on SeaWinds, although much of the algorithms are generically applicable for the ERS scatterometer and A S C A T on M E T O P , to be launched in 2005. K N M I is seeking user participation in a Visiting Scientist programme or as beta user, aiding in the development of our software or data products.

Figure 1 Sample archive SeaWinds product of JPL at 25 km resolution. A 1400-km wide central swath can be seen that is viewed by the inner and the outer beam of SeaWinds. Two outer swath strips of 200 km are visible that are viewed only by the outer beam. In the middle (nadir) region excessive noise is visible, which is due to the poorer azimuth sampling in this area. In a horizontal area at around I0N grey areas denote rain contamination as flagged by JPL (SeaWinds, 2002).

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Routine scatterometer winds for the Mediterranean

2. SeaWinds and DIRTH Figure 1 displays part of the SeaWinds swath as processed by JPL at 25km. The figure clearly denotes different parts of the swath, and developments are ongoing to use all these parts of the swath. The so-called sweet swath excludes the outer swath and the nadir region, and is the most uniquely determined and accurate part of the swath. The nadir (middle) region has the same amount of nadir views, but the views are closer together and as such provide less independent information on wind direction, leading to less unique and accurate winds, as may amongst others be noted from the figure. To avoid this noise, NOAA distributes the so-called DIRTH SeaWinds product (SeaWinds, 2002). In short, DIRTH does not retrieve four wind solutions, but four intervals with wind solutions. Subsequently, a spatial median filter determines a smooth streamline through these intervals. Scales smaller than 100km are generally lost in the process. REMSS (2002) distributes off-line SeaWinds data with a spatially smoothed wind vector (speed and direction). In the outer swath region only two azimuth views are available in a single radar polarisation, which means that no residual information is available for the quality control (QC) of the resulting winds. This is problematic, since the measurements are affected seriously by rain.

3. SeaWinds at KNMI To achieve noise reduction, at KNMI measurements are accumulated and a 100-km product has been developed. Outer swath data are ignored momentarily due to poor QC. Following similar methods in use for the QC of ERS scatterometer winds, KNMI developed a QC method for SeaWinds that rejects rain cases and cases with other geophysical interpretation problems (Portabella and Stoffelen, 2002). We apply this procedure on the 25-km SeaWinds data prior to the 100-km resolution wind inversion. Inverted scatterometer winds are ambiguous and Stoffelen (2000) presents a generic method for assimilating ambiguous scatterometer winds, using wind retrieval accuracy and solution probability information, exploited also in the KNMI ambiguity removal.

4. Product quality Figure 2 shows that the probability of a SeaWinds solution is much more meaningful, i.e. diverse, for a 100-km resolution product than for a 25-km product. As such the KNMI 2D-VAR ambiguity removal will be more successful, since it explicitly exploits this probability information, while in NOAA DIRTH no probability information is propagated to the spatial ambiguity removal filter. As such, the DIRTH wind direction field is rather smooth, while the wind speeds are much more variable then in the KNMI 100-km product.

Ad Stoffelen

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Figure 2 Probability density function of differences in probability of the highest and lowest probability SeaWinds solution for a 25-km wind-vector cell (WVC) (fight) and a 100-km WVC (left). The solid line denotes sweet swath and the dotted line the nadir swath. The probability discrimination or uniqueness of the 100-km wind solutions is much better than that of the 25-km winds; also, sweet swath is more unique than the nadir region (see Portabella and Stoffelen, 2002, for further evidence). Table 1 shows a comparison of differences between European Centre for Medium-range Weather Forecast (ECMWF, 2002) winds with both KNMI and DIRTH. For DIRTH, the wind in the KNMI Wind Vector Cell (WVC) closest to its centre is taken. The comparison is for the month of October 2002 and contains almost 300000 triple collocations. Indeed the wind speed and components of DIRTH compare rather poorly to the smooth ECMWF fields, while wind direction compares better. Since the ECMWF field is rather smooth it can not be concluded from the comparison that the DIRTH wind speeds are noisy, but it can be concluded that smaller scales are interpreted mainly as wind speed, while the wind direction only varies on the large (100km) scale. This is physically wrong, since the wind speed and direction sensitivities are scale invariant. Table 1 Comparison of the NOAA DIRTH SeaWinds product with the KNMI product as verified against independent ECMWF winds. Wind direction SD of differences are for DIRTH, KNMI, and ECMWF wind speeds above 4ms -1 only.

SD

KNMi

DIRTH

Speed

1.31

1.64

Direction

13.58

14.58

U-comp.

1.60

1.96

V-comp.

1.58

1.80

5. Spatial resolution and the Mediterranean Figure 3 shows an example of a Mediterranean wind field as depicted by the KNMI High-Resolution LAM (HiRLAM), the 100-km KNMI SeaWinds product, and the NOAA DIRTH product. It illustrates the smooth wind direction field of the latter and some wind direction ambiguity removal problem in the outer swath at the left. On the other hand the NOAA product shows more coastal winds due to its processing at 25 km. For many applications in the Mediterranean both coastal winds and high resolution winds would be particularly useful. However, many researchers documented the n o n -

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Routine scatterometer winds for the Mediterranean

uniform noise properties of SeaWinds (e.g. COAPS, 2002), as illustrated in Figure obtain more uniform noise properties spatial filtering of SeaWinds data is required. the comparison above it may be clear that coastal winds may be obtained by using km grid. Rather than filtering wind direction intervals, such as in DIRTH, a meteorologically balanced way of spatial filtering should be sought.

1. To From a 25more

Figure 3 Top: KNMI 100-km SeaWinds product in the Mediterranean (white) and corresponding NWP model winds (grey) on top of a MeteoSat IR cloud image showing a low. SeaWinds winds range from WNW to N in the upper left. Bottom: Same as top, but NOAA DIRTH SeaWinds product, which does shows a smoother wind direction field than KNMI. Note that for example the winds are from NW to N in the upper left here. The N winds at the far fight are in the outer swath and inconsistent with in situ surface data (not shown). On the other hand, NOAA DIRTH shows more coastal winds than KNMI due to the 25 km swath grid (Discard a few isolated black flags that are rejected, but shown)

6. Outlook K N M I developed a spatial filtering method that fully exploits the information obtained by scatterometer wind retrieval (Portabella, 2002) and which is meteorologically balanced, called MSS. This is expected to work better than a statistical (median) filter such as DIRTH. Therefore, KNMI is working on a 25-km version of MSS that will

Ad Stoffelen

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become available in early 2004 for the user community. It will be verified spatially by correlation analyses and against in situ observations. KNMI welcomes potential users and testers of this product, whom should contact the author. Scatterometers provide accurate and spatially consistent near-surface wind information. Hardware permitting, there will be a continuous series of scatterometers with at times ideal coverage of the ocean surface wind for the coming two decades. EUMETSAT provides user services in collaboration with KNMI, where these are now being set up and freely available at http://www.knmi.nl/scatterometer for the SeaWinds-I and - I I scatterometers. Near-real time FTP products or software can be obtained by sending a request to the author. Moreover, a visiting scientist scheme is funded in order to support the development programme and the use of the KNMI services. Again, the author will provide more information on request.

References Atlas, R., and R.N. Hoffman, 2000, The Use of Satellite Surface Wind Data to Improve Weather Analysis and Forecasting, in Satellites, Oceanography, and Society, edited by D. Halpern, Elsevier Oceanography Series 63, Elsevier Science, Amsterdam, the Netherlands, ISBN 0 - 4 4 4 - 5 0 5 0 1 - 6 . COAPS, 2002, COAPS home page with SeaWinds animations / characterisation, http://coaps.fsu.edu/~bourassa ECMWF, 2002, European Centre for Medium-range Weather Forecast, http://www.ecmwf.int EUMETSAT Satellite Application Facilities (SAF), 2002, http://www.eumetsat.de/saf HiRLAM, 2002, High-Resolution Limited Area Model, http://hirlam.knmi.nl Isaksen, Lars, and Ad Stoffelen, 2000, "ERS-Scatterometer Wind Data Impact on ECMWF's Tropical Cyclone Forecasts", IEEE-Transactions on Geoscience and Remote Sensing (special issue on Emerging Scatterometer Applications) 38 (4), pp. 1885-1892. KNMI, 2002, http://www.knmi.nl/scatterometer Portabella, Marcos, and Ad Stoffelen, 2002, Quality Control and Wind Retrieval by SeaWinds, final report on a EUMETSAT fellowship, available from KNMI. SeaWinds, 2002, Instrument: http://winds.jpl.nasa.gov/missions/quikscat/quikindex.html Archive data: http://podaac.jpl.nasa.gov/quikscat Near-real time data: http://manati.wwb.noaa.gov/quikscat Stoffelen, Ad, 2000, A generic approach for assimilating scatterometer winds, proc. ECMWF seminar 4 - 8 Sept. 2000, available from KNMI. Stoffelen, Ad, Aart Voorrips, and John de Vries, 2000, Towards the Real-Time Use of QuikScat Winds, BCRS project report, available from KNMI. REMSS, 2002, Remote Sensing Systems homepage with off-line SeaWinds products, http://www.remss.com

Sea Surface Salinity mapping with SMOS space mission Jordi Font *l, Gary Lagerloef 2, Yann Kerr 3, Niels Skou 4, and Michael Berger 5 l l n s t i t u t de Cikncies del Mar, CMIMA-CSIC, Barcelona, Spain 2Earth and Space Research, Seattle, USA 3CESBIO, Centre National d'Etudes Spatiales, Toulouse, France 4Technical University of Denmark, Lyngby, Denmark 5Earth Sciences Division, ESA-ESTEC, Noordwijk, The Netherlands

Abstract The European Space Agency SMOS (Soil Moisture and Ocean Salinity) mission is scheduled for launch in early 2007. SMOS will exploit an innovative instrument designed as a 2D interferometer acquiring globally brightness temperatures at L-band (1.4GHz) to retrieve soil moisture fields over the land surfaces and ocean salinity fields over the oceans. Considering the exploratory nature of the salinity measurement with SMOS, the GODAE open ocean requirement (0.1, practical salinity scale, over 200 km every 10 days) represents a technically challenging objective that has been set as a goal for the mission.

Keywords:

Salinity, remote sensing, microwave radiometry, ESA

1. Introduction Even though both Soil Moisture (SM) and Sea Surface Salinity (SSS) are used in predictive atmospheric, oceanographic, and hydrologic models, no capability exists to date to measure these key variables on a global basis with adequate space/time coverage performances. The reason this information is currently not available mainly stems from the fact that, while in situ measurements are very far from global, no dedicated, long term, space mission has been approved so far. The SMOS mission aims to first contribute to fill this gap through the implementation of a satellite system that has the potential to globally, frequently, and routinely provide this information. These surface data will complement the vertical profiles, at 300km resolution, provided by the Array for Real-time Geostrophic Oceanography (ARGO) floats, now under implementation. The most direct way to date to retrieve SM and SSS is by the use of L-band (21cm, 1.4GHz) microwave radiometer systems, which observe the radiometric brightness temperature (Tb) that provides access to surface emissivity, explicitly depending on SM and SSS. Recent development of the so-called interferometric design, inspired from the very large baseline antenna concept (radio astronomy), is an option to make such a venture possible. The idea consists of deploying small receivers in space (located on a deployable structure), then reconstructing a T b field with a resolution corresponding to the spacing between the outmost receivers. The two-dimensional MIRAS interferometer developed by ESA (Martfn-Neira and Goutoule, 1997) allows T b to be measured at large * Corresponding author, email: [email protected]

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incidence angles, for two polarisations. Moreover, the instrument instantaneously records a whole scene; as the satellite moves, a given point within the 2D field of view is observed from different view angles. A series of independent measurements is then obtained, which allows surface parameters to be retrieved with much improved accuracy.

2. Objectives for ocean salinity remote sensing Knowledge of the distribution of salt in the global ocean and its annual and inter-annual variability, are crucial in understanding the role of the ocean in the climate system. Ocean circulation is mainly driven by momentum and heat flux through the atmosphereocean interface, but salinity is also fundamental in determining ocean density, and hence thermohaline circulation. In some regions (e.g. the Arctic), salinity is the most important variable in this respect, and thus can control processes as the deep water formation, a key component in the ocean thermohaline circulation. Seasonal predictions systems including coupled ocean-atmosphere models use surface and subsurface temperature observations for the ocean initial conditions, but rely on a weak relaxation to climatology for salinity. Presently, the models assimilate temperature and/or altimeter derived sea level only. The absence of any specific treatment of salinity can lead to significant errors in the ocean model. For example, Troccoli et al. (2000) have shown that correcting temperature without updating salinity in a global ocean model could generate spurious convection and lead to first order error in the subsurface temperature and salinity fields. Other recent results (Maes et al., 2000, Ji et al., 2000) have shown that 5 - 8 c m dynamic height differences are associated with interannual salinity variations. Deriving salinity corrections based on temperature-salinity relation conservation properties can significantly reduce this problem. Methods have been developed to estimate salinity profiles from temperature profiles, altimeter heights and SSS using empirical orthogonal function techniques. This relation is not generally preserved in the mixed layer because of the different effects of heat and surface water fluxes. Therefore, no salinity correction is provided near the surface, and thus surface observations are necessary to reduce the error in these estimated profiles. Assimilating data in the NCEP (U.S. National Centers for Environmental Prediction) model with an estimated salinity error of about 0.5 over the top 130-150 m will have an effect on sea level determination in the western equatorial Pacific of the order of 5 cm (Vossepoel and Behringer, 2000). If this is not accounted for, any attempts to initialise the climate prediction models with altimeter data will be in error and degrade predictability. The influence of these factors is especially relevant in the western equatorial Pacific where there is a strong ENSO-related near-surface salinity signal, and where zonal advection is of main importance for ENSO mechanisms (Picaut and Delcroix, 1995). Primary scientific objectives for SSS remote sensing were defined in 1998 by an international Salinity and Sea Ice Working Group (Lagerloef, 1998) as: 9 Improving seasonal to inter-annual IENSOI climate

predictions

Effective use of SSS data to initialise and improve the coupled climate forecast models, and to study and model the role of freshwater flux in the formation and maintenance of barrier layers and mixed layer heat budget in the tropics.

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Sea Surface Salinity mapping with SMOS space mission

9 Improving ocean rainfall estimates and global hydrologic budgets

The "ocean rain gauge" concept shows considerable promise in reducing uncertainties on the surface freshwater flux on climate time scales, given SSS observations, surface velocities and adequate mixed layer modelling. 9 Monitoring large-scale salinity events

This may include ice melt, major river runoff events, or monsoons. In particular, tracking inter-annual SSS variations in the Nordic Seas is vital to long time scale climate prediction and modelling. It is the objective of the SMOS mission to provide global coverage of Sea Surface Salinity fields, with a repetition rate and an accuracy appropriate for oceanographical, climatological and hydrological studies.

3. Sea Surface Salinity determination by SMOS The measurement of SSS is one of the challenges of SMOS, and of ocean remote sensing for the near future (see for example Font et al., 2003). Due to the low radiometric sensitivity (from 0.75K to 0.25K per unit of salinity, depending on ocean temperature), and low spatial resolution expected with a spaceborne microwave interferometric radiometer, mesoscale (or regional) studies of SSS data cannot be expected. However, several phenomena extremely relevant for large-scale and climatic studies can benefit from such an observation approach: barrier layer effects on tropical Pacific heat flux, halosteric adjustment of heat storage from sea level, North Atlantic thermohaline circulation, surface freshwater flux balance, etc. These require an obtainable salinity accuracy of 0.1-0.4 over 100- 300 km in 10-30 days. The Global Ocean Data Assimilation Experiment (GODAE) aims to demonstrate the feasibility and practicality of real-time global ocean modelling and assimilation systems, and has proposed an accuracy requirement for satellite salinity for global ocean circulation studies as 0.1 for averages over 10 day time intervals in 200x200 km boxes. Considering the exploratory nature of SMOS, the GODAE open ocean requirement represents a valid scientific goal for the mission but is nevertheless a serious technical challenge. The image reconstruction errors, currently not fully known, their correlation characteristics, and the calibration stability, represent uncertainties about the capability of SMOS in achieving these requirements, particularly in higher latitudes. This is being addressed with the continued development of the SMOS demonstrator and numerical simulator to analyse the SMOS ocean retrieval accuracy. From the SMOS Extended Phase A study it follows that SMOS should provide a radiometric accuracy of 1.2 K and sensitivity of 4.73 K (RMS per snapshot) at the centre of the scene. It will be sufficient to average data over 30 days or longer periods for many climate studies and further reduce random measurement noise. Ten-day resolution will be less accurate but may be retained for certain operational applications related to GODAE. Monthly averages over 100km boxes would give data comparable to the standard climatologies, but with the time dependence not available from current climatologies. Lower salinity accuracy, and thus higher spatial or temporal resolution (typically 0.5, 50 km, 3 days) could provide a means to monitor moving salinity fronts in various regions of the world.

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Sea surface roughness is the major geophysical error source in SSS determination, as it can modify the measured brightness temperature by several K depending on the incidence angle. Several models consider the effect of roughness via parametrisation schemes using wind speed, and the validation and improvement for such effects are a current field of research. During 2000 and 2001 ESA sponsored several field campaigns, both airborne and tower-based, to measure and analyse polarimetric L-band emission under varying incidence and azimuthal viewing angles for a wide range of sea state conditions. Another dedicated salinity mapping mission is also expected for launch in late 2008. AQUARIUS-SAC/D, developed by the US in co-operation with Argentina, will carry a real aperture L-band radiometer system plus radar backscatter measurements to correct the effects of surface roughness. It is expected to provide data with better radiometric accuracy but less spatial coverage than SMOS. A strong collaboration exists between SMOS and AQUARIUS-SAC/D for both science and calibration.

Acknowledgements The authors are grateful to the European Space Agency, the French Centre National d'l~tudes Spatiales (CNES), the Spanish Centro para el Desarrollo Tecnol6gico Industrial (CDTI, Ministry of Science and Technology), colleagues from the SMOS Science Advisory Group, and the members of the different ESA studies and campaign teams.

References Font, J., G. Lagerloef, D. Le Vine and A. Camps, 2003, Open issues for SMOS salinity retrieval, Proceedings of SMOS Campaigns Workshop, ESA SP-525, 7-13. Ji, M., R. Reynolds, and D. Behringer, 2000, Use of TOPEX/POSEIDON sea level data of ocean analyses and ENSO prediction: some early results, J. Climate, 13, 216-231. Lagerloef, G., 1998, Report of the First Workshop, Salinity Sea Ice Working Group, La Jolla, USA, 7 - 8 Feb. 1998, http://www.esr.org/lagerloef/ssiwg/ssiwgrep 1.v2. Maes, C., D. Behringer, R. Reynolds, and M. Ji, 2000, Retrospective analysis of the salinity variability in the western tropical Pacific Ocean using an indirect minimization approach, J. Atmos. Oceanic Technol., 17, 512-524. Martin-Neira, M. and J.M. Goutoule, 1997, A two-dimensional aperture-synthesis radiometer for soil moisture and ocean salinity observations, ESA Bulletin, 92, 95104. Picaut, J. and T. Delcroix, 1995, Equatorial wave sequence associated with the warm pool displacement during the 1986-1989 E1 Nifio and La Nifia, J. Geophys. Res., 100, 18393-t 18408. Troccoli, A., M. Balmaseda, J. Segschneider, J. Vialard, D. Anderson, K. Haines, T. Stockdale, F. Vitart and A. Fox, 2002, Salinity adjustments in the presence of temperature data assimilation, Mon. Wea. Rev., 130, 89-102. Vossepoel, F., and D. Behringer, 2000, Impact of sea level assimilation on salinity variability in the western equatorial Pacific, J. Phys. Oceanogr., 30, 1706-1721.

Sea level prediction at the Portuguese coast based on model and remote sensed data C. Guedes Soares*l, Hafedh Hajji 2 and P. Sebasti~o 1

1Unit of Marine Technology and Engineering, Technical University of Lisbon, Portugal 2MdtdoMer, France

Abstract Sea level has been determined in the Portuguese coastal area by a hydrodynamic model and also from altimetry measurements for two points near Sines in the South-western Coast of Portugal. The comparison between both predictions yielded a very high correlation and the root mean square of the differences was small.

Keywords: Circulation model, sea level measurements, altimetry, Topex-Poseidon 1. Introduction Sea level calculations based on model results were made within the scope of a project aiming at hindcasting 40 years of atmospheric data, sea level and waves in the Coastal Areas of Europe (Guedes Soares et al., 2002). Satellite data was also used in the project (Bentamy et al., 2002) but was only available for the past 10 years. It is thus more limited for climate studies but, in view of its availability, calibration and validation of model results have been pursued in this paper.

Figure 1 Bathymetry used in the model (depth values expressed in metres)


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1.1 The circulation model

The circulation model used was HAMSOM (Backhaus, 1985; ,/dvarez et al., 1997), a three-dimensional oceanic circulation model. HAMSOM is based on a set of seven partial differential equations. In the horizontal plane the complete equations of NavierStokes are used and in the vertical the hydrostatic equation is adopted. These equations are used together with the continuity equation, the equations of conservation energy and mass of salt and the UNESCO seawater equation of state. The model can take into account the tides, wind, atmospheric pressure, heat fluxes and baroclinic gradients. The unknowns are the three components of the current velocity, the pressure, the density of the water, the salinity and the temperature. The model has been applied to the North-eastern Atlantic. The computational domain is shown in Figure 1. The boundary conditions of the model are the tidal constants that introduce the astronomical forcing. It considers the tidal harmonics M2, $2, N2, K2, K1, O1 and Q1. The tidal constants were supplied from the global ocean tide model GOT00.2 (Ray, 1999). HAMSOM has also the input of atmospheric forcing over the entire domain receiving the input of pressure and velocity fields every six hours.

Figure 2 Example of instantaneous sea level on January 2000 calculated by HAMSOM for the Coast of Portugal 1.2 Topex-Poseidon sea level observations

The repeating period (9.9156 days) as well as the accuracy of Topex-Poseidon measurements (TP), allows accurate sea-level measurements. The accuracy of the TopexPoseidon altimetry is 5 cm in the case of instantaneous measurement. When considering an average the accuracy rises to 2 cm (Hajji and Olagnon, 1997; Le Provost et al., 1997). The principle of the water level measurements is as follows: each altimetry measurement provides, after environmental corrections (tropospheric, earth tide, etc.), the distance between the ocean and the satellite every second. Considering the knowledge of the satellite position at each measurement instant, the sea level height can be computed with respect to a fixed reference level. The difference between the level obtained and its longterm average can be decomposed into oceanic tide and storm-surge. These are the most important phenomena affecting the sea level, particularly in coastal areas, which are the

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Sea level prediction at the Portuguese coast based on model and remote sensed data

target of the present study. Of less importance in these areas are eddies, Rossby waves or current variability, which also influence the sea level. To assess the mean water-levels and the tides from altimetry data, Le Provost et al. (1997) developed a method based on harmonic analysis. This method was applied to TP measurements, giving most of the significant tidal components as well as the storm-surge residual data over the North Sea (Hajji and Olagnon, 1997). Figure 3 shows three Topex-Poseidon tracks: two of them passing near Sines (137 and 198) and one crossing the Iberian shelf (61).

Figure 3 Topex-Poseidon tracks near the Iberian Peninsula.

2. Comparison between HAMSOM and Topex-Poseidon Figure 4 and Figure 5 present comparisons of instantaneous total sea-level (tide+ residual) subtracted by the respected long term average, between model simulations (dark grey) and satellite measurements (lighter grey) for two points of Figure 3 (Sines 1 and Sines 2) located near Sines, for 2000 and 2001. The correlation is very high for the two simulations (r=0.99 and RMS= 13cm for Sines 1 and r=0.98 and RMS= 13 cm for Sines 2).

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Figure 4 Comparison between sea-level simulated by the model HAMSOM and extracted from Topex-Poseidon for point Sines 1-Track 198.

Figure 5 Comparison between sea-level simulated by the model HAMSOM and extracted from Topex-Poseidon point Sines2-Track 137

3. Conclusions The comparison between the results of the model and altimetry measurements showed good agreement for the two sites studied. The information obtained in this way is useful for validating the circulation model in terms of the sea level calculation.

Acknowledgements The authors would like to thank Enrique ,/dvarez, from Puertos del Estado, Spain, for his valuable contribution and help on the set-up of the HAMSOM model. This work has been performed within the project HIPOCAS, which has been partially funded by the European Commission under the program "Energy, Environment and Sustainable Development" (Contract No. E V K 2 - C T - 1999-00038).

References Alvarez, E., I. Rodrfguez, and B. P6rez, 1997, A description of the tides in the Eastern North Atlantic. Progress in Oceanography, 40, 217-244. Backhaus, J.O., 1985, A Three-Dimensional model for simulation of shelf sea dynamics. Dt. Hydrogr. Z., 34, H.4, 164-187. Bentamy, A., H. Hajji, and C. Guedes Soares, OMAE 2002, Remotely Sensed Wind, Wave and Sea Level for European Sea Climatology, Proc. 21 st Int. Conf. on Offshore Mechanics and Arctic Engineering, Paper OMAE2002-28625. Guedes Soares, C., R. Weisse, E. Alvarez, and J.C. Carretero, 2002, A 40 Years Hindcast in European Waters, Proc. 21st International Conference on Offshore Mechanics and Arctic Engineering, Paper OMAE2002-28604. Hajji, H. and M. Olagon, 1997, Contribution of satellite data to storm-surge climatology, Proc. 7th Intern. Offshore and Polar Conf. Le Provost, C., F. Rabilloud, and M. Olagnon, 1997, Assessment of mean water levels and tides from satellite data. Proc. 7th Intern. Offshore and Polar Conf. Ray, R. D. (1999). A Global Ocean Tide Model From TOPEX/POSEIDON Altimetry: GOT99.2. NASA Technical Memorandum 209478.

Numerical Modelling and Data Assimilation


The Forecasting Ocean Assimilation Model (FOAM)

system

Mike Bell*, Rosa Barciela, Adrian Hines, Matt Martin, Michael McCulloch, and David Storkey Met Office, UK

Abstract The FOAM system produces daily 5-day forecasts of three-dimensional ocean temperatures, salinities, currents and sea-ice properties on a routine basis. It assimilates temperature profile, satellite surface height and satellite and in situ surface temperature data and is driven by 6-hourly surface fluxes from the Met Office numerical weather prediction (NWP) system. High resolution model configurations are nested inside a global configuration. Statistics on the difference between the model forecasts and observations are routinely produced and re-analyses can be generated from 1997 onwards.

Keywords: Operational oceanography, ocean forecasting, ocean data assimilation, ocean modelling. 1. Introduction The aim of the FOAM system is to provide real-time, operational analyses and forecasts of the three-dimensional structure of the deep ocean and of sea-ice. The ocean fields that are forecast are the temperature, salinity, currents and mixed layer depth. The velocity, concentration and depth of sea-ice are also forecast. The main long-term objectives are to forecast the surface mixed layer to 3 - 5 days ahead and the mesoscale structure to 10-20 days ahead. A global version of the FOAM system has run each day in the Met Office's operational suite since it was introduced in 1997 on a grid with 1~ spacing in the horizontal and 20 levels in the vertical. It is based on the z co-ordinate primitive equation ocean and sea-ice model developed by the Hadley Centre for coupled ocean-atmosphere climate change experiments. The model is forced by 6-hourly surface fluxes from the Met Office's NWP system and assimilates thermal profile and surface temperature data. The system is described in detail in Bell et al. (2000b). This paper outlines the developments that have been made to the FOAM system since that coarse resolution global model was introduced and indicates the directions in which further development is planned.

2. Developments to the Suite and Model Configurations In order to perform integrations for geographically limited areas using models with horizontal grid spacings of lOkm or finer, the facility has been developed to nest higher resolution, limited area, models inside larger area, lower resolution, models. The nesting * Corresponding author, email: [email protected], 9 British Crown Copyright

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The Forecasting Ocean Assimilation Model (FOAM) system

is one-way and based on the flow relaxation scheme (FRS, Davies, 1983). All prognostic variables (including the barotropic streamfunction) of the inner model are relaxed towards those from the coarser grid model over regions (4 to 8 model grid points wide) adjoining the boundary of the inner model. The bathymetries in the two models in this nesting region are prescribed to be as similar as possible. Typically the grid spacing of the inner model is about three times smaller than that of the coarser resolution model. The limited area models use a "rotated" latitude-longitude grid in which the pole need not coincide with the North-South geographical pole and is chosen to achieve a nearly uniform grid within the model domain. Accessible archives of 6-hourly surface fluxes from the Met Office' s NWP system have now been established dating back to 1997. Also a new suite control system (SCS) has become available. This is used for the operational forecasts, for pre-operational forecasts (which can also be run on a daily cycle), and for long period (e.g. 3 year-long) hindcasts using 6-hourly fluxes. A system has also been introduced to calculate the statistics of differences between observations and forecasts. A series of integrations has been performed using 6-hourly fluxes for the 3 year period 1997-1999. Each of the integrations was performed using three nested models: an "Atlantic" model coveting the Atlantic and Arctic with a grid spacing of 1/3 ~ nested in the standard global model; and a "Gulf of Mexico" model coveting the Gulf of Mexico and Caribbean with a grid spacing of 12 km nested inside the Atlantic model. Some of the integrations assimilated observations, others a subset of observations, and some no observations. The Atlantic model was implemented in the operational suite on 23 January 2001. Figure 1 illustrates a model covering most of the North Atlantic and the Mediterranean sea, with a grid spacing of 12km, that has also been nested within the Atlantic model. This model has been run on a daily pre-operational basis since May 2002 and the output made available on http://www.nerc-essc.ac.uk/las as a contribution to the GODAE project. Additional model configurations for the Indian Ocean and Mediterranean Sea and a global model with a grid spacing of 1/2 ~ and 40 levels have also been established.

3. Improvements to the observations supplied to the system Since 1997 the in situ measurement system has of course been greatly strengthened by the Argo project. Although the FOAM group had experimented with the assimilation of altimeter data for several years (Forbes, 1996; Hines, 2001), routine delivery to the Met Office of altimeter data (with suitable corrections) for operational use was only established in August 2001. Data are now supplied by CLS (Centre Localisation Spatiale) twice a week. They have been assimilated in the operational Atlantic model since 25 September 2001. Fields of sea-ice concentration data have been assimilated into the FOAM operational models since 6 July 1999 (Bell et al., 2000a). These data, provided by the Canadian Meteorological Center, are based on Special Sensor Microwave Imager data interpreted using the York/AES algorithm. On the same date the Met Office NWP system started to calculate surface fluxes using sea-ice fractions based on analyses provided by NCEP.

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Figure 1 Surface currents (cms -1) in the North Atlantic model on 11 April 2003. The AVHRR satellite surface temperature data available from NOAA/NESDIS via the Global Telecommunications System (GTS) has until this year had a rather coarse resolution (2.5~ Global data at 1~ and 0.5 ~ resolution are now being distributed on a daily and weekly basis respectively. We intend to assimilate these data and the Merged Atlantic Product (MAP) produced by the EuMetSat Ocean and Sea-Ice Satellite Application Facility (OSI-SAF) at Lannion.

4. Improvements to the ocean and sea-ice models A system has been developed for interpolating bathymetries and initial temperature and salinity fields for new model configurations. The bathymetry is slightly smoothed and important channels inspected and re-excavated if necessary. The tracer fields are taken from the Levitus et al. (1998) climatology. Grid points where climate values are missing are filled in by limited horizontal and vertical "extrapolation". The models are typically spun-up to real-time over a period of 6 - 1 8 months, assimilating data over the last 6 months of the spin-up. A semi-implicit free surface formulation is available for use but our main integrations still use a barotropic streamfunction in a formulation which avoids the Killworth instability (Bell, 2000, appendix A). The Brown and Campana (1978) pressure averaging technique is used in some configurations to increase the model timestep. Several experiments similar to Chassignet and Garraffo (2001) have been made exploring the choice of

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viscosity (and thermal diffusivities) for models of widely differing horizontal resolution. A third order accurate upwind interpolation scheme is used for advection of tracers (Holland et al., 1998). A parametrisation of exchange flow through unresolved straits and river inflow based on monthly-mean climatological values from the Global Rivers Data Centre (GRDC) database has been tested and will be implemented shortly. The Hadley Centre Ocean Carbon Cycle (HadOCC) "ecosystem" model (Palmer and Totterdell, 2001) has been coupled to the global and Atlantic FOAM configurations and will be developed further through collaboration in the Centre for Air-Sea Interface fluXes (CASIX). Some tests of the elastic-viscous-plastic (EVP) sea-ice dynamics formulation of Hunke and Dukowicz (1997) have also been performed in the Atlantic FOAM configuration. Tests using 1D mixed layer models and Argo profile data suggest that a modified version of the Large et al. (1994) scheme will provide better mixed layer analyses than our present scheme. We intend also to trial the impact of various versions of the Gent-McWilliams scheme

5. Improvements to assimilation of observations A number of useful sensitivity and tuning experiments have explored the assimilation of altimeter data (Hines, 2001) and surface temperature data. Assimilation of both types of data can be improved by attention to the horizontal correlation scales used. It has also been found that the Cooper and Haines (1996) scheme can be improved by limiting the displacement of isopycnals near the sea surface. Spurious circulations near the equator arising from the assimilation of thermal data into models with systematic errors have been explored in some detail. Martin et al. (2002a) proposed and implemented a scheme to improve the assimilation in these circumstances. The FOAM assimilation scheme has been re-organised in order to achieve two objectives. Firstly the assimilation scheme has been reformulated to enable observational data to be fully utilised on the day they are received and thereafter given appropriate weight relative to newly arriving observations. Analyses are calculated once a day by a process of iteration towards the statistically "optimal" solution. Secondly the model's error covariance is specified as the sum of "mesoscale" and "synoptic" scale components estimated using statistics of observation minus model values from three-year hindcasts (Martin et al. 2002b). The synoptic scale component has a relatively large horizontal scale (~300km) and small vertical scale (~50m). The mesoscale component has a relatively small horizontal scale (~50km) and larger vertical scale (~200m). The variance of these components is calculated as a function of depth and geographical location. The variance of the synoptic scale component depends relatively little on geographical location whereas the variance of the mesoscale component varies markedly and is largest in the western boundary current regions. Other developments to the assimilation system are also being tested. B. Ingleby and M. Huddleston have implemented improvements to the quality control of profile data as part of the ENACT project. Code is also in place to assimilate salinity data (from Argo profiles) and to estimate the bias in satellite surface temperature data.

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The new methods are being tested by six-month integrations of the new and old systems for the 1/3 ~ Atlantic model using observation minus analysis statistics for objective assessment. At present the results for the new system are better than the old near the surface, particularly in the north-west Atlantic, but are slightly worse than the old system in mid-thermocline. Figure 2 illustrates the improvement in the north-west Atlantic.

Figure 2 Timeseries of RMS differences between analyses and surface temperature observations before they are assimilated for the original scheme (solid line) and the new scheme (dashed line) from a six-month integration

6. Plans We will collaborate in the intercomparison between the FOAM, Mercator, MFS and TOPAZ systems within the Mersea Strand-1 project. We will also implement a model for the Intra-Americas Seas (IAS) with a 6km grid and 40 levels and assess the forecast skill of this and lower resolution models and the impact on the model skill of the altimeter and Argo profile data.

References Bell, M.J., 2000, An assessment of the value of semi-implicit schemes, semi-Lagrangian schemes and various grids for ocean dynamics. Ocean Applications Technical Note No. 25. Available from Ocean Applications, Met Office, Bracknell, UK. Bell, M.J., T. Allen, J.O.S. Alves, and A. Hines, 2000a, The FOAM system and the use of satellite information. Proceedings of SAF Training Workshop, Ocean and Sea Ice. EUMETSAT. Hosted by Meteo-France, Perros-Guirec, France 30 November-2 December 1999. Bell, M.J., R.M. Forbes, and A. Hines, 2000b, Assessment of the FOAM global data assimilation system for real-time operational ocean forecasting. J. Mar. Sys., 25, 122. Brown, J.A., and K.A. Campana, 1978, An economical time-differencing system for numerical weather prediction. Mon. Weath. Rev., 106, 1125-1135. Chassignet, E.P. and Z.D. Garraffo, 2001, Viscosity parameterization and the Gulf Stream separation. Pp 37-41 In "From Stirring to Mixing in a Stratified Ocean". Proceedings 'Aha Huliko'a Hawaiian Winter Workshop. U. of Hawaii. January 1519, Eds. P. Muller and D. Henderson.

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Cooper M., and K. Haines, 1996, Data assimilation with water property conservation. J. Phys. Oceanogr., 101, 1059-1077. Davies, H.C., 1983, Limitations of some common lateral boundary schemes used in regional NWP models. Mon. Weath. Rev., 111, 1002-1012. Forbes, R.M., 1996, Initial results from experiments assimilating satellite altimeter sea surface height data into a tropical Pacific ocean model. Ocean Applications Technical Note 12. Available from Ocean Applications, Met Office, Bracknell, UK. Hines, A., 2001, Implementation and tuning of an altimeter data assimilation scheme for high resolution FOAM models. Ocean Applications Technical Note No. 26. Available from Ocean Applications, Met Office, Bracknell, UK. Holland, W.R., J.C. Chow and F.O. Bryan, 1998, Application of a third-order upwind scheme in the NCAR ocean model. J. Climate, 11, 1487-1493. Hunke, E.C., and J.K. Dukowicz, 1997, An elastic-viscous-plastic model for sea ice dynamics. J. Phys. Oceanogr., 27, 1849-1867. Large, W.G., J.C. McWilliams, and S.C. Doney, 1994, Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization. Rev. Geophys. 32, 4, pp 363-403. Levitus, S., T.P. Boyer, M.E. Conkright, T. O'Brien, J. Antonov, C. Stephens, L. Stathoplos, D. Johnson, and R. Gelfeld, 1998, World Ocean Database 1998. Volume 1: Introduction. NOAA Atlas NESDIS 18, 346 pp. Martin, M.J., M.J. Bell and N.K. Nichols, 2002a, Estimation of systematic error in an equatorial ocean model using data assimilation. Int. J. Num. Meth. Fluids, 20, 435444. Martin, M.J., M.J. Bell, and A. Hines, 2002b, Estimation of three-dimensional error covariance statistics for an ocean assimilation system. Met Office Ocean Applications Technical Note 30. Palmer, J.R. and I.J. Totterdell, 2001, Production and export in a global ocean ecosystem model. Deep-Sea Research Part I, 48, 5, 1169-1198.

Coupled physical and biochemical data driven simulations of Black Sea in spring-summer: real-time forecast and data assimilation Sukru T. Besiktepe Institute of Marine Sciences, Middle East Technical University, Turkey

Abstract Data driven simulations in the Black Sea based upon observations during May-June 2001 in the SW part of the basin and coupled 3D physical and biochemical models have been carried out. The model was initialised with the data obtained during 22-28 May, 2001 and ran until 15 June, 2001. The data obtained in the second leg during 12-18 June, 2001 was assimilated into the model. At the time of the assimilation, the model forecast and the data were also compared. Quantitative and qualitative comparisons of the coupled model fields with data show that the predictive capability of the model was about one week.

1. Introduction The primary objective of this research paper was to explore, quantify and predict the ecosystem variability of the Black Sea through the development of coupled interdisciplinary models with data assimilation schemes that would allow: 9 prediction of future states of the sea (forecasting) 9 descriptions of the present (nowcasting) 9 descriptions of the past states of the sea (hindcasting). This goal has been achieved with the establishment of the regional forecasting system for the Black Sea, performing real-time forecast at sea. There are three stages in the development of a regional forecast capability involving exploratory, dynamical and predictive phases (Robinson, 1996). Determining features and processes existing in the Black Sea form the exploratory phase, leading to constructing the model framework and geometry. The dynamical phase involves the calibration of the model through the determination of synoptical dynamical events and interactions, and the elucidation of dynamical processes governing mesoscale evolution and sub-mesoscale events. Calibration and validation of the model is done at this phase. The forecast studies are done at the predictive phase. This 3-phase strategy has been considered in the development of a Black Sea forecast system. This paper presents the result of the validation of the coupled physical and biogeochemical model of the Black Sea using the data obtained from cruises designed for this purpose.


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Coupled physical and biochemical data driven simulations of Black Sea in spring-summer: real-time forecast and data assimilation

2. Data Two quasisynoptic surveys of the South-western Black Sea region with near mesoscale resolution were carried out during May-June 2001. The general plan was to obtain two quasisynoptic surveys of the region with near mesoscale resolution ~25 km and a submesoscale survey with a finer resolution in the areas of interest. The survey was divided into two legs, each of about 6 days. The initial quasisynoptic survey, Leg 1, was carried out during 22-28 May, 2001. The second leg was carried out during 12-18 June, 2001. The network of stations for each leg is shown in Figure 1.

Figure 1 The positions of the hydrographic stations used to initialise the model (above) and to assimilate the model (below) SeaBird CTDs equipped with temperature, conductivity, beam transmission, fluorescence, PAR/irradiance, pH and oxidation reduction potential sensors were lowered at every station from the surface to a nominal depth of 500m. Dissolved nutrients (PO 4, NO 3, NO 2, NH 4, and Si) were measured using two-channel Technicon autoanalysers for samples collected with a rosette system attached to the CTD. Chlorophyll-a and pheopigments-a were measured using a spectrofluorometer.

3. Interdisciplinary model system The Harvard Ocean Prediction System (HOPS) was used to carry out the data driven simulations of physical-biogeochemical variabilities. The system consists of coupled dynamic models, statistical models, initialisation procedures, data assimilation schemes, and various visualisation and post-processing tools. HOPS is a flexible, portable and generic system for interdisciplinary nowcasting, forecasting and data-driven simulations

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at sea (Robinson, 1996; Lozano et al., 1996; Robinson, 1999). It has been successfully applied to different regions including real-time shipboard forecast experiments with validation and verification of forecast skill (Robinson et al., 1996; Lermusiaux and Robinson, 2001). The physical dynamical model employed here is the four-dimensional primitive equation (PE) model of HOPS, which is based on the GFDL integration algorithm. The PE model includes terrain following coordinates and algorithms designed for accurate estimates of pressure gradients in steep and/or shallow topography. Horizontal subgridscale processes are parametrised using a Shapiro filter, which is applied on the sub-mesoscale component of the total PE fields. The bulk vertical diffusion is a Richardson number dependent parametrisation. The transfer of atmospheric fluxes to the water-column involves a vertical mixing-length turbulent model based on a locally computed Ekman depth. A bottom boundary layer and coastal friction parametrisations are also incorporated. The biogeochemical model coupled to the physical model includes phytoplankton, zooplankton, detritus, nitrate, ammonium and chlorophyll-a. The explicit modelling of chlorophyll-a is important as it allows the relatively direct use of satellite images (seasurface colour) and in situ fluorometer profiles for model validation and data assimilation. In the model, fluxes and state variables are expressed in terms of nitrogen. The details of the biogeochemical model can be found in Besiktepe et al. (2003). The biogeochemical model parameters used by Oguz et al. (1999) are used in this study. Data assimilation strategies in a forecast system provide the means for model initialisation and update, melding model fields and primary data, tuning of model parameters, and providing error estimates. Presently, the Optimal Interpolation (OI) scheme of HOPS is employed to assimilate the synoptic data in the ecosystem simulations. The data-forecast melding step of this OI scheme consists of a two-scale Objective Analysis (OA) of the observations, followed by a blending of the forecast with the OA fields. The model grid covers the southwestern Black Sea at a resolution of 4.5 km with 28 levels in the vertical (Figure 1). The other modelling issues related to application of the HOPS to the Black Sea are the same as in Besiktepe et al. (2001) except the open boundaries in the present study. At open boundaries, conditions based on an Orlanski radiation scheme are employed.

4. Data driven simulations Data driven simulations in the Black Sea based upon these observations and a coupled 3D physical and biochemical model have been carried out. The model was initialised using physical, chemical and biological data collected during the first leg of the survey. The model is forced with QuickSCAT Level 3 Daily wind fields. Physical and biological data collected during the second leg were assimilated to the model. The model results with and without biological data assimilation were compared. A time-line of these initialisation steps and subsequent data assimilations is given in Figure 2.The initial physical fields are computed for May 25, using the OAs of the initial T, S data, geostrophy and an imposed barotropic transport stream function along openboundaries. Chlorophyll-a, nitrate, and ammonium fields were computed by objective

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analyses of observed data. The remaining biological fields (phytoplankton, zooplankton and detritus) were derived from objectively analysed chlorophyll data. Phytoplankton biomass in terms of nitrogen was computed from chlorophyll-a using carbon to chlorophyll and carbon to nitrogen ratios for the region. Zooplankton and detritus biomasses were taken as a fraction of the phytoplankton biomass.

Figure 2 Time-line diagram for the data driven simulation Along the course of the simulation MODIS chlorophyll and AVHRR SST measurements were assimilated into the model. Validation of the physical model was done using the ADCP data collected on board R/V "Knorr" in the area on 29 May and validation of the biogeochemical model forecasts was done on 17 June.

Figure 3 Salinity and velocity at 10m for days 5, 10, 15 and 20. Salinity is shown by the grey scale code, and current by the arrow length.

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The time series (Figure 3) of salinity and velocity is given for a depth of 10 m, every 5 days between days 5 and 20. The main Black Sea current (Rim current) enters the region from the west, leaves the coast forming an anticyclonic gyre, and then reattaches to the coast in the Sakarya Canyon region. Low salinity river waters originated from the big rivers in the north-eastern Black Sea extend down to the Bosphorus, characterised by salinity less than 18. The distributions of nitrate, zooplankton, chlorophyll-a and new production at initialisation are given in Figure 4. Nutrient concentrations are generally low in the surface waters except at the north-western corner of the region where river waters intrudes. The chlorophyll-a concentrations are higher near the coastline and follows the structure of the rim current. Zooplankton distribution follows the chlorophyll-a distribution as expected, since zooplankton concentrations were calculated from chlorophyll-a concentrations as explained above. The distributions explained above display typical early summer conditions for the Black Sea and it is seen that the features in the study region are mainly the result of a sustained advection of the river originated nutrient rich waters from the northwestern part of the basin into the study area.

Figure 4 Distribution of nitrate and chlorophyll-a (from measurements) and zooplankton and new production (calculated) at initialisation By day 25 (Figure 5), the distributions of the biogeochemical constitutes are the result of the redistribution of the nutrient and biomass rich coastal waters in the region by the rim current. The local uptake of nitrate by phytoplankton as a proxy for new production

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indicates that the new production occurs only on the rim current. As is characteristic of early summer conditions, the new production is low and does not follow the detailed structure of Chl-a. The phytoplankton biomass is indeed sustained by the ammonium production rate (not shown). However the production in early summer 2001 is very low as compared to other years in the Black Sea. The quality of the model simulation was measured quantitatively by comparing model fields to data values at data-points and to objectively analysed data fields. The skill evaluations are illustrated in Figure 6 for the simulations described above. The ecosystem forecast integrated from 25 May (using wind forcings from QSCAT, and MODIS and AVHRR SSTs assimilated as they are available) to 17 June. The model forecast is compared to the data collected during 16-18 June (see Figure 1 for the station positions). Since Chlorophyll-a and nitrate are the biogeochemical fields most densely observed in the vertical and horizontal, they are chosen to illustrate the model skill. The skill measure shown is the RMS of the differences between model fields and data values at data points, averaged over each depth. A goal is to find out if forecasts have better skill than the initial conditions; i.e. does the forecast "beat persistence"? The 17 June forecast is largely superior to the persistence forecast for both chlorophyll-a and nitrate.

Figure 5 As in Figure 4, but for day 25

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Figure 6 Skill evaluation for the model forecast. Solid line is the persistence and the dashed line is model forecast.

5. Conclusion This work was a contribution to the development of a physical, chemical and biological regional forecast system for the Black Sea. The developed, calibrated and validated model within the context of this study will be used in the operational observation and prediction system for the region. The results show that the model is able to generate and maintain the 3D structures of the current, temperature and salinity fields. Model results are paffially validated using observations. The four-dimensional fields yield a useful framework for the interpretation of data acquired during recent years by multi-institutional, multi-ship experiments in the Black Sea. The simulations have been used to identify mesoscale and sub-basin scale processes and interactions; and they can now be used to setup pointed dynamical studies. The skill of the physical model was found to depend on the successful representation of the model topography and the strength and accuracy of the available atmospheric forecasts. It was shown that the physical and biogeochemical predictive capability was extended to about two weeks. This indicates that the structures of the real-time biogeochemical model likely contains most of the important biogeochemical processes and that the model parametrisations are suitable for the region at these scales. Such physicalbiogeochemical data-driven simulations can be of considerable use for obtaining accurate quantitative information on the dynamics of the lower trophic levels of the region.

Acknowledgements The cruise support came from the projects "Biogeochemical cycles in the Black Sea, Marmara Sea, Aegean Sea and the Mediterranean", supported by the Turkish Scientific and Technical Research Council and the NATO-SfP Black Sea Ecosystem Processes and Forecasting/Operational Database Management System project. This work was also supported by NSF and TUBITAK through the joint NSF TUBITAK programme between Middle East Technical University and Harvard University.

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References Besiktepe, S., C.J. Lozano and A.R. Robinson, 2001, On the Summer Mesoscale Variability of the Black Sea, Journal of Marine Research, 59(4):475-515. Besiktepe, S.T., P.F.J. Lermusiaux, and A.R. Robinson, 2003, Coupled physical and biochemical data driven simulations of Massachusetts Bay in late summer: real-time forecast and data assimilation, Journal of Marine Systems, 40-41, 171-212. Lermusiaux, P.F.J. and A.R. Robinson, 2001, Features of Dominant Mesoscale Variability, Circulation Patterns and Dynamics in the Strait of Sicily. Deep-Sea Research I, 48, 1953-1997. Lozano, C.J., A.R. Robinson, H.G. Arango, A. Gangopadhyay, N.Q. Sloan, P.J. Haley, Jr. and W.G. Leslie, 1996, An Interdisciplinary Ocean Prediction System: Assimilation Strategies and Structured Data Models. Modern Approaches to Data Assimilation in Ocean Modelling P. Malanotte-Rizzoli, Ed., Elsevier Oceanography Series, Elsevier Science, The Netherlands. 413-452. Oguz, T., H. Ducklow, P. Malanotte-Rizzoli, J. Rizzoli, J.W. Murray V.I. Vedernikov, and U. Unluata, 1999, A physical-biochemical model of plankton productivity and nitrogen cycling in the Black Sea. Deep Sea Research I, 46, 597-636. Robinson, A.R., 1996, Physical processes, field estimation and an approach to interdisciplinary ocean modeling, Earth-Science Reviews,39,3-54 } Robinson, A.R., H.G. Arango, A.J. Miller, A. Warn-Varnas, P.-M. Poulain and W.G. Leslie, 1996, Real-Time Operational Forecasting on Shipboard of the IcelandFaeroe Frontal Variability. Bulletin of the American Meteorological Society, 77(2), 243-259. Robinson, A.R., 1999, Forecasting and Simulating Coastal Ocean Processes and Variabilities with the Harvard Ocean Prediction System. In Coastal Ocean Prediction, Ed, C.N.K. Mooers, American Geophysical Union, Washington DC, 77-100. Robinson, A.R., P.F.J. Lermusiaux, P.J. Haley, Jr. and W.G. Leslie, 2002, Predictive Skill, Predictive Capability and Predictability in Ocean Forecasting. Proceedings of Oceans 2002 IEEE/MITS Conference, 787-794.

Data assimilation in an operational forecast system of the North S e a - B a l t i c Sea s y s t e m J. V.T. Sorensen* 1,2, H. Madsen 1, H. Madsen 2, H.R. Jensen 1, p.S. Rasch 1, A.C. Erichsen 1, and K.I. Dahl-Madsen I ! D H I Water and Environment, Denmark 2Informatics and Mathematical Modelling, Technical University of Denmark

Abstract The operational service "Water Forecast" gives daily forecasts for the North Sea, Baltic Sea and interconnecting waters. The basic computational units include a 3D hydrodynamic module, a 3D environmental module and a wave module. Ongoing development is focused on data assimilation of tide gauge and SST data. A cost-effective Kalman filter based procedure that uses a regularised constant Kalman gain is applied for the tide gauge data. For assimilation of SST data a simplified Kalman filter procedure is adopted. The combined approach gives an acceptable computational overhead for operational applications. Performance of the modelling system is evaluated.

Keywords: Data assimilation, Kalman Filter, operational modelling, SST data, tide

gauge data.

1. Introduction During the last decades a number of complimentary developments within oceanographic modelling and monitoring have been taking place. Numerical modelling has advanced to the stage where operational systems are now run on a routine basis, predicting an everincreasing number of physical and biogeochemical properties (Pinardi and Woods, 2002, Erichsen and Rasch, 2002). Simultaneously, a growing amount of observations of a wide range of these properties in the shelf and coastal seas are becoming available in real or near-real time. Hence with the advance of data assimilation schemes suitable for shelf and coastal seas, the potential of an integrated approach has become clear. It is now possible to estimate the state of the sea as a composite of on-line observations and model results through the use of data assimilation techniques. In this way, the relatively precise but sparse data can in essence be interpolated by the theoretical knowledge embodied in the physically consistent model. DHI Water and Environment operates a forecast system of the North Sea-Baltic Sea system called the Water Forecast (WF). This contribution demonstrates the application of cost-effective data assimilation schemes for assimilation of tide gauge and SST data into the high-resolution model, which provides the computational component of the WF.

2. The Water Forecast operational system In 1999 the development of an end-user oriented web based operational modelling system of the North Sea-Baltic Sea was initiated at DHI Water & Environment, (Jensen * Corresponding author, email: [email protected]

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Data assimilation in an operational forecast system of the North Sea-Baltic Sea system

et al., 2002). The system has produced operational forecasts since June 2001. The model area is depicted in Figure 1. It includes two open boundaries in the North Sea and stretches to cover the entire Baltic Sea. An area of particular interest is defined, which surrounds Denmark and southern Sweden as shown in Figure 1. The basic computational engine is composed of a two-way dynamically nested 3D baroclinic hydrodynamic module (MIKE 3 HD), a 3D environmental module (MIKE 3 EU) as well as a 2D wave module (MIKE 21 SW). Every 12 hours a 4 day forecast is provided, predicting a range of physical and environmental parameters. These include water level, currents, salinity, temperature, wave height, period, spectra and swell as well as chlorophyll-a, oxygen and algae growth. A thorough description of the system can be found in Erichsen & Rasch (2002).

Figure 1 The WF model area. The dotted line between Scotland and Norway indicates an open boundary whereas the dashed square shows the area in focus.

3. T h e data In principle all data which can be assimilated at an acceptable cost and yet provide an improvement to the ocean state estimation skill, ought to be considered. For the WF system two data sources are considered initially: Tide gauge water level observations and satellite sea surface temperature (SST) observations from the Ocean Pathfinder AVHRR sensors.

3.1 Tide gauge data Tidal data from 14 stations in the focus area have been selected for the present study. Eight of these will be assimilated and six used for validation. Figure 2 shows the positions of these stations and whether each station acts as a measurement (M) or a validation (V) station. Data are provided from the Danish Meteorological Institute and the Danish Coastal Authority.

J.V.T. Serensen*, H. Madsen, H. Madsen, H.R. Jensen, P.S. Rasch, A.C. Erichsen, and K.I. DahI-Madsen

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Figure 2 Tide gauge measurement (M) and validation (V) stations. 3.2 SST data

The Pathfinder AVHRR SST data was obtained from Collecte Localisation Satellites (CLS), who has pre-processed the data into 10-day interval products as part of the EU funded project, GANES. For the purpose of assimilation it is essential to note that the SST fields are derived from composite images and are therefore not snapshots in time. Data from 22/9, 2/10 and 12/10 1994 was used for assimilation. The SST field from the 22/10 1994 was used for validation. Figure 3 shows the data coverage by the SST data in the left panel. Further, all available temperature data from the ICES database (ICES) in the given period was used for validation. These are highly sparse in time and space. Their spatial distribution is shown in the fight panel of Figure 3.

Figure 3 Left: Data coverage for every 10 day period of the AHVRR SST data product. Right: Positions of in situ temperature measurements from the ICES database during the validation period.

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4. The data assimilation approach The final aim is to utilise data assimilation techniques that can have a widespread use in engineering and scientific applications and thus it is essential to develop schemes which are both cost-effective and robust. We define this as model execution time and memory requirement less than five times that of a pure model run and preferably below a factor of two. The following two approaches comply with these constraints while retaining a robustness and effectiveness caused by the advanced data assimilation approaches on which they build and the corresponding physical error assumptions. Thus both are applicable for operational systems. 4.1 Tide g a u g e assimilation

The implemented assimilation scheme for tide gauge data is a hybrid scheme that combines the ensemble Kalman filter approach (Evensen, 1994) with a barotropic dynamical approximation (Serensen et al., 2002), a steady gain assumption (Cafiizares et al., 2001) and a regularisation of the gain matrix. A one-layer barotropic version of the three-dimensional hydrodynamic model is run over a three day period with an ensemble Kalman filter using 100 ensembles. Errors are assumed to originate solely from the open boundaries and the wind field. The time varying Kalman gain is averaged over the last two days of the run and saved for application in the steady Kalman filter approach. This two-dimensional Kalman gain basically assumes the model errors to be barotropic and hence for application of the gain in the three-dimensional baroclinic model the same assumption can be followed to relate the full velocity field to the depth averaged velocities. Thus, an update of the full velocity field based on a depth averaged gain will merely shift the mean of the vertical profile, not the structure. For further detail refer to Serensen et al. (2001). Due to spurious correlations in the ensemble Kalman filter, which have not diminished in the averaging process, rather large Kalman gain values can be observed in data-sparse regions even when such correlations have no physical interpretation. Also the correlation between water level and velocity is dominated by noise in large parts of the area. Thus, in order to ensure robust results a rough manual regularisation of the gain is performed. This practically sets velocity gains to zero and cuts off water level gains at the 0.01 contour. More advanced regularisation, which allows the velocity to re-enter and significant negative correlations to remain must be considered as a future improvement. However, the present implementation is an important first step.

4.2 SST assimilation A module for cost-effective data assimilation of SST data (Annan and Hargreaves, 1999) has been implemented in MIKE 3 HD. Based on a few simple dynamic assumptions imposed on the Kalman filter approach the data assimilation module is able to correct the temperature field above the mixed layer. It is assumed that horizontal correlations are small enough to be ignored. Further, it is assumed that the areas above and below the mixed layer are both well mixed. This yields a one-dimensional gain vector for each SST data point approximating the Kalman gain. The SST data are interpolated in time to provide an observation at every time step. This represents the fact that the SST fields are averages over a longer period of time. The base of the mixed layer can be defined in a range of different ways. Note that the mixed layer is merely a theoretically constructed

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concept. In the present approach, it is taken to be the highest grid point with a diffusion coefficient of 10-4 or lower. However, the exact threshold value is a calibration factor.

5. Results and d i s c u s s i o n For the purpose of testing the developed water level data assimilation schemes, the period 3 - 2 7 February 2002 was selected, whereas a period between 20 September and 22 October 1994 was considered for the SST assimilation. The different periods were chosen to match the available data for assimilation and validation. In both periods the model was run both with (assimilation) and without (model) the appropriate assimilation scheme. 5.1 Tide g a u g e results The performance of the model with assimilation of tide gauge data is compared to a pure model execution in Figure 4, which shows the root mean square error of the validation points and the measurement points to the left and right respectively. The mean values are also shown. There is a clear improvement in all stations with an average o~ a 35% increased performance in validation points and 58% in measurement points. This significant improvement can be obtained at an overhead in execution time of less than a factor of two. All stations here are located in data dense regions. In more data sparse regions, performance converges to that of a normal model execution. When the regularised gain is not used a significant bias can be introduced from spurious correlations in the ensemble Kalman filter. Thus, an assimilation scheme has been implemented which meets the constraints of fast execution and robust improvements.

Figure 4 Root mean square error of water level results in validation points (left) and measurement points (right) with (white bars) and without (dark bars) assimilation.

5.2 SST results The performance of the model with assimilation of SST data is compared to a pure model execution in Table 1, showing the root mean square error of the 10 days forecast and the validation SST field from the 22 October 1994. Also shown is the root mean square error compared to the in situ measurements from the ICES database. The latter is divided into two bins above and below 20m in an attempt to roughly look at results above and below the thermocline. The assimilation scheme clearly improves the results, where expected. It was assumed that no information was available below the thermocline

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and thus it is consistent to maintain the performance below 20m. Above 20m we see an 18% reduction of the RMSE. However a more significant reduction must be expected to be obtainable if the SST data could be assimilated in time. The 10 day forecast gives a 30% RMSE reduction. These are encouraging results, but also in the forecast statistics, a further improvement must be expected when the validation is done timely, since movements of fronts and rapidly changing atmospheric conditions will be more accurately captured. Table 1 Root mean square error (RMSE) of temperature results with (Assimilation) and without (Model) assimilation RMSE (m)

CLS 10 days forecast

ICESAbove 20m

ICES Below 20m

Model

0.74

0.66

1.27

Assimilation

0.52

0.54

1.28

6. C o n c l u s i o n s

and future work

The successful assimilation of tidal data and satellite-derived SST data have been demonstrated in a model of the North Sea-Baltic Sea for operational use. For the assimilation of water level data a proper regularisation of the Kalman gain will be considered for further improvement. The SST assimilation scheme will be developed to use timely data in cloud free areas leaving the propagation of the information to the model dynamics. In the near future simple optimal interpolation schemes for the assimilation of chlorophyll-a and dissolved oxygen will also be implemented. References

Annan, J.D., and J.C. Hargreaves, 1999, Sea surface temperature assimilation for a three-dimensional baroclinic model of shelf seas. Continental Shelf Research 19, 1507-1520. Cafiizares, R., H. Madsen, H.R. Jensen, and H.J.H. Vested, 2001, Developments in Operational Shelf Sea Modelling in Danish Waters. Estuarine, Coastal and Shelf Science 53,595-605. Erichsen, A.C., and P.S. Rasch, 2002, Two- and Three-dimensional Model System Predicting the Water Quality of Tomorrow. Proc. of the Seventh International Conference on Estuarine and Coastal Modeling Eds. M.L. Spaulding, American Society of Civil Engineers Evensen, G., 1994, Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. of Geophysical Research 99(C5), 10,143-10,162. ICES, International Council for the Exploration of the Sea, http://www.ices.dk Jensen, H.R., J.S. MOiler, and B. Rasmussen, 2002, Operational hydrodynamical model of the Danish waters. Danish National Programme for Monitoring the Water Environment. Operational Oceanography 66, 87-97. Pinardi, N., and J. Woods, Eds., 2002, Ocean Forecasting. Springer-Verlag BerlinHeidelberg.

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SCrensen, J.V.T., H. Madsen, and H. Madsen, 2001, Data assimilation of tidal gauge data in a three-dimensional coastal model, Proceedings, 4th DHI Software Conference. Abstract No. 058 SCrensen, J.V.T., H. Madsen, and H. Madsen, 2002, Towards an operational assimilation system for a three-dimensional hydrodynamic model. Proceedings of the Fifth International Conference on Hydroinformatic, Volume Two, Eds. I.D. Cluckie, D. Han, J.P. Davis and S. Heslop, IWA Publishing.

Impact of the progress in operational oceanography

on oil spill drift forecasting in the Mediterranean S e a Pierre Daniel*, Fabien Marty, and Patrick Josse Mdtdo-France

1. Introduction M6t6o-France has national and international responsibilities to agencies fighting marine oil pollution. M6t6o-France can intervene at a national level within the spill response plan P O L M A R - M E R in case of a threat to the French coastline, and at an international level within the Marine Pollution Emergency Response Support System (MPERSS) for the high seas. In order to answer to these commitments, M6t6o-France developed MOTHY, a pollutant drift model. The model is operational and can be used 24h/24 for oil spills or drifting objects. MOTHY is an integrated system that includes an oil spill model linked to a hydrodynamic coastal ocean model with real time atmospheric forcing from a global or limited area model. MOTHY has proved its efficiency, giving results very close to reality, during the Erika crisis (December 1999) in the Bay of Biscay. So far, the effects of the general circulation and the associated large scale currents are not represented in the model. This paper focuses on evaluating the effects of these currents and comparing different methods to represent them in the MOTHY system. Currents from different origins can be used and the first part of this work is to compare: 9 Currents derived from climatology 9 Currents derived from altimetry 9 Currents produced by operational oceanography systems The potential benefit of currents from operational oceanography systems is obviously higher since forecasts will be available. However, currents from climatology and altimetry will be used as a reference. The optimal way to integrate this information into the MOTHY system for the different sources is also discussed. The impact of these currents from different origins and of different methods of integration on the oil spill drift prediction is evaluated. Two actual pollution cases will be used, for which observations are available: the accidents of the Haven (1991) and the Lyria (1993).

2. Large scale currents This paper focuses on evaluating the effects of large scale currents and comparing different methods to represent them in the MOTHY system. This effect is investigated in the Western part of the Mediterranean Sea where such currents are significant. We have mainly used two actual pollution cases, for which observations are available: the accidents of the Haven (1991) and the Lyria (1993).


Pierre Daniel*, Fabien Marty, and Patrick Josse

219

The Cypriot tanker Haven, a 109700 tonnes-313m long oil tanker, caught fire and suffered a series of explosions on 11 April 1991, while at anchor seven miles off the coast of Genoa. The vessel was carrying approximately 144000 tonnes of crude oil and it is estimated that over 50000 tonnes of fresh and partially burnt oil were spilled into the sea. This caused the worst oil pollution incident ever in the Mediterranean Sea. On August 1993, the 2400 ton submarine Rubis collided with the 278000 dwt 1 115 foot long oil super-tanker Lyria some 70 miles south of Toulon, tearing a hole in the tanker and causing an oil slick. The submarine damaged its bow. The spilled oil drifted for three weeks without reaching any coast. Without permanent current, simulations show a drift which does not fit the observations (Figure 1).

Figure 1 Left: Havenn2 weeks forecast without permanent current. Right: Lyria--3 weeks forecast without permanent current. The star is the starting point, diamonds are the observations, black spots show the final position of the slick forecasted by MOTHY. Due to the lack of an operational oceanography system on the Mediterranean Sea, the only available (total) currents are monthly or seasonal means (derived from climatology or produced by operational oceanography prototypes) at a given depth level. We studied a complete month of atmospheric forcing and its mean effect at the 5 metre level. It appeared that we could consider a monthly mean of total currents at the 5 metre level as the permanent part which is missing.

3. Use of currents derived from climatology The Mediterranean Oceanic Data Base (MODB) (Brankart, 1998) provides seasonal climatology of currents at a depth of 5 metres reconstructed from historical hydrological data. These data are available on a quarter degree grid mesh. With a simple addition of these currents to the final current given by MOTHY, we get results closer to the observations (Figure 2).

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Impact of the progress in operational oceanography on oil spill drift forecasting in the Mediterranean Sea

Figure 2 Left: Haven--2 weeks forecast with seasonal current from MODB. Right: Lyriam3 weeks forecast with seasonal current from MODB

4. Use of currents produced by operational oceanography prototypes We used monthly means calculated on a 1/8 degree grid mesh, with 3 years of simulation of the MERCATOR system (Madec, 1998). The MERCATOR mission seeks to develop and deploy a truly operational oceanography system capable of analysing and predicting ocean conditions around the globe. This system will describe and predict ocean conditions over the whole ocean column continuously and in real time, at scales ranging from global phenomena to regional eddies. The addition of the currents in the MOTHY system leads to better results than the MODB version, probably due to the better temporal and spatial resolution of these data (Figure 3).

Figure 3 Left: Havenu2 weeks forecast with monthly current from MERCATOR. Right: Lyria--3 weeks forecast with monthly current from MERCATOR

Pierre Daniel*, Fabien Marty, and Patrick Josse

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5. Use of current anomalies derived from altimetry From combined ERS-1 and TOPEX/POSEIDON altimetry data produced by the CLS Space Oceanography Division as part of the MAST-Ill MATER EC project (Ayoub, 1998), the circulation anomalies were calculated. These anomalies were then combined with the yearly general circulation given by the MERCATOR system. This combination leads to the best results (Figure 4).

Figure 4 Lyriam3 weeks forecast with monthly current from MERCATOR plus current from altimetric anomalies

6. Conclusions Monthly current means from Mercator ocean model lead to consistent results with the observations. The use of altimetric data improves the results still further. This is encouraging for a future use of currents from operational ocean models which will assimilate this type of data.

References Ayoub, N., P.-Y. Le Traon, and P. De Mey, 1998, A description of the Mediterranean surface variable circulation from combined ERS-1 and TOPEX/POSEIDON altimetric data, Journal of Marine Systems 18 (1-3), pp. 3-40. Brankart, J.M. and P. Brasseur, 1998, The general circulation in the Mediterranean Sea: a climatological approach, Journal of Marine Systems 18 (1-3), pp. 41-70. Daniel, P., 1996, Operational forecasting of oil spill drift at M6t6o-France. Spill Science & Technology Bulletin. Vol. 3, No. 1/2, pp. 53-64. Daniel, P., P. Josse, P. Dandin, V. Gouriou, M. Marchand, and C. Tiercelin, 2001, Forecasting the Erika oil spills, Proceedings of the 2001 International Oil Spill Conference, American Petroleum Institute, Washington, D.C, pp 649-655. Elliot, A., 1986, Shear Diffusion and the Spread of Oil in the Surface Layers of the North Sea. Dt. Hydrogr. Z. 39, 113-137. Madec, G., P. Delecluse, M. Imbard, and C. L6vy, 1998, OPA8.1 ocean general circulation model reference manual, Notes du p61e de mod61isation IPSL, 11.

The study of seasonal variability in the Adriatic Sea with the u s e of EOF a n a l y s i s

A.Grezio* 1, N. Pinardi 2, S. Sparnocchia 3, and M. Zavatarelli 2 l lstituto Nazionale Geofisica e Vulcanologia, Bologna, Italy 2 Universita'di Bologna, Italy 3Istituto di Scienze Marine-CNR, Trieste, Italy

Abstract Multivariate vertical Empirical Orthogonal Functions (EOFs) have been calculated in the Adriatic Sea in order to implement a System for the Ocean Forecasting and Analysis (SOFA) which will release weekly forecasts of currents. The EOFs define spatially coherent, multivariate modes of variability which are physically meaningful and enable truncation of the problem of modelling error correlations to some order leading to the best convergence. The model vertical EOFs indicate the regions of greater spatial and seasonal variability of temperature and salinity. Also the analysis shows that a higher number of modes is necessary in the Northern Adriatic.

Keywords: Adriatic Sea, ocean forecasting, vertical EOF, POM 1. Introduction The study of vertical EOFs can effectively synthesise the information contained in T-S diagrams (Sparnocchia et al., 2003). A further study is necessary in order consider the barotropic component of the motion and produce better data assimilation in the Adriatic Sea using the ocean forecasting system SOFA.

2. The Adriatic model The present analysis is based on the Adriatic Sea model implemented by Zavatarelli et al. (2002) using POM (Blumberg and Mellor, 1987). The model has a horizontal resolution of ~5 km and 21 layers in the vertical, high frequency forcing (6 hourly wind), daily PO river run off and monthly heat fluxes. The model was integrated from 1991 to 1996. The vertical EOFs are calculated considering model climatology. We grouped the seasons following Zavatarelli et al. (2002).

3. The vertical EOFs The vertical EOFs have been calculated for each grid point of the model domain for each season. The bi-variate vertical EOFs were calculated using T,S fields and the tri-variate vertical EOFs were calculated using T,S and 1"1fields extracted at various depths. Salinity variability is mainly distributed along the Western Adriatic Sea and is particularly pronounced in summer (Figure 1 left).


A.Grezio*, N. Pinardi, S. Sparnocchia, and M. Zavatarelli

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Figure 1 Left: Vertical bi-variate S-EOFs at (14.4N-43.1E). Right: Vertical tri-variate T-EOFs at (16.2N-42.5E) The strongest vertical variability in temperature is present in the Southern Adriatic at the thermocline depths in spring and summer (Figure 1 right).

4. The explained variance The total variance explained by the first few modes is generally higher in the bi-variate case (T,S) (Figure 2 left) than the tri-variate case (T,S, TI) (Figure 2 fight). In the North Adriatic a higher number of modes is necessary in the tri-variate case compared to the bivariate case in order to reach the 90% of the total explained variance, in particular in winter and summer. In the Middle Adriatic gyre and in the Southem Adriatic gyre in winter and spring the total explained variance reaches 80% in the second mode both in the bi-variate and tri-variate EOFs.

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The study of seasonal variability in the Adriatic Sea with the use of EOF analysis

5. Discussion and summary The aim of this study is to calculate a set of EOFs for the SOFA system in order to correctly model Temperature, Salinity and Sea Surface Height and produce the best forecasting in the Adriatic Sea. The bi-variate (T,S) vertical EOFs will be used for the background error covariance matrix in order to produce the model correction in Temperature and Salinity.

Figure 2 Left: Number of modes explaining 90% of variance for the bi-variate EOFs. Right: number of modes explaining 90% of variance of the tri-variate EOFs for each season The tri-variate (T,S, rl) vertical EOFs are not representative and a better approach can be the use of (T,S,W) for the correction of the barotropic component of the motion. This preliminary study indicates that 10 modes are sufficient in the Adriatic Sea for the data assimilation purposes using SOFA, as has been obtained by Sparnocchia et al. (2003) in the Mediterranean Sea.

Acknowledgements This study is supported by the Italian Ministry for the Environment and Territory and coordinated by the National Institute of Geophysics and Volcanology within the ADRICOSM Project.

References Blumberg, A.F., and G.L. Mellor, 1987, In Three dimensional ocean models N.S. Heaps, Ed. AGU De Mey, P., and M. Benkiran, 2002, In Ocean Forecasting, Ed. N.Pinardi, J.Woods

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Sparnocchia, S., N. Pinardi, and E. Demirov, 2003, Multivariate Empirical Orthogonal Function analysis of the upper thermocline structure of the Mediterranean Sea from observations and model simulations, Annales Geophysicae, in press Zavatarelli, M., N. Pinardi, V.H. Kourafalou, and A. Maggiore, 2002, Diagnostic and Prognostic model studies of the Adriatic Sea General Circulation: Seasonal variability, Journal of Geophysical Research, vol. 10, C 1


Next Generation Systems

Gwyn Griffiths


AUVs" designing and operating next generation vehicles Gwyn Griffiths*l and lan Edwards 2

/Southampton Oceanography Centre, UK 2Subsea7 Ltd., UK

Abstract Over the last decade Autonomous Underwater Vehicles (AUVs) have become capable marine platforms. AUVs built in Europe are now operational tools in the global offshore business. Industry has seen significant cost savings from this new technology. Data quality has improved to the point where some customers in the offshore industry now prefer not to use shipboard systems. But what of AUVs for operational oceanography? Is the successful collaboration between research and industry in the offshore business a transferable model? This paper examines AUVs in scenarios relevant to EuroGOOS against the capabilities of existing technologies and those under development.

Keywords: Autonomous vehicles, survey, technology 1. Introduction It is an oft-stated truism that in many branches of oceanography our progress in understanding is limited by our capacity to make observations on suitable time and space scales (e.g. Dickey, 2002; Fischer and Flemming, 2002). For over a decade, the autonomous underwater vehicle (AUV) has been promoted as one option, of many, that might lead to a significant increase in our data-gathering capacity. Now that AUVs are accepted tools within ocean research, commercial seafloor survey, subsea mineral exploration and defence communities, it is timely to review their potential as tools to aid the data-gathering needs of operational oceanography. Many research groups in Europe and North America have access to mature AUV technology incorporated in vehicles that are capable of performing science missions of real utility. The Autosub project (Millard et al., 2003) is a leading example of a large (ca. 1.5 tonne) AUV that has provided new insights into areas of oceanography as diverse as the ecology of krill in the Southern Ocean, fish stock assessment in the North Sea, the distribution of turbulence within organised flow structures in the upper ocean, sea ice thickness distribution in the Weddell Sea, the flow over the sill between the eastern and western Mediterranean Sea and the small scale distribution of different species of phytoplankton near the sea surface. While the Autosub has been successful at adding to our knowledge of the ocean, this has been through process study experiments, and not, as yet, through the provision of "operational" data, for example as envisaged in the GOOS 1998 Prospectus (p. 49 in IOC, 1998). This situation looks set to continue, at least over the next three years. The same seems true for other large research AUVs.


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AUVs: designing and operating next generation vehicles

In contrast, major companies in the offshore energy industry have embraced medium and large-sized AUV technology as a key solution to gathering high quality operational data in the deep ocean (below 400 m) at a significant cost saving over previous methods, that is, surface ships and deep-tow vehicles (Danson, 2003). While the offshore energy industry lagged behind the science community in driving the development of AUVs until the late 1990s, the subsequent pace of transition from demonstration trials to routine operations has been remarkable (Hill, 2002). Research groups and companies in Europe have also pioneered the use of small AUVs for coastal and shallow water applications. They are considerably less expensive than large AUVs, require much smaller support teams, and are far easier to deploy and recover, but have a shorter range. Within SUMARE, an EU-FP5 funded project, the AUV Mauve has been used for bathymetric survey of sand exploitation in Belgian coastal waters. Mauve is a reconfigurable torpedo-like vehicle, 1.80 m long, weighing ~30 kg in air, with a scientific payload space available for specific instrumentation (Figure l a). Using innovative adaptive sampling, Mauve, through specific guidance algorithms, can decide for itself, using the acquired data, where is the more appropriate place, and what is the best way to sample, to achieve the objective of the mission. Another small European AUV that is operational is the Gavia (Thorhallsson and Hardason, 2002). In addition to a CTD the Gavia can carry cameras, lights and sonar and has been used to investigate the effect on the benthos of new fishing gear and in studies of marine ecosystems. It is easily deployed from a small boat (Figure lb).

Figure 1 Small AUVs are easily deployed: a) the Mauve AUV ready for a North Sea deployment, b) the Gavia vehicle being deployed from a small boat. The central tenet of this paper is that designing and operating next generation AUVs for operational oceanography is primarily a challenge for the business model rather than for the technology. While improvements in technology are required in several areas, they do not present a barrier to the adoption of AUVs within an operational data gathering system. The remainder of this paper is organised as follows: Section 2 describes the change processes within the offshore energy industry that led to the rapid adoption of AUV technology. Section 3 presents some costed scenarios for using autonomous under-

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water vehicles as data-gathering platforms for operational oceanography in Europe. Some conclusions are drawn in Section 4.

2. Drivers for operational AUVs in offshore industry Several research groups world-wide were developing AUVs for marine science applications by the early 1990s. By the middle of the decade, these groups had gained a great deal of experience with autonomous vehicles. Yet, as late as 1995, many within the offshore energy industry were dismissing the new technology. It is apposite to quote one such response, given at a meeting convened by the UK Society for Underwater Technology ("The Potential of Robotic Systems in the Seas and Offshore") to bridge the gap in vision that then existed between marine researchers and those in offshore energy: "Mr. X gave what was perhaps the most firm rebuff of the day to the notion of using AUVs in industry" (Stevenson, 1995). However, by 1999 the situation was completely different. Marine technologists had persevered with AUVs, had designed, built and operated vehicles in the open ocean, and, of critical importance, survey sensors were becoming available that could be carried within the payload space of AUVs. Combined with the advances that had taken place in navigation systems for autonomous vehicles, key individuals in the offshore energy industry began to explore seriously the potential benefits of the new technology. Shell International Exploration and Production BV's "Gamechanger" project report in February 1999 was a major milestone. For the first time, end-users in the offshore energy business made available to their suppliers (but only their suppliers) an analysis of the effect that this new technology could have on its operations within 5 years (that is, to 2004). The report's main conclusions were later made public (Gallett, 1999): 9 Survey Class AUVs whose tasks would include seabed survey, pipeline route surveys and inspections, oceanographic and environmental data collection, at water depths ranging from 100-4000m were feasible within 1-2 years (that is, during 20002001). Work Class AUVs, designed for intervention, using manipulators and operating within and around structures, were not feasible before 2004. 9 Hybrid ROV/AUV vehicles, combining a work class ROV with AUV modules, was feasible before 2004. Following from these conclusions, the report estimated that "operational cost savings of over $30M and increased leverage of over $75M are in prospect within 5 years". In effect, this was a green light from a major customer for the service provider companies to invest in providing the new technology. An analysis along the lines of the Shell Gamechanger project does not seem to have been carried out by the customers that pay or commission data acquisition within the realm of EuroGOOS. Hence the reluctance of service providers to invest in the capacity to make those observations is understandable. The following section presents, for debate, examples of what could be done by existing and next generation vehicles, with some indication of costs.

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3. Scenarios for A U V s in European operational o c e a n o g r a p h y The technical maturity of current generation autonomous underwater vehicles, as described in the chapters within Griffiths (2003), is sufficient to tackle many tasks of value to the European operational oceanography community and their end-users. Nevertheless, some aspects of vehicle design and, perhaps more important, aspects of overall system design and cost do need to be tackled. Three examples are developed below to illustrate the possibilities and the challenges.

3.1 Exchanges between the Arctic/Nordic Seas and the North Atlantic The section from Scotland to the Faroe Islands, to Iceland and on to Greenland is key to understanding the meridional overturning circulation of the North Atlantic (Saunders, 2001). Some parts of the section have been observed over many years by cruises and moored instruments, other parts less frequently. Severe weather can preclude or curtail observations from ships in winter. How might an AUV substitute? 9 Type of A U V ~ t h e section traverses areas of strong currents where buoyancy-driven vehicles (gliders) with their slow forward speeds (4).3 ms -1 ) would be subject to excessive drift. The range exceeds that of small AUVs, leaving large AUVs as the most appropriate technology. 9 R a n g e ~ a section from Scotland, over the Wyville Thomson Ridge, over the Faroe Bank, crossing the Faroe Bank Channel to the Faroe Islands is 360-400 km. This is achievable with today's larger propeller driven AUVs. Launch and recovery could be from shore bases. From the Faroe Islands to Iceland along the Iceland Faroe Ridge is about 440 km. From Iceland to Greenland, and return, is about 600 km, which could be achieved with little technical difficulty. Multiple stages, that is Scotland to Iceland without refuelling at the Faroe Islands, would require a range capability beyond the cost-effective capability of today's vehicles, but could be achieved within three years based on planned development of lithium-ion batteries. 9 D e p t h ~ t h e maximum sill depth on this section of 840 m in the Faroe Bank Channel is well within the specification of a number of AUVs. Profiles would be obtained every 2 km i.e. ~800 profiles over the two-way section. 9 Sensor requirements~Sensors pose few difficulties in this application. Standard CTDs would be used. The calibration stability of the conductivity sensor would be less of an issue compared to profiling floats as large AUVs can carry water samplers and the duration of each mission would be measured in days. Absolute currents could be measured with ADCPs. Although not yet used in an AUV, 75kHz phased array ADCPs could be installed if the pressure rating of the acoustic transducer could be increased. These instruments would enable bottom tracking, hence absolute currents, over the entire section even when the vehicle was near the surface. Additional sensors, already proven within AUVs, would include fluorometers for chlorophyll, electrodes for dissolved oxygen, transmissometers, UV absorption for dissolved nitrate, microsensors for turbulence (heat and velocity) and even fisheries echosounders (Langebrake, 2003). 9 Navigation ~ with a bottom tracking ADCP for speed over ground and a fibre-optic gyrocompass for heading, with the vehicle surfacing twice a day for GPS fixes the


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position error would be less than 350 m between fixes. This would be adequate for the type of survey envisaged above. The vehicle would require bases at Stornoway (Scotland), Torshavn (Faroe Islands), and Seydisfjord (Iceland). The procedures for shore-based launch and recovery assisted by a small inshore boat have already been established (Griffiths et al., 1998). Table 1 Schedule and cost of a monthly AUV occupation of the Scotland-Iceland section Day of month

Activity

Vehicle lease & insurance cost (E)

Other cost (E) 8200

1-4

Preparation at Stornoway

8800

5-7

AUV on passage to Torshavn

6600

-

8-9

Retrieval, refuelling, inspection, remove samples and redeploy

4400

7600

10-12

AUV on passage to Seydisfjord

6600

-

13-14


4400

7600

15-17

AUV on passage to Torshavn

6600

-

18-19


4400

7600

20-22

AUV on passage to Stornoway

6600

-

23-30

Retrieval, contingency period

17600

1000

Total

66000

32000

Table 1 shows a schedule for an AUV occupying this section, performing east to west and west to east transects on a monthly basis from Scotland to Iceland and return. The cost has been derived as follows. We assume that the AUV can be leased for C2000 a day, given a long lease, and that insurance will cost ~200 a day. Each deployment and recovery by small boat is estimated to cost ~1000 and each shore preparation involves travel and attendance by two engineers (~2600-4200). The energy and maintenance cost per leg is estimated at ~3000, based on using secondary Lithium-Ion batteries. The total monthly cost of ~98000 compares to an estimated cost of ,-~275000 for occupying the section using an ocean-going research ship with a larger station spacing. 3.2 T r a n s - M e d i t e r r a n e a n section

It is within the current capability of more than one AUV to carry out repeat hydrographic sections across the Mediterranean Sea if a refuelling stop is available. As an example, the section from Toulon (France) to Oran (Algeria) and return could be occupied on a monthly basis given refuelling at Mallorca. Figure 2 shows a cross section of salinity from the OCCAM 1/12 x 1/12~ global ocean model along the track indicated in the inset map. The salinity shown is an instantaneous, winter-time field such as may be observed by a near-synoptic AUV survey. The maximum depth of ~2800 m is achievable with existing vehicles and Mallorca is almost midway between the two end-pointsmeach leg would be ~500 km. On the same cost basis as in Table 1, the cost of a mission from Toulon to Oran and return would be -4E99300.

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Figure 2 A cross section of salinity from the O C C A M 1/12 x 1/12 ~ global ocean model along the track indicated in the inset map. Africa is on the left, France on the right and the section crosses the island of Mallorca. 3.3 Transects of the Mediterranean outflow Figure 3 shows an instantaneous, winter-time salinity section from the O C C A M 1/leXl/le ~ global ocean model between Africa, near E1 Aaidn (Western Sahara) and ending in Portugal, near Lisbon. The track is indicated in the inset map. This section slices through the tongue of high salinity water resulting from the Mediterranean outflow (coming from the right). Some of this high salinity water detaches from the Iberian coast as sub-surface eddies. Such mesoscale features are intermittent and difficult to sample with traditional ship-based instruments. Regular transects by AUVs could be used to good effect.

Figure 3 An instantaneous, winter-time salinity section between Africa (left) and Portugal (fight) from the OCCAM 1/12 x 1/12~ global ocean model. The track is indicated in the inset map. The outflow of high salinity water from the Mediterranean has an impact on the regional and global ocean circulation (Candela, 2001). Programmes such as C A N N I G O have


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improved our understanding of the physics of the area, but routine in situ monitoring may have a place in future plans. If so, the requirements in terms of depth and range would require the capabilities of the next generation of AUVs. No AUV available today has the necessary combination of range and endurance. The range required (~1400 km) is within the endurance of gliders such as Spray, Seaglider and Slocum (Davis et al., 2003), but these gliders are limited currently to operate at depths of less than 1500 m, barely adequate to reach the lower part of the Mediterranean overflow. Several large propeller-driven AUVs in routine use today would not be able to reach the maximum depth of ~5000 m but would be able to reach 3000 m and hence the lower part of the overflow. However, they do not have the endurance. The Odyssey AUV, which uses glass spheres for buoyancy, has an adequate depth rating, but its endurance is far short of what' s required. Given direction and encouragement, marine technologists could provide AUVs with the combination of range and endurance within five years.

4. Conclusions Using examples from the applications of autonomous underwater vehicles in marine scientific process experiments and their recent success in providing efficient routine surveys for the offshore industry we have shown that it is no longer technology that is holding back the use of these vehicles in operational oceanography. Vehicles existing today could tackle sections such as Scotland to Iceland and France to Algeria with midsection refuelling. The cost of occupying such sections with AUVs is significantly less than when using a research ship. Technology developments will be needed, in terms of range and depth, before AUVs will be able to tackle sections such as Portugal to Africa (or the Canary Islands) across the Mediterranean outflow. Docking stations for AUVs are being developed that would allow battery recharging and download of data without the need for recovery. When these stations become available, the cost of mid-section refuelling could reduce and the risk of damage to the vehicle could also reduce, as recovery remains the most difficult operation with an AUV. Debate on the introduction of AUVs into operational oceanography should centre on the scientific requirement in the context of cost, with close dialogue between scientists and engineers on turning requirements into practical vehicles.

Acknowledgements We are grateful to Alain Norro of MUMM for the description and photograph of the Mauve vehicle and Torfi Thorhallsson of Hafmynd for details of Gavia. Andrew Coward of the OCCAM large scale modelling team at SOC kindly produced the simulated AUV sections in Figure 2 and Figure 3, hinting at the powerful combination of model and AUV.

5. References Candela, J., 2001, Mediterranean water and global circulation, pp. 419-429 in Ocean Circulation and Climate, Siedler, G., J Church and J. Gould (eds), Academic Press, London.

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Danson, E.F.S., 2003, AUV tasks in the offshore industry, pp. 127-138 in Technology and Applications of Autonomous Underwater Vehicles, Griffiths, G. (ed). Taylor and Francis, London. Davis, R.E., C.C. Eriksen and C.P. Jones, 2003, Autonomous buoyancy-driven underwater gliders, pp. 37-58 in Technology and Applications of Autonomous Underwater Vehicles, Griffiths, G. (ed). Taylor and Francis, London. Dickey, T.D., 2002, A vision of oceanographic instrumentation and technologies in the early twenty-first century, pp. 209-254 in Oceans 2020mScience, Trends and the Challenge of Sustainability, Field, J.G., G. Hempel, and C.P. Summerhayes (eds). Island Press, Washington D.C. Fischer, J. and N.C. Flemming, 2002, The EuroGOOS data requirements survey, pp. 3544 in Operational Oceanography--Implementation at the European and Regional Scales, Proc. 2nd International Conference on EuroGOOS. Elsevier, Amsterdam. Gallett, I.N.L., 1999, $10 Oil: Is underwater robotics an answer? Report of a workshop on AUVs and other underwater robotics, SUT, London. Griffiths, G., S.D. McPhail, R. Rogers, and D.T. Meldrum, 1998, Leaving and returning to harbour with an autonomous underwater vehicle. Proceedings Oceanology International '98, Brighton, Vol. 3, pp. 75-87, Spearhead Exhibitions Ltd, New Malden, ISBN 0 900254 23 8. Griffiths, G., 2003, Technology and Applications of Autonomous Underwater Vehicles, Taylor and Francis, London. Hill, A., 2002, AUV uptake in the offshore industry: maintaining a forward momentum, Proc. Unmanned Underwater Vehicle Showcase 2003, p. 1, Spearhead Exhibitions, New Malden, UK. IOC, 1998, The GOOS Prospectus 1998, Intergovernmental Oceanographic Commission, Paris. Langebrake, L.C., 2003, AUV sensors for marine research, pp. 245-278 in Technology and Applications of Autonomous Underwater Vehicles, Griffiths, G. (ed). Taylor and Francis, London. Millard, N.W., S.D. McPhail, P. Stevenson, M. Pebody, J.R. Perrett, A.T. Webb, M. Squires, G. Griffiths, S.A. Thorpe, M.B. Collins, P. Statham, C. German, P.H. Burkill, K. Stansfield, and D.A. Smeed, 2003, Multidisciplinary ocean science applications of an AUV: the Autosub science missions programme, pp. 139-160 in Technology and Applications of Autonomous Underwater Vehicles, Griffiths, G. (ed). Taylor and Francis, London. Saunders, P.M., 2001, The dense northern overflows, pp. 401-418 in Ocean Circulation and Climate, Siedler, G., J Church and J. Gould (eds), Academic Press, London. Stevenson, P., 1995, The potential of robotic systems in the seas and offshore--Report on the Collquium, Underwater Technology, 21 (2), pp. 44-46. Thorhallsson, T and H. Hardason, 2002, GAVIAma modular compact AUV for deep and shallow waters, Proc. Unmanned Underwater Vehicle Showcase 2003, pp. 4558, Spearhead Exhibitions, New Malden, UK.

Sustainability analysis in marine research, monitoring and forecasting systems Jun She

Danish Meteorological Institute, Denmark

Abstract End-users are driving forces behind the development of marine science. A sustainable Research and Technological Development (RTD) system serves as a basic tool to transfer basic research to service products. Successful development of next generation marine research, monitoring and forecasting systems relies on sustainable strategy in management human, monitoring and computing resources. This paper analyses relationships between European marine research, monitoring and forecasting systems, reveals existing problems (e.g. bottle-neck effects) and highlights possible solutions in order to maintain sustainable development in these systems.

Keywords: Sustainability, forecasting, monitoring, system optimisation 1. Introduction Current major marine services are provided by operational forecasting and environment agencies while the origin of the knowledge used in generating the service products is from basic research and technology development. Figure 1 shows a schematic flowchart of transferring research to service in a marine RTD system. Model, observation and operational platform are three major elements that generate service products. The importance of any of them should not be down-played. A sustainable RTD system requires these three elements to be developed in a balanced and harmonised way. This requires a smooth transfer from basic research to applied research and from technology to monitoring, and also requires a balanced development between the computing system and communication network. Otherwise "bottle-neck" effects will prevail and the system resources may be largely wasted. The sustainability of such a system is mainly determined by two factors: external enduser needs and internal consistency among different subsystems. The former is mainly related to service availability and quality while the latter to the nature of marine science as well as system engineering. To maintain the sustainability of marine research means optimising the RTD system to provide a better service. The purpose of this paper is to reveal existing problems in the European marine RTD system and try to highlight possible mechanisms in solving these problems. Section 2 analyses the interactions and existing problems of the subsystems shown in Figure 1, and describes possible solutions, and section 3 gives some concluding remarks.

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2.1 Research system 2.1.1 Research direction

Marine science is basically an application-oriented research. For historical reasons, basic research (e.g., theoretical research) is mainly conducted in universities and research centres. The mutual part of the knowledge from the basic research is then delivered to operational agencies such as met. offices or environmental agencies for applications. Recognising that marine science is application-oriented, marine research policies should be made for improving the service. Hence interactions between research centres, universities and application sectors should be strengthened, and in particular feedback from the latter should be used to steer the research policy. This requires a certain level of participation from application sectors (e.g. operational agencies) in providing scientific advice to both European and national marine research policies for next generation systems. In this way new knowledge creation is oriented to applications. This provides a sensible basis to transfer research to service products. On the other hand, research needs to be driven by improving knowledge and understanding, links between application sectors and basic research sectors (such as the Research Network of Excellence in FP6) should be strengthened.

2.1.2 Human resourcesand integratedforecastingsystem Due to the application role of operational agencies in marine RTD system, their human resources in marine research are limited. For example, the number of modellers in marine groups of operational forecasting agencies in Europe ranges from 2 to 10, normally around 4. They have to maintain operational running of a series of ocean models: 3D ocean models with different coverage, wave models, surge models, ice models, oil drift models and even ecosystem models. Few resources are left for developing state-of-the-art models. Without wide range networking, a critical mass of

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research can never be reached. On the other hand, many operational models have similar physical frames and are run in similar marine areas by several operational agencies though the areas of focus could be different. This means that a common knowledge base exists but work is duplicated. This is why, in the field of marine forecasting, the sharing of workload, knowledge and forecasts is becoming a natural option to deal with newly raised research problems. The BOOS (Baltic Sea Operational Oceanography System) and NOOS (NW Shelf-sea Operational Oceanography System) groups are good examples of such cooperation.

2.2 Monitoring system 2.2.1 Existing observation database and modelling requirements Many EU member states have their own national monitoring systems, usually operated by environmental agencies and/or forecasting agencies. Figure 2 gives an example of profile measurement locations in northern European seas. The observations are obtained either in near real-time or in delayed mode, differing in variables and countries. Generally, surface physical measurements are obtained in real-time, sub-surface physical measurements in near real-time or with days delay while biological variables in weeks delay in the monitoring institutes.

Figure 2 Oceanographic stations measuring profile data in the northern European seas by major national operational agencies and environment monitoring agencies (ICES, BOOS, NOOS members and from the ODON project): diamonds are buoys (only from Baltic countries) and black dots are ship stations

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Regional scale modelling needs full area coverage from observations. It is very difficult to develop a large scale application based on the database of a single country. This is why existing observation databases have hardly been used in operational data assimilation though the full regional coverage has been reached by the existing monitoring systems. To improve this situation, first a speedy observation exchange network is needed, with full coverage and secondly existing monitoring programmes must be coordinated and harmonised so that they are optimal to both regional and local scale modelling. In recent years observation exchange under a regional framework has started, albeit slowly. Currently two kinds of near real-time data exchange networks have been established, namely in BOOS and delayed mode environmental data exchange (delayed up to a few years) under supervision of HELCOM and OSPARCOM. The former is a network between operational forecasting agencies and only exchanges water level and wave data in limited stations in the Baltic Sea. This near real-time data exchange should be expanded to the European Shelf Seas and include hydrographical data from the environmental monitoring agencies. The observation analysis product can then be used in forecasting models via data assimilation. Exchange of bio-chemical measurements should also be sped up for rapid environmental assessment, as required by intergovernmental agencies such as HELCOM and OSPARCOM. There are many issues to be resolved before an observation data exchange system can be up and running. At the national level, there are often many different government agencies engaged in making and using marine observations and monitoring, and this can lead to issues of ownership, Intellectual Property rights and funding. Organisations such as BOOS and NOOS are appropriate platforms for discussion to reach consensus in resolving these issues. 2.2.2 Technology and monitoring systems There is no doubt that new marine technologies are solving many problems in marine technology frontiers while old, cheap and robust technologies still have their active role in routine operational monitoring network. For example, the technology used in the very successful ARGO open ocean profiler system has a history of tens of years. It seems that cost-effectiveness is still the most important index in a large scale monitoring system. Cost-effective marine technology does not automatically guarantee a cost-effective monitoring system because marine monitoring is system engineering. The performance of the entire system relies not only on marine technologies but also largely on where the instruments are deployed. An optimised monitoring strategy should be designed and used (Prandle et al., 2003). Most national monitoring programs will have been developed to address specific policy questions such as water quality, and may not have considered the possible additional use of the data in models, or the use of modelled data as a monitoring tool. Effort is required to gain wider acceptance or to develop the use of operational models as a monitoring tool, which is a fundamental output of observing systems.

2.2.3 Integrating existing monitoring systems Existing monitoring systems are mainly designed for local and historical reasons and are not optimised in regional scale monitoring. Measurements are collected for different time schedules by different countries and spatial sampling locations are not harmonised.

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Therefore the usefulness of these data in regional models is limited. This suggests a need for more multi-national integration in monitoring systems. However, for political and practical reasons it is very difficult to coordinate a variety of national monitoring programmes on a regional scale. The first step towards an integrated regional monitoring system is to design such a system and demonstrate its value. There are two ways to demonstrate it: Observing System Simulation Experiment (OSSE) and Observing System Experiment (OSE). The former is a virtual demonstration where different observing systems are simulated by using numerical models and their cost-benefit performance can then be tested. ODON (Optimal Design of Observational Networks), an EU FP5 program focusing on optimal 3D sampling strategy for Baltic-North Sea temperature and salinity observing system, is such a quantitatively and systematically designed optimal monitoring system. Naturally the optimising process should be extended to regional environmental monitoring networks. The OSSE is easier to perform (in comparison with OSE) but may not be sufficient to convince monitoring agencies to adjust their monitoring strategies. OSE, on the other hand, directly conducts field experiments according to a rationally designed sampling strategy, collects the measurements and then uses models to demonstrate the cost-benefit of the observing system. This is more convincing but harder to perform since a multi-national monitoring network has to be formed, for which members have to make agreement to share monitoring resources. Such an integrated initiative can only be possible with support from external funding agencies (e.g. EU) and a closely cooperated and trusted consortium. Since the regular monitoring programs are funded by Member States, this integrated monitoring system should be sustainable after the demonstration period.

2.3 Platforms 2.3.1 Computing system and communication network The computing and communication capabilities should match each other. Currently a bottle-neck effect in communications networks exists in some of the forecasting agencies, which means that the bandwidth of the communication network is not large enough (currently most have an order of Mbs -1) to transfer large amounts of data produced by faster supercomputers (of the order of 1 Gb). It is necessary to upgrade the communications networks in these agencies. This is also required by growing regional forecasting and data exchange networks.

2.3.2 Computing system and modelling requirements Figure 3 is the schematic evolution chart of computing power in the last two decades, shown in two categories: supercomputer and PC. In the current stage the fastest PC runs at about 3 GHz, which is equivalent to about 5 Gflops while the fastest class of supercomputer (e.g. Earth Simulator) has a peak speed of tens of Teraflops. According to Moore's law (i.e. computing speed doubles every 18 months), a Petaflops machine will be available within about 10 years. At the same time a PC will reach a speed of up to 1 Teraflop. If only considering physical 3D ocean prediction, a teraflop computer is sufficient to perform week-long global forecasts by using eddy-resolved ocean models (with ~2km resolution). However, if we consider sustainable development of marine forecast capability over then next 10-20 years, such as ensemble forecast for extreme events,

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coupled (maybe ensemble) atmosphere-ocean-ecosystem forecasts in time range of 5-90 days, even a Petaflop computer can't satisfy these requirements. Multi-national computing resources should be integrated so that local, regional and global forecasts are run on different computers. This could correspond to three levels of marine centre (national, regional and European levels). 1PF t

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3. Concluding remarks Europe is progressing to a European Research Area, marked by the launch of FP6 in November 13, 2002. Marine science is also facing challenges in its frontier, e.g. ecosystem prediction, intra-seasonal prediction and ensemble prediction. To resolve these challenges, sustainable RTD systems, monitoring systems and forecasting systems at European and regional levels have to be established. Based on analysis of existing systems, in order to reach a harmonised marine research, monitoring and forecasting system, priority should be given to integrated research, forecasting and monitoring systems with emphasis on reducing duplicated research, coordinated regional monitoring (integrated marine infrastructure), observation exchange and shared human and computing resources.

References Kantha L. and C.A. Clayson, 2000, Numerical models of oceans and oceanic processes. Academic Press. Prandle, D., J. She, and J. Legrand, 2003, Operational Oceanography~the Stimulant for Marine Research in Europe. In: Wefer, G., Lamy, F., and Mantoura, F. (eds), Marine Science Frontiers for Europe. Springer-Verlag, Berlin-Heidelberg-New YorkTokyo, pp. 161-171.

EC Operational Forecasting Works hop: Reports on EC Operational Forecasting Projects


The use of HF radar networks within operational forecasting systems of coastal regions K.-W. Gurgel,, H.-H. Essen, and T. Schlick

Universit(~t Hamburg, Institut fiir Meereskunde, Germany

Abstract Operational forecasting of current and wave fields in coastal regions has become more and more important over the last decades, both for coastal management and for security aspects. One of the key components in this context is high-resolution numerical modelling, which however requires accurate forcing and handling of the boundary conditions. HF radar remote sensed current and wave fields can significantly increase the data quality of the model products through data assimilation. In some cases, when the oceanographic processes induce high local variability, such as mesoscale eddies and fronts, this approach might be the only way to provide reliable nowcasts and forecasts.

Keywords: HF radar, remote sensing, monitoring system, ocean current. 1. Introduction In some regions of the world, mesoscale processes like eddies with diameters between 10 and 100 km and strong current fronts or coastal jets affect human activities in shipping, fishing and engineering. The coastal current dynamics in these areas is hard to describe and forecast with numerical models due to their partly chaotic behaviour. Ship surveys and moored instruments are the classic tools ofoceanographers to study these processes, however it is nearly impossible to track the oceanographic phenomena described above, as they are undersampled in space and/or time. Radar remote sensing methods have been available for about 20 years. Their main advantage is the area-covering synoptic view of the ocean, which is however restricted to the near sea surface. Besides the well known satellite-based systems, shore- or shipbased HF radars have been developed (Barrick et al., 1977, and Gurgel et al., 1999). These do not provide global coverage like the satellite systems, but they have the advantage of giving a contiguous observation in limited areas with high spatial (down to 300 m) and temporal (down to 10 minutes) resolution. This paper presents the design of the integrated measurement/model system which has been developed within the European FP4 project EuroROSE (European Radar Ocean Sensing, Gtinther et al., 2000), with the main emphasis on the application of HF radar. In addition, a vision on HF radar networks to cover basin-wide scales has been developed and questions like allocation of frequency bands and techniques of sharing frequencies by simultaneous operation of multiple HF radars are discussed.


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The use of HF radar networks within operational forecasting systems of coastal regions

2. The monitoring system developed for EuroROSE The general structure of a monitoring system is given in Figure 1. The aim is to provide accurate on-line access to the actual situation, on-line meaning a maximum delay of one hour, and to provide forecasts. This could be achieved by a synergy of observations and numerical models, i.e. by linking radar based measurements to fine-resolution models by data assimilation. The measured data are required to force the model close to nature, whereas the model is needed for interpolation and forecasts. Note that these kinds of operational models are completely different from those required for climate research. Observations/ Measurements Weather Stations Buoys Moorings Drifters Satellites HF Radar

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Vessel Traffic Services: Waves,Currents -+ EuroROSE Port Authorities: Water Level, Storm Surge, ETA Captains and Pilots: Waves, Currents, Route Optimization Search and Rescue: Waves, Drift (shipwrecked person) Environment Authorities: Water Level, Storm Surge, Drift (oil) Figure 1 The main components of an Operational Forecasting System and some examples of users of such a system. A monitoring system as described above has been demonstrated within the EU project EuroROSE to provide on-line current and wave data to VTS (Vessel Traffic Service) officers. On the measurement side, the University of Hamburg HF radar WERA and OceanWaveS' WaMoS (Wave Monitoring System, Reichert et al., 1999) microwave radar have been deployed to deliver remote-sensed ocean current and wave data. Both radar systems were operated from the coast and provided measurements three times per hour. The numerical models within EuroROSE have been operated by the Norwegian Meteorological Institute (met.no), while the data assimilation technique has been developed by the Norwegian Nansen Environmental and Remote Sensing Center (NERSC).

2.1 The WERA HF radar High-Frequency (HF) radars use frequencies between 3 MHz and 30 MHz. These systems make use of resonant backscattering of radio waves from the rough sea surface

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by a process known as Bragg scattering, i.e. 10 m electromagnetic waves couple to 5 m ocean waves. The transmitted electromagnetic wave travels along the conducting ocean surface far beyond the horizon.

Figure 2 The surface current field measured by HF radar at the 19th February 2000 04:00 UTC. A sharp front can be seen west off the ship entrance between Fedje and Lyngoy The HF radar WERA has recently been developed at the University of Hamburg. It is a modular system which can easily be adapted to different applications, cf. Gurgel et al. (1999). Depending on the working frequency of the HF radar, a working range of up to 150 km can be covered by WERA. Within EuroROSE a medium range system has been used. Current maps as shown in Figure 2 were sent to the data assimilation system every 20 minutes, as well as maps of significant wave height, wave direction, and peak frequency, which were processed using the University of Sheffield HF radar wave inversion algorithm (Wyatt, 2000). 2.2 The nested model chain

To supply the best available boundary conditions to the high-resolution current and wave models of the target area, a nested model approach is used. There is a three step model chain for currents: The outer model covers the North Atlantic and the Norwegian Sea with a resolution of about 20 km. This model delivers boundary conditions to an intermediate model (4 km resolution) of the coastal waters of southern Norway, which in turn provides boundary conditions to the high-resolution model. All models are Princeton Ocean Models as implemented and modified by the Norwegian Meteorological Institute.

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This sophisticated and costly model system can currently only be operated by national Met Offices or large companies. The inner high-resolution model with the data assimilation system, however, has been run on a powerful workstation which once an hour calculated the nowcast and hourly forecasts up to +6 hours.

2.3 The data assimilation system The data assimilation scheme used within EuroROSE has been implemented by NERSC. A sequential method called Ensemble Kalman filter (Breivik and Sa~tra, 2001) has been used: Instead of deriving the error covariance matrix from a number of simultaneous models runs, the matrix is derived from a number of model states stored from a run with a similar climatology. The error statistics of the measurement data to be assimilated into the model are derived from previous measurement campaigns and include the spatial variability due to geometrical effects. Actual measurement errors as provided by the HF radar have not been used within the data assimilation, because it would have been too time-consuming.

Figure 3 The surface current field calculated by the model after data assimilation of the HF radar current field shown in Figure 2. Figure 3 shows a current field delivered by the model/data assimilation system. The oceanographic front can still be seen, although it appears to be smoothed. The model results represent the top 10 m of the sea surface (this affects the navigating ships), while Figure 2 shows the measured current velocity at the very top 0.5 m.

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To get some information concerning the performance of the monitoring system, model results have been compared to the HF radar measurements for the same time. As expected, the nowcast and the measurement show an rms error as low as 10 cms -1 for a position in the centre of the measurement area. When comparing the 2- to 6-hour forecasts with the measurements taken at that time, the rms error increases to 20 cms -1. Breivik and Sa~tra (2001) present scatter plots and correlations for the different forecast steps. The correlation factor is 0.89 for the nowcast, 0.85 for the 2-hour forecast, 0.77 for the 4-hour forecast, 0.63 for the 6-hour forecast, and 0.27 for a free running model without data assimilation. The poor performance of the free running model shows the importance of including measured data in the monitoring system. The accuracy of the HF radar measurement itself is discussed by Essen et al. (2000).

3. An HF radar network To cover basin-wide scales, an HF radar network can be installed. Figure 4 shows an example of the western Baltic Sea with four HF radars installed along the coast to monitor the route of highspeed ferries, which connect Warnemtinde and Trelleborg.

Figure 4 An example of an HF radar network installed in the western Baltic Sea. The working range of the four radars shown is 100 km. Besides other factors, the working range of the radars depends on the operating frequency and water salinity. The indicated rage is 100 km which we expect at 8 MHz radar frequency. As in this example four HF radars are to be operated simultaneously in the same area, special care has to be taken regarding the electromagnetic frequency management to avoid interference.

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4. Conclusions HF radars play an important role within operational forecasting systems. They can provide the area-covering data which are needed to keep numerical models close to the oceanographic phenomena. On the other hand, the great advantage of the models is the complete 3-dimensional coverage and the possibility to provide forecasts. Within the EuroROSE project radar remote sensed current and wave data and numerical models have been coupled together for the first time to provide on-line nowcasts and forecasts to traffic control officers at VTS centres. Further information on the EuroROSE project can be found at http://wera.ifm.uni-hamburg.de/EuroROSE/.

Acknowledgments This work has partly been supported by the European Commission, DG XII, within the Mast-3 programme, project CT98-0168, EuroROSE (European Radar Ocean Sensing). The authors are grateful to the other partners of the EuroROSE group: H. Gtinther, W. Rosenthal, and M. Stawarz (GKSS, Germany), J. Guddal and M. Reistad (met.no, Norway), G. Evensen and 0. Breivik (Nansen Center, Norway), L. R.Wyatt and J. Green (University of Sheffield, UK), J. C. Nieto Borge (Puertos del Estado, Spain), and K. Reichert (OceanWaveS, Germany). We also wish to thank our technician M. Hamann and the local port authorities and computer centres for all their support in the experiments.

References Barrick, D.E., M.W. Evans, and B.L. Weber, 1977, Ocean surface current mapped by radar, Science, vol. 198, pp. 138-144. Breivik, 0. and 0. Saetra, 2001, Real time assimilation of HF radar currents into a coastal ocean model, Journal of Marine Systems (JMS),vol. 28, no. 3-4, pp. 161-182. Essen, H.-H., K.-W. Gurgel, and T. Schlick, 2000, On the accuracy of current measurements by means of HF radar, IEEE Journal of Oceanic Engineering, vol. 25, no. 4, pp. 472-480. GUnther, H., G. Evensen, J. Guddal, K.-W. Gurgel, J.C. Nieto Borge, and L.R. Wyatt, 2000, European Radar Ocean Sensing. In the proceedings of the EurOCEAN 2000: The European Conference on Marine Science and Ocean Technology of the European Commission, 29. August-2. September 2000, Hamburg, Germany, pp. 443-448. Gurgel, K.-W., G. Antonischki, H.-H. Essen, and T. Schlick, 1999, Wellen radar (WERA), a new ground-wave based HF radar for ocean remote sensing" Coastal Engineering, vol. 37, pp. 219-234. Gurgel, K.-W., H.-H. Essen, and S.P. Kingsley, 1999, HF radars: Physical limitations and recent developments, Coastal Engineering, vol. 37, pp. 201-218. Reichert, K, K. Hessner, J. C. Nieto Borge and J. Dittmer, 1999, WaMoSII: A Radar based Wave and Current Monitoring System", ISOPE '99, Proceedings, vol. 3. Wyatt, L.R., 2000, Limits to the Inversion of HF Radar Backscatter for Ocean Wave Measurement" Journal of Atmospheric and Oceanic Technology, vol. 17, pp. 16511665.

The DIADEM/ToPAZ monitoring and prediction system for the North Atlantic Laurent Bertino* and Geir Evensen Nansen Environmental and Remote Sensing Center, Norway DIADEM real time experiment: http://diadem.nersc.no/rtweb.html DIADEM homepage: http://diadem.nersc.no TOPAZ homepage: http://topaz.nersc.no Nansen Center homepage: http://www.nersc.no

Abstract The DIADEM/ToPAZ monitoring and forecasting system is currently being developed with support from projects funded by the European Commission and national research councils. The overall long term objective is to develop an operational ocean and ecosystem monitoring and prediction system for the North Atlantic, Nordic Seas and Arctic Ocean, using state of the art numerical model tools and data assimilation methodologies. The focus has so far been on the development remotely sensed data into ocean and ecosystem developments of real time assimilation systems sophisticated assimilation methodologies which iance statistics.

of methodologies for assimilation of models. In contrast to other ongoing we have chosen to work with rather also predict multivariate error covar-

The implementation of the assimilation systems has been completed and they have been applied in hindcast experiments for validation purposes and calibrated for use with different data types. Currently, the assimilation system is operated in real time and provides forecasts of physical ocean parameters. Results are freely accessible from both the DIADEM and TOPAZ home pages. In this paper the overall status of the DIADEM/ToPAZ monitoring and prediction system will be described in some detail and the first results from the data assimilation experiment will be presented.

1. Introduction The need for high quality predictions of marine parameters has been well identifiedufor example during recent years, offshore oil-exploration activities have expanded off the continental shelves to deeper waters. Drilling and production of oil and gas at depths of 2000 meters or more is carried out at several locations. This has introduced a need for real time forecasts of oceanic currents which in some cases may have a severe impact on the safety related to drilling, production and critical operations. Sustainable exploitation of marine resources are becoming increasingly important, e.g. commercial fisheries and fish farming. In future fisheries management systems, information about marine parameters such as nutrient and plankton concentrations, and pollutants, will be increasingly * Corresponding author, email [email protected]

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important for accurate monitoring and prediction of fishstocks. Thus, there are needs for operational monitoring and prediction of both physical and biological marine parameters. An operational ocean forecasting system will have to rely on integrated use of observations of physical, biological, and chemical variables and coupled physical and marine ecosystem models. This integration can best be done using data assimilation techniques. Thus, it will be necessary to further develop and implement consistent data assimilation techniques for primitive equation models and also new suitable methods for assimilation of data into the models of the marine ecosystem. Further, the real time processing and flow of observational data must be developed and maintained.

Figure 1 Model domain used for the ToPAZ prediction system. The plot shows sea surface temperature and ice concentration. The DIADEM/ToPAZ system is being developed to meet the needs from future users of marine parameters. It involves both the implementation and validation of state of the art ocean circulation models and marine ecosystem models, and the development of novel data assimilation methodologies. The system development has been centred around two ongoing European Commission funded projects, i.e. the DIADEM and TOPAZ projects, which are briefly explained in the following sections. By comparison, other state-of-the-art forecasting systems currently use traditional data assimilation techniques, including:

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9 FOAM (http://www.metoce.com/research/ocean/operational/foam/index.html) 9 MERCATOR (http://www.mercator.com.fr) 9 MFS (http://www.cineca.it/mfspp) The model domain used for the TOPAZ prediction system is shown in Figure 1. The grid is created using a conformal mapping of the poles to two new locations using the algorithm outlined in Bentsen et al. (1999). The figure shows sea surface temperature and ice concentration. The resolution of the model varies from 20km in the Arctic Ocean to 40km near the Equator. Within the TOPAZ project there is also a downscaling to coastal seas by nesting of high resolution regional models (illustrated by the frames in the figure). 1.1 DIADEM project The EC MAST-Ill project DIADEM has focused on the implementation of a data assimilation system for the North Atlantic and the Nordic Seas. The project involved partners from six European countries working with ocean and ecosystem modelling, data assimilation and processing of remotely sensed observations. The major objective of the project was to implement and demonstrate novel sophisticated data assimilation methods such as the Ensemble Kalman Filter (EnKF) (Evensen, 1994), the Ensemble Kalman Smoother (EnKS) (Evensen and van Leeuwen, 2000), and the Singular Evolutive Extended Kalman Filter (SEEK) (Pham et al., 1998), with the Miami Isopycnic Coordinate Ocean Model (MICOM) developed by Bleck et al. (1992) and a 3-dimensional implementation of the ecosystem model by Fasham et al. (1990) which has been coupled with MICOM by Drange (1994, 1996). The data which were assimilated were remotely sensed sea level anomalies, sea surface temperatures assimilated into the MICOM model, and ocean colour data used for assimilation in the ecosystem model (Natvik and Evensen, 2002a,b). The implementation of the assimilation systems has been completed and they have been applied in hindcast experiments for validation purposes and calibrated for use with different data types. The use of so called advanced methods has introduced the possibility of performing a multivariate and physically consistent analysis with statistical covariance functions which vary in space and time. This allowed us to extract a maximum amount of information from observations of surface quantities. The real time data flow from existing satellite observing systems and their capabilities in providing observations that could be used with the data assimilation system in an operational mode have been evaluated. The project has led to a prototype of a validated pre-operational monitoring and prediction system for the North Atlantic and the Nordic Seas. Estimates of error statistics such as correlation scales and cross-correlation between different variables which are crucial information in all data assimilation systems were produced routinely. The DIADEM project established for the first time an operational capability for coupled physical and ecosystem models in the North Atlantic and the Nordic Seas, where satellite information were assimilated using advanced data assimilation methods (Brusdal et al., 2002).

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1.2 TOPAZ project A new project TOPAZ has been funded by the European Commission under the fifth Framework Programme. TOPAZ extends the developments of DIADEM to a more realistic operational system. The project has a strong focus on end-user requirements and has established a particular link with off-shore oil industry operating in deep waters of the Gulf of Mexico. A major objective of TOPAZ is to establish a generic operational prediction system for ocean currents which will be applied and demonstrated for endusers. To meet the end-users' needs it is necessary to introduce nested regional models to allow for downscaling to very high resolution in the target areas where mesoscale processes must be properly resolved. In addition, the MICOM model used in DIADEM has been replaced by the recently developed Hybrid Coordinate Ocean Model (HYCOM), see the HYCOM web page http://panoramix.rsmas.miami.edu/hycom/. This model integrates the properties of the isopycnal MICOM model for the deep ocean with a level model for the surface boundary layer. Thus it is designed to work equally well for the coastal shelf areas as for the deep ocean. HYCOM has been coupled to a seaice model and a Carbon:Nitrogen Regulated Ecosystem Model (REcoM) with carbon and nitrogen being decoupled. The data assimilation system developed in DIADEM is being further extended to use new data types. A capability has been developed for assimilation of in situ data from the ARGO programme as well as additional remote sensing products such as ice concentration (SSMI), ice thickness (Cryosat), sea surface salinity (SMOS) and the improved sea level anomaly data which can be derived with the new geoid from the GOCE mission. With the inclusion of a nesting capability and the assimilation of both in situ data and data from a variety of satellite sensors, the TOPAZ project will develop a state of the art and flexible operational ocean prediction system. The model system has been designed to be easily extendible to other geographical areas including the global domain and it allows for nesting of an arbitrary number of regional high resolution models.

2. Participants The DIADEM project involved seven European partners with different responsibilities in the project: 1. The Nansen Environmental and Remote Sensing Center (NERSC) coordinated the project and supplied the model systems used by all partners. NERSC was responsible for model validation and an implementation of the EnKF with MICOM and the ecosystem model. 2. The Institute for Marine and Atmospheric Research, University of Utrecht (IMAU), implemented the EnKS with the physical model. 3. The Universit6 Joseph Fourier, Laboratoire des Ecoulements G6o-physiques et Industriels, (LEGI) developed a SEEK Filter with MICOM and the ecosystem model. 4. Calibration of the model parameters in the ecosystem model was done by the Alfred Wegener Institute (AWl).


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5. Gridded fields of sea level anomalies and sea surface temperature data were delivered by Collecte Localisation Satellites (CLS). 6. Gridded ocean colour data from SeaWifs were processed and delivered by the Joint Research Centre (JRC). 7. The HALO Laboratory for Oceanic and Atmospheric Sciences maintained the project web-page, where the forecasts from the assimilation systems were displayed during the real time operation of the system, and has also developed a marine information system which is used for the data management in the project. The partnership in TOPAZ is a subset of the DIADEM consortium with major activities distributed among partners as follows: 1. NERSC is coordinating the project and maintaining the HYCOM model system. The operation of the nested high resolution models are done at NERSC and the development of an assimilation system for ice is the responsibility of NERSC. NERSC will also operate the project database and the internet based display system for all the TOPAZ products. 2. LEGI is mainly responsible for the development of an assimilation system for in situ observations. 3. CLS is developing and operating a processing capability for ARGO in situ data and the remotely sensed products used in the project. 4. AWI will work on ecosystem model development and validation.

3. Hind-cast experiment A hind-cast experiment has been carried out over a three months period using a low resolution version of the physical model. In the experiment gridded fields of sea surface temperature and sea surface anomaly (produced by CLS) were assimilated. The gridded data were available every 10 days. Three assimilation methods were used: the EnKF, the EnKS, and the SEEK filter. All three methods provided a realistic prediction for the model error statistics which were consistent with the innovation sequence (Brusdal et al., 2002). Further, the assimilation methods could take the multivariate statistics into account and provided a realistic analysis where the whole model state was updated from the surface measurements. As an example, the impact of an SST measurement is to correct the mixed layer temperature in the model but in addition it will also introduce an update of the mixed layer thickness and the location of the thermocline. Similarly the SLA data contain more information about the mesoscale structures, e.g., the rings in the Gulf Stream extension, which could now be updated consistently with the thermocline depth etc. The model with its limited horizontal resolution has a tendency to a too northward location of the Gulf Stream separation. However, an interesting result was that the assimilation system was capable of correcting the location of the Gulf Stream axis and separation point. This is an important result since a successful assimilation system must be capable of correcting obvious model deficiencies. A twin experiment was carried out using the assimilation system with the marine ecosystem model. It proved that surface observations of ocean colour or Chlorophyll

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could be used to control the evolution of the marine ecosystem to some extent. When testing the assimilation system with real data from SeaWifs a similar conclusion could be drawn. The remotely sensed ocean colour data do have an impact on the accuracy of the ecosystem predictions, however, the accuracy of the data are currently rather poor and a greater impact is expected using data from more accurate future sensors.

4. Real time operation A real time prediction experiment was initiated November 2000. It has been ongoing until August 2001 with funding from the DIADEM project, and is now continued with funding from TOPAZ. The system was initially spun up into real time, first in simulation mode, then in a stepwise process the assimilation system for the physical model was introduced with assimilation of sea level anomalies from TOPEX and ERS and Reynolds SST data. During spring 2001, the marine ecosystem model was coupled to the system and following a spinup simulation an experiment was started with real time assimilation of SeaWifs ocean colour data which lasted until end of June 2001. The system has now been operated with a weekly assimilation cycle since November 2000. Results are displayed on the web under the DIADEM project. A major conclusion is that the system can be operated in real time and provides reasonable forecast results using available remotely sensed observations. Within the TOPAZ project the original MICOM ocean model has now been replaced with the new advanced Hybrid Coordinate Ocean Model (http://panoramix.rsmas.miami.edu/ hycom/). From the beginning of 2003, the real-time experiments have resumed with assimilation of sea level anomalies merged from three satellites (ERS2, Jason-1 and GEOSAT follow on) and Reynolds SST data. Further, an implementation has been developed for assimilation of remotely sensed ice data and in situ observations of temperature and salinity. These will be integrated into the real time system during spring 2003.

5. Summary This paper has discussed the formulation and implementation of a prototype of a preoperational monitoring and prediction system for the North Atlantic and the Nordic Seas. The system is based on sophisticated modelling and data assimilation tools and is set up for real time or near real time operation, and now assesses the real time data flow as well as the impact of the remote sensing products on the predictions. The real time operation of the system has proved to be feasible and relies on the availability of remote sensing products in near real time, and atmospheric forcing fields from the meteorological forecasting centres. Finally it should be stated that the DIADEM and TOPAZ projects comply with and contribute to the plans of international programmes such as GODAE and EuroGOOS. The system developed has similarities with the other major initiatives in GODAE and will in many respects be complementary to these. Further, the system is one of the major initiatives contributing to the EuroGOOS task teams, in particular the Atlantic Task Team by developing an assimilation system for predicting the ocean circulation in the Atlantic, the North West Shelf Task Team by introducing high resolution regional


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models for physics and ecosystem covering the European shelves, and finally the Arctic Task Team by the focus on ice modelling and assimilation of ice variables in the Arctic. The project has also realized the importance of merging these different areas into one model system to allow for a proper representation of the interactions between the different seas and oceans.

References Bentsen, M., G. Evensen, H. Drange, and A.D. Jenkins, 1999, Coordinate transformation on a sphere using a conformal mapping, Mon. Weather Rev., 127, 2733-2740. Bleck, R., C. Rooth, D. Hu, and L.T. Smith, 1992, Salinity-driven thermohaline transients in a wind- and thermohaline-forced isopycnic coordinate model of the North Atlantic, J. Phys. Oceanogr., 22, 1486-1515. Brusdal, K., J. Brankart, G. Halberstadt, G. Evensen, P. Brasseur, P.J. van Leeuwen, E. Dombrowsky, and J. Verron, 2002, An evaluation of ensemble based assimilation methods with a layered ogcm, J. Marine. Sys., in print. Drange, H., 1994, An Isopycnic Coordinate Carbon Cycle Model for the North Atlantic; and the Possibility of Disposing of Fossil Fuel CO2 in the Ocean, Ph.D. thesis, Nansen Environm. Remote Sensing Centre and Dep. of Math., Univ. of Bergen, Bergen, Norway. Drange, H., 1996, A 3-dimensional isopycnic coordinate model of the seasonal cycling of carbon and nitrogen in the Atlantic Ocean, Phys. Chem. Earth, 21,503-509. Evensen, G., 1994, Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10143-10162. Evensen, G., and P.J. van Leeuwen, 2000, An Ensemble Kalman Smoother for nonlinear dynamics, Mon. Weather Rev., 128, 1852-1867. Fasham, M.J.R., H.W. Ducklow, and S.M. McKelvie, 1990, A nitrogen-based model of phytoplankton dynamics in the oceanic mixed layer, J. Marine. Res., 48, 591-639. Natvik, L.J., and G. Evensen, 2002a, Assimilation of ocean colour data into a biochemical model of the North Atlantic. Part 1. Data assimilation experiments, J. Marine. Sys., in print. Natvik, L.J., and G. Evensen, 2002b, Assimilation of ocean colour data into a biochemical model of the North Atlantic. Part 2. Statistical analysis, J. Marine. Sys., in print. Pham, D.T., J. Verron, and M.C. Roubaud, 1998, Singular evolutive extended Kalman filter with EOF initialization for data assimilation in oceanography, J. Marine. Sys., 16, 323-340.

GAVDOS: A satellite radar altimeter calibration and sea-level monitoring site on the island of Gavdos, Crete S.P. Mertikas* l, E.C. Pavlis 2, P.G. Drakopoulos 3, K. Palamartchouk 1, and E. Koutroulis 1

/Technical University of Crete, Geodesy & Geomatics Lab, Crete, Greece 2joint Center for Earth Systems Technology, University of Maryland Baltimore County, Interdisciplinary Science Group, USA 3Institute of Marine Biology of Crete, Department of Oceanography, Greece Abstract An absolute sea-level monitoring and altimeter calibration permanent facility has been established on the island of Gavdos, Crete, Greece. The facility has been selected for various reasons, in particular because it is under a crossing point of the ground tracks of TOPEX/Poseidon and Jason-1, and adjacent to an ENVISAT pass. The island is also far from the mainland at a location where tides are small.

Keywords: Calibration/validation,

radar altimetry, sea level, J a s o n - l , ENVISAT,

GPS

1. Introduction This paper describes the objectives, current status and future plans for the establishment of the GAVDOS calibration facility for satellite altimeter missions. GAVDOS is an infrastructure research project. Its first objective is the establishment of an absolute sea level monitoring and altimeter calibration facility on the isle of Gavdos, south of Crete, Greece. The calibration facility is under a crossing point of the ground-tracks of TOPEX/ Poseidon (T/P) and J a s o n - l , and adjacent to an ENVISAT pass. The location of the Gavdos island is shown in Figure 1. The site has been chosen because 9 the small island is far from the main land, with relatively low topography, and rather simple coastal circulation 9 the surrounding geoid is known from in situ measurements and will be further improved using airborne measurements 9 the local tides are small 9 calibration can be made from the island, twice per cycle, on ascending and descending tracks 9 the cross-over information can be used to remove possible biases dependent on the direction of the satellite pass


S.P. Mertikas, E.C. Pavlis, P.G. Drakopoulos, K. Palamartchouk, and E. Koutroulis

259

it is possible to locate an altimeter transponder precisely under the crossing point for an additional, independent and innovative direct way of calibration.

Figure 1 The location of Gavdos island and the Jason-1 ground tracks. The purpose of such a permanent facility is 9 to conduct comparative laser distance measurements between the facility and satellite radar altimeters, such as TOPEX/Poseidon, Jason-1, ENVISAT, etc. 9 to ensure the unbiased establishment of the mean sea level, as realised by the globally distributed altimeter measurements 9 to consistently and reliably monitor any radar altimeter errors (either systematic or random) 9

to cross-calibrate different satellite altimeter missions, on a common and long-term basis. Our challenge is to meet the 1-cm accuracy level needed for the Jason-1 data products.

The second objective is to monitor deformations of the Earth' s surface at the tide gauges in the area as a contribution to the EuroGLOSS (Global Sea Level System) (Baker et al., 1997). This objective will be achieved by: 9 monitoring horizontal and vertical land deformation using GPS (Global Positioning System) permanent arrays on Gavdos and on Crete, collocated with tide gauges 9 determining, independently of GPS, the local tectonics by operating a DORIS beacon (Doppler Orbitography by Radio-positioning Integrated on Satellite) 9 by monitoring local sea-level variations with a regional network of tide gauges, and with auxiliary sensors (meteorological, oceanographic, Sea Surface Topography from scanning airborne lasers, etc.). The third objective is the development of a detailed regional geoid and Sea Surface Topography (SST) model, which is required for referencing the altimeter measurements over the calibration facility and for studying the regional sea current circulation. Finally, the fourth objective is to involve this project in other European and international programmes, and in particular, the European Union Cluster on Operational Forecasting, EuroGLOSS, WEGENER (Working Group for Earthquake Research), the IGS (Interna-

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GAVDOS:A satellite

radar altimeter calibration and sea-level monitoring site on the island of Gavdos, Crete

tional GPS Service for Geodynamics), and the TIGA (GPS Tide Gauge Benchmark Monitoring Pilot Project). Using this calibration experiment, the influence of potential error sources resulting, e.g. from the orbital modelling, instrument malfunction and deterioration, etc., decreases significantly. The site is also designed to be used for other altimeter missions, such as European ERS-2, and the US Geosat Follow-On (GFO) missions. The deployment of altimeter transponders at the site shows great promise in additionally making the facility the calibration site of the European ENVISAT altimeter.

2. GAVDOS Project Status At the time of this presentation, significant progress in the construction of equipment facilities, preparation of infrastructure for data transmission and processing, and in preliminary analysis of geodetic and gravimetric data has been made. Three locations for installing equipment have been chosen (see Figure 2) for the needs of the permanent facility.

Figure 2 Location of the measurement facilities on the Gavdos island The Theophilos Station, shown in Figure 3, is the central facility at Gavdos. It has been constructed on an area of approximately 4000m 2. The following instruments have been installed at this site: 9 A GPS receiver on a concrete pillar on stable limestone bedrock 9 A weather station, measuring wind speed & direction, solar radiation, ambient temperature and humidity and barometric pressure 9 A demultiplexer & multiplexer 9 A UHF radio modem link. The multiplexer output, containing the combined data stream (GPS, meteorological station and tide-gauge data) is interfaced to a radio modem. This radio modem will transmit the collected data via a repeater to the mainland of Crete. A 12V battery bank powers all devices at Theophilos. A solar charger is used to charge the battery bank from a photovoltaic source consisting of eight SM55 modules (Siemens) having a maximum output power capability of 440 Watt (under 1kWm -2 irradiance), placed at 60 ~ tilt and facing south. The estimated average daily energy production during winter is 45 Ah, while the daily energy requirements have been estimated to be approxi-


261

mately 25 Ah. The solar panels' energy production is stochastic and the power system design has been based on monthly mean irradiation values of previous years. Power over-sizing has been incorporated in the system design method in order to compensate both the variable energy production and future additional energy requirements (Markvart, 1994). The battery bank consists of three batteries (Gel electrolyte) with a nominal capacity of 210 Ah, resulting in a total nominal capacity of 630Ah. A gas generator has been installed as a backup power supply for a PC. At this stage, there is no absolute gravity station at this facility.

Figure 3 The Theophilos station and the design of various installations

Figure 4 The Karave station and the design of various installations The Karave station and the instrument functionality are shown in Figure 4. The following instruments have been installed at Karave station: 9 A data-logger to store the acoustic tide-gauge sensor measurements. Precision levelling of the tide gauge marks has been carried out to several geodetic benchmarks in the area 9 A back-up tide gauge (Pressure), a Doppler Current Meter and a datalogger 9 The combined data stream is transferred to a low power (1 watt) radio modem and then transmitted to the Theophilos permanent facility

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GAVDOS: A satellite radar altimeter calibration and sea-level monitoring site on the island of Gavdos, Crete

9 A solar charger to charge a 12-volt battery bank from a Photovoltaic power source in order to cover the data-acquisition units and radio modem power requirements 9 A stand alone wave and sea level recorder at a distance of about one mile offshore of Karave port at 10 m water depth. The main acoustic tide-gauge was installed by experts who have worked for similar installations for NOAA/USA and the United Nations all over the world. This gauge is tracking measurements on its own by measuring in the calibration tube and self-corrects for temperature variations in the measuring air-column. Control levelling is carried out at least twice per year and definitely after any changes on the system/environment. The back-up tide gauge measures differential pressure in a single channel with an accuracy of __.0.2% of the range (tides in the area are of the order of 40cm). Being a back-up gauge no precise datum control is planned at this early stage of the project. However, plans include implementing the method proposed by Smith et al. (1991) for its collocation. The Dias station is at the crossover point. It is located about 3.5 km away south from the Theophilos site. It has a concrete base of 1.5 x 2.0m for the installation of an altimeter signal transponder (Fu and Cazenave, 2000).

Figure 5 The equipment setup at the Operations Control Station in TUC, Crete. An Operations Control Center (OCC) has been established at the Technical University of Crete (TUC) (Figure 5). A radio modem at OCC communicates with a repeater station and finally with the facility on Gavdos. A central computer (called COSMOS) and a back-up computer are in regular operation at the OCC. A GPS receiver with a meteorological station is also in continuous operation. The web site for the GAVDOS Project can be found at http://www.gavdos.tuc.gr. All results from the project are disseminated through the official Web site via a public and a restricted area (Data Center). Data are planned to be disseminated to the European Sea Level Service and MedGLOSS. The total amount of data transmitted from Gavdos to TUC (Crete) every day is estimated to


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be about 1.1MB. An overhead of 30% of that must be anticipated to account for the "Error Correction" functions. Existing software packages cannot be used for GPS data collection in this project because there are no open-source software packages that can work with operational GPS receivers, while the cost of commercial software packages is significantly high. A software package is under development, in order to detect and automatically identify any failure signals in real-time, thus improving the accuracy, reliability and integrity of the GPS positioning. It is designed to confirm the existence of failures or changes of small magnitude, and to locate the origin of the failure in order to monitor and control the quality of GPS data for deformation and/or critical real-time applications. The Python 2.2.2 programming language was chosen because it is standard, object-oriented and available for almost all existing computer platforms. As a relational database management system a PostgreSQL 7.3 was chosen because it is regarded as a reliable and mature non-commercial RDBMS and also supports a wide range of built-in data types and embeds an extensive set of data processing functions. GPS data have been processed using precise orbital information (GAMIT, 2000), retrieved from IGS Analysis Centers. Preliminary results are presented in Table 1. A set of Digital Topographical Maps (DTM) and Digital Depth Maps (DDM) has been constructed. The DDM represents a gridded model of the bathymetry of the area around Gavdos (Lat =N33~ ~ and Lon=E21~176 This is to provide a first depth model for the region. Additionally, ship-borne depth soundings from the GEODAS database (US National Geological Survey) have been acquired. The validated global DDM together with the depth soundings from GEODAS will be a mesh of depths with grid spacing better than 0.75' and probably close to 0.25' (1.25 and 0.5 km respectively). A 1 kmdense grid of topographic heights for the inland Crete and Gavdos is also available (data from the Global Land 1-km Base Elevation, GLOBE Project, 2002). The final mesh of topographic heights will be close to or better than 500 m level (0.25'). Table 1 Preliminary WGS84 coordinates of geodetic sites involved (latitude, longitude and height with their root-mean-square (RMS) errors). Reference epoch is 2002.3 Site

Latitude

Longitude

Ellipsoid Height

TUC1

35 ~ 31' 54.96212"_+ 5.6cm

24 ~ 4' 11.02585" _+2.30cm

177.758cm _+3cm

SBTG

35 -0 29' 14.93260" _+l.60crn

24 ~ 4' 57.09439" _+3.37cm

24.156 m _+4.60cm

GVD0

34 ~ 50' 18.58134" _+5.63cm

24 ~ 6' 31.90211" _+8.52cm

124.649 m _+8.15cm

GVD1

34 ~ 50' 18.71625" _+1.69cm

24 -0 6' 31.75722" _+3.56cm

122.385 _+5.27crn

GVD2

34 ~ 50' 54.34317" _+2.62cm

24 ~ 7' 6.97096" _+0.20cm

17.131 _+3.33cm

GVD3

34 ~ 50' 17.44219" _+0.19cm

24 ~ 5' 27.43621" _+0.28cm

256.146 _+0.84cm

GVD4

34 ~ 50' 54.36821" _+0.22cm

24 ~ 7' 7.44944" _+0.37cm

16.724 _+1.01 cm

Further, four cruises of the Institute of Crete of Marine Biology research vessel, Philia, have taken place to collect Conductivity-Temperature-Depth profiles from 25 stations around Gavdos and on a 5-mile grid.

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GAVDOS:A satellite radar altimeter calibration and sea-level monitoring site on the island of Gavdos, Crete

3. F u t u r e P l a n s

Along the lines of this research project, the transponder equipment has been tested inhouse at the Space Research Institute of the Austrian Academy of Sciences, Graz (SRISG) and found in a satisfactory operation state. Further measurements will also be made to ENVISAT, E R S - 2 and Jason-1 satellites in the vicinity of SRISG to ensure that the transponder can manage signals from these altimeters successfully and to check if some modifications are required for changing over to the Jason-1 frequencies (provided that the specifications of the equipment are within the bandwidth of the transponder' s amplifier). Later, the transponder is to be installed permanently at the crossover point on Gavdos. It is expected that with the deployment of altimeter transponder will enable us to measure direct distances to the satellite with a = 5 mm precision. A site for setting up the French Transportable Satellite Laser Ranging (FTLRS) station for this GAVDOS project has been chosen on Crete, on the Technical University of Crete campus. Calibration results from Gavdos will be compared with those from the French Western Mediterranean site at Corsica and the NASA/JPL site at Harvest Platform, California, USA. The permanent facility is expected to become fully operational in 2003. Acknowledgements

The GAVDOS Project is funded by the European Union (contract E V R I - C T - 2 0 0 1 40019), The Swiss Federal Government and, the National Aeronautics and Space Administration (NASA, USA). References

Baker, T.F., P.L. Woodworth, G. B lewitt, C. Boucher, and G. Woppelmann, 1997, A European network for sea level and coastal land level monitoring. Journal of Marine Systems, 13: 163-171. GAMIT, 2000, Documentation for the GAMIT GPS analysis software, Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Scripps Institution of Oceanography, University of California, San Diego. Fu, L.L. and A. Cazenave, Eds., 2000, Satellite Altimetry and Earth Sciences: A Handbook of Techniques and Applications, Academic Press; 1st edition, New York. Markvart, T., 1994, Solar Electricity. Wiley, NY. Smith D.E., R. Spencer, J.M. Vassie, and P. L. Woodworth, 1991, Precise datum control for pressure sea level records, POL Internal Document.

EDIOS" European Directory of the Initial Ocean

Observing System

J e n n i f e r V e r d u i n * ! and J o h a n n e F i s c h e r 2

l lnstitut f~r Hydrobiologie und Fischereiwissenschaft, Universitdt Hamburg, Germany 2Northwest Atlantic Fisheries Organization, Canada Abstract EDIOS will be an innovative computerised directory that contains comprehensive information on all European ocean observing sites and is an initiative of EuroGOOS (European Global Ocean Observing System). EDIOS will constitute a prerequisite for the full implementation of EuroGOOS by allowing for the first time an analysis of the continuously available data for operational models in Europe, and hence the ability to optimise the deployment of instruments, and the design of sampling strategy and devices in routine and repeated operation. This includes technical specification of in situ and remote observing sites and devices such as stations, sections, repeat samples, buoys, platforms; geographical location of the observations; characteristics and frequency of observations; owners of sites and/or devices and data collected; links to data, and a visual user interface to facilitate accessibility of the directory to all categories of potential users. 1. I n t r o d u c t i o n 1.1 What is EDIOS? EDIOS will be a new service to marine science, agencies, and enterprises. It will be complementary to and supportive of meta-databases that contain archived records of projects, or which list scientific cruises, archival data centres, and catalogues of data centres and their holders. Figure 1 represents the functional positioning of EDIOS in relation to other directories and (meta)databases. EDIOS will include information on 9 technical specifications of the data collection methods (instruments, sensors, ships, nets, etc.) 9 geographic co-ordinates 9 specifications of the kind of measurements taken as well as of their spatial-temporal characteristics (but not the observational values) 9 approximate accuracy of measurements 9 present applications of the sampled data including derived products, 9 responsible agency/institute for each instrument or sensor 9 links to data-holding agencies and institutes. Those ocean observing sites/devices that meet minimum standards with regard to their geographic distribution and their reliability, frequency, and processing of the data


266

EDIOS: European Directory of the Initial Ocean Observing System

sampled will be classified for inclusion in EuroGOOS regional models. The importance of considering requirements and priorities of all kinds of users will be met by strong user involvement in all significant steps of the directory design (e.g. meta-data entries and user interface). A user-friendly visual interface will guarantee multipurpose use of the directory allowing a large variety of users from different sectors to perform individual searches through the directory. Regular update of EDIOS in the future will ensure that the directory will reveal most European ocean observing systems operating on a continuous level.

Figure 1 Functional types of databases: EDIOS will be a meta-database on ocean observing systems that networks with existing databases and meta-databases (links) 1.2 Project aim The main aim of the proposed project is to build a meta-database (computerised directory) that includes information on all European ocean observing sites/devices in routine and repeated operation (to be continuously updated) and to use this directory to define the initial European ocean observing system. There are a number of objectives related to different tasks:

1. Gather information on all European ocean observing sites/devices currently in repeated and routine use (stations, sections, repeat samples, buoys, platforms, etc.) in the Baltic, NE Atlantic including the NW European Shelf, and Mediterranean Seas, their geographical location, characteristics and frequency of observations, and dataholding archives, and transfer this information into a searchable database (directory). 2. Define the initial European global ocean observing system by classification of ocean observing sites/devices in use. 3. Create a visual user interface (with user involvement) to facilitate accessibility of the directory to all kinds of potential users, place EDIOS on the Internet, and provide for regular future update.

Jennifer Verduin* and Johanne Fischer

267

The proposed Thematic Network will collect the scattered information on ocean observing sites/devices (metadata only), organise it and combine it into a searchable European directory of operational ocean observing sites/devices currently in use.

2. EDIOS on the Internet EDIOS will be placed on the Internet and continuously updated. This information will include, for example: 9 Technical specifications of the data collection methods (instruments, sensors, ships, nets, etc.) 9 Geographic co-ordinates 9 Specifications of the kind of measurements taken as well as of their spatial-temporal characteristics (but not the observational values). Information on sites sampling biological and biochemical data can be included without the special considerations necessary for the storage of actual records of this kind. Links to databases/archives containing the actual records will be provided. 9 Approximate accuracy of measurements 9 Present applications of the sampled data including derived products 9 Responsible agency/institute for each instrument or sensor 9 Links to data-holding agencies and institutes Such a directory is a prerequisite for the full implementation of a co-ordinated European operational oceanographic strategy (EuroGOOS). It will provide the means to detect gaps and, in some cases, duplicate efforts in the current observation systems. It will enable rapid combination and co-ordination of national ocean observing stations to improve monitoring and modelling around European seas, and to plan investments to develop and refine observations. EDIOS will for the first time permit an analysis of the continuously available data for operational models in Europe, and hence the ability to optimise the deployment of instruments, and the design of sampling strategy. This will lead to a better exploitation of the data generated by operational oceanography as well as of the derived products. Easy access to EDIOS is guaranteed through its placement on the Internet and through a visual user interface that will allow users from various backgrounds to extract the information they need, with or without experience of handling complex meta-databases. Such accessibility will ensure that the use of EDIOS is not just confined to operational oceanographic centres and agencies. Students, scientists and entrepreneurs from all sectors will be able to locate types of ocean observing sites/devices in Europe and their owners along with information on observing characteristics, such as spatial and temporal patterns of measurements, variables observed and precision of the measurements.

3. EDIOS and existing Databases Over the past 1 0 m 2 0 years, several international databases on ocean scientific research data and meta-data have been created. For example, IOC/UNESCO has developed the Marine Environmental Data and Information (MEDI) referral system for cataloguing data sets. Initially a print-based directory, MEDI has recently been redeveloped as a PC-

268


based application. The database structure for MEDI is based on the Global Change Master Directory (GCMD). The GCMD, developed by NASA, is a directory of data sets of relevance to global change (including ocean data sets). In addition, a European Directory of Marine Environmental Data (EDMED) has been developed by BODC (with EU/MAST funding) and Australia has developed their 'Blue Pages' directory. EDMED is currently being updated as part of the EU/EURONODIM (European Network for Oceanographic Data and Information Management) project. This project also aims to improve input to the archive of Cruise Summary Reports (formerly ROSCOP), and disseminates information through its Sea-Search website (www.sea-search.net). In the field of marine data management, the EU/MODB (Mediterranean Ocean Data Base) initiative provided a comprehensive data set of temperature and salinity for the Mediterranean Sea. This work was further developed by the EU/MEDATLAS project, which produced the presently most complete data set including a climatological atlas of temperature and salinity for the Mediterranean region. The ongoing EU-MEDAR/ MEDATLAS-II project aims at advancing the aforementioned work by including chemical and biological parameters and by incorporating Black Sea data. Data and metadatabases for other regional European sea areas include BOOS (Baltic Ocean Observing System) that also assembled information on stations operated by Baltic Sea littoral states. For the North Sea, SeaNet (European Workshop on Fixed Monitoring Networks in the North Sea Region) offers comprehensive information on buoys and platforms. Other European efforts for the development of databases for ocean data and metadata are linked with marine data and informational management activities carried out within the framework of large scale research projects for regional seas, supported by the EU (OMEX, MTP, CANIGO, etc.). With the exception of the BOOS and SeaNet meta-databases, all the above mentioned data and meta-databases are largely or solely based on oceanographic data sets collected during classical oceanographic cruises. However, ocean forecasting in Europe requires observational networks with real-time data acquisition capabilities and analysis systems, numerical models and data assimilation procedures. For this purpose, most European coastal countries maintain operational oceanographic monitoring programmes usually carried out by national agencies and institutes or scientific research groups. These programmes, however, usually just operate within the national boundaries of each country and are only rarely co-ordinated with each other; they often are incompatible even between agencies in one country. Typically, there are 10 to 15 different agencies in a country making operational observations, a number that can be even higher if counting all those that perform on a more local level. In addition to national programmes, operational marine data are also collected within the framework of international projects (such as MFSPP, the "Mediterranean Forecasting System" Pilot Project).

4. What are the benefits of EDIOS? The benefits of EDIOS for European operational oceanography will be obvious in all components that make up the operational system, ranging from instrument manufacturers, design and implementation of the ocean observing systems, and modelling to value added processing and customer needs (see Figure 1).


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The Directory will find its own users in addition to international programmes such as IOC/GOOS, EuroGOOS, WMO, etc. These will be scientists and other staff at oceanographic research institutes and agencies, environmental and resource management agencies, meteorological offices, etc., as well as SME's operating in the marine sector. EDIOS will help marine resource users in general to find the sources generating data relevant to their applications. In addition, a compendium of European ocean observing sites/devices will facilitate collaborative scientific uses of data generated by operational oceanography and thus reduce redundant or duplicate data sampling. The resulting cost savings might well have the effect of attracting more investigators to study the seas and therefore add to our overall understanding of ocean processes. The classification of measuring systems in EDIOS will set European standards required for a European ocean observing system. These standards will deal with the formats, scales, units, geographic distribution, type and detail of information on ocean observing sites/devices stored by national and regional institutions. They will most likely encourage manufacturers and owners of ocean observing devices to improve their systems. With EDIOS, an analysis of the continuously available data for operational models in Europe will be possible for the first time. The Thematic Network will collect the scattered information on ocean observing systems, harmonise it and combine it in a European directory of operational ocean observing sites/devices currently in use (including the Black Sea). The set-up of EDIOS will follow that of existing international meta-databases, such as SeaNet and EDMED, as closely as possible. As argued above, such a directory is a prerequisite for the full implementation of EuroGOOS by enabling co-ordination of national ocean observing stations to improve monitoring and modelling around European seas. EDIOS will, of course, include links to all existing European directories as well as to the data-holding agencies and institutes and thus contribute to networking and data sharing among European oceanographic organisations, agencies and institutes. Provision for a regular future update of EDIOS will be taken. EDIOS will be a service to marine science, agencies, and enterprises which do not yet exist. It will be complementary to and supportive of meta-databases that contain archived records of projects, or which list scientific cruises, archival data centres, and catalogues of data centres and their holders (for the functional positioning of EDIOS in relation to other directories and (meta)databases see Figure 2). So far, information on ocean observing sites/devices including stations, repeat sections, moored buoys, remote imaging, etc. presently in use in European seas is scattered and not easily available. Many regional databases contain material on certain types of ocean observing sites/devices or concentrate on the variables measured and the institutions holding the data (for examples see below). National agencies and institutes usually hold lists of the ocean observing sites/devices they regularly use but this information is not organised in a compatible way between institutions, i.e. formats, scales, and units are often inconsistent as is the type and detail of information stored. Thus, a comprehensive inventory of ocean observing sites/devices in Europe will be a new and very useful tool in operational oceanography and in oceanographic science.

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5. User identification and user involvement User involvement will play an important role throughout the lifetime of the project. Cooperating users will contribute to the selection of metadata entries, testing of the visual user interface, and to a study that assesses the economic benefits users could gain from using EDIOS.

Figure 2 Functional types of databases: EDIOS will be a meta-database on ocean observing systems that networks with existing databases and meta-databases (links) Assessment of socio-economic relevance of different user sectors will use existing and accessible studies to ensure that all important marine sectors in Europe are represented among the envisaged 20 to 30 potential users selected to co-operate with the Thematic Network. The selection of cooperating users will involve a preliminary choice of a larger sample of potential users. The aim is to ensure cooperation from representative sectors. The most important users of EDIOS will be scientists and managers of oceanographic research institutes and operational agencies, but EDIOS will be also benefit many commercial enterprises in the marine sector. More information about the project can be found on the EDIOS website at http://www.edios-project.de, which also describes the EDIOS metadata sampling pages and presents information regarding data Metadata Information Forms (MIF) which can be downloaded together with MIF information and guidance notes.

6. Conclusion EDIOS represents a much-needed tool in operational oceanography and will fill a gap in the presently available European oceanographic meta-databases. EDIOS will help EuroGOOS to build a European ocean observing system by providing a comprehensive European catalogue of instruments and sensors in continuous use. In addition, its visual


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user interface will give easy access to the information contained in EDIOS to everybody interested, ensuring the usefulness of EDIOS to all oceanographic sectors, commercial and non-commercial.

IOMASA

lntegrated Observing and Modelling of the Arctic Surface and Atmosphere

Georg Heygster* 1, Soren Andersen 2, Nils Gustafsson 3, Klaus Kunzi 1, Thomas Landelius 3, Harald Schyberg 4, and Leif Toudal 5

lInstitute of Environmental Physics, University of Bremen, Germany 2Danish Meteorological Institute, Copenhagen, Denmark 3Swedish Meteorological and Hydrological Institute, Norrkrping, Sweden 4The Norwegian Meteorological Institute, Oslo, Norwco, 5Danish Centrefor Remote Sensing, Lyngby, Denmark Abstract IOMASA aims to improve the analysis and forecast of the Arctic weather and sea ice conditions using an integrated approach including remote sensing of the atmospheric parameters temperature, humidity and cloud liquid water, improved remote sensing of sea ice with more accurate and higher resolved ice concentrations, and improved numerical atmospheric models by assimilating the results. The usefulness of the concept will be shown in a demonstration phase with near-real time processing and online data distribution.

Keywords: Arctic, sea ice, atmosphere,

model, remote sensing.

1. Introduction At present, the polar regions belong to the regions for which the least information is available about the current and predicted states of surface and atmosphere. Because of sparse observations, we only have rough quality weather forecasts for northern Europe, and ice charts for the ice-frequented waters of the European Arctic. The objective of IOMASA, which started in October 2002, is to improve our knowledge about the Arctic atmosphere by using satellite information--which is continuously available, but currently not exploited. This progress will be achieved through an integrated approach involving the following 4 points (the outcomes of each point serving to improve the other ones): 1. remote sensing of atmospheric parameters temperature, humidity and cloud liquid water over sea and land ice 2. improved remote sensing of sea ice with more accurate and higher resolution ice concentrations (percentage of ice-covered sea surface) 3. improving numerical weather prediction (NWP) models by assimilating the results of the points 1 and 2 in order to prove the usefulness of this concept, a real time processing set-up and a user interface will be demonstrated. The following chapters are organised according to the first three points. * Corresponding author, email: [email protected]

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We expect IOMASA to enable progress in the fields of weather forecast for northem Europe ice charts for the ice frequented waters of the European Arctic estimation of the fraction of open water in the higher Arctic which is very important for the total heat budget of the region, affecting both local and regional weather and climate. The heat exchange between the ocean and the atmosphere is about two orders of magnitude larger when no ice is present.

2. Remote sensing of atmospheric parameters over ice A recently proposed procedure to retrieve the total water vapour (TWV) in the range of 0 to 6kgm -2 over Antarctic sea and land ice from data of the microwave humidity sounder SSM/T2 will be transferred to: 9 the similar sensor A M S U - B (part of ATOVS aboard the NOAA satellites) 9 Arctic conditions. Both sounders each have two window channels at 89 and 150 GHz together with three humidity channels at 183.31 _+ 1.0 GHz (ca 300 hPa), _+3.0 GHz (ca 500 hPa) and _+7.0 GHz (ca 700 hPa). A sample Antarctic result of the procedure is shown in Figure 1, together with a comparison to the ECMWF field of the same day. Figure 1 demonstrates an unprecedented capability to deliver daily measured values of the low TWV values over Antarctica. The rough structures agree with the model output which represents physical interpolations of the few radiosonde stations around the Antarctic coast. However, the satellite-derived map shows many more details. The comparison with AVHRR images has shown that e.g. the high TVW values near the east coast of the Antarctic peninsula are related to a low pressure system which is completely missing in the model (Miao et al., 2001).

Figure 1 Total water vapour over Antarctica from ECMWF (left) and the humidity sounder SSM/ T2 (fight) Fields of the cloud signature (roughly the cloud liquid water) will be derived from SSM/ I (Special Sensor Microwave/Imager) data after transferring the method originally derived for the Antarctic to Arctic conditions (Miao et al., 2000). A method to improve the retrieval of temperature profiles from microwave sounders working in the oxygen absorption band near 60 GHz (e.g. AMSU-A, SSM/T1) by including surface emissivity information at the frequencies and incidence angles of the

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sounder in regions (partially) covered by sea ice (Miao et al., 1995) will be adapted to direct assimilation and Arctic conditions.

3. R e m o t e s e n s i n g of sea ice 3.1 Sea ice concentration retrieval Ice concentration retrieval algorithms using SSM/I are well known and have been used for the last 20 years (e.g. Comiso et al., 1997). Work in recent years has concentrated on the optimisation of tie points, which are fundamental for sea ice concentration retrieval, as well as on the correction for the atmospheric influence (Andersen, 1998; Andersen, 2000; Kern 2001; Breivik et al., 2001). An example of the impact of the atmospheric correction scheme is given in Figure 2. Current SSM/I based algorithms are capable of retrieving sea ice concentration with an accuracy of only __. 5-10%, which results in corresponding inaccuracies in ocean/atmosphere fluxes. These fluxes vary dramatically with the addition of even small areas of open water in the form of leads and polynias within the consolidated ice cover. This in turn affects the performance of NWP models. Thus it is crucial that the sea ice cover is represented correctly.

Figure 2 Ice concentration retrievals based on atmospherically corrected and uncorrected SSM/I brightness temperature data from April 15 2002. a) Uncorrected Bootstrap and b) based on corrected SSM/I data. The primary objective will thus be to improve the ice concentration retrieval in regions where the above-mentioned leads and polynias occur. This will be carried out by way of 9 improved accounting for the atmospheric contribution to the satellite-measured radiances and backscatter values improved knowledge of the ice surface type which allows a more accurate specification of reference radiative properties, also known as tie points, that span the scale of ice concentrations. This will be obtained mainly from the surface emissivity and backscatter models (next section) in combination with synergies between Quikscat and SSM/I. The sea ice concentration algorithm developed is envisaged to take into account relevant parameters describing the radiative transfer in the atmosphere and ice/ocean surface. In

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connection with the development of the EUMETSAT Satellite Application Facility (SAF) on Ocean and Sea Ice, correction methods for SSM/I brightness temperatures based on NWP model output have been applied with good results and are being used operationally (Andersen, 2000; Breivik et al., 2001). More specifically, using NWP model estimates of surface wind and integrated water vapour content, it has proved possible to reduce the standard deviation of the SSM/I derived sea ice concentration estimates by 5-15% while reducing the bias to _+2%. This method will be adopted and possibly improved, e.g. by a more accurate specification of surface emissivity in the radiative transfer calculations. In light of recent advances in NWP and ocean model applications, the use of 85 GHz information will be considered in the development to optimise resolution. However, due consideration will be given, so as not to sacrifice precision of the concentration estimate. This is envisaged through the identification of conditions where a fail over to lower resolution methods with better signal to noise ratio would be preferable. The important sea ice information obtained from Quikscat is the differentiation of various ice surface types (Ezraty and Cavanie, 1999; Grandell, 1999; Tonboe, 2001). However, a major problem for ice retrieval from scatterometer data is the influence of the surface wind particularly in mixtures of sea ice and open water. It is planned to use the knowledge gained from the atmospheric correction of SSM/I data to improve the reliability of ice type retrievals by correcting them for the influence of winds as obtained from NWP models and current Ku-band wind model functions. Subsequently emissivity and backscatter models developed in the project will be combined with sea ice type information using state-of-the-art synergetic data combination techniques to further improve the sea ice concentration estimates. 3.2 Empirical model for emissivity and backscatter of sea ice

All algorithms to quantitatively derive ice concentrations from satellite passive microwave observations of the polar oceans rely on so-called tie points that are the expected signatures of 100% pure surface types. The most common algorithms utilise the signatures of ice free water, first-year ice and multi-year ice in order to calculate the relative amounts of these three surface types within the resolution cell. Unfortunately these signatures (tie points) are not constant in space and time. The microwave signature of the ice-free water depends on the roughness and whitecapping of the water surface, and thus on the local surface winds. Similarly, the signatures of the ice surfaces depend on ice surface properties such as snow cover, deformation (roughness), salinity. In addition, at lower salinities microwaves penetrate into the ice volume, and volume effects such as particle size and salinity become important as well. We will apply time series analysis of satellite data to establish an empirical model that relates the temporal evolution of the brightness temperatures to sea ice parameters. Ultimately, a physical model relating known or unknown sea ice parameters to microwave brightness temperatures will be used in the derivation of ice, atmosphere and ocean parameters from the satellite measurements. The model will be based on the microphysical snow and ice emissivity model Fuhrhop et al. (1998), in combination with the empirical model from above.

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In 2002, the two passive microwave sensors A M S R - E and AMSR have been launched aboard the AQUA (NASA) and ADEOS-2 (NASDA) platforms, respectively. In comparison to SSM/I, they both have similar channels, but the spatial resolution is increased two to three times. Moreover, there are additional channels at 6.9 and 10.6 GHz. The potential of improvement to be expected when shifting from the passive microwave sensors SSM/I(S) to AMSR(-E) will be assessed.

4. I m p r o v i n g n u m e r i c a l w e a t h e r prediction m o d e l s The improved knowledge about the Arctic can help to improve numerical weather prediction (NWP) models by means of data assimilation. This should result in improved forecasts of clouds and precipitation in the Arctic and surrounding areas. A significant part of the forecast errors in NWP comes from errors in the initial state, and in particular from the lack of observations of the atmospheric state in remote areas over oceans and polar ice caps. Especially short range forecast with high resolution limited area models (horizontal resolution of 20 km or better) should benefit from the higher density of high latitude passages for polar orbiting satellites. In this project the HIRLAM model will be used for NWP. HIRLAM is used operationally by all Scandinavian weathers services. It is typically run up to 48 hours with a short cut-off time for observations in order to provide fast availability for operational forecasters. The timeliness requirement for AMSU data will be met by the new EUMETSAT redistribution service. It will provide near real-time access to AMSU observations from a large part of the Arctic and is devoted to this kind of limited area modelling. Satellite remote sensing data from microwave channels from the ATOVS sensor package, A M S U - A for temperature and A M S U - B for humidity, play an important role in the meteorological observing system.

4.1 Assimilation of atmospheric humidity over sea ice Today the use of such data is limited over ice. A main goal in this project is to enhance the use of ATOVS data by also considering ice-covered surfaces. Since the Arctic atmosphere is rather dry, the surface will often influence the measurements and therefore a good description of the surface characteristics in terms of ice concentration and emissivity is necessary. The temperature dependence also calls for co-located temperature measurements so that the humidity information is assimilated in a consistent way. Such requirement constitutes a good example of the need for integrated processing of sea ice emissivity, atmospheric temperature and humidity which is one of the goals of the IOMASA project. 4.2 Surface heat flux modelling A closed sea ice cover reduces the heat flux by up to two orders of magnitude, with small openings contributing significantly to the atmospheric heat budget. Present operational NWP models applied to Arctic regions covered with sea ice only use information about the ice edge. Areas within the ice edge are assumed to be completely covered with ice. This can lead to large errors in surface fluxes in areas where open water is present, degrading the quality of Arctic forecasts.

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Recently the surface scheme in HIRLAM has been replaced by a new one that allows a surface grid square to be divided into tiles with different characteristics, e.g. conditions for fluxes. This opens up the possibility to remove the rigid assumption of a grid square being either covered with or free from ice. Instead, fluxes from open water and ice surfaces can now be treated in a consistent way. The HIRLAM NWP surface heat flux formulation in the Arctic will be improved by replacing the current assumption of full ice coverage with more realistic ice concentrations from remote sensing. As one result, a near real-time data flow will be set up to make recent ice concentration estimates available at the start up of each HIRLAM run. Furthermore, a new formulation for the calculation of surface fluxes in vertically stable planetary boundary layer conditions, with influence from gravity wave oscillations will be applied. This new formulation is expected to be of particular importance in the Arctic, due to the dominance of stable boundary layer conditions in that region. 4.3 Assimilation of temperature sounder data over sea ice

The main source of problems here is the inability to account for the surface contribution to the measured radiances. This has been circumvented by only using A M S U - A channels responding on the upper troposphere over the Arctic icecap or by introducing a very strict quality control which rejects observations where the discrepancy between the measurement and that simulated using NWP forecasts is too large, causing only a fraction of the observations to be used. We will improve the use of these data over ice by accounting for the surface contribution by applying information on ice concentration and emissivity. This will be done by implementing algorithms for ice concentration and sounding-channel ice emissivity for nearreal-time processing. It will be interfaced to a fast radiative transfer model to allow forward computation of A M S U - A and A M S U - B sounding channel radiances for use in a data assimilation system. We expect that this will enable us to use surface channels over ice and benefit from a much larger fraction of the A M S U - A observations than before. Another problem for optimal assimilation of the AMSU observations is that cloud liquid water (CLW) is not being assimilated in the NWP model. Therefore, in the data assimilation procedure, observations highly influenced by CLW must be detected and should be rejected before use. A quality check using cloud liquid water estimates must therefore be developed and applied before assimilation. A candidate for this check is the cloud signature procedure of section 2. The extended use of sounding data over sea ice is expected to give a positive impact on the forecasts, particularly over the Arctic region. The effect of the assimilation will be validated by performing an experiment with two parallel model c y c l e s m o n e with and one without use of sounding channels over sea ice. The validation will involve case studies of weather phenomena of particular interest (for instance severe weather) as well as studies of average improvements in forecast skill verified against time series of observations near the Arctic.

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5. Conclusions The improved knowledge about the Arctic environment will be available to users of many kinds. On a large scale, the models can be embedded in large-scale GCMs (global circulation models) to predict global climate change, while on a smaller space scale and shorter time scales they will be used to improve operational forecasting of weather, ice and ocean conditions, helping to reduce risks and costs of Arctic operations as well as to improve the living conditions of all members of the Arctic population.

Acknowledgements This work is supported by the EU under contract EVK3-CT-2002-00067.

References Andersen, S., 1998, Monthly Arctic sea ice signatures for use in passive microwave algorithms. DMI Technical Report 98-18, Danish Meteorological Institute, Copenhagen. Andersen, S., 2000, Evaluation of SSM/I sea ice algorithms for use in the SAF on Ocean and Sea Ice. DMI Scientific Report 00-10, Danish Meteorological Institute, Copenhagen. Breivik L.-A., S. Eastwood, 0. GodCy, H. Schyberg, S. Andersen, and R.T. Tonboe, 2001, Sea Ice Products for EUMETSAT Satellite Application Facility. Canadian Journal of Remote Sensing, in press. Comiso J.C, D.J. Cavalieri, C.L. Parkinson, and P. Gloersen, 1997, Passive microwave algorithms for sea ice concentration: A comparison of two techniques. Remote Sens. Environ, 60, 357-384. Fuhrhop, R., T.C. Grenfell, G. Heygster, K.-P. Johnsen, P. Schltissel, M. Schrader, and C. Simmer, 1998, A combined radiative transfer model for sea ice, open ocean, and atmosphere. Radio Sci. 33, 2, 303-316. Kern, S., 2001, A new algorithm to retrieve the sea ice concentration using weathercorrected 85 GHz SSM/I measurements. PhD Dissertation, Berichte aus dem Institut ftir Umweltphysik, 6, University of Bremen. Miao, J., K.F. Ktinzi, G. Heygster, T.A. Lachlan-Cope, and J. Turner, 2001, Atmospheric water vapor over Antarctica derived from SSM/T2 data. J. Geophys. Res. 106 (D10), 10187-10203 Miao, J., K.-P. Johnsen, S. Kern, G. Heygster, and K. Kunzi, 2000, Signature of Clouds over Antarctic Sea Ice Detected by the Special Sensor Microwave/Imager. IEEE Trans. Geosci. Rem. Sens. 38, 5, 2333-2345. Miao, J., T. Markus, and B. Bums, 1995: Retrieval of Temperature Profiles over Sea Ice with Multi-sensor Analysis: Combination of the DMSP's SSM/I, OLS, SSM/T1 sensors. Proc. IGARSS'95, 10-14 July 1995, Firenze, Italy, IEEE Catalog No.95CH35770. Tonboe, R., 2001, QuikScat~SeaWinds scatterometer observations of sea ice types around Greenland. Proceedings of SPIE Vol. 4544. Presented at 8th International symposium on Remote Sensing, Toulouse, 17-21 September 2001.

Marine EnviRonment and Security for the European Area, MERSEA Strand-1 J.A. Johannessen*l, p._y. Le Traon 2, I. Robinson 3, K. Nittis 4, M. Bell 5, N. Pinardi 6, P. Bahurel 7, and B. Furevik !

1Nansen Environmental and Remote Sensing Center, Bergen, Norway 2CLS, Toulouse, France 3Southampton Oceanography Centre, University of Southampton, Southampton, UK 4National Centre for Marine Research, Athens, Greece 5Met Office, UK 6ING V, Bologne, Italy 7MERCA TOR, Toulouse, France

Abstract The current capacity in provision of real time marine environmental information to different users occupied with aspects of the Global Monitoring for Environment and Security (GMES) are planned to be examined and classified by MERSEA Strand-l, notably within application sectors such as: maritime transports, naval operation, tourism, exploitation and management of ocean resources, environmental issues, and research and development. The expected outcome will document that integrated systems (in situ data, EO data and models) are cost-effective and greatly needed for hindcast, nowcast and forecast purposes in the context of specific GMES requirements including development, mitigation and assessment of policy agreements. Moreover, it will highlight the need to provide an information exchange platform between industry, research organisations, value adding companies and customers in order to make data, information and services more readily accessible.

1. Background The provision of satellite and in situ based data, and their integration into appropriate models of the Earth system are of paramount importance for monitoring the state of the environment and the climate, as required by national and international programmes, obligations and treaties. In addition, the products and services delivered from numerical weather prediction centres as well as from the growing number of operational and preoperational oceanographic monitoring and modelling systems all require access to integrated data systems. The EuroGOOS Space Panel report (Guymer et al., 2001), the EuroGOOS operational oceanography data requirements survey (Fischer and Flemming, 1999), the IGOS Ocean Theme report (Lindstrom et al., 2000) and the implementation and operations of EUMETSAT Satellite Application Facilities (i.e. CM-SAF, OSISAF) as well as the Argo profiling float programme are fully compliant with this integrated view, as are the requirements for integrated observations outlined in the plans for the Global Ocean Data Assimilation Experiment (GODAE) and the strategy for * Corresponding author, email: [email protected]

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Observing the Oceans in the 21st century (http://www.bom.gov.au/bmrc/ocean/GODAE; Koblinsky and Smith, 2001). The EU's new GMES initiative recognises that satellite-based multi-disciplinary Earth observation is a fundamental component of monitoring systems. In the context of the marine environment, integrated observing systems and numerical models are capable of producing a large range of information products, including physical, biological and chemical products. Established users range from the research community, via operational applications to commercial customers and governmental entities. The reliability and utilisation of these types of information products depend not only upon the performance of the models and assimilation tools, but also on the availability and quality of the observing systems, telecommunication networks, data processing and distribution, data access, and rapid information integration, flow and services.

2. Objectives The MERSEA Strand-1 project is directly related to the GMES Action Plan period 2001-2003) on global ocean monitoring and the marine theme. It builds current European capabilities for development, implementation and operational marine modelling and data assimilation systems, spaceborne observations and observing networks and systems. The overall objective is to

(initial on the use of in situ

"Integrate existing spaceborne observations with data from in situ monitoring networks through ocean modelling and data assimilation system to: a) deliver information products (physical, chemical and biological) needed by users concerned with European marine environment and security policies; b) report on the problems met and lessons learnt in supplying this information, and c) contribute to improve knowledge, methods and tools required for monitoring, information production and delivery to users occupied with marine environmental monitoring, management and security." In accordance with this overall objective and in order to support the assessment and definition of an optimum way forward for targeted research in marine environment and ecosystem monitoring and modelling the specific objectives include: 9 Operational Oceanography Model Systemmto analyse the strengths and weaknesses of existing oceanographic data assimilation, hindcast, nowcast and forecast systems; 9 Operational Observation N e t w o r k m t o analyse the strengths and weaknesses of existing marine observational network (in situ and spaceborne) and the flow of data to operational/pre-operational models and assimilation systems 9 Quality C o n t r o l ~ t o provide methodologies for distribution of and access to harmonised and quality-controlled outputs from such integrated observational and modelling systems 9 Demonstrationmto select specific cases and demonstrate the relevance, value and deficiencies of existing information products to specific users and policy-makers and their stakeholders with responsibilities for environmental monitoring and safety at sea

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Synthesis and recommendationmto synthesise important knowledge gaps and recommend cost-effective and sustainable solution to obstacles encountered for the development and implementation of a fully operational oceanography system beneficial to GMES.

3. Approach What makes the achievement of these specific objectives a reasonable expectation within 18 months (total duration of the MERSEA Strand- 1 project from 1 January 2003) is the present existence of a number of regional pre-operational systems for the Baltic sea, the Black Sea, the Mediterranean, the North Sea and Skagerrak Sea and the North Atlantic (see Table 1). In general these systems are more mature for what concerns the ocean physics, while they are still more inclined at the research level towards pollution and ecosystem simulations. However, it must be emphasised that this has important consequences in the context of provision of realistic boundary and initial conditions in coupled physical, biological and chemical modelling. This will become evident in the series of demonstration cases to be undertaken in the MERSEA Strand-1 project. The users will specifically include the European Environmental Agency (EEA), meteorological services, coastal protection agencies, national and international environment administrators, water basin authorities, and climate and environmental research organisations and communities (Peronaci, 2001; EEA, 1999). The information products will be tailored and evaluated against the users' documented needs specified for the development and implementation of a common European strategy for the protection and conservation of the marine environment. In so doing, shortcomings will be precisely identified thus allowing the development of specific plans to remedy what is lacking in data collection, knowledge, infrastructure and data integration methodology. In this context an important goal is to improve the ways in which observational data (in situ and from satellites) are made available for incorporation into marine numerical modelling systems such that they facilitate the generation of reliable output data products and management information for client agencies, stakeholders and users. MERSEA Strand-1 also offers the opportunity of presenting a GODAE European perspective starting from the European marine areas operational systems. This effort has been built in the past seven years inside a European GOOS community (Guymer et al., 2001; Fischer and Flemming, 1999) and the MERSEA Strand-1 project capitalises on and incorporates most of the pre-operational and operational systems developed in Framework IV and V of the EU Environmental and Marine Programme.

3.1 Delivery of reliable, high quality, information products Several pre-operational and operational marine integrated systems are available in Europe as indicated in Table 1. They can be subdivided into two main categories: 9 global ocean systems producing assimilated analysis of the ocean state and forecasts 9 regional/coastal high resolution systems producing user-oriented products, increasing the quality of the global models at the regional/shelf level and extending to ecosystem modelling.

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It is the view in MERSEA Strand-1 to use the existing systems with the final aim of evaluating the quality of the systems products and their usefulness for a large community of end-users for environment and security. Table 1 Overview of existing modelling and assimilation systems System TOPAZ

MERCATOR

Targeted area North Atlantic/Nordic Seas North Atlantic, Azores, Mediterranean

Key input Atmospheric forcing data. Remote sensing SLA, SST and ocean colour. From Spring 2002 also satellite sea ice, and Argo profiling floats. Atmospheric forcing data. Remote sensing SLA, SST and ocean colour, and Argo profiling floats.

Global, North Atlantic, Mediterranean Sea Mediterranean

Atmospheric forcing data. Remote sensing SLA, SST and sea ice, and Argo profiling floats, VOS XBTs. Remote sensing SLA and SST, VOS XBTs, Buoy and Argo profiler data

MI POMI NORWECOMI POSEIDON/ BOOS/POL3DBI ERSEM

North Atlantic, Northern European shelf seas, Baltic, Greek Seas, Adriatic Sea, etc.

Atmospheric forcing data, tides, buoy data information, river run-off, satellite SST, SSI and wind, boundary conditions from SYS 1 to SYS 4.

National Monitoring Programmes (NMP) at regional and local scales

North Sea, Skagerrak and Kattegatt, Mediterranean, Aegean Sea, Baltic

Atmospheric forcing data, river run-off, boundary conditions from SYS 1 to SYS 4. Ferry-box data

North Atlantic and adjacent seas

Meteorological forcing, wave buoy information, satellite SAR, scatterometer and altimeter data

FOAM MFS

WAM

Satellite and in situ observations are available in near real time and they are assimilated into dynamical models of the ocean hydrodynamics. Such systems deliver nowcasts and forecasts and analysed data to end-users in selected regions and for selected needs. Products include physical (currents, temperature, waves, sea level, etc.), biological (algae concentration, primary production, etc.) and chemical (oil spill, etc.) quantities. A global assessment of these systems has not been carried out yet and it is timely to do so in view of the need for assessment of environmental stress upon the marine ecosystem. This effort has a scale and impact that is larger than Europe itself since it will be the first of such endeavour and it should give indications for the future developments of the GMES directives. The marine community behind MERSEA Strand-1 has developed and operates both near real time distribution of satellite and in situ observations and forecasting models, with innovative data assimilation tools. The ecosystem modelling, available as first generation pre-operational systems, will be complementary to these existing operational oceanography systems. As indicated in Table 2 a series of demonstration experiments will be undertaken to demonstrate the capacity of these existing systems.

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Table 2 Generic overview of candidate cases to be considered for demonstration Candidate demonstration categories Algae Bloom

Candidate area

Satellite

Baltic Sea, SST, CHL Skagerrak, Med. Sea SPM, CDOM, PP

Southern North Sea, Skagerrak, Baltic Eutrophication Sea

Oil spill

Relevant data

European Shelf, Baltic Sea, North Sea, Galicia coast 1, Med. Sea

SST, CHL

SAR

Assim. system

Information product

T, Algae species & conc., Chl., Nutrients SPM, CDOM, PP, T, S, Chl., Nutrients

OI, EnKF

Chl., Nutrients, T, prim. prod.

OI, EnKF

Nutrients, ChI.,T,S, prim. prod., T, S, 0 2

Location Volume Type, SST and currents

01, EnKF

Drift vectors, dissipation

In situ

1Note that the Prestige oil spill case will be undergoing dedicated examination and analyses

3.2 Reporting on the problems met and lessons learnt MERSEA Strand-1 will establish interfaces between the different ocean operational systems so that inter-comparison can be made, quality of products demonstrated and further integration of satellite and in situ observations can be evaluated. This will, in turn, ensure that expected regional characteristics (i.e. advantages/disadvantages) can be reported in the context of problems and lessons learnt for European monitoring for environment and security. Based on this we will subsequently address the procedure for: 9 harmonising data gathering, utilisation and analyses

9 product generation and quality control 9 information dissemination 9 standardised and consistent reporting. 3.3 Contributing to longer-term improvement of knowledge, methods and tools

The primary deliverables of MERSEA Strand-1 will be used to assess the current status of European capacity in European monitoring for environment and security to be undertaken in Strand 2. Based on this a set of key recommendations for targeted research and development will be specified and eventually considered for integration in the EU FP 6 programme, notably with the aim to have GMES in operation by 2008. 4. S u m m a r y

MERSEA Strand-1 will deliver a report to identify the important knowledge gaps concerning the state of marine environmental information systems that, in turn, should be prioritised and filled via targeted research and monitoring. The report will address a wide audience including research scientists, managers and decision-makers, members from national marine authorities as well as from the marine-related industries and services sector. Feedback and comments from them shall in turn culminate in the recom-

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mendation for the way forward on the future strategy for implementation of a European operational ocean monitoring and forecasting system for environment and security.

Acknowledgement The MERSEA Strand-1 project is supported by the European Commission through contract no. EVK3-CT-2002-00089. There are 19 partners. In addition to those seven partners listed up front they include Ifremer, MeteoFrance, and LEGOS (France), DMI (Denmark), DFMR (Cyprus), Met.no and IMR (Norway), POL, PML and CEFAS (UK), DLR (Germany) and FIMR (Finland).

References Guymer, T.H., N.C. Flemming, J. Font, P. Gaspar, J. Johannessen, G.H. van der Kolff, C. le Provost, A. Ratier and D. Williams, 2001, EuroGOOS Conference on Operational Ocean Observations from Space, EuroGOOS Publication no. 16. Fischer, J. and N.C. Flemming, 1999, Operational Oceanography: Data Requirements Survey, EuroGOOS Publication No. 12. Lindstrom, E.J., J.-L. Fellous, M. Drinkwater, R. Navalgund, J. Marra, T. Tanaka, J. Johannessen, C. Summerhayes, and L. Bermann Charles, 2000, An Ocean Theme for the IGOS Partnership: Final report from the Ocean Theme Team, (http://www.igospartners.org). Koblinsky, C.J. and N.R. Smith, 2001, Observing the Oceans in the 21st Century, A strategy for global ocean observations, 2001 GODAE Project Office, Australia. Peronaci, M., 2001, Marine and Coastal Environment-Annual topic update 2000, European Environment Agency, EEA. EEA, 1999, Environment in the European Union at the turn of the century-Summary, European Environment Agency, EEA.

Integrated marine science in European shelf seas and adjacent waters Jun She* and Erik Buch

Dan&h Meteorological Institute, Denmark

Abstract The European Shelf sea Integrated Project (ESIP) and Integrated infraStructure for European Research Area in Marine Science (SERAM) are two proposed integration activities to establish an integrated, world-leading operational modelling and observing system in European shelf seas and adjacent waters. The ESIP-SERAM system is constituted by an integrated next generation operational modelling system, supported by an integrated marine observing system, a data exchange system, distributed data centres and a service system. It will serve as an efficient tool to transfer state-of-the-art marine science and technology into valuable information products for end-users, especially for: 1. operational agencies with value added forecast products and new forecasting capabilities 2. environmental monitoring agencies with optimised monitoring strategies 3. regional environmental protection organisations such as HELCOM and OSPARCOM with high quality and fast delivered environmental indicators (rapid environment assessment) 4. GMES with a full set of operational (near) real-time in situ marine observations in major parts of the marine Economic Exploration Zone in 15 countries (9 EU member countries and 6 associated countries) 5. being an integrated infrastructure basis for European Research Area in marine science.

Keywords: Integrated operational modelling, observing system, infrastructure 1. Introduction 1.1 Background The European shelf and adjacent seas (i.e., Baltic, North Sea, and entire European shelf waters, Figure 1) is one of the most active regions in the world within operational oceanography and environment protection. Ten countries in this region have carried out operational marine forecasting on physical or biochemical parameters and fifteen countries are running regular national marine environment monitoring programmes. Regional organisations have been formed for integrating operational oceanography and environmental protection. Under the auspicious of EuroGOOS, operational forecasting agencies have built up BOOS (Baltic Sea Operational Oceanography System) and NOOS (NW shelf sea Operational Oceanography System) organisations. A MoU has been signed by all participants. Data exchange and integrated marine forecasting have * Corresponding author, email: [email protected]

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been initiated. Supported by national environment agencies, the intergovernmental organisations HELCOM (HELsinki COMmission) and OSPARCOM (OSlo-PARis COMmission) have been established to protect the environment in the Baltic Sea and North Atlantic (including shelf seas). Joint monitoring and assessment projects have been running for years to exchange marine monitoring data in a delayed mode and assess basin scale marine environment. Despite the significant achievements, major challenges exist in building up an integrated and sustainable system for operational modelling, observing and service in European shelf sea and adjacent waters. This requires integration of existing operational modelling systems and national observing systems, maximised use of all observations to improve operational nowcast, hindcast and forecast, efficient use of models in designing costeffective monitoring strategies and finally implementation of the upgraded operational modelling, observing and service system as necessary.

Figure 1 European Shelf Sea and adjacent waters concemed by ESIP and SERAM (the pale area) 1.2 Operational modelling and observing system in generations The operational modelling here includes regular operations of hindcast, nowcast and forecast while the observing system contains all the in situ and remote sensing monitoring systems which have a possible direct use in operational modelling. Both systems are applied in European shelf seas and adjacent waters. Different generations of the two systems are briefly described in the following sections. The first generation (G1): current status Operational modelling has experienced a rapid development over the last decade. Today a short-term (2-3 days) forecast on surface physical parameters such as winds, waves, storm surge, sea surface temperature and ice has reached a certain forecasting skill. This has benefited from relatively mature Numerical Weather Prediction (NWP) and physical models for these parameters as well as available observations (from remote sensing and/ or in situ) for data assimilation. The operational modelling of sub-surface physical parameters and biogeochemical parameters, however, is still in an initial phase. Among the fifteen countries in the ESIP-SERAM region, 7 of them are running operational 3D

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ocean models in coastal and/or shelf waters and 2 of them are running operational ecosystem models. No sub-surface data assimilation has been applied in these operational models. This is one of the major limits in the operational modelling. Due to long persistence time of subsurface temperature and salinity (T/S) and internal nonlinearity (mainly eddy-related), the initial error will be enlarged with the operational period. The predictability transferred from the NWP model to the subsurface ocean layer will be distorted by the enlarged initial error. After years of operational running without assimilation, even the large scale distribution of salinity and residual currents can be shifted in the wrong way. This has been noticed at several operational agencies including DMI, BSH and SMHI. Such errors will be continuously spread into ecological models which are very sensitive to the distribution of water T/S. The status of the integration of operational modelling systems in different countries are still in a initial stage--forecasts are made independently in different countries and the best practices of operational modelling have not been widely shared. The current stage of operational modelling, featured by relatively mature surface physical marine forecasts and non-assimilated 3D ocean forecasts as well as ecosystem forecasts, as described in the above, is defined as the G 1 operational modelling system. As for the observing system, the satellite system is planned and managed at the European level while many ESIP-SERAM partners have facilities to receive operational satellite products. Major local activities in remote sensing include HF radar observing systems (e.g. in UK and Norway) and airborne environment monitoring (e.g. in UK). The in situ observing system mainly contains fixed platforms (buoys, gauges, oil platforms, etc.) and ship measurements (regular national monitoring programmes and ship of opportunity reports). Over 30 buoys are operated in the ESIP-SERAM region to report waves, profiles of T/S, currents, and/or biological parameters. Regular environmental monitoring programmes (monthly, quarterly or yearly) are conducted in 15 countries at thousands of stations. The observations made in this expensive system, however, have not been shared and made available to an operational modelling system at a basin scale. The national observing systems are mainly designed for local interest, little basin scale coordination and optimal design have been adopted in practical monitoring activities. This status of the marine observing system, featured by regular fixed platforms and cruise monitoring activities but without a shared (near) real-time common database for the operational modelling system, is defined as the first generation observing system. As shown above, the G1 operational modelling system and observing system have not been efficiently coupled except for a few surface physical parameters. 2nd and 3rd generation systems The 2nd generation (G2) operational modelling system features an operational physicalecological modelling system with 3D T/S assimilation in a reasonably short time interval (e.g. once a month), optimised forcing for lateral boundary conditions, fiver input and bathymetry, together with a calibrated ecological module. Recognising the current situation of operational forecasting in multi-countries, a multi-model based ensemble prediction system could be developed to improve the met-ocean forecast quality, especially in storm cases. The G2 operational modelling system will be supported by a

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G2 observing system which provides a common near real time T/S database for the assimilation and on-line model quality assessment. The 3rd generation (G3) operational modelling system features operational coupled physical-ecosystem models with assimilation of 3D T/S (with enhanced assimilation frequency to optimise the forecast), nutrients, oxygen and chlorophyll, optimised parametrisations with sufficient vertical resolution to resolve the major relevant processes, real-time river input and correct description of stratification and transport through major straits and sills. The G3 operational modelling system will be supported by a G3 observing system which is featured by optimal designed sampling strategy and costeffective monitoring platforms, sufficient observations to resolve large scale features and part of the eddy-scale features of assimilated parameters.

2. Objectives and activities The objective of ESIP-SERAM is to realise the G2 operational modelling system and the G2 observing system in European shelf seas and adjacent waters as well as to pave the way for G3 operational modelling and observing systems. To this end, the following activities are proposed: 1. carry out a network activity among operational modelling agencies to exchange real time forecast data and best practice in operational modelling 2. carry out a network activity among national monitoring agencies to exchange observations (physical parameters, nutrients and oxygen) in near teal time 3. develop an on-line multi-model forecast quality assessment system and a multimodel based ensemble forecasting system 4. develop an observation product analysis system (with common quality control, objective analysis and assimilation tools) to make exchanged observations ready for operational modelling and rapid environment assessment 5. establish distributed data centres and product dissemination network to distribute multi-model value-added forecasts and the observation products 6. coordinate and optimise national monitoring programmes, implement cost-effective marine monitoring instruments as necessary 7. upgrade operational modelling systems with an optimised operational scheme and assimilated observations to provide solid, high quality physical marine forecasts 8. pave the way to fully operational ecosystem forecasts by conducting a predictability study and a key technology study (e.g., physical-ecological coupling, biochemical data assimilation, etc.). Activities 1-5 are mainly related to an integrated and enhanced infrastructure based on existing resources. This is formed into an FP6 proposal SERAM (Integrated infraStructure for European Research Area in Marine science), joined by 23 operational agencies and environmental monitoring agencies from 14 countries. Activities 6 - 8 will be a major focus in ESIP. SERAM was submitted to the EU FP6 Call for Research Infrastructure (launched on 17 December 2002) while ESIP aims towards the Priority Thematic Area 1.1.6.3: Global change and ecosystem of Framework 6 with focus on operational forecasting, modelling and observation system.

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SERAM and ESIP are two closely related Integrated Projects. SERAM will build up a solid, efficient and sustainable infrastructure for operational forecasting, environment monitoring and rapid assessment (including near real time observation/forecast data exchange, data centres, quality controlled and analysed observation products as well as service network, etc.) while ESIP will further develop and apply key technologies to upgrade existing operational forecasting system and observing systems.

3. Expertise needed to achieve objectives The critical mass of research and development resources have to and will be used in ESIP-SERAM, including expertise in marine physical-biogeochemical ecosystems, numerical weather prediction and hydrological modelling, data assimilation, marine technology and monitoring, remote sensing, optimal design of observational networks, data analysis, information technology (network and database), ocean-eco-atmosphere coupling process and social-economics. The expertise is drawn from major national agencies responsible for operational forecasting and environment monitoring, research centres and SMEs in 16 countries around the European Shelf Seas. BOOS and NOOS organisations are the backbone of ESIP-SERAM (Figure 2). Almost all the BOOS and NOOS members are participants in ESIP-SERAM. BOOS and NOOS will also serve as a part of ESIP-SERAM management units.

Figure 2 Participants in BOOS and NOOS organisations

4. Long-term sustainability and structuring effects 4.1 Long-term sustainability ESIP-SERAM are two integrated projects which develop innovative scientific knowledge and sustainable marine infrastructure and then transfer the knowledge via the infrastructure to produce high quality information products for end-users. The ESIPSERAM system is designed with a sustainable strategy. In the post-ESIP-SERAM period, the systems developed will be operated by volunteer host institutions, with support from a sustained BOOS and NOOS infrastructure. Strong end-user requirements,

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a cost-effective financial solution and an efficiently managed organisation are three key factors to maintain post-ESIP-SERAM sustainability.

4.2 Long-term structuring effects ESIP, together with SERAM infrastructure, is an integrated system formed by operational modelling and environmental monitoring agencies, as well as marine research centres from 16 countries around the European shelf sea and adjacent waters. Their structuring effects can be analysed at two levels: regional and European. Regional structuring effects At the regional level, ESIP-SERAM can be regarded as a tool to raise the integration level of BOOS and NOOS. ESIP-SERAM will additionally support and establish close relations to HELCOM and OSPARCOM. They will be operated as a virtual centre organised in a networked way (it is not a specific central server that performs all the functions but several "distributed centres"). SERAM makes the system run smoothly from data collection to end-user products. This workflow ensures the ESIP-SERAM will be a complete system covering all elements from input raw materials to useful products.

European structuring effects Relation with GMES: GMES is a major EU effort in environmental monitoring and security, which aims to reach an autonomous operational global monitoring system in Europe by 2008. The ESIP-SERAM observing system and data centres will support GMES by making most of the in situ observations available (in near real time). Relation with MERSEA: MERSEA focuses on establishing a global ocean prediction and monitoring system for GMES. A European marine centre is planned to strengthen European resources in global marine forecasting. ESIP-SERAM activities are complementary to the MERSEA strategy in the sense that MERSEA does not have a regional in situ observational network and therefore its forecasting capability in the European shelf sea and adjacent waters will be limited. ESIP-SERAM will on the other hand use MERSEA forecasting results as lateral boundary conditions for the shelf models used in ESIP-SERAM, as well as quality controlled remote sensing data for data assimilation. Relation with the European marine research community: ESIP-SERAM will form the bridge connecting basic marine research communities (research centres and universities) to marine operational and environmental agencies, and will provide European marine research communities with observations and state-of-the-art modelling skills and research topics raised in the frontier of operational oceanography, while also benefiting from the research and development performed by the research community.

ESONET

European Sea Floor Observatory Network

lmants G. Priede*l, Juergen Mienert 2, Roland Person 3, Tjeerd C.E. van Weering4,Olaf Pfannkuche s, Nick O'Neill 6, Anastasios Tselepides 7, Laurenz Thomsen 8, Paolo Favali 9, Francesco Gasparoni l~ Nevio Zitellini 11, Claude Millot 12, Hans W. Gerber 13, and Jorge Miguei Alberto de Miranda 14. ! University ofAberdeen, Oceanlab, UK 2University of Tromso, Norway 3IFREMER, Plouzane, France 4NIOZ, Texel, Netherlands 5GEOMAR, Kiel, Germany 6CSA Group Ltd, Dublin, Ireland 7Institute of Marine Biology of Crete, Greece 8International University Bremen, Germany 9lstituto Nazionale di Geofisica e Vulcanologia, Roma, Italy !~ S.p.A. Venice, Italy l llstituto Per La Geologia Marina CNR, Bologna, Italy /2Lab. de Oc~anogr. et de Biogeochimie, La Seyne/mer, France !3TFH Berlin Germany /4Centro de Geofisica, Lisboa, Portugal Abstract ESONET proposes a network of sea floor observatories around the European Ocean Margin from the Arctic Ocean to the Black Sea for strategic long term monitoring as part of a GMES with capability in geophysics, geotechnics, chemistry, biochemistry, oceanography, biology and fisheries. Long-term data collection and alarm capability in the event of hazards (e.g. earthquakes) will be considered. ESONET will be developed from networks in key areas where there is industrial sea floor infrastructure, scientific/ conservation significance (e.g. coral mounds) or sites suitable for technology trials (e.g. deep water close to land).

Keywords"

Sea floor, data, biodiversity, observatories, networks

1. Introduction The submarine terrain around Europe from the continental shelves to 4000m depth known as the European Ocean Margin extends approximately 15000km from the Arctic Ocean to the Black Sea with an area of ca. 3 million km 2. This is comparable in size with the total land mass of Europe and is increasingly important for resources, such as minerals, hydrocarbons and fisheries. Only a small fraction of this realm has been explored and new features and communities of animals (e.g. cold water corals and mud volcanoes) are discovered every year. The biodiversity probably exceeds that of the European land mass. There are natural hazards such as submarine slides and earthquakes * Corresponding author, email: [email protected]

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with associated tsunamis. Human impacts on this zone are poorly understood. A prerequisite for management, conservation and protection from hazards of this zone is the establishment of a long- term monitoring capability. To provide the necessary spatial and temporal coverage it is important that different agencies, nations and scientific/technical disciplines work together sharing infrastructure, data, information and knowledge. The aim is to create means of co-operative development of an observatory network. The objective of ESONET is to produce a practical plan for long term monitoring of the ocean margin environment around Europe as part of GMES with capability in geophysics, geotechnics, chemistry, biochemistry oceanography, biology and fisheries. ESONET will be complementary to oceanographic networks such as GOOS, EuroGOOS, DEOS and will work with industries who are deploying sea floor cable networks. ESONET will be multidisciplinary, with stations monitoring the rocks, sediments, bottom water, biology and events in the water column. Both long-term data collection and alarm capability in the event of hazards (e.g. earthquakes) will be considered.

Figure 1 The ESONET Logo

2. The ESONET Approach There is worldwide recognition of the need for long term in situ monitoring of the marine environment. This has been manifested in recent reports by the US Committee on Seafloor Observatories (2000) and Romanowicz et al. (2001) sponsored by the International Ocean Network (ION). Off Europe there is a history of long-term stations in physical oceanography such as the Shetland to Faeroe transect of current meter stations monitoring interchange between the Atlantic and Arctic oceans which may play a significant role in climate change and monitoring of Mediterranean inflows and outflows. In biology the most noteworthy results have been from long term camera deployments (BATHYSNAP) in the NE Atlantic which first showed evidence of seasonal change in the abyss, and then during a 10 year time series, that major changes occur. A third tradition is the extension of seismic networks into the sea from land. In recent years the oil industry has installed sensors and networks of cables on the sea floor throughout oil and gas field areas for the management of production facilities.

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There are several ambitious international proposals for networks of sea floor stations. NEPTUNE proposes instrumentation of the Juan de Fuca plate in the NE Pacific Ocean with fibre optic linked observatories, to provide real-time, long term, plate scale studies. DEOS, Dynamics of Earth and Ocean Systems, proposes a world-wide network of moored buoys whereas MOMAR proposes instrumentation on the Mid Atlantic Ridge.

Figure 2 The University of Aberdeen Deep Ocean Benthic Observatory (DOBO) operational in the NE Atlantic Ocean at 4000m depth A number of EU programmes have developed autonomous observatory capacity, e.g. ALIPOR (Autonomous Lander Instrument Platforms for Oceanographic Research) included BOBO, MAP (Module Autonome Pluridisciplinaire) and VESP (monitors flow from vent sites). The UK BATHYSNAP included a pioneering instrument and the DOBO (Deep Ocean Benthic Observatory) is operational in the NE Atlantic. These systems all typically have 6 to 12 month power and data storage capability and provide a cost-effective means of obtaining long-term observations but without real-time data access. GEOSTAR is a geophysics platform but has been extended to accommodate multidisciplinary sensors and real time data transmission capability. OFOS is a platform currently deployed in the Adriatic, monitoring sediment pore water pressure and seismic activity.

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Existing Autonomous stations (BOBO, DOBO) are generally deployed on the sea floor by free fall so placing is not precise. To study events at e.g. vents or coral mounds there will be a need for accurate location. GEOSTAR is deployed and recovered using a special mobile docker equipped with thrusters that can manoeuvre above the sea floor. VESP is deployed using a video launcher that allows selection of the drop site. Installation of deep cabled observatories will require use of deep remote operated vehicles (ROVs). To create a unified GMES system around Europe ESONET will co-ordinate existing European technical and infrastructure capability in sea floor observatories. It is not envisaged that a complete cabled network around Europe is feasible in the medium term (the ESONET logo is purely symbolic). A first step will be development of autonomous platform capability. Cabled systems from the coast will be proposed in particular areas of scientific or strategic importance or where the coast is particularly suitable for laying of a cable into deep water or where old communication cables could be re-commissioned. In other regions, clusters of stations around a telemetry buoy may be more appropriate. Existing or new satellite communication links will provide bi-directional transmission. An important aspect to be examined will be the feasibility of GMES stations using existing or proposed industrial networks. Whilst many sensors are well developed in some areas (e.g. biology, biochemistry) sensor design still requires improvement. These aspects will also be examined in ESONET. Data management and distribution and archiving in a multi-disciplinary context will require significant innovations. ESONET will be implemented through a series of workshops to which all potential stakeholders will be invited and by a series of work packages undertaking feasibility and design studies.

Acknowledgements ESONET is funded as a Concerted Action by the European Commission FP5 EVK32002-00533. (www.abdn.ac.uk/ecosytems/esonet/)

References Priede, I.G. and P.M. Bagley, 2000, In situ studies on deep-sea demersal fishes using autonomous unmanned lander platforms. Oceanography & Marine Biology, Annual Review. 38:357-392. Romanowicz, B., K. Suyehiro, and H. Kawakatsu (eds.), 2001, Workshop Report of OHP/ION Joint Symposium on Long-term Observations in the Oceans, Japan. US Committee on Seafloor Observatories, 2000, Illuminating the Hidden Planet: The Future of Seafloor Observatory Science, National Academy Press.

In Sifu Monitoring


Use of a Ferry-Box system to look at shelf sea and ocean margin processes D.J. Hydes *l, A. Yooi l, J.M. Campbell l, N.A. Crisp 1, J. Dodgson l, B. Dupee 1, M. Edwards 1, S.E. Hartman 1, B.A. Kelly-Gerreyn l, A.M. Lavin 2, C.M. GonzfilezPola 3 and P. Miller 4

!Southampton Oceanography Centre (SOC), UK 2Instituto Espa~ol de Oceanografia (lEO), Spain 3Instituto Espa~ol de Oceanografia (lEO), Spain 4plymouth Marine Laboratory, UK Abstract In 2002 a robust minimum maintenance system to measure temperature, conductivity, and chlorophyll fluorescence was installed on a ferry running twice weekly between Portsmouth, UK and Bilbao, Spain. Logged data are transferred from the engine room to an Orbcom satellite communicator on the bridge. On shore data enters an SQL database with access from a public web page. Data from the Iberian margin is linked to monthly surveys by IEO. Target work includes:- the structure, transport of and seasonal development of water masses, and scaling of patchiness of plankton blooms in regions with different hydrographic characteristics.

Keywords: Bay of Biscay, English Channel,

salinity, satellite, plankton, temperature.

1. Introduction For the effective management of coastal and shelf sea ecosystems it is necessary to distinguish between the natural cycling from small and regional scale anthropogenic effects. In many aspects the north west European shelf is considered to be a well studied area. However it is evident from the data assembled during the NORWESP project (Radach et al., 1996), that data are lacking in many areas, particularly in the winter months when research ships have difficulty working. Research cruises allow extensive ranges of measurements to be made but the view is potentially highly aliased by the limited temporal coverage that is possible. Satellites potentially provide global coverage but are limited to a few determinands and problems leading to aliasing such as cloud cover. The technologies of measuring devices, control systems, data logging and transmission are advancing rapidly. This means that one of the oldest ideas in oceanography D the use of ships of opportunity n has great potential to provide regular cost effective cover of a wide range of determinands. The "Ferry-Box" concept is the installation of instrumentation on ferries running regular routes to collect systematic data. In EuroGOOS planning it has been highlighted that over 800 ferries operate in European waters. Uses of systems have been reported in open seasDthe Sea of Japan (Harashima et al., 1997), the North Sea (Swertz et al., 1999) and the Baltic Sea (Leppanen, 2000) and more recently shorter routes in estuaries have been instrumented * Corresponding author, email: [email protected]

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(e.g. Holley and Hydes 2002). An EU-Framework-5 project "FerryBox" will develop procedures for integrating the data from such systems. Eleven partners are working on 8 routes. SMEs, scientific, operational and policy-oriented institutions are involved. A key part of this work is the development of uniform quality controls. This paper focuses on the route between Portsmouth, UK and Bilbao, Spain (see map in Figure 1). Observations from the ferry are complemented by regular research ship cruises conducted adjacent to the southern end of the line. This work aims to:- monitor the intensity, timing and duration of algal blooms in the range of environments along the route; make detailed observations of mixing events particularly in winter and "upwelling" at the shelf break in summer; ground truth satellite observations of frontal features and changes in "bio-mass"; quantify events producing significant water movement across the shelf and through the English Channel; study physical features at the southern Bay of Biscay (such as thermohaline fronts due to upwelling processes, runoff, poleward winter currents, eddies shedding, etc. (Holligan, 1989, Lavin et al., 1998). The data from the ferry can be compared to data that are available from satellites. The Remote Sensing Data Analysis Service (RSDAS), at the Plymouth Marine Laboratory, processes SeaWiFS and AVHRR and images. Weekly composite images are generated for specific areas. These provide averaged data from the available cloud free areas. Data from pixels lying along the route of the ferry can be selected and compared to the ferry data. The percentage of satellite data available using this method high was close to 100% from April onwards in 2002, the monthly minimum was 40% in February 2002.

Figure 1 Northwest European Shelf, route of ferry "Pride of Bilbao" Portsmouth to Bilbao. X is IEO "station 6".

2. The Equipment The system on the P&O European Ferries Ltd. ship "Pride of Bilbao" was installed in April 2002. It is intended to be permanently on the ship and operate year round except for January when the ship is in dry dock for its annual refit. The ship makes two

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crossings weekly between Portsmouth (50.8 ~ N, 1.1 ~ W) and Bilbao (43.4 ~ N, 3.0 ~ W); the distance is approximately 1000km and the journey time is about 35 hours. Measurements are made of temperature, conductivity, chlorophyll-fluorescence, (Chelsea Technologies Group MINIpack). Data are logged at a rate of 1Hz on a SOC designed UNIX based logging and control system (DAPS). The 1Hz data are down-loaded weekly. The DAPS unit sends a sample of the data at 10 minute intervals to an ORBCOMM satellite data transmission and receiving unit on the bridge deck. On shore data are taken from the ORBCOMM email message that arrives at SOC and is entered into an SQL database. The database is attached to public web page which displays the current data readings and position of the ship. This allows the functioning of the system to be checked remotely. The plots presented in this paper were generated in Matlab using the 1Hz data averaged to 5 minute intervals joined with the position data (GPS) logged by the ORBCOMM unit. The lEO system was installed in summer 2002 on the R.V. Jos6 Rioja. The R.V. Rioja makes a monthly track along the coast between Santander, Gij6n and Cudillero (approximately 260km) and also works three standard CTD sections perpendicular to the coast. These sections have been worked regularly by lEO since 1991 (Lavin et al., 1998). The measurements made underway are temperature and conductivity (Seabird SBE21), and chlorophyll-fluorescence and spectral algae class determination (Bbe Fluoroprobe, Moldaenke).

3. Results and Discussion We consider here data available from the ferry from the installation of the equipment on 16 April until the 8 August, which covers the period of the spring bloom and establishment of the summer thermocline. The data for salinity are mapped in Figure 2. This map was generated from data from 64 crossings over 114 days. Matlab software was used to interpolate the data onto a grid with a Y dimension of 0.1 ~ latitude and an X dimension of 3 days. The contour map was then generated in Matlab. Figure 2 shows the fresher water associated with the harbours at the ends of the route and the generally restricted size of the region of fresh water influence on the narrow Iberian shelf up to around day 200. After this time along the track significant freshening occurred. The degree of freshening is consistent with observations at the IEO "Station 6" off Santander (Lavin et al., 1998; Cabanas et al., in press) as shown in Figure 3. The plots of mean profiles for April and August show a fall in salinity by 0.4. Another distinct feature in Figure 2 is the low salinity water at 49 ~ N. (The track crosses the 49 ~ N parallel north of Brest.) This area of fresher water corresponds to a patch of colder water that is a characteristic feature of satellite images and which develops in the spring and summer. Temperature data from the ferry can be compared to data from the satellite images. Figure 4 shows maps of temperature from both sources drawn in the same way as the salinity map Figure 2. As would be expected for these relatively reliable measurements the general patterns are similar. The data from the ferry are offset to higher temperatures. In 2002 the temperature data from the ferry that was that recorded by the MINIpack CTD. Water travelled through 4 metres of hose from the take off point to reach the unit. The offset is consistent with the warming of the water in this hose. In 2003 a second temperature sensor will be integrated with the FerryBox system. This will be a hull

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mounted temperature sensor (Sea bird SBE 39) that was successfully tested on the Pride of Bilbao at the end of 2002.

Figure 2 Map generated in Matlab of the variation in the sea surface salinity measured between Portsmouth and Bilbao {Y axis } with time from day 106-220 in 2002 {X axis }.

Figure 3 Plot of mean values the IEO data base (1991-2002) for the variation of salinity with depth in April and August at "station 6" 43.7 ~ N 3.8 ~ W.

Figure 4 Maps drawn in the same way as Figure 3, of the variation in temperature measured on the Pride of Bilbao and from AVHRR satellite, with both sets of data interpolated onto the same grid Interpolation and gridding of the two sets of temperature data onto a uniform grid allows them to be compared directly. The two gridded sets are plotted against one another in Figure 5. The offset from a one to one relationship is greatest at lower temperatures when the temperature differences between the engine room air temperatures and sea water temperatures were greatest. The relatively tight grouping of the data suggests that the algorithms used by RSDAS do effectively remove cloud errors from the satellite data. The highest ferry temperatures recorded also form a group that is offset by an error of about 5 ~ C which may be due to error removing cloud cover or due to transient events detected in the sparser satellite measurements. The difference between the values at corresponding grid points can also be mapped (Figure 6). This allows for the detection of variations in the data to be located in space and time. The localisation of the largest

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offset (detectable in Figure 6 at about day 215) suggests further investigation of these data is necessary to see if they are a system fault or an interpolation problem. In 2003 a statistical approach needs to be taken to find the optimum interpolation procedures (and associated errors) for interpreting changes in these data that have both space and time dimensions.

Figure 5 Plot regressing the two interpolated data sets for temperature from ferry and AVHRR satellite

Figure 6 Map of the difference in each grid cell between the two temperature data sets shown in Figure 5

Figure 7 Variation in concentration of chlorophyll (estimated from a single laboratory calibration of the Ferry-Box fluorimeter) measured on the Pride of Bilbao and from ocean colour measurements by the SeaWiFS satellite. Both sets of data are interpolated onto the same sized grid. In Figure 7 estimates of concentrations chlorophyll-a based on satellite and FerryBox data are compared. The estimate of the concentration of chlorophyll-a based on the SeaWiFS colour data is generated using the standard NASA OC4v4 algorithm for case 1 waters. For the FerryBox the estimate of chlorophyll-a is based on a single calibration of the FerryBox MINIpack fluorimeter against a standard solution of chlorophyll-a in acetone. There is an obvious but not unexpected difference between the two data sets.

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However the locations of areas of higher biological production occur in both data sets at similar positions and times and these correspond to expected variations (Holligan, 1989). To better quantify the ability of the FerryBox to estimate biomass, a detailed calibration of the FerryBox fluorimeter measurements will be undertaken each month during 2003. This will include collection of filtered samples for measurement of acetone-extractedchlorophylls, HPLC pigment analysis and microscopic determination of the dominant algal species in water samples.

4. Extension of the system A critical driver of primary biological production in these waters is the supply of nitrate. In 2003 two systems will be used on the Pride of Bilbao that will monitor concentrations of nitrate. A system determine concentrations of nitrate via measurements the UV absorbance spectrum of the water (Finch et al., 1998) will be run along side an autonomous analyser which will measure nitrate using a well established chemical method (Hydes et al., 2000). In addition fast repetition rate fluorometer (FRRF) system will also be added. The FRRF will allow estimates to be made of biological production rate in addition to biomass (Moore et al., submitted).

References Cabanas, J.M., A. Lavfn, M.J. Garcfa, C. Gonzalez-Pola, and E. Tel P6rez, In Press, Oceanographic variability in the northern shelf of the Iberian Peninsula 1990-1999. ICES Marine Science Symposium 2001, Edinburgh. Finch, M.S., D.J. Hydes, C.H. Clayson, P. Gwillam, B. Weigl, and J. Dakin, 1998, A low power ultra-violet spectrometer for the measurement of nitrate in seawater. Introduction, calibration and initial sea trials. Analytica Chimica Acta 337, 167-177. Harashima, A., R. Tsuda, Y. Tanaka, T. Kimdo, H. Tatsuta and K. Furusawa, 1997, Monitoring algal blooms and related biogeochemical changes with a flow through system deployed on ferries on the adjacent seas of Japan. pp 85-112 In Monitoring Algal Blooms; New techniques for detecting large scale environmental change (eds M.Kahru & C.W. Brown) Springer Verlag Berlin Holley, S.E. and D.J. Hydes, 2002, "Ferry-Boxes" and data stations for improved monitoring and resolution of eutrophication-related processes: application in Southampton Water UK, a temperate latitude hypernutrified estuary. Hydrobiologica, 475/ 476.99-110. Holligan, P.M., 1989, Primary productivity in the shelf seas of north-west Europe. Advances in Botanical Research, 16, 193-252. Hydes, D.J., P.N. Wright and M.B. Rawlinson, 2000, Use of a wet chemical analyser for the in situ monitoring of nitrate, pp 95-105. In "Chemical Sensors in Oceanography". (ed M. Varney) Gordon and Breach, Amsterdam. Lavin, A., L. Valdes, J. Gil, and M. Moral, 1998, Seasonal and inter-annual variability in the properties of surface water off Santander, Bay of Biscay, 1991-1995. Oceanologica Acta, 21, 179-190. Leppanen, J.M., 2000, Alg@line--Operational Monitoring of the Baltic Sea Environment; httD://meri.fimr/Alualine v

D.J. Hydes et al.

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Moore, C.M., D. Suggett, P.M. Holligan, J. Sharpies, E.R. Abraham, M.I. Lucas, T.P. Rippeth, N.R. Fisher, J.H. Simpson and D.J. Hydes, Submitted, Physical controls on phytoplankton physiology and production at a shelf sea front: An FRRF based field study. Marine Ecology Progress Series Radach, G., J. Gekeler, G. Becker, P. Bor, P. Castaing, P. Damm, D. Danielssen, L. Foeyn, J. Gamble, R. Laane, J.P. Mommaerts, D. Nehring and K. Pegler, 1996, The NOWESP Research Data Base. Deutsche Hydrographische Zeitschrift, 48, 241-259. Swertz, O.C., F. Colijn, H.W. Hofstraat and B.A. Althuis, 1999, Temperature, salinity and fluorescence in the southern North Sea: High resolution data sampled from a ferry. Environmental Management, 23,527-538. Tziavos, C. and N.C. Flemming (eds), 1998, "The EuroGOOS Technology Plan Working Group Report" EuroGOOS Publication No. 13, Southampton Oceanography Centre, Southampton. ISBN 0-904175-37.

Monitoring the marine environment operational practices in Europe Jacques Legrand* 1, Marta Alfonso 2, Roberto Bozzano 3, G~rard Goasguen 4, Henrik Lindh 5, Alberto Ribotti 6, Ignacio Rodriguez 2, and Christos Tziavos 7

l lfremer, France 2puertos del Estado, Spain 3CNR-ISSIA, Italy 4CETMEF, France 5SMHI, Sweden 6IMC, Italy 7NCMR, Greece Abstract A survey of existing environmental monitoring networks operated in European waters and a review of their main characteristics has been completed. Individual questionnaires have been filled out by persons in charge of operations enabling gathering of up-to-date, accurate information about equipment used and actual operational practices. A map of the location of these networks is shown and a table gives their main characteristics. These data have been used to conduct the analysis of practices employed to maintain these installations. The various difficulties met by operators as well as the costs of these operations are analysed. Methods and procedures are specific to the geographic situations and to the physical characteristics of the monitored sites. The means and resources used for maintenance operations and the related costs are strongly dependent on the distance between the monitoring site and the closest harbour (logistic bases). In the same way, the frequency of on-site interventions are heavily dependant on the bio-productivity of the surrounding environment, bringing a heavy constraint on the choice of the technique for biofouling prevention. Last but not least, the level of data quality seems to be related to the methodology used for maintenance (both procedure and frequency). The choice of the architecture of measurement systems is also an important factor in the efficiency of the systems.

Keywords: operational oceanography, monitoring, automated measurement maintenance.

1. Introduction: There is a need for in situ data As the impacts of perturbations on the natural environment caused by natural and anthropic factors are becoming more and more visible and sensitive, the need to sense precisely and quantitatively not only the bio-geochemical processes but their dynamic nature, in correlation with the physical and meteorological conditions, is clearly expressed. The ultimate goal is to forecast unwanted environmental events and enable * Corresponding author, email: [email protected]

Jacques Legrand*, Marta Alfonso, Roberto Bozzano, Gerard Goasguen, Henrik Lindh, Alberto Ribotti, Ignacio Rodrigues, and Christos Tziavos

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decision-makers/authorities to take actions to mitigate or, even better, to prevent problems which may affect the various activities of the population. To reach this goal, numerical models represent the keystone to the edifice but, when they are used as operational tools, they need data that can describe the initial state and bring periodic and recurring concrete references to guarantee the reality of the output prediction. These data dedicated to assimilation in numerical models are largely provided from space, as the number of satellites dramatically increases as well as the number of parameters that can be measured. But satellite sensors measure only a thin layer at the ocean surface and they are not very accurate in coastal areas as the presence of land masses alters their response. Data for assimilation in numerical models may come from databanks which today are well organised and interconnected, providing a source of confident climatology. Data may also come from manual monitoring which is a source of very low frequency (typically 1 month to 1 year) but with the capacity to deal with a very large number of parameters. High frequency data (more than 1/day) are needed when the dynamic of the variability is high such as in highly energetic coastal areas and when high resolution knowledge of the processes at work is needed. In these situations, automatic measurements are the only way to get information. Recently, the European Union adopted the so-called Water Framework Directive (WFD) which is a common legal framework accepted by all member states to ensure the sustainability of the use of waters. This means: 9 increasing the biodiversity of the aquatic population 9 reducing water pollution 9 mitigating the consequences of overflows and dryness The WFD aims to reach a state of "good environmental quality" for all European waters--i.e, surface, underground, transitional and coastal w a t e r s - - b y 2015. This ambitious goal necessarily implies a large number of measurements and reliable organisations to perform this task. The present work is the result of a working group constituted in within the framework of EuroGOOS. Four steps were identified: 9 identification and survey of existing stations or networks in operation in European waters 9 collection of practices and methods used in routine operation of these installations 9 analysis of resources needed 9 recommendations The existing installations were classified in four categories according to the water depth at which they are installed and to the type of parameters they measure. Figure 1 shows the locations of the installations taken into consideration in this survey and Table 1 shows their main characteristics.

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Monitoring the marine environment operational practices in Europe

Figure 1 Monitoring stations in European seas used to support of this survey

2. The limiting factors A number of limiting factors to the deployment of the equipment are identified. Biofouling is reported to be the most important limiting factors especially for sensors, leading to the obligation of frequent cleaning operations or replacement of sensors. The quality of collected data decreases dramatically when suitable methods are not used. Biofouling may also act on the float itself by initiating biological production, leading, for


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example, to an increase of the drag of the surface float and the mooring, which can bring about complete destruction of the installation. Sensor drift may cause a rapid decrease in the data quality if sensors are not well suited to long term operation. The sensor, from the calibration laboratory to the monitoring installation, is subject to a series of physical and environmental aggressions (e.g. changes in ambient temperature, vibrations, shocks, humidity, corrosion, biofouling, etc.) which may affect its response. Communication system is the only link between the station and land. The performance of this link (i.e. data rate, mono or bi-directional, etc.) may directly influence the overall efficiency of the installation as well as the operating costs.

Access to installations by operators is needed regularly to complete the maintenance and/or repair of the installation. Therefore, distance to harbour, mean sea conditions on this route, ease of stepping from a boat onto the buoy and to transfer the necessary equipment are important factors which affect the running cost of the installation. Exposure to extreme meteorological events will define the percentage of time the station will be accessible and, therefore, will affect the data availability. Moreover, this factor also defines the degree of risk of partial or total destruction of the installation due to natural events. Vandalism may cause major operating problems to installations. Industry/customer relations is a question which is posed when new installations are to be installed. Off the shelf equipment may be cheaper with short delivery time but will not be specifically adapted to the customer needs or to the characteristics of the site. Conversely, customised realisations may respond exactly to the specified needs but will have to support the development costs and will obviously cause a long delay before delivery. Costs (both acquisition and running costs) represent the integrating factor of all the points mentioned above.

3. Elements of recommendations The various limiting factors are reviewed and solutions derived by designers in industry or adapted by users are described in the following sections. 3.1 Mitigating the effects of the limiting factors

Biofouling characteristics are very specific of the geographical site directly related to the bio-productivity and meteo-oceanic conditions that affect the site. Therefore, no unique solution exists and the choice of the method will take into account not only the site characteristics but also the amount of energy available and the general architecture of the monitoring station. Generally, passive antifouling methods use copper as the constitutive material of the parts that are in contact with sea water. Specific coatings to prevent biofilm adhesion can also be used. Active methods include chlorinated ion injection in the vicinity of the sensor or exposition to UV of the surfaces to be protected. It should be stressed that the techniques used for biofouling protection should not affect the measurements themselves nor be aggressive to the environment.

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Sensor drift in long term operations is a sensitive question. The choice of sensors is, most of the time, made according to these characteristics as specified by the provider. Additional testing may be performed to experimentally verify the performance of the candidate sensors and to make the final choice. Auto-calibration techniques have been successfully used. In autonomous analysers, this question is solved by using standard solutions of known concentration. The stability in time of that solution has to be carefully verified. Communications systems are chosen regarding the distance from the station to shore. GSM should be used when available because it is by far the cheapest solution, with best performance. Use of communications protocols such as TCP-IP, offers a bi-directional, fully duplex link which permits direct links from the station to the internet and enables tele-maintenance operations from any laptop computer connected to the network. Industry/customer relationships may be eased by independent experts committed to evaluating the commercial propositions and the matching of the technical specifications with the functionalities expressed by the customer. 3.2 Data quality

It has been remarked that, when dealing with continuous, automated monitoring activities, the measurements do not require the same level of accuracy as point measurements performed, for instance, in the framework of scientific cruises. Confidence intervals for each parameter have to be defined by the system specifications. The data quality control will then consist of comparing the collected data with this interval. The confidence intervals have, generally, a fixed amplitude but their absolute positions will depend on the local climatology. Some groups use automated data quality control procedures before archiving and distributing the data. The absolute necessity of archiving metadata (i.e. information concerning the site and the monitoring system) and technical data (i.e. information related to working conditions and configuration of the monitoring station) together with the useful environmental data has been stressed. 3.3 The security of personnel and installation It is a good practice when working at sea, and not only necessarily when dealing with buoys, that all the personnel participating in the operations wear suitable and appropriate security means (life jacket, safety shoes, belts, helmets, gloves, etc.).

Moreover, it is also important that personnel working on the system are well acquainted with the system characteristics and the most effective way to operate on it at a high level of security. To this end, written documents such as technical maintenance notices and procedure manuals should be made available to the personnel in charge. If the structure can accommodate people, particular attention should be used when stepping onto the buoy from the service boat/ship. This operation should be limited by prescribed maximum sea states, except in cases of emergency. No accidents of this kind in the European seas have been reported in recent years. However, the Data Buoy Cooperation Panel (DBCP) reported a significant quantity of accidents due to battery explosions. Though experience demonstrates that these events can be easily avoided by correctly configuring and installing the power supply system


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(e.g., by protecting the batteries from overcharge by using regulators that are usually shipped with the solar panels) and by installing the batteries in a vented compartment, caution must always be taken when operating on electrical power sub-systems of the buoys. Institutions managing the buoys are engaged both in preserving the structure itself and its equipment. The electronics equipment and other materials such as connectors are usually fragile and expensive. Adequate protection means must be provided and established safety procedures must be carefully followed. Electronic parts should be protected and made immune to electrostatic discharge by adequate shields and properly isolated from the power supply source. At present, deliberate vandalism acts have not been reported to be a problem within European seas. But every structure at sea becomes a fishing aggregating device (FAD) thus attracting fishermen. Hence, unintentional damages (e.g., fishing lines or nets into the rotors of mechanical current meters) have been frequently experienced. However, all the compartments should be closed and made inaccessible in order to prevent theft. Nevertheless, buoys at sea are always exposed to the normal risk such as extreme weather conditions and collisions with ships that may provoke damage to the structure of the system or its complete loss. Such damage, caused by unidentified ships, was reported in the Ligurian Sea (North West Mediterranean Sea) in early 1991 and in the bay of Seine in 2001.

4. The costs of operations Operating costs represent the ultimate integration of all factors. They should include all running costs (i.e. personnel, travel/expenses, external costs, etc.) in the following operational phases: 9 at sea interventions 9 in lab refurbishing 9 periodic sensor recalibration 9 data management (i.e. quality assurance, archiving and diffusion) It does not include investment or depreciation costs. The following factors were found to have the greatest influence on the operating costs: 9 the site location (distance from harbour) and availability of qualified sub-contractors 9 the number of personnel required for maintenance operations and their skill 9 the characteristic (size, speed, specific equipment requested) of the boat/ship needed for at sea operations Minimum operating costs are observed for coastal stations which are physically linked to shore followed by maintenance operations that do not require any nautical means. On the other hand, maximum costs are shown by deep water subsurface monitoring networks which require both a specialised ship and higher educated personnel. The frequency of maintenance operation of first level (i.e. routine maintenance on sensors mainly) necessary to maintain the data quality at a satisfactory level is a dimensioning factor in term of costs.

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Acknowledgements The EuroGOOS Office is thanked for its help and encouragement. The Technical Coordinator of the Data Buoys Coordination Panel (DBCP) of IOC and WMO gave his advises and support to the group. He is warmly thanked. Table 1 Networks and station characteristics

Smartbuoy" A marine environmental monitoring buoy with a difference D.K. Mills .1, R.W.P.M. Laane 2, J.M. Rees l, M. Rutgers van der Loeff 2, J.M. Suylen 2, D.J. Pearce 1, D.B. Sivyer l, C. Heins 2, K. Platt 3 and M. Rawlinson 3

/Centrefor Environment, Fisheries and Aquaculture Science, UK 2RIKZ, National Institute for Coastal and Marine Management, The Netherlands 3Eco-Sense Ltd., UK Abstract High frequency measurements of physical, chemical and biological variables are being routinely made employing a moored platform (SmartBuoy) in UK shelf-seas. The rationale for the design and configuration of SmartBuoy are described together with details of the system control, software environment and telemetry capability. An 18month time series of results, showing different scales of variability from annual, through seasonal to daily of a range of measured variables, is presented. Future developments to extend the capability of the current version of SmartBuoy to measure wave direction and current speed are described together with a description of the next generation SmartBuoy 3 platform.

1. Background Moored buoys have been used as platforms for making untended, fixed point and highfrequency observations of physical variables such as current speed and direction for more than 30 years. Such observation strategies play a critical part in resolving environmental variability at temporal scales that are otherwise difficult or impossible to observe using other means. Until relatively recently the lack of reliable sensors and measurement systems for chemical and biological variables have limited our observations to primarily ship-based approaches. The SmartBuoy (Figure 1) has been developed jointly by CEFAS and Eco-Sense to provide a platform that can reliably and cost effectively collect physical, biological and chemical data over extended periods (> 1 year) with the temporal resolution necessary to detect the rapid changes that characterise our shelf seas. It addresses shortcomings of ship-based sampling that provide good spatial cover at the expense of temporal resolution and therefore may provide only limited insight into marine ecosystem variability. A unique aspect of the SmartBuoy is the built-in redundancy for key measurements. For example, plant nutrient concentrations are derived water samples collected using an automated water sampler (Aqua Monitor) and also by the NAS2E in situ nutrient analyser (Figure 2). Currently, nitrate is measured hourly whilst nitrate and silicate are measured daily on preserved water samples. Water samples can also be analysed for phytoplankton biomass and species composition as well as for gravimetric determination of suspended matter concentration. Such measurements on discrete water samples can be * Corresponding author, email: [email protected]

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Smartbuoy: A marine environmental monitoring buoy with a difference

used to validate or calibrate sensor-derived measurements. Measurements of salinity, temperature, irradiance and chlorophyll fluorescence are also made in parallel. Meteorological variables can also be measured on SmartBuoy.

Figure 1 SmartBuoy alongside after deployment showing the surface met. sensor package

2. Smart control and real-time data acquisition SmartBuoy Data acquisition and control is performed by ESM controllers, which employ a distributed data acquisition technique comprising several linked autonomous submodules. This makes for a highly robust system designed to withstand catastrophic failure of individual modules. Each SmartBuoy sub-module acquires and stores data and then makes the data packet available to the main ESM controller. Data are then replicated between the main controller and individual modules. SmartBuoy data are stored on high-capacity removable memory in the form of Compact Flash cards. A GIS enabled web-publishing capability has been developed (www.cefas.co.uk/ monitoring) to display data returned in near real-time from the Marine Monitoring Network of operational SmartBuoys. The ESM generates a data email and transfers it to a satellite transceiver. Data then goes to a user-designated mailbox at the user's site. The SmartBuoy software package acquires the necessary ESM information from the email, retrieves the deployment record from the database, and checks the email for the correct sensors and any spurious characters. The current calibrations from the database are then applied to the values in the email and from calibrated data added to the telemetry result table on the database. The program also generates graphics for the internet and intranet websites (e.g http://www.cefasdirect.co.uk/monitoring, Pearce et al., 2002).

D.K. Mills*, R.W.P.M. Laane, J.M. Rees, M. Rutgers van der Loeff, J.M. Suylen, D.J. Pearce, D.B. Sivyer, C. Heins, K. Platt and M. Rawlinson

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Figure 2 SmartBuoy--a view of the subsurface instrument payload with NAS2E in situ nutrient analyser and Aqua Monitor The SmartBuoy software environment is designed for secure and reliable data processing of a data stream from either a single buoy or from a monitoring network employing multiple buoys. Data from new buoy locations are added as the data buoy network expands. The web-publishing and database infrastructure developed for SmartBuoy has been designed with flexibility in mind, so as to rapidly meet other users' needs. A recent example is the display of time-critical data from the UK wave-monitoring network, WaveNet (www.cefas.co.uk/wavenet). The buoy is equipped with a telemetry system that is integrated with the data archive, dissemination and visualisation software installed at base. Generally SmartBuoy utilises a satellite data transport system to enable wide area cover and global reach. A GPS receiver is included to provide position data with every transmission. If the SmartBuoy moves out of its 'watch-circle' an out-of-position alert is automatically generated and disseminated by email message as an operator's option. Other telemetry options include UHF radio modems and GSM.

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Figure 3 Example of the GIS/Database environmentmsee www.cefas.co.uk/monitoring or www.cefas.co.uk/wavenet for examples of output.

3. SmartBuoy in operation Currently, 3 SmartBuoys are deployed in UK coastal waters at locations that are part of the UK's National Monitoring Programme. Observations from SmartBuoy augment the ship-based annual survey carried out to determine the over-winter plant nutrient concentrations. Observations from SmartBuoy are playing a key part in the development of a more effective monitoring programme for the UK where future assessment of ecosystem health will be required. Between March 2000 and April 2002 a SmartBuoy was deployed in Dutch coastal waters as part of a jointly funded UK-Netherlands programme between CEFAS and RIKZ. The work was undertaken as part of a study on sediment transport in Dutch coastal waters (Mills et al., 2002; Rutgers van der Loeff et. al., 2002) and in order to explore the capability of SmartBuoy for monitoring plant nutrients and phytoplankton response. For 4 months a bottom lander (MiniPod, EcoSense Ltd., Alton, UK) was deployed in conjunction with SmartBuoy with results reported in Rees et al. (2002) Data were successfully returned by SmartBuoy from Noordwijk 10 with Figure 4 showing temporal variability over a continuous 18-month in a range of variables over a range of time scales. Inter-annual variability is apparent in the record for plant nutrients (Figure 4f), phytoplankton chlorophyll biomass (Figure 4e) and temperature (Figure 4c). Over-winter maxima and summer minima in plant nutrients are generally inversely related to chlorophyll concentration, which peaks in the spring with minor peaks in the summer and with lowest concentrations in the winter. There is also evidence of short term variability particularly in turbidity (Figure 4b) and the vertical attenuation coefficient (Kd; Figure 4c). The wave climate recorded nearby (Meetpost Noordwijk) shows the general relationship between increased wave height and turbidity. For example, the extreme wave heights in May 2000 were associated with force 10 winds and a large increase in turbidity (see Mills et al., 2002).

D.K. Mills*, R.W.P.M. Laane, J.M. Rees, M. Rutgers van der Loeff, J.M. Suylen, D.J. Pearce, D.B. Sivyer, C. Heins, K. Platt and M. Rawlinson

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Instruments were replaced every 4 - 6 weeks, and periods when biofouling became problematic were identified by comparison of signals output from duplicate sensors. Biofouling varies with location, depth and season. We encountered little or no evidence of biofouling in winter and sometimes intense biofouling in summer. Between 2-4 weeks of biofouling free operation was possible on average. Periods when biofouling was identified for a particular sensor resulted in deletion of that record from the time series.

Figure 4 Time series data from Noordwijk 10 showing (a) significant wave height (b) turbidity measured using an optical backscatter sensor (c) the vertical attenuation coefficient (Kd) derived from measures of downwelling irradiance at 1 and 2 m depth (d) salinity and temperature (e) fluorescence and (f) plant nutrient data. Total oxidisable nitrate (nitrate + nitrite) was measured using the NAS2E in situ nutrient analyser as well as on preserved water samples (WMS TOXN) together with silicate (WMS Si). Data collected from a vessel alongside the buoy is also shown (RIKZ TOXN).

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4. The future The open architecture central to the design of SmartBuoy allows changes to the payload to meet changing or additional user needs. The flexible design of SmartBuoy permits multiple use, from measurement of environmental health to flood and coastal defence applications and support of shipping operations. For example, SmartBuoy is undergoing further development to measure additional physical parameters, including directional waves and currents, to meet the needs of the UK Flood Management Division of DEFRA with trials currently underway. An important implication of SmartBuoy flexibility is the economic benefit where more than one customer supports the cost of operations from the same platform. SmartBuoy has been in operation for five years, developed by a process of continual refinement in response to operational and scientific requirements. A third generation of SmartBuoy is currently in an advanced state of development with improvements designed to specifically address the issue of biofouling by macro- and microorganisms. By taking a pragmatic approach of avoidance and limitation of biofouling the new design is intended to meet the severe biofouling threat posed by tropical waters where high rates and magnitude of biofouling may be expected. SmartBuoy 3 has also been designed to minimise operational costs associated with deployment, recovery and servicing of the buoy with a view to increasing reliability to maximise data return and give a world-wide deployment capability. The new design also anticipates future requirements by increasing payload capacity to allow new sensors and systems to be incorporated.

Acknowledgements This work was supported by the UK Department of Food and Rural Affairs (CDEP 84/5/ 262) and by the Netherlands Rijkswaterstaat. Wave data was kindly supplied by A.P. Roskam. It is also a pleasure to acknowledge the officers and crew of R/V Mitra and staff of Directie Noordzee (DNZ) for deploying and recovering the SmartBuoys and MiniPod.

References Mills, D.K., M. Rutgers van der Loeff, R.W.P.M. Laane, and J.M. Rees, 2002. Continuous measurement of suspended matter. Sea Tech. Oct. 2002, 43, (10) 5pp Pearce, D., D.A. Lees, M.A. Scriven, C. Grobbelaar, J.M. Rees, B.J. Robinson and D.K. Mills, 2002. A GIS enabled website for publishing near real-time data from autonomous marine observation systems. Poster, 3rd EuroGOOS Conference, Athens 3 - 6 Dec., 2002 Rees, J.M., R.W.P.M. Laane, D.K. Mills, N.D. Pearson, D.B. Sivyer, M. Rutgers Van der Loeff, R.J.M. Suylen, D.J. Pearce, C. Heins, A. Leadbetter, C.E. Vincent and R. Sonneveldt, 2002. Long time-series of Horizontal and vertical fluxes of suspended sediments and chlorophyll in the southern North Sea, presentation at 3rd EuroGOOS conference, Athens, 3 - - 6 Dec. 2002 Rutgers van der Loeff, M., D.K. Mills, J.M. Suylen, D.B. Sivyer, J.M. Rees, D.J. Pearce, C. Heins, A. Reeve, M. Guertz, R. Hoogervorst, and R.W.P.M. Laane, 2002. Observation strategies for measurement of suspended matter in the Dutch coastal zone: past and future. Poster, 3rd EuroGOOS Conference, Athens 3 m 6 Dec. 2002.

ARGOS capabilities for global ocean monitoring Christian Ortega CLS, Collecte Localisation Satellite, France

Abstract The world's oceans contain over 3000 drifting buoys, moored buoys, floats and other platforms monitored by the Argos satellite-based location and data collection system. Following successful deployments in such programmes as TOGA and WOCE, and now ARGO, Argos is going through fundamental changes to better meet the needs of its main users: oceanographers. New features include two-way communicationBoperational in April 2003Bincreased data transmission capacity, and fully customised access to data and results. Designed for and with its scientific users, Argos will remain the only global satellite-based system dedicated to monitoring and protecting the environment. This paper reviews the reliable tools used for in situ observations with Argos, the related global networks, and describes the enhancements to the system designed to enable the ocean community to satisfy increasingly difficult data relay needs with a proven, reliable, and robust data collection link.

Keywords/n situ observation, data collection, location,

satellite

1. Argos system Since the late 1970s, satellites have offered scientists the capability they need to study the oceans. For global oceanographic studies requiring in situ data, low earth-orbiting polar satellites are particularly attractive as they provide worldwide coverage. Their low altitude also means they can receive signals from low-power transmitters.

Figure 1 The Argos System: transmitters relay environmental data to scientific and operational communities worldwide.

Email: christian.ortega @cls.fr

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Argos, which has been developed specifically for scientists, combines these characteristics with simple transmitters operating without the need for a sea-to-sky transmission protocol. Developed under a collaboration between US NOAA and the French space agency CNES, Argos can locate any platform carrying a suitable transmitter, anywhere in the world, and collect data from sensors connected to the transmitter.

Figure 2 Argos is built on a simple and robust one-way link: messages are repeated without any protocol to guarantee their collection by the satellites in any environmental conditions

Figure 3 The smallest Argos drifter, a pop-up tag on a whale shark (transmission power 125 mW). Argos can locate and relay signals from miniaturised and low-power transmitters, thus enabling long lifetime for autonomous platforms These unique characteristics of the Argos system have fostered the development over the past twenty years of a wide range of reliable in situ platforms, leading to the implementation of most global ocean observation networks in operation today.

2. Reliable ocean monitoring tools 2.1 The buoy family The first Argos platforms were 200 FGGE type buoys deployed in 1978 for the Global Atmospheric Research Program (GARP) experiment. Since then, oceanographers and manufacturers have refined these platforms and developed reliable low-cost models such as the SVP type buoy which were extensively deployed during the Surface Velocity Programme of the World Ocean Circulation Experiment (WOCE). The expendable SVP drifters are fitted with a holey sock drogue to drag the buoy with the currents and hence accurately measure the current speed and direction. New sensors are being regularly added to this simple buoy concept starting with a barometer (SVP-B) which suits both operational meteorology and science, wind and conductivity sensors, thus offering a full range of cost-effective oceanographic platforms. Some 1500 Argos drifters are roaming the oceans. The buoys' performance has been regularly monitored by users and manufacturers and the reliability level has increased to reach an average lifetime over one year, with some units having lasted more than three years.

Christian Ortega

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Figure 4 The buoy family, the ODAS moored buoy transmitting via GOES or Meteosat, but backed-up with Argos transmitter, and the SVP-B drifter 2.2 The ARGO floats

The float technology has been maturing over the past 15 years to reach the so-called "ARGO" models which are being implemented for the international ARGO programme. The floats were originally developed to make direct observations of the subsurface ocean circulation. They dive to a given "parking" depth, float freely with the currents, then surface to be positioned by Argos and/or relay acoustic signals--sent by fixed sourcesm recorded during their journey. The ARGO units are multi-cycle floats fitted with temperature, conductivity (i.e. salinity) and pressure sensors, which sense the water characteristics on their way up to the surface, and relay the data profiles via the satellites.

Figure 5 examples of multi-cycle subsurface floats, from left to right, the ALACE without sensor, the APEX, and PROVOR both fitted with CTD sensors. The reliability requirement on this type of equipment is quite high as the mission duration is to exceed 4 years with no intervention. In January 2003 there were 620 ARGO floats in the seas, all but few units using Argos transmission. Argos floats have been deployed since the early 80s and their reliability has proved to match the stringent

320


ARGO requirements. New models are expected to carry Argos two-way transceivers which will allow them to transmit more data while spending less time at the surface.

2.3 Other examples of reliable tools Figure 6 shows the smallest V.O.S. meteorological station, the Minos station, which can be installed in a few hours.

Figure 6 The Minos station includes atmospheric pressure, air temperature under shelter, GPS receiver, display terminal in cabin, and of course, Argos transmitter. A new feature coming soon will be the addition of wind speed and direction. The smallest autonomous water level station, Hydro Argos (Figure 7), is also installed in few hours.

Figure 7 The Hydro Argos station includes water level from pressure sensor, air intake (pressure compensation), LR20 batteriesmAutonomy >1 year, evolution: water quality multi-sensor probe, T-S, pH, turbidity

Christian Ortega

321

3. Global Argos Network

Figure 8 The Argos network of antennas, subsidiary and agents worldwide. 38 antennas are shared or owned by CLS worldwide. The lower diagram displays the Argos platforms seen last year. Over 9000 Argos platforms transmit each month.

4. Global Argos applications

Figure 9 The IOC/WMO Data Buoy Co-operation (DBCP) action groups and the related platforms which were transmitting in January 2003

322


Figure 10 T/S profile sample from one of the 620 floats in the ARGO float network (Jan. 2003)

5. System and service enhancements 5.1 A strong international cooperation Argos has been operating since 1978. The United States, Europe, Japan and Brazil have been combining efforts to build the Argos system of the future. Satellite launches are planned for the next ten years.

Figure 11 Satellite launches, space agencies involved and Argos payload generations. Today there are six NOAA satellites, three of them equipped with Argos-2 generation, and ADEOS-II fitted with an Argos 2 two-way system.

5.2 Argos two-way: sending commands to your platforms Further to the launch of ADEOS-II in December 2002, Argos is now in the process of supplying two-way capability to the users. The Argos downlink is to be operational in April 2003, following the completion of the satellite tests by NASDA and CNES. The Argos Downlink enables you to: 9 Send commands to your platforms, once they are deployed, to tune their mission, switch them OFF/ON, adapt transmission parameters, etc. 9 Optimise platform performance. Argos-defined information such as satellite configuration, orbit parameters, UTC time is regularly broadcast and can be use by the platforms to transmit more efficiently.

Christian Ortega

323

Send more data by using the new interactive data collection mode: Satellite will acknowledge the reception of messages sent by your platform.

Figure 12 Sending messages to the platforms: User inputs a message request via our web site. The message is then relayed to the satellites via a network of four master platforms. Once above the platform, the satellite downloads the message, several times if necessary, until it receives an acknowledgment from the platform. The user receives the acknowledgement. 5.3 Longer lifetime, more data The successive designs of the Argos Data Collection System have been following the evolution of the in situ observation requirements. If, originally, the main challenge was to relay any observation at all from remote areas, often exposed to harsh environments, today the general trend is to transmit larger volumes of data while increasing the experiment lifetime. Also, cost-effective reliable instrumentation are needed more than ever. Without going into the details of the Argos payload enhancements, the diagrams below provide an overview of the performance evolution in term of platform lifetime and data volumes.

Figure 13 Platform lifetime (left) and average data volume collected at each satellite pass with a repetition rate of 90' Lifetime increase is due to the lowering of the transmission power thanks to the 3dB increase of the DCS antenna sensitivity from Argos 1 to Argos 2, and the further 6 dB

324


from Argos 2 to Argos 3. The major lifetime increase comes from the Argos two-way capability which, by providing satellite ephemeris, will enable the platform to transmit only when there is a satellite above. The data throughput is at least doubled with Argos 2 + downlink (Argos two-way) with the use of an interactive data collection mode--the satellites acknowledge the reception of good messages which reduces transmission redundancy. The significant step is brought up by the 4.8 kbits/sec high data rate channel of Argos 3.

5.4 The Web interface New data processing and management systems for the Argos processing centres are being phased in along with the evolutions of the Argos payloads. These will provide a more open system that lets users access and modify platform processing, program characteristics and access to results, on-line and made easier via an improved user interface. From mid 2003, users will access their data via a new web interface and use it to send messages to their platforms.

Figure 14 The Argos data access interface, home page

6. Conclusion Argos has been serving oceanographers for more than 20 years, in a spirit of worldwide cooperation. The best example of this is the way the system continues to evolve as a partnership between its users and operators. The enhancements described will enable the ocean community to satisfy increasingly difficult data relay needs with a proven, reliable and robust data collection system as Argos continues its Earth observation and monitoring mission into the next decade.

Acknowledgments To all users and manufacturers who trusted the Argos system and made it grow. To CNES, NOAA, NASDA, EUMETSAT, IMPE space agencies who provide satellites, payloads, and regular and steady enhancements to the Argos system capabilities.

FerryBox systems for monitoring coastal waters Wilhelm Petersen*, Michail Petschatnikov, Friedhelm Schroeder, and Franciscus Colijn GKSS Research Centre, Institute for Coastal Research, Germany

Abstract A new operational tool which uses ferryboats as the carrier system for automated monitoring equipment has been developed. Such systems can be operated with lower costs than automatic buoys and have better performance with regard to biofouling. Currently the system is being tested in a EU project in which eight ferry routes are used for comparison of different European "FerryBoxes" in different European waters. The "German FerryBox" consists of a fully automated flow-through system with sensors and automatic analysers for the measurement of physical, biological and chemical parameters (T, salinity, turbidity, pH, oxygen, chlorophyll-a, nutrients). It provides automatic cleaning cycles and position-controlled sampling (GPS). Data are transferred to shore and the system can be remotely operated via mobile phone. The system has been installed on the ferry Cuxhaven (DE)-Harwich (UK) and has been in operation since November 2001. Results from the experiences of the first year demonstrate the functionality and applicability of the ferry system.

1. Introduction The contamination of coastal waters with nutrients and toxic substances is of growing concern in European countries. For an assessment of the water quality of these regions operational monitoring programmes have been implemented. In this context the main aims of water quality monitoring are: 1. preventing potential danger to human health 2. assessing the impact of anthropogenic substances on aquatic ecosystems 3. documenting the present state of water pollution 4. showing the efficiency of water protection measures by means of emission values. Looking at the present practice in monitoring of rivers, coastal areas and shelf seas it is evident that operational monitoring is mainly carried out by manual sampling during ship cruises and followed by analyses in the laboratory. In the "official" monitoring programmes for the North Sea this is carried out three to six times per year, which is not frequent enough to observe the spatial and temporal extensions of phenomena such as algal blooms, which have a typical random short time distribution. Information about spatial distributions is therefore strongly hampered by a fixed station strategy (e.g. Althuis et al., 1994). Whereas manual sampling is the only feasible method for counting biological species and the analysis of trace contaminants such as heavy metals or organic micropollutants, there are other, complementary methods for the automatic unattended measurement of * Corresponding author, email: [email protected]

326

FerryBox systems for monitoring coastal waters

standard oceanographic parameters, such as temperature, salinity and currents, and in some cases other parameters including turbidity, oxygen, nutrients and chlorophyll fluorescence. These automated measurements are mainly carried out from different types of moored buoys or other fixed marine stations (Hydes et al., 1998, Knauth et al., 1997, Nies et al., 1999, Sanders et al., 2001). There are many advantages to these operational systems on buoys among which are the assessment of short-term events (storms, fresh water discharge, etc.) and production of consistent long-term time series with high temporal frequency. However there are also some serious disadvantages in that they provide only point measurements and often data gaps occur due to biofouling of sensors and maintenance difficulties due to the difficulty of accessing stations during bad weather. The operational costs are high due to maintenance by ship cruises. Based on all these problems and limitations, it seems logical to investigate the role that could be played by ships of opportunity (Flemming et al., 2002). There are many routes for ferryboats and "ships-of-opportunity" which run quite frequently. Already for 60 years the "Continuous Plankton Recorder (CPR)" (Reid et al. 1998) has followed the idea of using scientific equipment on such ships for continuous recording of environmental data. This method has now been improved and shows an impressive data set of semi-quantitative phytoplankton data over the world oceans. Over the last few years some more sophisticated systems have been implemented on ferry boats, which allow more precise measurements of temperature, salinity and chlorophyll (Harashima and Kunugi, 2000; Rantaj~irvi et al., 1998; Ridderinkhof et al., 2002, Swertz et al., 1999; Koske, 2002). Applying such measuring systems on ferry boats or ships-of-opportunity has several advantages: 9 the system is protected against harsh environment, e.g. waves and currents 9

bio-fouling can be more easily prevented (inline sensors)

9 no energy restrictions (in contrast to buoys) 9 easier maintenance when ferry comes back "to our doorstep" 9 lower running costs since the operation costs of the ship do not need to be calculated 9 instead of point measurements (buoys) transects yield much more spatial information. Within the GOOS (Global Ocean Observing System) and EuroGOOS framework we have started initiatives to develop automatic measuring systems for bio-oceanographic parameters. As a measuring platform, ferries on regular routes offer a cheap and reliable possibility to obtain regular observations on near surface water parameters. Present activities are both nationally and internationally EU funded. Some of these tools and methods are currently being developed and tested in the EU project "FerryBox" (http:\\www.ferrybox.org), in which eight ferry routes will be used for an comparison of different European "FerryBoxes" (Figure 1).

Wilhelm Petersen*, Michail Petschatnikov, Friedhelm Schroeder, and Franciscus Colijn

327

Figure 1 EU project "FerryBox" and the ferry routes involved

2. Material and methods The "German FerryBox" consists of a fully automated flow-through system with different sensors and automatic analysers. Figure 2 shows a schematic drawing of the German FerryBox system and the measured parameters.

Figure 2 Schematic drawing of the German FerryBox system

328


Water is pumped into the ship from an inlet in front of the ship's cooling system. A debubbling unit removes air bubbles, which may enter the system during heavy seas. At the same time coarse sand particles which may be introduced in shallow harbours and which settle and tend to block the tubes are removed. Coupled to the debubbler is an internal water loop in which the seawater is circulated with a constant velocity of about l ms -1. This velocity decreases the tendency for build-up of bacterial slime on sensors and tube surfaces. A small amount of the water is filtered by a ribbon-type filter for automatic nutrient analysis (this type of filter regularly provides new filter material avoiding bacterial processes which may influence the concentration of nutrients). For a reliable unmanned operation the system is supervised by an industrial programmable logic control, which can shut off the system in case of errors and operates automatic cleaning cycles, e.g. in harbours. Data acquisition, data storage and data transfer to shore is controlled by an industrial standard PC (Pentium II). Data can be transferred to shore and the system can be remotely operated by GSM (mobile phone). Biofouling is prevented by cleaning the sensors with fresh water and rinsing with acidified water or under severe conditions (tropics) by chlorination. Sometimes clogging of the water inlet in the ship' s hull by debris or small fish causes problems. Since all flow rates are monitored and controlled by the system in such cases an automatic pressure back-flushing cycle is initiated, which clears the inlet. Table 1 gives an overview of the specification of the different sensors/analysers. Currently a quality assurance programme for qualifying all ferry modules is under way. Table 1 Instruments used in the FerryBox Parameter water temperature salinity

Manufacturer

FSI (USA)

turbidity-I dissolved oxygen

Range -10 to 50 ~ 0 to 50 0 to 9999 FNU

Endress&Hauser (D)

0 to 20 mg/I

pH

0 to 15

turbidity-II

0 to 50 NTU

chlorophyll-a

Turner design (USA)

Accuracy

Resolution

0.1 ~ 0.02

0.01 0.001

10%

0.001 FNU

0.2% F.S.

0.01

0.1

0.01

to be tested

0.05 NTU

0 to 200 pg1-1

10%

0.5

0.1 to 301Jmol1-1

15%

0.001

0.5 to 500 pmol1-1

15%

0.01

0.2 to 50 pmol1-1

15%

0.05

0.1 to 100 pmol1-1

15%

0.01

nitrate (UV detect.) Trios (D)

0.05 to 200pmol1-1

to be tested

0.05

algal groups

1 to 200 pg1-1 chlorophyll to be tested

ammonia nitrate o-phosphate

ME Meereselektronik (D)

silicate bbe-moldaenke (D)

1

Of the sensors used, the analysers for nutrients and the sensor for algal groups require a special mention: Nutrient analysis (nitrate, ammonium, o-phosphate, silicate) is carried out by chemical analysers which automatically perform the same colorimetric reactions as are applied in standard marine chemistry in the laboratory.


329

In addition a new optical method for nitrate determination is under test: By measuring the spectrum of sea water from 200nm to 400nm and applying a multi-component analysis to the known spectra of nitrate, bromide and humic substances nitrate can be measured quantitatively in the range from about lpmol1-1 to 100pmol1-1 (automatic change of cuvette length). Algal groups are measured by sequential excitation with five LEDs (blue, green, yellow, orange and red) and measurement of the emitted fluorescence signal (Ruser, 2001, Beutler, 2003). By careful calibration phytoplankton with distinctive different pigment patterns can be distinguished, e.g. green algae, blue-green algae and diatoms. The instrument is now being tested during cruises. Samples are taken at regular intervals and plankton is counted and chlorophyll measured by HPLC for comparison. The first installation of the system has taken place on the ferry between Hamburg (Cuxhaven) and Harwich and has been tested since November 2001. The ferry route covers the southern part of the North Sea and crosses the waters flowing from the English Channel to the North.

3. Results and Discussion Since November 2001 measurements have been obtained regularly. Figure 3 shows the DFDS ferry route and the results from a single transect in May 2002.

Figure 3 Chlorophyll-a concentrations between January to November 2002 on the transect Cuxhaven-Harwich

330


In the temperature curve the higher temperatures in the coastal waters (Cuxhaven and English coast) can be seen. The salinity curve near Cuxhaven shows the fresh water outflow of the river Elbe, whereas the decrease near Harwich is only marginal. It is also evident that lower salinities are observed along the Dutch and German coast compared to the English Channel due to fresh water influence of the IJsselmeer and the fiver Rhine outflow. High turbidity is measured near Harwich which is characteristic of the coastal areas of the English coast. The total chlorophyll concentration shows two maxima, one near Cuxhaven and one between the English Channel and the Dutch coast. The maximum near Cuxhaven is typical for the first spring bloom in front of the Elbe estuary in the German Bight (Moll and Radach, 2001). The main reason for this could be the high nutrient concentrations in combination with the relatively moderate turbidity and therefore relatively high light availability (Colijn and Cad6e, 2003). In this peak diatoms are dominant; but green algae are also present. The other chlorophyll peak at the Dutch coast is a mass occurrence of algae in this area in spring. Here, green algae and diatoms are both prevalent. The pH varies between 7.9 and 8.4 pH units. Near the Elbe estuary (Cuxhaven) the values are lower due to the fresh water input. However, other low values can be found near Harwich where the freshwater influence is negligible. Since the oxygen concentrations here are low, turbidity is high and ammonium is high as well, a guess can be made that oxygen consumption processes of re-suspended sediments contribute to these lower pH values (Radach and Lenhart, 1995). As expected, pH and oxygen also show maxima at the chlorophyll peaks, thereby reflecting the higher primary productivity of these areas. Along the ferry transect the nitrate concentrations vary between 0.1 and 90 ~mol1-1 (1.5-1400 ~tg-N1-1) and the ammonium concentrations vary between 0.1 and 1.3 ~tmol 1-1 (1.518 ~tg-N l-l). The largest nitrate concentrations are measured near Cuxhaven due to high nitrate input from the fiver Elbe. Near the English coast the largest ammonium concentrations are found whereas nitrate is slightly enhanced. One result which demonstrates the potential of FerryBox systems to detect phenomena that have previously been missed is the broad maximum of ammonium between km 300 and km 430, which can be observed during most ferry cruises in spring and summer. The origin of this is currently unclear. In Figure 4 a contour plot of the chlorophyll concentrations from Harwich to Cuxhaven is shown for the time interval between January and November 2002. On the vertical axis the time is depicted; the horizontal axis shows the distance from Harwich. As can be seen, until the end of February hardly any algal growth exists. Growth starts between km 180 and km 220 in early March. Later in April plankton growth occurs along the whole Dutch and German coast almost simultaneously. In early May this bloom breaks down, leaving only the patches at km 150 and km 5 6 0 - - a s shown in Figure 3. Later in June and July higher algae concentrations are only found at the Dutch and German coast and from August to October just about no blooms are detectable.


331

Figure 4 Chlorophyll-a concentrations between January to November 2002 on the transect Cuxhaven-Harwich From the chlorophyll data it is evident that due to patchiness, samples taken from research ships would only be representative if the cruise had been carried out at the "right" times and at the "right" locations. On the other hand, stationary (fixed) buoys would only by chance detect these algal blooms if they had been anchored within the area of the bloom. Contrary to this, transects with ferries have a greater chance for the observation of algal blooms. If these measurements are combined with satellite measurements n as we plan to do in the near future with E N V I S A T n the "fate" of algal patches can be followed. In the case of Figure 4 this means that we could decide if the algal bloom broke down or was drifting out of the region. Simultaneously the ferry data can be used for calibration of satellite data (in situ truth). Since the satellite coverage in the North Sea is poor due to clouds, a numerical model could be used for further improvement of the assessment of algal blooms.

4. Conclusions It has been shown that ferry boats or ships-of-opportunity equipped with automated systems for water quality measurements are a suitable tool for monitoring water quality and can supplement existing "conventional" monitoring in coastal areas and shelf seas. However, integrated monitoring strategies which are carried out by combining sampling from ships, automatic fixed stations, automated ferry systems, remote sensing and numerical models do not only require good concepts and strategies in order to achieve the maximum of information with the minimum of resources, but also require suitable tools to merge the different types of data (different spatial and temporal scales). After completion of the EU funded FerryBox project, strategies should be developed to demonstrate how future integrated marine monitoring systems can be operated and which benefits of enhanced coverage and information density can be obtained.

332


Acknowledgement The authors would like to thank DFDS Seaways (Copenhagen, DK) for the opportunity to use the 'Admiral of Scandinavia' as a platform for the FerryBox and the chief engineers Mr. Philippi and Mr. Btiker and their crew for their support onboard. Many thanks are also given to Jan B6dewadt for programming the LabView program for remote control and data acquisition.

References Althuis, I.J.A., W.WC. Gieskes, L Villerius, and F. Colijn, 1994, Interpretation of fluorometry chlorophyll registrations with algal pigment analysis along a ferry transect in the Southern North Sea. J. Sea Res. 33 (1): 37-46. Beutler, M., 2003, Spectral fluorescence of chlorophyll and phycobilins as an in situ tool of phytoplankton analysis~models, algorithms and instruments. Dissertation at University Kiel, Germany Colijn, F. and G.C. Cad6e, 2003, Is phytoplankton growth in the Wadden Sea light or nitrogren limited?. J. Sea Res. 49, 83-93 Flemming, N.C., S. Vallerga, N. Pinardi, H.W.A. Behrens, G. Manzella, D. Prandle, and J.H. Stel, 2002, Operational Oceanography: implementation at the European and regional seas. Proc. Second International Conference on EuroGOOS, Elsevier Oceanography Series Publication series 17. Harashima, A., and K. Masayuki, 2000, Comprehensive Report on Marine Environmental Monitoring and Related Studies Using Ferry Boats. CGER-Report, National institute for Environmental Studies, Environmental Agency of Japan, CGERM007-2000, ISSN 1341-4356 Hydes, D.J., P.N. Wright, I. Waddington, and M.B. Rawlinson, 1998, Real-time monitoring of eutrophication processes using a data buoy. Conference Proceedings Volume I. Oceanography International 98, 10-13 March 1998 Brighton, U.K. ISBN 0900254203, pp 59-67. Knauth, H.-D., F. Schroeder, R. Menzel, E. Gebhart, S. Marx, D. Kohnke, and F. Holzkamm, 1997, Marine Pollution Network EURMAR-MERMAID: Results of the Experimental Operation. Dt. Hydrogr. Z., 49(2/3), 385-407 Koske, P., 2002, Ferries in operational oceanography~the German Ferry Box Project. In Flemming, N.C., S. Vallerga, N. Pinardi, H.W.A. Behrens, G. Manzella, D. Prandle, and J.H. Stel, 2002, Operational Oceanography: implementation at the European and regional seas. Proc. Second International Conference on EuroGOOS, Elsevier Oceanography Series Publication series 17, 317-324. Moll, A. and G. Radach, 2001, Synthesis and new conception of North Sea Research (SYCON)~Working Group 6: Review of three-dimensional ecological modelling related to the North Sea shelf., Ber. Zent. Meeres-Klimaforsch. Univ. Hamburg (Z Interdiszipl. Zentrumsber.), No. 8, 225 pp. Nies, H., B. Brtigge, D. Sterzenbach, N. Theobald, S. Dick, H.-D. Knauth, and F. Schroeder, 1999, Erste Ergebnisse des Projektes CANVAS (Contaminants and Nutrients in Variable Sea Areas). Dt. Hydrogr. Z, Supplement 10 Radach, G., and H.J. Lenhart, 1995, Nutrient dynamics in the North Sea: Fluxes and budgets in the water column derived from ERSEM. J. Sea Res., 33,301-335


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Rantaj~irvi, E., R. Olsonen, S. H~illfors, J.H. Lep~innen, and M. Raateoja, 1998, Effect of sampling frequency on the detection of natural variability in phytoplankton. Experiences based on unattended high-frequency measurements on board ferries in the Baltic Sea. ICES J. Mar. Sci. 55; pp.697-704. Reid, P.C., M. Edwards, H.G. Hunt, and A.J. Warner, 1998, Phytoplankton change in the North Atlantic. Nature, London. 391. 546 Ridderinkhof, H., H. van Haren, F. Eijgenraam, and T. Hillebrand, 2002, Ferry observations on temperature, salinity and currents in the Marsdiep tidal inlet between the North Sea and Wadden Sea. In Flemming, N.C., S. Vallerga, N. Pinardi, H.W.A. Behrens, G. Manzella, D. Prandle, and J.H. Stel, 2002, Operational Oceanography: implementation at the European and regional seas. Proc. Second International Conference on EuroGOOS, Elsevier Oceanography Series Publication series 17, 139-147 Ruser, A., 2001, Untersuchung der Erkennung von Algengruppen und deren photosynthetischer Aktivit~it im marinen Bereich. Dissertation at University Kiel. Berichte, Forschungs- und Technologiezentrum Westktiste d. Univ. Kiel, Nr. 25, 206S, ISSN 0940-9475 Sanders, R., T. Jickells and D. Mills, 2001, Nutrients and chlorophyll at two sites in the Thames plume and southern North Sea. J. Sea Res. 46, 13-18. Swertz, O.C., F. Colijn, H.W. Hofstraat, and B.A. Althuis, 1999, Temperature, salinity and fluorescence in the Southern North Sea: High resolution data sampled from a ferry. Environmental Management, 23(4): 527-538

Real-time oceanographic measurements using the M3A system I. Thanos* l, K. Nittis z, and C. Tziavos 2

1MartedecS.A. Athens, Greece 2National Center for Marine Research, Athens, Greece Abstract The design and development of a prototype buoy system able to provide biochemical data in the euphotic zone and physical measurements in the upper main thermocline are presented in this paper. The satellite data telemetry and the large autonomy of the system allow its operation in both near-shore and open ocean sites. The system architecture is described and selected results from the in situ measurements during its pilot operation in the Mediterranean Sea are presented. It is shown that the system can provide high quality measurements that improve our understanding of open sea ecosystem functioning and contribute towards reliable environmental predictions.

Keywords:

Ocean monitoring, buoy systems, marine technology, sensors, Mediter-

ranean Sea

1. Introduction Operational monitoring and forecasting of marine environmental conditions are necessary tools for the effective management and protection of the marine ecosystem, requiring the use of multi-variable real-time measurements combined with advanced physical and ecological numerical models. Fixed oceanographic buoys contribute significantly to operational monitoring especially when high frequency and multiple variable measurements are among the requirements (Glenn et al., 2000). Such a multi-variable observing system was developed for the needs of the Mediterranean Forecasting System within the EU funded MFS-PP project. The project aims at the prediction of coastal primary producer variability within time scales of days to months (Pinardi et al., 1998). One of the main goals of the pilot phase was to demonstrate that multi-parametric operational monitoring and near real time forecasts of the large-scale basin currents are feasible. The Mediterranean Moored Multi-sensor Array (M3A) was one of the observing components of MFS and was designed to provide real time biochemical data of the euphotic zone and physical parameters of the upper 500m. The development of the M3A system was based on the experience of the Tropical Atmosphere-Ocean (TAO) array of the Equatorial Pacific (McPhaden et al., 1998) and similar developments on multi-parametric measurements at the Bermuda Test-Bed Mooting (Dickey et al., 1998). The system architecture and the specifications of its components are described in section 2. The results of the pilot operation during 20002001 are presented in section 3 while the final conclusions are presented in section 4 of the paper. * Corresponding author, email: [email protected]

I. Thanos*, K. Nittis, and C. Tziavos

335

2. Architecture of the M3A system The M3A system (Figure 1) is composed of independent mooring lines that host the various instruments and communicate via underwater acoustic telemetry. This configuration allows separate, and thus easier, maintenance of lines 2 & 3 that need more frequent maintenance (every 2-3 months) and can be handled by a medium size vessel. The central mooting line 1 has a lower maintenance frequency (every 12 months) but requires the use of a larger research vessel. Furthermore, this modular configuration of M3A guarantees the expandability of the system that could be enriched in the future with additional mooring lines. The central line hosts a surface buoy (of Medusa type), and the sensors for physical parameters at deep layers (150-500m). Four SBE 37-IM CTD instruments (element #2 of Figure 1) are used at 150, 250, 350, 500m to measure temperature, conductivity and pressure. Data are transferred from the deep sensors to the surface buoy by a 600m inductive-modem cable. This cable is connected to the subsurface umbilical by a conductive swivel at 30m (#3). At 30m, the hydroacoustic modem Orca-MATS-12 (#4) receives the data from line 2 and transfers them to the surface buoy through the umbilical. The surface buoy hosts sub-surface sensors for temperature, conductivity, turbidity, dissolved oxygen and chlorophyll-a, the meteorological and wave sensors (wind speed and direction, air temperature, atmospheric pressure, humidity, wave height and direction), plus the data storage and transmission system. For the transmission of data to the ARGOS satellite system, an IESM-PTT07 transmitter has been used. A backup method for data transmission is through the GSM network using a mobile phone. This system allows two-way communication between the operational centre and the M3A system. _~~

approx. BOO m

~'ic~i'~r[

[]

-

5

1

~

l

[] ,50~goo~b...... [] /f--~Sm (('~-------P.......... tOi. . . . .

ith doto logger . . . . . gosb. . . . .

_30m/~[_400 . . . . . . . . . . . . floater [] J --;~ l ] ~ e r i hYdmro~176 LFI-4-] I -65m I ~ h t .... y of 4 multi. . . . . . . e ~1,9,,} L---gl0~ II r of: CTD ,t ....... y Inductive modem coi~. ocoustlc releeser swivel / I j DissolvedOxygen ' / chlorophyll L'150m l 9Conducthdty.Temperoture,Depth ~. PAR A0cP(Ro,)§ J-ZSOm,Coodoct!,~*y.Temperot~ro ~ n u t r i e n t onolyser

/

/

Line2

Line1

12 mm polyester 16 mm wire rope acoustic ~ releaser...~

~

o

n

f

o

/Clump beton onchor ...... ie ]/1350kq r t h Anchor{lO0 kq)

ocoustic

1000_~ or train wheels or beton clamp

Figure 1 Outline of the M3A system Mooring line 2 hosts four CTD (SBE-16) instrument packages, at 4 0 - 6 5 - 9 0 - 1 1 5 m , and a NAS-2E nitrate analyser (WS-OCEAN) at 45m depth (#12). Each SBE-16 package is additionally equipped with a Wetlabs C-star transmissometer, a WETStar fluorometer, a Li-Cor (LI-193-SA) PAR (Photosynthetically Active Radiation) sensor and a SBE-23B dissolved oxygen sensor. A pressure container at 35 m depth (#11) hosts

336

Real-time oceanographic measurements using the M3A system

the computer that controls line 2, stores the data and transmits them to mooting line 1 through the acoustic modem (#10). Mooring line 2 is also equipped with two releasers at 125 and 1000m and an Orca-BASM500 Argos beacon that transmits the mooring position if the floating device accidentally surfaces. Mooring line 3 hosts a RDI 75kHz Long Ranger ADCP at 500m depth for measurements of the current profile in the 0-500m layer. The system does not include real-time data transfer to the surface buoy since the large volume of ADCP data does not allow their transmission through satellite (at least with the current system capabilities). With a sampling interval of 30 min the autonomy of the system is approximately 6 months, which is also the maintenance interval for mooring line 3.

3. The pilot system operation The M3A system was deployed in January 2000, at a depth of 1030m approximately 30nm NW of the city of Heraklion (Crete) in the south Aegean Sea. The area was selected because: a) it has open-ocean characteristics while being relatively close to the coast b) its phenomenology had been extensively studied during previous MAST projects c) it is an area for which ecological models are being developed by MFS partners. Up to the end of 2001, five maintenance cruises were performed; each expedition had a duration of 3-4 days that included recovery and redeployment of the system during the first and last day respectively and lab work during the remaining time. The most significant problems during the pilot operation were related to the satellite data transmission and the performance of optical sensors. The satellite transmission operated for only two months due to hardware failure of the antenna and the transmitter. The data retrieval during these two months of satellite transmission was not satisfactory: due to the large amount of data and the relatively low throughput of ARGOS, only 60% of the measurements were successfully retrieved in real time. The backup GSM transmitter was successfully used but this solution cannot be applied at open ocean sites. The light attenuation sensors were found to be very sensitive to biofouling, even in the extremely oligotrophic conditions of the Cretan Sea. Dissolved oxygen sensors gave reliable data for the first six months but an attempt for in situ repair was not successful and thus data are missing after August 2000. Chlorophyll-a (chl-a) and dissolved oxygen data were calibrated using in situ reference measurements. For the calibration procedure, water samples were collected at the respective depths during each maintenance cruise, once during the recovery of the instruments and once during their redeployment 2-3 days later. The calibration coefficients were calculated separately for each period between maintenance cruises, using the reference data collected during each re-deployment when all sensors had been cleaned. The same procedure was applied for temperature and salinity sensors using reference measurements from standard CTD but the result indicated that there was no need for re-calibration during the 22 months period. Time series of chl-a concentrations at the 4 sampling depths of line 2 are presented in Figure 2. Data have been calibrated against water samples following the above-described procedure. Overall, chl-a values in the Cretan Sea are very low (less than 0.51ag1-1 at the


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chl-a maximum layer and less than 0.1~tg1-1 below) in total agreement with previous studies in the area (Tselepides et al., 2000). During winter, chl-a concentrations are more uniform in the upper 100 m due to vertical mixing, while the development of the thermocline (Figure 3) creates a chl-a maximum layer since concentrations above 70m decline significantly. The effect of biofouling is mainly visible in 3 periods: between August and October 2000 at almost all depths, at the end of March 2001 for the 90m sensor and at the end of June 2001 for the 40m sensor. In the two summer periods the problem appears shortly after maintenance while in the winter event it appears only after a long operation of the system without cleaning. This suggests that a more frequent maintenance programme (every 2 months) is required in summer when favourable conditions for fouling are created, compared to the winter period (>4 months).

Figure 2 Chlorophyll-a time series at different depths

Figure 3 Temperature time series at different depths

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The evolution of temperature in the upper 500m (Figure 3) indicates that no deepconvection event took place in this region during 2000-2001 although the area is known to be the site of Cretan Intermediate Water formation. The euphoric zone is dominated by the seasonal cycle that leads to the development of a strong thermocline at the 40-70m layers during summer. The strong synoptic scale signal in this layer is related to variability of the semi-permanent mesoscale structures that dominate the dynamics of the area (Cardin et al., 2003). Despite the relatively short operation of the system, there are indications of important interannual variability in the area: the temperature time-series indicate that the summer of 2001 was warmer that 2000 and this signal penetrated even below the seasonal thermocline affecting layers down to 150m.

4. Summary and conclusions The prototype M3A system was developed and tested during the pilot phase of the Mediterranean Forecasting System with the primary objective of collecting multiparametric data for ecosystem model development. The modular design of M3A allows independent handling of sub-systems that have different maintenance requirements and makes the system expandable for future upgrades. The main problem during this pilot operation was related to biofouling on optical sensors. The light transmittance sensors were found to be the most sensitive to this effect while Chl-a sensors were affected only during certain periods of the year and at the end of long deployments. Overall, dissolved oxygen and chlorophyll-a sensors gave reliable data after consistent re-calibration against in situ measurements during each maintenance cruise. There was no need for recalibration of temperature and salinity sensors that provided reliable data. The preliminary analysis of data showed their value for studies of physical-biological coupling in the open sea at various time scales and demonstrated the importance of continuous multidisciplinary deep sea monitoring (Cardin et al., 2003). The M3A data of the pilot operation are already being used for the development and validation of ecological models of the Cretan Sea. It is the first time that such models can be evaluated against long time series of coherent physical and biochemical data with adequate vertical resolution. The overall experience from the pilot deployment during 2000-2001 suggests that operational multi-parametric ocean observations are feasible. Part of the problems (telemetry, stability of mooting lines) can be solved with engineering improvements of the system, while problems of biofouling will benefit from the new developments in sensor technology. The M3A system can be used as a test-bed for evaluation of new sensors and methods. For the second phase of MFS the evaluation of different antifouling techniques (copper tubes, bromine solutions) has been scheduled while new communication platforms (Inmarsat, Orbcom) will be tested and used operationally.

Acknowledgements The work was carried out in the framework of the Mediterranean Forecasting SystemPilot Project (MFS-PP) funded by the European Union DG Research under the MAST3 Program (contract No MAS3-CT98-0171).


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References Cardin, V., M. Gacic, K. Nittis, V. Kovacevic and L. Perini, 2003, "Sub-inertial variability in the Cretan Sea from M3A buoy", Annales Geophysicae, 21, (in press) Dickey, T., D. Frye, H. Jannasch, E. Boyle, D. Manov, D. Sigurdson, J. McNeil, M. Stramska, A. Michaels, N. Nelson, D. Siegel, G. Chang, J. Wu and A. Knap, 1998, "Initial results from the Bermuda Testbed Mooring program", Deep-Sea Res. I, 45, 771-794 Glenn, S.M., T. Dickey, B. Parker and W. Boicourt, 2000, "Long-term real-time coastal ocean observation networks", Oceanography, Vol. 13 (1), 24-34 McPhaden, M.J., A.J. Busalacchi, R. Cheney, J.R. Donguy, K.S. Gage, D. Halpern, M. Ji, P. Julian, G. Meyers, G.T. Mitchum, P.P. Niiler, J. Picaut, R.W. Reynolds, N. Smith, and K. Takeuchi 1998, "The Tropical Ocean-Global Atmosphere (TOGA) observing system: A decade of progress", Journal of Geophysical Research, 103, 14169-14 240 Pinardi, N. and N.C. Flemming, 1998, "The Mediterranean Forecasting System Science Plan", EuroGOOS Publications No. 11, Southampton Oceanography Centre, Southampton, ISBN 0-904175-35-9 Tselepides A., V. Zervakis, T. Polychronaki, R. Danovaro and G. Chronis, 2000, "Distribution of nutrients and particulate organic matter in relation to the prevailing hydrographic features of the Cretan Sea (NE Mediterranean)", Progress in Oceanography, 46 (2000), 113-142

EGOS--European Group on Ocean Stations providing real time buoy observations from data sparse areas of the North Atlantic Ocean and adjacent seas Voiker Wagner*l, Anne A. Hageberg 2, and Christian Michelsen 2

1Deutscher Wetterdient, Germany 2Research AS, Norway

Abstract Within Europe a number of countries have combined their national programmes on drifting and moored buoys into the European Group of Ocean Stations (EGOS). The group was established on December 1, 1988, as a joint operational project for near realtime acquisition of meteorological and oceanographic data in the North Atlantic Ocean. The Group also functions as an Action Group of the Data buoy Co-operation Panel.

Keywords European co-operation, drifting and moored buoys, real-time observations, North Atlantic Ocean, deployment strategy. 1. Introduction EGOS was born from the question: Would it be possible to make meteorological and oceanographic data from buoys in European waters permanently available on GTS? This was a real challenge in the 1970s. Funding was given to begin with by a COST project (Cost-43) at a European level, created for the period 1979-1988: To have two buoy years of data on the GTS. 1988 was the end of COST-43 and start of the operational programme, EGOS. EGOS is a living system so the most up-to-date information and activities are available from the EGOS official home pages http://www.meteo.shom.fr/egos/. Here you will also find more information about EGOS and reports.

2. Programme objectives EGOS aims to maintain an operational network of drifting and moored buoys in data sparse areas of the North Atlantic Ocean and adjacent seas (Lothe, 2000). This is done by co-ordinating deployments of drifting buoys and other contributions and services provided by EGOS members. Data quality is being monitored and efforts are made to achieve a high level of data dissemination. EGOS provides information on the operational status of the buoys to members and co-operate parties on a regular basis, and cooperating with JCOMM as an Action Group of DBCP.


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3. E G O S area of interest EGOS provides more than 90% of operational meteorological data from drifting buoys in the North Atlantic (Figure 1).

Figure 1 The distribution of EGOS drifting and moored buoys on 22 August 2002.

Figure 2 The number of operational EGOS drifting buoys by the end of each month for 1992August 2002.

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EGOSnEuropean Group on Ocean Stations providing real time buoy observations from data sparse areas of the North Atlantic Ocean and adjacent seas

The increase in number of drifting buoys shown in Figure 2 is due to constant input by 9 members since 1997. This contribution seems to stabilise a steady state of 40-50 permanently operational buoys.

Figure 3 The average operational lifetime for EGOS drifting buoys 1990-August 2002. The operational lifetime is defined as the mean age of those buoys ceasing to operate in the year concerned. Due to technical improvements the lifetime of the drifting buoys has doubled in the last 12 years (Figure 3) (Hageberg, 2002a). The decrease 1997-2000 is due to increased use of the SVP-B drifter of lower quality. The quality improved significantly in 2001.

Figure 4 Causes of stopped operation for EGOS drifting buoys 1997-August 2002. Drifting buoys tend to drift ashore more frequently in the northern part of the North Atlantic Ocean whereas in southern part the majority fade out due to battery exhaustion. Failed (Figure 4) means transmission interruption, instrument failures, damage or operational stop due to unknown reasons. Pickup is either unauthorised (fishermen) or intentional (for tests).

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4. Deployment strategy EGOS tries to maintain a maximum number of buoys permanently operational together with a good spatial distribution in data sparse areas of the North Atlantic, e.g. apart from the big shipping routes, and to have an extended number of buoys in special areas of sensitivity. There is continuous use of and search of economic and suitable deployment opportunities. Deployment services are mostly provided by merchant ships on standard routes, and research vessels (normally free of charge). Increasing flexibility is achieved by air deployment. EGOS has good co-operation with North American institutions (NAVOCEAN) that perform this service. Limiting factors in deployment strategy are the finite number of buoys available in EGOS, and the drifting pattern, which is not totally predictable.

5. Buoy types in EGOS Two main types of drifting buoys are used in EGOS; the FGGE style and the SVP-B drifters. Use of the latter has been increasing in EGOS since 1997. Buoys, irrespective of type, should meet the EGOS minimum standards, which have been summarised and published by the Technical Secretariat and have been updated during the present intersessional period. Parameters measured on the drifting buoys are air pressure, pressure tendency, and sea surface temperature. Some EGOS buoys also report air temperature and wind. As the meteorological aspect dominates the intention of EGOS the pressure measurement is of great concern. The reports include the latest asynoptic observation and usually also the last synoptic observation. Besides the drifting buoys a network of moored buoys on the eastern Atlantic has been built up as a part of EGOS. 8 are operated by the UK, 2 by France and 2 are co-operative projects between France and UK (Hageberg, 2002c). Currently Ireland, in co-operation with UK, is developing a network of moored buoys round Ireland with 3 platforms already operational. These buoys have an extended observing programme, also containing humidity and wave measurements. The number of operational buoys is currently 16.

6. Quality monitoring Quality control is carried out on a day-to-day basis and on a weekly basis. The workload is shared between members. Information on dubious data and monthly quality statistics are distributed on the Intemet according to DBCP guidelines. The mailing list, operated by the Icelandic Meteorological Office, is used together with all parties of the DBCP. On a broader time scale (quarterly) the report on the drifting buoys in the North Atlantic by the Met Office is also used.

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7. Membership In 2002 EGOS had 9 members from DE, DK, FR, IE, IS, NL, NO, SE and UK. The Technical Secretariat is provided by CMR, Bergen and France contributes voluntarily with a Technical Coordinator. The members contribute with buoys and any kind of hardware, deployment facilities (ships, opportunities), LUT services, monitoring, PTT for common use, payments to common funds, support of special projects (e.g. test of GPS on buoys), representation of EGOS at meetings. All the contributions are on a voluntary basis and administrated by the Meeting Committee of EGOS.

8. Liaison with international organisations EGOS maintains close co-operation with international organisations working in the same field (Hageberg, 2002b): 9 W M O (e.g. administration of Common Fund, mutual reporting) 9 IOC (mutual reporting) 9 I)BCP (EGOS is action Group of DBCP, mutual reporting). 9 EUCOS (EGOS is planned to become integral part of EUCOS project structure, response to EUCOS needs) 9 BOOS and EuroGOOS (informal contacts, possibly to be intensified) 9 Environment Canada (co-operation in deployment services, information on deployment strategies) 9 US Naval Oceanographic Office (NAVOCEANO) (Deployment Services for EGOS) 9 US National Data Buoy Center (NDBC) (deployment support, buoy storage)

References Lothe, T., 2000, EGOS Technical Document No. 178 EGOS Basic Documents, Bergen Hageberg, A.H., 2002a, EGOS Technical Document No. 248 Intersessional Report CGC, Bergen Hageberg, A.H., 2002b, EGOS Technical Document No. 249 EGOS MC Oslo 2002, Bergen Hageberg, A.H., 2002c, EGOS Technical Document No. 252 Monthly Report August 2002, Bergen

CORIOLIS, a French project for in situ operational

oceanography

S. Pouliquen* 1, T. Carval l, Y. Desaubies l, L. Petit de la Vill~on 1, G. Loaec 1, and L. Gourmelen 2 I lFREMER, PlouzanO, France 2EPSHOM, Brest, France

Abstract The seven French agencies (CNES, CNRS, IFRTP, IRD, METEO-FRANCE, SHOM and IFREMER) concerned with ocean research are together developing a strong capability in operational oceanography based on a triad including satellite altimetry (JASON), numerical modelling with assimilation (MERCATOR), and in situ data (CORIOLIS). The CORIOLIS project aims to build a pre-operational structure to collect, validate and distribute ocean data to the scientific community and modellers.

1. CORIOLIS objectives CORIOLIS aims to achieve four goals: 1. To build up a data management centre, part of the ARGO network for the GODAE experiment, able to provide quality-controlled data in real time and delayed modes. 2. To contribute to ARGO floats deployment mainly in the Atlantic with about 250 floats during the 2001-2004 period. 3. To develop and improve profiling ARGO floats. PROVOR is a self-ballasted float, able to drift at a user-defined parking depth and then to dive to 2000m before profiling up to the surface where data are transmitted using the Argos system. More than 100 cycles can be performed during its 3-year lifetime. 4. To integrate into CORIOLIS other data presently collected at sea by French agencies from surface drifting buoys, PIRATA anchored buoys, oceanographic research vessels (XBT, thermosalinograph and ADCP transmitted on a daily basis). CORIOLIS has three phases: 9 A Preparation phase (2000-2002) synchronised with MERCATOR demonstration phase, which sets up the system, 9 A Demonstration phase (2003-2005) during which CORIOLIS will operate in an operational mode, 9 Lastly, an Evaluation Phase (2004-2005), which will provide recommendations starting from this experience, on what, should be a sustainable operational structure, in accordance with international plans that will follow the ARGO/GODAE experiment.


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CORIOLIS, a French project for in situ operational oceanography

2. The CORIOLIS data centre The CORIOLIS data centre has been set up progressively to collect, control, and distribute physical oceanography in situ data, initially temperature and salinity profiles. The core of this centre is located in Brest built on the experience acquired over twenty years at the Ifremer oceanographical data centre SISMER. It handles in situ data available in real time coming from the GTS (Global Transmission System of meteorological data whose French partner is METEO-FRANCE) and also from other sources including French floats, buoys, and research vessels. 3500 profiles are now provided to MERCATOR on a weekly basis. These data will then be assimilated by MERCATOR and are also used in real-time by other customers, such as defence authority (SHOM) and METEO-FRANCE.

Figure 1 Salinity time series from a Provor profiler during 18 months offshore Spain

Figure 2 Weekly temperature analyses for Atlantic Ocean The CORIOLIS data centre also operates in delayed mode for: 9 Instruments and sensors monitoring 9 Sensor drift estimation 9 Re-analysis and data syntheses: gridded fields in different areas

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The CORIOLIS data centre already provides on-line data access within 24 hours working day to the meteorological community on GTS and on Internet to the scientific community (http://www.coriolis.eu.org). The CORIOLIS data centre is also one of the two Argo Global Data Centres together with the US GODAE centre, providing a unique access to all the ARGO data.

Figure 3 Access to the two Argo Global Data Centres

Figure 4 Acceptance test in the Ifremer tank

3. Profiling floats development Within the Coriolis framework, Ifremer has developed a free-drifting hydrographical profiler named PROVOR, based on MARVOR technology, through an industrial partnership with the MARTEC company. The MARVOR drifting float is usually used for deep current studies at depths of up to 2000m. Some 200 units have been built already. Neither PROVOR nor MARVOR require any ballasting before launch. PROVOR units typically drift for more than three years at a user-defined parking depth for 10 days, dive to 2000 metres, come up to the surface, transmit data and then dive back for another cycle. The float volume is modified using a hydraulic system that transfers oil from an internal reservoir to an external ballast and generates enough buoyancy variation to move from the surface to 2000 metres even through high variations of sea water density. Temperature and salinity measurements are performed during the ascent and/or descent. The sampling strategy is set before launch and parametrised for at least two user-defined layers. PROVOR floats represent 20% of the Argo floats now in operation.

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CORIOLIS, a French project for in situ operational oceanography

4. Integration of national activities related to in situ measurements Many in situ measurements necessary for operational oceanography are made regularly by the French agencies involved in CORIOLIS: SHOM (XBT, hydrographical cruises), Ifremer (4 large research vessels), IFRTP (one large research vessel cruising in Indian and Antarctic oceans), IRD (TOGA XBT lines and thermosalinometers), Meteo-France (drifters and several anchored buoys), CNRS (floats). But data are not always transmitted in real time to data centres. CORIOLIS aims to organise the systematic collection in real time of such in situ measurements made either in routine or within the framework of specific research, in order to meet the operational oceanography needs. It harmonises reduction, control and calibration processes to cope with operational constraints.

5. Prospects The CORIOLIS project implementation by the French agencies in charge of oceanography, will contribute to the ocean observing system, providing world coverage of the oceans in real time. CORIOLIS is a multi-disciplinary pilot project and is involved in development of new autonomous instruments with up-to-date transmission capability, in float deployment in the Atlantic Ocean then world oceans and in data collection, processing and distribution to users (public authorities, scientific community, industry sector, etc). The aim is to sustain the contribution when the world assessment programmes, of which CORIOLIS is a part, have drawn their conclusions for the future. We will then witness an evolution similar to the one observed in the meteorology field twenty years ago: deep-sea oceanography will move from science to operational for the benefit of the world population on a sustainable base. Nevertheless it will then be necessary to assume the recurring cost of such a programme.

References A large amount of information and data can be found on the CORIOLIS web site: http://www.coriolis.eu.org

ASSEM" Array of Sensors for long term SEabed Monitoring of geohazards J. Blandin l, R. Person *l, J.M. Strout 2, P. Briole 3, V.Ballu 3, G. Etiope 4, M. Masson 5, S. Smolders 6, V. Lykousis 7, and G. Ferentinos 8 / IFREMER, France eNGI, Norway 31PGP, France 41NGV, Italy 5CAPSUM, Germany 6THALES Geosolutions SA/NV, Belgium 7NCMR, Greece 8University of Patras, Greece

Abstract Recent marine and geophysical research have demonstrated that long-term time series of critical parameters are needed to understand several ocean systems and underwater geological systems as well. ASSEM is the first application of a new concept of sea bed observatories dedicated to long term monitoring a small area (a few km2), lying on a network of interconnected measurement nodes. It is a project that enhances marine technologies allowing real time monitoring of the sea bed. The main component of the system is the COmmunication and STOrage Front end, COSTOF. It is an electronic unit, structured on an internal CAN bus, providing a set of sensors with the means to communicate with the external world through an underwater network, and to locally store the produced data. The evaluation of this new concept will be made through two experiments addressing two sea bed problems: slope instability risks and seismic risks.

Keywords: Sea bottom observatory, slope stability, acoustic network, monitoring. 1. Introduction Continental margins are the focus of increasing human activities that are moving towards deeper waters. Some of these margins are also places where drastic phenomena like slope failures occur. There is a need to better understand the phenomena leading to these instabilities by measuring a set of geotechnical, geodetic or chemical parameters of the sediment and seafloor. Long term seafloor observatories are already deployed, but even if they are multidisciplinary, they are mainly oriented toward seismic measurements. These measurements require high bit rate data links. The most efficient but expensive solution is to deploy cabled seismic seafloor observatories. For example nine cabled seafloor observatories are in use in Japan, and three real time ones are operated by JAMSTEC (Mikada, 2001). To reduce the cost of a cabled system, it is sometimes possible to re-engineer retired coaxial undersea telephone cables. The US navy has repowered SD cables to SOSUS * Corresponding author, email: [email protected]

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arrays. The Hawaii-2 Observatory (H20), sited south of the Moonless Mountains, uses the Hawaii-2 Cable (HAW-2) in its part from Oahu to the observatory (Bums, 1999). In order to achieve low-cost long term seafloor monitoring, JAMSTEC developed and tested at sea the Mobile Seafloor Observatory as reported by Momma (2001). The observatory consists of a multi-sensor Mother Station surrounded by four satellites at distances from 20 to 50 kilometres. Data can be retrieved by recovering the stations. Part of the sensor data in the Mother Station can be monitored through satellites by releasing pop-up buoys. The same approach is presented by Gasparoni (2002) in the GEOSTAR European project. All these systems are too large and too expensive for long term monitoring of geohazards. The project ASSEM consists of developing suitable means to measure and monitor a set of geotechnical, geodesic and chemical parameters distributed over a seabed area in order to better understand the slope instabilities phenomena, to assess and possibly anticipate the associated risks (such as seismic risks). The means are studied and developed in order to deploy a selection of adapted sensors on a seabed area (a few km 2) and transmit the sensor data to shore for exploitation.

2. Technical description To achieve these objectives, ASSEM brings several innovations: 9 A detailed pre-site survey in order to determine the proper sites for the selection and deployment of sensors 9 A deployment of sensors and stations assisted by ROV or submersible 9 A protection against trawling 9 A modular design, with standard connecting and installation interface enabling easy configuration of the system to the site of interest, addition of new sensors, and replacement of components for maintenance 9

two-way communication link between sensors and shore, built on an acoustic network (a wired interconnection exists as an option

A

9 Local storage of all raw data in each node, with local analysis able to generate alarms, 9 Enhanced sensors for long term monitoring. An array is composed of several nodes, (see Figure 1 and Figure 2). Each node includes an electronic unit, named COSTOF (COmmunication and STOrage Front-end), providing a set of enhanced sensors (pore pressure, methane, geodesy, tilmeter, CTD, turbidity, currents, etc.) with the means to communicate with the external world through an underwater network, and to locally store the produced data. Alarms can also be generated by processing these data. The architecture is organised around an internal CAN/CANopen bus, hosting sensors, communication and memory devices on a common transmission backbone. The software resources enabling a monitoring node to act as a network node (routing algorithms throughout the network, network configuration management, data transmission protocol and other network layers) are implemented in every COSTOF. Alarms can be generated for example if a critical parameter, or a group of parameters, exceeds a programmed threshold for a given time. The acoustic network is developed from a new

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version of the MATS 12 acoustic modem produced by ORCA Instrumentation. This digital modem, based on micro-controller and DSP cards, is capable of data transmission under adverse channel conditions at data rates up to 2400 bits per second (bps). Original network protocols, with autonomous handshaking and adaptive bit rate, adaptive modulation and adaptive routing were implemented. Such a network offers much more reliable and much higher data rates links than standard modems currently available. Directional transducers were developed to decrease power consumption.

Figure 1 Monitoring node architecture

Figure 2 Mechanical structure of a node

Two complementary pilot experiments are planned. The first one will take place at a site in Norway where slope instability is studied after a major landslide: Finneidfjord in Norway (Figure 3). The experiment should include a new design of pore pressure sensors and communication system for data transfer to the surface.

Figure 3 Finneidfjord Norway

Figure 4 Trizonia islandmGulf of Corinth

The second experiment will take place in the Gulf of Corinth (Figure 4). The shelf (with its pockmarks), the slope and the margin of the basin off the coast of a faulted area are selected for the deployment of the ASSEM array of sensors. It is the most active extensional basin in Europe, with more than 1 cm/year of deformation across the Gulf and

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high rates of margin uplift. Together with physical oceanographic measurements, both horizontal and vertical deformation devices will be installed. Horizontal deformation estimates will be based on acoustic traveltime measurements and vertical deformation estimates will be based on pressure measurements. The array will be deployed in the south of Trizonia island and will also host a seismometer satellite of the EU-ORION system, demonstrating the compatibility between the two systems. Data collected in real time or after recovery of storage devices, will be available to endusers over the intemet. Results from the EU project EMEW for the data handling of warning systems will be applied, especially for the Corinth experiment.

3. Conclusions ASSEM is a new concept of real time sea floor observatories dedicated to collecting data with low sampling rate and presenting a high level of modularity. It is also a warning system. It can be operated as a stand alone or linked to another observatory system, such as GEOSTAR/ORION. This concept could be applied to other long-term studies in biology.

Acknowledgements This work is supported by EC FP5 in the specific research and technological development programme "Energy, Environment and Sustainable Development" (contract EVK3-CT2001-00051, ASSEM project).

References Bums R.F., 1999, Hawaii-2 Observatory, Sea Technology, Vol. 40, No. 9, 10-18. Gasparoni, F., L. Beranzoli, G. Etiope, P. Favali, G. Smriglio, D. Calore, J.M. Coudeville, J. Marvaldi, and H. Gerber, 2002, GEOSTAR seafloor observatory successfully completes first deep-sea mission, Proc. Oceanology Intemational, London. Mikada, H., K. Kawaguchi, T. Goto, R. Iwase, K. Mitsuzawa, K. Hirata, Y. Kaihom, and K. Suyehiro, 2001, Long term strategy for the development of JAMSTEC cabled observatories, Proc.OHP/ION symposium: Long term observations in the oceans: current status and perspectives for the future, Yamanashi, Japan. Momma, H., K. Kawaguchi, and R. Iwase, 2001, Long term seafloor monitoring data recovery--new approach, Sea Technology, Vol. 41, No. 7, 55-59.

Adaptive sampling for coastal environmental monitoring using a geo-referenced mobile instrument platform and correlative data visualisation T.O. Ojo 1, M. Sterling 1, J.S. Bonner *z, F.J. Kelly 2, C.A. Page z, J. Perez z and C. Fuller z 1Civil Engineering Department, Texas A&M University, College Station, USA 2Conrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi, USA

Abstract Coastal observations and monitoring often involve the collection and analyses of disparate data sets. The amount of data collected and the understanding of the information they represent pose a challenge to researchers who study events occurring and how they affect the ecosystem. We developed a geo-referenced mobile instrument and sensing platform for characterising and mapping bays, estuaries and coastal environments. Consisting of a towed array of instruments specifically designed for shallow embayment, it employs correlative visualisation as an interpretative tool. Adaptive sampling can therefore be performed to aid and guide the data acquisition exercise with improved resolution and extended coverage.

Keywords: Sampling, monitoring, visualisation, adaptive, correlative 1. Objectives 9 Coastal monitoring of environmental water quality 9 Emergency response 9 Build towards fully integrated sampling network 9 2D/3D contouring of interpolated data between sampling stations 9 Mobile platform running randomly selected transects to validate interpolation scheme

2. Hardware/software platform We deployed multiple sensors as a towed array and established a multi-parameter instrument interface with real-time visualisation of up to six parameters. The postprocessing software generated a colour-coded trace with the individual colours corresponding to the percentage of peak value for each measured parameter. Each data point was being geo-referenced through a GPS unit with DGPS/WAAS capability. In operational mode, the data acquisition scheme will not be based on arbitrary transects but will be guided through model predictions and computer simulation to the location and extent of the constituent of interest. In other words, the sampling regime will be adapted based on information from computer simulation and instantaneous feedback from the * Corresponding author, email: [email protected]

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Adaptive sampling for coastal environmental monitoring using a geo-referenced mobile instrument platform and correlative data visualisation

shipboard real-time data visualisation module. This implementation of a "Trajectory Tracking and Vessel Guidance System" will generate a set of waypoints corresponding to the particular transect pattern to be selected. The waypoints (in NMEA sentence format) will then be uploaded to the shipboard navigation system.

3. Data acquisition, archival and real-time visualisation 3.1 High speed data acquisition network Data acquisition from the subsurface in situ sensors was implemented through a highspeed data link. We established a local area network (LAN) between the sensors and the main computer through a multi-port, submersible device server that we developed using COTS. Up to four RS232 serial devices can be hooked up to the submersible device server, which also distributes 18 VDC power to the in situ sensors from a shipboard DC power supply. Each of the four RS232 ports is capable of a data rate of 115Kbaud and individually addressable through a bi-directional 10Mbs -1 Ethernet link using TCP/IP network protocol. The computer interface for data control, visualisation and archival was implemented on a rugged notebook computer (Panasonic Toughbook CF-28 running Windows 2000 Professional on Intel Pentium III processor at 600 MHz with 356 MB of RAM). 3.2 Instrument control and data visualisation A software interface on the computer, "The Multi-Parameter Instrument Array and Control System", MPIACS, allowed for real-time visualisation of four parameters measured by the instruments (the interface has provision for simultaneous display of up to six parameters). These include rhodamine and fluorescein concentrations (from a multi-spectral fluorescence sensor), salinity and temperature (from a CDT sensor). For each parameter, the percentage of the parameter (measured value against a pre-set peak value) generated a colour trace with the horizontal travel of the towed-array through the water, giving a visual indication of the spatial distribution of intensity of the constituent of interest or sampled parameter. This visual reference helped in guiding the data acquisition effort during the exercise. Each colour-coded trace was displayed against the outline of the Corpus Christi Bay by geo-referencing the instrument raw data through the data-acquisition GPS unit (Furuno model GP-37). The GPS was run in the auto-selection mode during this exercise, which allowed the unit to operate in GPS, DGPS or WAAS mode, depending on service availability. The accuracy of the unit varies with the mode as follows: DGPS mode ( l m accuracy), WAAS mode (Sm accuracy), or GPS mode (10m accuracy).

3.3 Data sampling rate and spatial resolution The sampling rate of the instrument array is governed primarily by the maximum data rate from the GPS unit (fixed at 1Hz) since all data points require geo-referencing for visualisation. Travel speed of the towed-array was governed by the speed of the vessel, which averaged 5 knots. Under this set of conditions, a spatial sampling resolution of ~3 m was expected. Actual spatial resolution during the exercise was ~10m due in part to the particular operational mode of the GPS unit and the fact that the sampling rate was, for the purpose of this exercise, set at 3 Hz in attempt to eliminate (or at least minimise)

T.O. Ojo, M. Sterling, J.S. Bonner*, F.J. Kelly, C.A. Page, J. Perez and C. Fuller

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data dropouts. Preliminary runs with the setup suggested that GPS data might not always be available due to obscurity of satellite views and other peculiarities associated with GPS units in marine applications. This adopted method to prevent dropouts in data was a software solution, and hardware solutions have been identified but could not be implemented in time for the exercise. For future exercises, we will establish the availability and location of DGPS beacons within the operational zone and lock the GPS unit into operating in this mode to improve the sampling resolution. Other considerations include antenna location, travel speed and the limiting GPS data rate.

3.4 Ship-to-shore data telemetry Raw data was locally archived on the main computer by generating an ASCII text file for each sensor as well as the GPS unit. The data set was sent at pre-determined intervals by telemetry through a ship-to-shore wide area network (WAN). A shore-based computer at Command Center had a version of the MPIACS running on it and retraced the instrument colour-coded track-lines in near real-time. With a zero data dropout from the instrument array, the Command Centre computer was able to regenerate the track-lines. It remains to be determined whether the ca. 30m spatial resolution is adequate in resolving the horizontal gradients for the constituents of interest.

4. Summary As we attempt to draw inference over the spatial extent for the domain of interest from information available from a finite number of fixed sampling stations, we fall back on spatial smoothing techniques such as Kriging, Objective Analysis, and Delaunay Tessellation among others. However for real-time data acquisition such as emergency response, adaptive sampling techniques can be implemented on a mobile sampling platform. This paper described an implementation of this technique during an exercise in Corpus Christi Bay, Texas. We were not able to commission the WAN for this test to enable the implementation of the Trajectory Tracking and Vessel Guidance System described in this paper and will be performed as part of ongoing development work in this area. The system comprises a shipboard user interface showing the predicted instantaneous location of the plume. The operator selects from a set of patterns defining the sampling transect and a set of waypoints. The resultant waypoints are interfaced with the vessel electronic charting system (or autopilot for extended automated operations in open waters). The vessel GPS unit tracks the current position relative to the defined sampling transects and corrections made as necessary with immediate feedback being available in real-time to guide the sampling scheme. A towed instrument array used for measurements is interfaced with data acquisition, post-processing and visualisation software.

5. Acknowledgements Project funded by the Texas General Land Office (TGLO).

A comparison with the Argo observing system-Gyroscope 0302 cruise Gregorio Parrilla-Barrera *1, Manuel Vargas-Y~fiez 1, Pedro V~lez-Belchi l, Alicia Lavin l, C~sar Gonz~ilez-Pola 1, Eugenio Fraile 2, Alonso Hermindez-Guerra 2, Elena Tel 1 and Daura Vega 2 l lnstituto Espa~ol de Oceanografia, Spain 2Universidad de Las Palmas de Gran Canaria, Spain

Abstract In March 2002, 20 Argo profilers were deployed in the Subtropical North Atlantic, within the European project Gyroscope. In this contribution we compare the 0-distribution on a hydrographical section along the 24.5~ parallel using two different data sets. The first set consists of the first data transmitted by an array of autonomous profilers deployed along the section, and the second set is made up of CTD stations accomplished during the deployment and discusses the representativeness of the field at different scales.

Keywords: Gyroscope, subtropical North Atlantic, CTD-Argo profilers comparison. 1. Introduction Most of our present knowledge about the water mass properties and its general circulation around the oceans comes from hydrographical cruises or oceanographic data obtained from opportunity ships. Traditionally, these surveys were accomplished by means of bottle samples, XBTs or CTDs obtained or launched at a set of predefined stations. Data of the different properties are interpolated into a regular grid for contouring and further calculations. One of the basic hypotheses in the treatment of oceanographic data is its sinopticity, i.e. it is assumed that time variability can be ignored and we can consider the whole data set as sampled simultaneously. Nevertheless, hydrographical surveys are rarely repeated at different times and time variability can not be addressed. For this reason the synopticity hypothesis can not be checked. The distribution of these properties contains different length scales, mainly a macroscale and a mesoscale field. Usually, the interpolation and contouring methods only consider the limitation imposed by the station distance, that is, we can not solve length scales shorter than twice this distance. Nevertheless, the different time variability of the different length scales is not considered. In this work we consider that long length scales have longer time scales and they can be considered as synoptic. On the other hand, shorter length scales, though resoluble by our sampling scheme, may have shorter time scales. The lack of synopticity when dealing with the mesoscale field can produce spurious structures. The recent use of autonomous profilers can provide a sequential repetition of hydrographical surveys, allowing the study of both spatial distribution and time variability of * Corresponding author, email: [email protected]

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water mass properties. This can provide an invaluable tool for checking the time scales associated with different length scales. It can be used in traditional CTD surveys to find out which length scales can actually be considered as synoptic and which ones should be filtered out before contouring or computing derived magnitudes. Also for the TS profiles from autonomous profilers, which are not simultaneous either, it is also important to know which length scales should be retained and which ones have to be removed. In this work we compare the 0-distribution on a hydrographical section along the 24.5~ parallel using two different data sets. The first one is obtained from the first data transmitted by an array of autonomous profilers deployed along the section, and the second one from the CTD stations accomplished during the deployment survey. Both sections are a few days apart. The time lag between both data sets is similar to the time needed for accomplishing the whole section. Accordingly, if synopticity is an acceptable hypothesis, no main changes should be observed from both data sets. We check that this is true for the macroscale, while important differences appear in the mesoscale field.

2. Data From 2 to 23 March 2002, Instituto Espafiol de Oceanograffa (IEO) in collaboration with Universidad de Las Palmas de Gran Canaria, carried out the cruise GYROSCOPE 0302, within the scope of the European Project Gyroscope, which is the European contribution to ARGO (Grupo Gyroscope-Espafia. 2003). The main objective of the cruise was to deploy 20 profilers out of a total of 80 planned for Gyroscope Project. 39 CTD profiles were accomplished during the cruise. The profilers and the CTD casts were distributed along two sections between the Canary Islands and the Mid Atlantic Ridge and the latitudes 24.5~ and 30~ (Figure 1). The survey was carried out on board the Spanish research Vessel "Vizconde de Eza" from the Secretarfa General de Pesca Maritima (Ministerio de Agricultura Pesca y Alimentaci6n). Of the 20 profilers deployed, 15 were Provor CT-F2, developed by IFREMER through an industrial partnership with MARTEC (SERPE-IESM), and 5 APEX from Webb Res. Co. Both are designed to provide CTD profiles. They are programmed to achieve about 100 profiles from a depth of 2000m to the surface. They feature active depth control and, prior to their profiling phase, will stabilise at 1500m. When they surface, after a 10 day cycle, data and location are transmitted via an ARGOS satellite before diving again. PROVOR profilers are equipped with FSI CTD sensors. APEX profilers use Sea Bird sensors that provide salinity instead of conductivity. Profiler records can already be seen and downloaded from www.coriolis.eu.org. The CTD data was calibrated with a Guideline-Autosal, achieving differences in salinity smaller that 0.002. In this work we focus on the data obtained along the 24.5~ both using CTD profiles (from 03 March to 18 March) and the profiles from the first transmission of the profilers (from 06 March to 25 March). It should be noticed that there is a delay between the profiler deployment and the first recorded and transmitted profile, so the two data sets are not simultaneous.

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A comparison with the Argo observing systemwGyroscope 0302 cruise

3. Comparing CTD and profilers data. Synopticity of 0-sections To check the sinopticity of hydrographical sections we use the first set of profiles transmitted by the profilers and the CTD casts along 24.5~ To represent the distribution of potential temperature along this section, we use the OI method described in Gomis et al. (2001).

Figure 1 Location of float deployment sites (larger filled circles) and CTD stations (smaller circles) The basic statistics of the temperature field using both CTD and profiler data are compared. Then, using the same autocovariance function and the same noise to signal ratio, the background field and the mesoscale field for both data sets are obtained. The potential temperature field is split into a background field IXand a signal 0, assuming that our observations contain noise (white) e due to instrumental errors and length scales not solved by our sampling. The background depends on the position and this dependence is usually modelled by a second order polynomial. The signal is interpolated on a regular grid to obtain the background using a linear combination of observations. This gives an optimal linear estimator (in the sense that it has the minimum mean square error) if the autocovariance function of the signal and the variance of the noise is known. The autocovariance function is modelled as r

2

y(r) = 0 e

2

2L 2

(1)

+ o 5(r)

It represents the expected value , (brackets denote expectation), 0 2 is the 2 signal variance and o~ is the noise variance, r is the distance, and u the position vector where we calculate the autocovariance. Usually it is assumed that this is homogeneous and does not depend on u. For a discrete set of observations, and taking into account that the hypothesis of isotropy can not be accepted for vertical sections, we can write: 2

Yij = (OiOj) = O exp

(

Ax 2 2L 2

•

2L2) + OeSij

(2)

To find the unknown parameters in the above expression, we calculate the variogram, defined as the expected value

Gregorio Parrilla-Barrera et al.

2

2 ((0i- Oj) 2)

2

= ~ + ~-

359

2

(7~j + 8 i j ~ )

(3)

8/j being the kronecker delta (Petitgas, 1992). At a certain distance, the variable 0 is not correlated and the variogram becomes flat, being the value equal to the signal variance plus the noise variance and is usually called the sill of the variogram. At lag zero it presents a discontinuity. The limit when r tends to zero or i tends t o j represents the noise variance. Figure 2a is the variogram estimated for the horizontal direction at different depths. Figure 2b is the same for the vertical direction. The x-axis shows the lag in km for the horizontal and db for the vertical. The value of the sill, and the distance for which it is reached (no correlation) is estimated visually. For the noise variance a straight line is fitted for the initial part of the variogram to obtain the interception with y-axis.

Figure 2 a) Variogram for the horizontal direction at 10, 50, 100, 150, 200 db. X-axis shows lag in km, and y-axis is ~ b) The same as in figure a) for the vertical direction. X-axis is in db. c) Complete field of potential temperature (background plus mesoscale field) for the CTD section (solid line) and profilers (dashed). d) Potential temperature difference between CTD and profiler sections. For the horizontal the calculations are repeated at 10, 50, 100,150, 200, 300, 500, 1000 db, to get an average for the parameters estimated at each depth. The reason for using the upper 1000db layer is that statistics are similar in this layer, being very different from 1000db to the bottom. This simply indicates that the homogeneity of the signal is not a good hypothesis (this is quite obvious). These statistics were repeated using both data sets (CTDs and the first profiler records along the 24.5~ Results did not differ

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A comparison with the Argo observing systemwGyroscope 0302 cruise

substantially, indicating that though there can be a lack of synopticity in the sampling, the statistics of the signal are stationary (another usual hypothesis). Finally, for the OI the following covariance function and noise variance are used: 0.22exp(

AX2 2Lx2

2L2)+O'lSq

(4)

Lx=250km, Lz=33 db This function was used to obtain the background for the 24.5~ section using both CTDs and profilers. There were no differences, indicating that, for long length scales, the section can be considered as synoptic. When constructing the complete field (background plus mesoscale), important differences appear. Figure 2c is the complete field for the data from the CTDs and profilers. Figure 2d is the complete field from the CTD section minus the complete field for the profilers' data. The standard deviation of this difference calculated along the whole section is 0.2~ Note that the standard deviation of the signal and noise is: 2 d0.222+0.1 = 0.24 This is consistent with the fact that differences in both sections are due to the mesoscale variability. Our conclusion is that profiler data are invaluable for studying how the spatial distribution of water mass properties evolve in time. Though we have focused in a vertical section of potential temperature, the same can be applied to horizontal sections or other variables such as salinity or derived magnitudes. These sections will frequently be made of TS profiles measured at different moments in time. Short time variability, linked to mesoscale structures can make the synopticity hypothesis fail. In such a case, spurious structures can appear when contouring different water properties, leading to false conclusions. We should inspect which length scales can be considered as synoptic and which should be filtered out. Another possibility is to include the time in the interpolation process in order to obtain synoptic sets of data.

References Gomis, D., S. Ruiz, and M.A. Pedder, 2001. Diagnostic analysis of the ageostrophic circulation from a multivariate spatial interpolation of CTD and ADCP data. DeepSea Res. I, 48, 269-295. Grupo Gyroscope-Espafia. 2003. T6cnicas de medida y calibraci6n de variables oceanogr~ificas. Campafia Gyroscope E03-2002. Datos y resdmenes. Instituto Espafiol de Oceanograffa, no. 17, 21 pp. Petitgas, P., 1992. Geostatistics and their applications to fisheries survey data. In Computers in fisheries research, B. A. Megrey and E. Moksness (Eds.). Chapman and Hall, London, 254 pp.

Coastal oceanographic station at the entrance of the

Gulf of Trieste (Northern Adriatic)

V. Mala~i~*l D. Sonc 2 and B. Petelin 1

1National Institute of Biology, Marine Biology Station, Slovenia 2University of Ljubljana, Faculty of Computer and Information Science, Slovenia Abstract The Coastal Oceanographic Station Piran, which is mainly composed of an instrumented buoy and a data reception station, continuously records oceanographic and meteorological conditions at the southern part of the entrance of the Gulf of Trieste, and the data products are updated on the web every half hour. The buoy and the control system were developed and constructed by a local engineering team.

Keywords: Operational oceanography, coastal oceanography, buoy, Adriatic Sea,

Gulf of Trieste

1. Introduction In 2001 the Coastal Oceanographic Station Piran became operational. This station comprises an instrumented oceanic buoy (2.5m in diameter, 5m in height, weighing about 3 tons) deployed at the southern entrance of the Gulf of Trieste, a data reception station located at the Marine Biology Station (MBS) of the National Institute of Biology (3.5km away from the buoy), and a video-surveillance system of the buoy at the lighthouse at the tip of Cape Madonna in the town of Piran (2.3 km from the buoy). The buoy is located at position 45 ~ 32.90' N, 13 ~ 33.00' E, 2.3km from the cape, where the depth of the sea floor is 23 m. The conductivity-temperature (CT) probe (Idronaut OS316), that is fixed on a buoy hull 2m below the sea-surface, has active (powered) anti-fouling protection from Idronaut S.r.1, while the acoustic Doppler current meter profiler from Nortek AS (500kHz) at the sea-floor is cleaned monthly by a scuba diver. The CT probe is to be calibrated every six to eight months, while air temperature and humidity sensors will be calibrated once a year. Spare calibrated CT probes, and air-temperature and humidity sensors are always ready to replace the active ones. Since October 2002 the buoy continuously records currents, sea surface temperature and salinity, together with winds, air temperature and humidity. Every half hour data retrieval for a period of 10 min is started, and after the retrieval all the data are transmitted via radio link to the data reception station, stored in a database, and presented graphically on the Internet (http://buoy.mbss.org). Wind and compass data are sampled every 0.25 s, while temperatures, humidity and salinity are sampled every 10s. Values of the latest data are presented to any web visitor, while graphs of the time series of 10 min averages, updated every half hour for the period of the previous 24 hours, are available to registered usersm registration is free of charge and available to any web visitor.


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Coastal oceanographic station at the entrance of the Gulf of Trieste (Northern Adriatic)

The goal was recently achieved under the auspices of the completed Phare CBC project Slovenia/Italy 1999, which was to set up a system of continuous data exchange among institutions that control stationary measurement platforms on the Adriatic Sea. This system is intended to offer environmental information to the general public and to strengthen collaboration between the institutions that will participate in the data exchange. Data exchange in near-real time with an institution in Italy (Osservatorio Geofisico Sperimentale in Trieste) is already functioning. Recently (May 2003), the system was upgraded in such a way that the meteorological parameters (wind, air temperature and humidity) are sampled continuously, also during the data transmission that is repeated every half hour. The data processing is as before, but routines have been added to calculate the statistics of instantaneous data assembly of half hour periods, as well as the statistics of the data within a period of 10 minutes every half hour.

2. Control system of the oceanographic station The Coastal Oceanographic Station Piran represents a reliable and low-cost solution to the problem of acquisition of oceanographic data on the Slovenian coast. Apart from the measuring equipment, it was developed and constructed by local engineering companies in Slovenia. The main design objectives were high reliability in a harsh marine environment, low cost, low power consumption and use of standard and open (source) solutions wherever possible. The custom-made processing unit of the oceanographic buoy is based on an industry standard microcontroller. It incorporates a Flash ROM storage unit, a power control unit and a communication unit with eight configurable RS232/RS422 communication ports, which can easily be expanded to 32 ports. Most measuring instruments on the market are equipped with such interfaces. The processing unit also monitors operating conditions such as battery voltage, current consumption, operating temperature and humidity. Standard embedded PC, PLC and other solutions found on the market, which were also considered during development, did not meet the low power, communication or price requirements. The major difficulty that software had to overcome was a set of different communication protocols. Many measuring instruments on the market do not support, or only partially support, the NMEA 0183 standard communication protocol, which is designed for marine electronics equipment. The software also had to provide a reliable communication protocol between the buoy and the land station with as high as possible utilisation efficiency of the existing 19.2 kbit/s radio link. To meet all communication requirements a simple ARQ protocol, which is based on OSI seven layer ISO standard, was devised, but only three layers are implemented: the physical, link and application layers. The data acquisition process consists of two phases: 1. Data acquisition and storage of data on the Flash ROM unit. 2. Transmission of data to the land station where measurements are stored in the relational SQL database for further processing. Data in the Flash ROM storage unit are retained until a reliable transmission to the land station is accomplished. During both phases it is possible to use a terminal to access instruments that do not take part in the automatic data acquisition cycle.

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363

Software at the land station is based on open source software (Linux, MySQL) and is responsible for data reception and insertion into the MySQL relational database together with basic data quality information. The land station runs the client part of the communication protocol, while the processing unit on the buoy runs the server part of the protocol. A simple and efficient user interface is also integrated into the land station software package. This enables modification of the operating parameters of the buoy, downloading of new software versions to the processing unit on the buoy, diagnostic testing, and access to the instruments via a terminal.

3. Data presentation on the Web and quality assurance The base of the web graphic presentations is the open source Java SGT library of NOAA, which we found to be the most suitable tool. Additional routines for graphic presentations of time series of vector and scalar quantities were jointly developed by the MBS external collaborators and the engineering staff at MBS. Raw data processing, quality assurance, storage of the processed data and their presentation on the web are performed with several procedures written in Bash, Octave and Python script languages, Java and SQL queries. They run under Linux at regular 30 minutes intervals. Quality assurance (WMO, 1983; Gilhousen, 1988; IOC, 1993) and data processing are described as follows: Raw data in the MySQL database are unchangeable. The first group of quality control flags is filled when the raw data enter into the database. These flags are provided by the instruments and the data acquisition software on the buoy. They signal the communication errors and instrument failures. The second group of quality control flags is applied on the raw (instantaneous) wind data and is related to checks on the number of data, gross error limits, stationarity and fluctuation (IOC, 1993; WMO, 1983). Calculations of statistical quantities on the raw data of the 10 minutes intervals are performed. For typical scalar variables (e.g. air temperature, humidity, sea temperature, salinity) these statistical quantities are mean, standard deviation, kurtosis and skewness. For wind data, calculations are rather more complex. Here the vector average of raw wind data (magnitude and direction) is calculated together with the scalar average (magnitude), where in the latter the direction is a median of instantaneous wind directions. Maximal wind gust speeds and directions are calculated as the maximum of averages of three consecutive measurements of raw wind data (IOC, 1993). Newly calculated (mean) data are stored in separate tables in the same MySQL database. Recently, since continuous sampling of meteorological parameters took place, the statistics of data within half hour intervals is also calculated and stored. The third group of quality control flags is implemented to the postprocessed meteorological data (wind, air temperature and humidity), that is to 10 min averages. The flags are for the number of proccessed data, data limits tests, data rate of change limits and stationarity checks (IOC, 1993). The implementation of this group of quality control check on other data (currents, salinity and sea temperature) is ongoing.

364

Coastal oceanographic station at the entrance of the Gulf of Trieste (Northern Adriatic)

The data from the last 24 hours that passed all three quality control tests are presented on the web in a graphic form of scalar and vector charts, together with values of the last 10 min averages. On the scalar plots air temperature and humidity are presented together with wind speed. Sea temperature and salinity at a depth of 2 m are shown, as well as the temperature at the sea floor, compass data (azimuth, pitch and roll) and the battery voltage. Wind and currents are displayed on vector plots (Figure 1).

Figure 1 Wind and surface currents from 1100 UTC, 8/12/2002 to 1100, 9/12/2002 during a bora wind event, as could be seen on the web. Currents at two neighbouring layers indicate strong vertical shear near the surface. While currents in the thin surface layer represent an outflow, there is an inflow of water mass at greater depths that is contrary to bora wind (not shown).

4. Conclusion The Coastal oceanographic station Piran is efficiently monitoring the oceanographic conditions at the southern entrance of the Gulf of Trieste. It is giving valuable, near realtime information to numerous end-users and is generating necessary data for the scientific exploration of the gulf. Time series of low pass filtered winds and currents for the first three month period for 2003 are presented in Figure 2. On time series of hourly data (10 minute averages with a period of one hour) a 72h low pass filter (Pugh, 1987) was applied to remove the tidal signal in currents, and daily sea-breeze in winds. These plots demonstrate the reliability of the measurement system, since there are no interpolations to fill any data gaps. These plots show that during three winter months the ENE bora wind was very frequent. Currents a few metres below the sea-surface (21 m above the sea-floor) follow the wind. However there are deeper counter currents in roughly the opposite direction, bringing the water mass into the Gulf of Trieste. They usually lag behind the surface currents. This simple pattern, however, is masked with a presence of coastal waves that cause the oscillation of inflow currents with a period between two to 15 days, that make a clockwise turn of 180 ~ from the direction of 270 ~ to 90 ~ These waves will need more attention in the future.

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365

Figure 2 Winds, currents at three depths, and the volume transport (sum of currents along the water column) in January-March 2003, when the water column was completely mixed. A 72-h low-pass filter was applied (Pugh, 1987) on all time series of hourly values (10 min averages), that were retrieved in near real time and had no data gaps. For clarity, only every sixth vector in the time series is plotted.

References UNESCO, IOC, 1993, Manual of Quality Control Procedures for Validation of Oceanographic Data. Manuals and Guides, 26, 436 pp. WMO, 1983, Guide to Meteorological Instruments and Methods of Observation, 8, p. 6.7-6.8. Gilhousen, David B., 1988, Quality control of meteorological data from automated marine stations, Proceedings of Fourth Internal Conference on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, Anaheim, CA, Jan. 31-Feb. 5, 1988, 248-253. Pugh, D. T., 1987, Tides, Surges and Mean Sea-Level, John Wiley, New York, 472 p.

The NOR-50" a fast research vessel for operational

oceanography

Philippe Marchand*l and Jacques Servain 2 l lfremer, Plouzand, France 2IRD, Plouzan6, France

Abstract The NOR-50 is a fast vessel specially suited for operational oceanographic missions such as maintenance of observation networks in the tropical and south Atlantic oceans. The total mission cost of a NOR-50 is expected to be 1/3 of a classical oceanographic vessel of the same size because of a much lower construction cost and a doubling of the cruising speed.

Keywords: Operational oceanography, PIRATA, Argo, research vessel R/V, fast vessel concepts The development of operational oceanography (OP/OC) dedicated to climate monitoring will need the deployment and maintainance of large ocean networks and optimised at-sea costs. In the Atlantic Ocean, two major in situ arrays will be improved in the coming years: 9 The PIRATA array, consisting of 20 moored buoys in the tropical zone 9 The Argo network made up of 700 profiling floats. Those networks need ship time, about 8 months of conventional ship for the fully developed PIRATA array and several months for the maintenance of the Argo floats in the remote South Atlantic basin. A proposal is made here to the physical ocean science community to facilitate and to optimise OP/OC field activities in the tropical and South Atlantic Ocean. The OP/OC missions differ from classic oceanographic missions: 9 They are repetitive, long-lasting and have to adapt themselves quickly to a multitude of events related to the maintenance of the network of observations 9 They involve light autonomous equipment (e.g. Argo floats), and semi-heavy (e.g. ATLAS buoys) and limited scientific staff (3 to 4 persons) 9 They cover oceanic distances on the basin scale (e.g. the tropical Atlantic Ocean) 9 The transit time dominates the time spent at stations. The main criterion for optimisation of an operational oceanic vessel is its operating cost for recurring missions, and assuming a full time utilisation. This paper focuses on a fast vessel, a feature that makes sense only if it is a light vessel. The simplicity of missions enables consideration of a vessel with a limited payload capability, and an accordingly small displacement, resulting in fuel costs noticeably smaller than for a classic R/V. The


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following functional specifications will be adopted for such a fast oceanographic vessel (named "NOR-50" for "Navire Oc6anographique Rapide de 50m"): 9 Autonomy: 5 000 miles trans-oceanic capability 9 Speed: 22 knots when cruising (twice that of a classic R/V) 9 Payload: 15 tons (e.g. four complete PIRATA/ATLAS buoys) 9 Capability to moor/remove at least PIRATA/ATLAS buoys 9 Crew: 10 persons, and 4 scientists. What kind of fast vessel concept can answer to such a specification? In our view the catamaran and the planning monohull concepts are to be rejected because of limited comfort on swell, as well as other too coastal or too expensive concepts such as first and second generation hydrofoils. In the authors' view the trimaran concept is suitable for long range missions mainly because of acceptable comfort in a well formed sea at 20 knots. A classic trimaran is made of a main thin hull stabilised by two lateral floats. The concept proposed here is an improved version of such a trimaran. The stabilised slender monohull (SSM) patented concept (Marchand, 1992-94) is a "monohull-trimaran" (Figure 1 and Table 2) consisting of a very slender hull flanked with two small side floats which give stability at rest. When the main hull is ballasted with water, and therefore "low at sea", it becomes a trimaran. En route, the vessel is emptied of its ballast water (now "high at sea", with the side floats above the water), and it becomes a slender monohull stabilised by two active (piloted) foils protruding from beneath the side floats. The transformation of the trimaran into a stabilised monohull remedies the main defect of the trimaran, which suffer a high resistance to movement due to the drag of floats, at high Froude indices.

Figure 1 NOR-50, a stabilised slender monohull The construction cost for the NOR-50 ship itself would be 5 M$ US. This should be compared with the 30 M$ US estimated cost for a classic 55m monohull R/V, 10 times heavier. Table 1 gives results of an economical comparison of typical PIRATA and Argo

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The NOR-50: a fast research vessel for operational oceanography

missions made with a classic 55m R/V and the NOR-50. The cost reduction is substantial: about three times less for a NOR-50 typical cruise. This is a consequence of both the six times lower construction cost and the doubled cruising speed. Table 1 Economic comparison between a classical R/V (i.e. Le Suro~t) and NOR-50 NOR-50

Classic R/V

Cost Hypothesis Initial investment ($ US millions)

5.0

27.8

PIRATA mission, 3 ATLAS buoys (deploy/remove) Transit duration (days) Station duration (days) Total cruise duration (clay) Crew cost ($ US thousands/clay) Depreciation ($ US thousands/clay) Fuel at 0.28 $ US/Kg Other costs ($ US thousands/day) Total cost of PIRATA cruise cost/day ($ US thousands/day) Relative cost / NOR-50

6,3 1.5 8.8 18.7 5.9 9.9 3.7 38.1 4.3 1.0

11,5 1.5 13.0 41.5 39.6 15.6 9 105.8 8.1 2.8

Argo mission, 30 floats (deploy), 4,700 Miles Total cruise duration (days) Cost of Argo mission ($ US thousands/clay)

9,9 45.8

18,8 154.5

cost/clay ($ US thousands/clay) Relative cost / NOR-50

4.6 1.0

8.2 3.4

In our simulations, The NOR-50 is devoted to the maintenance of the PIRATA network and the deployment of ARGO floats in South Atlantic. If we supposed the vessel based in Natal (Brazil), she would cruise 44 000 nautical miles/year to maintain the 21 buoys of the PIRATA network during 4.5 months including 10 stops between 2 following cruises. To deploy 187 Argo floats in the South Atlantic (between 15~ and 40~ during 3 years, the distance to cover would be 50000 miles for a total duration of 5.2 months including 18 stops. The NOR-50 is a proposal at the present stage. And some questions remain which have to be addressed during detail studies, for example: 1. the effective speed of the vessel versus sea-state (preliminary results suggest that the average speed of 22 knots could be sustained in sea-state 4, The NOR-50 concept is much more comfortable than planning hulls because of a very slender hull piercing waves) 2. The impact of regulations under the operating flag (for instance, according with French flag rules, the deadweight would be heavier, decreasing the maximum speed). The authors recommend that the NOR-50 be financed and managed within an international framework, coveting the initial investment cost (5 M$US), as well as subsequent operations.

Philippe Marchand* and Jacques Servain

369

Table 2 Characteristics & Performances of NOR-50 vs. classic R/V (le Suro~t) Parameter

Length (m) Overall breadth (m) Main hull breath @waterline (m) Light displacement (tons) Full-load displacement (tons) Installed power (KW) Average speed (Knots) Consumption (kg/day) Fuel tank capacity (tons) Cruising range (miles)

NOR-50

Classic RN

51.8 14.4 3.2 90 165 1500 22.0 5670 57 5000

56 11 1130 1200 11 4900 117 6000

References Marchand, P., and J. Servain, 2002, NOR-50: Fast Research Vessel for Operational Oceanography, Sea Technology, Vol.42, N~ 49-54. Servain, J., P. Marchand, and R. Zaharia, 2001, NOR-50 Position Document, 44p, available under the PIRATA Web site http://www.brest.ird.fr/pirata/piratafr.html (click on "informations").

Monte Carlo simulation of NaI(TI) gamma-spectra in sea water D.S. Vlachos* and C. Tsabaris

National Center for Marine Research, Institute of Oceanography, Greece

Abstract In the field of gamma spectroscopy, detection systems such as NaI(T1), HPGe, CdZnTe, CsI, BaF 2, BGO, GaAs are widely known. In a marine environment only the NaI(T1) and HPGe detectors have been used for in situ monitoring of radioactivity due to their high efficiency and to their capability of detecting in a wide energy range. HPGe detectors have been used for a number of applications in seawater, but these detectors could only function for limited period of time (2 hours) due to overheating of the crystal. On the other hand, NaI(T1) based detectors suffer from high background radiation, so that the real information consists of the gamma ray emission from various radionuclides. The most significant contribution to the background is produced by the natural constituent of the seawater 4~ which has an abundance of 0.012% and emits gamma rays at 1461 keV with approximate activity of 12000 Bqm -3. In this work, Monte Carlo techniques have been used in order to calculate the gamma ray spread from any monochromatic photon, taking into account the geometry and the interactions between the photons with the atoms of the water (folding in the water) and the NaI(T1) crystal (folding in the NaI(T1)). Incorporation of the measured energy resolution of the detecting system to the previous processes has then been performed to produce the folded spectrum for any monochromatic gamma ray.

1. Introduction The measurement of radioactivity in seawater has over the last few years focused mainly on the improvement of the prescriptions of the sensors and on the use of a suitable method for the detection of the respective radiation. The real time gamma spectroscopy method has many difficulties with setting up the whole system (sensor and communication), but a lot of advantages compared with the widely used method of sampling and measuring with laboratory facilities (off-line method) (Florou et al., 1994). The requirements of such a remote sensing system are low consumption, low cost, high efficiency, good energy resolution and robust construction for long runs. Lately the most widely used sensing systems for real time monitoring are the NaI(T1) and HPGe scintillators (Osvath et al., 2001). The HPGe detector has been used for on-line measurements but the maximum measuring time was only 2 hours, due to the overheating of the crystal. The use of cryogenic sensors solved this problem, but the consumption of such a system is very high (~500W), so it can not be used in fields without power supply. On the contrary the NaI(T1) sensors have high efficiency, low cost, stability for long runs in the seawater but their energy resolution (7% at 662 keV) is relatively high compared with the Germanium detectors. * Corresponding author, email: [email protected]

D.S. Vlachos* and C. Tsabaris

371

Thus, the development of autonomous measurement system for radioactivity in the water environment is today of high scientific priority for the marine sciences and especially for Operational Oceanography. Such a system involves high demands in constructing sensors that produce reliable measurements for long runs, as well as in designing a communications system to allow on-line transmission of the data to the operational centre. In this work the set up of the NaI(T1) detection system is performed, specifically the efficiency, energy and energy resolution calibration by using three calibration gamma ray sources covering the energy interval from 100-2000 keV. The reference spectra were reproduced accurately by performing a simulation code which takes into account the energy spread of the photons into the water and the various interactions of the photons with the nuclei of the NaI(T1) materials.

2. Set up of the detection system The set up of a system for radioactivity measurements by using the real time gamma spectrometry method consists mainly of the sensor and the communication between the system and the operational centre for further analysis of the measured data. The National Centre for Marine Research (NCMR) owns and maintains RADAM III sensors constructed by the Norwegian Company OCEANOR. A description of the system is given in Tsabaris et al. (2001). In order to use this system for continuous monitoring the sensor has been energy calibrated and tested for its stability within temperature variations and its energy resolution. In particular, five reference sources (emission of eight gamma rays) have been used to perform the energy resolution calibration. Additional measurements of the detector efficiency and absolute calibration have also been performed (Tsabaris et al., 2001).

3. Results The efficiency and the shape of the "f- ray spectra of the specific spectrometer were both calculated by using the developed simulation code named "NaIFold" (Vlachos et al., 2003 and Vlachos et al., 2002). This code has as inputs the quantity of the radioactive source in Bqm -3 and the energy of the emitted gamma rays of a specific source. Typical runs of the code are represented in Figure 1 for the contribution of 4~ (Ey= 1461 keV). The energy-deposition spectra were resolution broadened by folding with a Gaussian resolution function according to measured data taken from the calibration sources. Experimental spectra as acquired from the RADAM III sensor are also shown in Figure 1 in order to compare with simulated data. The background has been removed experimentally from the measured spectrum by subtracting data recorded for the same measuring time without the gamma ray contribution of the 4~ The agreement between simulation and experiment is excellentmclose to the photopeak of 4~ In particular, the amount of 4~ measured by the system (2950 Bqm -3) was predicted by the simulation code with an uncertainty less than 3%. On the other hand, non-linearity and insufficient response of the electronic subsystem of RADAM III in high counting rates, leads to a disagreement between simulated and measured spectra at low energies.

372

Monte Carlo simulation of NaI(TI) gamma-spectra in sea water

Figure 1 Energy Photon distribution with the RADAM sensor (black line) and the simulated result (dotted line) for the 4~ emission.

4. Conclusions 9 The deduced values of absolute calibration were tested by performing a parallel measurement with a similar detection system installed on an oceanographic buoy in the North Aegean Sea. Comparison of the recorded data with the simulated data for a specific salinity as given in the literature shows promising results for the simulation code as well as for the quantitative estimation of the 4~ in seawater. 9 The gamma ray response of the NaI(T1) system has been estimated theoretically by using Monte Carlo techniques and taking into account all the responsible processes and interactions of gamma rays into the water, as well as in the material of the detector and its housing. The background of the system is suppressed by 40% at the energy 661 keV by subtracting the strong contribution of 4~ (about 12500 Bqm-3).

References Florou, H., and P. Kritidis, 1994, The dispersion of Cs-137 in the Aegean Sea, Radiochimica Acta 66/67 pp.415-417. Osvath, I., and P.P. Povinec, 2001, Seabed y-ray spectrometry: applications at IAEAMEL. Journal of Environmental Radioactivity 53, p.335. Tsabaris, C., D.S. Vlachos, C.T. Papadopoulos, R. Vlastou, and C.A. Kalfas, 2001, Application of an Underwater y-ray Spectrometer for Radioactivity Measurements, Proc. In the International Symposium on North Aegean System Functioning and Interregional Pollution, Kavala. Vlachos D.S., C. Tsabaris, C.T. Papadopoulos, R. Vlastou and C.A. Kalfas, Software Development for the Calculation of the Response Function for an Underwater Gamma Ray NaI(T1) Spectrometer, Proceedings CD-ROM, International Conference on Radioactivity in the Environment, September 2002, Monaco. Vlachos D.S. and Tsabaris C, Response Function Calculation of an Underwater Gamma Ray NaI(T1) Spectrometer, to be published in Nuclear Instruments and Methods A.

In situ calibration of biofouling-prone oceanographic s e n s o r s in the framework of the POSEIDON project Vassilis Zervakis*, Eva Krasakopoulou, Georgia Assimakopoulou, Panagiotis Renieris, Dionysios Ballas, Aggelos Mallios and Emmanuel Papageorgiou Institute of Oceanography, National Centre for Marine Research, Greece

Abstract This work presents the in situ calibration procedures developed for the sensors of conductivity, dissolved oxygen and chlorophyll-c~, attached to eleven Oceanor Seawatch-type buoys deployed since 1999 in the Aegean Sea. The sensors are deployed in the upper 50m of ocean, where bio-fouling is a significant cause of measurement error. The limited resources in instrumentation and the cost of ship-time have dictated the development of parallel procedures allowing the calibration of 4 - 5 CT sensors, one chlorophyll-~ fluorometer and one dissolved oxygen sensor within two hours. Through our calibration we have achieved significant improvement of the chlorophyll and conductivity measurements.

1. Introduction The observational component of the POSEIDON system consists of 11 oceanographic and meteorological buoys, distributed throughout the Aegean Sea. Each mooring is equipped with meteorological, oceanographic and sea-state (wave-climate) sensors. The oceanographic sensors are all distributed within the upper 50m of the water column: Temperature and conductivity are measured typically at 3, 10, 20, 30 and 40m depth, temperature and pressure at 45m; current speed and direction, chlorophyll-a fluorescence (chl-c~) and dissolved oxygen concentration (DO) sensors are typically attached to the surface buoy, at 3 m depth (Figure 1). The upper 50m of the water-column are well within the euphotic zone, and intense biological activity can alter the response of the sensors, thus introducing error to their measurements. The sensors sensitive to biofouling are the dissolved oxygen concentration (DO), the chlorophyll-c~ fluorescence (chl-c~) and the conductivity sensors. The DO sensor is a Royce Instruments model 94, membranebased sensor. The chl-~ sensor is a Chelsea Instruments MINItracka Mark II fluorometer, while the conductivity sensors are Aanderaa 2994 cells. The large number of buoys that need to be periodically maintained and serviced, combined with the limited number of available sensors, require the use of sensors on a rotational basis, on each mooring site, which makes laboratory maintenance and calibration impossible. Thus, new in situ sensor calibration procedures had to be devised in order to ensure data quality.


374

In situ calibration of biofouling-prone oceanographic sensors in the framework of the POSEIDON project

Figure 1 Typical distribution of Figure 2 Three Seamos CT sensors attached to the tank for sensors along a POSEIDON calibrationrelative to a Seabird SBE19 also shown. Seawatch-type mooring

2. Methodology The requirements of the POSEIDON observational network dictate that the moorings are maintained on a rotational basis. Upon arrival at a mooring site, the ship deploys the onboard mooting that is already maintained and calibrated. Then, the old mooring is recovered, and the ship departs to the next mooring site. During passage, the mooting that has been recovered undergoes regular maintenance, and the instruments are tested and cleaned. Then, the mooring is reassembled, and the sensor calibration begins. The in situ, on-board calibration procedure has been developed conforming with the additional requirement of ship-time minimisation. In order to minimise the required ship time, all three calibrations are performed simultaneously. The mooting is programmed so that sampling of all parameters is repeated every 15 minutes. Thus, a minimum number of 6 values for calibration takes about 1.5 hours. The reference values for conductivity are recorded simultaneously with the buoy sampling. After each buoy measurement, there is about 10 minutes to change samples for all parameters measured. For the calibration of conductivity, a thermally-insulated tank has been built, and its characteristics have been recorded in a specially designed experiment. The tank is initially filled with high-salinity water from the sea, and the wide range of conductivity values is achieved through mixing with fresh water from the ship's tanks. The combination of the tank with a portable SBE-19 CTD as reference instrument allows calibration of salinity within 0.005, and temperature within 0.01 ~ aboard ship. We expect that higher accuracies could be achieved under controlled experiments at shore, with a higher accuracy CTD and/or bottle samples for reference. The calibration of the dissolved oxygen sensors is performed against the Winkler method, as modified by Carpenter (1965a, b) using samples varying in dissolved oxygen

Vassilis Zervakis*, Eva Krasakopoulou, Georgia Assimakopoulou, Panagiotis Renieris, Dionysios Ballas, Aggelos Mallios and Emmanuel Papageorgiou

375

concentration and temperature. The chlorophyll- (z sensor is calibrated against local phytoplankton populations. Sampling throughout the euphoric zone and subjecting phytoplankton cultures to nutrient-rich seawater provide a wide range of chl-o~ values. Through the in situ calibration, the quality of chlorophyll-~ measurements has been greatly improved, as the factory calibration provided values too high for the region. The conductivity calibration provides minor improvements to the salinity measurements; the measurements of the dissolved oxygen sensors have not required correction.

Figure 3 A good test of the goodness-of-correction is the homogenisation of the upper water column in the winter. The non-corrected time series of sensors distributed throughout the top 40m exhibits significant variance (top). After correction, the variance has significantly decreased (bottom). The values of the sensor at 3 m have not undergone correction. References Carpenter, J. H., 1965a, The accuracy of the Winkler method for dissolved oxygen analysis, Limnology and Oceanography, 10, 135-140. Carpenter, J. H., 1965b, The Chesapeake Bay Institute technique for dissolved oxygen method, Limnology and Oceanography, 10, 141 - 143.


Waves Monitoring and Forecasting


Wave and current forecasting along the Spanish Catalan coast A. Sanchez-Arcilla 1., M. Espino l, R. Bolafios 1, J. Gomez l, G. Jorda l, S. Ponce de Leonl, and A. Sairouni 2

!Maritime Eng. Lab. Universitat Politecnica de Catalunya, Spain 2Servei de Meteorologia de Catalunya, Dpt. Medi Ambient, Spain Abstract Wave and current predictions along the Spanish Catalan coast (NW Mediterranean) are considered in this paper. Wave predictions are obtained using the WAM 4.0 code while current predictions come from the POM model. Their validation with a network of buoys and the quality and improvement of predictions are also briefly considered.

1. Introduction The North-western Mediterranean is characterised by a high industrial and tourist activity and is, therefore, vulnerable to environmental phenomena such as snow, rain and storms (Figure 1). Waves and currents therefore have significant implications for the utilisation and resources along the Spanish Mediterranean coast. Operational predictions are very much required to mitigate coastal risks such as those associated with the November 2001 storms (Figure 2). At the same time operational predictions are of greater relevance as the coastal population and activities increase and there is an improvement in numerical models. The main aim of this paper is to present the wave and current forecasting presently carried out at LIM/UPC in Barcelona.

Figure 1 Illustration of the densely built Spanish NW Mediterranean coast (left), vulnerable to storm wave events (fight)


380

Wave and current forecasting along the Spanish Catalan coast

Figure 2 Illustration of some of the damage suffered along the Spanish coast during the November 2001 storms: direct wave attack on the promenade (left), and flooding of coastal towns (fight)

2. Wind forecasting Operational wave and current forecasting in the NW Mediterranean require the development of a wind prediction system. In the Meteorological Service of Catalonia (SMC) the MASS (Mesoscale Atmospheric Simulation System) (Codina et al., 1997) model has been implemented. The model incorporates a high resolution Blackadar type planetary boundary layer parametrisation and detailed surface energy and moisture budget that includes the parametrisation of surface hydrology and evaporation. This code is used as a general meteorological tool and, in particular, to predict wind fields that are later used as input for the wave and current models.

3. Wave forecasting The wave model implemented by the LIM/UPG at the SMG is the W A M

cycle 4.0 code.

It is a third generation spectral model in which the main equation solved is the transport equation (Komen et al., 1994): 8--t + ~-~(+F) +

(~F) +

(OF) = S

(1)

where F(f, 0, % ~, t) represents the spectral density; f i s frequency; 0 is direction; q0 and are latitude and longitude, and t is time. The term S represents generation and dissipation processes. The (0, ~, 0 terms are the rate of change of the position and propagation direction of a wave package. The operational wave prediction uses two grids (coarse and medium) as shown in Figure 3. The coarse grid has a resolution of 0.1666 ~ (ca. 18km) coveting the area between 34 ~ N to 45 ~ N a n d - 5 ~ E 18~ E. The WAM model is forced with wind fields every 6 hours producing wave fields with a horizon of +36 h. The medium grid is nested in the coarse one and it has twice its resolution, covering the area between 38 ~ N to 43 ~ N a n d - 1 ~ E to 5 ~ E. The boundary conditions are supplied from the coarse grid but the wind fields are obtained

A. Sanchez Arcilla et al.

381

from a nested "improved physics" medium grid run of the MASS model. This system is run twice a day.

Figure 3 Schematisation of the two domains used for the wave simulations.

Figure 4 Schematisation of the domain geometry and bathymetry, including also the position of the XIOM wave buoys Wave forecasting in this area faces a number of additional difficulties associated with the small time and space scales involved. This can be illustrated by the average duration of storms in the area which is about 12 hours (Gomez et al., 2001) and the Ebro river valley in the coastal mountain chain whose width is of the order of 10km. Another difficulty is the high wind variability (speed and direction) which, together with the short fetches

382


involved (10km for north-west winds and 800km for north-east winds) pose a tough challenge to accurate wave predictions. These predictions can, in principle, be improved by reducing the 6 hour interval between wind fields and the 12-hour cycle for forecast runs. These 6 and 12 hour intervals were selected from the physical and computational constraints of the atmospheric model. Now the wind field is being generated every 3 hours to test the changes in predicted waves, particularly for the sharp storm gradients typical of the NW Mediterranean.

4. Buoy network In order to provide measured data to validate these models, the XIOM (Oceanographic and meteorological instruments network) buoys have been used for operational wave measurements along the Catalan coast. The buoys are named by their coastal position as Tortosa (40.72N, 0.98E) at a depth of 60m, Rosas (42.18N, 3.2E) at a depth of 46m, Blanes (41.65N, 2.82E) at a depth of 74m, and Llobregat (41.28N, 2.14E) at a depth of 45 m (Figure 4). Additionally to the model validation these data are used to characterise the wave climate in the area, with more than 10 years of directional wave data. Some compilation of long tong term observations can been seen in Figure 5 and Figure 6 where the wave and wind climate are described. Another important activity for the operational system is forecasting of important storm events (because of their intensity and/or damage to the coast) such as the ones occurring during November 2001. Figure 7 shows the significant wave height Hs predicted for this storm (at the three indicated buoy stations and for the two presented domains).

Figure 5 Summary of recorded wind parameters


383

Figure 6 Summary of recorded wave parameters: duration and H, T and direction

Figure 7 Predicted and recorded Hs time series for the three buoy positions and the two grids and domains used in this paper

5. Current forecasting Pre-operational 3D current simulations have been carried out within the framework of the EU MFSTEP project. The simulations carried out at LIM/UPC covered the shelf domain (Figure 8) off the Catalan coast. The resolution was 1 - 1 . 5 k m and these shelf runs will be linked (via a "one-way" boundary condition) to regional and basin simulations with a resolution of 3 km and 6km, respectively. The high-resolution shelf model is based on an adapted version of the POM code (Blumbery and Mellor, 1987) and uses 300x 100 nodes and 25 levels in the vertical (see Figure 9 for some sample results).

384


Figure 8 Schematisation of the shelf domain considered in the pre-operational simulations carried out in the MFSTEP project. 1 point in 5 has been plotted.

Figure 9 Sample computation from the high resolution shelf model corresponding to February 2001 showing the velocity field at about 17m depth (left) and temperatures (fight, also at 17 m) The presented circulation field is typical of NW wind (locally called Mestral) conditions. The pattern shown corresponds to the velocity field at 17 m water depth, where a current-


385

meter was deployed for validation purposes. However, the same type of field is normally found in the near surface layer, down to the thermocline (located around 50m depth).

6. Conclusions Wave and current predictions along the Spanish Mediterranean coast are being used operationally or pre-operationally for a number of applications. Wave predictions are being improved with alternative nesting strategies and different parametrisations of the wind drag. Additionally the driving wind field should be calculated every 1 hour and with a smaller grid (about half the size of the present one). This should result in a better reproduction of the rapid storm development typical of the NW Mediterranean. Current predictions are also being improved with different air-sea coupling algorithms and nesting techniques. The vertical distribution of the forcing terms and boundary conditions should allow an improved simulation of the 3D circulation field. The assimilation of observed data and the initialization with a variational method are also being considered.

Acknowledgements The authors would like to acknowledge the support of the Spanish PREVIMED research contract (no. REN2002-03415) and the EU MFSTEP research contract (no. EVK3-CT2002-00075). The contribution of the Catalan Meteo-Service (SMC), and in particular, that of Mr. E. Vilaclara is also duly appreciated.

References Blumbery and Mellor, 1987, A description of a 3D coastal ocean circulation model. 3D Coastal Ocean Models, vol. 4, Dy. N. Heaps, p. 208. Codina, B., S. Young, and A. Redafio, 1997, Prediction of a mesoscale convective system over Catalonia (Northeastern Spain) with a nested numerical model, Meteorology and Atmospheric Physics, 62, 9-22 G6mez J., A. Sanchez-Arcilla, J. Puigdefabregas, J. Sospedra, and S. Ponce de Leon. 2001, Clima maritimo en la costa catalana. Implicaciones para prediccion de oleaje, VI Jornadas Espafiolas de Ingenieria de Costas y Puertos, Spain Komen, G. J., L. Cavaleri, M. Donelan, K. Hasselmann and P.A.M Janssen, 1994, Dynamics and modelling of ocean waves, Cambridge University Press, 532 pp.

Progress in building a wave climate database along the French c o a s t s through numerical hindcast simulations over a 20-years period M. Benoit* 1, D. Violeau l, J-C. Fournier 1, J. L'Her 2, and G. Goasguen 2 1Laboratoire National d'Hydraulique et Environnement (LNHE), EDF R&D, France 2Centre d'Etudes Techniques Maritimes Et Fluviales (CETMEF), France

Abstract The modelling of coastal morphodynamics as well as the design of sea defences require long-term series of wave data, in order to estimate average and extreme wave conditions in the coastal zone and near the shoreline. Unfortunately, often only limited amounts of observations and measurements are available. The TOMAWAC spectral wave code is used to make a hindcast of wave conditions along the French coasts over the past 20 years, in order to extend our knowledge of wave climate. Preliminary results obtained over a period of two years compare fairly well with available buoy measurements.

Keywords: Wave climate, extreme waves, database, numerical models 1. Introduction The modelling of coastal morphodynamics as well as design of coastal structures are mainly based on the knowledge of mean and extreme events, and in particular storm surges and waves. From a statistical point of view, extreme wave conditions can be associated to high return periods (e.g. 50 or 100 years). Therefore, the determination of extreme values of significant wave height at a given location requires long-term time series. For example, a time-series covering 10 years of observations allows engineers to get suitable information on the one-in-50-years wave height. However, continuous longterm measurements are rare. In addition, the limited number of buoys available along the coastlines leads to a poor spatial resolution of wave conditions. The project described here aims at improving our knowledge about the return periods of mean and extreme values of significant wave height along the French Atlantic coasts. We try to obtain continuous series of wave data with a high spatial resolution by applying a numerical wave prediction model driven by re-analysed wind fields over the past 20 years. Some benefits of such a database would be the following: 9 improving our knowledge of wave climate along the French coastline 9 providing charts showing wave statistics and areas of high potential risks 9 allowing a better design and reducing costs of sea defences 9 obtaining precise wave-climate data for modelling coastal morphodynamics.


M. Benoit*, D. Violeau, J-C. Fournier, J. L'Her, and G. Goasguen

387

2. N u m e r i c a l m o d e l s u s e d f o r w a v e h i n d c a s t 2.1 The TOMAWAC wave code A single wave can be characterized by its period (or frequency) and direction of propagation. However, under real sea conditions, the wave energy is spread over a spectrum covering a large range of frequencies and directions. This energy spectrum (or "wave action density") can be considered as the most complete way of describing a sea-state condition (e.g. Dean and Dalrymple, 1984). TOMAWAC is a third order generation spectral wave model based on the wave action density balance equation (e.g. Bretherton and Garret, 1969; Komen et al., 1994). Developed at E D F - L N H E within the TELEMAC hydro-informatics suite, TOMAWAC computes the evolution (in space and time) of the full wave spectrum on a finite element spatial grid, under unsteady forcing due to wind influence. It takes into account transfer processes due to non-linear interactions between frequency triads and quadruplets, as well as dissipation due to white-capping, bottom friction and depth-induced breaking. In addition, the code may deal with unsteady currents and water levels, in order to study the interactions of waves with tides and storm-surges (Benoit et al., 1996). 2.2 Numerical models built for the study TOMAWAC is now used for the purpose of the above-mentioned project. Two models were developed, based on different nested grids. The first one, called the "oceanic model", covers the Northern part of the Atlantic Ocean, with a finite element grid of variable mesh size: from about 1 degree offshore down to 20 km along the French coastline (see Figure 1). The spatial mesh has 2279 nodes and 4218 elements, while the wave spectrum grid uses 21 frequencies (between 0.04Hz and 0.4Hz) and 36 directions (corresponding to an angular resolution of 10 degrees). Time-step is 15 minutes (900s) and shallow-water processes (depth-induced breaking and triad interactions) are not considered in this oceanic model. No wave spectra are imposed on the boundaries of this model: all the wave energy is generated inside the oceanic domain. Earlier tests have shown that this area is sufficient to generate storm waves that may reach the Atlantic coasts of France. This however may lead to a slight under-estimation of wave height in the case of pure swell events coming from the Southern part of Atlantic Ocean. The second model, the "coastal model", covers the continental shelf with a mesh size of about 2 - 3 km along the French coasts (see Figure 2). The spatial mesh has 5028 nodes and 9261 elements; the frequency-direction grid for the wave spectrum is the same as above. The use of unstructured mesh allows a very fine resolution of the nearshore domain. The time-step is 3 minutes (180s) and shallow-water processes are activated. The coastal model uses the results of the oceanic model as boundary conditions (through a series of 40 directional spectra on its boundaries) and the same wind fields at this stage. This coastal model is presently under test and calibration. Both models are run with steady-state water levels (corresponding to the mean tidal level) and without tidal current. Interactions of waves with tides will be considered in a future step of the project. These effects are expected to be significant at least for the coastal model, in areas where the water depth is lower than about 20m, as shown by previous numerical studies on hindcasting several storms in the Channel.

388

Progress in building a wave climate database along the French coasts through numerical hindcast simulations over a 20-years period

Figure 1 Spatial mesh of the "oceanic" model.

Figure 2 Mesh of the "coastal" model (fight) compared with the mesh of the "oceanic" model zoomed near the French coasts (left).


389

3. A p p l i c a t i o n of the m o d e l s for w a v e h i n d c a s t o v e r a p e r i o d of t w o years

3.1 O v e r v i e w of the work p r o g r a m m e The project includes the following steps: 1. collect wind data (10m wind) provided by M6t6o-France and the European Centre for Medium-range Weather Forecast (ECMWF) over the period 1981-2000 2. calibrate the two wave models over a given duration by comparison of the computed results with buoy measurements provided by CETMEF 3. perform a hindcast simulation over the 20-year period 4. extract useful results and create a numerical wave database 5. perform statistical treatment of the results and construct charts of representative wave height conditions. 3.2 Presentation and discussion of some results for the year 1999 According to the work programme previously described, we are now proceeding with step 2. Two complete years of wave conditions (1999 and 2000) have been reproduced by using ECMWF wind fields (given on a 0.5x0.5 degrees grid) and compared with measurements coming from directional wave buoys operated by the CETMEF. Some results obtained from the oceanic model (described in subsection 2.2) are shown in Figure 3 and Figure 4 for the last three months of year 1999 at two particular locations of the domain (see Figure 2). The site of Ile d'Yeu (water depth 32m CD) is exposed to swell from the Atlantic ocean and local wind-waves, whereas the site of Les Minquiers (water depth 38 m CD) is located inside the Channel and is more representative of continental shelf conditions. It should be noted from Figure 2 that the spatial resolution of the oceanic wave model around these places is quite coarse.

In spite of this, computed significant wave height, mean wave period and mean incoming direction are in very good agreement with buoy measurements, as shown by Figure 3 and Figure 4. The evolution in time of these three parameters is precisely reproduced by the model, each event being caught at both the locations. In particular, the evolution of the computed mean period compares favourably with the measured one, even for a succession of swell and wind-sea conditions. These results were obtained with the standard version of TOMAWAC, simply by increasing slightly (about 5%) the proportionality coefficient in the wind input source-term due to Janssen (1991). However, some discrepancies appear from time to time. For example the peaks of significant wave height are sometimes underestimated by the model. Various explanations can be invoked to explain that, particularly the temporal resolution of wind data. They are provided every 6 hours on input in the wave model (with linear interpolation in time between two successive wind dates). A sensitivity study will be carried out on a limited period of time by considering wind-fields with finer spatial resolution (0.1 degrees) from M6t6o-France model and lower time-steps (3h and l h). This will help in studying the effect of wind data on input, which may be sensitive especially for the coastal model. Another improvement will also be made in the future by considering the tidal effects

390

Progress in building a wave climate database along the French coasts through numerical hindcast simulations over a 20-years period

(which are quite significant along these coasts, in particular near the location of Les Minquiers in the Channel).

Figure 3 Computed significant wave height, mean wave period and direction at ~le d'Yeu island during the last 3 months of 1999. Comparison with buoy measurements.

Figure 4 Computed significant wave height, mean wave period and direction at Les Minquiers during the last 3 months of 1999. Comparison with buoy measurements.


391

As a general conclusion, the results of the comparison over a period of 2 years (performed in total at six buoy locations, but not fully reported here) are globally satisfactory for the oceanic model. The use of the coastal model presented on Figure 2 will also probably improve the accuracy of these hindcast simulations. 3.3 Evaluation of computational r e s o u r c e s

All the simulations are performed on a workstation (type Hewlett-Packard H P - 9 0 0 0 785 Series C3700). For the oceanic model (described in subsection 2.2) the simulation over a whole year (with a time-step of 15 min) requires 14 hours computing time, which permits the performance of various runs with different parameters for the calibration. For the coastal model (which has a larger number of nodes and a smaller time-step), the CPU time is in the order of 5 days, which is higher, but remains acceptable. Another point of concern is the amount of data produced by the hindcast simulations. For the oceanic model, the storage of 5 representative parameters (Hmo,fp, fo2, qm, s) at each node of the mesh every 3 hours requires a storage capacity of 128Mb per year. The storage of the full directional spectrum (21 frequencies, 36 directions) every 3 hours at 40 points represents a volume of 339 Mb per year.

4. Ongoing and further work During the year 2003, the last four steps of the work programme described in subsection 3.1 will be carried out. In particular, the coastal model will allow a better description of wave climate conditions in shallow water areas and in the coastal zone. In a next step, new runs will be performed, including a coupling with unsteady currents and water depth due to tide and storm surges. A statistical analysis of the results will then give fruitful information about the spatial distribution of joint probabilities regarding the combination of extreme significant wave heights and mean sea levels.

Acknowledgements This project is a collaboration between EDF-LNHE, CETMEF and M6t6o-France. The authors gratefully acknowledge Mr. Ph. Dandin, from M6t6o-France (Forecasting Service), for providing the re-analysed wind fields used as input of these simulations.

References Benoit M., F. Marcos, and F. Becq, 1996, Development of a third generation shallowwater wave model with unstructured spatial meshing. Proc. 25th Int. Conf. on Coastal Eng., ASCE, Orlando (Florida, USA), pp 465-478. Bretherton, F.P., and C.J.R. Garret, 1969, Wavetrains in inhomogeneous moving media. Proc. Roy. Soc. London, Series A, Vol 302, pp. 529-554. Dean, R.G. and R.A. Dalrymple, 1984, Water wave mechanics for engineers and scientists. Advanced Series on Ocean Engineering, Vol. 2, Ed. World Scientific. Janssen P.A.E.M., 1991, Quasi-linear theory of wind-wave generation applied to wave forecasting. J. Phys. Oceanogr., vol. 21, pp 1631-1642 Komen, G.J., L. Cavaleri, M. Donelan, K. Hasselmann, S. Hasselmann, and P.A.E.M. Janssen, 1994, Dynamics and modelling of ocean waves. Cambridge Univ. Pr., 532p.

Modelling of sea states sequence along a ship route

using Markov theory

Chrysoula Diamanti* and Takvor Soukissian

Institute of Oceanography, National Centre for Marine Research, Greece

Abstract Knowledge of the wave climate on the routes of new-technology fast boats is an important factor for the estimation of their dynamic behaviour and the optimization of passenger safety. A wind and wave statistical analysis can be performed on available time series of wind and wave characteristics. By using Markov chain theory, we proceeded to establish a stochastic model for describing the sequence of sea states expected to be encountered by a ship during her route. With this reduced information, the optimization of the ship route would be possible by operating, for example, wave buoys in selected locations.

Keywords: Markov theory, wave climate, ship routing, stochastic modelling 1. Introduction In Greece, over the past few years the use of new-technology fast boats for the region of the Aegean Sea has increased. These ships, being more sensitive to waves than the other ones, require an accurate knowledge of the wave climate on their routes, since it is the most important factor for the estimation of their dynamic behaviour and the optimization of passenger safety. In addition, a valid Greek law, instituted after the shipwreck of "Herakleio" in 1966, inhibits sailing when winds greater than 7 Beaufort prevail, making the significance of the wave climate study even more necessary. Given a time series of wind and wave characteristics, obtained by means of the European wave model WAM with a resolution of 1/20 degree and a sampling interval of 3 hours, the sequence of the sea states that a ship can expect to encounter during her route may be described. First, a wind and wave statistical analysis for selected ship routes is performed. In the sequel, by using basic results of Markov chain theory, the possible states of the system can be defined and the transition probabilities and, consequently, the transition matrix can be estimated by using the hindcast data. The current work regards the time series (process) in discrete time, that is, the examined parameters remain unchanged during the period of a time interval. The implementation of the Markov chain theory offers the advantage of reliably estimating the transition probabilities among the states of a process from relatively short time series, typically of the order of 1-2 years. The method has already been applied in cases of statistics ,of marine environmental parameters, e.g. Athanasiou and Tsekos (1996). A brief introduction to the theory of Markov chains is presented in Section 2. The statistical analysis for selected ship routes is illustrated in Section 3 and in Section 4 the transition matrix is calculated. Lastly, synoptical conclusions are presented in Section 5. * Corresponding author, email: [email protected]


393

2. Markov chains in discrete time Consider that the significant wave height obtained every 3 hours is the random variable X n, n > 0 of the system (sea states). The system can be in any one of the states x n which may be defined as closed intervals with appropriate chosen values of significant wave heights for upper and lower bounds. The set of these states is a finite irreducible closed set, meaning that state x leads to state y for all choices of x and y in the set. Let P(x,y) be the probability that a transition to state y at time n+ 1 may occur, given that at time n the system is at state x, independently of time n: P ( x . y ) = P ( X . + ~ = V l X o = 0 . . . . . x . _ ~ = x . _ ~. iv. = x . ) = P(X.

+ ~ = ylX.

(1)

= x.)

In other words, if the Markov chain is in state x at time n, then no matter how it got to x, it has probability P(x,y) of being in state y at the next step. The probabilities P(x,y) are called one-step transition probabilities and describe first order Markov chains. If the transition probabilities are independent of time the process is also stationary. These assumptions have also been made when dealing with significant wave height and wind speed data. Particularly, Bern and Houmb (1984) pointed out that wind speed, wind direction and significant wave height data can be treated as stationary first order Markov chains. Consider that every state of the system can be reached by every other state, indicating that Markov chain is irreducible and recurrent. The transition probabilities can be calculated from: N~y X~

*xy = P ( x , y ) -

(2)

where Nxy is the number of the observed transitions from state x to state y and N x is the number of occurrences of state x in the time series. Consider also that for a given state (closed interval of significant wave height) the system can either remain at this state or visit any other state of the system. Then the transition probabilities can be described as a ( d • 1 ) • ( d • 1) matrix, called a transition matrix P, given by:

0

...

]

...

d

P(O, O) ... P(O,j) ... P(O, d) 9 9

,

P(i, O ) . . .

,

.

.

P ( i , j ) ... P ( i , d )

9

P ( d , O)

P(d,j)

P ( d , d~_

The elements P(x,y) of the non-negative matrix P must satisfy the relationship:

394

Modelling of sea states sequence along a ship route using Markov theory

d

~.~ P ( x , y ) = 1 , x = 0 , 1 . . . . , d

(3)

y=O

3. Statistical analysis for ship routes In order to perform a wind and wave statistical analysis, the ship route PiraeusHerakleio (Crete) (Figure 1) was chosen as it is one of the most important ship routes for navigation concerning the number of passenger travelling. Furthermore, this ship route is considered to be an "easy" one, due to the lack of numerous islands around. The wind and wave data (wind speed, wind direction, significant wave height, wave direction) used for the statistical analysis are obtained along the ship route at 21 grid points of the WAM wave model for a 3-year period (1999-2002). The statistical analysis is performed for the complete route and for three time periods: the whole year, winter season (October-March) and summer season (April-September).

Figure 1 The ship route Piraeus-Herakleio The histograms of the predicted significant wave height and wave direction as well as of the wind speed and direction for the three periods are presented in Figure 2-Figure 4. Notice that the dominant H s lies in the interval 0.25-0.5 m while the dominant Ow,ve lies mainly in the N - N E direction. As concerns wind conditions, the dominant Uwind lies in the interval 2.25-4.5ms -1 for the whole period and during summer, but values greater than 9ms -1 dominate during winter. Accordingly, the dominant Owindlies mainly in the E - S E direction, but during summer dominant E - N E directions are observed. Another result revealed from the statistical analysis is the frequency of the so-called extreme events, for Hs>2.5m and for Uwind>9ms -1. As concerns the whole period, 0.5% of waves propagating from E-SE and N - N E direction and 7.5% of winds blowing from E - S E and N - N W direction are observed. During winter the corresponding percentages are 1% of waves from N - N E direction and 12% of winds from E - S E and N - N W direction. A very small percentage (only 0.02%) is observed for waves during summer, while 3.1% of winds both coming from E-SE direction.


395

The statistical analysis has also pointed out the region of the most severe conditions along the ship route. This region extends from 36 ~ 0 6 ' N to 36 ~ 3 8 ' N and from 23 ~ 17'E to 24 ~ 15' E. Values of H s greater than 2.5m and Uwind greater than 11.5ms -1 (~22 knots) are observed.

Figure 3 Histograms of H

s,

{~wave,llwindand Owindduring winter

396

Modelling of sea states sequence along a ship route using Markov theory

Figure 4 Histograms of H s, ~)wave, blwindand Owindduring summer

4. Transition matrix using Markov chain In order to apply Markov chain theory, a three-year significant wave height time series for the ship route Piraeus-Herakleio with a time interval of 3 hours is used. The process ( H s time series) consists of 182848 data and is divided in six states with significant wave height limits 0.3, 0.5, 0.8, 2.0 and 3.0m, respectively. Exploiting equation (2), the 6 x 6 transition matrix corresponding to the six sea states is calculated.

o d 0

p

o (6 0

o ~ 0

aJ o5

0

~ d

C~l

,~d 03

0.786 0.116 0.027 0.067 0.004 0.001

[0.0,0.3]

0.135 0.719 0.142 0.004 0.000 0.000

[0.3,0.5]

0.026 0.171 0.714 0.089 0.000 0.000

[0.5,0.8]

0.134 0.002 0.140 0.710 0.014 0.000

[0.8,2.0]

0.103 0.004 0.004 0.212 0.633 0.044

[2.0,3.0]

0.075 0.000 0.000 0.003 0.271 0.641

[3.0,4.2]

_


397

An example for the procedure of the transition-matrix calculation follows: There are a total of 44487 occurrences of state [0.5,0.8] from which 3971 are followed by a transition to state [0.8,2.0] at the next time interval. Consequently, the transition probability from state [0.5,0.8] to state [0.8,2.0] is 3971 - 0.089. P34 44487 This means that the transition from state [0.5,0.8] to state [0.8,2.0] has a probability of 8.9% of occurring in the next 3 hours.

5. Conclusion This paper models a sea state sequence along the ship route Piraeus-Herakleio (Crete) by using Markov chain theory and exploiting wind and wave data derived from WAM wave model. Furthermore, the statistical analysis has shown that along the ship route Piraeus-Herakleio there are not frequent extreme wind and wave conditions. The most intense period is winter, while the region along the route where the most severe conditions were observed is effected from the high wind and wave potentials of the straits between Crete and Kythera (Soukissian et al., 2002). In order to confirm our conclusions a further statistical analysis along the ship route based on long time series as well as a comparison with in situ measurements has to be made.

6. References Athanasiou, K., and C. Tsekos, 1996, Persistence statistics of marine environmental parameters from Markov theory, Part 1: analysis in discrete time. Applied Ocean Research, Vol. 18, pp. 187-199. Bern, T.I. and O.G. Houmb, 1984, Simulation of offshore operations by a combined statistical physical model. Proceedings of 3rd Int. Offshore Mechanics and Arctic Eng. Symp., ASME, Vol.III, New Orleans, Louisiana, USA, 12-17 February 1984, pp.342-353. Hoel, Paul G., Sidney C. Port, and Charles J. Stone, 1991, Introduction to stochastic processes, Universal Book Stall, New Delhi. Soukissian, Takvor H., Aristides M. Prospathopoulos and Chrysoula Diamanti, 2002, Wind and wave data analysis for the Aegean s e a m Preliminary results, The Global Atmosphere and Ocean System, Vol. 8, No. 2-3, pp. 163-189.

Real time monitoring of Spanish coastal waters" The deep water network E. Alvarez Fanjui*, M. Alfonso, M.I. Ruiz, J.D. L6pez, and I. Rodriguez Puertos de Estado, Spain

Abstract Puertos del Estado has developed a series of networks to measure and monitor the marine environment in Spanish waters. This paper will focus on the so called "Deep Water Network", consisting of 12 buoys measuring waves, currents, wind, atmospheric pressure and temperature, sea surface temperature and salinity. Information from the buoys is transmitted every hour via satellite Inmarsat in order to be quality controlled and distributed in real time via Internet. Additionally, directional wave information is propagated in real time to the mouths of the harbours by means of a wave phaseaveraged model.

1. Introduction The Deep Water Network (Figure 1) has been an important improvement in the measuring networks existing in Spain for several reasons: the variety of sensors in every buoy, their location in deep water and the real time transmission of the measured data. The main goals of the project are to obtain a complete picture of the Spanish seas, useful both for scientific and engineering purposes, and to prevent and alert the users with real time information about the ocean.

Figure 1 Deep water network instruments locations.


E. Alvarez Fanjul*, M. Alfonso, M.I. Ruiz, J.D. L6pez, and I. Rodrfguez

399

This network is the result of the integration of two previously existing systems: the EMOD network and the RAYO project. The EMOD network consisted in three stations based on Wavescan buoys measuring directional waves (Hippy-120 sensor), wind (Lambrecht $2 F1000), atmospheric pressure (Vaisala PTB200A(D)) and air temperature (Omega 905). Two stations are operational since 1990 (Bilbao) and 1993 (Mah6n), being the third one established at 2001 (Begur). The RAYO project was designed as a series of networks. The main part consisted of 9 SeaWatch buoys (provided by Oceanor) measuring waves (Waverider sensors, three of them are directional), currents (UCM-60 sensor), wind (Aanderaa 2740 for speed and Aanderaa 3590 for direction), atmospheric pressure (Vaisala PTB200A(D)) and temperature (Aanderaa 3455), sea surface temperature and salinity (Aanderaa 2994S). The RAYO project was launched in 1997 and the set-up phase ended in December 1998 with all the planned instruments working properly in the assigned positions. The project set-up phase was co-funded by the European Community through EFTA resources (85%) and by Puertos del Estado (15%) The Deep Water Network buoys measure information every hour which is then transmitted in real time via Inmarsat to both the port authorities and to the Puertos del Estado premises at Madrid, where the information is processed and posted into the Internet for public access (http://www.puertos.es). The information measured by the buoys is transmitted in real time and saved on the hard disk inside the buoy. This way, we generate two sets of information which should be identical if there are no problems. However, sometimes, the satellite transmission or the data reception does not work correctly. At other times, the hard disk on the buoy is defective and we cannot retrieve the information. Thus, these two sets may not coincide in dates. The measured parameters, for the same dates, are the same with a small margin due to the codification for the satellite transmission. For the waves, the raw time series are also saved on the hard disk so, when this information is recovered, we can apply our quality control and analysis. For these reasons, the transmitted information and the hard disk data are treated separately. The Deep Water Network is complemented by three current meter chains, each one based on three Aanderaa R C M - 7 (see Figure 1). These instruments provide further oceanographic information useful for understanding the measurements from the buoys and the main processes taking place at the coasts under study. In this paper, the Deep Water Network is introduced and experience from the set-up phase is presented (making special focus on operational aspects).

2. Deployment and maintenance of the Network From a harbour engineering point of view, the measurement of sea state was one of the main objectives of the project. The buoys are located at points with depths greater than 300 metres, so, wave measurements are not affected by local bathymetry and data are representative for large portions of the coastline. Increased spatial coverage was provided to the north west corner of the Iberian Peninsula, a region usually suffering

400

Real time monitoring of Spanish coastal waters: The deep water network

severe storms. Figure 1 shows the position of the instruments deployed in the Deep water network. The lack of instruments in some Spanish regions of the Mediterranean Sea (like portions of the Mediterranean coast) was due to the fact that, as previously mentioned, the RAYO project was partially funded with EFTA resources, and those parts of the country, being richer than the others, are not able to receive the benefits of such funding. The buoys are maintained by service visits every 3 or 4 months on average. This period is a compromise between the existing funding and the needs of the instruments. In the case of the conductivity sensor, this period is insufficient for some locations characterised for high biological activity, like the Canary Islands. 3. Real time data m a n a g e m e n t and d i s s e m i n a t i o n The real time data reception via Inmarsat at Puertos del Estado allows the computation of the propagation of the wave spectra from deep to shallow water at points of special interest (for example, the entrance of the harbours). All the raw and processed information from the buoys is posted on the Internet and distributed to the harbours. This section contains detailed information about this data treatment and dissemination processes. 3.1 Spectral wave propagation in real time Directional wave information is propagated in real time to the mouths of the harbours by means of the PROPS wave propagation model (Rivero and Arcilla, 1993). The propagation calculation is based on the so-called "one-point spectral propagation" (Alvarez Fanjul et al., 1997), developed at Puertos del Estado. The system is based around "wave transfer tables" computed with the model during a set-up or preprocessing operation. The main advantage of this system is the extremely low computational cost. This will allow the computation of the spectral propagation to the coastal points of interest in literally dozens of places along the Spanish coast without a representative computational effort in addition to the operational system. In order to couple this system to the real time data from the buoys, in November, 1998, directional spectral transmission was implemented at the directional buoys. The transmission of the whole directional spectrum or the complete time series becomes economically prohibitive, so, the transmitted directional information is limited to the scalar spectral density, and the mean direction and the mean angular spreading for every frequency band. From this data, directional spectrum is rebuilt by fitting a Mitsuyasu directional distribution in every frequency band (Mitsuyasu et al., 1975). The one-point propagation is based on linear theory and thus, should only be applied to areas where generation and nonlinear effects, such as bottom friction, are considered to be non-critical. Results obtained from this method are identical to those obtained with the linear version of PROPS at the points of interest. Due to the fact that the Spanish shelf is very narrow, these conditions are fulfilled along almost all the Spanish coast. 3.2 Real time data dissemination The parameters measured and processed on board by the buoy are transmitted in real time via Inmarsat satellite to Puertos del Estado. When received, an automatic quality

E. Alvarez Fanjul*, M. Alfonso, M.I. Ruiz, J.D. L6pez, and I. Rodriguez

401

control is applied to the data in four levels: instrument range, physical range of every parameter, spurious peaks and stationarity of values. Every value is marked with a quality index and introduced into a database, where we have connected our web pages, in order to extract and present this data every time a connection is produced. This real time data is also used for the numerical model's real time verification, also presented in the web pages. Figure 2 shows the flow of the real time data treatment.

Figure 2 Deep water network: real time data treatment scheme.

4. Data reports Every year, after a detailed quality control of the measurements, several codes are employed to produce data reports containing representations of the measured parameters (i.e., progressive vectors of winds and currents) as well as the following analysis of the results: 9 Statistical analysis, including wind and current roses, histograms, monthly evolution tables, etc. 9 Power spectra of several magnitudes by means of Fourier transform smoothed in the frequency domain using a Parzen window (Jenkins and Watts, 1968). 9 Decomposition of currents into tidal and residual components by means of the Foreman harmonic analysis program (Foreman, 1977). 9 Computation of the inertial component of currents using a filter in the frequency domain (Jenkins and Watts, 1968). 9 Sub-inertial time-series computation through an A24A24A25 Godin filter (Godin, 1991). The final reports obtained with this software can be found for free on the Internet (in PDF format) under http://www.puertos.es.

5. Conclusions and future plans The Deep Water Network is providing a complete picture of the marine environment surrounding the Spanish coast.

402

Real time monitoring of Spanish coastal waters: The deep water network

The idea of placing the buoys in deep water and employing propagation models to provide information on the coastline has been proved to be successful. For future extreme event studies, the data acquired at those depths will be by far more representative and useful than data obtained in shallow water, representative only for the point where the measurement is being taken. Results from our propagation system (the "one point spectral propagation") are accurate enough to be useful for daily harbour operations. Most of the well known oceanographic processes in the region have already been detected and recorded, promising future long time series capable of producing a better understanding of those phenomena and their impact on economical activities. With the exception of the salinity sensor, all the instruments in the Seawatch buoys proved to be accurate and reliable. For most of the instruments, the maintenance visits every three or four months proved to be sufficient. For the current and salinity sensor, a problem with fouling is present, especially in the highly productive Canary Islands waters. Alternative solutions to a complete service visit, such as cleaning the sensors in situ with the help of divers every month, are being evaluated. Future plans include the extension of the network to the poorly covered regions of the Mediterranean Sea and the change of all the scalar waveriders to directional units. Additionally, more wave transfer matrices will be constructed with a propagation model in order to provide propagated wave information at other coastal points. Finally, we can conclude that the Deep Water Network promises an unprecedented amount of quality data in the Spanish coastal waters that will provide a new vision of the dynamics in the region, useful both from the scientific and economical point of view.

References Alvarez Fanjul, E., M. G6mez Lahoz, J.C. Carretero, P. Vega, and F. Rivero, 1997, Acoplamiento de dos modelos espectrales de oleaje en el marco de un sistema de previsi6n. Libro de ponencias de la cuartas jornadas espafiolas de ingenierfa de puertos y costas. Vol 1.61-75 pp (in Spanish). Foreman, M. G.G., 1977, Manual of tidal heights analysis and prediction. Pacific Marine Sciences Report 77-10. Institute of Ocean Sciences. Victoria, British Columbia Godin, 1991, Tidal analysis and prediction techniques. Tydal hydrodynamics. Pp 675711. John Wiley & Sons Jenkins, G.M. and D.G. Watts, 1968, Spectral analysis and its applications. Holden Day. Pp 525. Mitsuyasu, H., F. Tasai, T. Suhara, S. Mizuno, M. Onkusu, T. Honda, and K. Rukiiski, 1975, Observations of the directional spectrum of ocean waves using a cloverleaf buoy. Journal of Geophysical Oceanography, Vol. 4, pp. 750-760. Rivero, F.J. and A.S. Arcilla, 1993, Propagation of linear gravity waves over slowly varying depth and currents. Proceedings on "WAVES'93" Symposium. New Orleans, USA, 518-532.

Adaptive neural network for wave forecasting D.S. Vlachos* and A. Papadopoulos


Abstract The physical process of generation of wind waves is extremely complex, uncertain and not yet fully understood. Despite a variety of deterministic models presented to predict the heights and periods of waves from the characteristics of the generating wind, a large scope still exists to improve on the existing models or to provide alternatives to them. In this work, an adaptive neural network has been designed and used in order to predict the wave height. The system has been proved to produce a 90% successful 24 hour prediction of wave height after 2 months of operation.

Keywords: Neural networks, wave forecast, operational oceanography 1. Introduction The knowledge of heights and periods of oscillatory short waves is essential for almost any engineering activity in the ocean. These waves are generated by the action of wind through pressure as well as shear mechanism. Wind-wave relationships have been explored over a period of five decades in the past by establishing empirical equations and also by numerically solving the equations of wave growth (Kissman, 1965; WMO, 1988). However, the complexity and uncertainty of the wave generation phenomenon is such that despite considerable advances in computational techniques, the solutions obtained are neither exact nor uniformly applicable at all sites and at all times. Moreover, errors in the wind forecasting model propagate and may be amplified in the final wave forecast. On the other hand, many of the most important properties of biological intelligence arise through a process of self-organisation whereby a biological system actively interacts with a complex environment in real time. The environment is often noisy and nonstationary, and intelligence capabilities are learned autonomously and without benefit of an external teacher. For example, children learn to visually recognise and manipulate complex objects without being provided with explicit rules for how to do so. The main problem encountered in the design of an intelligent system capable of autonomously adapting in real time to changes in his world, is called the plasticity-stability dilemma, and a theory called adaptive resonance theory is being developed that suggests a solution to this problem (Vlachos et al., 1998). The plasticity-stability dilemma asks how a learning system can be designed to remain plastic, or adaptive, in response to certain events, yet also remain stable in response to irrelevant events. In particular, how can it preserve its previously learned knowledge while continuing to learn new things? The proposed neural network-based wave height prediction system has been designed taking into account the above considerations. The system takes as input the wave height, * Corresponding author, email: [email protected]

404

Adaptive neural network for wave forecasting

the wind speed and direction time series and predicts the wave height up to the next eight expected values. The data arrive every three hours so a twenty-four hour point prediction is performed.

2. The dynamics of the system Figure 1 shows a schematic diagram of the neural system used for the wave height prediction and its subsystems. The input of the system is the significant wave height, the wind speed and the wind direction time series. The data arrive every three hours and predicted values of wind speed and wind direction are already included. These values are produced by the POSEIDON system (Soukissian et al., 1999).

Figure 1 Schematic diagram of the adaptive prediction system The different subsystems are explained in detail as follows: The role of the S 1 Switch is to feed the actual value of the wave significant height either to the long-term memory or the error control Subsystem. When a new value of Hm0 arrives, the S 1 Switch feeds this value to the Error Control Subsystem in order to correct the previous predictions of the system. After the correction is performed, the S 1 Switch feeds the incoming value to the $2 switch. The role of the $2 Switch is to feed the long-term memory either with the actual value of the wave height or the predicted one in order to obtain a deeper prediction. More precisely, the value of wave height at time t n can be used to predict the wave height at time tn+ 1. Then this value can be used in order to predict the wave height at time tn+ 2 and so on. In our case the prediction is performed up to time tn+ 8 which corresponds to a twenty-four hour prediction. The wind speed is analysed in u-v components (WindN, WindE). The neural network used to predict the wave height is a three layer back propagation network (Kosko, 1992). The first layer consists of neurons that simply hold the input values. These values are passed through the weighted connections to the second layer, which consists of neurons that sum their input and pass it to their activation system. The output of the second layer is passed through the weighted connection to the third layer, which have only one neuron. Let I n be the network input, Wnk the weights of the connections between the first and second layer, j~ the activation functions of the neurons of the second layer, gk the

D.S. Vlachos* and A. Papadopoulos

405

weights of the connections between the second and third layer, N the number of input neurons and K the number of the neurons of the second layer. The activation function of the neurons of the second layer is given by the formulae: 2

+

2

fk(x) = tanh (ckx), x > O,

tanh (ckx), x < 0

(1)

The slope of the activation function is very important because the sensitivity of the system depends on it. Moreover, there is a different slope between positive and negative values in order to account for the effect of wind direction in coastal zones. In these cases, the direction of the wind is critical for the wave growth. The weights Wnk and gk and the + parameters c k and c~ are the long-term memory of the system. These (N+3)*K parameters are modulated during adaptive learning. In every correction step, each parameter is modified in such a way that the overall error in prediction will be decreased.

3. Results and discussion Figure 2 shows the prediction results of the system after three months of operation. The results are compared with the measured values of weight height and the values calculated by the arithmetic model of the POSEIDON system. The solid line is the significant wave height measured by a floating buoy, the dashed line is the 24 hour prediction of the POSEIDON system and the dotted line is the 24 hour prediction of the adaptive system. The period shown was purposely selected in order to show the effect of the failure of the wind forecasting model to both the wave forecasting model and the adaptive system. It is clear from this figure that errors in wind speed and wind direction prediction reflect directly on the wave height prediction by the numeric model. On the other hand, the adaptive system is less sensitive to these errors. This is due to the fact that the output of the adaptive system is balanced between that predicted by the wind forecast and that predicted by the wave height time series. 2.5

Significant Wave heighl

2: ~o 1.5 S

,J,.

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15/'05

16/'05

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Figure 2 Comparison of 24 hours prediction between the numerical model and the adaptive system. See text for details.

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Adaptive neural network for wave forecasting

References Kissman, B., 1965, Wind Waves, Prentice Hall, Englewood Cliffs, NJ. Kosko, B., 1992, Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, NJ. Soukissian, T., G. Chronis and K. Nittis, 1999, POSEIDON: Operational Marine Monitoring System for Greek Seas, Sea Technology, 40, 7, 31. Vlachos, D.S., D.K. Fragoulis and J.N. Avaritsiotis, 1998, An Adaptive Neural Network Topology for Degradation Compensation of Thin Film Tin Oxide Gas Sensors, Sensors and Actuators B 43 (1998) 1. World Meteorological Organization, 1988, Guide to Wave Analysis and Forecasting, WMO no. 72. Secretariat of the World Meteorological Organization, Geneva, Switzerland.

User Perspectives

Christos Tziavos


Demand side "pull" for EuroGOOS products" Identifying market and policy decisions impacted by new environmental information Mary G. Altalo* 1, Colin Summerhayes 2, Nicholas Flemming 3, and Patricio Bernal 4

/Science Applications International Corporation Ltd. (SAIC Ltd.), London, UK 2GOOS, IOC, UNESCO, Paris, France 3Southampton Oceanography Centre, UK 4IOC UNESCO, Paris, France Abstract As the ocean observing system network becomes mature and information flow is enhanced, knowledge of the business and policy applications of the information will guide further design improvements. An analysis has been carried out from the "demand side" of the optimal temporal and spatial resolution and information format necessary for the ingestion of environmental data into business forecast models of EU industries. Data is presented from desktop studies made with partners from the European energy (utilities, oil and gas), leisure, construction, emergency management, and financial services sectors, in which the information "pathway" in decisions is examined. The paper analyses how the environmental information and forecast is transformed into a business and policy product, and how businesses have to re-engineer themselves to maximise the use of the information. The paper discusses how the information impacts the value chain decisions as well as the business plans. Selected bottom line indicators are used as performance metrics of successful incorporation of the information into decisions and policy guidance for regions and nations. Business case studies include 9 the role of improved weather/climate forecasts examined in energy demand/supply forecast business cycle and in the formation of electricity pricing and trading strategies 9 the role of seasonal forecasts in regional oil and gas energy management planning 9 the use of ocean and climate forecasts in determining test conditions for performance evaluation of building codes and standards 9 the role of bio-geo-chemical information in financial risk rating for investment business practice improvement and insurance premium setting 9 the use of short term and seasonal temperature and precipitation parameters in the yield-management models for tourist demand forecasting 9 the value of combined meteorological/air quality forecasts in formulation of health alerts/emergency management preparedness.

1. Introduction There are a number of concepts explored in this paper. The first concept is that environmental information from integrated observing systems plays a critical role in the * Corresponding author, email: [email protected]

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Demand side "pull" for EuroGOOS products: Identifying market and policy decisions impacted by new environmental information

sustainable development of nations. The second concept is that "demand pull" from

industry or other social sectors can have important implications for observing system design, management, planning and funding. Examining the value chain structure of organisations allows us to develop the concept that the "demand pull" approach has universal application to governments and agencies as well as to the business community. In the industrial context, transforming an environmental forecast into a business forecast is critical for the decision making process of business operations and management. By examining this process the critical pathway can be defined from data, through information, into the knowledge upon which to base an action that is industry specific. This can be demonstrated through case studies that are "industry trials" of environmental informationmmuch like pharmaceutical trials--where an information product is tested under real business operational conditions, thereby enabling its performance and its incremental value to the business operation to be assessed. Performance metrics such as improvement in cost, in reliability, or in risk reduction can be developed for the assessment process. In this paper we use case studies involving industry partners from economic sectors that are not often thought of as being "beneficiaries" of observing system products. Finally, the importance of public-private partnerships and joint investment planning is examined, as these can lead to the establishment of observing systems that are sustained and that foster the development of nations.

2. Observing systems for sustainable development

Guide marketplace decisions

Figure 1 How observing system products contribute to sustainable development The sustainability of each nation is tied to the development and best practice management of economies, the fair governance and protection of societies and the cultivation of a habitable environment. Only when the economic, social and environmental well being of nations are jointly addressed will communities develop in the global society. Allowing observing system products to inform national policy, to guide market decisions, and to safeguard the environment will engender support for such systems from governments and businesses alike (Figure 1).

Mary G. Altalo*, Colin Summerhayes, Nicholas Flemming, and Patricio Bernal

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Taking this approach to thinking about observing systems and their role changes the focus from designing a sustained observing system to designing an observing system for sustainable development. What does this refocusing mean in practice? It means we will require new metrics for evaluating the performance of environmental information products. The value of an observing system product will be judged on its ability to answer practical economic, social and governance questions as well as to address research needs. Focusing product development to serve both societal and economic needs will create both government and business customers for observing system information. It will raise the potential value of the product and convey a responsibility for applying the information. New products developed with this approach will be robust and have dual use. As the ocean observing system network becomes mature, and information flow is enhanced, knowledge of the business and policy applications of the information will guide further design improvements.

3. Industry trials for observing system information There are many approaches to estimating the value of information from observing systems. The traditional approach is cost-benefit analysis, which may significantly underestimate the value of the information if the product is new and not routinely assimilated in stakeholder operations. The second, rather different, but complementary approach is through an industry trial. In this proactive approach, a business end-use partner and an environmental information product-provider team up to run a performance assessment in a real world operational setting. Here, the environmental information product from the observing system is inserted into an operational business model and is transformed into a business decision tool. The business models are run with and without the environmental information, scenarios are generated, consequences analysed, cost factors applied and performance assessed. As the skill of the environmental forecast product increases, so too does the skill of the business forecast. Thus the demand grows for the information product, and we have demand-pull rather than supplier-push. This approach requires integrating information from all the observing systems--ocean, terrestrial, weather and climate. The industry trial approach actively engages the supplier, the end-user, and the organisational business unit in which the decision is made. The outcome is used to guide marketplace decisions. In national policy settings the same approach can be used to inform national policy and to prioritise the science and technology strategies for governments or agencies.

4. Relevance of the observing system products to the value chain The business units of most major corporations are organised around their value chain. Figure 2 demonstrates this principle in the organisational structure of CONOCO (modified from Parker, 2001). Each business unit (shown along the top) functions in a specific role to contribute to the overall performance of the corporation. Each unit adds value at each point in the chain. Some functions are unique to a particular business unit, such as government negotiations, which are the primary responsibility of the "Access" business unit. Other functions are cross cutting, such as long range planning, compliance reporting, operations management, risk management, and fiscal accounting. Each unit requires some environmental information for operational and planning decisions. Many

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of the activities of these units or functions are formalised in models such as demand and supply forecasting to determine ranges of possible outcomes depending on ranges of possible information inputs. Others are treated in a non-numerical, more ad hoc way, such as planning for crisis (risk) management in the event of a natural disaster. Viewing the information needs of a corporation in this way attaches the information to series of decision functions within an overall structure. Tests can be made of the value of any given type of information at any given point within the overall value chain of the corporation.

Figure 2 Value chain organisation of CONOCO business units and functions

5. Generic application of observing system products across all organisations Just like private industries such as CONOCO, all public organisations of national scale, such as government agencies and ministries, have cross cut management and governance activities, strategic planning and tactical planning activities for operations, infrastructure concerns, fiscal concerns and resource allocation activities. All have technical and safety concerns and all have stakeholders, regulators or the public to whom they report and by whom they are held accountable for the "investment returns". It is our thesis that the value chain approach described above therefore applies equally to industry, governments and nations, and that using environmental information in this way should enable any organisation to function more efficiently, more cost effectively, and more safely. In other words, the concept outlined above is a generic one portable to all governments and industries as illustrated in Figure 3, because it addresses the common needs of organisational structures in general. Given that observing system products can be used to address the needs of organisations such as governments, ministries, and businesses, then they can also be used to address the needs of nations, which are an "organisation of organisations".


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Figure 3 Generic organisational structuresmobserving system products apply to the value chain of all organisations

6. The industry trials approach The business and policy applications that we describe here are associated with several European regions and industries. The overall purpose has been to use industry trials to assess the performance of weather, climate, and ocean information in the tactical operation and strategic planning decisions of some major sectors of the EU economy. The approach is to determine how environmental information is currently used in each of the operations, develop a roadmap for the infusion of the new environmental information into the business models for those operations, and measure how the skill of the business projection increases with the increased accuracy of the environmental information. Figure 4 presents an overview of some representative studies.

Figure 4 Industry trials n observing system product performance assessment in business operations and policy development

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The first trial was in the utilities sector, as represented by Scottish Power. Weather forecast information is critical in the pricing models; more precise short-term forecast accuracy enhances the value of the output of these models in the market. The second trial was in the oil and gas sector, hosted by British Petroleum; regional energy management plans developed with and without climate projections can be used as a guide to future demand. The third trial was in the construction industry, hosted by the Building Research Establishment; the analysis of out-year environmental conditions can be used to assess the robustness of building codes and bridge design standards. The fourth trial was in the recreation and tourism industry in the accommodations sector, as represented by Starwood, to assess the value of seasonal coastal forecast conditions in revenue projections of coastal resorts. The fifth trail was in the financial services sector, hosted by SERM, a Risk Rating Agency, where enhanced water/air quality, climate and biodiversity information can aid the financial risk rating of companies. The final trial was in the health and emergency management sector, where real time meteorology and air and water pollution data can be used in the emergency management strategies of large cities. In this short paper it is impossible to show details from all of the case studies, so we have selected examples from the financial services, recreation and tourism and construction industries.

7. Industry trial in the financial services sector The financial services sector is a diverse sector, which has a need for environmental information across the industry. This sector has a wealth of sophisticated model and forecast products and decision tools to guide investment decisions. Its data processing and distribution systems are highly developed, and its products include weather derivatives. This sector underpins all of the other sectors of the country as it provides capital and risk management tools such as insurance for any facility. During recent years there has been increasing stakeholder and consumer pressure on the financial services sector to make investment decisions that take account of environmental factorsmso called green investments. Risks "ratings" have been developed to include exposure to natural environmental risks (including climate, weather, air and water quality, and ocean conditions) as well as man-made environmental hazards (e.g. industrial effluents). This is still quite a new area for banks and other funding bodies, and relevant data, which is already available, is not always fully exploited, usually because institutions are unaware of its availability or are unfamiliar with how to use it. Institutions supplying weather, climate and other environmental data are not always conversant with the needs of the financial services sector, so there is in many cases a disconnection between the suppliers and the end-users of this information. All businesses in one way or another are exposed to risks. Controlling and managing these risks often determines the success of a company or organisation. Individual companies use their own company risk rating analysis to determine what measures to take to improve their overall performance. This information is factored into the strategic and operational plans of corporations, so serves as a guide for better performance.


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The case study (trial) was designed to assess the benefits of incorporating new environmental information into the statistical models that generate the performance risk rating of individual companies and regions, particularly in the Eastern European area. The case study organisation that we are working with is a risk-rating agency, SERM, (Safety and Environmental Risk Management) in the UK. The objective of our study is to demonstrate an increase in the assessment rigor of a generic risk evaluation model, the output of which provides a "rating" of the overall risk to the capital of an organisation. Asset managers use this information to assemble portfolios of socially and environmentally responsible companies. It is standard practice in the finance industry to "rate" companies as a means of evaluating company viability. Financial rating is used as an information tool by the investment community to help guide investment decisions. Therefore, financial rating as an instrument exerts a powerful influence in all business sectors, in particular (although not exclusively) over publicly listed companies.

Figure 5 Example of Risk Rating Report using environmental information To date, most accepted rating schemes do not incorporate environmental data in their input parameters. Our industry partner, however, has developed an extended rating system to include health, safety and environmental management conditions in companies as shown in Figure 5. This rating model has begun to factor in environmental information in the form of weather, air quality, climate, ocean and biodiversity data. While it is generally accepted that such environmental conditions may either favourably or adversely affect a wide range of business activities, few attempts have been made to formally incorporate these parameters into rating models. The mathematical model that is used for the work in the finance sector currently takes into consideration the combined financial consequences (both tangible and intangible) of potential incidents on companies (e.g. pollution leakages and consequent costs of clean-up, breaches of health and safety regulations and consequent liability costs, etc.). The model can then determine the likely effectiveness of risk management in avoiding or mitigating known and control-

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lable risks. The key rating outputs are a "net risk to capital" figurewbased on market v a l u e - - a n d a point on a 27-point scale from AAA+ to C-, which is analogous to the credit ratings with which the financial community is familiar. This type of analysis can provide a clear view of a company's risk to market value from its operational risk exposure. A few climate and air quality variables have recently been added that aid companies to identify the sources of related risk, to assess the degree to which they are exposed to such risks, and to facilitate management of the risks. This case study is an opportunity to test a diverse set of variables from observing systems in their model to demonstrate the value of the environmental information to the outcome rating. A more comprehensive rating system will result in the management of a wider range of business risks, and increased investor confidence in highly rated businesses and organisations.

8. Industry trial in the recreation and tourism industry In this case study, we are working with the hotel and accommodation industry in Spain, specifically, The Westin La Quinta Golf Resort in Marbella. The organisational chart is shown in Figure 6. It is part of Starwood Hotels & Resorts Worldwide, Inc., owners of the Westin and Sheraton chain. It operates in 80 countries and employs 120000 people.

Figure 6 Overview of industry trial in the tourism sectormcan seasonal environmental information improve the accuracy of revenue forecasting in the Iberian peninsula? The Starwood Group sets standards for all its properties, and ensures adherence to a number of policies and procedures, such as the environmental risk/disaster preparedness procedures. Every business unit in the Starwood group uses environmental information to some degree in its decisions and operations. The revenue manager is responsible for proper "yielding" (i.e. a good return on investment), implementation of revenue management strategies, demand and capacity forecasting, and market analysis. He is responsible for the revenue projections for the resort, and reports them through the president to the shareholders. The business-planning model that is used is a yield


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management model where revenue per available room (Rev PAR) is calculated for different time periods. Rev PAR is one of the industry performance metrics that allows the shareholders to judge the company's performance. It is a pricing model, influenced by tourist demand (forecasting). The performance question that we want to answer by the trials is "can the improved seasonal forecast of temperature and precipitation, surf and ocean conditions result in better demand forecast for the property and the region?" The business forecast models incorporate observing system data on two scales: the regional and the local scale. The various scenarios that are generated will be compared and evaluated in terms of the resulting impacts on the actions to be taken by the hotel management. These assessments will be assigned a monetary value by the revenue manager for the purpose of weighing the consequences of incorporating the environmental information into the scenarios. The information will be fed back to the observing system-partner for consideration. The organisational chart shown in Figure 7 demonstrates that the information gleaned from the trial in Spain would be applicable to any one of the Starwood properties, thus multiplying the need for such information.

Figure 7 The importance of environmental information in the value chain of Starwood While the hotel example demonstrates the general need for information in the recreation and tourism industry, the industry as a whole is highly diverse and its environmental information needs range across all time and space scales, as shown in Figure 8. At every scale of environmental forecast product, there are business and policy models that can ingest the information to improve a business operation or a decision. For example, the proper function of the building engineers in HVAC (heating, ventilating, and air conditioning) control requires short-term forecasts with a time frame of less than a day. Cruise ship positioning requires ocean condition forecasts on the 1 to 2 week time scale. Sales and earnings of the hotel industry require forecasts one month ahead. The fuel procurement process for convention and resort facilities requires seasonal forecast information, and planning for new recreational boating facilities requires 5 to 10 year forecasts of sea level, precipitation, and temperature. As the skill of the environmental forecast information improves, so too does the "correctness" of the business or policy decision for this sector. Incorporating environmental information into the business model may also help businesses make policy decisions directed toward green issues, in addition to those

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decisions that enhance the bottom line. For instance, hotels responsible for golf courses in hot dry areas may need to use such information to evaluate the effects of excessive watering of their courses on the local water table.

Figure 8 Requirements for weather, climate and ocean forecast information by the recreation and tourism industry ranges across all time scales. Environmental forecast improvements lead to business forecast improvements (from Altalo et al., 2002)

9. Industry trial in the construction sector In a third trial, the construction industry will examine the impact of using regional decadal scale forecasts of precipitation, heat and wind to setting codes and standards for construction materials and design of shelter in Southern Europe. Our host partner is the Building Research Establishment (BRE), a NFP solely owned subsidiary of the Foundation for the Built Environment. It is a leading national centre of expertise on building and construction, risk sciences, and the prevention and control of fire. It helps determine European and international loading standards, design philosophy and structural safety, and is a major contributor to the Structural Euro codes and ISO standards. It is responsible for bridge standards and development of software for bridge design and port infrastructure. It has a testing facility with large laboratories for the assessing the performance of structures and materials. It is also an implementing b o d y m f o r the European Construction Products Directive (CPD)mthrough certification and training. The project manager for risk and insurance is responsible for developing BRE's business plan for the insurance industry, and reports findings to the standards setting bodies of the EU and other nations.


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The test scenario shown in Figure 9 focuses on an area in the southern Mediterranean region, where insulation against heat flow to the inside is an important factor. The interaction of various weather parameters in addition to seismic, volcanic, wild fire and periodic flooding risks, are factored into the model to determine minimum resilience levels of building structures. From the conclusions drawn from the running of the model with forecast environmental conditions, an assessment of the need for new or revised building codes can be made. The weather and climate information is obtained from the national meteorological services along with the ECMWF. Information on seismic and vegetation (for fire risk) predictions is obtained from the terrestrial observing networks and programs. The ocean information, such as sea level changes and wave height/storm prediction, is obtained from EuroGOOS agencies.

Figure 9 Overview of the industry trial in the construction industry. The changing role of publicprivate investment over technology development phases is shown conceptually in the top diagram as targeted government cost share % over the lifetime of the contract. The changing role of investing partners over project time is shown conceptually in the bottom diagram. The environmental information is used to set up initial conditions for design testing. New information is accommodated in the scenario runs, and software is developed to ensure that the data and information from the providers is in a format readily assimilated by the testing models. The uncertainties associated with the forecast are also built in to the model runs. Once the environmental predictions are incorporated, scenarios can be run for different building materials. Information on the best format for the variable can be valuable feedback to the forecast provider. The increased accuracy of the forecast will allow a greater safety margin to be built into the designs of the structures, which can save lives in the future. In addition, better design will be more cost efficient with fewer repairs or retrofits. Information, such as the optimal scales of the forecasts, both geographically as well as temporally, can be of value to the observing system provider community so as to optimally configure observing systems for optimal future construction industry considerations.

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10. The role of public private partnerships Businesses will not incorporate environmental information into their decision-making if the information is not sustained and standardised. Thus a business plan for the long-term financing of the observing system must be a top priority. There are many models, such as the technology transition strategies shown in (top), which may shed some light on the best approach to take to engage both the public and private sector in partnerships. In these strategies, the players/partners may consist of foundations, intergovernmental, economic development, and aid agencies, national science agencies, the private sector, local banks and venture capitalists. The role of the investors will change with risk and maturity of the project. Many governments are assessing their appropriate level of funding for the out-years if the product aids the public good (Mullin, 2001). Engaging all of the potential investors at the outset in the initial investment strategy planning process will allow sufficient time for the out-year investors to acquire the pre-agreed upon resources.

Figure 10 The development of a long-range investment strategy for GOOS which involves public and private contributions is a critical element in the overall governance of the programme Also the declining role of government investment in research must be taken into account as the observing system moves forward towards full-scale operation. The legacy planning must start now, and bring all of the potential investment partners to the table. The IOC Business Partners for Global Observing Systems scheme is one such effort, aimed at engaging the stakeholders in dialogue and training to raise their awareness of the value of observing system products to their industries. The business community is a potentially powerful voice in support of the establishment and maintenance of the


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observing system and its products, and will become more so when it is well prepared to receive environmental information and to optimise its use. The economic and social, as well as the environmental needs of nations can serve as guides to observing system design and prioritisation. In the spirit of Kofi Annan, this engagement of the private sector is not an option--it is a necessary step to global peace and social justice:

"In today's world the private sector is the dominant engine of growth--the principal creator of value and managerial resources. I f the private sector does not deliver economic growth and economic opportunity--equitably and sustainably--around the world, then peace will remain fragile and social justice a distant dream. " Kofi Annan, Secretary-General of the United Nations

References Altalo, M., M. Hale, O. Anastasia, H. Alverson, 2002, Requirements of the US Recreation and Tourism Industry for Climate, Weather, and Ocean Information. NOAA technical report, http://ioc.unesco.org/goos/key5.htm Mullin, 2001, ISSN, 168/2001, UNESCO Parker, D., 2001, The Role of Environmental Information and Technology in the Oil and Gas Value Chain, p. 35-42. In: M. Altalo (ed) The Critical Role of New Environmental Information and Technology in National Energy Needs: Conference Proceedings, US DOC and DOE.

International public goods and operational oceanography Martin Brown Retired international civil servant from OECD, France.

Abstract Notions of "public goods" are embedded in most discussion of the provision of oceanographic products and their finance, but the issues are unclear. The focus of operational oceanography is becoming increasingly international. At the same time, discussion of international development and its finance is increasingly emphasising public goods issues. This paper reviews some of the emerging issues from these divergent standpoints.

Keywords: International public goods, operational oceanography 1. International public goods There is a huge economic literature on public goods. The term has passed into public discussion and policy-making, where its use is fairly loose. The issues and references are reviewed in an important recent publication by the United Nations Development Programme (UNDP) entitled "Global Public Goods: International Co-operation in the 21st Century" (Kaul et al., 1999). It has very little to say about oceanography, but it does summarise the issues about public goods and discusses them from a global perspective. It also includes a glossary of important terms used in the public goods discussion (Kaul et al., 1999, pp 509-511). For academic economists, the discussion starts with a theoretical paper by Nobel Laureate economist Paul A. Samuelson (Samuelson, 1954). However, the discussion goes back to David Hume's 1739 "Treatise of Human Nature" (Hume, 1961) and Adam Smith's 1776 "Wealth of Nations" (Smith, 1993). The recent literature has been reviewed by Shmanske (1991). For environmental economists, one key paper is from 1968 by Garret Hardin: "The Tragedy of the Commons" (Hardin, 1968). For economists, public goods issues are about "market failures" in a functioning economic market system that is in equilibrium. The equilibrium requirements include that all "factors of production" (labour, capital, land) are fully employed. They also require, that if we shift the equilibrium, there should be no losers (or that they should be fully compensated). Obviously, this does not correspond to anyone's national, regional or global reality. But, if we reject this methodological framework, we should be careful about talking about "public goods". The economic framework for discussing "public goods" is about two principles: nonexcludability and non-rivalry. Under the first, once a good is produced, no one can be excluded from its access. Under the second, no individual agent's access to the product or service can damage that of someone else. These are very constraining restrictions. We have very few "pure public goods". Email: [email protected]

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"Market failure" gives rise to "externalities" and "spin-offs". Market agents (public and private) will produce goods and services if they can recover the costs (make profits) from their sales. However, their actual or potential production may result in outputs for which they cannot charge (non-excludability)mpositive (e.g. a publicly broadcast detailed weather forecast, whether or not it has been cost-recovered, is available to anyone) externalities m or may create costs to society, which the agents do not paymnegative externalities (e.g a sewage outfall, whether or not it is legal, may pollute a neighbouring beach). These externalities represent one form of public goods. With positive externalities, it is usually assumed that a market system will "under-produce" the public goods. Conversely, with negative externalities, "public bads" will be over-produced. There is thus need for a public policy response to maximise public welfare. The policy issues are generally about how best to "internalise" the externalities, that is, make the producer economically sensitive to the external impacts of the production. PRIVATE GOODS

EXCLUDABLE

food

CLUB GOODS

regulated fisheries

EuroGOOS end-products

NON-RIVAL

RIVAL

EuroGOOS semiprocessed products COMMON POOL RESOURCES

open ocean

International peace knowledge/information

NON-EXCLUDABLE

PURE PUBLIC GOODS

Figure 1 International public goods Another form of market failure occurs when there are no (or inadequate) property rights and regulations. Here, we can distinguish "natural" and "man-made" public goods. Without regulation, fish have traditionally been a public goodmnon-excludable--and sea fishing was assumed to be non-rival. The usual assumption is that, without property rights and property, there may be over-use of natural public goods. For man-made public goods, we make more complicated use assumptions. In the case of "knowledge", we generally assume that there will be under-use (see below). In both cases, market failure may be aggravated by uncertainty among the actors involved. This has sometimes between represented in terms of the "prisoner's dilemma" (e.g. Kaul et al., 1999, p.7). If individual actors knew better the intentions of other actors and trusted them, they could be more willing to supply public goods, through either individual or concerted action. Because they do not, the supply is sub-optimal. So, trans-

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International public goods and operational oceanography

parency is important, particularly to the extent that the public goods are international or global (Sandier, 1999). More generally, market failure is aggravated by "transaction costs" and inertia. Without perfect prior information about a market transaction takes place, any individual incurs costs for a transactionClearly, most people use the term "public goods" loosely. There are very few examples of "pure" public goods, which are neither excludable nor rival. So, it seems useful to explore the spectrum between "pure" public goods, through "impure" public goods to fully "private" goods. The public goods literature distinguishes two broad sorts of impure public goods: "club goods" and "common pool resources" (Kaul et al., 1999, p. 509). Club goods are generally non-rival, but may be excludable. GOOS products would be an example. EuroGOOS members are committed to sharing their products and one member's use does not harm another's--it may well enhance the general value. Common pool resources are generally non-excludable, but their use may well be rival (e.g. mining or fishing from open ocean resources). It is relevant to a EuroGOOS discussion to explore the differences between, national, regional, international and global public goods. It is important to consider whose welfare we wish to maximise. Even within the regional EuroGOOS framework, we cannot simply assume that provision of public goods by internalising externalities will not disfavour some member countries. For example, with the provision of better regional information to fishing fleets or to guide policies towards conservation of regional fish stocks, there will be national losers. Of course, to the extent that we believe that European economic and social integration is leading towards a social market equilibrium, we may assume that the losers will be compensated. Similarly, the provision of EuroGOOS products as public goods to non-EuroGOOS countries may, in an open trading system, shift the international social market in ways which disfavour some countries (including EuroGOOS countries). In any international valuation of public goods, we should use international prices to value costs and benefits (Brown, 1997 and 2002). Another typology would be in terms of the different actors who are concerned in the provision and use of public goods. We can use the notion of "civil society" to signal that our global concerns cannot always easily be translated into intergovernmental or national policy processes (Kaul et al., 1999, pp. 2-19). Civil society players include academic institutions, NGOs, multinational corporations, other interest groups.

2. Operational oceanography Operational oceanography has been defined and discussed in various EuroGOOS reports. EuroGOOS products and user requirements are heterogeneous. For a discussion of EuroGOOS' survey of end-users' requirements in six countries, see Fischer and Flemming (1999) and Flemming (1999). However, the scale and importance of potential EuroGOOS products may well change as EuroGOOS expands its activities. Moreover, the survey may not adequately cover public good possibilities: it was essentially of informed (potential) users who were willing to pay for marketable products. We should distinguish between different stages in producing useable GOOS products:

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1. data gathering 2. data processing and modelling 3. preparation and dissemination of products to different users One could argue that the products of 1 and 2 are or should be (almost) pure public goods, although at present there may be rather few users, while 3 produces club public goods or private goods. There are also different time scales in the production and use-value of EuroGOOS products. Flemming (1999) has identified five categories of products, with their public goods implications. EuroGOOS foresees a shift in its activity from its recent emphasis on coastal monitoring and applications towards the development of open ocean monitoring with large financial, and potentially large institutional and public goods, implications (Woods, 2002). Much of the public goods discussion has been as though the public goods associated with a project would be small in relation to the private goods. We refer to "externalities", "spinoffs" and the need to "spread the benefits" from private to potential public good users. In contrast, with the emerging EuroGOOS strategy, the private goods may become subsidiary to the public goods. We should be careful about the assumptions that we are making about the global social market equilibrium.

3. Financing and provision of public goods The fact that certain goods/services are public does not imply that they need to be provided free or by public agencies (Heal, 1999). Nor does it imply that providing the extra public goods will be a costless spin-off. Indeed, the marginal cost of providing impure public goods (e.g. extending the use of a club good) may sometimes be higher than the average cost of the private goods. On the user side, there might be customers willing to pay, but market failure means that the goods are simply not available or not adequately provided. Or, the potential users of public goods are not sufficiently aware of their existence or usefulness. In other cases, potential users may not be able to afford the public goods and public finance may be needed. However, there are cases of market failure where a private supplier would be able to provide the goods free to some users while still increasing his profitsn"cross price subsidisation". There could be significant economies of scale from extending the market to public good users. Governments can intervene to correct market failure in various ways. They can intervene directly to (finance and) conduct the R&D, data collection and processing that would be necessary. They can finance private suppliers to do the same, by subsidies or by fiscal and monetary incentives. They can make commercial contracts to purchase the public goods on behalf of the users. They can form joint ventures with the private sector, including with potential users, within which the government shares the costs and the risks of failure. They can give similar fiscal and financial incentives to potential users. There are, at least, two principal issues. First, it may be argued that private suppliers are more efficient than bureaucrats at judging market possibilities and risks: to the extent that this is so, the less a government intervenes directly, the better the outcome. Second, fiscal or financial intervention increases government budget costs. Government budgets are constrained in aggregate, have limited fungibility between different competing uses and particular activities are subject to shifting unpredictable priorities over the longer

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term. Moreover, funding for operational oceanography typically comes from a variety of government budgets (including defence), with different appropriation labels. Regulation, of various kinds, may complement financial intervention as a means to correct market failure. The market structure of potential private/public suppliers ranges from monopoly (particularly for data collection and primary processing) through to fairly competitive (for hardware and processed products). Regulation is normal in other sectors (e.g. utilities) where the market structure has monopolistic features. However, such regulation frequently needs to be supplemented by public subsidies and price regulation. Meteorology has obviously similarities to operational oceanography, but some important differences. This form of regulation seems likely to become more important as EuroGOOS activities increase. However, to the extent that the activities are collaborative between European nations and, still more, international, there could be increasing problems in harmonising national regimes. Measures to correct market failure raise "free rider" problems (Kaul et al., 1999, p. 509). The community and each individual would benefit by regulating a public good, but any individual has an interest in avoiding the regulation. It is an empirical question as to how far the different communities can accept free-tiding.

3.1 Knowledge as a special form of public good~lPR It can be argued that "information" or "knowledge" is a pure public good (Stiglitz, 1999, Adams et al., 2000). Once the information is available, the marginal cost of another person using it is zeromit is non-rival. Also, because the marginal cost of reproducing the information is zero, the information should be available to whoever wants to use it so, non-excludable. Of course, there will be costs in processing the information, and transmitting it, for useful application. As the primary information becomes useful, it passes from a pure to an impure, club public good. We should want to extend the public good use of club goods. As noted above, one policy response to market failure is to reinforce property rights, subject to government regulation. With information products, this brings us to the difficult issues about Intellectual Property Rights (IPR). Without IPR, the incentive to invest in R&D is reduced and science and technology developments may be sub-optimal. However, IPR tend to limit the optimal spread of public good use. Stiglitz (1999) sees this as a difficult conflict between IPR-driven "dynamic efficiency" and the "static efficiency" which comes from wider spread of existing information. Trade-offs need to be found. EuroGOOS has not so far made much significant use of IPR, except perhaps among associated equipment and software firms, but the issues may well become more important.

4. Public goods and developing countries There is growing international interest in the role of public goods in relations between developed and less developed countries (Kaul et al., 1999; SIDA, 2001; Ferroni, 1999). This comes partly from a growing "aid weariness" in both donor and recipient countries. Introducing the notions of public goods could promote the efficiency of "overseas development assistance" (ODA). ODA is traditionally justified for a fairly murky mixture of reasons, with objectives ranging from altruism to self-interest, with sustainable devel-

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opment somewhere in the middle. Public goods may blur the distinctionhrelatively costless to the donors and both sides may ultimately benefit. In the case of GOOS, its future development strategy points to continuous monitoring within the EEZ' s of developing countries. However, there are difficult problems about extending public goods from developed to developing countries. Much of the frustration and unease about ODA effectiveness comes from a lack of governance in recipient countries and their inability to resolve civil society conflicts. The question for public goods suppliers is about how far they can effectively operate within this general context. GOOS development requires stable, long-term observations, with associated secure funding. Clearly, in most recipient countries, the extension of GOOS activities will require "capacity-building", for data collection and dissemination of final products. The need at the intermediate stagehprocessing and modellingmis less obvious. Capacity building covers some very diverse, but important, activities, which are not normally a problem in EuroGOOS countries. GOOS institutions face difficulties intervening in developing countries. This could point to taking GOOS public goods into a wider institutional framework, or, alternatively, tailoring GOOS products so as to short-circuit the general governance problems. For international finance, especially ODA, accountability is very important. Donors want their ODA to lead to efficient and visible development. Thus, they seek "conditionality" (where ODA is linked to macro-economic and social policies) and "additionality" (where they can be satisfied that their ODA increases the net development effort). So, ODA is focused on specific countries and, to a considerable extent, on specific projects. Moreover, donors are allergic to open-ended activities, with unclear or uncertain outcomes. The same considerations apply to most multilateral funding. The focus of these international public goods activities should be regional or international, rather than national. Most EuroGOOS activities have regional partners and international components. Some of the challenges and potential benefits concern groups of neighbouring developing countries, so it would make sense to develop existing and future GOOS activities on a regional basis. (Some of this is happening through the GOOS Associations). Some of the funding constraints might be eased. In this context, there could be an important (but hitherto largely unexplored) role for the UN regional development banks. The IOC and GEF (the Global Environment Facility) have been suggested as international agencies which could sponsor and support regional institutional arrangements to promote public goods applications of GOOS activities. The IOC has very considerable experience, but is not itself a capital-funding agency. It is constrained at present by dependence on annual financial budgeting. GEF is less constrained financially and has been specifically concerned with establishing selfsustaining institutional arrangements, which could eventually be self-financing through participating countries. However, GEF has so far shown little interest in operational oceanography. The need for stable, relatively long-term funding has raised interest in the potential for endowment or trust funding. The IOC depends considerably on trust funding, which has allowed member states (and non-members--notably the US) to contribute finance and staff to specific IOC activities or to the general budget (Brown, 1999). It is not clear how

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far the IOC and its sponsors would want, or be able, to expand sponsorship of dedicated activities with longer-term time horizons.

5. Key public good issues for EuroGOOS Five key issues are identified for discussion against a background with: 9 a shift from research to operational oceanography 9 a shift in emphasis from coastal to open ocean monitoring and modelling 9 a rapid increase in activity with consequent funding requirements 9 increasing public funding constraints 9 an increased role for (impure) public goods. These issues are: 1. Potential conflicts between dynamic and static efficiency. How far is there a conflict in priorities and financing between continuing technological advance and wider spread of products and users? 2. Civil society and access to EuroGOOS products. There is growing public concern about issues that are not adequately reflected in the actions of national governments and still less in international negotiations and agreements. In a public goods sense, present EuroGOOS products are "club" goods: they are non-rival to club members, but may be excluded from non-members. How far does EuroGOOS have an adequate public profile to match its aspirations for operational oceanography, especially its global aspirations? Are there institutional implications for EuroGOOS in trying to reach a wider audience? Should there be greater transparency in, and public knowledge of, its present and projected funding? 3. Whose welfare is EuroGOOS targeting? Present end-users for EuroGOOS products are primarily national, but increasingly regional and international. Because EuroGOOS products are club goods (excludable), there may be losers. 4. Issues about EuroGOOS, public goods and poor countries. There could be large benefits to EuroGOOS as well as to recipients. There is growing interest in global public goods to re-focus foreign aid and multilateral financial transfers. However, there remain very big problems. For EuroGOOS, there is a need for stability and flexibility in financing longer-term activities with uncertain outcomes. For the donor community, there is a need for "accountability". For the recipient community, there is a widespread need for governance, and capacity building, which would involve civil society actors. There is increasing interest in promoting problem-defined regional activities, with corresponding self-sustaining financial and institutional arrangements.

5. Implications for transparency and socio-economic research. There is much about EuroGOOS and its potential public goods role that is unclear. The subject is of growing European and international importance and would justify further socioeconomic research to contribute to future policy discussion, which could lead to more focused policy management. Several lines of inquiry deserve priority: -

mapping of EuroGOOS programmes from a public goods standpoint

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in-depth analysis of individual programmes the eventual role of IPR.

References Adams, R., M.S. Brown, C. Colgan, N.C. Flemming, H. Kite-Powell, B. McCarl, J. Mjelde, A. Solow, T. Teisberg, and R.F. Weiher, 2000, The Economics of ISOOS: Benefits and the Rationale for Public Funding, NOAA, U.S. Department of Commerce. Brown, M.S., 1997, "Cost/benefit Analysis of GOOSmSome Methodological Issues", in Operational Oceanography: The Challenge for European Co-operation, Elsevier Oceanography Series 62, Elsevier Science B.V. Brown, M.S., 1999, Economic Evaluation and IOC Activities, IOC/EET/Brown, IOC. Brown, M.S., 2002, "Valuing marine activities in Europe: provisional estimates, concepts and data sources", in Operational Oceanography: Implementation at the European and Regional Scales, Elsevier Oceanography Series 66, Elsevier. Ferroni, M., 1999, Reforming Foreign Aid: The Role of International Public Goods Fischer, J. and N.C. Flemming, 1999, "Operational Oceanography: Data Requirements Survey", EuroGOOS Publication No. 12, Southampton Oceanography Centre, Southampton. Flemming, N.C., 1999, Dividends from Investing in Ocean Observations: a European Perspective, Proceedings of OceanObs 99 Hardin, G, 1968, The Tragedy of the Commons, Science 162; 1243-48. Heal, G., 1999, "New Strategies for the Provision of Global Public Goods: Learning from International Environmental Challenges", in Kaul et al. Hume, D., 1961 [1739], A Treatise of Human Nature. Garden City. NJ: Dolphin Books. Kaul, I., I. Grunberg and M.A. Stern, 1999, Global Public Goods: International Cooperation in the 21st Century, UNDP, Oxford University Press, New York. Samuelson, Paul A, 1954, The Pure Theory of Public Expenditure, Review of Economics and Statistics 11: 387-89. Sandler, T., 1999, Intergenerational Public Goods: Strategies, Efficiences and Institutions, in Kaul et al. Sasssone, P.G., and R.F. Weiher, 1997, "Cost benefit analysis of TOGA and the ENSO observing system", in Operational Oceanography: The Challenge for European Cooperation, Elsevier Science B.V., Amsterdam. Shmanske, S, 1991, Public Goods, Mixed Goods, and Monopolistic Competition, College Station: Texas A & M University Press. SIDA, 2001, Transboundary Water Management as an International Public Good, Ministry for Foreign Affairs of Sweden, Stockholm. Smith, Adam, 1993 [ 1776], Inquiry into the Nature and Causes of the Wealth of Nations, New York, OUP. Stiglitz, J.E., 1999, "Knowledge as a Global Public Good", in Kaul et al. Woods, J.D., 2002, The Role of Monitoring in Global Change, ESF.

Global operational oceanography and the role of the Joint WMO/IOX Technical Commission for Oceanography and Marine Meteorology Peter Dexter*l and Johannes Guddal 2

1Chief Ocean Affairs Division, World Meteorological Organization, Geneva 2Co-president of JCOMM, Norwegian Meteorological Institute, Bergen, Norway

1. Introduction The Joint WMO/IOC Technical Commission for Oceanography and Marine Meteorology (JCOMM) was formally established by its parent Organizations, WMO and IOC, in 1999, and held its first session in Akureyri, Iceland in June 2001. JCOMM has been designed and implemented to provide the international coordination, regulation and management mechanism for a global, operational oceanographic and marine meteorological observing, data management and services system. As such, it performs a role equivalent to that of the WMO Commission for Basic Systems with regard to operational meteorology and the World Weather Watch. JCOMM is inherently multi-disciplinary and multi-organisational in concept and operation. It seeks to pool the expertise and resources of the meteorological and oceanographic communities, both nationally and also internationally through WMO and IOC, to coordinate operational oceanography in support of the requirements of governments, industry, commerce, global climate studies and individual marine users for marine data, products and services. JCOMM provides the implementation mechanism for a large part of GOOS, and has been explicitly recognised by OceanObs99 as the intergovernmental body to coordinate an operational ocean observing system for climate, which constitutes the common module of GOOS and GCOS. This paper reviews the background to and role of JCOMM. It summarises the present status and future development of the operational ocean observing systems, data management and services coordinated through JCOMM, with emphasis on the immediate priority areas for such development. It also summarises certain aspects of the World Weather Watch of WMO, including the work of the body responsible for its international coordination, the Commission for Basic Systems, as an existing parallel to the work of JCOMM. Finally, the paper addresses present and future interactions between JCOMM and regional mechanisms such as the GOOS Regional Alliances, with particular emphasis on the role of EuroGOOS.

2. Background to JCOMM "The JCOMM vision JCOMM coordinates, regulates and manages a fully integrated marine observing, data management and services system that uses state-of-the-art technologies and capabilities, is responsive to the evolving needs of all users of marine data and products, and includes an outreach programme to enhance the national capacity of all maritime countries." * Corresponding author, email: [email protected]

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Prior to 1999, marine meteorological and oceanographic observations, data management and service provision programs were internationally coordinated by two separate bodiesmthe World Meteorological Organization (WMO), through its Commission for Marine Meteorology (CMM), and UNESCO's Intergovernmental Oceanographic Commission (IOC). While enhancing safety at sea remained the primary objective of marine forecast and warning programmes, requirements for data and services steadily expanded in volume and breadth during the preceding decades. Other applications such as coastal area management, optimisation of commercial fishing activities, ship routing, offshore resource exploration and development, pollution prevention and clean-up and, most recently, climate modelling and prediction became increasingly important. Moreover, many of these applications required observational data sets and predicted products for both the oceans and the overlying atmosphere. Responding to these interdisciplinary requirements necessitated the development of ever-closer working relationships between oceanographers and marine meteorologists. This was reflected at the global level by growing collaboration between the IOC and the WMO in organising and coordinating ocean data acquisition, data management and provision of related services. The increasingly close relationship between the two agencies' operational activities in the oceans culminated when the Thirteenth WMO Congress (May 1999) and the 20th IOC Assembly (July 1999) formally agreed that a new IOC/WMO Joint Technical Commission for Oceanography and Marine Meteorology (JCOMM) should be established. This new body brought together the marine meteorological and oceanographic communities in a common global forum charged with overall responsibility for worldwide marine meteorological and oceanographic services and their supporting observational and data management programmes. As formally constituted, JCOMM is an intergovernmental body of experts that provides the mechanism for international coordination, regulation and management of oceanographic and marine meteorological observing, data management and services systems. The creation of this Joint Technical Commission results from a general recognition that worldwide improvements in coordination and efficiency may be achieved by combining the expertise and technological capabilities of the WMO and the IOC. JCOMM has a mandate to prepare both regulatory and guidance material for WMO Members and IOC Member States related to marine observing systems, data management and the design and delivery of meteorological and oceanographic services. In its formal sessions, the Commission acts as a final review body for activities, proposals and recommendations prepared by a sub-structure of working groups, expert teams and rapporteurs. JCOMM, in turn, prepares and submits formal recommendations for actions to its governing bodies, the WMO and the IOC, for consideration, endorsement and, ultimately, for implementation by the appropriate agencies of maritime countries and other responsible bodies. It is, in other words, the single global coordinating and reporting body for the full range of current and future operational data collection and service provision activities related to these disciplines.

3. JCOMM structure and programme areas JCOMM has a current membership of approximately 250 experts, with most national delegations comprising roughly equal numbers of oceanographers and marine meteorol-

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Global operational oceanography and the role of the Joint WMO/IOX Technical Commission

ogists. It is co-chaired by a meteorologist and an oceanographer, reflecting its integrated responsibilities for meteorological and oceanographic programs. Under the overall direction of a Management Committee chaired by the co-presidents, the Commission is organised into four Programme AreasmObservations, Data Management, Services and Capacity Building. Each Programme Area is, in turn, managed by a Coordinator, with support from a small coordination group and with specific activities being undertaken by designated teams or panels of experts. The establishment of these four Programme Areas is intended to facilitate the delivery of JCOMM's mandated responsibilities by subdividing them into logical and coherent groupings (see Figure 1).

Figure 1 JCOM_M structure The Observations Programme Area is primarily responsible for the development, coordination and maintenance of moored buoy, drifting buoy, ship-based and spacebased observational networks and related telecommunications facilities. It also monitors the efficiency of the overall observing system and, as necessary, recommends and coordinates changes designed to improve it. It has inherited lead responsibility for a number of important and well-established observational programmes, which are managed by bodies that now report through JCOMM. The Services Programme Area deals with the provision of marine meteorological and oceanographic services around the globe. Consequently, it facilitates and supports the delivery of the most visible outputs of the world's marine meteorological and oceanographic organisations. These include warnings of gales, storms, severe tropical weather systems such as typhoons, hurricanes and tropical cyclones and other hazardous phenomena, information on sea ice conditions and other products disseminated through the Global Maritime Distress and Safety System (GMDSS) in response to requirements established under the International Convention for the Safety of Life at Sea (SOLAS). The continuing provision of safety-related weather and oceanographic services is an absolutely fundamental priority of JCOMM and of its Services Programme Area. The J C O M M Data Management Programme Area addresses the quality assurance, archiving and provision of access to marine meteorological and oceanographic data and related metadata. Most marine meteorological and oceanographic data are currently held


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in IOC and WMO data centres with differing data storage and management systems operating on a variety of computer platforms. However, some clients require highly integrated marine data flows that include meteorological, oceanographic and physical and non-physical data. The ultimate aim of JCOMM is to meet the needs of all users by developing and implementing a fully integrated data management system able to deliver high-quality data spanning the complete spectrum of marine meteorological and oceanographic observations. At the same time, the JCOMM has inherited responsibility for several established data management programmes. The Capacity Building P r o g r a m m e Area has as its focus the provision of assistance to countries to enhance their capabilities in ocean data collection and management and in the provision of marine services. Building capacity is a high priority activity directed at ensuring that maritime nations can not only contribute meaningfully to JCOMM's various programs but also gain optimum benefits from the global system. The Programme Area arranges or delivers training, facilitates transfer of technology, assists in providing equipment and works closely with the capacity building programmes of donor countries and other UN Agencies. In summary, the present global mandate of JCOMM encompasses: 1. Observations and data management Planning and coordinating the acquisition, exchange and management of marine observational data involving (see Figure 2): -

Around 6000 volunteer vessels reporting weather and surface oceanographic observations

-

More than 100 volunteer vessels reporting sub-surface temperature profiles

-

Around 20 volunteer vessels making upper air observations at sea

-

More that 1400 drifting buoys reporting weather and surface oceanographic data

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More than 100 moored ocean data buoys reporting weather and ocean data

-

Around 160 tide gauges measuring and reporting sea level

-

Global partners such as satellite remote sensing agencies and the Argo Project

-

Distributed data archives managed by designated data centres for specific data types

2. Services Planning and coordinating the preparation and dissemination of marine meteorological and oceanographic products and services, involving: -

-

-

A global network to prepare and issue weather, sea ice, sea state and other bulletins for marine users A global network to prepare and issue oceanographic products Global information dissemination and telecommunication mechanisms Development and implementation of new ocean products and services

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Globaloperational oceanography and the role of the Joint WMO/IOX Technical Commission

3. Capacity building Planning, coordinating and undertaking related capacity building initiatives, involving: -

Analysing national and regional needs for education, training and technology transfer

-

Developing projects aimed at enhancing national and regional capacities

-

Supplying technical publications, guidance materials, expert lecturers and trainers

-

Conducting workshops

Figure 2 Operational ocean observing system status, February 2003 4. J C O M M ,

WWW

and

GOOS

JCOMM is now clearly recognised as a primary implementation mechanism for global GOOS, by the GOOS Steering Committee, the Intergovernmental Committee for GOOS, and also within JCOMM itself. Partly because of the existing expertise within JCOMM, but also in view of the current level of development of the requirements for the operational exchange and management of data within GOOS, the initial priority for such implementation remains in marine meteorology and ocean physics (including related observational data such as salinity). However, the likely future need for JCOMM to address also some types of chemical and biological data has been recognised in both JCOMM and GOOS, and a JCOMM rapporteur has been appointed (who is also co-chair of the Coastal Ocean Observations Panel of GOOS), to provide the necessary liaison. At the same time, JCOMM is recognised by the WMO Commission for Basic Systems (CBS) as the implementation and coordination mechanism for the marine component of the World Weather Watch (WWW). It also provides support to GCOS other than through strictly oceanographic observational data; e.g. surface meteorological and upper air observations over the ocean. In this context, the Commission therefore receives guidance on scientific and operational requirements from a number of bodies, including OOPC and COOP (through the GOOS Steering Committee), the AOPC of GCOS, and CBS. In a reciprocal sense, both JCOMM and GOOS contribute directly to the work of CBS, in


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particular in developing observational data requirements and the re-design of the Global Observing System of the World Weather Watch. Overall coordination of interactions between global GOOS and JCOMM takes place at the management oversight level, where there is reciprocal membership on the JCOMM Management Committee, the GOOS Steering Committee and the I-GOOS Board. In addition, there is close interaction at the implementation level, such as between the OOPC and the JCOMM Observations and Data Management Coordination Groups, while the Intergovernmental Committee for GOOS provides a mechanism for facilitating interaction between JCOMM and the GOOS Regional Alliances, in particular.

5. W W W structures Meteorological services are required for the safety of life and property, the protection of the environment, and for the efficiency and economy of a wide range of weathersensitive activities. Central to the provision of these services is the receipt by National Meteorological Centres of observational data, analyses and forecasts. The World Weather Watch (WWW) is the international cooperative programme that arranges for the gathering and distribution in real-time, on a worldwide scale, of meteorological information required by individual Members, by other WMO programmes and relevant programmes of other international organisations. The overall objectives of the WWW are: 1. To maintain an effective worldwide integrated system for the collection, processing and rapid exchange of meteorological and related environmental data, analyses and forecasts 2. To make available, in real-time and non-real-time, as appropriate, observational data, analyses, forecasts and other products to meet the needs of all Members, of other WMO programmes and of relevant programmes of other international organisations 3. To arrange for the introduction of standard methods and technology which enable Members to make best use of the WWW system and ensure an adequate level of services and also the compatibility of systems for cooperation with agencies outside WMO 4. To provide the basic infrastructure for GCOS and other WMO and international programmes for climate monitoring and studying of climate issues. The WWW operates at global, regional and national levels. It involves the design, implementation and further development of three closely linked and increasingly integrated core elements: 1. Global Observing System, consisting of facilities and arrangements for making observations at stations on land and at sea, and from aircraft, environmental observation satellites and other platforms, designed to provide observational data for use in both operational and research work 2. Global Telecommunications System, composed of an increasingly automated network of telecommunication facilities for the rapid, reliable collection and distribution of observational data and processed information

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3. Global Data Processing System, consisting of World, Regional/Specialised and National Meteorological Centres provide processed data, analyses and forecast products. The current WMO information systems have been developed to meet a diverse set of requirements. The principal system is the GTS along with the related data processing and management functions that have been developed to serve the WWW. The GTS has a number of significant strengths: it is an operational private network that mainly provides for the exchange of real-time high-priority data, and it is mature, well tested and operated according to well-defined procedures and shared responsibilities. Other information systems that have been developed to meet the needs of other programmes and Commissions have their own advantages. The multiplicity of systems operated for different Programmes has, however, resulted in incompatibilities, inefficiencies, duplication of effort and higher overall costs for Members. Therefore, a new approach is proposed: a single coordinated global infrastructure, the Future WMO Information System (FWIS). It is envisioned that FWIS would be used for the collection and sharing of information for all WMO and related intemational programmes. The FWIS vision provides a common roadmap to guide the orderly evolution of these systems into an integrated system that efficiently meets all of the international environmental information requirements of Members. FWIS should provide an integrated approach to meeting the requirements of: 9 Routine collection and automated dissemination of observed data and products ("push") 9 Timely delivery of data and products (appropriate to requirements) 9 Ad-hoc requests for data and products ("pull") FWIS should be: 9 Reliable 9 Cost effective and affordable for developing as well as developed Members 9 Technologically sustainable and appropriate to local expertise 9 Modular and scalable 9 Flexible and extensible-able to adjust to changing requirements and allow dissemination of products from diverse data sources and allow participants to collaborate at levels appropriate to their responsibilities and budgetary resources FWIS should also support: 9 Different user groups and access policies, such as WMO Resolutions 40/25 9 Data as well as network security 9 Integration of diverse datasets Taking into account that information systems technology is evolving rapidly, FWIS should utilise industry standards for protocols, hardware and software. Use of these standards will reduce costs and allow exploitation of the ubiquitous Internet and web services.


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The ultimate implementation of FWIS will build upon the most successful components of existing WMO information systems. It will continue to rely upon the WMO communication system (initially the GTS) to provide highly reliable delivery of time-critical data and products. To clarify the concept of FWIS, three functional components are defined: National Centres (NC), Data Collection or Product Centres (DCPC) and Global Information System Centres (GISC). FWIS will be built upon existing systems and these systems can continue to carry out their current tasks without modification. The system concept is illustrated in Figure 3.

Figure 3 Future WMO Information System The WMO bodies primarily concerned with WWW are CBS and the Regional Associations. CBS is responsible for technical matters relating to worldwide cooperation in the planning, implementation, operation and further development of the WWW system. At the regional level working groups on the regional aspects of the WWW assist the RAs in coordinating the implementation of WWW. The GOS consists of facilities for making observations on land and at sea, and from aircraft and satellite. Each WMO Regional Association (RA) draws up a regional network of observing stations, called a Regional Basic Synoptic Network (RBSN), to meet the collective needs of its Members. The level of implementation of the RBSN surface stations in 2002 varied from 40 per cent in Region III to 95 per cent in Region VI, with a global average of 80 per cent (see Figure 4). The number of SYNOP reports actually received at Main Telecommunications Network (MTN) centres on the Global Telecommunication System (GTS) varied regionally, from 50 per cent of those required in the RBSN of Region I, to 93 per cent in Region VI, with a global average of 75 per cent. In addition to stations in the RBSNs, a large number of supplementary stations have been established to meet regional and national needs. Most of these stations are automated and record observations hourly. There were a total of 2391 automated stations as of October 2002.

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Figure 4 The surface-based component of the Global Observing System of the WWW The GDPS generates nearly all the Numerical Weather Prediction (NWP) products required by Members. The GDPS is made up of WMCs, Regional Specialised Meteorological Centres (RSMCs) and NMCs. Most RSMC operations have shown sustained improvement by enhancing their forecast systems and computer facilities. Global models are now running at 16 GDPS centres; 67 centres run NWP models operationally; 34 run Limited Area Models (LAMs) (with resolution coarser than 35 km); and 44 run mesoscale models (resolution 35 km and finer) (see Figure 5 and Figure 6). Several centres are running Ensemble Prediction Systems (EPSs) for short-, medium- and extended-range forecasts, and an increasing number of centres are using EPS for longrange forecasting. Most RSMCs apply statistical models to supplement deterministic numerical models. Eight RSMCs share the responsibility for disseminating atmospheric transport model products in the framework of the international coordinated response plans for nuclear emergencies, thus achieving a global coverage.

Figure 5 Global Data Processing System centres


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Figure 6 Evolution of NWP models

6. EuroGOOS, JCOMM and the

WWW

JCOMM already structures its work in many areas along lines similar to that of the WWW. This is the case for aspects of ocean observing systems, where regional and national implementation bodies contribute to the overall global system. It is also likely to be increasingly the case with regard to operational ocean modelling and product preparation, where we will see an eventual cascade of global to regional to local models generating products for different classes of users (c.f. the WWW data processing centres described above). Finally, JCOMM (and GOOS) will, in future, have to be closely associated with, and make use of, the structures and facilities being put in place for the FWIS. In the context of the structures and activities of JCOMM and CBS as described above, it is clear that EuroGOOS can and should make major contributions. While such contributions may be expected to expand in the future, currently they include areas such as: 1. Regional contributions to a global operational ocean observing system: -

moored and drifting buoy networks

-

VOS for surface and upper air meteorology and surface oceanography

-

regional XBT lines, Argo and similar sub-surface oceanography

-

-

regional sea ice monitoring eventually, special regional projects such as ferry-box support to JCOMMOPS, including links to regional metadata and data bases and data products, and possible resource support

2. Regional contributions to global climate studies: -

-

support to the VOS Climate project regional climate modelling and products regional sea ice products

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-

ocean carbon measurements

3. Regional contributions to global operational modelling and services, such as through the operation of ocean basin models and the provision of maritime safety services through the globally coordinated system of WMO. 4. EuroGOOS support to JCOMM regional applications, in fields such as: -

-

-

Marine pollution emergency response support services Regional wave and surge modelling and climate Regional ocean models Natural disaster preparedness and response Sea ice services

At the same time, EuroGOOS can expect to benefit substantially from the global systems coordinated and managed by JCOMM and CBS. It is clear that many/most user interests exist primarily at the national, local or regional level. In this context, EuroGOOS provides the regional bridge whereby data, products, information and services cascade from global through regional and national organisations to the user. Thus EuroGOOS will benefit directly from the contribution of the global observing systems to regional and national data requirements. In addition, the global systems will model output and data products suitable for regional applications, while the global models themselves provide boundary conditions necessary for the operational of regional and local models. Finally, there will also be direct regional user benefits in areas such as transport and fisheries, while the JCOMMOPS facility provides a valuable resource to EuroGOOS in terms of information, metadata and technical support to regional observing system operators and products and services centres.

7. Conclusions JCOMM has been designed and is being implemented to provide a global framework for the intergovernmental coordination, management and regulation of operational marine meteorology and oceanography. In addition to its traditional responsibilities in areas such as maritime safety services, its role includes that of the implementation mechanism for a large part of global GOOS, including in particular the common GOOS/GCOS global climate module. JCOMM has an organisational structure similar to that of the Commission for Basic Systems in its coordination of the World Weather Watch. This includes, in particular, a structuring of observing and data processing systems at global, regional and national levels. Such a structuring both necessitates and benefits greatly from a close interaction with existing regional structures and bodies, including the GOOS Regional Alliances and in particular EuroGOOS. In this context, EuroGOOS can and should provide a critical input to the work of JCOMM in a number of areas, such as through: 9 regional contributions to global observing networks (buoys, volunteer ships for meteorology and ocean physics, chemistry and biology, upper air observations, research ships, Argo floats, etc.)


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9 the establishment and operation of both regional and global data management networks and centres 9 regional modelling, ocean, interface and lower atmosphere 9 the development of a range of regional products and services, to serve national, regional and some global requirements. At the same time, EuroGOOS will benefit from the work of JCOMM in various ways, including: 9 Access to data and products delivered by the global networks, which also provide boundary conditions for the regional networks operated under EuroGOOS 9 Access to the metadata and services provided through the work of JCOMMOPS.

References Integrating and modernizing global ocean data and services for the benefit of the maritime communitymthe Joint WMO/IOC Technical Commission for Oceanography and Marine Meteorology. WMO and IOC, April 2003. Twenty-first status report on the implementation of the World Weather Watch, WMO no. 957, March 2003.

The Global Ocean Observing System" design and implementation of the coastal module Thomas C. Malone

Horn Point Laboratory, University of Maryland Center for Environmental Science, USA

Abstract The combined effects of climate change, extreme weather and human alterations of the environment are especially pronounced in the coastal zone where people and ecosystem goods and services are most concentrated and inputs of energy and matter from land, sea and air converge. These realities call for a more integrated and adaptive approach to resource management, environmental protection, coastal zone management, coastal engineering, an approach that considers the effects of both human activities and natural variability change in an ecosystem context. Implementing such an approach requires the capability to routinely and rapidly detect and predict changes in the state of the coastal environment. Developing these capabilities is the purpose of the coastal module of GOOS, which is the subject of this presentation.

Keywords: Coastal, monitoring, GOOS, GOOS Regional Alliances 1. Introduction The combined effects of natural hazards, human activities, and climate change are and will continue to be most pronounced in the coastal zone where people and ecosystem goods and services are most concentrated and inputs of energy and matter from land, sea and air converge. Large scale drivers of change (Table 1) that impact social, economic, and ecological systems of the coastal zone include: 1. basin scale changes in the oceans (e.g. the E1 Nifio-Southern Oscillation, the Pacific Decadal Oscillation, and the North Atlantic Oscillation) 2. human alterations of the environment (changing inputs of water, sediments, nutrients, and contaminants from land-based sources; inputs of human pathogens; introductions of non-native species; and extraction of marine resources) 3. extreme weather (e.g., tropical storms) 4. global climate change (global warming, sea level rise, and changes in the hydrological cycle). Associated with these drivers of change there is a variety of phenomena of interest (Table 1) in the coastal marine ecosystem that a coastal observing system needs to consider. Although rapid detection and timely prediction of each phenomenon have unique requirements for data and information, they have many data needs in common. Likewise, the requirements for data communications and management are similar. These realities are the basis for developing the coastal module of GOOS. Consequent changes in coastal marine and estuarine ecosystems affect public health and well being, the safety * Email: [email protected]

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and efficiency of marine operations, ecosystem health, and the sustainability of living marine resources. Table 1 Drivers of change (natural and anthropogenic forcings) and associated phenomena of interest in coastal marine ecosystems that are the subject of the coastal module of GOOS. Although rapid detection and timely prediction of each phenomenon have unique requirements for data and information, they have many data needs in common. Likewise, the requirements for data communications and management are similar. These realities are the basis for developing the coastal module of GOOS FORCINGS

Natural

9Global warming & sea level rise 9Storms & other extreme weather events 9Seismic events 9Ocean scale currents 9Waves, tides & storm surges 9 River & ground water discharges

Anthropogenic

9 Physical restructuring of the environment 9Alteration of the hydrological cycle 9Harvesting living & nonliving resources 9Alteration of nutrient cycles 9Sediment inputs 9Chemical contamination 9 Inputs of human pathogens 9Introductions of non-native species

Marine Services, Natural Hazards & Public Safety

9Fluctuations in sea level 9Changes in sea state 9Changes in surface & sub-surface currents 9Coastal flooding events 9Changes in shoreline & shallow water bathymetry

Public Health

9Seafood contamination 9 Increasing abundance of pathogens (in water, shellfish)

Ecosystem Health

9Habitat modification & loss 9Changes in biodiversity 9Eutrophication 9Changes in water clarity 9Harmful algal events 9Invasive species 9Biological affects of chemical contaminants 9Disease & mass mortalities of marine organisms 9Chemical contamination of the environment

Living R e s o u r c e s

9Abundance of exploitable living marine resources 9Harvest of capture fisheries 9Aquaculture harvest

PHENOMENA OF INTEREST

These realities call for a more integrated, ecosystem-based approach to resource management, environmental protection, coastal zone management and coastal engineering, i.e. the implementation of an integrated coastal management strategy that considers the effects of both environmental variability and of human activities. Implementing such an ecosystem-based strategy depends on the capability to engage in

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adaptive management, a process that requires routine and rapid detection of changes in the condition of coastal ecosystems, their causes, and their consequences. As indicated by recent attempts to quantify the condition of the world's ecosystems, (e.g. www.wri.org/wr2000/coast page.html, www.heinzctr.org/ecosystems), we do not have this capability today. The current mode of fisheries management exemplifies these problems, i.e., fisheries scientists advise managers based on annual stock assessments and fisheries managers weigh this advice against their socio-political implications to set quotas for the upcoming fishing season. For the most part, science loses out in this process, in part because of two chronic problems that inhibit the development of ecosystem-based fisheries management: 1. the lack of long, high resolution time series and spatially synoptic observations lead to large uncertainties in stock assessments that undermine the credibility of scientific advice, especially when it calls for reductions in catch limits 2. the time taken to make measurements, access and analyse the required data is much too long. The rates of data acquisition and analysis are not well tuned to the time scales on which decisions should be made for the purposes of adaptive management. This puts managers in a difficult position where politics usually wins the day. These problems are magnified when considering the relations between the marine sciences, environmental protection, and fisheries management. In this arena, linkages between science and management are even more uncertain and are rarely institutionalised.

2. The solution A new approach is needed that enables adaptive management through routine, sustained and rapid provision of data and information over the broad spectrum of timespace scales required to link ecosystem scale changes to regional and global scale drivers of change (hours-decades). We are, in fact, on the cusp of a revolution that is making such an approach feasible. The revolution is occurring on two related fronts: 1. advances in environmental sensing and modelling capabilities 2. the emergence of operational oceanography in the form of the Global Ocean Observing System (GOOS). I focus on the latter here. Under the oversight of the GOOS sponsors (IOC, UNEP, WMO, ICSU, FAO), the observing system is being organised in two related and convergent modules: 1. the global ocean module being developed by the Ocean Observations Panel for Climate 2. the coastal module being developed by the Coastal Ocean Observations Panel. The former is primarily concerned with changes in and the effects of the ocean-climate system on physical processes & the global carbon budget. The latter is primarily concerned with the effects of climate and human activities on coastal ecosystems and socio-economic systems of coastal nations.

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The purpose GOOS is to continuously provide data and data-products in forms and at rates required to achieve six goals: 9 improve the safety and efficiency of marine operations 9 mitigate the effects of natural hazards on coastal communities and ecosystems more effectively 9 improve predictions of climate changes and their effects on coastal communities and ecosystems 9 minimise public health risks 9 more effectively protect and restore healthy coastal marine ecosystems 9 sustain living marine resources. Achieving these goals depends on more rapid detection and timely prediction of changes in the marine environment than are currently realised. Clearly, this is a formidable task. However, although each goal has unique requirements for data and information, they have many data needs in common. Likewise, the requirements for data communications management are similar across all six goals. Thus, an integrated approach to the development of a multi-use observing system is both sensible and cost-effective. Monitoring and modelling are mutually dependent, and linking the two processes for more rapid detection and timely prediction requires a managed, two-way flow of data and information among three essential subsystems: 1. an observing (measurement) subsystem for monitoring required variables on specified time and space scales 2. a data communications and management subsystem for serving and archiving data of known quality in real-time or delayed mode as needed 3. a modelling subsystem for data assimilation and analysis. With the exception of marine operations and to some extent natural hazards, such an "end-to-end" system that is routine and sustained is a new concept for both the environmental science and management communities. The World Weather Watch (WWW) provides a model of the kind of operational, end-toend system GOOS is envisioned to be. The first national weather service with a permanent observing network was established in France during the mid-1800s. Its primary purpose was to forecast the weather for farmers. By the early 1900s, a real-time global observing system was in place that consisted of a sparse network of unevenly spaced land-based monitoring sites; by the late 1960s, satellite-borne radiometers were providing global coverage; and by the mid-1990s, an upper air observing network was in place. Today, the global atmospheric observing system of the World Weather Watch (WWW) serves many user groups routinely, including meteorologists and other scientists. In this model, there is a synergy between meteorological research and weather forecasting in which the WWW observing system supplies and manages the data required for numerical weather predictions (NWP) and meteorologists both benefit from the data streams required for weather forecasting and enable improved forecasting skill. This arrangement not only sustains the integrity of meteorological research, it strengthens it.

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GOOS is intended to perform a function similar to that of the WWW. It is envisioned as a network of national, regional, and global systems that rapidly and systematically acquire and disseminate marine data and data products to serve the needs of many user groups including government agencies, private enterprise, scientists, educators, NGOs and the public that are responsible for or use marine goods and services. Just as weather forecasts are not possible without sustained observations and operational models, implementation of an ecosystem-based approach will not be possible without operational models of marine ecosystems and the uninterrupted provision of oceanographic data required to initialise and up-date model runs. The development of GOOS is not only required to implement adaptive management practices, it will enable advances in marine science and help to maintain the integrity of the scientific method in the face of the growing demand to be more "relevant." The W W W is a good model for an operational system that routinely provides weather forecasts and promotes advances in the science of meteorology. However, it is not an integrated system in that it is not multidisciplinary. The WWW has a singular purpose that depends on a relatively small set of data streams for variables that are relatively easy to measure. Today, programmes that are well integrated in terms of synoptic measurements of physical, chemical, and biological variables are, for the most part, limited to research projects that are finite in duration and are not routine by their very nature. The target of operational oceanography is the development of an observing system for marine ecosystems that is routine, sustained and integrated. To achieve these goals, the movement to establish GOOS is an attempt to network and enhance existing programmes for: 1. more cost-effective use of existing knowledge and infrastructure 2. more rapid detection and timely prediction of changes 3. more rapid access to diverse data from many sources 4. more effective use of environmental data and information. It is an effort that, if successful, will not only significantly increase the value of environmental research, it will enable more integrated approaches to achieving the related goals of environmental protection, resource management, coastal zone management, coastal engineering and marine research.

3. Conceptual design of the coastal module The design plan for the coastal module must consider many factors. These include the need to address a broad diversity of phenomena encompassed by the 6 goals; the fact that the phenomena of interest are globally ubiquitous and tend to be local expressions of regional and global scale processes; and ecosystem theory which posits that the phenomena of interest are related through a hierarchy of interactions that can be modelled. The design must also take into consideration certain important realities: priorities vary among nations and regions; many of the elements required to build the observing system are already in place; those elements of the observing system required to improve marine services and forecast natural hazards are most developed while those required for ecosystem-based environmental protections and management of living

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resources are least developed; and most nations do not have the capacity to contribute to and benefit from GOOS at this time. These considerations have important consequences for the design of the coastal module: 9 the design must respect the fact that priorities vary among regions and should leave system design on the regional scale to stakeholders in the regions 9 regional bodies provide the most effective venue for specifying user group requirements for environmental data and information 9 economies of scale can be achieved by establishing a global system that measures variables and manages data streams required by most regions 9 the global coastal module will come into being through a combination of national, regional and global processes 9 the system can be implemented by selectively linking existing elements and can be developed by enhancing and complementing these elements over time 9 high priority must be placed on capacity building in developing countries; on the establishment of the data communications and management infrastructure; and on marine research to develop the sensors and models required to achieve those goals that require biological and chemical data. Clearly, the coastal module must include both global and regional scale components. This can best be achieved through the establishment of a Global Federation of Regional Systems in which regional observing systems are nested in a Global Coastal Network. The latter establishes a network of reference and sentinel stations; develops international standards and protocols for measurements, data exchange and management; and measures and processes a small set of common variables that are required by most, if not all, regional systems. This provides economies of scale and improves the cost-effectiveness of regional systems by minimising redundancy and optimising data and information exchange. GOOS Regional Alliances (GRAs), guided by national and regional priorities, develop regional system for the provision of data and data-products that are tailored to the requirements of user groups in the region. This will be achieved through national and regional enhancements, i.e., more variables are measured with greater timespace resolution as dictated by national and regional priorities. In this way, GRAs both contribute to and benefit from the global network. It must be emphasised that the global network will not, by itself, provide all of the data and information required to detect or predict changes in the phenomena of interest. There are categories of variables that are important globally, but the variables measured and the time-space scales of measurement change from region to region. These include variables in the categories of fish stock assessments; distribution and condition of essential fish habitats such as sea grasses, kelp beds, tidal wetlands and coral reefs; distribution and abundance of large organisms such as turtles, marine mammals, and seabirds; invasive species; harmful algae; and chemical contaminants. For these categories, decisions concerning exactly what variables to measure, the time-space scales of measurement, and the mix of observing techniques are best made by stakeholders in the regions affected. Thus, regional observing systems are critical building blocks of the coastal module of GOOS, especially for achieving the goals of sustaining and restoring healthy marine ecosystems living marine resources.

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4. Selecting the common variables Given these considerations, an objective process is needed to select the common variables to be measured as part of the global coastal system. In brief, the global network must measure and manage a set of common variables that are required by most regions to achieve the six goals. The objective is to select the minimum number of variables required to detect or predict changes that are important to a maximum number of users. The process begins with the compilation of a comprehensive list of variables. The variables for detection are then ranked based on the number of phenomena they are relevant to and the number of user groups that are likely to benefit. A similar analysis is done for variables required for prediction based on model requirements. The analysis yielded a provisional list of variables that are recommended for incorporation into the global coastal network: 9 physical variables~temperature, salinity, sea level, vector currents, surface waves spectra, shallow water bathymetry, and shoreline position 9 chemical variables~sediment grain size and organic content, dissolved inorganic nutrients, and dissolved oxygen 9 biological variables~chlorophyll-a, attenuation of downwelling radiation, benthic biomass, and faecal bacteria.

5. Linking observations to models The data management and communications subsystem is the "life-blood" of the observing system and the primary means by which an integrated system will emerge. Thus, the development of an integrated data management and communications subsystem is arguably the highest priority for implementation. Under current conditions, data are often not exchanged freely among nations and, even when data are not proprietary, data management and analysis tend to be programme-specific and analyses that require multi-disciplinary data from many sources take far too much time. The goal is to establish an integrated data management subsystem that serves data in both real-time and delayed mode and allows users to exploit multiple data sets from many different sources through "one-stop-shopping". The integrated plan is based on a hierarchical, distributed network of local-, regional- and global-scale data management activities that build on, link and enhance existing data management centres and programmes.

6. Building the coastal module The development of both the global ocean and coastal components of GOOS are critically dependent on selectively and effectively linking, enhancing and supplementing existing programmes. Although some elements of the system will be global in scale from the beginning (e.g., GLOSS, observations from space), national and regional coastal observing systems will be the building blocks of the global coastal network. GOOS Regional Alliances (GRAs) are planning and implementing regional observing systems that will become the building blocks of the coastal module of GOOS. GRAs are expected to be formed through coalitions among national and regional GOOS programmes, Regional Seas Conventions, Regional Fishery Bodies, Large Marine Ecosystem Programmes, and other bodies and programmes as appropriate.

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In this context, implementing the coastal module will require an unprecedented level of cooperation, coordination and collaboration among nations and existing programmes to ensure the emergence of a global network as national and regional systems come on-line. A critical aspect of this process will involve harmonising the need for global coordination with user needs based on national and regional priorities. At present, there is no formal international mechanism in place to promote and guide this process. An intergovernmental commission, such as the Joint Technical Commission on Oceanography and Marine Meteorology (JCOMM with the appropriate advisory bodies), will be needed to facilitate multi-lateral agreements and to address legal issues that will arise from implementation of UNCLOS and other international conventions. The "Integrated Design Plan for the Coastal Module of GOOS" has recently been completed by the COOP and can be found at http://ioc.unesco.org/goos/gsc6/COOPDesign-Plan.doc.

Acknowledgements This work represents the work of the COOP and invited experts including Dagoberto Arcos, Bodo von Bodungen, Alfonso Botello, Robert Bowen, Lauro Julio Calliari, John Cullen, Michael Depledge, Eric Dewailly, Michael J. Fogarty, Juliusz Gajewski, Johannes Guddal, Julie Anne Hall, Hiroshi Kawamura, Anthony Knap (Co-Chair), Coleen Moloney, Nadia Pinardi, Hillel Shuval, Vladimir Smirnov, Keith R. Thompson, MVM Wafar, Rudolph Wu, Robert R. Christian, Chris Crossland, Savithri Narayanan, Worth Nowlin, Shubha Sathyendranath, and Neville Smith.

High-resolution wind fields from synthetic aperture radars and numerical models for offshore wind farming S. Lehner* 1, j. Horstmann 2, and C. Hasager 3

1DeutschesZentrumfar Lufi und Raumfahrt, Germany 2GKSS Research Center, Germany 3RISOE, Germany Abstract All European countries with shallow coastal waters and strong mean wind speed at the coast have begun planning and construction of offshore wind farms, and large parts of the North Sea and the Baltic are under investigation as to whether they are suitable for offshore parks. This paper demonstrates how satellite images taken by spaceborne radar sensors can be used to determine mesoscale wind fields and thus help in the task of planning offshore wind farms. High resolution SAR (Synthetic Aperture Radar) images acquired by the European remote sensing satellite E R S - 2 are presented which show single wind turbines. The derivation of high resolution wind fields from SAR images is explained and comparisons with numerical models are presented.

Key words: Wind energy, wind farms, synthetic aperture radar, high resolution wind field retrieval, turbulence 1. Introduction Data from active microwave radar satellites transmit and receive radar signals with wavelengths in the range of centimetres to one metre and measure the roughness of the sea surface, which allows retrieval of wind or ocean wave fields. The SAR aboard the European remote sensing satellites ERS-1 and E R S - 2 and the Canadian satellite RADARSAT-1 operate in the C-band (5.3GHz) at moderate incidence angles between 20 ~ and 50 ~. For this electromagnetic wavelength and range of incidence angles the backscatter of the ocean surface is primarily associated with the small-scale surface roughness, which is strongly influenced by the local wind field and therefore allows the backscatter to be a measure of wind. As the radar signals penetrate clouds these sensors have all weather capability and can acquire data both during the day and night and are therefore especially suited to sea surface observations during severe weather conditions. Since the launch of the European remote sensing satellites ERS-1 and ERS-2, and the Canadian satellite RADARSAT-1, synthetic aperture radar (SAR) images have been acquired over the oceans on a continuous basis. Their high resolution and large spatial coverage make them a valuable tool for measuring wind fields, especially in coastal areas. In the past few years much effort has been undertaken to develop algorithms for the derivation of wind vectors from SAR images. The wind direction can be retrieved from * Corresponding author, email: [email protected]

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the direction of wind-induced streaks visible in most SAR images, which are approximately in line with the mean wind direction. The direction of these streaks can either be retrieved by using spectral methods (Lehner et al., 1998), or in the spatial domain by a method based on local gradients (Koch, 2000). The wind speed is derived from the normalised radar cross section (NRCS) measured by the SAR using semi empirical Cband models for vertical (VV) polarisation (Lehner et al., 1998 and Horstmann et al., 2000a). These models have been extended to horizontal (HH) polarisation (Horstmann et al., 2000b and Horstmann et al., 2002) and allow retrieval of wind fields of an area of up to 500kmx 500km with a resolution of up to 200m. These high-resolution wind fields give the unique opportunity to improve numerical models especially in coastal regions with complicated land topography.

2. Offshore wind farms The last few years have seen rapid development of wind energy production in Europe with 23056MW installed by the end of the year 2002. The offshore power installed up to date is about 280MW. In the near future many projects and plans for wind farms with an output of more than 5000MW are being made. Some of these projects are already under construction. Currently, wind farms with over 100 single wind turbines and an area of more than 200km 2 are planned or under construction. The E R S - 2 SAR scene in Figure 1 shows the Horns Rev offshore wind park, which is situated 15km off the Danish west coast at Blavands Huk. At the moment of the data acquisition on July 30, 2002 it was still under construction. At the final stage, there will be about 80 single wind turbines installed with 70m hub height and rotor diameters of over 80m.

Figure 1 ERS-2 SAR scene from the Danish offshore wind park Horns Rev, Denmark, acquired on July 30, 2002 (30x50km) The short- and long-term effects of these parks on the environment are not yet well understood. One important parameter that can be observed with spaceborne SAR is the high-resolution wind field in the vicinity of the wind parks. The SAR allows investigation of changes of the high-resolution wind field due to the wind turbines e.g. turbulent wakes, as well as ocean surface wave fields. During the planning stage of wind farms

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High-resolution wind fields from synthetic aperture radars and numerical models for offshore wind farming

SAR retrieved wind fields will help to solve issues like optimal siting or perfect positioning of the turbines within the wind farm. For the first point it is helpful to have a long record of historic data, providing information on the wind and wave parameters relevant for wind farms. Relevant geophysical parameters are mean wind speed and direction, wind variability and turbulences. Using synthetic aperture radar data from satellites will offer the following opportunities: a synoptic overview of the wind field, using in situ measurements to improve the algorithms, improve mesoscale models, increase the awareness of wind farmers to use remote sensing data at the planning stage, estimate the effects of wind farms on the environment, optimise the output and minimise the impact on environmental conditions. While many studies are on the way, concerned either with improving the technology of wind turbines, e.g. constructing larger and more powerful ones for offshore use, or with environmental issues like the effect on birds and fishery, no joint studies have been undertaken on how this new technology can be assisted by operational forecasting. Similar experience exists in respect to offshore oil industry. Another research aspect is to investigate how these large wind farms alter the wind field and the related processes themselves.

3. Utilised SAR data and numerical models The European remote sensing satellites ERS-1 and ERS-2, and the Canadian satellite RADARSAT-1 are positioned in a near-circular, polar and sun-synchronous orbit at an altitude of ~790km. In the image mode ERS provides well-calibrated high-resolution images of the earth' s surface in a range of incidence angles, between 20 ~ and 26 ~ perpendicular to flight direction corresponding to a coverage of 100kmx 100km. The spatial resolution is 26m in range (antenna look direction) and 6 to 30m in an azimuthal direction (flight direction). In contrast to ERS, RADARSAT-1 is capable of acquiring images in the ScanSAR mode with incidence angles between 20 ~ and 49 ~ Each processed image covers an area of approximately 500kmx500km with a pixel size of 50m. The resolution of the four beams varies from 86.5 to 146.8m in range and from 93.1 to 117.5 m in azimuth. The numerical model GESIMA is a 3-dimensional non-hydrostatic mesoscale model of the atmospheric circulation which was developed at the GKSS Research Center (Horstmann et al., 2002) and already applied for wind farming purposes (Lehner et al., 1998). GESIMA operates with terrain-following coordinates and the lower boundary conditions for the friction velocity over the sea are given by Charnock's relation, while over land a variable bottom stress is taken into account according to land-use charts from the German weather service. For the examples in Figure 4 the model was set up with a horizontal spatial resolution of l kmx l km in the area around Rtigen and with a resolution of 2km• in the Odra Lagoon both located at the German coast of the Baltic Sea. Both areas were adjusted to include the important topographical structures in the surroundings of the lagoons. The 3-dimensional grid of GESIMA has 13 layers between heights of 0 and 1700m. Wind output of the second layer represents the wind at 10m height. The time step of the model was chosen to be between 4 and 10s according to the geostrophical wind velocities used as the upper boundary condition of the model.


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In order to compute the vertical wind velocity profile as additional boundary information to the GESIMA model, a one-dimensional model was run in a pre-processing step that assumes stable atmospheric conditions.

4. Methods for SAR wind retrieval SAR wind field retrieval is a two-step process. In the first step wind directions are retrieved which are a necessary input into the second step to retrieve wind speeds. The wind directions are retrieved from wind-induced phenomena aligned in wind direction, which are visible in most investigated SAR images. Their imaging by SAR is caused by sources such as boundary layer rolls, Langmuir cells and wind shadowing. The orientation of these streak-like features is approximately in direction of the mean surface wind. The method used hereafter, defines the wind direction as normal to the local gradients derived from smoothed amplitude images. Therefore the SAR images are smoothed and reduced to an appropriate pixel size, e.g., 100m, 200m, and 400m. From these pixels the local directions, defined by the normal to the local gradient is computed leaving an 180 ~ ambiguity. From the resulting directions the most frequent and most probable local direction is selected using additional assumptions about the wind flux pattern. The 180 ~ ambiguity can be removed if wind shadowing is present, which is often visible in the lee of coastlines and large objects such as offshore structures. For retrieving wind speeds from SAR data a model function relating the NRCS (normalized radar cross section) of the ocean surface ~Y0to the local near-surface wind speed u, wind direction versus antenna look direction 9 and incidence angle 0, o'~0~ = a(0)uV(~

+ b ( 0 ) c o s ~ + c(0)2cosO]

(1)

is applied. Here a, b, c and y are coefficients that in general depend on radar frequency and polarization pol. These coefficients were determined empirically in the case of the model functions CMOD4 by evaluation of scatterometer (SCAT) data of the European satellite ERS-1 and wind fields from the ECMWF (Stoffelen and Andersen, 1997). The CMOD4 has been applied successfully for wind speed retrieval from C-band VV polarized ERS-1 and ERS-2 SAR images (Lehner et al., 1998 and Horstman et al., 2000a). For wind speed retrieval from C-band HH polarized SAR images no similar well developed model exists so that a hybrid model function is applied that consists of the VV HH CMOD4 and a C-band polarization ratio P R = o 0 /G o (Horstmann et al., 2002 and VV HH Horstmann et al., 2000b), where o 0 and o 0 are the VV- and HH-polarized NRCS, respectively. The polarization ratio considered in the hybrid model function neglects the wind speed and wind direction dependency. The hybrid model function showed good results when applied for RADARSAT-1 ScanSAR wind retrieval (Horstmann et al., 2002).

5. Comparison of wind fields from SAR to numerical models A first comparison was carried out using a RADARSAT-1 ScanSAR image of the southern North Sea acquired on September 27, 1996 (Figure 2). The grey levels in the

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ScanSAR image are proportional to the roughness of the ocean surface and therefore to the wind speed. The ScanSAR retrieved wind field (white arrows) was compared to results of the mesoscale atmospheric model REMO (black arrows). The wind vectors represent the mean wind in an area of approximately 45kmx 45km. In the open waters (50km off the coast) SAR and model wind speeds agree well, however, near to the coast especially of the southern North Sea wind speeds from REMO are significantly overestimated. This is due to the too coarse resolution of REMO, which cannot deal with the temperature gradients between land and water with sufficient accuracy to resolve the land sea breeze. It is the near coastal area that is of greatest interest for the offshore wind farming and therefore the region where models have to be improved.

Figure 2 RADARSAT-1 ScanSAR image of the southem North Sea, acquired on September 27, 1996. Wind vectors retrieved from the ScanSAR (white arrows) and from REMO (black arrows). 9 International In a further example high-resolution wind fields from E R S - 1 SAR images and from the GESIMA model are compared to each other. The first comparison is in the area of the island of RiJgen in the Baltic Sea, and the second in the area of the Odra lagoon and Pomeranian Bay of the Baltic Sea. These areas were chosen because of their complex wind field structure resulting from the topography. To retrieve the wind direction, each ERS SAR image was analysed for wind-induced streaks and wind speeds using the method as described in the section before. The mean wind direction resulting from the SAR images around Rtigen was ~290 ~ and in the Odra estuary ~300 ~ These wind directions were taken as input to the C-band model to retrieve the wind speed over the water surface.


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Figure 3 Map of the German and Polish coast of the Baltic Sea with superimposed ERS-1 SAR images from August 12, 1991, at 21:07 UTC and from June 18, 1995, at 21:04 UTC

Figure 4 Comparison of wind speeds from SAR to GESIMA results

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On the left hand side of Figure 4 the resulting isotaches are given for both areas. For the Rtigen area, the upper boundary condition at 1700m height assumed a stationary wind from 306 ~ at 16ms -1, and in the case of the Odra estuary, the wind measurements at Ueckermtinde station were taken into account. The results of the GESIMA model for each area are plotted on the right hand side of Figure 4. Wind directions and relative changes in wind speed due to shadowing effects, particularly behind the white cliffs of Rtigen (northeastern tip of Rtigen), these shadowing effects are also visible in the Odra estuary. The magnitude of wind speeds agree overall quite well too. Wind-shadowing effects show the same order of magnitude in both cases. Behind the island of Rtigen, wind speed drops down to about 6 ms -1 before picking up again toward the open water. Comparison also shows differences in the geographical location of the wind-shadowing effects and a much smoother solution from GESIMA. The model seems to overestimate the effect of wind shadowing, which could be due to uncertainties in the land-use charts. Furthermore, SAR-retrieved wind fields show a much finer detail of the wind structure and a much higher variability of the wind.

Acknowledgement:

The authors were supported by the German Bundesministerium for Bildung und Forschung (BMBF) in the framework of the project ENVOC. All RADARSAT-1 ScanSAR data were kindly made available by RADARSAT International, the ERS SAR data by an ESA AO.

References Horstmann, J., W. Koch, S. Lehner and R. Tonboe, 2000, Computation of wind vectors over the ocean using space-borne synthetic aperture radar, John Hopkins APL Tech. Dig., vol. 21/1, pp. 100-107. Horstmann, J., W. Koch, S. Lehner and R. Tonboe, 2002, Ocean Winds from RADARSAT-1 ScanSAR, Canadian Journal of remote Sensing, Vol. 28, No 3, pp. 524-533 Horstmann, J., W. Koch, S. Lehner and R. Tonboe, 2000, Wind retrieval over the ocean using synthetic aperture radar with C-Band HH polarization, IEEE Trans. Geosci. Remote Sens., vol. 38/5, pp 2122-2133 Kaptza, H. and D.P. Eppel, 1992, The non-hydrostatic mesoscale model GESIMA, I. Dynamical. equations and test, Contrib. Atmos. Phys., vo165/2, pp 129-146 Koch, W. 2000, Semiautomatic assignment of high resolution wind directions in SAR images, in proceedings of OCEANS 2000, Providence, Rhode Island, USA, vol. 3, pp. 1775-1782 Lehner, S., J. Horstmann, W. Koch and W. Rosenthal, 1998, Mesoscale wind measurements using recalibrated ERS SAR images, J. Geophys. Res., vol. 103, pp. 78477856 Lehner, S., J. Schulz-Stellenfleth, B. Sch~ittler, H. Breit and H. Horstmann, 2000, Wind and Wave measurements using complex ERS-2 wave mode data, TGARSS, vol. 38, no. 5, pp 2246-2257


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Lehner, S., J. Schulz-Stellenfleth, and J. Horstmann, 2001, Marine parameters from radar satellite data, Archive of Hydro-Engineering and Environmental Mechanics, Vol. 48, no 2, pp 17-29. Stoffelen, A. and D. Anderson, 1997, Scatterometer data interpretation: Estimation and validation of the transfer function CMOD4, J. Geophys. Res., vol. 102, pp. 57675780.


EuroGOOS Task Teams


Towards N O O S - - T h e EuroGOOS NW Shelf Task Team 1 9 9 6 - 2 0 0 2 Martin Holt (Chair, NOOS Steering Group) Met Office, UK

Abstract EuroGOOS initiated the NW European Shelf Seas Task Team in 1996. With members from operational agencies around the NW shelf the team set about an initial inventory of models and observations, and project proposals were formulated for MAST III and later FP5. In 1998 the EC-funded concerted action ESODAE (European Shelf Seas Ocean Data Assimilation Experiment) was initiated. During 2000-2001 a strategic plan for the NW Shelf Operational Oceanographic System was drafted and published, and the NOOS memorandum of understanding was finalised. In 2002, with eight agencies signed up to the MoU, NOOS was established at a meeting held at RIKZ. This paper gives an overview of the activities of the task team and the plans for NOOS.

Keywords: NW European Shelf, operational modelling, marine ecosystem, data assimilation.

oceanography,

ocean

forecast

1. The NW Shelf Task Team EuroGOOS initiated the NW European Shelf Seas Task Team in 1996. With members from operational agencies around the NW shelf, and chaired by Howard Cattle, the team set about an initial inventory of models and observations, and project proposals were formulated for MAST III and later FP5. In 1998 the EC-funded concerted action ESODAE (European Shelf Seas Ocean Data Assimilation Experiment) was initiated, with the first meeting in February 1999. ESODAE went on to host three workshops. The first of these, held in January 2000, examined data assimilation and shelf seas modelling, and the second workshop brought together modellers and the users of forecast data. ESODAE was also a co-sponsor of the EuroGOOS space workshop held in 2001. The "ESODAE Plan" was prepared, reviewing the available data, models and data assimilation techniques for the NW Shelf. One conclusion was that before a Shelf Seas Ocean Data Assimilation Experiment could be carried out, the infrastructure (boundary forcing for nested models, surface forcing) for 3D circulation modelling of the NW shelf required development. Also the data available for assimilation were sparse, and assimilation techniques for models of tidal waters were in their infancy, except for the use of tide gauge water level data in a storm surge model. The development of data assimilation schemes for 3D models of the strongly tidal waters of the NW shelf was addressed in the ESODAE phase 2 project proposal to the European Commission Framework Program 5, which unfortunately was not selected for funding.

* Corresponding author, email: [email protected], www.noos.cc 9 British Crown Copyright

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Towards NOOS--The EuroGOOS NW Shelf Task Team 1996-2002

Through discussions during ESODAE meetings, a daily routine exchange of modelled storm surge forecast data for selected locations (see Figure 1) around the North Sea coast was set up, starting in December 1999.

Figure 1 Locations around the North Sea used in the storm surge forecast exchange This daily exchange of data by ftp-boxes continues, and is used for example by the Danish Meteorological Institute to construct an ensemble of storm surge predictions. By having available the results from several independent storm surge models, each run with different meteorological surface forcing, the forecaster can gauge the reliability of a particular storm surge forecast.

2. NOOS Under the chairmanship of Leen Droppert, during 2000 and 2001 the task team began to formulate a plan for a NW shelf Operational Oceanographic System "NOOS", closely modelled on the successful BOOS activity in the Baltic. The NOOS Strategic Plan was published in November 2001. The task team continued to draft a Memorandum of Understanding for NOOS, which was finalised by summer 2002. The goals of NOOS are outlined in the Strategic Plan. They are: 9 To give a reliable description of the actual marine condition of the NWS area including physical, sedimentological and ecosystem variables 9 To develop and implement on-line marine data and information services 9 To provide analysis, forecasts and model-based products describing the marine conditions 9 To establish a marine database from which time-series and statistical analyses can be obtained including trends and changes in the marine environment and the economic, environmental and social impacts To do this, NOOS partners will collaborate with national and multi-national agencies in the NWS area to maximise the efficiency of the ocean observing system, and to maximise the value of the information products. NOOS will maintain and strengthen

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working relations with other European agencies and bodies concerned with the NW European shelf seas and will consult user communities on a routine basis to improve products and services, and to introduce new components into the observing system. The strategic principles under which NOOS is established are that NOOS will make formal agreements and commitments between participating agencies to create networks of existing systems and services delivering operational ocean data products, and NOOS will be developed in the context of GOOS, EuroGOOS and Coastal Ocean GOOS. After eight agencies had agreed to sign the MoU, NOOS was initiated with a meeting held at RIKZ, Den Haag in September 2002. A steering group was appointed with membership from BSH, DMI, Met.no, RIKZ, and chaired by the Met Office. A NOOS website (www.noos.cc) is being prepared, hosted by DMI in Copenhagen.

3. NOOS projects and activities It is intended that NOOS will be implemented primarily through self-funded projects, building on the existing daily work of the partners. The first NOOS projects to be jointly agreed are an exchange of data for sea level observations, building on the successful storm surge forecast exchange, and NOOS-ESODAE 1 which will set up an exchange of boundary data for nested modelling of the North Sea coasts. Figure 2 shows a schematic of this, with a ~12km shelf wide model nested into a deep ocean model of the Atlantic, providing boundary forcing to a higher resolution model of the North Sea. This model in turn provides boundary forcing to a very high resolution coastal model. A key point here is the shelf-wide model does not need to be run by every agency wishing to model a part of the North Sea coast. Member agencies of NOOS are also participating in several projects funded by the European Commission under Framework Programme 5, including FerryBox, EDIOS and ODON.

Figure 2 Schematic showing a shelfwide model providing boundary forcing to a nested North Sea model, and then from that to a coastal model

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Towards NOOS--The EuroGOOS NW Shelf Task Team 1996-2002

4. NOOS: Challenges for the future An immediate priority for NOOS is to bring in full membership of the NW shelf coastlines. Already we have a "North Sea OOS", but need to extend this to the full NW shelf. The first steps to achieve this are in hand, through discussion with the relevant national agencies. An Implementation Plan for NOOS needs to be prepared, identifying the necessary work required, and setting out a road-map for completing the work under a range of possible funding scenarios, making full use of national funding. NOOS must achieve increased efficiency in the delivery of existing services, whilst developing the ongoing introduction of new and more challenging services. NOOS is a partner with ICES in planning the ICES-EuroGOOS North Sea Pilot Project (NORSEPP) for nowcast ecosystem modelling. A first step towards this is contained in the MERSEA Strand 1 GMES project, where a demonstration nowcast of coupled physical-ecosystem models for the North Sea and for Norwegian waters will be carried out respectively by the Met Office and by Met.no (Figure 3).

Figure 3 Example from Met.no of predictions of nutrients (bottom row), diatoms and zooplankton, from a coupled physical-ecosystem model of the northern North Sea and Skagerrak NOOS is also preparing proposals for FP6, with input to the MERSEA large integrated project and the European Shelf Seas Integrated Project ESIP. To do all this, NOOS must develop stronger links with service providers and users of oceanographic data on the NW shelf, and also with research centres, particular the proposed "Networks of Excellence" for shelf seas modelling and science. NOOS must also draw on the research and development activities carried out by other EuroGOOS projects such as MFSTEP, which for example will develop the next generation of data assimilation schemes for coastal oceanography.

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The ultimate challenge for NOOS is to establish truly networked "European ocean forecast modelling and monitoring" for the North West European Shelf Seas. This could form one component of a distributed ocean forecast and monitoring system for Europe.

References Droppert, L.J., H. Cattle, J.H. Stel, W.H.A. Behrens, (eds), 2000, "The NOOS Plan: North West Shelf Operational Oceanographic System 2002-2006" EuroGOOS publication No. 18.

Present status of BOOS Baltic Operational Oceanographic System BOOS Steering group: E. Buch* 1, j. Elken 2, j. Gajewski 3, B. Hhkansson 4, K. Kahma 5 and K. Soetje 6

1DMI, Denmark 2Marine Institute, Estonia 3Maritime Institute Gdansk, Poland 4SMHI, Sweden 5FIMR, Finland 6BSH, Germany 1. What is BOOS? BOOS is a formal association of institutes from Sweden, Finland, Russia, Estonia, Latvia, Lithuania, Poland, Germany and Denmark taking national responsibility for operational oceanographic services, which will support the protection of life and properties and the promotion of the development of society. BOOS focuses primarily on observations, analysis and model predictions for water level, waves, currents, temperature, salinity, sea ice, oxygen, nutrients, algae, and chlorophyll, and thereby contributing towards improving efficiency of marine operations, reducing the risk of accidents, optimising the monitoring of the marine environment and climate, improving the assessment of fish stocks and improving the foundation of public marine management.

2. Present activities of BOOS

Figure 1 The production line in generating operational oceanographic services The driving force in operational oceanography is the needs and requests from users for operational oceanographic services and products. It is therefore vitally important to have a good, open and positive running dialogue with all potential users. The most important marine related areas which require operational oceanographic services in the Baltic are:


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9 Shippingmall kinds 9 Navigation in shallow waters and entrances to harbours 9 Rescue operations, drift forecasting 9 Military purposes 9 Storm surge warnings 9 Flood protection 9 Coastal protection 9 Transport calculations of water, substances and passive biological material, e.g. algae and fish eggs 9 Bottom water renewal, oxygenation 9 Environmental protection, impact assessment and management 9 Ecosystem assessment 9 Fisheries planning and management 9 Recreation purposes 9 Public warnings 9 Research BOOS is based on existing national observation programmes, which are already extensive, but can be made more operational. At present the physical oceanographic observations such as water level, temperature, salinity, waves, currents, sea ice are the most operational in the sense that real-time or near real-time data delivery is implemented by most BOOS member organisations. BOOS is therefore in its present stage of development primarily focusing on the production of services related to physical parameters. One of the most important subjects to address in order to create an operational oceanographic system is the establishment of an effective data exchange system. BOOS has built a simple but effective FTP-box system to exchange data (Figure 2). The experience of BOOS is however that the most severe problem in establishing a data exchange system has not been the technical set-up but institutional and national rules and regulation regarding exchange and release of data. All the legal aspects concerning the data exchange, i.e. the right to use data for institutional and commercial purposes, is addressed with great care in the EuroGOOS data policy, which the members have agreed to follow. Despite of this agreement it is still not easy and takes some time for the individual BOOS members to achieve the necessary permission to exchange their data in real-time.

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Present status of BOOS n Baltic Operational Oceanographic System

Figure 2 BOOS data and information exchange using the Internet. Members of the BOOS co-operation exchange data using a system of ftp-boxes. Each member institution puts the data it wants to exchange with the other members in its own ftp box, where it can be collected by the other partners. The system is protected by usernames and passwords Several physical models are run in an operational mode by BOOS members forecasting water level, waves, temperature and salinity fields, currents, transports through sections, sea ice, drift patterns for various substances (oil, chemicals) and objects. Ecological models are in the development phase at several BOOS member institutions and some of these are at present run in a pre-operational mode. Taking into consideration the complexity of the marine ecosystems, including both processes and variability in time and space, it is part of the BOOS strategy that coupled 3-dimensional biogeochemical ocean models should be part of a system for making an integrated assessment of environmental information available. Such a model system will be an important tool for planning and decision, for example within: 9 environmental status in different areas 9 assessment of the spatial influence of polluting activities 9 calculating source apportionment from different areas 9 forecasts of pollutants (spatial and temporal) when the load is changed (floods, reduction scenarios) 9 improved basis for causal relationships, e.g. algae blooms and oxygen deficiencies 9 monitoring and forecasting of the environmental situation Dissemination of the operational products and services is a crucial task, which BOOS handles at different levels. BOOS has its own web page (www.boos.org) where real-time observations of water level and waves are displayed as well as weekly maps of SST. From the BOOS page there are links to the web pages of the BOOS members, where


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additional products can be foundmsuch as prognoses for various parameters displayed either for the entire Baltic Sea or for national waters only. Additionally the individual BOOS members may deliver special products to specific users/customers.

3. Projects BOOS is developed through projects financed by the BOOS members. At present BOOS is working on the following projects: 9 Data exchange and homepage 9 Sea Surface Temperature 9 Transports 9 Waves 9 Optimising Baltic-sea Observational Networks (OBON) 9 Baltic Sea Water Level Project 9 Harmful algae blooms project (HABWARN) 9 Zooplankton On November 1, 2002 the BOOS members formally started the EU-funded thematic network project: "Programme for a BAltic network to assess and upgrade an oPerational observing and forecAsting system in the region - PAPA" PAPA will in a cost-effective manner integrate and further develop the present operational ocean monitoring, data management and modelling activities within the Baltic Sea with the purpose of producing data products and ocean forecasts of a higher quality to the benefit of the users. PAPA is considered by the partners to constitute a valuable advance towards the establishment of an effective operational oceanographic service for the Baltic Sea. The objectives of PAPA are to: 9 Build the basin-wide network for ocean monitoring and forecasting, linking all the Baltic countries, broadening and strengthening the existing network of national institutions already established by PAPA partners

9 Identify the gaps in the monitoring systems in the region and in the capability to measure, model and forecast the ecosystem, taking stock of current RTD projects and of the EuroGOOS and BOOS activities 9 Build capacities for expertise in the setting up and running of observing platforms, in managing data, in modelling and forecasting the ecosystem 9 Design an effective observing and forecasting system, inter-comparing experiences and standardising practices, towards the co-ordinated upgrading of the observing and forecasting capabilities in all Baltic countries 9 Raise awareness on the benefits of ocean forecasting at local, regional and global scales, involving stakeholders and disseminate PAPA results and products.

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Present status of BOOS m Baltic Operational Oceanographic System

4. BOOS and HELCOM Although HELCOM and BOOS work in different time frameworks i.e. HELCOM reporting on Baltic Sea environment back in time and BOOS monitoring the environmental conditions and forecasting the oceanographic conditions a few days ahead, there are obvious intersections of interest that can serve as a platform for cooperation between the two organisations. In the future this platform can be expanded when BOOS becomes more developed and mature. Several BOOS members also carry national responsibilities towards the HELCOM MONAS programme - a fact that can ease the initialisation of cooperation. HELCOM wishes to be able to display the present situation of the Baltic Sea environment and to produce forecasts for given conditions (what-if scenarios). HELCOM regards BOOS as an organisation that can help in this work especially regarding data exchange and data processing, which can be valuable contributions to the HELCOM Indicator Reports. HELCOM has the following expectations from PAPA: 9 Contribute to the HELCOM basin wide observation network 9 Identify gaps in the present monitoring programme 9 Design an optimal observation network 9 Modelling and forecasts 9 Operational production of indicator reports on: -

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Water exchange between the Baltic- and North Sea Stratification in the water Winter nutrient concentration

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Phytoplankton

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Zooplankton

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Against this background it was decided at the October 2002 HELCOM MONAS meeting that HELCOM should take the initiative to invite BOOS to formal negotiations for future cooperation between the two organisations.

5. Conclusions The establishment of an efficient and cost-effective operational oceanographic system for the Baltic Sea has been initiated through a formal cooperation between 15 institutions representing all countries around the Baltic Sea. The observational system is based on existing national and international observations systems, which are gradually made operational, improved with new instrumentation and extended with new stations. A system for real-time exchange of data between the partners has been established. Forecasting of basic physical variables such as water level, current, waves, temperature, salinity and sea ice is done in operational mode by several members of the BOOS community using Baltic scale models as well as local or coastal models.


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Dissemination of products is done via the BOOS homepage www.boos.org and on BOOS member Internet pages.

Figure 3 Examples of products and services generated by BOOS and its member organisations.


Regional Systems 2

Ola M. Johannessen


Review of the last 15 years with the Seawatch system Svein Erling Hansen

OCEANOR ASA, Trondheim, Norway Abstract Seawatch was launched in 1990 under the Eureka/Euromar umbrella. In 1997, when the first EuroGOOS Conference was held, Seawatch-Europe had already become history. The market for integrated systems has been very limited. Today, there are many service and instrument providers that offer their products. Competition has hardened-what are the consequences for the industry. Who is now asking for operational oceanographic services? Are the public institutions still dominant or have private customers finally discovered the benefits? This paper discusses what has happened to Seawatch and gives examples from the Seawatch history demonstrating applications in operational oceanography over the last 10-15 years.

1. Introduction The last 15 years have been revolutionary with respect to ocean forecasting. Previously, few people felt any association with the term operational oceanography. At that time there was not any technology available to allow automatic data transfer from the open ocean. The only applications were buoys located close to the coastline transmitting the data by radio. All processing of data took place at a nearby coastal receiving station. Only a few parameters were measured, very often only waves. With the boost in communication technology over the last 20 years this has completely changed. Communication with remote stations is today possible using radio, telephone or satellite links. Two-way communication permits the user to reconfigure remotely located buoys or fixed platforms even from the opposite side of the globe. New sensors are on the market for measuring physical, biological and chemical parameters. Remote sensing techniques from satellite or aeroplanes provide a large number of applications of ocean measurements such as water temperature, sea surface wind, waves, and phytoplankton. Further capabilities in ocean modelling including models for ocean circulation, waves, pollution transport and biological production have improved dramatically with access to more and more powerful computers. The Internet provides the market place and offers a fast and reliable link to the users. Oceanographers are today technically prepared to offer on-line services comparable to what meteorologists have already been able to offer for many years. The challenge now is to educate the public to understand and to make use of the products that will be offered to them in the field of operational oceanography. There are quite a few examples already, and in particular the oil industry involved with offshore oil and gas production has played an important role. Seawatch was introduced in 1990 as system combining data from buoys in real-time, numerical models and transmission of information to the end-users. Seawatch was Email: svein.hansen @oceanor.no

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developed in Norway as part of the E U R E K A / E U R O M A R programme in the period 1990-1995, with participation from Sweden, Germany, UK and the Netherlands. Over the last 10 years the system has been installed in Thailand, Indonesia, Vietnam, India, Peru, Spain and Greece. This paper gives some highlights from Seawatch systems world-wide, and further discusses the challenges of the years to c o m e m w h a t we have learnt from the last 15 years and what can we expect to come in the future?

2. The start of operational oceanography Based on environmental studies that took place before development of some of the largest oil and gas fields in Norway, strong episodic currents were observed, far from the coast. The strong current was clearly baroclinic and limited to the upper 50-100m. These episodes were normally associated with a drop in salinity and a marked signal in temperature suggesting that the current was related to the coastal current system that follows the Norwegian coastline. This current is generated in Skagerrak, the semienclosed ocean basin between Denmark and Norway. In the beginning of the 1980s AVHRR pictures become available and close studies based on satellite images clearly demonstrated the distribution of this water. It was not like a coastal jet, but often appeared as a large meander with whirls that occupied large ocean areas and could reach some of the oil/gas fields far from the coast. It became clear to the oil companies that this caused large implications for planned operations. The research institutes in Norway were invited to study the mechanisms of the coastal whirls, and were encouraged to develop ocean-forecasting systems to be able to predict the whiffs. It became clear that real time oceanographic data was needed in order to make ocean forecast on currents, waves, etc., and to make available ocean forecasting models for ocean circulation and waves. This was in 1982. At that time there was no experience at all for transmitting data over long distances. Ocean wave buoys could only be deployed close to the coast as all "raw" data had to be transmitted to a receiving station on land for further analytic processing. The challenge was to make systems that could process data internally in the buoys and utilise the new ARGOS facilities for transmitting the data by satellite links. ARGOS again was a set of orbital satellites that was limited in messages and the communication was uni-directional. It was only in 1992, as part of the Seawatch system, that Inmarsat introduced data transmission from a remotely operated buoy. This was a great advantage because Inmarsat allowed much more data to be transferred, and also because it was possible to communicate bi-directionally with the buoys. Seawatch was launched at the EUREKA conference in Rome in 1990 and was presented as the first commercial attempt to offer a turnkey system in ocean forecasting. The term Operational Oceanography was for the first time used for this type of service to better explain what it was all about. Operational Meteorology or weather forecasting was known to almost everybody. The first Seawatch system, named Seawatch Europe, was partly operational and partly experimental. The idea was to deliver data and forecasts to users in Europe and in Norway in particular. At the same time it would be necessary to develop new instrumentation, improve processing and data communications and introduce the first ocean

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models in operational mode. Seawatch Europe received funds from the Research Council in Norway, from four oil companies, State pollution Control Authorities in Norway and internal research funds from Oceanor, the owner of the Seawatch system. An international advisory board with representatives from nearly all coastal countries in Northern Europe was established. The Director of the GOOS office at IOC in Paris represented IOC. Seawatch soon become a first realisation of a GOOS system at least on a regional scale. Data was collected from 14 multi-parameter buoys deployed in the Barents Sea, North Sea and in Kattegat, Baltic and Skagerrak. Daily forecasting meetings addressed the state of the ocean and reported to customers on a daily basis using the Ocean-Info system where customers extracted data and forecast information tailored to their needs. Seawatch was introduced to new countries and has over the years been implemented in Indonesia, Thailand, Vietnam, India, Spain, Greece, Peru, South Africa and Equador. The Seawatch applications vary from country to country, but it has the same components. Real-time ocean data from buoys, data processing and ocean prediction, and data and forecast dissemination to end-users. The following examples illustrate the capabilities and benefits of operational oceanographic services.

3. The inflow to the Baltic in January 1993 The Baltic is a large estuary connected with the open ocean only via the Kattegat and sounds with relatively shallow thresholds; Oresund with a sill depth of 8 m, and the Darss sill with a threshold depth of 18m connecting the Baltic to the Belt Sea, which again connects to the Kattegat. These sounds provide the only way of exchanging water between the Baltic and the open ocean. The general hydrographic situation in the Baltic is that there is a vertical thermohaline upper layer with salinity S = 7 - 8 and thickness ~50m, a ~10m thick halocline overlying the deep water with S = 11-13. However, for many years before 1993 a general reduction in the deep-water salinity had taken place, documented by salinity measurements in the Gotland Deep, which showed salinity decreasing from 14 in 1953 to 11.3 in 1992. The decreasing tendency was interrupted a few times by a temporary increase in salinity. The most pronounced event was in 1977 when salinity increased from 12.5 to 13.2, due to a major inflow in 1976. Since 1976 the situation worsened with respect to oxygen content in the deep water and the salinity level dropped below the reproduction threshold for cod reproduction~a factor of economic importance for the countries surrounding the Baltic. Knowledge about the development in salinity conditions is therefore of vital importance for the proper management of the cod population. The deep water in the Baltic can only be replaced by water from the Kattegat if the inflowing water has a density greater than that of the Baltic deep water. In January 1993 the conditions were favourable for inflow to the Baltic. Strong SW to W winds forced inflow of saline waters from the North Sea into the Kattegat, and caused high water levels to be maintained in the Kattegat throughout the month, causing a net inflow to the Baltic. From water level measurements the inflow volume was estimated to ~300km 3, and measurements taken in Oresund showed that the inflowing water had S--25-27. Further investigations confirmed that the inflow had continued over the Darss Sill down into the Arkona Basin, where the salinity near the bottom of the basin reached 22.5. The

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Swedish Meteorological and Hydrological Institute (SMHI) (Hans Dahlin, personal communication) presented these data in reports from the Swedish Ocean Information Centre. As a member of Seawatch Europe, SMHI suggested a buoy be deployed at the deepest part of the basin. They wanted to measure salinity and temperature from a string under the deployed buoy. This was a real challenge because it had never been done before. The only automatic sensors on a string were temperature sensors connected by cables. SMHI was however aware that in the Seawatch Europe experimental programme development was underway of a salinity/temperature sensor with inductive coupling. These tests took place in the Trondheimsfjord, and some very successful results had been achieved. As an experiment a buoy was deployed for SMHI at the Bornholm basin. The buoy was equipped with prototypes of temperature and salinity sensors at 50, 60, 70, 80 and 85 m, in addition to the standard buoy instrumentation. The "test" was very successful and for the first time a continuous record of salinity and temperature measurements was obtained showing the intrusion of the saline rich water into to the deep parts of Baltic (Figure 1).

Figure 1 Temperature and salinity recording of the Bornholm Seawatch Europe buoy The main halocline was found between 60 and 70m depth at the beginning of the period, but was elevated to 50 and 60m at the end of March. This indicated that the Bornholm Basin was filled up by inflowing saline water. The temperature and salinity measurements at 85 m depth showed salinities of 18.5-19 during the first week of measurements, dropping to 18 toward the end of the month. These values were lower than those

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observed in the Arkona Basin on 26 January the same year, but definitely higher than the initial salinity in the Baltic before the inflow. This again supports the idea that the inflowing water had started to fill the Bornholm basin from Arkona but was influenced and mixed further with the old and less saline waters on the way down the slope. It is seen that the pycnocline is lifted in the middle of the observation period, from --65 m depth to ~55m depth, and at the same time there is a reduction in the bottom water salinity of ~1.

4. Deep water formation in the Norwegian Trench February 1994 Statoil became a member of Seawatch Europe in 1993. Their decision to join the Seawatch Europe partnership was to get access to water temperature from the deepest part of the Norwegian Trench. Not only data in real time, but also a forecast of what was expected up to 48 hours ahead. Statoil operated a gas condensate pipeline from their platform at the Sleipner field to their refinery on-shore. If the surrounding water of the pipeline was colder than 6~ it was necessary to inject chemicals to prevent hydrate formation of the condensate. This process was expensive because the chemicals later had to be separated from the condensate at the refinery. It was a clear objective for Statoil to minimise the use of chemicals and the temperature measurements and forecasts given by the Seawatch team made an important contribution to this (Figure 2).

Figure 2 Seawatch buoy deployed over the gas/condensate pipeline One of the Seawatch buoys was deployed over the pipeline in the middle of the Norwegian Trench with temperature sensors 3 and 5m above the sea floor. The data from the instruments was cabled to the surface buoy and transmitted to shore. Meteorologists and oceanographers on the Seawatch Europe Team carefully assessed the data on a daily basis and prepared forecasts. For nearly a year the temperature was stable, averaging 6.5~ then in February 1994 a sudden change occurred. The water temperature dropped by nearly 2~ in a couple of days (Figure 3). In this case Statoil was warned several days before it happenedmthe heat loss from the sea surface to the atmosphere at the shallow part of the North Sea had totally homogenised the water column and created an unstable situation with dense surface water overlaying the less dense bottom water in the Trench. This is the classical mechanism behind deep-water formation and

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the Seawatch Europe buoy recorded the process continuously. It also demonstrates how operational datasets may contribute significantly to scientific investigation of fundamental oceanographic processes.

Figure 3 Temperature recording from the sea floor of the Norwegian trench

5. Radioactivity monitoring in Kattegat In 1994 chemical oceanography had few opportunities to obtain continuous data records. One field of development of Seawatch was to prepare the buoy to measure gamma radiation. A sensor was developed with an accuracy of 7bqm -3 to be attached to the buoys. The first prototypes were tested in 1993 and one by one the buoys in Seawatch Europe were equipped with these sensors. In April 1994 one of the Seawatch buoys deployed in Kattegat recorded an incident where a "cloud" of radioactive water passed the buoy. It was measured to be of the order of 1000bqm -3. This would have only limited value if it was not for the fact that the Seawatch buoys also supported the observations with supplementary data that could highlight the origin of the "radioactive water cloud" (Figure 4).

Figure 4 Radioactivity monitoring in Kattegat

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It is seen clearly that as the turbidity changed, the salinity dropped, indicating that the water was of Baltic origin. The current direction changed from SE to NW. The incident was reported to Swedish authorities. A week later it was confirmed on the radio that had been a minor accident at a nuclear power plant located in Oresund. A striking feature of the gamma radiation parameter is the variability in the time domain. This is not for this chemical parameter only, but could probably be expected from most chemical and biological components in the ocean. This fact strongly supports a sampling strategy for ocean chemical processes with high time domain resolution.

6. Typhoon forecasting in Vietnam Since the development period in Seawatch Europe terminated in 1995, Seawatch has been introduced and implemented in many places throughout the world. One of these systems was implemented in Thailand. The objective was to support typhoon forecasting. During the passage of the typhoon "Linda" it was possible to determine the track of the typhoon, based on the recordings from the buoys. Just by examining the pressure gradient, wind speed and direction it was possible to observe the first signs of the typhoon and follow it as it crossed the Bay of Bangkok and also to make sure that the track was not directed towards the northern densely populated area near Bangkok.

7. "Prestige" oil spill accident in Biscay In late November 2002 the Greek-owned tanker "Prestige" sank off the coast of Spain/ Portugal. A large amount of oil was spilled from the tanker before it was towed out to deep water far from the coast and forced to sink. During the first few days after the accident a buoy from the Spanish Seawatch Rayo continuously recorded the wave height, wind and temperature. This information supported the Spanish authorities in their preparation and efforts to collect the oil spilled and to make their decision of adequate response to the situation. It also gave the combat teams information on the drift of oil. The information was complemented by satellite images giving a horizontal coverage of the oil spread. Wind and weather reports from the Seawatch weather forecasting models combined with wave models provided valuable forecasts.

8. Algal bloom forecasts When combining observations of light beam attenuation, oxygen saturation, and physical parameters, e.g. salinity, temperature, waves, and currents, algal blooms may be distinguished from dynamic changes due to other effects, e.g. resuspension of bottom sediments. A specific case is presented in Figure 5. In the case shown in Figure 5, observations taken from the Seawatch buoy located in Kattegat and Skagerrak show the same features. A clear signal in light attenuation is associated with an increase in oxygen content suggest that it is an algae bloom. The analyses of phytoplankton samples showed that these changes were caused by transport of diatom populations, dominated by Skeletonema costatum, advected in water masses from the Oslofjord and the fjords north of the Swedish-Norwegian border.

482


Figure 5 Light attenuation and oxygen observations Seawatch Europe, and species Skeletonema Costatum

9. European industry role in operational oceanography Seawatch, like many other related projects launched under the EUROMAR programme in 1990, was given prospects of a fast growing market. The idea of operational oceanography had been born and the demands from professional users like the oil industry in the North Sea created a market place for certain services. Almost at the same time as EUREKA/EUROMAR was established as a financial mechanism to foster research and development in the marine sector, the GOOS was created by IOC, WMO, ICSU and UNEP. This was clearly recognition that the strategy for European industry to develop this market was correct. However, the academic sector was clearly hesitant to accept operational oceanography as a stand-alone service comparable to weather forecasts in meteorology. When EuroGOOS was established as the European component of GOOS there was hope from industries that this would be the instrument to foster a fruitful communication between the academic sector and European industry and strongly recognise the initiatives and results achieved by EUREKA/EUROMAR. This never happened and instead EuroGOOS became a forum with members only from academic institutions or public authorities. Further, with the Mast and later the Framework programmes the academic institutions that were very reluctant to accept operational oceanography in the first place, changed their attitude and received generous funds to increase their capability in this discipline. This would have been wise if the intention from the EUREKA/EUROMAR programme had been followed up. Instead the European market completely vanished and the industry had to look to markets outside Europe to survive.

Svein Erling Hansen

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Today we can see a large number of activities and strong academic controlled initiatives totally dominate the arena for operational oceanography in Europe. This has been a disaster for the industry that in the first place was fostered through the EUREKA/ EUROMAR. What was expressed by EUREKA, as a major effort to make European industry competitive globally by preparing them to serve a European market has not been achieved. Many companies broke their neck on unrealistic prospects. Those who are left from the "early stage" of operational oceanography have succeeded not because of Europe, but because they have survived outside Europe.

Real-time forecast modelling for the NW European Shelf seas Martin Holt*, Zhihong Li, and Jeff Osborne Met Office, UK

Abstract The Met Office have run a 3D circulation model of the NW European shelf seas daily in the suite of operational forecast models since June 2000, providing a nowcast and 48 hour forecast. The model POLCOMS is implemented on a 1/9 ~ by 1/6 ~ grid, giving approximately 12km resolution. During 2002 the model was upgraded to cover a larger area, to be nested into the Met Office deep ocean FOAM model, and to use the latest formulation of POLCOMS. Boundary forcing is provided to a high resolution nested model of the Irish Sea. Data from the shelf seas model have been used as input to the SST analysis used by a mesoscale weather prediction model, to assess the impact on prediction of coastal fog. This paper describes the operational shelf seas model run at the Met Office, and outlines future plans for development of the system, including a demonstration nowcast of the POLCOMS coupled physical-ecosystem model.

K e y w o r d s : NW European modelling, marine ecosystem.

Shelf,

operational

oceanography,

ocean

forecast

1. The operational NW Shelf model The Met Office have run a 3D circulation model of the NW European shelf seas daily in the suite of operational forecast models since June 2000, providing a nowcast and 48 hour forecast. The model POLCOMS (Proctor and James, 1996), developed by the Proudman Oceanographic Laboratory (POL), is implemented on a 1/9 ~ by 1/6 ~ grid, giving approximately 12km resolution over the NW shelf. Between 2000-2002 the model domain covered 48~176 and 12~176 The model is driven at the surface by hourly winds and pressures and 3-hourly heat fluxes from the Met Office mesoscale weather prediction model, and at open ocean boundaries with tidal harmonics, and climatological values for temperature and salinity. Freshwater inflow is provided as an annual mean value from the Baltic and from 46 rivers. Vertical mixing in the model is calculated using a bulk Richardson number scheme. Comparison with moored buoy and AVHRR satellite observations shows that the model represents well the evolution of sea surface temperature (SST) in the North Sea. A time series comparison of daily averaged model SST and bed temperature, with hourly observations between January 2001 and November 2002 is shown in Figure 1 for two sites. In the English Channel the waters are predominantly well mixed, in the northern North Sea there is seasonal stratification. * Corresponding author, email: [email protected], 9 British Crown Copyright

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Figure 1 Timeseries of observed SST and modelled SST and bed temperature at two sites on the NW European shelf, January 2001 to November 2002. Top: at 57 ~N 0 ~E, bottom: at Channel Light Vessel 49.9~ 2.9~ Model SST (solid line), model seabed temperature (dashed line), observations (+).

2. SST in coastal waters For a case study of coastal fog, SST data from the shelf seas model have been input to the SST analysis used by a mesoscale numerical weather prediction (NWP) model, with impact on the predictions of fog clearance. In areas without observations, the SST analysis can be somewhat featureless, lacking any spatial detail. Figure 2 compares the analysis for 11 May 2000 with shelf seas model data in the coastal waters of the Moray Firth, north east Scotland. In Figure 2 a pixel represents approximately 12km by 12km.

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Real-time forecast modelling for the NW European Shelf seas

Figure 2 Comparison of sea surface temperature data on 11 May 2001 in the Moray Firth 57.25 ~N to 58.5 oN, 2.5 oW to 4.5 oW. Left: Analysis from operational mesoscale Numerical Weather Prediction model. Right: Shelf Seas model. Contour interval 0.2~ range 7.6~ (light grey) to 9.4 ~ (dark grey). With representation of the surface heating, and the water column mixing processes, the shelf seas model provides temperature data that can contain significant spatial variability. When this data is incorporated into the SST analysis used by the NWP model, the representation of coastal meteorology in the ~12km NWP model is improved.

3. The Atlantic Margin Model During 2002 an upgrade is being prepared. The model will be extended to include the full shelf break, covering 40~176 20~176 (Figure 3); will take boundary forcing data from the Met Office deep ocean FOAM model; and will move to the latest formulation of POLCOMS, including use of a hybrid vertical co-ordinate which will improve representation of SST in deeper waters. This "Atlantic Margin" implementation of POLCOMS will provide boundary forcing for nested high resolution models of coastal waters, such as a model of the Irish Sea at 1/60 ~ by 1/40 ~ resolution. Boundary data for nested models of coastal waters will also be provided to participants in NOOS, the NW Shelf Operational Oceanographic System. The formulation of POLCOMS Version 3 is described by Holt and James (2001). In addition to the move to use a hybrid vertical co-ordinate, the major difference to the earlier operational model is that the vertical mixing is now calculated using the MellorYamada level 2.5 turbulence closure (with parametrisation for internal wave breaking, surface wave breaking, and Galperin stability functions). Freshwater inflow is provided as a daily climatology for 36 rivers, with data from the UK Environment Agency, and water temperature is specified for river inflow where available. Salinity and volume flow for exchange with the Baltic is taken from the DYNOCS experiment. The surface flux of precipitation minus evaporation is now specified, with six-hourly mean values taken from the Met Office global numerical weather prediction model. At the deep ocean boundaries the Atlantic Margin model is nested into the deep ocean Atlantic FOAM model (presently 1/3 ~ resolution), see Bell (2000), for temperature and salinity profile, barotropic current and water level. The Atlantic Margin model uses 33 levels in the vertical, with equal spacing in "s". For water depths less than 150m the coordinates revert to sigma.


487

Figure 3 The Atlantic Margin model, showing bed temperature, ~ 3.1 The nested Irish Sea model The Atlantic Margin model can provide boundary forcing data for nested regional models at higher resolution. Figure 4 shows the domain of the Irish Sea model, which uses a 1 nautical mile grid (1/60 ~ by 1/40~ This model is being prepared for operational implementation in early 2003. At this resolution the coastline is much better represented, and with good bathymetric data, the model representation of currents and temperature is much improved compared to the ~12km model. 4. F u t u r e D e v e l o p m e n t s Future plans for the modelling system include an extension of the domain of the nested eddy resolving model, as computing resources permit. Firstly to cover the shelf seas to the west of the UK, and later to cover the whole NW European shelf seas at a resolution of 1 nautical mile. Boundary forcing for the Atlantic Margin model will be taken from a higher resolution Atlantic FOAM model as this become operational. As a contribution to the ICES North Sea pilot project NORSEPP, and as part of MERSEA strand 1, a nowcast demonstration of the coupled POLCOMS ERSEM ecosystem model will be set up for the North Sea, at --6km resolution. This model system has already been evaluated in hindcast mode (Allen et al., 2001).

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Real-time forecast modelling for the NW European Shelf seas

Figure 4 The Irish Sea model, showing surface current speed, with vector arrows at every eighth gridpoint. Colour scale ranges between zero (white) and 200 cms-1 (black), and the length of an arrow representing 20 cms-1 is shown. Within the UK, output from the Atlantic Margin model and nested Irish Sea model will be provided to POL as part of their Liverpool Bay observatory (Proctor, 2003). Assessments from this will guide future development of the modelling system. The POLCOMS model has already been demonstrated by POL for Liverpool Bay on a 300m grid, including wetting and drying, and this could be applied for real time forecast modelling of other estuary systems if required. Within Europe the Atlantic Margin model, nested into the deep ocean FOAM model, is a key component of the NOOS-ESODAE project, providing boundary conditions for NOOS partners to establish nested higher resolution models of their North Sea coastline. Plans are being developed for shelf seas model SST data to be taken at selected locations as input to the SST analysis used by the Met Office mesoscale numerical weather prediction model.


489

References

Allen, J.I., J. Blackford, J. Holt, R. Proctor, M. Ashworth, and J. Siddorn, 2001, A highly spatially resolved ecosystem model for the North West European Continental Shelf. SARSIA 86:423-440 Bell, M.J., R.M. Forbes, and A. Hines, 2000, Assessment of the FOAM global data assimilation system for real-time operational ocean forecasting. J Marine Systems Vol. 25 No 1. Holt, J.T., and I.D. James, 2001, An s coordinate density evolving model of the northwest European continental shelf 1, model description and density structure. JGR 106 C7 pp. 14015-14034 Proctor, R., and I.D. James, 1996, A fine-resolution 3D model of the Southern North Sea. J Marine Systems 8, p285-295 Proctor, R., 2003, The POL Liverpool Bay observatory (this volume)

Arctic climate change--will the ice disappear this

century?

Ola M. Johannessen*l'2, Martin W. Miles 3'4, Anne-Mette Olsen 1,2, Lennart Bengtsson s'l and Cathrine Myrmehl I

lNansen Environmental and Remote Sensing Center, Norway 2Geophysical Institute, University of Bergen, Norway 3Bjerknes Centrefor Climate Research, Norway 4Environmental Systems Analysis, USA 5Max Planck Institute for Meteorology, Germany Abstract A new set of multi-decadal and century-scale sea-ice data is compared with coupled atmosphere-ocean model simulations in order to understand Arctic sea ice and climate variability. It is evident that the two pronounced 20th-century warming events--both amplified in the Arctic m were linked to sea-ice variability. The area of sea ice is observed to have decreased by ~8x 105km 2 (7.4%) since 1978, with record-low summer ice coverage in 2002. Model predictions are used to quantify changes in the ice cover through the 21st century. A predominantly ice-free Arctic in summer is predicted for the end of this century.

1. Introduction A consensus result from 19 different coupled atmosphere-ice-ocean climate models is that greenhouse global warming should occur, enhanced in the Arctic (R~iis~inen, 2001). The Intergovernmental Panel on Climate Change (IPCC, 2001) states that the winter warming of northern high-latitude regions by the end of the century will be at least 40% greater than the global mean, based on a number of models and emissions scenarios, while the warming predicted for the central Arctic is ~3-4~ during the next 50 years, or more than twice the global average). These scientific concerns have generated efforts for monitoring, assessment and prediction of the Arctic sea-ice cover, as well as applied operational oceanographymboth pertinent EuroGOOS activities. Recent overviews of results from observational studies of atmospheric, oceanic and seaice parameters and other climate-sensitive variables have concluded that a reasonably coherent portrait of recent change in the Arctic is apparent (e.g. Serreze et al., 2000). However, it is debatable whether the Arctic warming and sea-ice melting in recent decades are an enhanced greenhouse-warming signal or natural decadal and multidecadal variability, e.g. as possibly expressed by the Arctic warming observed in the 1920s and 1930s (Johannessen et al., 2003). Key uncertainties include: 1. How are Arctic sea-ice conditions changing and to what degree are they a consequence of natural climate processes and/or external factors such as anthropogenic greenhouse-gas (GHG) forcing * Corresponding author, email: [email protected]

Ola M. Johannessen*, Martin W. Miles, Anne-Mette Olsen, Lennart Bengtsson, and Cathrine Myrmehl

491

2. To what degree may anthropogenic forcing induce the Arctic sea-ice cover to decrease or even disappear in this century? In order to address these questions, a set of pertinent multi-decadal to century-scale data is compared with global coupled atmosphere-ice-ocean climate model output from the ECHAM4 model (Roeckner et al., 2001) and the HadCM3 model (Gordon et al., 2000). The results reported here are detailed in Johannessen et al. (2003).

2. Sea ice extent---satellite observations

Figure 1 SMMR-SSM/I satellite-retrieved monthly sea-ice area (upper) and anomalies (lower) for the Northern Hemisphere, 1978-2002, with linear trend shown. Sea-ice concentration and derived parameters such as ice extent (the area within the iceocean margin) and area (extent minus open water area) can be reliably retrieved from satellite microwave sensor measurements, e.g. Scanning Multi-channel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I), available continuously since 1978. Satellite data show that the winter maximum ice area (Figure 1, upper) is typically about 14 x 106km 2 while the summer minimum ice area is about 7 x 106km 2. Although absolute inaccuracies of sea-ice concentration retrievals can be several percent using different algorithms, estimates of absolute and relative trends in sea-ice area and extent have been shown to be consistent + 1% regardless of the algorithm--e.g. Bjergo et al. (1997); Vinnikov et al. (1999). The SMMR and SSM/I data, here updated from Bjergo et al. (1997) and Johannessen et al. (1999) through September 2002, indicate an ~8.1xl05km 2 (~7.4%) decrease in the Northern Hemisphere ice area from 1978-2002 (Figure 1, lower). During this period, the decreases have been larger in summer, leading to a 7 - 9 % per decade reduction in the area of thicker, multi-year ice (ice that has survived at least one summer melt) over the last two decades (Johannessen et al., 1999).

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Arctic climate change--will the ice disappear this century?

Regarding the mass balance of the ice cover, it is possible that ice redistribution--e.g. convergence and ridging m may partially compensate for reduced areal coverage. However, it is notable that our updated time series confirms that the diminished summer ice cover recently reported (Serreze et al., 2003) is indeed unprecedented in the satellite record (end data points in Figure 1).

Figure 2 Satellite-retrieved sea-ice concentration in winter (March) and summer (September) for the Northern Hemisphere, 1978-2002: Mean ice concentration for (upper left) winter and (upper right) summer, and the linear trends (% change from 1978-2002) for (lower left) winter and (lower fight) summer. The spatial patterns of the mean winter (March) and summer (September) sea-ice cover 1978-2002 are shown in Figure 2 (upper left and upper right, respectively). The winter and summer trends (linear regressions) in sea-ice concentration from 1978-2002 are indicated in Figure 2 (lower left and lower fight, respectively). During this period, the

Ola M. Johannessen*, Martin W. Miles, Anne-Mette Olsen, Lennart Bengtsson, and Cathrine Myrmehl

493

decreases in winter have been most pronounced (as large as ~50%) in the Barents and Greenland seas, whereas the summer decreases have been greater than 50% in some areas of the Beaufort and Chukchi seas, and as large as ~30-50% in the Siberian marginal seas.

3. S e a ice e x t e n t ~ c e n t u r y - s c a l e

observations

A pronounced reduction in the Northern Hemisphere sea-ice cover, such as seen in the satellite record of last two decades, is scarcely apparentmat least in the most widelyused century-long sea-ice dataset (Chapman and Walsh, 1993; Vinnikov et al., 1999)-in the early 20th century warm period. To ascertain whether this has a physical explanation or is due to data deficiencies before the 1950s, we have analysed these data together with a new century-long Zakharov (1997) dataset, which includes hitherto under-recognised Russian data. The dataset comprises sea-ice extent for ~77% (11.3 x 106kin 2) of the area of the Arctic Ocean. This region occupies the central Arctic Ocean and the Greenland, Barents, Kara, Laptev, East Siberian and western Chukchi seas. Time series of annual sea-ice extent based on these data and annual sea-ice extent from the standard Walsh dataset are compared with the zonal annual mean surface air temperature (SAT) between 7 0 - 9 0 ~ since 1900 (Figure 3).

Figure 3 Annual sea-ice extent (area within the ice-ocean margin) derived from the Zakharov (1997) sea-ice dataset ("Zakharov"), Northern Hemisphere sea-ice extent from the Chapman and Walsh (1993) dataset ("Walsh") and zonal (70-90~ mean annual surface air temperature ("SAT") since 1900. The time series shown are 5 year running means. In contrast to the Chapman and Walsh data, these new data indicate a substantially reduced (..6x 105km 2) ice cover in the 1920s-1930s warming period, nearly as large as in the last two decades. The correlation between the SAT and the Zakharov sea-ice extent is r~0.6 and is maximum at 0 lag. This indicates that the interannual variability in the Arctic sea-ice extent in the last century was coupled to the high-latitude SAT variability to a large degree, though the r-value may partially reflect feedback processes from

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Arctic climate changemwill the ice disappear this century?

the ice cover to the atmosphere, e.g., ECHAM4-model sensitivity experiments have demonstrated a strong SAT response to model-imposed changes in sea ice (Bengtsson et al., 2003).

4. Sea ice--modelled

Figure 4 ECHAM4-modelled sea-ice concentration in winter (March) from (a) 2001-2010 and (b) and 2081-2090, and in summer (September) from (c) 2001-2010 and (d) 2081-2090. The variability of annual sea-ice extent has been modelled and compared to observations in previous analyses (e.g., Vinnikov et al., 1999), which predicted a reduction of ~15% in the mean ice extent up to 2050. However, potentially large and important spatial and seasonal aspects were not considered. Here, we consider both the spatial and seasonal variability of the ice cover and its modelled response to anthropogenic forcing to 2100, using ECHAM4 and HadCM3 model predictions using different IPCC emissions scenarios. The spatial distributions of the ECHAM4-modelled sea ice cover for the present decade (2001-2010) and towards the end of the century (2081-2090) are indicated in Figure 4. In order to assess the robustness of the ECHAM4 estimates, we have compared them with a different coupled atmosphere-ocean model, the HadCM3 (not shown, see Johannessen et al., 2003), using two different scenarios from the IPCC Special Report on Emissions Scenarios (SRES)mA2 and B2, which are "medium-high" and "medium-low" scenarios, respectively. The independent results from ECHAM4 and

Ola M. Johannessen*, Martin W. Miles,Anne-MetteOlsen, LennartBengtsson,and Cathrine Myrmehl

495

HadCM3 support each other, both predicting moderate reductions in winter and drastic reduction in summer. The spatial distributions of the ECHAM4 and HadCM3-modelled summer ice cover in late century (Figure 4d) indicate essentially ice-free Arctic marginal seas except north of Greenland (particularly in the ECHAM4 results) and the Canadian Arctic Archipelago (particularly in HadCM3).

5. C o n c l u s i o n s and implications The observational analysis and comparison with model results leads to the following conclusions. First, the Arctic warming and sea-ice reductions in the 1920s-1930s were due to natural fluctuations internal to the climate system. Second, there are strong indications that neither the warming trend nor the decrease of the sea-ice cover over the last two decades can be explained by natural processes alone. Third, the ECHAM4 and HadCM3 models both predict a dramatic decrease of the ice cover, which could result in a nearly ice-free Arctic Ocean during summer at the end of this century. There is a range of potential consequences of a shrinking ice cover: 1. Reductions in albedo and increased open water would significantly affect energy balances and atmospheric and oceanic circulation in the high latitudes 2. Exposure of vast areas of the Arctic Ocean with cold open water, which has a high capacity to absorb CO 2, could become an important sink of atmospheric CO 2 (Anderson and Kaltin, 2001) 3. Broad changes in the marine ecosystem could have a negative impact on marine biodiversity (Beaugrand et al., 2002). However, there would be a larger area for potential fisheries, as well as increased offshore activities and marine transportation, including the Northern Sea Route (Ragnar, 2000) 4. Changes in the pathways and spreading of melt water and stratification, and the effects of reduced Greenland Sea deepwater formation (Alekseev et al., 2001) on the global thermohaline circulation.

Acknowledgements This work has been supported by the European Union 5th Framework Programme project "Arctic Ice Cover Simulation Experiment (AICSEX)" and the Research Council of Norway projects "Norwegian Ocean Climate (NOClim)" and "Role of Arctic Sea Ice--Atmosphere Processes (ROLARC)".

References Alekseev, G.V., O.M. Johannessen, A.A. Korablev, V.V. Ivanov and D.V. Kovalesky, 2001. Interannual variability of water mass in the Greenland Sea and the adjacent areas. Polar Res. 20, 207-210. Anderson, L.G., and S. Kaltin. 2001, Carbon fluxes in the Arctic Ocean--potential impact by climate change. Polar Res. 20, 225. Beaugrand, G., P.C. Reid, F. Ibafiez, J.A. Lindley and M. Edwards, 2002. Reorganization of North Atlantic marine copepod biodiversity and climate. Science 296, 1692.

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Bengtsson, L., V.A. Semenov and O.M. Johannessen, 2003. The early century warming in the Arcticma possible mechanism, Rep. No. 345, Max Planck Institute for Meteorology, Hamburg, Germany. BjCrgo, E., O.M. Johannessen and M.W. Miles, 1997. Analysis of merged SMMRSSMI time series of Arctic and Antarctic sea ice parameters. Geophys. Res. Lett. 24, 413. Chapman, W.L. and J.E. Walsh, 1993. Recent variations of sea ice and air temperature in high latitudes. Bull. Amer. Meteor. Soc. 74, 33. Delworth, T.L. and T.R. Knutson, 2000, Simulation of early 20th century global warming. Science 287, 2246. Gordon C., C.A. Senior, H.T. Banks, J.M. Gregory, T.C. Johns, J.F.B. Mitchell and R.A. Wood, 2000. The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim. Dynam. 16, 147. Intergovernmental Panel on Climate Change (IPCC), 2001. Climate Change 2000m Third Assessment Report, Cambridge University Press. Johannessen, O.M., E.V. Shalina and M.W. Miles, 1999. Satellite evidence for and Arctic sea ice coverage in transformation. Science 286, 1937. Johannessen, O.M., L. Bengtsson, M.W. Miles, S.I. Kuzmina, V.A. Semenov, G. Alekseev, V.F. Zakharov, A.P. Nagurnyi, L.P. Bobylev, L.H. Pettersson, K. Hasselmann and H. Cattle, 2003. Arctic climate changemobservations and modelling of temperature and sea ice, 2003. NERSC Tech. Rep. 218, Nansen Environmental and Remote Sensing Center, Bergen, Norway. Ragner, C.L. (ed.), 2000. The 21st CenturymTurning Point for the Northern Sea Route? Kluwer Academic Publishers, Dordrecht, Netherlands. R~iis~inen, J., 2001. CO2-induced climate change in CMIP2 experiments: Quantification of agreement and role of internal variability. J. Clim. 14, 2088. Serreze, M.C., J.E. Walsh, F.S. Chapin, T. Osterkamp, M. Dyurgerov, V. Romanovsky, W.C. Oechel, J. Morison, T. Zhang and R.G. Barry, 2000, Observational evidence of recent change in the northern high-latitude environment. Clim. Change 46, 159. Serreze, M.C., J.A. Maslanik, T.A. Scambos, F. Fetterer, J. Stroeve, K. Knowles, C. Fowler, S. Drobot, R.G. Barry and T.M. Haran, 2003. Record minimum sea ice cover in the Arctic Ocean for summer 2002. Geophys. Res. Lett. 30, 1110. Vinnikov, K. Ya., A. Robock, D.J. Cavalieri, V.F. Zakharov and J.E. Walsh, 1999. Global warming and Northern Hemisphere sea ice extent. Science 286, 1934. Zakharov, V.F., 1997. Sea Ice in the Climate System, WMO/TD-No. 782, World Meteorological Organization, Geneva, 80 pp.

Approach to the operational Ocean Observing System in the Yellow Sea through China-Korea bilateral cooperation D.Y. Lee*, G.K. Tan, C.S. Kim and J.Y. Han

Sino-Korea Joint Ocean Research Center (SKORC), People's Republic of China

Abstract Cooperation between the countries sharing the same water body is essential for the establishment of an operational ocean observing and prediction system for the regional sea. There are many on-going international cooperation programmes aiming at building regional ocean observing systems. However, it is not easy to secure the required resources from all the participating countries for the establishment of a regional ocean observing system. One of the effective ways to implement a cooperative regional observing system is to initiate bi-lateral or sub-regional cooperation programmes between neighbouring countries in the same sub-regional sea by preparing a specific action plan for a specific sub-regional sea rather than coveting the entire regional sea. The People' s Republic of China and the Republic of Korea share the Yellow Sea, and the development, utilisation and conservation of this sea are common problems to both countries. The Sino-Korea Joint Ocean Research Center (SKORC) was established as a bridge linking Chinese and Korean institutes and researchers in the field of ocean sciences. Recently, SKORC decided to pursue an initial operational oceanographic system for the Yellow Sea as one of its main functions by preparing a specific plan to build an operational ocean observing and prediction system in the Yellow Sea through bi-lateral cooperation between the two countries.

Keywords: Regional ocean observing system, Yellow Sea, sub-regional sea, operational oceanographic system, bi-lateral cooperation 1. Introduction The utilisation of the coastal and ocean areas for various activities has been increasing for the coastal area of the Yellow Sea, and this tendency is expected to increase in the future. One of the major concerns in the development of coastal area is the preservation and management of the marine environment and marine resources. For the scientific management of the coastal and ocean areas, marine environmental information needs to be produced along the coast of a regional sea by means of coastal and ocean prediction models. Basic information on the coastal and ocean processes that are generated from the open sea and propagate to the local area of interest is essential as the offshore boundary conditions of the local coastal models to solve various coastal problems. This information could well be obtained from ocean models coveting the whole regional sea. The oceanographic community all over the world realised that the regional cooperation between countries sharing the same water body is essential. Many regional ocean * Corresponding author, email: [email protected]

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A p p r o a c hto the operational Ocean Observing System in the Yellow Sea through ChinaKorea bi-lateral cooperation

observing systems have already been initiated, such as NEAR-GOOS and EuroGOOS. NEAR-GOOS was established in the middle of the 1990s as a pilot programme of the regional GOOS (IOC, 1966; Lee et al., 1997). However, progress was rather slow mainly due to the problem of implementing specific programmes to cover all the sub-regional seas. A specific action plan can be prepared more effectively for a sub-regional sea, and it may also be easier to find sponsoring agencies in the region especially where there are no multinational funds available like Europe. One of the effective ways of implementing a cooperative regional ocean observing system is to establish a sub-regional ocean observing system with a limited number of participating countries for the well defined sub-regional sea instead of trying to bring all the countries in the region together from the beginning.

2. Problems in building a regional ocean observing system A regional sea can be composed of many sub-regional seas with rather independent and different marine environmental conditions such as the Yellow Sea and Japan/East Sea in the North East Asia Region. In such a case the specific implementation plan for such sub-regional seas may be better pursued rather independently under the same guidelines of the regional ocean observing system. Through the first phase implementation of NEAR-GOOS it was found that the initiation of a larger scale regional cooperation programme is not so feasible, especially in a region where previous experiences in regional cooperation is rare and communication problems exists. The area covered by NEAR-GOOS is shown in Figure 1. Initiation of a programme coveting the whole NEAR-GOOS region is difficult since the marine environment and scientific issues and countries involved are different for three different marginal seas in the region. One of the practical solutions to this problem is to encourage national and bi-lateral programmes in the participating countries. It is much easier to initiate specific action programmes to improve ocean observing and prediction systems for sub-regional seas rather independently under the umbrella of NEAR-GOOS. Phenomena resulting from direct forcing such as wind waves, wind-driven circulation and storm surges, and the phenomena like astronomical tides and tidal currents can be predicted by using boundary conditions at the air-sea interface and at the offshore boundaries, which are successfully produced by sub-regional scale models, hence the cooperation among the countries sharing the same sub-regional sea is more important. The NEAR-GOOS Coordinating Committee realised this and decided to pursue such a sub-regional approach at the last Coordinating Committee Meeting held in Vladivostok, Russia in October 2002. The regional GOOS such as NEAR-GOOS and EuroGOOS can lead to the formulation of such sub-regional programmes in the region. The area of the sub-regional ocean observing system of NEAR-GOOS for the Yellow Sea and the East China Sea is shown in Figure 2. The sub-regional and regional models now need to be interfaced with a Global scale model, hence the local, sub-regional, regional and global ocean observing systems should be linked accordingly.

D.Y. Lee*, G.K. Tan, C.S. Kim and J.Y. Han

Figure 1 Area of NEAR-GOOS

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Figure 2 Area of YOOS

3. China-Korea bi-lateral cooperation To be able to implement the action plan of a sub-regional ocean observing system through the cooperation between neighbouring countries we may need lots of discussion and coordination between the countries involved. Dialogue between Korea and China and hopefully with North Korea in the future is very important to initiate a sub-regional system for the Yellow Sea. Fortunately there already is a rather strong coordinating mechanism in ocean science and technology between China and Korea. The Ocean Science Joint Committee composed of the Chinese and Korean government meets annually to discuss the ocean related issues between the two countries. There is also the Sino-Korea Joint Ocean Research Center (SKORC) agency to activate the cooperation in ocean science and technology between the two countries especially for the Yellow Sea. The People's Republic of China (PRC) and the Republic of Korea (ROK) are close neighbours sharing the Yellow Sea. The development, utilisation and conservation of the Yellow Sea are problems common to both PRC and ROK. According to the relevant agreement between the two governments, the Sino-Korea Joint Ocean Research Center (SKORC) was established in Qingdao on 12 May, 1995, as a bridge linking Chinese and Korean oceanographic institutes and scientists in the field of ocean sciences. SKORC is a cooperative institution fostered by China and Korea to enhance the levels of ocean sciences and technologies of both sides, promote marine environment protection and development and utilisation of ocean resources, and expand and develop the cooperation between China and Korea. The management committee is composed of Chinese and Korean government officials and meets once a year to discuss and to coordinate ocean science cooperation between the two countries and operation of the SKORC. The key staff of SKORCmthe director and deputy directormare appointed at this management committee and serve for two years at the Center located in Qingdao, China.

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Approach to the operational Ocean Observing System in the Yellow Sea through ChinaKorea bi-lateral cooperation

SKORC operates two networks: one for data and information exchange and the other for coordination and promotion of research cooperation. The data and information network consists of more than 40 member agencies in China and Korea and the coordination network at present links about 30 oceanographic agencies including universities, local government agencies and institutes in China. Agencies in Korea will be invited to join the coordinating network in 2003. The coordinators representing member agencies meet regularly to discuss the research cooperation for the Yellow Sea. SKORC acts as a bridge and window in the China-Korea exchange and cooperation.

4. Development of Yellow Sea Operational Observing System (YOOS) The establishment of an Operational Ocean observing System for the Yellow Sea is of common interest to the two countries. NEAR-GOOS is conceived as an umbrella under which sub-regional ocean observing system such as YOOS can be pursued. SKORC chose the task of initiation of YOOS and coordination of its implementation as one of its main functions through the bi-lateral cooperation programme. From the 2002, SKORC is managing a project entitled "Feasibility study and strategic planning for China-Korea cooperation for the establishment of the operational oceanography of the Yellow Sea", linking marine scientists in China and Korea. They will prepare a specific plan to build an operational ocean observing and prediction system in the Yellow Sea through bilateral cooperation between the two countries. The marine environment monitoring system in China and Korea is being reviewed. China operates a 10 metre buoy in the northern part of the Yellow Sea, while Korea is building an ocean observation tower in the southern part of the Yellow Sea, which will begin operation in 2003. Both China and Korea are interested in deploying offshore buoys in the middle of the Yellow Sea. SKORC is studying the feasibility of building a joint platform in the middle of the Yellow Sea through China-Korea bi-lateral cooperation. The meteorological buoys need to be extended to cover ocean environment parameters such as surface water temperature, suspended sediment concentration and chlorophyll, which can be used as a ground truth to validate remote sensing data. Monitoring of the marine environment parameters along the international ferry route in the Yellow Sea between China and Korea using a ferry boat is also planned. An integrated ocean monitoring system can be established by developing a real-time field observation system and complementary methods such as coastal and ocean numerical modelling and remote sensing. By synchronising the real-time coastal data with numerical modelling, detailed information on the spatial and temporal variations of the coastal environmental conditions are provided. The concept of designing a coastal monitoring programme is that coastal environmental information can be obtained by means of coastal prediction technology using state-of-the-art coastal models and utilising the field data taken from a limited number of coastal stations to improve the reliability of the prediction model (Lee et al., 1998). Integration of the in situ, real-time data and remote sensing data can become a crucial component of YOOS. Conservation of the marine environment and ecosystem is one of the main topics of concern of the two countries. Building the capacity to predict changes in the marine environment and ecosystem is essential. A long-term research programme for both China

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and Korea for studies of the Yellow Sea needs to be initiated. For such an approach, a training workshop has been designed to bring the experts of both China and Korea together to discuss further cooperation in building operational oceanographic system for the Yellow Sea. A series of meetings between Chinese and Korean marine scientists have taken place to provide an overview of the present status and to set proper goals to be achieved in the near future. The meetings will also establish a proper strategy to achieve the goals through different presentations and discussions between those who are closely related to the operation and research development in the region.

5. Discussion and conclusion The environment has changed considerably since the establishment of the SKORC. The way of coordinating the ocean science cooperation and operation of SKORC may need to be revised accordingly. SKORC is now preparing a more effective implementation plan for the development of the ocean research cooperation programme between China and Korea with emphasis on the initiation of YOOS through its coordinating mechanisms between China and Korea. The Yellow Sea receives a lot of international interest and concern due to its rich coastal sea features of strong tidal current, suspended sediment and ecosystem. One of the main ocean science goals is that Operational Oceanography will meet the needs of the users. It is certain that China and Korea have to work together to establish an Operational Oceanographic System successfully in the Yellow Sea. Once the bi-lateral programme between China and Korea has successfully initiated the YOOS, North Korea needs to be invited to participate in the YOOS so that the system will be more complete and efficient in producing the data products required by many applications for the Yellow Sea. It could also be extended to include the East China Sea by inviting Japan to participate in an action implementation plan. The leading agencies involved in the Japarl~ast Sea such as Kyushu University, Korea Ocean Research and Development Institute, Pacific Institute of Oceanography, etc. need to get together to design the strategic plan and also specific implementation plan of the sub-regional programme for the Japan/East Sea Operational Oceanographic System. When both sub-programmes are initiated and implemented properly within a limited number of countries in the sub-regional sea and combined together, the final goal of the establishment of a regional ocean observing system in the North East Asia Region, NEAR-GOOS, can be achieved. We will be able to make greater progress on YOOS once a strategic and implementation plan is properly established. Emphasis is placed on the strategy plan so that the marine experts of China and Korea will be able to prepare the proper implementation action plan accordingly. SKORC would be able to take the secretarial job for the YOOS when it is implemented, since the coordination between the two countries is the most important function of SKORC. The marine community of both countries would be able to enjoy the benefit of the YOOS.

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References IOC, 1996. Draft Pilot Implementation Plan for North-East Asian Regional Ocean Observing System (NEAR-GOOS). Lee, D.Y. and K. Taira, 1997, Development of NEAR-GOOS, Proceedings of the First International Conference on EuroGOOS, The Hague, The Netherlands. Lee, D.Y. et al., 1998, Integrated Coastal Monitoring Systems, Tech. Rep. BSPN 9734500-1092-2, KORDI, Ministry of Science and Technology, Korea.

Operational products and services for the Belgian coastal waters Virginie Pison* and J o s 6 0 z e r Management Unit of the North Sea Mathematical Models, Belgium

Abstract The maritime zone that is under the jurisdiction of the Belgium state may be rather limited (about 3600 km 2) nevertheless it is characterised by a very high density of human activity. This paper shortly describes some of these activities and presents the management approach that is followed for some of the concrete problems raised by these activities. An initial brief description of the hydrodynamic model used operationally is followed by links with various problems (oil pollution, sediment transport, sand and gravel extraction and functioning of the ecosystem). The system used to disseminate the information will also be presented as well as some of the international activities in which MUMM is involved.

Key words: Belgian maritime zone, operational oceanography,

management,

monitoring.

1. Overview of some activities in the Belgian maritime zone It is well known that the maritime traffic in the English Channel is very intense (on average, 250 ships cross the Strait of Dover each day) and, therefore, very dangerous. To limit the risk of accidents, shipping lanes and anchorage areas have been established. Moreover, as the area is rather shallow, most of the shipping lanes and sea harbours have to be dredged. The dredged material (of the order of 12 million tons each year between 1997 and 2001) is dumped at sea raising various questions dealing with the efficiency of the chosen dumping sites as well as with the impact of the dumping activity on the functioning of the ecosystem. Sand and gravel are also heavily extracted in the area (in 2000, around 1.8 million m 3 of material have been extracted). These are used either in the building sector or for beach maintenance. Here also, the activity raises various questions dealing with its impact on the morphology of the exploited sandbanks and/or the functioning of the ecosystem in the area. A lot of communication cables as well as pipelines are deployed on the seabed. For each of them, a security zone has been defined. Military activities also take place in the area. Shooting activities for example take place from the shore, but more importantly, the "Paardenmarkt", the area the west of Zeebrugge where a lot of WWI munitions have been dumped, clearly requires a specific monitoring programme. To finish up this brief overview dealing with human activities in Belgian maritime zone, it should be mentioned that, in the near future, one or several windmill parks could be * Corresponding author, email: [email protected]

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Operational products and services for the Belgian coastal waters

implemented in the area (one has already received the required authorisation). For each of them an a priori environmental impact assessment study has to be carried out and for each actual implementation a monitoring programme will be put in place.

2. Management tools Within the framework of a sustainable development of the area, all the activities already mentioned (as well as others not mentioned here) clearly required a complex management approach. To a large extent, this must be specifically developed for the area of interest. It must rely on tools specifically developed and optimised for that zone. It must take into account the impact, on the maritime zone, of human activities on land (80% of the chemical pollution in the area come from terrestrial activities; most of the nutrients come from the agricultural and household activities). Clearly, for a successful sustainable development of the area, a multidisciplinary approach that includes monitoring, modelling and management is necessary. Monitoring helps to observe and control the activities. It enables investigation of the way the system evolves in time and provides data necessary for validating the models. Modelling provides a better understanding of some processes and is used to forecast the evolution of the system. Both activities are clearly linked and are certainly necessary for any management decision that has to be taken. Operational oceanography is precisely the activity that combines all these aspects in an attempt to provide useful information to all people involved: decision-makers, economists and citizens.

3. The hydrodynamic model At MUMM, the COHERENS model (Luyten et al., 1999) is progressively implemented in operational mode. The model has been developed by an international team within the framework of various EU funded projects ( M A S T - 0 0 5 0 - C [Profile I], M A S 2 - C T 9 3 0054 [Profile II] and MAS3-CT97-0088 [Coherens]). The model is now public domain and more than 500 people already have a copy of it. Apart from the hydrodynamic module, the model also includes a biological module as well as a module for sediments and contaminants. Currently the model is used in operational mode to forecast the surface elevation and currents. Temperature and salinity will be introduced later when the procedures for the exchange of boundary data foreseen within the framework of NOOS (Holt, 2003) will be implemented. A 2D implementation (barotropic mode only) over the North West European continental shelf provides the open boundary conditions to a 3D North Sea version (20 sigma layers). For the Belgian maritime zone, a third model implementation on a highresolution grid (800m) is used. Further grid refinements are possible for model applications in specific areas. Model results are made available at http://www.mumm.ac.be, updated twice daily. In situ data are essential for model validation. As an example, a comparison between

computed and measured (with an ADCP) currents at one point in the Belgian maritime zone is presented in Figure 1. Model results are close to the observations (except maybe close to the bottom, but the model is not yet completely calibrated).

Virginie Pison* and Jose Ozer

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Figure 1 Comparison between currents computed by COHERENS and measured by an ADCP

4. Oil pollution Following the Bonn agreement, an aerial surveillance programme was set up in 1991. This surveillance is carried out to detect pollution and to help combat it. Moreover, it aims to prevent violations of the anti-pollution regulations. About 250 flight hours are planned every year. Although most of them are still used for anti-pollution surveillance, the control of other activities (e.g. fishing) and/or the observation of some particular processes (e.g. algae bloom) are now included in the programme. In case of pollution, its behaviour (transport, spreading and ageing) is forecasted with the help of a mathematical model (Scory, 1991) so as to provide useful information to the teams in charge of the surveillance and of the wrestling. The model usefulness has been demonstrated in various incidents. Just as an example, in July 1995, within the frame of the incident between two ships (the CARINA and the SAMIA), wave and oil spill forecasts were used for the determination of the optimal time window for an intervention at sea. It is foreseen that the model will be used in the near future search and rescue operations in collaboration with RSC/WOPS (Rescue Sub Centre / W i n g OPS), which is the search and recue team for the Belgian coastal zone.

5. Sedimentology and spatial imagery The sediment transport model is used within the frame of various research programmes: efficiency of the dumping sites used in the dredging activities, balance of sediment

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transport in the area, etc. It enables a better understanding of the dynamics of cohesive sediments as well as determination of the main sources (Strait of Dover, coastal erosion, etc.). The model forecasts the concentration of suspended matter and the thickness of mud on the bottom, taking waves and currents into account. A strong spring-neap cycle is observed in the behaviour of mud in the area. During spring tides, most of the material is in suspension while it remains on the bottom during neap tides. The model simulates the essential features of sediment transport well and, in particular, the coastward recirculation of fines as observed in a series of radio-active tracer experiments (Van den Eynde, 2003). Concentration of suspended sediments can be derived from data collected by sensors on satellites. An example of a picture obtained after the processing of SeaWIFS data is given in Figure 2. MUMM is very active in this field. The standard SeaWIFS atmospheric correction has been extended for use over turbid water (Ruddick et al., 2000).

Figure 2 Concentration of suspended sediments [mgl-1]JProduct derived from SeaWiFS image taken on 14 February 1998 at 12:58 UTC

6. Sand and gravel extraction The sediment transport model has also been used to estimate the impact of a reduction (5m) of the height of an exploited sandbank (the Kwintebank). The results obtained so far indicate that, in the present situation, there is almost a balance between erosion and sedimentation. Such a balance seems to be lost when the actual height of the bank is reduced. The impact on the functioning of the ecosystem, due to an increase in turbidity in the area where the activities take place has been evaluated with a coupled physical-


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biological model. Fortunately this impact seems to be very limited. This is explained by the fact that if light controls the timing of the phytoplankton bloom the strength of the blooming is determined by the amount (and composition) of the available nutrients. Sandbanks have so far been monitored mainly with traditional methods (a surface vessel equipped with a single beam or, since 1999, with a multi-beam echo-sounder).

Figure 3 The autonomous vehicle MAUVE The aim of the SUMARE project (http://www.mumm.ac.be/SUMARE) is to demonstrate the added value of autonomous sensors within the frame of the monitoring of the marine environment. Two applications are studied: the monitoring of the volume of sandbanks and the mapping of living/dead marl. The main advantages expected are efficiency (quick deployment and reduced costs) and accuracy (better spatial resolution and appropriate sampling rate). For the monitoring of sandbanks, the use of a miniaturised, re-configurable, mobile and autonomous vehicle (MAUVE) is foreseen.

7. Ecosystem modelling In 1998, faced with the problem of eutrophication, European countries agreed, within the OSPAR strategy on eutrophication, to make every endeavour "to reach, by 2020, and maintain a healthy marine environment where eutrophication does not occur". Ecosystem models can be used to provide a better scientific basis to reach this goal by assessing the probable consequences of environmental policy on phytoplankton levels. The models allow alternative scenarios to be simulated, such as reductions of nutrients by different percentages. A further source of data for ecosystem model validation will be provided by satellite images from Envisat/MERIS, which was launched on March 1, 2002. MUMM's ecosystem modelling activities are funded by the Belgian Science Policy Office "Sustainable Development North Sea programme" and are carried out in collaboration with the "Ecologie des Syst6mes Aquatiques" (ESA) laboratory of the Universit6 Libre de Bruxelles, the College of Oceanic and Atmospheric Sciences of Oregon State University, and the Laboratory of Ecology and Systematics of the Vrije Universiteit Brussel in the AMORE project.

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8. Monitoring and exchange of the data The real-time data acquisition system ODAS (Oceanographic Data Acquisition System) gathers and processes up to 200 parameters. The corresponding database is updated virtually in real time. In addition to the physical and chemical parameters measured constantly since 1984 during each Belgica campaign (~200 days/year), it contains a substantial collection of current and wave measurements taken during long-term anchorage in the 1970s and 1980s, as well as CTD profiles since 1984. The database on the quality of the marine environment (IDOD, Integrated Dynamical Oceanographic Data Management) mainly contains the concentrations of numerous substances in the air, the water, the sediment and the biota (that is, in living organisms). These values are the result of measurements taken in situ and analyses carried out in laboratories. In addition to the concentrations, quantitative information on the biota is also stored. In an attempt to deliver an optimal service to scientists and decision-makers, tools for the visualisation and statistical analysis are also made available. This database has just been brought into use (http://www.mumm.ac.be/datacentre).

Figure 4 Spatial visualisation of some of the information available from the data centre

9. Management--national and international activities Nothing can be done for the marine environment in the North Sea without real international co-operation. This is most effective at the regional level, which for us means the north-east Atlantic and the North Sea. This explains the great interest of MUMM in all co-operative efforts at the European level like those developed within the framework of EuroGOOS and NOOS. The key instruments in this context are the OSPAR Convention for the Protection of the Marine Environment of the North-East Atlantic (Paris, 1992), the Bonn Agreement for cooperation in dealing with pollution of the North Sea by oil and other harmful substances (1983) and the system of International Conferences on the Protection of the North Sea. MUMM leads the Belgian delegation at these fora. Consequently it has to define the Belgian positions beforehand, uphold them in meetings and ensure that the decisions taken are implemented, in particular by means of appropriate legislation. To be complete, we have also to mention the EU framework directive on water 2000/60/EC that is an important new factor in marine environmental policy as well


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as debates and discussion held within specialised United Nations Agencies, such as the International Maritime Organisation.

Conclusion The actual cases described in this document show the usefulness of the activities of modelling and monitoring and the advantages of combining them to help the management of the maritime zone. These activities must follow the technological advances and have to be implemented in a local approach to best meet the needs of the various stakeholders. Nevertheless the greatest importance is placed on the necessity of an efficient collaboration to solve and prevent the problems on the marine domain which is intrinsically without boundaries.

References Luyten, P.J., J.E. Jones, R. Proctor A. Tabor, P. Tett, and K. Wild-Allen, 1999, COHERENS--A Coupled Hydrodynamical-Ecological Model for Regional and Shelf Seas: User Documentation. MUMM Report, Management Unit of the Mathematical Models of the North Sea, 991 pp.(http:Hwww.mumm.ac.be/coherens) Holt, M., 2003, Towards N O O S m T h e EuroGOOS NW Shelf Task Team 1996-2002. Proceedings of 3rd EuroGOOS Conference (this publication), p. 461. Ruddick, K.G., F. Ovidio, and M. Rijkeboer, 2000, Atmospheric correction of SeaWIFS imagery for turbid coastal and inland waters. Applied Optics, Vol. 39, No. 6, pp. 897-912. Scory, S., 1991, Assistance technique aux Autorit6s portugaises pour la mod61isation des pollutions par hydrocarbures dans les eaux marines sous juridiction portugaise. Contrat C C E - D G XI B6612-90-006913, Rapport final. Van den Eynde, D., 2003, Interpretation of tracer experiments with fine-grained dredging material at the Belgian Continental Shelf by the use of numerical models. Paper submitted to Journal of Marine System, Special Issue of the 34th Li6ge Colloquium on Ocean DynamicsmTracer Methods in Geophysical Fluid Dynamics.

Co-ordinating UK inputs to EuroGOOS and GOOS M.J. Cowling* and I.H. Townend Inter-Agency Committee on Marine Science and Technology, UK

Abstract This paper describes recent developments in the UK strategy for the conservation and sustainable development of the marine environment, and the current arrangements for the co-ordination of monitoring and observing of the marine environment, as the basis for the UK contribution to GOOS and EuroGOOS. The paper also describes processes which lead to improved co-ordination and access to spatial data and mapping of the marine environment. These arrangements are overseen by the Inter-Agency Committee on Marine Science and Technology (IACMST), which itself is composed of representatives of UK Government Departments and Agencies with significant marine interests.

Keywords: Co-ordination, marine, environment, monitoring, data. 1. Introduction The United Kingdom Government has recently published a new strategy for the conservation and sustainable development of the marine environment (DEFRA, 2002), which has become known as the 'Marine Stewardship Report', MSR. The key marine stewardship commitments include: 9 Developing better integration of marine environment monitoring and observation 9 Improved co-ordination and access to spatial data and mapping of the marine environment. To this end the UK participates in several inter-governmental monitoring activities, including the Global Ocean Observing System.

2. Co-ordination Marine observations and marine data are to be found widely distributed in various Government Departments and Agencies. To make UK participation in EuroGOOS and GOOS most effective, two mechanisms have been established for inter-Departmental coordination: a GOOS Action Group and a Marine Environmental Data Action Group, both under the auspices of the Inter-Agency Committee on Marine Science and Technology, which reports to the Government' s Chief Scientific Adviser. Working across the various Departmental and Agency interests for an overall national contribution to GOOS presents many difficulties. The UK IACMST GOOS Action Group is working to develop a national GOOS strategy, including a programme of dissemination of information, evaluating the economic benefits, preparing assessments of the marine environment, and interacting with the EuroGOOS and GOOS offices. The Action Group is currently contributing to a the development of new UK marine * Corresponding author, email: [email protected]

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monitoring strategy and undertaking a cost-benefit analysis of a range of targeted monitoring programmes. The work of the UK GOOS Action Group may be viewed through the IACMST web site at www.marine.gov.uk The Marine Environmental Data Action Group has a small co-ordinating secretariat based at the British Oceanographic Data Centre (BODC) at the NERC Proudman Oceanographic Laboratory. The UK marine environmental data network facilitates scientific research and underpins other operations. Its core activities are: 9 To develop, maintain and make available inventories of data 9 To improve mechanisms to facilitate data exchange (including contributing UK data to global databases) 9 To develop guidelines for data management so as to improve curation and utility of data 9 To raise the visibility of the UK marine environmental data network and the availability of data, leading to greater efficiency in data use. Access to UK marine environmental data and data products is possible through the IACMST website at www.marine.gov.uk or the Action Group website at www.oceannet.org. The list of on-line data catalogues includes directories of marine environmental data sets, inventories of research cruises, moored current meter data and marine monitoring observations. The specific coastal data resource contains information on coastal data initiatives and links to appropriate coastal data collecting organisations.

3. Products Recent products arising from the IACMST co-ordination and action group activities include an assessment of the intrinsic value of marine sample collections (Rothwell, 2001) and a climate status report for UK waters (Alcock and Rickards, 2001). The latter is the first in a planned series of updates and the pre-cursor to a web-based time-series atlas for the waters around the UK. Further planned products for 2003 include a report reviewing international metadata standards.

4. Conclusions It is apparent, from this summary of one country's attempts at co-ordinating national marine observations in the context of EuroGOOS and GOOS, that there are still many difficulties to be overcome. Problems of a technical, administrative and accounting nature, augmented by cross-governmental Department co-ordination will be common to many countries. Now that EuroGOOS and GOOS are moving towards an operational phase, it will be interesting to compare such internal co-operation within the various contributing countries.

References Alcock, G. and Rickards, L.J., Eds., 2001, 'Climate of UK Waters at the MillenniumD Status and Trends', IACMST Information Doc. No. 9. www.marine.gov.uk/publications/InfoDoc9.pdf

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DEFRA, 2002, 'Safeguarding our Seas', DEFRA Publications UK, ISBN 0-85521005-2. www.defra.gov.uk/environment/marine/stewardship/pdf/marine stewardship.pdf Rothwell, R.G., 2001 'Marine Sample Collectionsmtheir value, use and future', IACMST Information Doc, No. 8. www.marine.gov.uk/publications/InfoDoc8.pdf

3D, EOF-based spatial analysis of Gyroscope observations in the North Atlantic Ocean S. Ruiz*l, D. Gomis 2 and J. Font !

1Centre Mediterrani d'lnvestigacions Marines i Ambientals, CSIC, Barcelona, Spain 2Institut Mediterrani d'Estudis Avangats (CSIC-UIB), Mallorca, Spain

Abstract A three-dimensional Empirical Orthogonal Function analysis is applied to density profiles collected by autonomous floats in the North Atlantic Ocean. Preliminary results reveal that two modes account for most of 90% of the variance of the density field.

Keywords: EOF analysis, floats, North Atlantic Ocean 1. Introduction The EU-funded Gyroscope (2001-2003) project aims at developing a component of the in situ observing system for the North Atlantic Ocean. It is a contribution to the international ARGO project, which plans to deploy a global array of some 3000 autonomous profiling floats to observe the large scale ocean variability. During 2002, Gyroscope has completed deployment of 80 floats. The objectives of the project are: 1. To deploy an initial array providing real-time data to users 2. To estimate the information content of the array 3. To develop procedures for real time ocean state estimation 4. To estimate time varying ocean transports and structure 5. To develop a cost effective sampling strategy to observe ocean variability in the North Atlantic This paper presents results related to objective 2. In particular we show preliminary results of a fully 3D analysis based on Empirical Orthogonal Functions applied to the density profiles obtained by the Gyroscope floats.

2. Data set By the end of November 2002, a total of 1790 profiles had been performed by the Gyroscope floats. The floats used in this project are of the type PROVOR and APEX. After launch, a float reaches the predetermined depth ('parking depth'), drifts there with the current for 10 days, then descends to the maximum depth (1500 or 2000m) and ascends to the surface while measuring temperature, salinity and pressure. Once on the surface, the data are transmitted through the ARGOS system. After 4 to 8 hours on the surface the float starts a new cycle.


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3D, EOF-based spatial analysis of Gyroscope observations in the North Atlantic Ocean

In this work we have selected the profiles for the July-October 2002 period (the 80 profilers were operating). The main issue is to investigate the information that can be extracted from the Gyroscope floats.

3. Methodology Steps of the EOFs methodology: 9 Compute the "climatological" modes and amplitudes for the period July-October 2002. 9 Objective analysis of amplitudes on grid points and on October stations 9 Compute October amplitudes using "climatological" modes and original October profiles 9 Obtain the anomaly amplitudes for October 9 Objective analysis of anomaly amplitudes on grid points 9 Obtain the total amplitude for October adding the "climatological" and anomaly amplitudes 9 Finally, recover density profiles for October.

4. Preliminary results on density field Figure 1 shows the density modes obtained from the floats cycles in July-October. A

priori, the smooth modes ensure the vertical consistence of the structures captured by the analysis. The first mode explains 77% of the total variance of the density field. The percentage increases to 92% if the second mode is included (Figure 1, variance table).

Figure 1 Vertical density modes (left) and percentage of variance of the total density field explained by each mode (fight) Figure 2 shows the climatological density field at 20m depth, while Figure 3 shows the density field for October at 20m depth (climatological field plus October anomaly). Small but significant differences are detected in structures such as the eddy next to the Spanish coast. However, the statistical significant of these results (mainly climatological

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EOFs) have to be improved using the available historical databases and the new data collected every month by the floats.

Figure 2 Climatological density field at 20m

Figure 3 Climatological density field plus October anomaly at 20m

5. Further work Comparison between the EOFs analysis and 2D standard analysis is being carried out in order to investigate the most adequate technique to apply to the data distribution. Particular attention will be given to the coherence between horizontal and vertical structures, which is essential for the computation of derived variables such as potential vorticity.

A unified model system for the Baltic Sea Lennart Funkquist Swedish Meteorological and Hydrological Institute, Sweden Abstract

The operational oceanographic system at the Swedish Meteorological and Hydrological Institute delivers daily forecasts on both hydrographic and ecological conditions for the Baltic Sea region. The modelling system is modular and contains blocks for atmosphere, ocean, wind waves, sea ice and ecology. To take full advantage of today's computer capacity, the system is built to run on machines with both distributed and shared memory. 1. I n t r o d u c t i o n

The coupled hydrodynamic/sea ice/ecology system (Figure 1) is built on the existing ocean prediction system with ecology as the last incorporated module. Both the resolution and the forecast length are variable within the system. Operational models for runoff (HBV), wind waves (HYPNE), NE Atlantic storm surge (NOAMOD) and atmospheric deposition (MATCH) are forced by atmospheric data from the ECMWF global model and the HIRLAM (HIRLAM, 1993) limited area model. The ecological module SCOBI solves for seven variablesmnitrate, ammonia, phosphate, oxygen, phytoplankton, zooplankton and detritus. The model system constitutes the main part of the HIROMB project (Funkquist, 2002), a collaboration between countries around the Baltic Sea. ATMOSPHERIC MODELS HIRLAM ECMWF

Limited area model

HBV

Hydrological runoff model

HYPNE

Wind wave model

Global model

NOAMOD

North Atlantic storm surge model

HIROMB/SCOBI

MATCH

Atmospheric chemical transport model

3D baroclinic ocean model fully coupled with a sea ice rood and a biogeochemical model

Figure 1 The coupled operational system at SMHI

Email: lennart.funkquist @smhi.se

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2. Model domain The model system runs on 3 nested grids with a resolution ranging from 12 nautical miles for the North Sea to 1 nautical mile for the Baltic Sea (Figure 2). The forecast length is 48 hours for the 1 nm grid and 168 hours for the 3 nm grid. Nested grids for HIROMB

12Ow

0o

12~

2g E

Figure 2 Different grids used for the operational ocean model. Grid A is the storm surge model for the NE Atlantic with a resolution of 24 nautical miles (n.m.). Grids B, C and D are for the 3D model with a resolution of ]2.3 and ] n.m. respectively.

3. Forcing A storm surge model for the NE Atlantic, together with prescribed tidal elevation, controls the water elevation at the open boundary to the North Atlantic. The river runoff model covers the whole Baltic Sea drainage basin and the Norwegian rivers entering the Skagerrak. Climatological values on nitrate and phosphate are used for the river input. The forcing from the wind wave model produces enhanced mass flux (Stokes' drift) and mixing in the surface layer. The atmospheric deposition model provides wet and dry deposition of ammonia and nitrate. The hydrodynamic model itself (HIROMB, see Funkquist and Kleine, 2000) is forced by 10m wind, sea level pressure, 2m humidity, 2m temperature and cloudiness from either the ECMWF or HIRLAM model.

4. Domain decomposition The two model systems run on an SGI 3800 and a Linux cluster. The model is parallelised to take full advantage of multi-processor vector machines with distributed memory. The model domain is decomposed into a number of blocks. The optimum blocking structure is given a weighting based on the minimum number of inactive points and the administrative overhead.

5. Real-time validation A number of selected products from the model, such as time series and synoptic pictures, are put on a web site as a daily service for the Marine Forecasting Department at SMHI. When available, real-time observations are shown together with the forecasts thereby

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constituting continuous validation. As the main driving force together with its own realtime validation is produced at the same institute, it is possible to separate between the quality of the atmospheric and ocean forecasts.

6. Operational products for users The main product from the system is the current field, which is mainly used for searescue operations and oil-drift forecasts. An internet-based dispersion program (SeaTrackWeb), which calculates the drift of all kinds of objects ranging from oil to ships, is available on-line for the Coast Guard. By keeping all data at one centre it is only the drift forecast that has to be transmitted, thereby reducing the data transfer time. It is also possible to find the source of found objects by making hindcasts. The surface currents, sea ice conditions and wind waves are used daily by the Marine Forecasting Department. For example, strong currents in narrow straits have effect on optimal use of fuel. Other examples, where a forecast of the hydrographic conditions is important, are risk of high waves when transporting large constructions and severe ice conditions or low water level when accessing harbours. Predictions of hydrographic conditions have also been used in connection with scientific fishing expeditions where the vertical distribution of salinity and temperature determines where to fish. The forecast fields are put on an ftp server, to which all members of the HIROMB project have access. These fields are then used either directly or as driving forces to regional models covering parts of the coastal area.

7. Forecast database The output from the forecast system is put on an on-line data base giving an excellent opportunity to directly assess the environmental status of the Baltic Sea. Another use could be a complement to regular measurements in monitoring programs. The database has also been used in biological experiments, e.g. tracking different kind of larvae either as passive particles or with a rhythmic vertical behaviour.

Acknowledgement The Nordic Ministry Council has financed part of the development and implementation of the ecological module.

References HIRLAM, 1993, The HIRLAM 2 Final Report, ed. N. Gustafsson. HIRLAM Techn. Rept. 9. Available from SMHI, S-60176 Norrk6ping, Sweden. Funkquist, L. and E. Kleine, 2000. An introduction to HIROMB, an operational baroclinic model for the Baltic Sea. SMHI Reports, Oceanography, available from SMHI, S-60176 Norrk6ping, Sweden. Wilhelmsson, T., J. Schtile, J. Rantakokko, and L. Funkquist, 2002, Increasing resolution and forecast length with a parallel ocean model. In Operational Oceanography; Implementation at the European and Regional Scale. Elsevier Oceanography Series, 66, pp 77-85.

AIg@line joint operational unattended phytoplankton monitoring in the Baltic Sea Lotta Ruokanen, Seppo Kaitala*, Vivi Fleming, and Petri Maunula Algaline, Finnish Institute of Marine Research, Finland

Abstract The Baltic Sea is a unique continental brackish water sea. Today the Baltic Sea is eutrophied and the blooms of harmful planktonic algae are annual phenomena. The blooms are harmful to the marine ecosystem as well as to the recreational and economic use of marine resources. High-quality research gives reliable information on the state of the ecosystem and its changes. Adequate monitoring information is a prerequisite for sound protection measures and only research is able to reliably show the effects of the protection investments. Because the phytoplankton blooms are extremely patchy and temporally rapidly changing, they often remain unobserved when using traditional sampling methods.

1. AIg@line has monitored and reported the events in the phytoplankton community and the state of the Baltic Sea for 10 years Alg@line is a forerunner in the field of monitoring research and is based on co-operation between several research institutes and shipping companies. In 1992 the Finnish Institute of Marine Research started systematic measurements onboard the ferry Finnjet, crossing the Baltic Sea Proper, using unattended recording and sampling. Alg@line monitors the fluctuations in the Baltic Sea ecosystem in real-time using several approaches. It combines studies onboard research vessels with high-frequency automated sampling onboard several merchant ships (ship of opportunity), satellite imagery, buoy recordings and traditional sampling and observations in coastal waters. Ecosystem models are under development. Without the high-frequency observations with the ship of opportunity technique, the rapid fluctuations in the Baltic Sea ecosystem could not be monitored. Alg@line has analysers and sample collectors on five ships and a sixth vessel will be tested in spring 2003. Yearly 1.5 to 2 million flow-through observations (in vivo chlorophyll a, salinity and temperature), 7 000 semiquantitative species observations and 1000 nutrients observations are gathered. Alg@line is the only research project in the Baltic Sea region, even in the whole world, which utilises the ship-of-opportunity technique in the monitoring of the state of the environment on this scale.

2. AIg@line method and equipment Water is pumped constantly through the sensors from a fixed depth (ca. 5 m) while the ship is moving. The in vivo chlorophyll a fluorescence, temperature and salinity are recorded quasi-continuously with a spatial resolution of 100-300m while the ferries are moving. Concurrently, water samples are taken. The measurements and sampling are repeated every 0.5-3 days in the same sea area depending on the schedule of the ferry. * Corresponding author, email: [email protected], website: www.itameriportaali.fi, www.fimr.fi

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2.1 E q u i p m e n t o n b o a r d

the ship

9 Computer 9 GPS navigator

9 Flow-through fluorometer 9 Thermosalinograph 9 Refrigerated water sampler 2.2 Measured parameters

9 latitude and longitude (spatially 100-200m accuracy) 9 time (date and time) 9 S, T, chl a fluorescence 2.3 Activities at laboratory

9 Parameters: phytoplankton species, chl a, PO 4, NO 3, NH 4, Si, Tot P, Tot N, partly turbidity 9 Data processing Sampling and analyses are quality controlled using ISO GUIDE 25 and S F S - E N 45001, and laboratory methods are accredited. After the preliminary quality control, the data is used operationally to produce graphs and quick statistics. The final quality check of the data is done annually and data is used for annual monitoring reporting. Finally the data is stored in a database for further analysis and research.

3. Reporting On-line reports on phytoplankton blooms and general information on the Baltic Sea are published in the Baltic Sea Portal: www.itameriportaali.fi. Annual monitoring reports are published in partners, own publications and e.g. within HELCOM. The 10th anniversary report of Alg@line was published in Spring 2003. Alg@line is a collaboration between: 9 Estonian Marine Institute 9 Uusimaa Regional Environment Centre 9 City of Helsinki Environment Centre 9 Southwest Finland Regional Environment Centre 9 West Finland Regional Environment Centre 9 Southeast Finland Regional Environment Centre 9 Shipping companies (Silja Line, Transfennica) 9 Finnish Frontier Guard 9 Finnish Sea Scouts 9 Finnish Environment Institute Alg @line also co-operates with HELCOM, ICES, EuroGOOS, BOOS, etc.

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Figure 1 Annual variation in the concentration of phosphate (mmol1-1) in the surface water of the western Gulf of Finland. The squares represent measurements made in 2001 and the triangles measurements made in 2002. The dots represent measurements made in 2003.

Figure 2 Annual variation of phytoplankton (measured as mg chlorophyll a m -3) in the western Gulf of Finland. The curve represents the average for the years 1992- 2002, the dots measurements made in 2003.

Figure 3 Concentration of chlorophyll a (algal biomass) in the surface layer along the route of the ferry Finnpartner from Travemtinde to Helsinki, 27-29 July 2003. Data: Finnish Institute of Marine Research.

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4. Projects which have used AIg@line-data EU projects at FIMR: 9 BOING (information system, not a scientific project, 2000-spring 2003) 9 HABES (2001-2003) 9 HABILE (2002-2004) 9 FerryBox (will start in 2003 and carry on for 3 years)

4.1 Other projects and co-operation 9 co-operation with EMI in 2001-spring 2003, funded by the Finnish Ministry of the Environment 9 FEI, calibration of satellite images, started 2001 9 FEI, cyanobacterial bloom forecast model 9 GEF BSRP SOOP, to be started in 2003

5. Advantages The versatility and openness make it possible to tailor the combination of sensors and analysers according to the specific requirements of the user. The basic equipment is relatively inexpensive and the final costs are determined by the prices of the sensors. The environmental circumstances on ferries correspond almost to those in laboratories and therefore no special requirements for the analysers and sensors are needed. The regular visits of the ships in the harbours make the maintenance of the system easy to carry out and enable the water samples to be fetched rapidly for laboratory analysis. Dense spatial sampling in combination with frequent voyages enables the reliable detection of patchy plankton blooms. The high-resolution sampling provides comprehensive data for long-term time series and trend analysis. On the basis of fluorescence recordings, the water samples for the time consuming phytoplankton species determination can be preselected: only samples that coincide with high chlorophyll a values are analysed. The number of samples analysed can be reduced but the necessary information on the bloom forming species is still obtained.

Acknowledgements This paper is a contribution to the EU-FP5-funded project, "From On-line Oceanographic Observations to Environmental Information", contract E V K 2 - C T - 2 0 0 2 00144.

Pre-operational system for oil spill simulation P. Sebasti~o and C. Guedes Soares*

Instituto Superior T6cnico, Unit of Marine Technology and Engineering, Technical University of Lisbon, Portugal

Abstract A system to simulate and visualise the trajectory and fate of oil spills near the Iberian Peninsula is presented. It comprises a set of modules to calculate the weathering of an oil spill and its trajectory. A database stores geo-referenced information, such as bathymetry and mean conditions of wind, waves and currents, and, the physico-chemical properties of the most common crude oils. When used operationally the system receives the input of online met-ocean forecast data and uses it to make predictions. If forecast data is not available the predictions can also be made using the mean met-ocean conditions resident in the system. An example is provided of its application to the oil spill originated by the accident involving the tanker Prestige off the Coast of Spain, in November 2002.

Keywords: oil spills, modelling, marine pollution 1. Introduction Oil spill models are important to support decision for operations to combat oil spills at sea. Often the positions of the slicks are monitored with the help of aircraft and thus the model is expected to make forecasts of 24-48 hours. In these situations it is important that quick answers are provided but the accuracy is not as critical as ensuring that the deviations that can occur within these time periods are not very large. The oil spill forecasting system that is being developed has a database and three main modules: a weathering module, a trajectory module and a visualisation module (Guedes Soares et al., 2000). The last one is a graphical interface that allows visualisation of meteorological data as well as the model outputs. By making use of monthly mean met-ocean data stored in the database a first guess of the trajectory of an oil spill can be made, before receiving real met-ocean information. If real met-ocean data, or forecast data is available then it is introduced in the system to make the calculations. The system is being developed in order to be able to receive online met-ocean data, so that it can work operationally. A case study is presented using the system to predict the trajectory of the recent oil spill caused by the accident of the tanker Prestige off the Spanish Coast of Galice. The trajectory was calculated from three different locations where the tanker passed during the first and the second day after it started to release oil into the sea.

2. The oil spill model The trajectory module calculates the trajectory of the spilled product taking into account the local current, wind, the Stokes' drift and a deflection angle due to Coriolis effect, in a * Corresponding author, email: [email protected]

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Pre-operational system for oil spill simulation

Lagrangian approach, as described in more detail in Guedes Soares et al. (2000). The modules to compute the weathering of the spills are described in Sebasti~o and Guedes Soares (1995, 1998). They take into account the processes of spreading, evaporation, dispersion and emulsification and compute the evolution of the viscosity and density of the spilled oil. The database of the system has geo-referenced information of the bathymetry, monthly mean values of wind (COADSmComprehensive Ocean-Atmosphere Data Set, see Dfaz et al., 1992) and currents (Richardson et al., 1984, 1986), and the properties of the most common crude oils. The data covers the Exclusive Economic Zone of Portugal (Figure 4) with a resolution of 1~ Lat x 1~ Lon (approximately 60x 46 nautical miles).

Figure 1 Exclusive Economic Zone of Portugal, which covers Continental Portugal, Aqores and Madeira Islands. The line from A to B represents the trajectory of the tanker Prestige from 13 November 2002 (A) until it sank on 19 November 2002 (B), as described below. The operational aspect of system, which is under development, consists basically of setting up the process of receiving met-ocean data via web and automatically store it in a database. Then, the oil spill model will retrieve the necessary data from the database to perform the calculations.

3. Case study: Prestige oil spill 3.1 Brief description of the accident On the 13th November 2002, the Bahamas-flagged tanker Prestige, carrying 77 000 tons of heavy fuel on board, suffered a large crack in the starboard side of the hull when it was sailing off the West of Cape Finisterre in the Galician coast of Spain, under stormy weather conditions. The tanker started taking water and leaking oil into the sea. On the same day the tanker started receiving the assistance of tugs, and initiated a journey that took it away from the coast, first to the North and then to the South, until its structure collapsed and broke into two parts that sank, with a great part of its cargo still inside, on the 19th November, some 200 km off the Spanish and the Portuguese Coast. Along that journey the tanker kept spilling oil and when it sank an additional spill occurred. The total amount of spilled oil, until that moment, was of the order of 103 tons.

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3.2 Trajectory simulations Figure 2 shows the trajectory of the tanker Prestige from the location where it first suffered a crack on the hull to the site where it would sink 6 days later.

Figure 2 Trajectory of the tanker Prestige while it was being towed.

Figure 3 Trajectory simulation using mean wind and current for November.

Figure 3 shows the simulation of the Prestige oil spill from the initial position using the mean wind and current resident in the database of the system. The simulation indicates that the spill will reach the coast. Three other simulations were done, considering measured wind and current data supplied by Puertos del Estado (2002), from their deep-sea buoy network. A Seawatch buoy station moored at 323 m water depth performed the measures. The measurements are transmitted every hour via satellite to Puertos del Estado and directly posted on the web. Three instantaneous spills were simulated starting at points 1, 2 and 3 on Figure 4 - - t h e first one starting on 13 November 2002 19h (point 1) and the other two on 14 November 2002 at 10h (point 2) and 18:05h (point 3). The simulation period was 8 days.

Figure 4 Three oil spill trajectories simulated starting from three locations (points 1,2 and 3) of the trajectory followed by the tanker Prestige. The tanker symbol highlights the trajectory of Prestige while the other trajectories correspond to simulated spill trajectories.

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Pre-operational system for oil spill simulation

In this way the three simulations intend to represent three different trajectories followed by the spill at different points and different instants of time, along the trajectory followed by the tanker, as shown in Figure 4. Although there was in fact a continuous spill, the simulations that were done showed a general good agreement both in space and time with the observations. Simulating more instantaneous spills would represent better the continuous spill situation and this would lead to intermediate trajectories and landing sites. It can be observed that at the different locations the oil released was submitted to the action of the local met-ocean conditions, which induced different trajectories. The three simulations indicate that part of the spill would clearly reach the Galician Coast and that another part of it would travel along the Northern Coast of Spain towards the coast of France. These results were in fact observed: the spilled oil reached the Galician Coast (both the Western and Northern parts), the Cantabrian Coast (Northern Coast of Spain) and reached the French Coast by the end of December 2002.

4. Conclusions The system that was presented proves to be useful to predict the trajectories of oil spills at sea. It was able to simulate approximate trajectories of the Prestige Oil Spill using measured currents and wind, which can be very important when planning response measures. The mean conditions of current and wind stored in the database allow a first estimate of the trajectory while met-ocean forecast data allow more accurate estimates.

Acknowledgements This work has been performed within the project "Simulation of the Fate of Oil Spills in the Portuguese Coast" which is conducted in cooperation with the Service to Combat Pollution at Sea from the Directorate General of the Maritime Authority and is funded jointly by the Foundation of Portuguese Universities and the Ministry of Defence.

References Dfaz, H.F., K. Wolter, and S.D. Woodruff (Eds.), 1992, Proceedings of the International COADS Workshop, Boulder, Colorado, 13-15 January 1992, NOAA Environmental Research Laboratories, Climate Research Division, Boulder, Colo., 390 pp. Guedes Soares, C., P. Sebastiao, and F. Silva, 2000, System for Oil Spill Prediction, Hydraulic Engineering Software VIII (Hydrosoft 2000), W.R. Blain and C.A. Brebbia (Eds.), WITPress, Southampton, pp.217-226. Puertos del Estado, 2002, Oceanography and Meteorology--deep-sea network, Minist6rio do Fomento, Spain, www.puertos.es. Richardson, P.L. and T.K. McKee, 1984, Average seasonal variation of the Atlantic equatorial currents from historical ship drifts, J. Phys. Oceanogr., 14, 1226-1238. Richardson, P.L. and D. Walsh 1986, Mapping climatological seasonal variations of surface currents in the tropical Atlantic using ship drifts, J. Geophys. Res., 91, 1053710550. Sebastiao, P., and C. Guedes Soares, 1995, Modelling the Fate of Oil Spills at Sea. Spill Science & Technology Bulletin. 2, 2/3, 121-131.

Coastal Systems


Monitoring the Norwegian Coastal Zone Environment (MONCOZE) J.A. Johannessen* 1, B. Hackett 2, E. Svendsen 3, H. Soiland 3, G. Evensen 1, L.P. Roed 2, N. Winther l, J. Albretsen 2, M. Skogen 3, L. Pettersson 1, D. Durand l, and D. Obatonl, 4 !Nansen Environmental and Remote Sensing Center (NERSC) 2Norwegian Meteorological Institute (met.no) 3Institute of Marine Research (IMR) 4Ifremer, Brest, France (visitng scientist at NERSC) Abstract The need for better monitoring and managing of the coastal zone environment has been recognized and agreed in many international declarations and legislations such as MARPOL, HELCOM, OSPARCOM, BARCELONE etc. Moreover, the Water Framework Directive (WFD) requires increased attention to the near shore (lkrn) environmental zone and how it is influenced by the terrestrial hinterland. The MONCOZE project is therefore highly relevant with respect to the concerns and underlying commitments for these agreements. In this project the strengths and deficiencies of the current monitoring capabilities of the southern Norwegian coastal zone will be investigated and characterized. This paper outlines how this is implemented and carried out.

1. Introduction The Norwegian Coastal Current (NCC) is the highway for transporting nearly all the pelagic, chemical and biochemical material entering the North Sea, and spreads it from the Skagerrak to the Barents Sea (Johannessen et al., 1993). As such, it strongly influences the water quality of the coastal zone, which is of major importance for the rapidly increasing fish farming industry. Blooms of harmful algae, such as the Chrysocromulina polylepis toxic bloom in 1988 (Dundas et al., 1989; Johannessen et al., 1988), have clearly demonstrated that this major industry is highly vulnerable. Over the past two decades, the means to observe and model the Norwegian coastal zone, including the Norwegian Coastal Current, have gradually improved through a) developments of in situ and remote sensing observational technologies; b) advances in numerical simulation and high performance computing; and c) new methods for assimilation of heterogenic, timedependent atmospheric, oceanic and chemical data. Despite these developments there are still major deficiencies in our ability to understand and describe the temporal and spatial variability of the NCC and its influence on the marine environment and ecology, locally as well as downstream. These deficiencies arise from lack of regular observations, gaps in our knowledge of the many processes involved, and lack of properly validated models capable of assimilating the heterogenic data and simulating the state and evolution of the system with its large range of underlying components. * Corresponding author, email: [email protected]

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This is therefore a concern in the context of future increasing demand for quality flags which document that marine food comes from a "clean" environment. An integrated system which can meet these demands will rely on the combination of in situ measurements, satellite observations and numerical models. High-accuracy in situ measurements are essential in order to obtain sub-surface observations, as well as for the calibration and validation of satellite data and models. Satellite observations provide wide area (in some cases regular) quantitative information on surface variables, which in some cases may be related to upper layer phenomena. Via systematic combination of these observing methods, a 3D picture of the ocean state may be drawn and used for validation of models. Ultimately, combining well-validated numerical models with observations by means of data assimilation techniques may provide a system that can realistically describe, and eventually forecast, the state of the marine coastal environment.

2. Objectives The overall objective of the MONCOZE project (jointly conducted by NERSC, met.no and IMR) is to develop, test and demonstrate a pilot system for monitoring and prediction of the Norwegian marine coastal environment with particular focus on dominant physical and coupled physical-biochemical interactive processes within the Norwegian Coastal Current and along its open boundaries. The project is aimed at making significant contributions to four specific themes: 1. Norwegian Coastal Current variability: Advance the understanding and description of the mesoscale and sub-mesoscale variability, including the formation, propagation and decay of eddies, the generation and decay of convergent (divergent) zones in frontal regions, and the strength and extent of episodic upwelling. 2. Algal blooms: Develop and demonstrate methods to combine multiple data sources, heterogenic in time and space, for consistent and reliable analysis and estimation of algal blooms, including location of source area and its spatial distribution according to dominant oceanic processes and transport characteristics. 3. Contaminant exposure time: Develop a method for the combined estimation of contaminant and plankton/fish larvae distribution to be able to integrate the total contaminant exposure time on specific populations. 4. Extreme events: Provide monitoring and warnings of extreme and potential harmful events in water properties, such as for instance associated with anomalously cold water outbreaks impacting aquaculture, and fiver floods affecting salinity and turbidity.

3. Approach The MONCOZE approach is based on utilization of existing capabilities combined with incremental advances in technology and scientific research. A sketch of the pilot monitoring system concept that can be applied at a range of time scales, e.g. daily nowcasts, annual assessments, etc. is shown in Figure 1. It consists of three main modules: 1. an Observation Inbox, which takes care of acquiring, handling, archiving and disseminating observations as they become available

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2. a Hindcast/analysis module, in which numerical hydrodynamic and ecosystem models are run to produce hindcasts, nowcasts and forecasts 3. a Value-added module, in which the data products from the other two modules are analysed and presented together with other information and knowledge to create information products for users.

Figure 1 Modules and integrated operations of the pilot monitoring system which will be delivered at the end of the project. 3.1 Observation Inbox

The observational element aims to combine all available sources of observation that are operational. Currently they include repeat monitoring cruises; volunteering observing ships (VOS); drifting buoys in the Norwegian Sea (Argo); satellite derived sea surface temperature and ocean colour; and coastal HF radar system coveting a 50x50 km zone off the west coast of Norway. Each of these measurement types is highly relevant and important, but only the optimum combination and synergetic analysis of the various types of observation can provide a sufficient stream of consistent, repeat observations to the monitoring system. For instance, as we can only rely on ocean colour observations and derived chlorophyll concentration under cloud free conditions, the availability of fluorescence data and chlorophyll estimates from Ferrybox systems ensures regular supply of measurements, e.g. along the transect across the Skagerrak from Oslo to Hirtshals.

3.2 Hindcast/analysis module In MONCOZE a distributed, nested model system, ranging from basin-scale to coastal/ shelf scale, is used, encompassing three ocean model codes: 9 MIPOM is a three-dimensional, baroclinic, primitive equation hydrodynamic ocean model which computes the time evolution of sea surface elevation, currents, salinity and temperature. MIPOM is met.no's version of the widely used Princeton Ocean Model (POM) (Blumberg, 1987; Martinsen et al., 1997). It has been used for operational ocean forecasting at met.no since 1989. 9 Building on the Miami Isopycnical Coordinate Model, the HYCOM (Hybrid Coordinate Ocean Model) (Bleck, 2002) combines different coordinate systems to

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optimise model representation of deep water as well as shallow water coastal and shelf regions. HYCOM is part of the TOPAZ (Towards an Operational Prediction system for the North Atlantic and European coastal Zones) model system. In addition, this model system includes ecosystem models, ice-model and a data assimilation module. The TOPAZ model system is now running in real time (http:// topaz.nersc.no) at NERSC. NORWECOM has been extensively used in multi-year studies of the primary production in the North Sea and the Skagerrak/Kattegat and operationally in connection with harmful algal blooms in the area by met.no and IMR. Figure 2 the depth integrated primary production for April is shown. Along the Norwegian coast there is a band of high production due to an ongoing diatom bloom. High production also take place in the continental coastal water on the west coast of Denmark and in parts of the Kattegat The results shown are from runs performed with the 4km resolution model nested into the 20km model covering the North Sea, with realistic meteorological forcing, fiver runoff and nutrient loads.

Figure 2 Example of user-defined numerical forecast product. Diatom concentration at the surface (left) and the change in concentration 5 days. Assimilation of satellite altimeter and SST data is presently carried out in the basin-scale model component (HYCOM/TOPAZ), and the influence of the observations is propagated into the nested coastal models (HYCOM, MIPOM, NORWECOM) through nesting zones (see Figure 3). A key area of development in the project is methods for assimilation of SST and ocean colour at shelf and coastal scales. Likewise, the nested coastal models need to be consistently and regularly driven by atmospheric forcing and supplied with river inflow estimates. Atmospheric forcing: The ocean models require wind (10m height), air and dew-point temperature (2m height) as well as precipitation data to calculate surface fluxes of momentum, heat and salt. For hindcast runs, these forcing data are obtained from the numerical weather prediction (NWP) model of the ECMWF (operational analyses at -.40km resolution). For forecasting, NWP forecast fields from the ECMWF and from met.no's HIRLAM (20km resolution) are used, for the large-scale and local-scale ocean models, respectively. Open Boundary Conditions: In MONCOZE the open boundary conditions are supplied by the basin-scale HYCOM model run on a domain covering the North Atlantic (Figure

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3). At the lateral open boundary the HYCOM will provide the following physical variables: surface elevation, current velocity, temperature and salinity at selected depths at a resolution on the order of 20km. The intermediate model has a horizontal grid resolution of about 7km, while the regional nested coastal models have grid resolution down to 2 km. River inflow: In the North Sea and Skagerrak the distributions of salinity and nutrients are strongly influenced by the freshwater and nutrient loads supplied by the river runoff. Technically the freshwater may be supplied as a source or as a boundary inflow. The freshwater supplies may be based on a hydrological model or observed flow and nutrient concentration. In the MONCOZE hindcast studies, observed river flows and nutrient concentrations from the major rivers entering the North Sea are used. In nowcast and forecast mode, near real time data from the Baltic Sea and a limited number of Norwegian rivers are available; for other rivers, monthly climatology are used.

Figure 3 Proposed configuration of nested model domains for use in MONCOZE. The target area for MONCOZE is the North Sea and the Skagerrak. 3.3 Value added module

The three models employed in MONCOZE are being extensively validated and compared using in situ observations and satellite remote sensing data. The results from these experiments will, in turn, be used to evaluate and characterize how the three models perform. In particular the modelled fields can be a) instantaneous on the same day as the data, b) daily average for the same day as the data, or c) picked out along the section at the same time as individual stations are taken. The discrepancies between

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models and measurements are estimated using a cost function (Berntsen et al., 1999) that normalizes the difference between the modelled sections and the measured sections.

4. Conclusion The MONCOZE pilot operational system will provide real-time updating of standard monitoring products and facilities for dissemination to users. Pilot demonstrations of the system will be carried out in spring 2003 and 2004 for the coast of southern Norway, and the strength and limitations of the system will be documented. A priori, one limitation is the lack of a European hydrological network that can provide real time data on freshwater and nutrient transport to the North Sea and Skagerrak Sea. It is expected that the primary users of the monitoring system will be public authorities responsible for coastal zone management (e.g. fisheries, pollution, ship traffic) and the marine scientific community. Close cooperation with users is necessary to develop and refine suitable products from the diverse data sources within the system. For instance, as shown in Figure 2, a forecast product can be tuned to the request of a specific user. In this case the user is interested in a quickly scanned estimate of today's state and the expected change over the next 5 days.

Acknowledgment The MONCOZE project is supported over 5 years (2001-2005) by the Research Council of Norway under contract 143559/431.

References Bleck, R., An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates, 2002, Ocean Modelling, 4, 55-88. Blumberg, A. and G. Mellor, 1987, A description of the three-dimensional coastal ocean circulation model. In Three-dimensional Coastal Ocean Models, edited by N. Heaps, vol. 4 of Coastal and Estuarine Sciences, AGU. Berntsen, J. and E. Svendsen, 1995, Using the skagex datasets for evaluation of ocean model skills, Journal of Mar. Sys., 18, pp. 313-331. Dundas, I., O.M. Johannessen, and B. Heimdal, 1989, Toxic algal bloom in Scandinavian waters, May-June 1988, Oceanography, 2, 9-14. Johannessen, J.A., L.P. Reed, O.M. Johannessen, G. Evensen, B. Hackett, L.H. Pettersson, P.M. Haugan, S. Sandven, and R.A. Shuchman, 1993, Monitoring and modelling of the marine coastal environment, Photogrammetric Engineering and Remote Sensing, Vol. 59, No. 3,351-361. Johannessen O.M., T.I.Olaussen, L.H. Pettersson, J.A. Johannessen, P.M. Haugan, K. Kloster, S. Sandven, L. Hansen and C. Geiger, 1988, The toxic algal bloom in May 1988, with recommendations for future application of remote sensing. A NERSC special report No. 1 to the Norwegian Space Center. Martinsen, E., B. Hackett, L.P. Reed and A. Melsom, 1997, Operational marine models at the Norwegian Meteorological Institute, Operational Oceanography, The challenge for European Co-operation, edited by J.H. Stel et al., Elsevier.

Sensing the coastal environment J.S. Bonner *1, F.J. Kelly 1, P.R. Michaud 1, C.A. Page 1, J. Perez 1, C. Fuller 1, T. Ojo 2, and M. Sterling 2 1Conrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi, USA 2Civil Engineering Department, Texas A&M University, USA

Abstract The spatial and temporal dynamics of near-shore ecosystems are being studied by the Conrad Blucher Institute for Surveying and Science at Texas A&M University-Corpus Christi with special consideration of episodic events associated with anthropogenic activity. Our growing array of estuarine and coastal monitoring stations in the Corpus Christi region supplies real-time, continuous input from a broad array of sensors (physical, chemical and biological) to feed comprehensive converged data sets that in turn foster, in a new context, the interpretation and evaluation of environmental perturbations (episodic events) and their ecological effects.

Keywords: Coastal, sensors, environment, converged-data, HF radar, Texas 1. Introduction Near-shore regions are some of the most productive regions in the world, providing habitat for diverse marine communities and natural resources of national economic and social importance. In many countries, these same coastal areas are also the fastest growing regions, leading to increasing anthropogenic degradation. Current pressures and stresses in near-shore regions include over-fishing, mineral depletion, sewage disposal, aquifer depletion, freshwater inflow diversion, vulnerability to coastal hazards, beach/ wetland loss, contaminant releases, eutrophication, ecosystem health and integrity degradation, nuisance species invasions and harmful algal bloom inducement, and decreases of biodiversity. A changing climate can cause even more pressure on this interface between land and ocean. These regions also play a key role in on-going debate over human influence on the global carbon cycle. Many management policies associated with near-shore ecosystems do not provide adequate solutions to these problems, primarily due to a lack of scientific consideration and a citizenry uninformed of the elements compromising and controlling near-shore environmental quality and health. There is a critical need to understand near-shore environmental history and the processes driving environmental changes on a range of spatial and temporal scales. Near-shore regions are non-linear and highly complex, and easily disrupted by external and internal forces and natural and human-induced processes that are not well understood. In the Gulf of Mexico, shallow embayments tend to be weather driven rather than tidally driven as in Atlantic or Pacific coasts. This leads to activity pulses during short time periods concurrent with episodic events. These pulses are stochastic, making prediction more * Corresponding author, email: [email protected]

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Sensing the coastal environment

difficult. A vast majority (~99%) of the environmental activity in coastal waters occurs during a very small percent (~1%) of the time. The "environmental activity" can be characterised as a series of episodic events (i.e. pulsed systems). These pulses may be nature-driven such as storm systems and harmful algal blooms, or they may be manmade such as oil spills and other chemical spills. Standard observational research uses long-term trend analysis to characterise system changes. The coastal water sampling regimes for most governmental agencies is infrequent (seasonal to annual sampling) due to cost and logistical constraints. This approach is limited in its ability to provide data about short-term or broad spatial scale processes sufficient to distinguish between cause and effect. For example, Figure 1A depicts the total suspended solids (TSS) concentrations in Lavaca Bay (Texas) over a 20-month period. In the 1940s, an aluminium manufacturing plant (that used a Chlor-alkali process) was built on this bay. The plant has discharged significant amounts of Mercury (Hg), resulting in an underwater Superfund site (http://www.epa.gov/oerrpage/ superfund/sites/npl/f940223.htm). Particle transport is one mechanism for Hg movement within this bay. Figure 1B depicts the correlation between TSS and wind data over a 3day period, where the higher wind speeds produced an increase in TSS concentrations (sediment resuspension) and the concomitant movement of Hg. If sampled biannually, only a handful of samples would have been collected during this time and the breadth of environmental activity (Hg transport, in this case) would have been grossly underestimated.

Figure 1 A) Total suspended solids (TSS, in mg1-1) over a 20-month period in Lavaca Bay, Texas; B) correlation between wind speed (ms-1) and TSS (mg1-1) over a 3-day period in Lavaca Bay. In the previous example, the traditional sampling approach would miss the environmental activity during pulsed events, but increasing the frequency of this sampling regime is cost prohibitive. Thus, a "smart sampling" regime is needed to capture the effects of these episodes or pulses. This can be accomplished by integrating existing observation networks, new technologies, and basic research from diverse engineering and natural and social sciences disciplines. Integrated data management and modelling can ensure the results obtained are analysed and synthesised for optimal use by end-users and stakeholders. Optimal operation and feedback require an understanding of the interrelationships among physical, ecological, and human political and regulatory activities (e.g. Adams et al., 1998).

J.S. Bonner*, F.J. Kelly, P.R. Michaud, C.A. Page, J. Perez, C. Fuller, T. Ojo, and M. Sterling

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2. Problem definition Most ecological response measurements of normal seasonal and annual cycles in nearshore ecosystems rely on sampling strategies that often under-sample, providing aliased results (e.g. Kostic, 2000), thus incorrectly portraying long-term trends. Monitoring cyclical and episodic events in near-shore ecosystems and integration of changes in both the physical environment and ecological integrity of such dynamic systems requires observations recorded in real-time and at different spatial and temporal scales. Storms influencing current patterns, increased stream flow into estuaries, dramatic changes in water quality, algal blooms, and anthropogenic activities such as oil spills can be considered events that directly or indirectly result in increased particle (trace metals, polycyclic aromatic hydrocarbons, nutrients, toxins from harmful algal blooms, etc.) suspension in the water column. A new, integrated, real-time, observational paradigm can supply the cross-disciplinary data needed to pursue an integrated research approach.

3. Research plan Our research plan can be conceptualised by the creation of an Environmental Field Facility (EFF), a concept supported by the Collaborative Large-scale Engineering Assessment Network for Environmental Research (CLEANER) workshop, sponsored by the National Science Foundation (NSF) in December 2001. CLEANER defines an EFF as a well-instrumented site with remote and in situ sensors designed to characterise an anthropogenically-stressed environmental region in real-time. We are in the process of turning Corpus Christi Bay into an EFF. It is well instrumented and new sensor technologies are being applied as they become available. Eventually, this local network can be linked to regional networks and then linked to regional and global networks, which is a concept supported by other NSF programs such as the National Ecological Observatory Network (NEON; http://www.sdsc.edu/NEON/) and the Long-Term Ecological Research (LTER; http://lternet.edu/) program. The NEON has as its mission scientific infrastructure and intellectual capital development to take on global research challenges (e.g., biogeochemical imbalances, carbon dynamics, invasive species). This is accomplished through a full integrated and nationally distributed proposed network of environmental research instrumentation networks. The LTER network, in existence since 1980 with 24 current network sites nationally, has as its mission to develop ecological understanding, synthesise long-term data, disseminate valuable information, create a legacy of high quality data, create scientists experienced in long-term research, and provide community outreach on complex environmental issues.

3.1 Approach In situ Observations

We currently utilise the data from existing observation systems such as Texas Coastal Ocean Observation Network (TCOON, http://www.cbi.tamucc.edu/projects/tcoon/) (Michaud et al. 1994), Texas Automated Buoy System (TABS) (http:// tabs.gerg.tamu.edu/tglo) (Kelly et al. 1998), and Galveston/Houston PORTS. They currently provide observations mostly of physical oceanographic and meteorological parameters. We are upgrading selected sites with extended sensor arrays, including an alternate water quality sonde, an automated water sampler, and sensors to measure

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particle size and distribution, turbidity, horizontal and vertical current profiles and directional wave parameters, nutrients (Iron II, Iron III, nitrate, nitrite, ammonium, phosphate, silicate), and high-resolution particle source indices. We are extending the sensor technology by collaborating with industrial partners to develop new technology for underwater particle size analysis, low-level dissolved oxygen probes, and biofouling countermeasures. Currently in Corpus Christi Bay, there are 5 TCOON stations around the bay perimeter. In addition, there are 3 platforms in the bay with 3 more under construction. Instruments deployed include acoustic Doppler current profilers, water-quality sensors, oxygen sensors, fluorometers and optical backscatter instruments. Both a nutrient analyser and an in situ flowcytometer are scheduled to be deployed soon. There are also 2 platforms in the offshore area just beyond Corpus Christi Bay with similar instrumentation as those within the bay. A geo-referenced survey boat (with sensor packages like those at the fixed sites) complements fixed observing platforms by spatial surveys during events exceeding trigger thresholds for parameters such as particulates, chlorophyll a, dissolved oxygen, and nutrients. A submersible, towed vehicle (Acrobat | by Sea Sciences Inc.) is used to deploy the in situ instrument package. Both manual and software control allow the Acrobat to maintain either constant depth/elevation or undulating flight paths. The ability to follow an undulating flight path is a necessary sampling regime to determine vertical parameter gradients. Coastal Radars

High Frequency (HF) Radar systems map surface currents and directional waves in realtime (Barrick, 1972; Teague et al., 1997), and can be utilised in modelling contaminant movement in coastal waters (Ojo et al., 2002; Tissot et al., 2001). The Conrad Blucher Institute (CBI) operates an HF radar system in Corpus Christi Bay, with several more systems scheduled to become operational along the Texas coast in the near future. In addition, CBI operates the only emergency-response HF radar in the Gulf of Mexico (http://www.cbi.tamucc.edu/projects/hfradar) (Kelly et al., 2002). The mobile system produces hourly grids (1-3 km) of bays and coastal waters out to 80km. Surface velocity vectors (0.5 to 1.0m, depth averaged, from HF radar observations at CBI) are highly correlated with wind direction and speed. For example, Figure 2 depicts the "before" (left figure) and "after" (right figure) as a weather front passes through Corpus Christi Bay. The surface current vectors (very small arrows) have shifted after the frontal passage. Environmental fluxes and environmental gradients are shown (larger arrows on the left figure). Fluxes indicate the forcings on an environmental system, while gradients indicate the transport potential within the system. Figure 2 demonstrates an example of the analysis possible through systemic deployment of sensors in synergy. These types of changes have environmental, social, and political impacts that need to be conveyed to stakeholders.


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Figure 2 Corpus Christi Bay observations showing the change in wind velocity and resulting surface currents (very small arrows) over a two-hour period of time. The left figure occurred at 13:00 UTC on January 24, 2002 and the right figure occurred nine hours later. The left figure also shows environmental fluxes (short dark thick arrows) and gradients (long thick arrows). All of this shows the measured parameter accumulation in the Environmental Field Facility (EFF) concept.

Data Communication and Management The communication links for the system handle data being generated from different remote sources (boat, fixed platform(s), HF Radar) in a timely fashion. This is achieved by establishing several communication links (dial-in, direct radio connection) to the various sites. A network terminal server (Lantronics) along with a network filesystem (Snap Appliances) act as the core data infrastructure for the system. The terminal server allows the data logger/visualisation computer on the boat or fixed platform to effectively "dial-in" to the local network that exists in the land-based, mobile, HF-Radar trailer through spread-spectrum radios (FreeWave Technologies) and access the network file system. Information from the HF-Radar system also uses the local network to transfer the generated JPG images of surface current maps onto the network file system. The data management strategy is to 1. preserve source data 2. annotate source data 3. automate acquisition 4. maintain standard formats 5. avoid proprietary components complicating dissemination 6. emphasise long-term reliability. The use of these principles results in a robust, stable, and flexible data support system. The integrated components include data acquisition, data archival, data extraction, and data world-wide-web interfaces. 3.2 Benefits and Contributions

Benefits to the overarching "smart sampling" research include 1. understanding complex cyclical near-shore ecological and physical parameters in real-time, allowing for accurate physical and biological coupling

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2. building a foundation for quantitative modelling and for designing long-term, costeffective monitoring strategies 3. predicting environmental stressor, fate and effects 4. formulating operational tools for environmental managers 5. disseminating results in "user-friendly" formats for the general public, educators, and policy-makers 6. providing replicate assessment of key processes 7. highlighting regional differences for comparative study (e.g. high turbidity in west Gulf of Mexico vs. low turbidity in east Gulf of Mexico), and offering opportunities for others (e.g. Caribbean and Mexican collaborators) to join and expand our efforts 8. through industrial collaboration, developing sensor technology for critical water quality parameters and methods to combat sensor problems (e.g. biofouling). The multiple facets of this research are on-going efforts, operating in parallel. A recent demonstration of the "smart sampling" concept was a simulated oil spill that was sanctioned by the U.S. Coast Guard. In this simulation (dye study), our geo-referenced boat was the designated monitoring vessel, arrayed with a fluorometer and a conductivity/temperature/depth instrument. The real-time data was successfully transmitted and spatially visualized at the command post onshore.

Acknowledgements We acknowledge numerous state, federal, and industry sponsors of this synergistic, leveraged programme. In particular, we thank the Texas General Land Office, the National Science Foundation, and the Texas Higher Education Coordinating Board.

References Adams, S. M., K.D. Ham, and R.F. LeHew, 1998, A framework for evaluating organism responses to multiple stressors: mechanisms of effect and importance of modifying ecological factors, pp. 13-22. Multiple Stresses in Ecosystems, J. J. Ceck and B. W. Wilson (eds.), Lewis Publishers. Barrick, D.E., 1972, First-order theory and analysis of MF/HF/VHF scatter from the sea, IEEE Trans. Antennas Propagat, vol. AP-20, pp 2-10. Kelly, F.J., R.H. Weisberg, J.S. Bonner, M.E. Luther, J.C. Perez, J.S. Adams, D. Prouty, D. Trujillo, R. He, R. Cole, J. Donovan, and C. Merz, 2002, An HF-Radar test deployment amidst an ADCP array on the West Florida shelf. Proceedings of the Oceans 2002 IEEE/MTS Conf., October 29-31, 2002, B iloxi, MS, pp 692-698. Kelly, F.J., N.L. Guinasso, L.L. Lee III, G.F. Chaplin, B.A. Magnell, and R.D. Martin, 1998, Texas Automated Buoy System (TABS): A public resource. Proceedings of the Oceanology International 98 Exhibition and Conference, 10-13 March 1998, Brighton UK, Vol.1, pp 103-112. Kostic, M., 2000, Interactive Simulation with a LabVIEW TM Virtual Instrument Including Magnitude Change, Phase Shift and Aliasing: "What we see is not what it i s m P A R T II!" NIWeek2000 Annual Conference, National Instruments, Austin, TX. http://www.kostic.niu.edu/myPublications.html


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Michaud, P, C. Thurlow, and G. Jeffress, 1994, Collection and dissemination of marine information from the Texas Coastal Ocean Observation Network. U.S. Hydrographic Conference Proceedings, Hydrographic Society Special Publication, 32, pp 168-173. Ojo, T., and J. Bonner, 2002, Three-dimensional self-calibrating coastal oil spill trajectory tracking and contaminant transport using HF radar, 2002, Proceedings of the Twenty-fifth Arctic and Marine Oilspill Program (AMOP) Technical Seminar. Environment Canada, Ottawa, Canada, Vol. 1, pp 215-226. Teague, C., J. Vesecky, and D. Fernandez, 1997, "HF Radar Instruments, Past to Present" Oceanography, vol. 10, no. 2, pp 40-44. Tissot P., J.C. Perez, F.J. Kelly, J. Bonner, and P. Michaud, 2001, Dynamic neural network modeling of HF radar current maps for forecasting oil spill trajectories, Proceedings of the Twenty-Fourth Arctic and Marine Oilspill Program (AMOP) Technical Seminar. Edmonton, Alberta, Canada, Vol. 1, pp 224-231.

The Bay of Biscay project Jean Boucher and Philippe Marchand*

Ifremer, Plouzan~, France

Abstract The Bay of Biscay is important for French fisheries. It is a complex ecosystem that has been scientifically investigated for many years, and has now been chosen by Ifremer for a major integrated project for the next decade. The general objectives are: 9 To understand interactions between fishing resources, the environment and the human pressure on a regional scale 9 To determine how social and economic factors control the behaviour of the various system components 9 To analyse, understand and forecast the evolution of the system according to various climatic and economic scenarios This multidisciplinary project, involving an important Ifremer task team of 80 man/ years, was launched in 2001.

Keywords: Bay of Biscay, ecosystem modelling, human impact, fishery management 1. Introduction The Bay of Biscay is located on the western side of France, between Brittany and the northern Spanish coast. It is one of the biggest marine ecosystems and has been intensively studied over the last two decades, mainly because half of French catches come from this complex region and because Ifremer is officially in charge of fish stock assessment. A considerable expertise was accumulated in all disciplines.

2. What is the present situation of the Bay of Biscay? An evaluation was made by the OSPAR Commission (2000 a and b). All the commercial fish stocks show signs of overfishing such as decrease in longevity, decline in abundance, and variability. Three quarters of them are beyond their safe biological limit. Despite a decrease in the power of the fishing fleet, no restoration of stocks has occurred over the last twenty years. An explanation is found in the adjustment of the fishing capacity and the growth of exploited population within physical and biotic capacity of the ecosystem. Fishing activity impacts on the growth of the population through mortality, selective pressure on stocks and degradation of habitats. In addition, over the last two decades the environmental conditions have changed under both climatic variations and the impact of human activities.


Jean Boucher and Philippe Marchand*

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2.1 Climate variation

The 1990s are characterised by warmer temperature conditions than during the previous century (Planque et al., 2002). Especially in the south-western part of the Bay of Biscay where the warming reaches values up to 0.6~ It seems the NAO influence is low because of the location of the Bay of Biscay, between the North Atlantic sub-polar and sub-tropical gyres. As a consequence of the global warming of the region, a northward migration of tropical fish species (i.e.: Zenopsis conchifer) has been observed, together with a change in the main plankton species.

2.2 Human impact on the environment The increasing fertilisation of the water is a consequence of the development of agriculture and discharge of nutrients into the rivers. Some quiet locations such as the Bay of Vilaine are now subject to anoxia crises during the summer period. Human activities impact on the fisheries of the coastal zone where spawning and nursery areas are located. The fishing activity itself impacts on the environmental conditions of fish stocks through mortality due to catches, the physical impact of fishing gear on the sea bed, and the low selectivity level of gears. Because of such crucial changes in the ecosystem of the Bay of Biscay, in 2001 Ifremer decided to launch an ambitious integrated project, following a year of preparation. The challenge of the bay of Biscay project is to understand how the ecosystem responds to the combination of natural changes and human increasing activities and to estimate the socio-economic consequences of those dynamics. The key question is how to preserve in a sustainable way, the environment, the species which live there and their exploitation?

3. G e n e r a l o b j e c t i v e s The general objectives of the Bay of Biscay project are: 9 to understand interactions between fishing resources, environment and human activities on a regional scale 9 to determine how social and economical factors control the behaviour of the various system components 9 to analyse, understand and forecast the evolution of the system according to various climatic and economic scenarios The project is mainly fishery oriented. The ultimate goal is to forecast the ecosystem evolution, including catches, according to several exploitation scenarios. The complexity of the ecosystem dynamics is very high because it is made of living organisms linked between them, interacting with physical and chemical environment, itself controlled by continental discharge (product of human activity) and climate change. The project then addresses a large thematic content with the following five components: 9 Physical dynamics of habitat 9 Population ecology 9 Communities 9 Characterisation of the fishing activity, scenario of management 9 Technological developments

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The Bay of Biscay project

3.1 Physical dynamics of habitat The objective is to determine how climate fluctuations modify the physical structure of habitat in order to determine the "hydroclimate" of the Bay of Biscay or more simply "the weather that prevails in the ecosystem". The climate variations impact on the biological production through hydrodynamic structures, which determine the habitat of species and their migrations during their life and determine the production of food (Figure 1). Each species is able to use the physical structures in a specific way.

Figure 1 Habitat dynamics--Physical process The project will then determine typical meteorological situations and associated hydrodynamic disturbances (fiver inflows, climatic anomalies, wind regimes). It will try to clarify the role of both thermocline and vorticity anomalies as controlling factors of the biological productivity. Hydrodynamic indicators for biology and fishing activity will be constructed. Another key factor impacting on the dynamic of habitats is the geological nature of the sea bed. In the Bay of Biscay, the "Grande Vasi~re" is a muddy area of great importance for demersal and benthic fisheries, but this zone is vulnerable because of low thickness. The evolution of such a sedimentary structure is controlled by hydrodynamic processes (which determine mud transportation), and the fishing activity which impacts on the sea bed and on the turbidity. One of the questions is: have we, in the past, observed large modifications of the "Grande Vasi6re" habitat?

First result of the physical dynamics of habitat study The joint analysis of river inflows and abundance of juvenile soles in the bay of Vilaine over the last two decades shows a good relation between the juvenile abundance (recruitment) and the climate regime at the beginning of the year. The explanation is that the extension of the river plume determines the area and the production of food (benthic invertebrates) for demersal species.

3.2 Population ecology The objective is to understand the effect of the synergies between natural variability and anthropogenic impact on the growth of populations.


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The basic principle of fishery management is to adjust, in average, the death rate (through catches) to the birth rate (recruitment). A consensus exists on the second term, the recruitment, which depends on physical phenomena, production and availability of food. The first term, death rate, is the result of interaction between the environment and the anthropogenic pressure. Such pressure varies with habitats (i.e. estuaries, shelf, bays, etc.). Different populations are considered in the project such as: 9 Anchovies, whose growth depends more on the hydroclimate than exploitation 9 sole, bass, hake, and eel productions are closely dependant on nurseries located in bays and estuarine areas where the anthropogenic impact is great 9 phytoplankton, the first level of the marine food chain, whose abundance is greatly determined by physical disturbances Kinetics of such populations will be simulated in various biological models taking into account anthropogenic and climatic perturbations. 3.3 Communities

The nature of the observed changes in production and species structure depends on the relative part of anthropogenic and climatic impacts on the community' s dynamics. These impacts can be synergetic or antagonistic. Concerning human activities, anthropogenic nutrient runoff from land catchment basins changes the primary production and the species structure of the phytoplankton community; exploitation by fishing changes the species structure of the high levels of the food web. Concerning the environment, climate change alter the surface of habitat areas, hydrological conditions of transport, time and level of food production available to the various stages of life history. These changes may spread through the food web. An illustration of the evolution of various trophic flows in the Bay of Biscay during the 1973-2000 period is given in Figure 2.

Figure 2 Evolution of trophic flows in the bay of Biscay (1973-2000) Between 1973 and the present day a shift of the main trophic flows is observed for the benefit of the prey species and the pelagic ones and to the detriment of demersal predator

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The Bay of Biscay project

species of most important commercial value. The positive effect of climate warming on the Sardine (Sardinapilchardus) biomass is not observed, probably because Sardine is depleted by fishing in the Iberian Peninsula. The Horse mackerel (Trachurus trachurus) and Boarfish (Capros aper) biomass increase probably because of a stronger effect of warming than the fishing effect. Blue whiting (Micromesistius poutassou) biomass increases despite warming, probably because its main predators decrease. Whiting (Merlangius merlangus) biomass decreases because of fishing and the negative effect of warming. Hake (Merluccius merluccius) and Anglerfish (Lophiuspiscatorius) decrease: they are overexploited and climate warming may be unsuitable to the Anglerfish. Climate and exploitation can interact negatively so that no temporal trend may be observed (Sardine) or positively so that trends may be observed (Anglerfish, Whiting).

3.4 Characterisation of the fishing activity, scenario of management The first objective is to characterise the present structure of activities related to the exploitation of living resources in the Bay of Biscay and to determine the evolution factors of these activities. The second objective is to simulate the evolution of resources as a response to new management scenarios. A better knowledge of the behaviour of fishing firms regarding the past regulation measures is essential for two main fisheries: the coastal exploitation along the south Brittany, the demersal exploitation of the Bay of Biscay and in the Celtic sea. The controlling factors to quantify are economical (incomes and costs, market prices, etc.), institutional (access to the resource, general rules), environmental (short term or long term fluctuations), technical (fishing gears, innovations). Several management scenarios will be tested from extreme scenarios (i.e. individual quotas) to softer ones (i.e. modification of rules). New scenarios will be envisaged such as limitation of bycatches or multiyear stock management. Forecasts will be performed to estimate the acceptable ecological objective (according to climatic scenarios) and the economical efficiency of the envisaged measures.

4. Technological developments To understand how the ecosystem functions in order to forecast it, especially for fisheries, it is necessary to build and run complex physical and biological models. To perform good forecasts, those numerical models need to be calibrated and fed by at-sea data. Most data presently comes from scientific cruises and are transmitted in delayed mode. In order to collect more regular data, and to increase their flux, Ifremer will develop and deploy adapted marine instruments, such as: 4.1 Multibeam echosounder (MBES)

This device has been developed in cooperation between Ifremer and Simrad for specific needs of fishery research and will be installed on the R/V Thalassa by 2005. With a refined angular resolution of 2 ~ in a widened across-track angular sector of 60 ~ to 80 ~ the MBES is a very innovative system which will perform the job of 30 narrow-beam echosounders! It will enable 9 detection of demersal species closed to the bottom


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9 detection of pelagic schools 30 ~ across-track central sector and a 3D both morphological and energetic description 9 analysis of the fish behaviour (i.e.: fish avoidance of trawl). 4.2 Multiparameter fixed station

The idea is to monitor biomass and environmental parameters evolution at a fixed location. This is a new concept, which will give an Eulerian description of the water column in fishing areas, complementary to the annual fish survey made by research vessels. A first test of horizontal detection was recently performed successfully giving an acoustic detection of schools more than 2km away in the 12 KHz band. Deployment of autonomous profilers, derived from the oceanic profiler Provor, to get systematic temperature and salinity profiles. The effort in the field of fishing gear technology will be put on tests of more selective fishing trawls, evaluation of the trawl' s impact on the sea bed.

5. Conclusion The Bay of Biscay project is very ambitious and everyone is aware that forecasting the evolution of such a complex ecosystem in all dimensions will take several years. Nevertheless the integrated approach will oblige various disciplines to interact and specialists to work together. For the next four years, Ifremer is deploying an important task team, with 80 men/year, 1.5 million Euros/year and about 5 months of dedicated scientific cruises.

References Boucher, J., C. Bacher, J.-F. Bourillet, P. Gentien, J.-F. Guillaud, O. Guyader, A. Herbland, F. Lagard6re, P. Lazure, P. Le Hir, V. Loizeau, P. Marchand, Y. Morizur, P. Petigars, D. Pelletier, V. Rigaud, M.-J. Rochet, and C. Talidec, 2001, Chantier Golfe de Gascogne-D6finition du projetmfascicules I (20pp) and II (128pp). Ifremer report (in French). OSPAR Commission, 2000a, Quality Status Report 2000. OSPAR Commission, London. 108+vii pp. OSPAR Commission, 2000b, Quality Status Report 2000 : Region I V - - B a y of Biscay and Iberian Coast. OSPAR Commission, London. 134+xiii pp. Planque, B., P. Beillois, A.-M. J6gou, P. Lazure, P. Petitgas, and I. Puillat, 2002, Large scale hydroclimatic variability in the bay of Biscay. The 1990s in the context of interdecadal changes. ICES Marine Science Symposia.

The POL Coastal Observatory R. Proctor* and M.J. Howarth

Proudman Oceanographic Laboratory, Bidston Observatory, UK

Abstract POL is implementing a Coastal Observatory in Liverpool Bay to integrate (near) realtime measurements with coupled models into a pre-operational coastal prediction system. The aim is to understand a coastal sea's response to natural forcing and to the consequences of human activity. The foci are the impacts of storms, variations in river discharge (especially the Mersey), seasonality, the effect of nutrients discharged into Liverpool Bay and plankton blooms. An extensive suite of real-time measurements will be interfaced with a nested suite of 3dimensional models, covering the ocean/shelf of northwest Europe, the Irish Sea, and eastern Irish Sea / Liverpool Bay which run daily. All results from instrumentation and models (e.g. recorded time series, modelled daily mean sea surface and sea bed temperature, currents, waves and sea surface height) are displayed on the Coastal Observatory website (cobs.pol.ac.uk).

Keywords: Coastal observatory, Irish Sea, real-time measurements, pre-operational modelling, website. 1. Introduction Coastal ocean observing systems are now technically feasible (e.g. Glenn et al., 2000 on the east coast of the USA; Blaha et al., 2000 in the Gulf of Mexico; Buch and Dahlin, 2000, in the Baltic Sea; IEEE J. Oceanic Eng, vol 27 for a summary of recent developments in the US, Edson et al., 2002). No single organisation in the UK (or Europe) alone has sufficient capability to design or support such a system to address the full range of marine issues (Prandle and Lane, 2000). POL, however, is uniquely placed in the UK, through its measurement and modelling capabilities and existing interactions with key agencies, to act as the focus for the development of a pilot coastal zone observing and monitoring system. This represents a major UK innovation in the approach to testing process understanding in shelf seas. Through collaborations with the UK Met Office (operational numerical weather predictions (NWP) and ocean/shelf circulation models), the Environment Agency (routine monitoring of fiver discharge, nutrients and contaminants) and Natural Environment Research Council (NERC) facilities (operational production of regional remote sensing data) the necessary inputs required by a pre-operational coastal ocean modelling system will be secured. Regulatory bodies (Centre for Environment Fisheries and Aquatic Sciences (CEFAS) and the Environment Agency (EA)) are partners in the project through deliberate alignment of their own measurement programmes with the Coastal Observatory. * Corresponding author, email: [email protected]

R. Proctor* and M.J. Howarth

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2. The pilot Coastal Observatory The Observatory is located in Liverpool Bay in the Irish Sea. This region is an archetypal coastal sea system with strong tides, occasional large storm surges and waves, freshwater input (the rivers Mersey and Dee), stable and unstable stratification, exposed banks, high suspended sediment concentration and biogeochemical interaction. The Bay is also stressed (near eutrophication from river-borne nutrients and subject to river-borne pollutants) and so of concern to regulatory agencies. The Observatory will integrate (near) real-time measurements with coupled models into a pre-operational coastal prediction system whose results will be displayed on a website. The concept is founded on obtaining data in (near) real-time, using telemetry, from underwater to the sea surface to land to POL to a website ("armchair oceanography"). This, the aspiration of every oceanographer, is now feasible. The emphasis is on a modest, pragmatic approach to the initial establishment of the Observatory (drawing on existing technology and partnerships). The Observatory is expected to evolve as the concept and its effectiveness becomes established in the UK context, all the while building up long time series of measurements. The foci are the impacts of storms, variations in river discharge (especially the Mersey), seasonality, and blooms in Liverpool Bay.

3. Measurements

Figure 1 Proposed Irish Sea monitoring system. White dots=tide gauges; grey dots=moorings; square=meteorological station; dotted lines=ferry routes/university monitoring routes; shaded areas=HF radar coverage; white lines=airborne (satellite, lidar) monitoring.

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The POL Coastal Observatory

An extensive array of different measurement techniques are planned to complement each other and provide information on the four-dimensional variability in Liverpool Bay (Figure 1). The first measurements were started in August 2002. The measurements currently include: 9 A central mooring at 53 ~ 32' N, 3~ 21.8' W, installed in August 2002, providing in situ time series of current, temperature and salinity profiles. A second site, and an expanded measurement set (including turbidity and fluorescence) is eventually planned. At present data are not available in real-time, being stored and then downloaded on the routine maintenance cruises carried out 6-weekly by the R/V Prince Madog. Development of sub-surface acoustic transmission is in progress so that the data can be transmitted in real-time. Figure 2 shows an example of the current structure recorded by the ADCP during August 2002. This shows the progressive vector at each level recorded by the ADCE The end point of the vector indicates the distance a passive particle would travel in the time period of the vector. An estuarine circulation is evident, as expected close the Mersey estuary at this time of the year. 9 A CEFAS SmartBuoy (www.cefasdirect.co.uk/monitoring), installed in November 2002. This records surface properties including salinity, temperature, turbidity, nutrients and chlorophyll and transmits the data in real-time. 9 A WaveNet directional wavebuoy was also installed in November 2002. This transmits spectral wave components in real-time (www.cefas.co.uk/wavenet). Both the SmartBuoy and the WaveNet buoy are located adjacent to the central mooting. 9 Servicing of the moorings is carried out by the R/V Prince Madog at approximately six-week intervals (four weeks in the summer to overcome biofouling of the SmartBuoy sensors). Spatial surveys of Liverpool Bay are carried out on each cruise. 9 The Liverpool-Isle of Man ferry belonging to the Isle of Man Steam Packet Company has been equipped with instruments for near surface (5m depth) temperature, salinity, turbidity, chlorophyll (Figure 3 shows an example of the fine scale structure obtainable). Later, a nutrient analyser will be added. This ferry is one of the nine ferry routes under study in the EU FERRYBOX project (www.gkss.de/ ferrybox). Instrumenting other ferry routes (e.g. Liverpool-Dublin, LiverpoolBelfast) is under consideration. 9 The UK Tide Gauge Network is currently being upgraded to allow real-time transmission of data from all tide gauges around the UK. The tide gauges in the Irish Sea, with additional sensors for met, waves, temperature and salinity where appropriate, are being incorporated into the Observatory. 9 Bidston Observatory has been a meteorological recording station since 1867. This provides local real-time weather information (atmospheric pressure, wind speed and direction, cloud cover, rainfall). 9 Satellite dataminfra-red (for sea surface temperature) and visible (for chlorophyll and suspended sediment)mis provided by the Remote Sensing Data Acquisition Service (RSDAS) of NERC. Daily and weekly composite images are available via ftp but the extensive cloud cover over the Irish Sea means the daily images are often of little use. Composite images are usually 90-100% complete.


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In 2003 the Observatory has additional planned expansion with the implementation of two different radar instruments: an HF radar (16MHz) with range capability out to 80km recording surface currents in 2 k m x 2 k m bins at 20 minute intervals and surface waves in 5 k m x 5 k m bins at 60 minute intervals; an X-band radar (9GHz) with a range of 2 k m recording surface waves at 84 second intervals. The latter forms part of a near shore coastal sedimentation experiment off Hilbre Island, Wirral.

Figure 2 Progressive vector diagram from central mooring, from 7 August to 25 September 2002, of ADCP currents at different heights through the water column showing outflow near the surface and inflow near the bed.

Figure 3 Salinity measured on 5 crossings between Douglas, Isle of Man, on the left, and Liverpool, on the right, on 10 and 11 January 2003.

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The POL Coastal Observatory

4. Modelling The Coastal Observatory will use POLCOMS (Proudman Oceanographic Laboratory Coastal Ocean Modelling System (www.pol.ac.uk/home/research/polcoms), a 3-dimensional modelling system whose main elements are a 3-dimensional baroclinic hydrodynamic model (Holt and James, 2001) linked to a surface wave model (WAM), a sediment resuspension and transport model and an ecosystem model (ERSEMmEuropean Regional Seas Ecosystem Model (Barretta et al., 1995)), see for example Allen et al. (2001). In collaboration with the UK Met Office nested 3-dimensional models coveting the ocean/shelf of northwest Europe (12km resolution) and the Irish Sea (1.8km) will focus on Liverpool Bay (100-300m resolution), see Figure 4.

Figure 4 Nested set of POLCOMS pre-operational models. Left: Irish Sea domain of the Met Office ocean-shelf (12km) model, centre: Irish Sea (1.8 km) model, right: Liverpool Bay (200 m) model. At the Met Office POLCOMS on an ocean/shelf domain (20~176 40-65~ forced by NWP (numerical weather prediction model) mesoscale (12km) meteorology and ocean forcing from North Atlantic 1/3 degree FOAM (Forecast Ocean Assimilation Model, Cattle et al., 1998), operational since December 2002, provides the boundary conditions for the Irish Sea model (which in turn provides boundary conditions for the Liverpool Bay model). Local river discharges will be included in real-time through a link-up to the Environment Agency river-flow network. Initial implementation of the 3-dimensional baroclinic models for the Irish Sea and Liverpool Bay (planned for early 2003) will include wave-current interaction provided by two-way linking between wave (WAM) and the 3-dimensional currents with performance checked against the in situ temperature, salinity, current and wave measurements and coastal sea-level measurements. Methods of data assimilation (initially, for example, for AVHRR sea surface temperature, later for HF radar surface currents and waves) are being explored both to enhance the value of the data and to improve the accuracy of the model forecasts. In the latter part of 2003 nutrients and plankton dynamics will be simulated by including the coupling to ERSEM and the sediment transport and resuspension module (for


553

estimating concentrations of suspended particulate matter, an important influence on light attenuation and hence biological processes in the shallow waters of the eastern Irish Sea). The SmartBuoy, the ferry and the SeaWifs satellite ocean colour sensor will provide validation data. Models will run daily in near-real time, either at the Met Office or at POL with the necessary forcing information transferred by ftp between the two computers.

5. Web site A major component of the Observatory is the dissemination of the results through a web site (http://cobs.pol.ac.uk), both as figures and as data. The web site aims to target several different audiencesmscientists, coastal zone managers and the general p u b l i c ~ by producing outputs tailored to their requirements. The model predictions (up to 48 hours ahead for most variables, further ahead for tidal sea levels) will be displayed (e.g. daily mean sea surface and sea bed temperatures, currents, waves and sea surface heights) and compared where possible with measurements.

References Allen, J.I., J. Blackford, M.I. Ashworth, R. Proctor, J.T. Holt, and J. Siddorn, 2001, A highly spatially resolved ecosystem model for the northwest European continental shelf. Sarsia, 86: 423-440. Baretta, J.W., W. Ebenhoeh, and P. Ruardij, 1995, The European Regional Seas Ecosystem Model, a complex marine ecosystem model. Netherlands Journal of Sea Research, 33, (3-4): 233-246. ISSN: 0077-7579 Blaha, J.P., G.H. Born, N.L. Guinasso, H.J. Herring, G.A. Jacobs, F.J. Kelly, R.R. Leben, R.D. Martin, G.L. Mellor, P.P. Niiler, M.E. Parke, R.C. Patcheon, K. Schaudt, N.W. Scheffner, C.K. Shum, C. Ohlmann, W. Sturges Ill, G.L. Weatherly, D. Webb, and H.J. White, 2000, Gulf of Mexico Ocean Monitoring System. Oceanography, 13, 2: 10-17. Buch, E., and H. Dahlin, Eds., 2000, The BOOS Plan: Baltic Operational Oceanographic System, 1999-2003. EuroGOOS Publication No. 14. Southampton Oceanography Centre, Southampton. ISBN 0 - 9 0 4 1 7 5 - 4 1 - 3 Cattle, H., M.J. Bell, and M.W. Holt, 1998, Operational analysis and forecasting of the global oceanmthe UK Met Office FOAM system. Oceanology International 98: The global ocean, held 10-13 March 1998, Brighton, U.K. Volume 1: 161-169. ISBN: 0900254203. Glenn, S.M., W. Biocourt, B. Parker, and T.D. Dickey, 2000, Operational observation networks for ports, a large estuary and on an open shelf. Oceanography, 13, 1: 1223. Edson, J.B., A.D. Chave, M. Dhanak and F.K. Duennebier, 2002, Guest Editorial. IEEE J. Oceanic Engineering, 27, 145. Holt, J.T. and I.D. James, 2001, An s-coordinate density evolving model of the north west European continental shelf. Part 1: Model description and density structure. Journal of Geophysical Research, 106, C7:14015-14034. Prandle, D. and A. Lane, Eds., 2000, Special issue: Operational oceanography in coastal waters. Coastal Engineering, 41(1-3): 1-359.

Optical variability associated with phytoplankton

dynamics in the Cretan Sea during 2000 and 2001

Panos Drakopoulos* 1,2, George Petihakis 2, Vasilis Valavanis 2, Kostas Nittis 3, and George Triantafyllou 2 1Dept. of Optics, Technological Education Institute of Athens, Greece 2Institute of Marine Biology of Crete, Greece 3Institute of Oceanography, National Centre for Marine Research, Greece

Abstract Time series of in situ optical data and concurrent SeaWiFS observations in the Cretan Sea are used to assess the phytoplankton dynamics in an area with Case I waters. The in situ data were collected during the pilot operation of the multi-parametric M3A buoy system in the Cretan Sea during 2000 and 2001. The synergy and limitations of the two different data sets are discussed. The optical properties in the area of interest are found to be highly correlated with local circulation dynamics. Finally, the value of these observations for the calibration and validation of ecosystem models is demonstrated.

1. Introduction In the framework of EuroGOOS, the EU funded Mediterranean Forecasting System (MFS) has been developed as a multinational effort for integrated operational forecasting in the Mediterranean (Pinardi and Flemming 1998). Among other components, the system embraces the Mediterranean Moored Multi-sensor Array M3A (Nittis et al., 2003) with the prime purpose being the validation of ecosystem models. The system is able to monitor meteorological and wave parameters at the sea surface, physical parameters (temperature, salinity, currents) in the upper 500 metres and optical-biochemical parameters (dissolved oxygen, chlorophyll-a, nutrients, light attenuation) in the euphotic zone. This mooting is situated in the Cretan Sea, 20 miles north of the port of Heraklion, and its design was based on similar experiences for multi-parametric measurements (e.g. the Bermuda Test-Bed Mooring, Dickey et al., 1998). The hydrological structure in the Cretan Sea is dominated by multiple scale circulation patterns and is characterised by intense mesoscale activity (Georgopoulos et al., 2000), with multiple scales of temporal variability (from synoptic to interannual). The circulation in the Cretan Sea is dictated by the combined effect of two gyre features, an anticyclonic eddy in the west and a cyclonic eddy in the east (Georgopoulos et al., 2000; Theocharis et al., 1999). The position of the dipole is not fixed but highly variable and the system becomes more energetic during winter. During spring, summer and autumn the Cretan Sea is stratified and exhibits an oligotrophic ecosystem characterised by a food chain composed of very small phytoplankton cells and a microbial loop, both of which have a negative effect on energy transfer (carbon and nutrients) to the deeper water layers and the benthos. This is magnified by the high water temperatures (> 14~ and high oxygen concentrations (>4ml1-1) enhancing decomposition rates of organic * Corresponding author, email: [email protected]

P. Drakopoulos*, G. Petihakis, V. Valavanis, K. Nittis, and G. Triantafyllou

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matter leaching out from the euphotic zone. During this period the picoplankton is the dominant fraction followed by nano+microplankton and ultraplankton (Ignatiades et al., 2002). In early spring intense mixing occurs and the euphotic zone is re-supplied with nutrients from deep waters. Even so phytoplankton biomass remains at relatively low levels due to phosphate limitation (Krom et al., 1991; Thingstad and Rassoulzadegan, 1995). In this work we assess and quality-check the optical data collected during 2000 and 2001. The array was moored at the rim of the anticyclone (Figure 1), which resulted in rather complicated dynamics. Frequent failure of the instrumentation and the significant problems with bio-fouling do not allow for a full seasonal interpretation of the time series data. Therefore only a few events will be examined and the focus will be placed on the inter-comparison between mooring optical data time series, remotely sensed ocean colour and ecological modelling outputs.

Figure 1 Surface layer concentration as observed by SeaWiFS sensor in the Cretan Sea. The anticyclone wake in the chlorophyll distribution is evident. 2. Data The M3A array hosts four CTD instruments at depths of 40, 65, 90 and 115 metres, fitted with additional sensors of beam transmittance at 660nm, fluorescence, spectrally integrated irradiance (PAR) and dissolved oxygen. The sampling period is set to 3 hours, however for the purposes of this work daily averages are used. In order to calibrate the optical sensors, during the bi-monthly maintenance cruises, water samples were collected and water column profiles with a similar pre-calibrated CTD were taken. Serious technical complications involving PAR sensors and bio-fouling problems with the optical sensors were encountered during the period under consideration, thus the available dataset is severely limited. Concurrent daily SeaWiFS data for the period January 2000-December 2001 were provided by NASA's Goddard Space Flying Center (GSFC). The Institute of Marine

Optical variability associated with phytoplankton dynamics in the Cretan Sea during 2000 and 2001

556

Biology of Crete (IMBC), as an Authorised SeaWiFS Data User, received level-3 data as standard mapped image (SMI) product with a resolution of 9 km (at the equator). Images were received in Hierarchical Data Format (HDF) files and were processed using Windows Image Manager (Kahru and Brown, 1997), which allowed for automated image scaling conversion, georeference and data extraction for the location of M3A array. The conversion formula that was applied to the images is NASA's level-3 logarithmic scaling equation: Chla(mg.m

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3. Results and discussion From scalar irradiance profiles in the visible (PAR) the average diffusion attenuation coefficient KpAR was estimated and the attenuation depth was found to be around 20 m during the deployment period. This defines the layer from which 86% of remotely sensed water-leaving radiance emanates. A straightforward comparison of SeaWiFS data to the upper layer chlorophyll bottle data taken during different cruises indicates that both datasets follow a similar seasonal cycle, however, SeaWiFS derived chlorophyll concentrations are higher by 37% (Figure 2).

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Moreover, the total depth-integrated chlorophyll concentration which exhibits a strong peak during May (Figure 2), does not produce a similar signature in the layer sensed by SeaWiFS. Given the strong component of the interannual variability in the local ecosystem, the use of statistically derived vertical modes might be in doubt. Thus the surface concentration as sensed by satellites cannot be easily extrapolated in depth. In general, the c(660) beam attenuation coefficient was found to have low correlation with fluorescence as was expected (Kichen, 1982). The coefficient has a tendency to decrease with depth, indicating its association with microorganism and detrital particle concentration.


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From the entire 2-year record, three distinctive events will be presented in this work. The first depicts the spring bloom as was observed during late April and early May 2000 (Figure 3). This was a period when the mixed layer was shallower than 40m and the stratification process was underway. Records from the ADCP suggest that relatively calm water conditions prevailed with current speeds below 20cms -]. The pressure recorded by the 115m pressure sensor also indicates that the mooring line was held vertical during this period and confirms that the monitored parameters are representative of the respective instrument's nominal depth. Recorded chlorophyll concentration at 65 m increased and reached a maximum during early May. The system is largely driven by the hydrodynamics and especially by the occurrence of deep mixing events. Thus in late winter to early spring the increase in light and temperature in conjunction with the transportation of nutrients in the euphotic zone results in an increase in chlorophyll. It is interesting to note, that the SeaWiFS sensor misses this bloom, which is a column process with a strong component below the first optical depth. Another remark is that during this event the correlation between beam attenuation coefficient and fluorescence is high. This association can be attributed to the high phytoplankton concentration during the bloom period consisting of cells having such size distribution that act as effective scatterers and probably to the increased chlorophyll pigment absorption at this wavelength. 0.4 q

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Optical variability a s s o c i a t e d with p h y t o p l a n k t o n d y n a m i c s in the C r e t a n S e a during 2 0 0 0 and 2001

558

tration and beam attenuation coefficient. Both decrease since the sensors are placed below the chlorophyll maximum of the season, which is expected to be around 90m. The third event occurred during the period from July to September of 2001 (Figure 5). The beam attenuation coefficient exhibits a smooth increase at all depths, starting in early July 2001. This condition corresponds to an intrusion of relatively cold water masses that halt the process of seasonal temperature build up. Moreover there is indication in the SeaWiFS record of a short-lived chlorophyll peak. All these suggest a slow intrusion of the cyclonic gyre in the M3A location. This situation ends abruptly in early September. Nutrient depleted and warm water is re-established in the area when the anti-cyclone is re-positioned much closer to the mooring site. 0.3

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4. Ecosystem model validation Time series of the diagnostic variable of chlorophyll concentration as obtained from two ecosystem models that exist in the area are compared here against observed data. The first, the 1D Cretan sea ecosystem model (Triantafyllou et al., 2003) is based on the European Regional Seas Ecosystem Model (ERSEM) (Baretta et al., 1995) describing the biogeochemical cycles. The other, the 3D ecosystem model (Petihakis et al., 2002) consists of two on-line, coupled sub-models: the three-dimensional Princeton Ocean Model (POM) (Blumberg and Mellor, 1987), which describes the hydrodynamics of the area providing the background physical information to the 1D ecological model. The 3D model is interannually forced whereas the 1D is perpetually forced (same external forcing every year).


559

The phytoplankton pool is described by four functional groups based on size and ecological properties. These are diatoms P1 (silicate consumers, 20-200ja), nanophytoplankton P2 (2-20p), picophytoplankton P3 (20p). All phytoplankton groups contain internal nutrient pools and have dynamically varying C:N:P ratios. The nutrient uptake is controlled by the difference between the internal nutrient pool and external nutrient concentration. The microbial loop contains bacteria, heterotrophic flagellates and microzooplankton, each with dynamically varying C:N:P ratios. Bacteria act to decompose detritus and can compete for nutrients with phytoplankton. Heterotrophic flagellates feed on bacteria and picophytoplankton, and are grazed by microzooplankton. Microzooplankton also consume diatoms and nanophytoplankton and are grazed by mesozooplankton. In Figure 6 the outputs of the two models at 15m and 65m are compared against SeaWiFS and mooting data respectively. It is apparent that at the near surface layer the evolution of chlorophyll concentration is underestimated by the 1D model in contrast with the 3D where simulated concentrations are very close to field values, with the exception the period mid winter-mid spring when the 3D model is overestimating. At a depth of 65m the chlorophyll from the 1D model follows the mooting data seasonal cycle reasonably well, while the 3D produces a maximum during summer. Oligotrophic systems like the Cretan sea exhibit two productivity maxima (end of winter and autumn), which depend on the hydrological properties. During summer a considerable production is sustained in the deeper layers close to the thermocline through a nutrient regeneration mode. Although both models depict the above pattern, the 3D model is overestimating chlorophyll presumably because the thermocline is wrongly displaced at a shallower depth due to the influence of the cyclone-anticyclone dipole. 0.5[

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560

Optical variability associated with phytoplankton dynamics in the Cretan Sea during 2000 and 2001

oligotrophic systems remotely sensed chlorophyll concentration is difficult to interpret since its maximum is close to the deep thermocline and not visible to the satellite sensor.

Acknowledgements The work was carried out within the framework of the System-Pilot Project (MFS-PP). We acknowledge the Commission MAST3 Programme that financed the project CT98-0171. The project was co-financed by the General Technology of the Hellenic Ministry of Development.

Mediterranean Forecasting support of the European under contract no. MAS3Secretary of Research and

References Baretta, J.W., W. Ebenhoh and P. Ruardij, 1995. The European Regional Seas Ecosystem Model, a complex marine ecosystem model. Netherlands Journal of Sea Research, 33: 233-246. Blumberg, A.F. and G.L. Mellor, 1987. A description of a three-dimensional coastal ocean circulation model. In: N.S. Heaps (Editor), Three-Dimensional Coastal Ocean Circulation Models. Coastal Estuarine Science. AGU, Washington, D.C., pp. 1-16. Dickey T., D. Frye, H. Jannasch, E. Boyle, D. Manov, D. Sigurdson, J. McNeil, M. Stramska, A. Michaels, N. Nelson, D. Siegel, G. Chang, J. Wu and A. Knap, 1998. Initial results from the Bermuda Testbed Mooting program. Deep-Sea Research I, 45, 771-794 Georgopoulos, D., G. Chronis, V. Zervakis, V. Lykousis, S. Poulos and A. Iona, 2000. Hydrology and circulation in the Southern Cretan Sea during the CINCS experiment (May 1994-September 1995). Progress in Oceanography, 46: 89-112. Ignatiades, L., S. Psarra, V. Zervakis, K. Pagou, E. Souvermezoglou, G Assimakopoulou, and O. Gotsi-Skreta, 2002. Phytoplankton size-based dynamics in the Aegean Sea (Eastern Mediterranean). Journal of Marine Systems, 36:11-28. Kahru, Mati and C.W. Brown, 1997. New Techniques for Detecting Large-Scale Environmental Change, 155pp, Landes Bioscience. Kichen, J.C., J.R.V. Zaneveld and H. Pak, 1982. Effect of particle size distribution and chlorophyll content on beam attenuation spectra, Applied Optics, 21: 3913-3925. Krom, M.D., N. Kress, and S. Brenner, 1991. Phosphorus limitation of primary productivity in the eastern Mediterranean Sea. Limnology Oceanography, 36(3): 424-432. Nittis, K., C. Tziavos, I. Thanos, P. Drakopoulos, V. Cardin, M. Gacic, G. Petihakis and R. Basana, 2003. The Mediterranean Moored Multi-sensor Array (M3A): System Development and Initial Results. Annales Geophysique, (In press). Petihakis, G., G. Triantafyllou, J.I. Allen, I. Hoteit, and C. Dounas, 2002. Modelling the Spatial and Temporal Variability of the Cretan Sea Ecosystem. Journal of Marine Systems, 36(3-4): 173-196. Pinardi, N. and N. Flemming (editors), 1998. The Mediterranean Forecasting System. Science Plan. EuroGOOS Publication No 11, SOC, Southampton, ISBN 0-90417535-9 Theocharis, A., E. Balopoulos, S. Kioroglou, H. Kontoyiannis and A. Iona, 1999. A synthesis of the circulation and hydrography of the South Aegean Sea and the Straits


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of the Cretan Arc (March 1994-January 1995). Progress in Oceanography, 44: 469509. Thingstad, T.F. and F. Rassoulzadegan, 1995. Nutrient limitations, microbial food webs, and 'biological C-pumps': suggested interactions in a P-limited Mediterranean. Marine Ecology Progress Series, 117: 299-306. Triantafyllou, G., G. Petihakis and J.I. Allen, 2003. Assessing the performance of the Cretan Sea ecosystem model with the use of high frequency M3A buoy data set. Annales Geophysicae, (In Press).

Contemporary problems of navigation safety and sea pollution in the Georgian Exclusive Economic Zone George Metreveli andKakhaber Bilashvili* Tbilisi State University, Georgia Located on the crossroads of Europe and Asia, Georgia represents a natural corridor between the two continents. Across this corridor runs the shortest line of the ancient "Silk Route", which for many centuries connected the countries of Europe and Asia. The construction of a new Silk Route, a necessary new bridge between the East and West, called Transport Corridor Europe-Caucasus-Asia (TRACECA) will enhance the trading market for energy and mineral resources. During the next decade in the sea sector of TRACECA there will be five functioning ports, from which four will transport oil, oil products and liquefied gas. Problems will appear regarding safe navigation and the marine environment in this case. Georgia, as an independent state, holds an Exclusive Economic Zone (GEEZ) of about 27000 sq. miles of the Black Sea (Metreveli et al., 1999b, Bilashvili et al., 2002). Accordingly permanent ecological monitoring of this zone is the responsibility of Georgia. On several occasions, ballast and municipal sewage water and other pollutants from ships have been dropped in the GEEZ including its seaports. In order to prevent unlawful actions, it is necessary to improve the qualitative parameters of operational oceanography, marine meteorology and ecological monitoring networks and establish the hydrometeorological provisional system for safety navigation. This system, which is oriented on the needs of end-users, must include: 1. A network of coastal oceanography with autonomous buoy station (ABS) and meteorology stations working with identified programmes for date collection, processing and transmission. 2. A weather ship for fulfilling ecological monitoring and hydrometeorological observations in open sea. In this case it is of great interest to prevent tanker accidents and area of the Supsa pipeline oil-loading buoy. This is located 3 km communicates with the Supsa Terminal over a subsea pipeline. floating 10-metre diameter welded metal construction, linked to two 16-inch diameter flexible submarine hoses. Two additional the tanker during the loading process.

oil overflow in the sea from the shoreline and The loading buoy is a the subsea pipeline by hoses link the buoy to

Local squalls which traditionally occur in many regions of the Black Sea, where the Caucasus mountain slopes closely come down to the sea coast, are a danger to tankers during loading time. In the initial period the squall progresses like a strong breeze. This is followed by constant acceleration and usually in 1.0-1.5 hours wind speeds of more than 35.0ms -1 are reached. At the peak of its development stage the squall becomes stronger--up to 4 0 . 0 - 4 2 . 0 m s - l - - w i t h a drifting current of 1.0-1.5ms -1, wave heights 5.0-5.5ms -1 * Corresponding author, email: [email protected]

George Metreveli andKakhaber Bilashvili*

563

and a total hydro and air pressure on the tanker body of up to 120-130 tons (Metreveli et al., 1999a, Metreveli, 1997). In such a critical situation a tanker can not resist the squall, because it is unable to pick up sufficient speed and the wind and current will cast it ashore. In this case oil overflow into the sea would be inevitable, causing an ecological catastrophe. Accidents like this would be disastrous not only for Georgian shore infrastructure and coastal zone ecology, but also for the entire Caucasian and Crimea coastal zone (Metreveli et al., 1999a). To avoid tanker wreckage, early squall forecasts are needed. The most significant signs of a squall are a rapid decrease in air pressure and temperature. In the Supsa buoy area, squall forecasting can be realised only by coastal meteorological observations of air pressure, temperature and other weather elements. The hydrometeorology station has to be set up in an area from where the loading buoy can be seen, i.e. on the Ureki shore. In the initial stage of a squall, tankers have to be informed about the squall at least 40-50 minutes in advance. This is the minimum time needed for the ship to escape from the dangerous zone. The Observation Network System of operational oceanography and marine meteorology is necessary not only for the safety of navigation. It is impossible to preserve the East Black Sea ecology and unique beaches of Ureki, Shekvetili, Kobuleti and all marine environment, without an ecological monitoring network (e.g. remote sensing). The elements of such a network can be put into the oceanography stations, ABS, on board ferries and the weather ship. This problem will be partly resolved within the EC funded ARENA project. One of the main requirements for successful functioning of an observation and ecological monitoring network is the implementation of modern observation and other appropriate equipment.

References Metreveli, G., G. Gigineishvili, and A. Demetrashvili, 1999, Safety Problems of the OffShore Delivery Terminal and Tanker at Georgia Oil Pipeline under a sudden Squall. NATO/CCMS Workshop on Environmental Security of Oil Pipeline in Georgia. Tbilisi, p57-60 Metreveli, G., 1997, Distributions of Poor Visibility and Wind Speed in the Area of the River Supsa Mouth. Georgian State Project Design Institute and Georgian International Oil Company Workshop on Information for Hydrometeorological Conditions in the Area of Supsa Pipeline Terminal. Tbilisi, p31. Metreveli, G., G. Gigineisvili, T. Gzirishvili, and B. Beritashvili, 1999, Impact of Current Global Warming on the Coastal Zone of Georgian. UNDP/GEF Project GEO/96/G31. National Climate Research Center, p75. Bilashvili, K., G. Metreveli, N. Beradze, J. Dolidze, and G. Kordzakhia., 2002, Georgian Observing Plans. GOOS Report #109, UNESCO.

Outfall of storm sewers in the sea - - a technical review J. D. Demetriou

N. T. UA.--School of Civil Engineering, Laboratory of Applied Hydraulics, Greece

Abstract This paper is a technical review concerning the sewer or culvert or small river outflows in the sea, mainly based on experiments by the author or on well established equations. The relative vertical positions of the exit of the sewer and the sea surface are examined, and the various flow and diffusion fields are presented according to the relative magnitudes of densities of the outflowing and sea waters. A number of equations are presented concerning the geometries, velocities and dilutions of all presented flow fields. It is believed that the results are useful when hydraulic and marine works designers are collaborating on the above topic.

Keywords: Storm sewers, culverts, sea outfalls 1. Introduction An interesting problem on marine environment protection, coastal engineering and pollution control, is the outfall of urban storm sewers or culverts or small rivers in the sea, which is unavoidable--in the sense that almost any rainfall is finally directed into the sea. In this paper a review of the technical part of the above topic is presented, based on experimental results by the author or other authors, and the "engineering approximation" is more pronounced in some cases for practical use and design purposes. To the best knowledge of the author this is a first practical attempt to connect the hydraulics of storm sewers with environmental hydraulics, predicting the behaviour of the issuing water into the sea. When a storm sewer, culvert or small river, of rectangular cross section (water depth h o and width bo, bo>ho), is horizontally issuing (with velocity Vo), at a vertical wall of a q u a y - - i n contact with the calm sea (sea water density, Ps=COnSt.), then the liquid effluent (of initial density Po=COnSt.) mixes with the sea water--usually with the mechanism of turbulent diffusion and entrainment. There are three distinct arrangements for the sewer water exit to be installed: 1. above the sea surface 2. at the same level as the sea surface 3. submerged, i.e. under the sea surface. In the latter arrangement the flow in the sewer is under pressure, and an extreme position is that it touches the sea bottom. There are also three distinct varieties among liquid densities: A) po = Ps, B) po < Ps and C) po > Ps- The varieties A and C may be created if the * Corresponding author, email: [email protected]

J. D. Demetriou

565

sewer, culvert or river water, contains suspended sediments or dissolved fertilisers transported within the flowing water. The entire flow and mixing field may function in several ways if the tidal rise and fall are considerable. Figure 1, Figure 2 and Figure 3 schematically show all relative arrangements and varieties (A, B, C), creating 9 flow cases. When two miscible liquids (of initial densities Ps and Po), one of initial velocity V o and the other at rest, are mixed, two flow parameters are most important. If the Reynolds number Re = V o 9

~/bo " ho V

(where v=kinematic viscosity)

(1)

has large values, then the flow and mixing has turbulent characteristics. In flows under a free surface the Froude n u m b e r is

Fr = U. (g'. l)

1 2

(2)

where U, l are reference velocity and length scales and g ' = . . I P s - P o l ' p gs modified intensity of gravity.

is the

If the local density at any point of the mixing field is P, then the local mass dilution of the moving fluid into a small sample of the mixture is given by the expression

s = ( p ~ - Po) ( P s - P)

(3)

When the sewer carries a pollutant, then its dilution within the sea is analogous to s and varies between 1 (exit) and oo (complete dilution). Most of the results presented here are based on previous measurements by Demetriou (1996), Noutsopoulos et al. (1980), Demetriou et al. (1987) and Demetriou (2002).

2. Flow geometries-velocities-dilutions Figure 1 shows all three flow varieties (A, B, C) for water exit over the sea surface (on the left). It is well known that 1

(t,g.22/3 b o)

Y b = 0.715 9

(where Q is the discharge)

the trajectory (axis) to the sea surface:z m = 0.5 9 g

while from continuity and energy

(4)

"X m

with tan0 =

(5)

566

Outfall of storm sewers in the sea m a technical review

1

V = [ 2 g A H + V~o] , b = A B = Yb where z m = A H - h axis.

,withL

=

I

2.z m.

1

(6)

o + 0 . 5 y b for L, and m shows corresponding quantity on the flow

Figure 1 Cases 1A, 1B, 1C

Figure 2 Cases 2A, 2B, 2C

Figure 3 Cases 3A, 3B, 3C For the above 9 flow cases the following equations are concluded within the sea water (at a distance from the bottom):

J. D. Demetriou

567

Cases IA, 3A

1

"~

-

2.4.

(7)

expE

u IAm

(8)

where for case 3A, 8 = x m, b = h o, V= Vo Case

IC: For 0=90 ~ 4

and 5

Figure 1 IWICOS metadata, metadata validation process and supported data types The IWICOS metadata standard consists of a set of XML Schema files that conform to the recommendations of the World Wide Web Consortium (W3C). The schema files define a unique vocabulary for describing meteorological, oceanographic, sea-ice data and satellite imagery within the IWICOS project. From this vocabulary, single metadata documents are instantiated and these describe the available data products precisely. The vocabulary is presented in a total of 17 separate schema files in order to simplify the development environment and to ease revisions and updates. The IWICOS metadata standard defines a hierarchical structure for the metadata. The XML elements are used as the means of data structure presentation, elements can hold sub-elements, whereas the data values are presented as XML attributes contained in the elements. The values have to conform to a specific value type and may furthermore be

638

IWICOS metadatamdescribing met-ice-ocean information with metadata

constrained by a set of facets. The constraints ensure that the skeleton for metadata instantiation is non-ambiguous. The metadata instance documents are validated against the schema definition to ensure that the files are standard compliant and that the structure and the values match the definition. The validation can be an automatic part of the service chain. Two major versions of the IWICOS metadata standard e x i s t - - a baseline version and an extended system version. They reflect the different states of the IWICOS project regarding data products and system internal architecture. The extended version matches the new schema recommendations by W3C. The IWICOS system supports a selected set of IWICOS data exchange formats. Format selection is based on the requirement of presenting raster, vector, grid and text data types. The selected formats are respectively BSQ, ESRI Shapefile, GRIB (Gridded Binary) and XML. The implementation of these formats can be exemplified as a NOAA AVHRR satellite image in BSQ, a sea ice analysis in ESRI Shapefile format, a wind prognosis in GRIB and a text based weather forecast in XML (see Figure 1). The metadata is used to store the information of available products from the producers at the IWICOS Broker (Haajanen et al., 2003). User agents, such as IWICOS Faqade instances (e.g. Kotovirta et al., 2003), can query the Broker for suitable data. A metadata file contains the URL of the corresponding data file residing at the relevant data producer in a distributed production environment. Thus the large data volumes are left with the data producers. The search results are returned as a set of metadata files that correspond to the available data files satisfying the search criteria. The user can subsequently retrieve the relevant data-files from the producer (Lind, 2001).

4. Conclusions The IWICOS metadata development clearly shows the potential and applicability of XML in GIS metadata presentation tool. The IWICOS metadata has been used in project demonstrations (e.g. the Fram strait demonstration in Sein~i and Gr6nvall, 2003) and has proved to also have practical value.

References Haajanen, J., R. Berglund, V. Kotovirta, and R. Tergujeff, 2003, Iwicos architecturem software architecture for marine GIS-data interoperability. Proceedings of 3rd EuroGOOS Conference (this publication), p. 633. Kotovirta V., R. Berglund, J. Haajanen, J. Mansner, and R. Tergujeff, 2003, Delivering near real-time met-ice-ocean observation and forecast data--the IWICOS Facade. Proceedings of 3rd EuroGOOS Conference (this publication), p. 639. Lind, M., 2001, A GIS Application for Integrating Meteorological, Oceanographic and Sea Ice Data, Proceedings, 16th ESRI European, Middle-Eastern and African user conference in Lisbon, October, 2001. Sein~i, A., R. Berglund, J. Haajanen, V. Kotovirta, J. Mansner, R. Tergujeff, J. Launiainen, P. Eriksson, M. Johansson, J. Vainio, M. Lind, and H. Gronvall, 2003, Digital, high resolution weather, sea ice and ocean information to the users at sea: the IWICOS demonstration during the Aranda expedition in the Fram Strait. Proceedings of 3rd EuroGOOS Conference (this publication), p. 627.

Delivering near real-time met-ice-ocean observation and forecast d a t a - - t h e IWlCOS Facade Ville Kotovirta*, Robin Berglund, Jyrki Haajanen, Jenni Mansner, and Renne Tergujeff Technical Research Centre of Finland (VTT), Finland Abstract Access to meteorological, sea ice, and oceanographic (met-ice-ocean) data is essential for ships navigating in harsh and ice covered marine environment. Furthermore, the need for efficient monitoring of the ongoing climate change poses new requirements for the accessibility of this kind of data by researchers. This paper describes how new web technologies, such as SOAP and XML, are used in an implementation of the IWICOS Faqade system, which delivers near real-time met-ice-ocean data to users at sea. The Faqade is part of a service chain developed in the EU-funded IWICOS project (Integrated Weather, Sea Ice and Ocean Service System).

Keywords:

Web technologies, portal, remote sensing data, operational data

1. Introduction The use of forecast and near real-time remote sensing data in strategic and tactical route planning improves the safety of the ship, the crew, and the cargo, and provides significant savings by decreasing fuel consumption and delay times. The system transmitting the data to the ships should operate as real-time as possible, since the vessels at sea may operate in a very dynamic environment--the more recent the data concerning the prevailing weather and ice conditions are, the more valuable they are for the users. The IWICOS project (Integrated Weather, Sea Ice and Ocean Service System) is targeted to research, evaluate, and demonstrate the technologies and approaches required for an interoperable meteorological, ice, and oceanographic data service. The IWICOS Faqade is part of a service chain developed in the IWICOS project (Haajanen et al., 2003). In general, the implementation of the Facade depends on the capabilities of the Client, and the communication links bandwidth between the Client and the Facade. The Facade can be considered as a portal, which delivers forecast and near real-time remote sensing data to users at sea. In this paper we describe how new web technologies are used in implementing the Facade system, show how the Faqade was used operationally in a demonstration, and conclude with a consideration about future research possibilities. The IWICOS Facade (herein after referred to as the Facade) actively queries, retrieves, and sends new data to the Client instead of just passing the client requests to the Broker and the Data Producers. It must not be confused with the object-oriented design pattern, called Fa9ade, which provides a unified interface to a set of interfaces in a subsystem (Gamma, 1994), but where the Facade is not an active component. * Corresponding author, email: [email protected]

640

Delivering near real-time met-ice-ocean observation and forecast datamthe IWlCOS Fas

2. Fas

architecture

Figure l depicts the generic Facade architecture on which we have based our implementation. The Facade consists of two logical subsystems, the Metadata & Product Manager, and the Profile Manager. The former is responsible for getting the metadata, selecting and downloading the products, and delivering them to the users. The latter provides the Client Software with methods for inputting and editing the profile data.

Figure 1 The Faqade architecture. The two subsystems are divided further in several sub-modules, which take care of the different functions of the Facade. As the user inputs and edits the profile data, the Client Software communicates with the Profile Server, which stores the profile data in the database. The Metadata Retriever communicates with the Broker and receives metadata, and based on the metadata the Product Selector selects useful products, which are retrieved from the producers and prepared for sending to the Client Software. In the implementation we used freely available components, such as SOAP and XML, server side scripting technology PHP, MySQL database, and Java programming language. Figure 1 illustrates the technologies that were used in the implementation of different sub-modules of the Facade.

3. Profiles In the general IWICOS architecture model the Faqade can have a passive or an active role in delivering the data. Our implementation is a profile-based Faqade, which actively searches for, selects, and delivers relevant data products to the users. User-defined profiles are an important part of the functionality of the Faqade. A profile describes a user's preferences regarding data products, area of interest, time of interest, data type, and some details about the capabilities of the client software and the data communication channel. The Faqade receives a bundle of metadata instances from the Broker as an answer to a query. Each instance contains a set of attributes, which describe the contents

Ville Kotovirta*, Robin Berglund, Jyrki Haajanen, Jenni Mansner, and Renne Tergujeff

641

of one data file located somewhere on the Web. By combining the profile data with the metadata, the Faqade infers which data files suit the user best. 3.1 Aranda demonstration The Facade system was demonstrated in operational use in March 2002, when weather data produced by Danish Meteorological Institute, satellite image data by Finnish Institute of Marine Research, and ice drift buoy data by the University of Hamburg were delivered to R/V Aranda during its North Atlantic expedition (Sein~i et al., 2003).

Because of the low-bandwidth communication link the file sizes were minimised in order to reduce the communication costs and data transmission times. The area of coverage and the resolution of the files were reduced, and the images were compressed with a lossy compression technique. Altogether the ship downloaded 196 files with an average size of 26.5Kb, consisting of 66 satellite images, 73 wind forecasts, 25 pressure forecasts and 32 ice drift data files. Although some minor problems appeared during the demonstration the users found the system applicable, and the data they downloaded were useful in the research work. 3.2 Future research possibilities

The basic philosophy in the IWICOS architecture is that the Faqade uses the catalogue services provided by the Broker to find ready-made static data files, which best suits the user profile. If the Broker was extended to also provide the Faqade with a catalogue of services, providing dynamic data products, the Faqade would have more possibilities and alternatives in serving the Client. So far it has been assumed that contracts between the users and the data providers already exist, and the Faqade has free access to the data files. In the future, when there are many clients and data providers involved in the system, this may not be the case, and the Faqade should have a mechanism to perform the business transactions needed to purchase the products applicable to the users. References

Gamma, E., 1994. Elements of reusable object-oriented software. Addison-Wesley publishing company. Reading, Massachusetts. ISBN 0 - 2 0 1 - 6 3 3 6 1 - 2 . Haajanen, J., R. Berglund, V. Kotovirta, and R. Tergujeff, 2003, Iwicos architecture m software architecture for marine GIS-data interoperability. Proceedings of 3rd EuroGOOS Conference (this publication), p. 633. Seinfi, A., R. Berglund, J. Haajanen, V. Kotovirta, J. Mansner, R. Tergujeff, J. Launiainen, P. Eriksson, M. Johansson, J. Vainio, M. Lind, and H. Gronvall, 2003, Digital, high resolution weather, sea ice and ocean information to the users at sea: the IWICOS demonstration during the Aranda expedition in the Fram Strait. Proceedings of 3rd EuroGOOS Conference (this publication), p. 627.

Interactive Internet coastal wave information production and retrieval system K.C. Jun, D.Y. Lee*, S.D. Kim and K.S. Park Korea Ocean Research and Development Institute, Korea

Abstract The Korea Ocean Research and Development Institute (KORDI) conducted a continuous wave hindcast for 20 years from 1979 for the area 2 0 - 5 0 N, 120-150 E to cover the North East Asia Regional Seas. An interactive database of hindcast wave data was built to handle large quantities of wave information to provide comprehensive information of the long-term wave climate in the region. To produce the coastal wave information at an arbitrary point of interest using the offshore boundary conditions retrieved from the database, a coastal wave information system has been developed. The user-friendly interactive coastal wave information system gives users direct access to the data through Internet and provides various statistic wave information in terms of tables and figures for better ocean and coastal services.

Keywords: Wave simulation, wave climate, shallow water wave, internet, database 1. Introduction Two types of long-term statistical information on wave conditions are desired for many marine applications: the information on the design waves and operational waves. Since the availability of the quantitative field wave data in the coastal regions is limited to cover climatically significant periods of time to be able to provide a reliable wave statistics, the wave climate information needs to be generated by means of long-term wave hindcasting. The coastal wave information system had been developed by means of a shallow wave model using offshore boundary conditions obtained from long-term simulation of the sea state for 20 years from 1979, for the open waters of the North East Asia Regional Sea. By means of such system, it is possible to generate coastal wave information for any area of interest for the period of 20 years, from which several coastal and environmental problems can be approached scientifically. The coastal information production system needs to be developed for better coastal oceanographic services along the coastal area. The Korea Ocean Research and Development Institute (KORDI) developed a system to make the database accessible to the users for the support of projects such as design of coastal structures study of shoreline erosion. KORDI has established the coastal and ocean information service using an Internet home page to demonstrate the potential of the operational coastal and ocean services that is the aim of the NEAR-GOOS.


K.C. Jun, D.Y. Lee*, S.D. Kim and K.S. Park

643

2. Long-term simulation of wind waves A basic database has been established, consisting of hindcasted wave and wind parameters, such as significant wave height, peak period and direction, wind speed and direction with a 3 hour interval for the period of 20 years starting from 1979 for each grid point of North East Asia Regional Seas with grid size of 27km. From the database the input offshore wave condition of the shallow water wave model can be retrieved. The time series of wind waves obtained from the long-term continuous wave simulations for more than 20 years using European Centre for Midrange Weather Forecasts (ECMWF) reanalysed wind data are archived as a basic database. In the long-term simulation, which takes a considerable amount of computer time, the second-generation wave model, Hybrid Parametric wave model, was used for continuous simulation, while the WAM wave model was used in cases of high sea states. The simulated waves were evaluated against time series of field wave data obtained from the wave monitoring system of the Korea Ministry of Marine Affairs and Fisheries (MOMAF), Japan Meteorological Administration (JMA) and also with satellite measurement data from GEOSAT and TOPEX/POSEIDON in order to provide guidance on the quality of the hindcast work.

3. Long-term database of shallow water wave information The coastal waves can be calculated by means of a fine mesh coastal wave model using the offshore boundary conditions retrieved from the databases. However, the shallow water wave hindcasting for a long period of time is a time consuming job. A simple but rather accurate prediction of the shallow water waves can be made by preparing database of ratio of incident wave to local wave at grid point. For a given offshore wave condition, which is retrieved from the data base of long-term wave simulation, the shallow water wave conditions at each grid point are produced by the conversion rate data base calculated from SWAN for 81 different combinations of incident wave direction, period and height. The long-term time series of the coastal waves for any given point in a fine grid with a grid size of 250m can be simulated almost instantly using the databases of long-term offshore wave simulation.

4. Interactive Internet system The output of the long-term wave hindcast has been prepared in the form of an Internet on-line database. The user can retrieve the long-term wave synthesis data including various wave climates through a web site. The system allows the users to extract the time series dataset for a chosen location and time period from which various statistics can be produced and displayed on the terminal. An interactive W W W service system (http:/! wave.kordi.re.kr) is developed to provide wave information for deep and shallow water produced by several numerical models. The system consists of six main menus and 20 sub-menus, which can produce information on average wave height, wave rose, extreme wave height, spatial distributions and so on. HTML documents have been prepared to get the user' s requirement through CGI, and several programs coded in multi languages have been developed to extract and service wave information. ASP scripts files and C language programs are used for responding to CGI requests, FORTRAN programs are

644

Interactive Internet coastal wave information production and retrieval system

used for data extraction from binary data files, and JAVA applets are for real-time visualisation of spatial distribution. A flowchart of the system is shown in Figure 1 and an example of the output is shown in Figure 2 and Figure 3. ~'-W r-~ Request eb Browse q Result

~'l

Web Server

Search Condition ] CGI Programs I (ASP,C)

Write ~-I Condition Files Search Condtion

Search Condition~ Main Control Program (C)

Result

HTML Documents

HTML

Result Display Programs (C)

Extraction Request.~

,11

Result Data

Extraction Programs (FORTRAN)

vWrite

Result Data Files

Includem JAVA Applets Figure 1 Flow chart

Figure 2 Example of output of interactive Figure 3 Example of output of interactive Internet system (average wave height) Internet system (local design wave height)

References Lee, D.Y., 1991, Development of Integrated Coastal Monitoring Network in Korea, BSPG000119-383, Korea Ocean Research and Development Institute. Lee, D.Y., 1996, Development of Shallow Water Wave Prediction System, Korea Maritime and Port Administration. Ris, R.C., N. Booji, L.H. Holthuijsen, and R. Padilla-Hernandez, 1997, SWAN Cycle 2 User manualmSimulation of Waves in the Nearshore Zone. Delft University of Technology, The Netherlands. The WAMDI Group, 1988, the WAM ModelmA Third Generation Ocean Wave Prediction Model, J. Phys. Oceanogr., 1775-1810.

MEDARIMEDATLAS 2002: A Mediterranean and Black Sea database for operational oceanography M. Fichaut *1, M.-J. Garcia 2, A. Giorgetti 3, A. lona 4, A. Kuznetsov 5, M. Rixen 6 and the MEDAR Group /Centre de Brest, Plouzand, France 2 lEO, Spain 30GS, Italy 4NCMR/HNODC, Greece 5RIHMI_ WDC, Russia 6GHER, Univ. de LiOge, Belgium

Abstract A comprehensive database of temperature, salinity and bio-chemical parameters in the Mediterranean and Black Sea has been constructed through comprehensive co-operation between the bordering countries. Statistical climatologies have been computed with all assembled and quality controlled data. The database, designed to initiate and validate prediction models, also represents a system to quality-check new incoming data produced by ocean observing systems.

Keywords: Database, temperature, salinity, nutrients, climatology, quality control 1. Introduction Access to basic oceanographic parameters like temperature, salinity and nutrients is requested for model initialisation and validation, and to perform quality checks of the new data collected by the observing systems. However these data are in general dispersed among many laboratories, and under different systems, and long term responsibility is not always ensured. To answer these needs, the EU concerted action MEDAR/ MEDATLAS II (MAS3-CT98-0174 and ERBIC20-CT98-0103) was initiated, through a wide collaboration between a Mediterranean and Black Sea countries.

2. The MEDAR data management system and the database The data management structure was distributed between 20 national oceanographic data centres or designated national agencies (NODC/DNA), 4 regional data centres (RDC) and one co-ordinating global assembling centre. Each NODC/DNA compiled and safeguarded copies of the data sets dispersed in the scientific laboratories of its country and reformatted them to the common MEDATLAS format (MEDAR Group, 2001). Through this strong international cooperation, the volume of available data now represents 286426 stations (vertical profiles) from CTD (36054 profiles), bottle casts (88453) Xbt and Mbt bathythermographs (161848), with the released parameters summarised in Table 1. The spatial distribution, shown in Figure 1 for salinity, provides evidence for the heterogeneity of the data coverage, including empty zones in the middle of the basins * Corresponding author, email: [email protected]

MEDAR/MEDATLAS 2002: A Mediterranean and Black Sea database for operational oceanography

646

and on the Libyan shelf. These empty zones are enlarged when considering the seasonal distributions and indicate the areas where any operational system will be most useful. Table 1 Content of MEDAR 2002 Database Parameter Temperature

Profiles

Parameter

284371

PH

Profiles 14512

Parameter

Profiles

Alkalinity

2548

Salinity

118009

Nitrate

10572

Total P

2381

Oxygen

44 928

Nitrite

10 508

Total N

153

Phosphate

20761

Ammonium

5239

H2S1

1843

Silicate

15920

Chlorophyll

4672

1H2S is measured only in Black Sea

118009 SALINITY profiles

Figure 1 Location of the salinity profiles in MEDAR 2002 Database 3. Q u a l i t y control of the data

To ensure compatibility and coherence of data coming from many different sources, the data sets has been checked for quality (QC) according to a common protocol (MEDAR Group, 2001) based on the international IOC, ICES and MAST recommendations. After reformatting to a unique auto-descriptive ASCII format, the MEDATLAS format, the data were submitted to three series of automatic and visual tests: QCO: check of the format and completeness of information QC 1: check of the date, time, latitude, longitude, ship velocity QC2: check of the data points by comparison with regional statistics. As a result, a quality flag was added to each numerical value (international GTSPP flag scale). The QC procedures have been implemented in four regional data qualification centres and completed by global checks, mainly to track the remaining duplicates, in the assembling centre. This protocol is presently used by several ongoing operational projects like the Mediterranean Forecasting System (MFS), and, with regional adaptation, in the Atlantic.

M. Fichaut*, M.-J. Garcia, A. Giorgetti, A. Iona, A. Kuznetsov, M. Rixen and the MEDAR Group

647

4. Objective analysis The selection of all "good" data has been interpolated at 25 standard levels and objectively analysed to produce gridded climatological fields. A variational method described in Rixen et al. (2001) has been used. When the data coverage was sufficient, seasonal and monthly fields have been computed and maps of horizontal and vertical distributions drawn as shown in Figure 2.

Figure 2 Temperature at 50 m from MEDAR 2002 annual climatology

5. Conclusion All observed and analysed data, maps and documentation are published on a set of 4 C D - R O M s (MEDAR Group, 2002) to facilitate the data access to any users. On-line access to information and climatological data is also possible through the portal www.ifremer.fr/sismer/to the distributed Internet servers. In addition to the released database, the E U - M E D A R provides 9 information on the data availability, which is a valuable issue for the design of any observation system 9 an extended protocol for data validation, already used to process real time data, 9 a distributed data management infrastructure for long term archiving and dissemination Established by close collaboration with scientists in charge of data collection and validation, it also intends to provide an efficient answer to the fast evolving data needs. This network should be developed at the global scale in the frame of the Sea Datanet data system (in preparation) to provide integrated and validated multidisciplinary data and products, adapted to the operational oceanography requirements.

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MEDAR/MEDATLAS 2002: A Mediterranean and Black Sea database for operational oceanography

Acknowledgements The MEDAR Group acknowledges with thanks the contribution of the laboratories and scientists who published data within MEDAR and EU for the financial support to the project (contracts MAS3-CT98-0174 and ERBIC20-CT98-0103).

References MEDAR Group, 2001, Medar-Medatlas Protocol (Version 3) Part I: Exchange Format & Quality Checks for Observed Profiles; Rap. Int. IFREMER/TMSI/IDM/SIS02-006, 50 P. MEDAR Group, 2002, MEDAR 2002 Database. Mediterranean and Black Sea cruise Inventory, Observed Data, Analysed Data and Climatological Atlas (4 CD-ROMs). Rixen, M., J.-M. Beckers, J.-M. Brankart, and P. Brasseur, 2001, A numerically efficient data analysis method with error map generation. Ocean Model., 2( 1-2):45-60.

POLLS" Poseidon On-Line Information System D.S. Viaehos


Abstract POLIS is a user-friendly information system that is exported to the World Wide Web. The system provides routing information for the Aegean Sea. The user can draw his route by selecting waypoints, defining the mean speed and time of departure. The system answers with a detailed report, which contains initial routing recommendations and information about the weather and sea condition along this route. Optional field equipment is available for continuous monitoring, providing corrections on the initial route recommendation. The system is connected with the POSEIDON system, which contains a network of observation buoys and a specialised operational centre for the processing of the data collected and the production of forecasts. POLIS can be found very valuable especially in small and medium size ship cruise programming.

Keywords:

Weather forecast, ship optimum routing

1. Introduction POLLS is a web-based application, which develops an optimum track for ocean voyages based on forecasts of weather and sea conditions computed by the POSEIDON system. Within specified limits of weather and sea conditions, the term optimum is used to mean maximum safety and crew comfort, minimum fuel consumption, minimum travel time, or any desired combination of these factors. The system consists of a ship-routing server located at the POSEIDON operation centre and optional field equipment, which offers position monitoring and a bi-directional satellite link with the routing server. The ship-routing server, acting as an advisory service, attempts to avoid or reduce the effects of specific adverse weather and sea conditions by issuing initial route recommendations prior to sailing, recommendations for track changes while underway (diversions), and weather advisories to alert about approaching unfavourable weather and sea conditions which cannot be effectively avoided by a diversion. Adverse weather and sea conditions are defined as those conditions which will cause damage, significant speed reduction or time loss. The field equipment, if available, establishes a bi-directional communication between the shiprouting server and the vessel providing routing corrections, recommendations and alerts during sailing. By this process of initial route selection and continued monitoring of the ship' s progress for changes, it is possible to maximise the ship' s speed and safety. Use of the above advisory service in no way substitutes prudent seamanship and safe navigation. There is no intent by the routing server to inhibit the exercise of professional judgement and prerogatives of commanding officers and masters.


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POLLS: Poseidon On-Line Information System

2. Ship and cargo considerations Ship and cargo characteristics have a significant influence on the application of ship routing. Ship size, speed capability and type of cargo are important considerations in the route selection process prior to sailing and the surveillance procedure while underway. A ship's characteristics identify its vulnerability to adverse conditions and its ability to avoid them. Ship performance curves (speed curves) are used to estimate the ship's speed while transiting the forecast sea states. The curves indicate the effect of head, beam and following seas of various significant wave heights on the ship' s speed. With the speed curves it is possible to determine just how costly a diversion will be in terms of the required distance and time. A diversion may not be necessary where the duration of the adverse conditions is limited. In this case, it may be better to ride out the weather and seas knowing that a diversion, even if able to maintain the normal ship's speed of advance, will not overcome the increased distance and time required.

3. Environmental factors Environmental factors of importance to ship routing are those elements of the atmosphere and ocean that may produce a change in the status of a ship transit. In ship routing, consideration is given to wind, seas, fog and ocean currents. While all of the environmental factors are important for route selection and surveillance, optimum routing is normally considered attained if the effects of wind and seas can be optimised. The effect of wind speed on ship performance is difficult to determine. In light winds (less than 20 knots) ships lose speed in headwinds and gain speed slightly in following winds. For higher speeds, ship speed is reduced in both head and following winds. Wave height is the major factor affecting ship performance. Wave action is responsible for ship motions, which reduce propeller thrust and cause increased drag from steering corrections. The relationship of ship speed to wave direction and height is similar to that of wind. Head seas reduce ship speed, while following seas increase ship speed slightly to a certain point, beyond which they retard it. In heavy seas, exact performance may be difficult to predict because of the adjustments of course and speed for ship handling and comfort. Although the effect of sea and swell is much greater than wind, it is difficult to separate the two in ship routing. Fog, while not directly affecting ship performance, should be avoided as much as feasible, in order to maintain normal speed in safe conditions. Although the route may be longer by avoiding fog, transit time may be less due to not having to reduce speed in reduced visibility. In addition, crew fatigue due to increased watch keeping vigilance can be reduced. Ocean currents do not present a significant routing problem, but they can be a determining factor in route selection and diversion. The important consideration to be evaluated is the difference in distance between a great circle route and a route selected for optimum current, with the expected increase of speed of advance from the following current.

D.S. Vlachos

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4. POLLS recommendations and advisories An initial route recommendation is issued to a ship normally 24 to 48 hours prior to sailing and the process of surveillance begins. Surveillance is a continuous process, maintained until the ship arrives at its destination (the optional field equipment must be present). Adjustment of departure time is a recommendation for delay in departure, or early departure if feasible, and is intended to avoid or significantly reduce the adverse weather and seas forecast on the first portion of the route, if sailing on the original estimated date of departure. A diversion is an underway adjustment in track and is intended to avoid or limit the effect of adverse weather conditions forecast to be encountered along the ship's current track. Adjustment of speed of advance is a recommendation for slowing or increasing the ship's speed as much as predictable, in an attempt to avoid an adverse weather situation by adjusting the timing of the encounter. This also an effective means of maintaining maximum ship operating efficiency, while not diverting from the present ship's track. Evasion is a recommendation to the commanding officer or master to take independent action to avoid, as much as possible, a potentially dangerous weather system. A weather advisory is a transmission sent to the ship advising of expected adverse conditions, their duration and geographic extent. The ability of the routing server to achieve optimum conditions for the ship is aided by the commanding officer adjusting course and speed where necessary for an efficient and safe ride. At times, the local sea conditions may dictate that the commander officer take independent actions.

5. Conclusion The success of POLIS in ship routing is dependent upon the validity of the forecasts. POSEIDON forecasts and real time measurements of weather and sea conditions establish a solid base for this action. Technological advancements in the areas of satellite and automated communications will increase the amount and type of information to and from the ship with fewer delays. Ship response and performance data included with the ship's weather report will provide the routing server with real time information with which to ascertain the actual state of the ship. Being able to predict a ship's response in most weather and sea conditions will result in improved routing procedures.


GMES Marine Forum

Michel-Henri Cornaert


An introduction to the Global Monitoring for Environment and Security (GMES)initiative Peter Ryder *l and Jan H. Stel 2

1Environmental Information Services, UK 2Netherlands Geosciences Foundation, Netherlands

Abstract This paper sketches the objectives and different development phases of the GMES process. GMES aims to establish information systems at a European scale, but in an international and national context, for the environment and for (civil) security. This paper focuses on the results of the GMES Marine Forum that was organised during the third EuroGOOS Conference.

1. Overall Objectives The GMES overall aim is to support Europe's goals regarding sustainable development and global governance, in support of environmental and security policies, by facilitating and fostering the timely provision of quality data, information, and knowledge. The GMES objective is to "establish by 2008 a European Capacity for Global Monitoring of Environment and Security"-- see EC (2001a). The scope of the initiative has been further defined in the E C - E S A joint strategy on GMES (EC/ESA, 2001a), as well as in the Commission Communication on the GMES Action Plan (EC, 2001b) and the related EU Council Resolution of 13 November 2001 (Official Journal, 2001). These documents indicate that the European capacity to be developed should: 9 be autonomous and operational 9 guarantee sustainable, long term and coherent monitoring 9 combine in situ (land, sea and air) and space based monitoring.

2. The approach 2.1 Matching demand and availability~guaranteeing coherence and securing continuity Over the past twenty years, environmental managers and policy-makers have repeatedly complained about the lack of adequate information. The necessary information was often not available in time, and when available was frequently not reliable, or did not meet policy needs, and could be difficult to understand. At the same time the combined advances in monitoring techniques and in information technologies have dramatically increased the amount of observation data and the ability to process these. This situation is largely due to the difficulties of producing synthetic and consolidated information, which requires the fusion of an extremely wide variety of data, gathered by * Corresponding author, email: [email protected]

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specialist groups, for different time-periods and geographic levels, for totally different purposes, using a diversity of measurement techniques. Hence the key starting point and motivation for GMES is that the overall system of production of environmental information lacks efficiency and that the delivery of the final information products is rather ineffective as well. A central objective and a challenge for GMES will be to help to introduce coherence, structure and efficiency in the system of production and delivery of information. In addition, in a significant number of areas, monitoring suffers from discontinuity in time, leading to gaps and incompatibilities in data series or even interruption of series. This happens in particular when monitoring is not a compulsory activity backed by legislation. As a result, a number of monitoring activities happen to be funded under research programmes. These however deliver data for the specific research areas and only for the duration of the programmes. A key challenge for GMES therefore is to identify the means to secure sustainable and coherent monitoring activities. 2.2 W o r k i n g P r i n c i p l e s

9 Activities have been selected and will be steered in a way to produce, before the end of 2003, results able to underpin proposals for establishing a European capacity for GMES. 9 Users will be involved in all activities (e.g. GMES Steering Committee; Thematic projects; Forum) in particular through the existing groups and committees associated to the monitoring and development of the relevant European policies. 9 Complementarity with ongoing or planned related activity will be ensured and the development of synergies will be sought: -

within the European Commission, EC (e.g. INSPIRE 1, Joint Research Centre activities),

-

between EC and European Space Agency (ESA),

-

with e.g. European Environmental Agency (EEA), EUMETSAT, the EU Satellite Centre.

9 Activities will build upon existing research (e.g. EC 5th Framework Programme key action on Global change, climate and biodiversity and sustainable marine ecosystem and ESA Data User Programme, market development and other relevant studies), experience (e.g. Centre for Earth Observation) and networks (e.g. the Environment Information and Observation Network (EIONET) of the European Environment Agency), seeking added value. 9 In order to facilitate the implementation of the above working principles, special emphasis will be put on information exchange and dissemination.

The INfrastructure for SPatial InfoRmation in Europe initiative (INSPIRE) aims at making available relevant, harmonised and quality geographic information for the purpose of formulation, implementation, monitoring and evaluation of Community policy-making

Peter Ryder* and Jan H. Stel

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3. GMES Initial and Implementation Periods The European capacity for GMES is intended to be operational in a staggered way by 2008. Its construction is planned over two periods known as the Initial Period (20022003) and the Implementation Period (2004-2008). The EC and ESA have joined efforts and resources to perform a coordinated build-up process and have established a single schedule and a repartition of tasks. The European Union (EU) and ESA Member States, together with all interested GMES stakeholders, are associated to the overall activities, primarily through the GMES Steering Committee and the GMES Forum.

3.1 Initial Period (2002-2003) The objective of the GMES Action Plan's Initial Period is to prepare proposals for the Implementation Period (2004-2008), jointly between EC and ESA, for the establishment of a European capacity for Global Monitoring for Environment and Security by 2008. The strengths and the weaknesses of the current European capacity for monitoring and information production are to be identified, as well as the needs for improvements, in the scientific, technical, legal, economic and institutional domains. A final report should ensure the transition from the Initial Period to the next one:

...A report for the Initial Period, jointly prepared with ESA, to be submitted to the Council and the European Parliament, will be prepared for approval by the end of 2003 to prepare for GMES activities in the 2004-2008 timeframe. The review will focus on" (i) the analysis of lessons learned," (ii) the proposal to the relevant authorities and actors of an organisational set-up; (iii) and an analysis offinancial requirements with a long-term perspective to establish fully-fledged GMES activity. The report will be based on a thorough analysis and structuring of user requirements. It will also entail descriptions of the services and system architecture meeting those requirements, the organisational framework and the assessment of the international dimension, to be adopted by the relevant authorities. An analysis of the costs and benefits of GMES will be included. The EU Council Resolution confirms and completes the above expected result:

...REQUESTS therefore the Commission to report to the Council and to the European Parliament at the end of the initial period (2001-2003) on the definition of the system, based on users' requirements, the expected services, the possible support to the various Community policies, the results obtained from the pilot services, the economic and social benefits, the possibilities for international cooperation at global level and the possible scenarios for an organisational framework.

3.2 Implementation Period (2004-2008) The Implementation Period's detailed work programme will be established on the baseline given in the Initial Period final report. The Implementation Period detailed Work Programme will be established by the partner institutions, and possibly other stakeholders, on the basis of the results of the discussions on the final report. Research, technological development and demonstration activities would involve:

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9 The construction of elements (centres, interfaces, communications, processes, management organisations, etc.) for mature GMES applications. 9 The research activities for new GMES applications. Recommendations for other operational activities are expected to emerge from the recommendations of the final report taking into account national deployment activities.

4. G M E S A c t i o n Plan ( 2 0 0 2 - 2 0 0 3 ) The Initial Period's w o r k programme is to be performed through complementary

activities of EC and ESA and comprises two main strands: 9 Delivering Information and Services (strand 1) Includes the thematic projects and the consolidation phase of the ESA GMES Services Element (GSE), see http://earth.esa.int/gmes/. 9 Assessments and Recommendations (strand 2) Will be performed and obtained through the cross-cutting assessment studies, see http ://212.219.37.118/GMES/X-cutting% 20As sessments.htm.

Figure 1 The GMES Initial Period work flow. 4.1 EC thematic projects The thematic projects will produce information of direct interest to users concerned primarily with EU Environment and Security policies and, on that basis, contribute to identifying the obstacles to the production of adequate "information products". 4.2 The GMES Service Element of ESA The objective of the GMES Service Element is the development of sustainable information services, which respond to well identified user needs in support of environment and security policies The GMES Service Element is organised in a two-phased approach, of which the first phase, the consolidation phase, identifies and further develops potential candidate services, which are developed to Service Centres in the second phase (post 2003).


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A key element of the GMES Service Element is to be the pro-active involvement of the user, either individually or as an organised community. The GMES Service Element will develop integrated solutions, hence fostering the dialogue between the user community, R&D institutions, operational entities and industry. The services will primarily be based on Earth Observation data sources, by maximising the use of existing ESA and non-ESA missions. The gradual build-up of the users communities will help investigate the adjustments required in terms of future operational systems (e.g. Earth Watch satellites).

4.3 Cross-cutting assessment studies The purpose of the cross-cutting assessment is to assemble and synthesise the results coming from: 9 Lessons learned from the thematic projects 9 Inputs from Member States national projects 9 State of the art knowledge coming from former EC initiatives (4th and 5th Framework Programmes, Joint Research Centre activities) 9 Inputs from the ESA GMES Services Element consolidation phase together with inputs from the Data User Programme and Earth Observation Market Development programme 9 GMES Forum involving partners from the user community to industry 9 Analysis of the EC 6th Framework Programme Expressions of Interest. They will also build upon and further develop the INSPIRE position papers to be issued during the autumn of 2002 on closely related issues, see http://www.ec-gis.org/inspire/. They will constitute part of the basis and the technical justification of the proposals for the Implementation Period of GMES. Cross-cutting assessment studies will look into particular issues facing the development of a sustainable European capacity for GMES: 9 Analysis and structuring of users requirements 9 Scientific issues linked to gaps in knowledge, tools and technology 9 Technical issues include the adequacy of monitoring networks and systems, data quality and coherence (continuity over time, comparability across space and thematics); data standards; data processing and archiving; space and in situ data integration 9 Identification and prioritisation of the policy requirement driving the definition of required information products (indicators, maps, warnings, etc.) 9 Data policies (accessibility, management, cost, etc.) 9 Organisational, institutional funding and policy issues; consultation and co-operation between data producers and information users 9 Societal and economic aspects.

5. The G M E S Marine F o r u m A Forum was held to obtain the advice of the participants at the 3rd EuroGOOS Conference on these cross-cutting issues; in particular on:

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9 Gaps in knowledge, tools and technology 9 The adequacy of monitoring networks, observing systems and data quality 9 Data management and policy issues 9

Socio-economic and institutional issues.

5.1 Current Scientific and Technological Capabilities Findings concerning relevant scientific and technological capabilities available at present included: 1. There are modelling and observing programmes in place and providing operational services globally, for the North Atlantic and in all the European regional seas and shelf areas, including the Black Sea. 2. Models are beginning to be eddy-resolving and capable of better representation of the thermocline. Increased computer power, variable grids and nesting were much in evidence to achieve these improvements. Sea ice models are in use where appropriate. 3. There will be a capable satellite-based observing system in place for environmental monitoring purposes during the GMES build up phase, on the assumption that the Jason-2 and other relevant programmes announced by the space agencies go ahead as planned. ENVISAT will be a key component. The overall system should be able to provide data at a useful accuracy to monitor SST, SSH, ice cover and height, ocean colour, sea state, intemal waves, wind speed and direction, shallow water bathymetry, oil slicks and ship location. 4. A few examples of prototype services based on single (remote sensing) technologies are being promoted but the vast majority of operational services are based on multiple data streams assimilated (in one form or another) into predictive models. 5. The overwhelming majority of operational services are focused upon the physical environment and on processes and parameters that obtain their predictability from astronomical effects (tides) or atmospheric coupling (waves, sea level surges, currents, sea ice cover). It follows that the limits on atmospheric predictability also limit most of the latter services. 6. There is a rich range of technology available to make the physical, near-surface ocean and atmospheric observations that are required, based on remote sensing from satellites and coastal radars, and in situ observations from moorings, floats and instrumented ships of opportunity. Effective technologies exist (XBTs from ships of opportunity, ADCPs on moorings and landers) and are also emerging to monitor subsurface physical properties, based on profiling floats, fixed and profiling moorings, and AUVs (including gliders). Their actual operational use is very limited at present, seriously limiting the prediction of deep currents for example. 7. The technology available to make needed marine biogeochemical measurements is more limited and still remains largely in the RTD domain. Data were reported from ocean colour measurements from space and a few ongoing transects employing 'Ferry Box' technology or towed vehicles, typically carrying the SAHFOS Continuous Plankton Recorder (CPR) or derivatives. Recent progress in equipping


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buoys and landers for routine physical and biogeochemical monitoring is encouraging and a few ships of opportunity are being equipped to make pCO2 measurements. The experimental use of airborne fluorescence lidar was reported. However, large uncertainties continue to exist in the interpretation of the ocean colour measurements. Biofouling remains a problem when (some physical and biogeochemical) sensors are exposed for long periods in the photic zone; calibration techniques for reducing the impact of this were reported. AUVs can be equipped to carry a wide range of sensors, but are not yet in regular use. The point was made during the conference that the business case for the deployment of AUVs for environmental monitoring purposes remains to be constructed. 8. Little progress was reported in the development and validation of ecosystem models, although many groups avowed their intention to nest such models within their physical ocean models. 9. Very few papers concerning the middle and higher trophic levels of marine life were posted; none was presented. 10. There is continuing interest in the development and validation of novel monitoring techniques (e.g. based on GNSS reflections, remote sensing of salinity and acoustic detection of precipitation). 11. Risk-based sampling strategies (i.e. in vigorous or environmentally/economically sensitive locations) are practised. The importance of feature (eddy, ecosystem or event) resolving observation and modelling was emphasised, particularly in such regimes. In this context, the construction of well-equipped coastal observatories to carry out marine research at sensitive locations, perhaps possessing simple topography and/or well-behaved boundary conditions, is an interesting development, as is the use of adaptive sampling techniques.

5.2 Gaps in Knowledge, technology and tools. Specific findings were identified as follows: 1. Although good progress has been made in implementing operational oceanography, there is a lack of knowledge that is preventing implementation of the ecosystem approach. We are still far away from being able to model fish behaviour, which is necessary for stock prediction. 2. Marine ecosystem models are still in an early stage of development, particularly with respect to benthic processes. A 4D view needs to be taken. 3. Ocean colour from satellites and monitoring from vessels equipped with the Ferry Box or undulating towed vehicles will provide useful near surface data. But it is far from clear how the sub-surface data required for verification and routine operation of ecosystem models is to be captured.--Landers, buoys and AUVs/gilder instrumented for sub-surface biogeochemical measurements are candidates for this. 4. Measurements of chlorophyll-a from space still have large error bars. There is need for standardisation of techniques. 5. There are large gaps in our knowledge of currents at depths below a few hundred metres and concern that high-resolution sampling will be necessary to resolve this,

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particularly in areas of complex topographymlanders, buoys and AUVs/gliders carrying ADCP provide possible technical solutions. 6. There is a need for improved knowledge about inflow from the major rivers, e.g. of the inflow of fresh water, temperature, sediment and nutrient loading.mMonitoring to meet the requirements of the WFD should help with this. 7. Much of the suspended sediment is lifted or generated by erosion in episodic (storm) events. There are large implications of this for monitoring. 8. EC (2002) identifies a number of knowledge gaps. These are important drivers for the FP6 research programme that is serving the GMES. 9. There are unresolved issues for the GMES concerning the handling of uncertainty in policy-making. Uncertainty in the science makes it difficult to construct and enforce effective compliance and liability legislation. 5.3 Adequacy of monitoring networks and data quality Specific findings were identified as follows: 1. Spatial resolution and representativeness are almost always a problem in the marine environment, where important eddy structures have dimensions ~ 5 - 1 0 k m and strong topographically induced, unstable flows are common. There are dangers of aliasing signals and biasing statistics. Targeted, adaptive sampling may be the only solution for subsurface measurements. 2. Measurement error statistics are almost as important as the measurements themselves. 3. Many of the environmental science and change detection problems require long time series of data. It is unrealistic to expect research activities to provide these. 4. Reliable scientific conclusions about the behaviour of the marine environment demand at least a minimum set of standards and effective quality control. 5. 'Variable or inadequate data quality' is often the first challenge by vested interests to findings and policies based on them; the second is 'inconsistency'. 6. Specific gaps in data are identified in EC (2002). 7. Gaps in knowledge, data and information are intimately connected. The transformation of data into useful products and services based upon them is usually a substantial task, which tends to be underestimated. 8. There is much to be done to enable existing databases to be accessed in near real-time (for modelling purposes) and in delayed mode. The systems for this are not in place. In particular biogeochemical data are highly dispersed and generally without quality control. Concerted actions and networking will be necessary to insure the widest and fastest access to comprehensive, coherent and compatible data sets by the operational and scientific communities. Only a professional, semi-distributed, multidisciplinary data management infrastructure will be able to give an appropriate access to the national data holdings, merge them with new data collected in real time and delayed mode and prepare the best timely integrated data products, that the scientific, technical and economic studies require.


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9. Attention was drawn to a new Strategic OSPAR Plan for Joint Assessment and Monitoring, which should be signed off in June 2003. It is likely to be useful in the planning of the GMES.

5.4 Data management and policy A basic proposition was put forward for debate "Effective data management and policies are essential to ensure that the right information can be made available to those who need it, in the right form and at the right time. To date, data management practices and policies within European countries can differ greatly and are poorly adapted to the changing monitoring and surveying technologies, and the changing needs of policy. " There was general recognition that if the GMES Initiative is to succeed, proper data system strategy(s) and policy(s) must be established. Furthermore, it was felt that the data system strategy(s) could not be discussed in isolation from data policy issues. Any pricing policy(s) adopted with respect to GMES data would have a direct impact on the data system strategy(s). Whilst some policy issues (e.g. relating to IPR) were not addressed three were discussed:

Pricing Policy--under 3 scenarios Scenario 1: European funding for GMES as a public good. The data policy of IOC, to which EU Member States subscribe, promotes free access to oceanographic data. Further, Resolution 40 of Cg XII of the WMO also requires marine data to be made freely available, in particular with regard to matters of human health, safety and security. Scenario 2" A European public-sector organisation funds environmental data collection for its own purpose as a private good. This public-sector organisation would make the data widely available within GMES. This is the model proposed for INSPIRE. Scenario 3: GMES could promote a multiplicity of end-user customers for regular environmental monitoring. The customers would buy data and information as private goods and the demand would be federated within a GMES context. It was accepted that different actors within GMES favour different data pricing scenarios. The first is most familiar to the marine community concerned with safety of life and property and global climate change monitoring but commercial activities are undertaken under scenario 3.

Safeguarding and archiving It was accepted that there is great value in safeguarding environmental data over the long-term. However, environmental archives are not cost-effective when seen from a short-term micro-economic viewpoint. The perception is that recovering stored items from an archive can often be very difficult and time-consuming. This view of an archive was contrasted with that of a library, where information is not only stored, but where it is readily available for consultation and where trained staff are present to assist people in their search to gain access to and recover specific items. In the framework of GMES, it would therefore seem to be preferable to talk about the concept of libraries, rather than archives.

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Data Systems Figure 2 was introduced and discussed. It was evident that there was a measure of agreement about the functionality of and need for the various components but a lack of consensus about some of the linkages.

GMES LIBRARY(S)

I

Data 1~,, ataarchive Model output Info products i i i

End

In situ

systems Data~

Remote sensing sys

Quality control

Data~ ~

1 "Fit for the purpose" DATA WAREHOUSES

J, Real-time alarms

Modelling factories Service providers

Policy info

Real-time

forecasts

Predic-

tions

Reports

users

Figure 2 A possible example of a (very) simplified GMES data system An initial issue concerns the methodologies required for bringing together data from different observing systems, e.g. in situ and remote sensing networks. Much work has already been carried out in this area and best practices will need to be identified and built upon. A second consideration is whether the required capacity exists to archive, in a proper and consistent manner, the complete GMES data stream. It was envisaged that "Data Warehouses" could be used to assemble, on the necessary timescales, the datasets that are "fit for the required GMES purposes". These "Data Warehouses" would also ensure that the data are passed to the "GMES Library". A further function of the "Data Warehouses" would be to ensure that overall quality control standards are applied to the "fit for the purpose" datasets. (All data provided to the "Data Warehouses" would have to have undergone initial "Quality Control" by the data provider to agreed standards.) "Modelling Factories" would take their input as required from various "Data Warehouses", and from the "GMES Library(s)", (this link is not shown on the figure due to lack of space). The model outputs should be stored in the "GMES Library" and passed to "Service Providers". Model output would also be subject to agreed "Quality Control" standards. All discussions have shown the difficulties that both "data providers" and "modellers" have considerable difficulties in matching their "products" to the "information requirements" of the "end-user". The "Service Providers" will therefore have a vital role to play as an interface between the "data providers" and "modellers and the "end-users" to ensure that the final information products are available in with relevant content in the correct format. The application of "Quality Control" at the level of the "Service Providers" is an issue that must also be addressed.


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All "GMES Information Products" should be passed to the "GMES Library(s)" for safekeeping and for dissemination. 5.5 S o c i o - E c o n o m i c and Institutional i s s u e s

The presentation focused around the Prestige accident and discussed the actions and decisions taken or not taken, at the local, national, regional and European level. Despite the availability of oil spill models, wave and weather forecasts etc., a decision-making structure and chain of command was apparently lacking to bring the relevant information to the attention of the top decision-makers. As a consequence a series of non-sustainable decision were taken forcing the vessel to be towed into the open sea, where it sank. It was noted that there also is no integrated European policy for the sustainable use of the European Exclusive Economic Zone, EEZ. Most European actions are focused on sectors as maritime safety, pollution and the transport of hazardous cargo, as in the case of the Prestige. There is a need for a nested integrated policy and legislation for the sustainable use of all stocks and resources of EEZ of Europe and its coastal Member States. In discussion the following points were made: 1. A socio-economic study of GMES as such is not realistic and not desirable. GMES cannot be compared with the launching of a (series of) satellite(s). It is the development of a marine information system that will have effects in many societal sectors, including policy-making at a European, regional, national and local level. For this stakeholder involvement is a prerequisite. Integrated assessment is an interesting methodology for designing policy options. Yet, cost-benefit analyses of elements of GMES are possible and should be performed to underpin investments at the different scale levels (national, regional, European, outside Europe in a capacity building scheme). 2. There was a discussion about the involvement of the marine industry. It was noted that the marine environmental industry is very small compared with the strong space related industry that has a forceful lobby at high political levels. Some participants expressed their concern that space based instruments would therefore be favoured at the cost of in situ instruments, that are needed to observe the interior of ocean space. 3. The institutional issues of GMES were not further discussed during the session. But during the conference the vision of operational oceanography as an end-to-end system, similar to that of operational meteorology was articulated. It was argued that there was a current opportunity to build such a system based on the plans of the space agencies, in situ monitoring technologies (e.g. the Greek Poseidon system) and emerging from research programmes as well as the capabilities of several modelling groups in Europe. GMES was seen as a valuable stimulus for this and the MERSEA project as a mechanism to bring it about in the marine environment. The creation of a regional modelling centre (a European Centre for Ocean Monitoring and Forecasting) was seen as an important step towards the goal. It was noted that ECOMF might be created through an inter-agency agreement, as a European company or via an intergovernmental agreement. The challenge would lie in integrating this with national and regional needs and initiatives such as those being provided within the context of the existing Conventions.

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Acknowledgements Alan Edwards and Michel-Henri Cornaert, of DG Research, European Commission, contributed to the presentations on which this paper is based and provided helpful advice during its preparation. The financial support of DG Research is also gratefully acknowledged.

References EC, 2001a, A sustainable Europe for a Better World: A European Union Strategy for Sustainable Development, COM(2001)264 final. EC, 200 lb, Global Monitoring for Environment and Security. Outline GMES EC Action Plan (Initial Period: 2001-2003), COM(2001)609 final. EC, 2002, Towards a strategy to protect and conserve the marine environment, COM(2002)539 final. EC/ESA, 2001a, Joint document of the European Commission and the European Space Agency and ESA(PB-EO)2001, 56, SEC(2001)993. Official Journal of the European Communities, 2001, Council Resolution of 13 November 2001 on the launch of the initial period of global monitoring for environment and security (GMES), 2001/C350/02.

Closure

Nic Flemming


Conference Valedictory Speech Nicholas Flemming

Southampton Oceanography Centre At the end of the 3rd EuroGOOS Conference, Hans Dahlin asked me to say a few words by way of rounding out the conference, and completing the proceedings. Having attended three EuroGOOS Conferences, 1996 in Den Haag, Netherlands; in 1999 in Rome, Italy, and now in 2002 in Athens, I accepted to do this with pleasure. Hans asked me to put the Conference in the context of the development of EuroGOOS since 1994. The organisers of the Conference, NCMR, deserve the most profound congratulations and thanks for the excellent organisation and spirit of the meeting. The hospitality was provided by a friendly team of assistants who were always ready to help. I would particularly like to thank Kostas Nittis for his untiring energy, and Christos Tziavos as Conference Chair. I hate retrospective pseudo-historical reviews in which elderly scientists or politicians reminisce about how things used to be done, and how past and vanished greatness or success should make us confident about the future. This is a false equation! If we are going to succeed in the future it depends totally and only on what we are capable of doing today and tomorrow, on the quality of our decisions today, and the competence and professionalism of the skill which we have today. History is for historians. Since making that impromptu speech (illustrated on p. 667), I have had the privilege of proof-reading every page and sentence of the Conference Proceedings in this book. It has been an illuminating and profoundly encouraging experience. In 1996 it would have been totally beyond anyone's imagination to conceive of the range and depth and variety and power of the operational oceanographic systems described in this book. In spite of my distaste for using history to inspire pride and self-confidence, I did use my speech to summarise how the agreed and established policies of EuroGOOS have led to the present success of operational Oceanography in Europe, and how the continuation of these technical policies will generate success in the future. The phrase I used then, and still believe was correct, was that I would try to describe the arrow of time as defined by the activities of EuroGOOS. Although our success today and tomorrow depends upon our knowledge and efforts now, they also depend upon the projects, analyses, funding commitments, infrastructure, and new technologies which we are progressively assembling and installing for European operational oceanography. These projects and decisions started several years ago. There has to be a time dimension to describe this process, and to show the direction in which EuroGOOS is developing. This sense of direction helps us to understand what we need to do today. EuroGOOS has momentum, not just position! A fundamental virtue of the structure of EuroGOOS is that it was established as an association of agencies, and was not set up as an inter-governmental body. This gave us flexibility, the advantage of a simple Memorandum of Understanding, a minimum of bureaucracy, and the ability to make decisions quickly, or adapt to changing situations.

670

Conference Valedictory Speech

Of course, in the long run, a more permanent or governmental status may be an advantage, but if we had tried to establish a governmental body for European Operational Oceanography in 1994 1 think we would still be discussing how to do it now, and trying to figure out which politicians to lobby. By starting at agency level EuroGOOS was able to start with just 9 Members, and then grow steadily over the first 7 - 8 years until the membership settled at around 30 agencies. Within this agency-based structure we formed sub-groups to study the needs of each European regional sea, and the benefits which operational oceanography could create there. We collaborated with all the nations and agencies in the Mediterranean, and this approach was already set out in 1996. We also established Scientific and Technology Working Groups which laid the foundations for the global activities of EuroGOOS and its members. Under the guidance of John Woods as first Chairman of EuroGOOS we started a strong series of technical publications which reached out to those people who had only a vague idea of what operational oceanography might be. These documents range from regional studies to an analysis of the role of satellite remote sensing. We also produced in 1996 a large strategy report, looking forward to 2006, and followed this in 1999 with a brief but practical forward look, trying to forecast developments up to 2010. Many of the targets and objectives set out in those documents have been met, or are well on track to be achieved by the proposed dates. The achievable resolution of operational models, the inclusion of adaptive routines into models, the ability to include chemical, sedimentary and biological parameters in models, all these are developing as fast or even faster than planned. Collaboration between EuroGOOS and the space agencies has matured, so that there is a real possibility of a future series of ocean observing satellites designed to support operational services. EuroGOOS is now self-funding, and its members provide the resources for running the secretariat through their annual subscriptions. Under the leadership of Dik Tromp we have planned the 3rd International Conference in Athens, where we had the honour and the pleasure to welcome representatives from the other Regional Alliances of GOOS. During 2002 the Coastal Ocean Observing Panel of GOOS recommended a structure for developing GOOS in the coastal regions of all countries which focuses on the idea of the GOOS Region Alliances doing most of the work, and collaborating with each other. As just such a GOOS Region EuroGOOS supports and applauds this approach. The great frontier now is ensuring that operational oceanography can extend its competence into the area of ecological and biological forecasting and ocean management. I am sure that EuroGOOS will succeed.

Index of Keywords A 349 acoustic network 353 adaptive 222, 361 Adriatic Sea 64 Aegean Sea 146, 190 altimetry 128 ambient noise 141 Aral Sea 272, 490 Arctic Argo 366, 611 180 ASCAT 109 atmosphere automated measurement maintenance 304 autonomous vehicles 229 awareness 47

B Baltic Sea Bay of Biscay beach Belgian maritime zone bi-lateral cooperation biodiversity Black Sea

516, 519 297, 542 574 503 497 291,574 168, 203,

buoy systems buoys drifting and moored

586, 645 334 361 340

C calibration capacity building Caspian Sea chlorophyll a circulation model climatology coastal coastal observatory coastal oceanography coastal processes coastal protection coastal zone environment

258 47 141 168 190 645 442, 535 548 361 174 574 529

converged-data co-ordination CORIOLIS correlative Cretan Sea Crete CTD-Argo culverts current forecasts

535 510 345 353 554 258 356 564 379

I) data data assimilation data collection database defence deployment strategy DIADEM diagnostic model digital delivery system directional wave spectra dust cycle dust deposition

291,510 211,461 317 386, 611, 642, 645 579 340 251 100 627 154 57 57

E ecosystem modelling English Channel environment environmental protection ENVISAT EOF analysis ESA EuroGOOS European co-operation Exclusive Economic Zone extreme waves

542 297 510, 535 574 258 513 186 592, 598 340 562, 592, 598 386

F fast vessel concepts FerryBox fishery management floats

366 325,568 542 513

672

fluorescence lidar forecast skill score forecasting Fram Strait

Index of Keywords

71 64 109, 237 627

Kalman Filter

Galileo gamma spectroscopy Georgia global GMES GNSS - R GOOS GOOS Regional Alliances GPS group velocity Gulf of Trieste Gyroscope

146 370 562 430 655 146 442, 592 442 146, 258 115 361 356

H HF radar

161,245, 535 high resolution wind field retrieval 450 human activity 592, 598 human impact 542 hydrological trends 78 hydrology 100 hydrophone 128

I calibration 373 observation 317 operational oceanography 345 satellite data 168 information system 621 infrastructure 285 integrated operational modelling 285 international public goods 422 internet 642 interoperability 633 inventories 47 Irish Sea 548 situ situ situ situ

J Jason- 1

258

211

L location

G

in in in in

K

317

M management marine marine ecosystem marine meteorology marine pollution marine technology market decisions Markov theory Mediterranean Sea

503 510 461,484 87 523 334 409 392 47, 57, 78, 97, 334, 645 NW 379 mesoscale features 100 meteorology 633,636 microwave data 141 microwave radiometry 186 modelling 523 modelling system 516, 586 monitoring 180, 237, 304, 349, 353,442, 503, 510, 529 monitoring network, long-term 78 monitoring system 245 Monte Carlo simulation 370

N near-real time networking networks neural North Atlantic numerical modelling nutrients NW European Shelf

180 47 291 403 251,340, 356, 513 87, 97, 122, 386 645 461,484

673

POSEIDON

O observatories 291 observing system 285 Observing System Simulation Experiments 97 ocean colour remote sensing 122 ocean current 245 ocean data assimilation 16, 197 ocean forecast modelling 461,484, 605 ocean forecasting 16, 197, 222 ocean model products 605 ocean modelling 16, 197 ocean monitoring 334 ocean waves 115, 154 ocean-atmosphere interaction 78 oceanographic information 615 oceanographic services 16 oceanography 633,636 offshore buoy 87 oil spill model 586 oil spills 71,523 operational data 639 operational modelling 211 operational oceanography 16, 47, 64, 197, 304, 361,366, 403, 422, 430, 461, 466, 484, 497, 503, 579, 605, 611

pre-operational modelling profilers

O quality control

71,568 554 519 366 161 297 409 592 222 639

180, 645

R radar 128 radar altimetry 258 rainfall 128 real aperture radar 115 real-time 279 real-time measurements 548 real-time monitoring 398 real-time observations 340 Regional ocean observing system 497 remote sensing 71,146, 174, 186, 245 remote sensing data 639 research vessel 366

S salinity sampling Sardinia Sea satellite scatterometer sea bottom observatory sea floor sea ice

P phytoplankton phytoplankton dynamics phytoplankton monitoring PIRATA Pisces plankton policy decisions policy-making POM portal

64, 141,190, 258, 373 548 356

sea level sea level measurements sea outfalls SeaWinds sensors services shallow water wave ship optimum routing ship routing

186, 297, 611,645 353 100 128, 297, 317 180 349 291 141,272, 490, 621, 627, 633, 636 258 190 564 180 334, 535 109 642 649 392

674

Index of Keywords

SKIRON system 57 slope stability 349 SmartBuoy 311,568 software architecture 633 Southampton water 568 spillet 586 SST data 211 stakeholders 592, 598 stochastic modelling 392 storm sewers 564 sub-regional sea 497 subtropical 356 survey 229 Suspended Particulate Matter 122 sustainability 237, 592, 598 synergy 174 synthetic aperture radar 174, 450 system optimisation 237

T technology temperature Texas Thames thermohaline circulation tide gauge data TOPAZ TOPEX turbulence

229 297, 611, 645 535 568 78 211 251 141, 190 450

V validation vertical EOF visualisation

258 222 353

W wave climate wave wave wave wave wave wave

forecast groups measurement model predictions simulation

386, 392, 642 403 115 161 161 379 642

WAVENET weather weather forecast web technology website wind energy wind farms wind product

161 627 649 639 548, 615 450 450 180

X X-band radar XBT observations XML XML-schema

154 105 636 636

Y Yellow Sea

497

Index of Authors A Aarup, T. Abdelbaki, A. Abdul Fattah, A. Abousiarov, Z.K. Abuissa, A. Albretsen, J. Alfonso, M. Altalo, M.G. Alvarez Fanjul, E. Andersen, S. Assimakopoulou, G. Astraldi, M. Awad, H. Awad, M.B.

46 46 78 109 46 529 304, 398 409 398 272 373 78 46 46

B Bahurel, P. Baker, D.J. Baldursdottir, H.B. Ballas, D. Ballu, V. Barciela, R. Beken, C. Bell, M. Belov, S. Bengtsson, L. Benoit, M. Berger, M. Berglund, R. Bernal, P. Bertino, L. Besiktepe, S. T. Bilashvili, K. Binks, L. A. Birch, K. G. Blandin, J. Blouch, P. Boargob, A. F. Bolafios, R. Bonner, J. S. Borghini, M.

16, 279 5 621 373 349 197 46 16, 197, 279 615 490 386 186 621,627, 633,639 409 251 46, 203 562 161 128 349 611 46 379 353, 535 87

Boscolo, R. Boucher, J. Bozzano, R. Briole, P. Brovchenko, I. Brown, M. Brundrit, G. Bryden, H. Buch, E. Buharizin, P.I.

78 542 87, 304 349 586 422 46 78 285,466 141

C Campbell, J.M. Capari, M. Caparrini, M. Carlier, A. Carval, Th. Casazza, G. Castellari, S. Cazenave, A. Cecchi, G. Cermelj, B. Chapron, B. Civili, F.S. Civitarese, G. Cohen, Y. Colijn, F. Connolly, N. Cornaert, M.-H. Cowling, M.J. Cretaux, J.-F. Crisp, N.A. Cummins, V.

128, 297 46 146 46 345, 611 46 87 141 71 46 135, 146 46 78 46 325 135 655 510 141 297 135

D Dahlin, H. vii, 46 Dahl-Madsen, K.I. 211 Dalla Costa, M. 46 Daniel, P. 218 Dankert, H. 115 Demetriou, J.D. 564 Demirov, E. 36

676

Index of Authors

Desaubies, Y. 345 Dexter, P. 430 Diamanti, C. 392 Dodgson, J. 297 Dozortseva, J. 141 Drago, A. 46 Drakopoulos, P.G. 46, 258, 554 Dupee, B. 297 Duporte, E. 611 Durand, D. 529

E Edwards, A. Edwards, I. Edwards, M. Elken, J. Erichsen, A.C. Eriksson, P. Espino, M. Essen, H.-H. Etiope, G. Evensen, G.

655 229 297 466 211 627 379 245 349 251,529

F Favali, P. Ferentinos, G. Fichaut, M. Fischer, J. Fleming, V. Flemming, N.C. Font, J. Fontes, J.B. Fournier, J.-C. Fraile, E. Frasheri, A. Fuller, C. Funkquist, L. Furevik, B. Fusco, G.

291 349 645 265 519 46, 409, 669 46, 78, 186, 513 154 386 356 92 353, 535 516 279 46

G Gacic, M. Gajewski, J. Garcia, M.-J. Garello, R. Gasparini, G.P.

78 466 645 135 78

Gasparoni, F. 291 Gayer, G. 122 Georgiou, G. 36, 46 Georgopoulos, D. 78 Gerber, H.W. 291 Germain, O. 146 Gertman, I. 46, 78 Giorgetti, A. 645 Goasguen, G. 304, 386 Gomez, J. 379 Gomis, D. 513 Gonz~ilez-Pola, C. M. 297, 356 Gould, J. 78 Gourmelen, L. 345 Green, J.J. 161 Grezio, A. 222 Griffiths, G. 229 Gr6nvall, H. 627 Guddal, J. 430 Guedes Soares, C. 154, 190, 523 Gtinther, H. 115, 122 Gurgel, K.-W. 245 Gustafsson, N. 272 Guymer, T.H. 128

H Haajanen, J.

621,627, 633,636, 639 Hackett, B. 529 Hafsteinsson, G. 621 Hageberg, A.A. 340 Hajji, H. 190 H~kansson, B. 466 Hamre, T. 135, 621 Han, J.Y. 497 Hansen, S.E. 475 Hartman, S.E. 297, 568 Harzallah, A. 46 Hasager, C. 450 Heins, C. 311 Hern~indez-Guerra, A. 356 Herrouin, G. 46 Heygster, G. 272 Hines, A. 197 Holt, M. 161, 461,484 Homminga, T. 598

677

Horstmann, J. Howarth, M. J. Hydes, D. J.

115, 122, 450

L

548 297, 568

L'Her, J. 386 Laane, R. W. P.M. 311 186 Lagerloef, G. 272 Landelius, T. 36 Lardner, R. 78 Lascaratos, A. 627 Launiainen, J. 297, 356 Lavfn, A. M. 279 Le Traon, P.-Y. 497, 642 Lee, D. Y. 46, 304 Legrand, J. 450 Lehner, S. 484 Li, Z. 621,627, 636 Lind, M. 304 Lindh, H. 345 Loaec, G. Loeff, M. R. van der 311 Lognoli, D. 71 L6pez, J.D. 398 Lopez-Jurado, J.L. 46, 78 Lucion, C. 579 Lykousis, V. 349

I Ibrahim, A. Iona, A. Izquierdo, P.

46 645 154

J Jensen, H. R. Johannessen, J. A. Johannessen, O.M. Johansson, M. Jones, C.E. Jorda, G. Josse, P. Jourdan, D. Jun, K.C.

211 135, 174, 279, 529 490 627 128 379 218 78, 579 642

K Kabbara, N. 46 Kahma, K. 466 Kaitala, S. 519 Kallos, G. 57 Katsafados, P. 57, 64 Kelly, F.J. 353, 535 Kelly-Gerreyn, B. A. 297 135 Kern, S. 186 Kerr, Y 497 Kim, C. S. 642 Kim, S. D. 78 Klein, B. 46 Kljajic, Z. 78 Kontar, E. 627, 633, 639 Kotovirta, V. 141 Kouraev, A. V. 258 Koutroulis, E. 46 Kouyoumjian, H. Krasakopoulou, E. 373 109 Krasjuk, V. S. 272 Kunzi, K. 586 Kuschan, A. 645 Kuznetsov, A.

M Maderich, V. 586 Madsen, H. 211 Magni, P. 46 Mahmoud A1-Sheikh, A. 46 Maillard, C. 46, 78 Malacic, V. 46, 361 Malanotte-Rizzoli, P. 78 Mallios, A. 373 Malone, T.C. 442 Manca, B. 78 Mansner, J. 627, 639 Manzella, G. M.R. 46, 78 Marchand, P. 46, 366, 542 Martin, M. 197 M arty, F. 218 Masson, M. 349 Maunula, P. 519 McCulloch, M. 197 Mertikas, S.P. 258 Metreveli, G. 562

678

Michaud, P. R. Michelsen, C. Mienert, J. Mikhalov, N. Miles, M. W. Miller, P. Millot, C. Mills, D. K. Minster, J.-F. Miranda, J. M. Mochi, I. Moncheva, S. Moorhead, M. Morovic, M. Muijen, H. van Myrmehl, C.

Index of Authors

535 340 291 615 490 297 78, 291 311,568 10 291 71 168 161 46 598 490

N Nesterov, E. S. Nittis, K.

109 46, 64, 105, 135, 279, 334, 554

O O'Neill, N. Obaton, D. Ojo, T.O. Olsen, A.-M. Ortega, C. Osborne, O. Ozer, J.

291 529 353, 535 490 317 484 503

P Page, C.A. Palamartchouk, K. Pano, N. Papa, F. Papadoniou, G. Papadopoulos, A. Papageorgiou, E. Park, K.S. Parlichev, D. Parlichev, G. Parrilla-Barrera, G. Pavlis, E.C. Pearce, D.J. Perez, J.

353, 535 258 92 141 105 57, 64, 403 373 642 574 574 356 258 311 353,535

Perivoliotis, L. 64, 105, 135 Person, R. 291,349 Petelin, B. 361 Petersen, L.T. 621,636 Petersen, W. 325 Petihakis, G. 554 Petit de la Vill6on, L. 345, 611 Petschatnikov, M. 325 Pettersson, L. 529 291 Pfannkuche, O. 36, 46, 78, 222, 279 Pinardi, N. Pison, V. 503 Pissierssens, P. 46 311 Platt, K. Pleskachevsky, A. 122 Ponce de Leon, S. 379 345 Pouliquen, S. Povinec, P. 78 291 Priede, I. G. 78 Prieur, L. 548 Proctor, R. 100 Puillat, I.

Q Quartly, G. D.

128

R Raicich, F. Raimondi, V. Rasch, P. S. Rawlinson, M. Reed, G. Remy, F. Renieris, P. Ribotti, A. Rixen, M. Robinson, I. S. Rodriguez, I. Reed, L. P. Roether, W. Romeiser, R. Rosen, D. S. Rosenthal, W. Ruffini, G. Ruffini, L. Ruiz, M. I.

97 71 211 311 46 141 373 46, 100, 304 645 135, 174, 279 304, 398 529 78 135 46 115, 122 146 146 398

679

Ruiz, S. Ruokanen, L. Ryder, P.

513 519 25,655

S Sairouni, A. Salat, J. Saldo, R. Salihoglu, I. Sammari, C. Sanchez-Arcilla, A. Sandven, S. Sauzade, D. Schiano, M. E. Schlick, T. Schroeder, F. Schyberg, H. Sebastiao, P. Sein~i, A. Selenica, A. Servain, J. Shannon, K. M. She, J. Shliakhtun, M. Siccardi, A. Silvestri, C. Sivyer, D. B. Skogen, M. Skou, N. Smolders, S. Snoussi, M. Soetje, K. Sr H. Sokolov, S. T. Sonc, D. SCrensen, J. V. T. Sorgente, R. Soukissian, T. Soulat, F. Sparnocchia, S. Stel, J. H. Sterling, M. Stoffelen, A. Storkey, D. Strout, J. M. Suetin, V.

379 78 621 46 46 379 621 46 87 245 325 272 190, 523 621,627 46 366 128 237, 285 586 87 46 311,568 529 186 349 46, 78 466 529 109 361 211 46, 100 392 146 222 592, 598, 655 353,535 180 197 349 168

Summerhayes, C. Suslin, V. Suylen, J. M. Svendsen, E.

46, 409 168 311 529

T Tan, G. K. Tarchi, D. Tber, M. Tel, E. Tergujeff, R. Thanos, I. Theocharis, A. Thomsen, L. Toudal, L. Townend, I. H. Triantafyllou, G. Triki, M. Tsabaris, C. G. Tselepides, A. Turton, J. Tziavos, C.

497 135 78 356 627, 633, 639 334 78 291 272 510 554 78 370 291 605 46, 105, 304, 334

U Ufermann, S. Umgiesser, G.

135, 174 46

V V ainio, J.

Valavanis, V. Vallerga, S. Vargas, M. Vargas-Y~ifiez, M. Vega, D. V61ez-Belchi, P. Verduin, J. Vilibic, I. Violeau, D. Vlachos, D. S. Vucijak, B. Vyazilov, E.

627 554 46 46, 78 356 356 356 265 78 386 370, 403, 649 46 615

W Wagner, V. 340 Waniek, J. 568 Weering, T. C. E. van 291

680

Winther, N. Woods, J. Wyatt, L. R.

Index of Authors

529 46 161

Y Yool, A. Yunev, O. A. Yuschenko, S.

297 168 586

Z Zavatarelli, M. Zervakis, V. Zheleznyak, M. Zitellini, N. Zodiatis, G.

46, 222 105, 373 586 291 36, 46

List of Reviewers We are very grateful to all the reviewers who gave their time to help us maintain a high standard in this publication.

Aarup, Thorkild IOC, UNESCO France

Boulluec, Marc le Ifremer France

Allen, J.Icarus Plymouth Marine Laboratory UK

Bourles, Bernard Ifremer France

Alvarez Fanjul, Enrique Puertos del Estado Spain

Buch, Erik DMI Denmark

Ambj6rn, Cecilia SMHI Sweden

Buchner, Werner CEA France

Aoustin, Yannick Ifremer France

Colijn, Fransiscus GKSS Germany

Artale, Vincenzo ENEA Italy

Dahlin, Hans EuroGOOS Sweden

Axe, Philip SMHI Sweden

Dankert, Heiko GKSS Germany

Behrens, Willem RIKZ The Netherlands

Dessier, Alain IRD France

Blaine, Stephane UBO France

Dexter, Peter WMO Switzerland

682

List of Reviewers

D6scher, Ralf SMHI Sweden

Guddal, Johannes met.no Norway

Elken, Jtiri Estonian Marine Institute Estonia

Guerzoni, Stefano CNR Italy

Eleftheriou, Anastasios Institute of Marine Biology of Crete Greece

Guedes Soares, Carlos Technical University of Lisbon Portugal

Flemming, Nic UK

Gurgel, Klaus-Werner University of Hamburg Germany

Funkquist, Lennart SMHI Sweden

Guymer, Trevor SOC UK

Gerard, Fran9ois M6t6o-France France

G~istgivars, Mafia Finnish Environment Institute Finland

Gillou, Jacques UBO, France

Hackett, Bruce met.no Norway

Gommenginger, Christine SOC UK

Havsteen, Charlotte RDANH Denmark

Grafstr6m, Torbj6m SMHI Sweden

Herisson, Christian Geosciences consultant France

Griffiths, Gwyn SOC UK

Holt, Martin Met Office UK

Groom, Steve University of Plymouth UK

Hydes, David SOC UK

683

Johannessen, Johnny NERSC Norway

Loaec, Gerard Ifremer France

J6nsson, Lennart University of Lund Sweden

Loeng, Harald IMR Norway

Kaitela, Seppo FIMR Finland

Luyten, Patrick MUMM Belgium

Kloster, Kjell NERSC Norway

Maillard, Catherine Ifremer France

Kohnke, Dieter BSH Germany

Manzella, Guiseppe ENEA Italy

Korres, Gerasimos University of Athens Greece

Marvaldi, Jean Ifremer France

Koutitas, Chris Aristotle University Greece

Mattson, Johan RDANH Denmark

Krestenitis, Yannis N. Aristotle University Greece

Mercier, Herle Ifremer France

Laxon, Seymour University College London UK

Minster, Jean-Franqois Ifremer France

Lefort, Olivier Ifremer France

Narayanan, Savi MEDS Canada

Legrand, Jacques Ifremer France

New, Adrian SOC UK

684

List of Reviewers

Nittis, Kostas NCMR Greece

Proctor, Roger POL UK

Nystuen, Jeff University of Washington USA

Pugh, David SOC UK

Oguz, Temel METU Institute of Marine Sciences Turkey

Quilfen, Yves Ifremer France

Onken, Reiner GKSS Germany

Romafia, L. Axel Ifremer France

Parrilla, Gregorio IEO Spain

Reistad, Magnar met.no Norway

Petersen, Wilhelm GKSS Germany

Repecand, Michel Ifremer France

Petihakis, George Institute of Marine Biology of Crete Greece

Robertson, Colette SOC UK

Pichot, Georges MUMM Belgium

Roy, Gilles Ifremer France

Piechura, Jan IOPAS Poland

Ryder, Peter Environmental Information Services UK

Prandle, David POL UK

Salihoglu, Ilkay METU Institute of Marine Sciences Turkey

Prevosto, Marc Ifremer France

Sandven, Stein NERSC Norway

685

Schiano, Elisabetta CNR-ISMAR Italy

Triantafyllou, George Institute of Marine Biology of Crete Greece

Schott, Fritz University of Kiel Germany

Turton, Jon Met Office UK

Sein~i, Ari FIMR Finland

Vallerga, Silvana CNR Italy

She, Jun DMI Denmark

Verduin, Jennifer University of Hamburg Germany

Smith, Neville Bureau of Meteorology Research Centre Australia

White, Jonathan Marine Institute Ireland

Soetje, Kai BSH Germany

Woerther, Patrice Ifremer France

Staskiewicz, Antoni Maritime Institute Gdandsk Poland

Woods, John Imperial College, London UK

Stel, Jan H. NWO Earth and Life Sciences Council The Netherlands

Wyatt, Lucy Sheffield Centre for Earth Obs. Science UK

Stoffelen, Ad KNMI The Netherlands

Zavatarelli, Marco Universit?a di Bologna Italy

S6rensen, Jacob V.T. DHI Denmark

Zecchetto, Stefano CNR Italy

Topliss, Brenda SOC UK


List of Participants Aarup, Thorkild IOC France

Awad, Hassan AUDO Dept. of Oceanography Egypt

Ahanhanzo, Justin UNESCO/IOC France

Bahurel, Pierre MERCATOR Ocean France

Ali, Mahmoud MAMA Project Palestine

Baker, D. James Academy of Natural Sciences USA

Allan, Tom Satellite Observing Systems UK

Barbetseas, Stavros NCMR Greece

Altalo, Mary Science Applications International Corp. Virginia, USA

Barth, Alexander GHER, University of Liege Belgium

Alvarez Fanjul, Enrique Puertos del Estado Spain

Behrens, H.W.A. RIKZ The Netherlands

Alvera Azcarate, Aida GHER, University of Liege Belgium

Bell, Michael Met Office UK

Ardhuin, Fanny ENST Bretagne France

Berge, Edward van den Flanders Marine Institute Belgium

Asanuma, Ichio NASDA Japan

Bergen Henegouw, Cok van OCN B.V. The Netherlands

Awad Morad, Bacily NIOF Egypt

Besiktepe, Sukru METU- Institute of Marine Science Turkey

688


Bjerrum, Anders Maridan Denmark

Chedrawy, Tony Metocean Data Systems Canada

Borst, J.C. RIKZ The Netherlands

Christensen, Charlotte RDANH Denmark

Bosman, J. RIKZ The Netherlands

Chronis, George NCMR Greece

Bozzano, Roberto CNR-ISSIA Italy

Civiterese, Giuseppe CNR Italy

Brown, Martin France

Clark, Andrew Marine Technology Society USA

Buch, Erik Danish Meteorological Institute Denmark

Colijn, Franciscus GKSS Germany

Carlberg, Stig SMHI Sweden

Connolly, Niamh ESF Marine Board France

Casazza, Gianna APAT Italy

Cornaert, Michel European Commission Belgium

Cattle, Howard International Clivar Project Office UK

Cowling, Michael University of Glasgow UK

Cecchi, Giovanna CNR Italy

Cummins, Valerie University College Cork Ireland

Cermelj, Branko National Institute of Biology Slovenia

Dahlin, Hans EuroGOOS Sweden

689

Daniel, Pierre M6t6o-France France

Drakopoulos, Panos IMBC Crete

Dankert, Heiko GKSS Research Center Germany

Dumon, Guido Waterways & Maritime Affairs Admin. Belgium

Davidson, Fraser CLS France

Edwards, Alan European Commission Belgium

Dehenauw, David Royal Met Institute of Belgium Belgium

Erb, William UNESCO Australia

Demetriou, John National Technical University of Athens Greece

Espino Infantes, Manuel Catalonia Polytechnic University (UPC) Spain

Desaubies, Yves Ifremer France

Etiope, Giuseppe Istituto Nazionale di Geofisisca e Vulcan. Italy

Dexter, Peter WMO Switzerland

Evensen, Geir Nansen Center Norway

Diamanti, Chrysoula NCMR Greece

Fischer, Johanne Bundesforschungsanstalt fiir Fischerei Germany

Dombrowsky, Erik Collecte Localisation Satellites France

Flemming, Nicholas SOC UK

Dongen, Frans van OCN B.V. The Netherlands

Font, Jordi Institut de Ciences Del Mar Spain

Drago, Aldo University of Malta Malta

Frasheri, Alfred Faculty of Geology and Mining Albania

690


Fratianni, Claudia INGV Italy

Griffiths, Gwyn SOC UK

Funkquist, Lennart SMHI Sweden

Grimes, David Meteorological Service of Canada Canada

Fyrberg, Lotta SMHI Sweden

Gr6nvall, Hannu FIMR Finland

Gajewski, Juliusz Maritime Institute Gdansk Poland

Guddal, Johannes Norwegian Meteorological Institute Norway

Garcia Lafuente, Jesus University of Malaga Spain

Guedes Soares, Carlos Instituto Superior Tecnico - UETN Portugal

Garcia Montero, Guillermo IOCARIBE-GOOS Steering Committee Cuba

Gurgel, Klaus-Werner University of Hamburg Germany

Garello, Rene ENST Bretagne France

Guymer, Trevor SOC UK

Gerritzen, Peter Cytobuoy B.V. The Netherlands

Haajanen, Jyrki VTT Information Technology Finland

Gertman, Isaac Oceanographic & Limnological Research Israel

Hackett, Bruce Norwegian Meteorological Institute Norway

Giraud St. Albin, Sylvie Collecte Localisation Satellites France

Hansen, Svein Erling OCEANOR ASA Norway

Grezio, Anita Istituto Nazionale di Geofisica e Vulcan. Italy

Hensen, H. Rene DHI Denmark

691

Herman, Rudy Science and Innovation Administration Belgium

Johannessen, Ola M. Nansen Center Norway

Heygster, Georg Institute of Environmental Physics Germany

Jourdan, Didier SHOM France

Hjollo, Bj6rn Norwegian Meteorological Institute Norway

J6nsson, Lennart University of Lund Sweden

Hogan, Patrick Naval Research Laboratory USA

Kabbara, Nijad National Centre for Marine Sciences Lebanon

Holt, Martin Met Office UK

Kahma, Kimmo FIMR Finland

Homminga, Tjerk Technical University of Delft The Netherlands

Kaitala, Seppo FIMR Finland

Hydes, David SOC UK

Kallos, George University of Athens Greece

H~kansson, Bertil SMHI Sweden

Kantidakis, Antony Marac Electronics Greece

Ibrahim, Amir High Institute of Marine Research Syria

Kargioti, Irine NAUS Ltd. Greece

Irmisch, Andreas Forschungzentrum Tulich GMBH Germany

Kelly, Frank Texas A&M University USA

Johannessen, Johnny A. Nansen Center Norway

Kern, Stefan University of Hamburg Germany

692


Knorring, Mary von Swedish Research Council Sweden

Kyriakidis, Hector-Lysis RD Instruments Europe France

Koersel, Ton van TNO-Physics and Electronics Lab. The Netherlands

Lane, David Heriot-Watt University UK

Kohnke, Dieter BSH Germany

Le Provost, Christian LEGOS/CNRS France

Koranteng, Kwame Marine Fisheries Research Division Ghana

Le Traon, Pierre-Yves CLS France

Korotaev, Gennady National Academy of Sciences Ukraine

Lee, Dong-Young China-Korea Joint Ocean Research Cen. China

Korres, Gerasimos University of Athens Greece

Legrand, Jacques Ifremer France

Kouraev, Alexei Moscow State University Russia

Lehner, Susanne DLR German Aerospace Center Germany

Kouts, Tarmo Estonia

Lindstrom, Eric US GOOS/Ocean US USA

Kromjongh, Joost TNO-FEL The Netherlands

Loeng, Harald Institute of Marine Research Norway

Krzyminski, Wlodzimierz IMWM Poland

Lominadze, George Georgian Academy of Sciences Georgia

Ktistaki, Maria Aegean University Greece

Lopez-Jurado, Jose Luis Inst. Espanol de Oceanografia Spain

693

Luyten, Patrick MUMM Belgium

McEwan, Angus Bureau of Meteorology Australia

Lymberopoulos, Spyros Aegean University Greece

Mertikas, Stelios Technical University of Crete Greece

Maderych, Volodymyr Inst. of Math. Machines and Systems Ukraine

Miller, Jerry Office of Naval Research UK

Maillard, Catherine Ifremer France

Minster, Jean-Franqois Ifremer France

Malacic, Vlado National Institute of Biology Slovenia

Monteny, Frank Belgian Federal Science Policy Office Belgium

Malone, Thomas Univ. of Maryland Center for Envir. Sc. USA

Morovic, Mira Institute of Oceanography and Fisheries Croatia

Manzella, Giuseppe ENEA Italy

Mykoniatis, George NTUA Greece

Marchand, Philippe Ifremer France

Nesterov, Eugene Roshydromet Russia

Martinez, Rodney Oceanographic Institute of the Navy Ecuador

Nittis, Kostas NCMR Greece

Martyschenko, Valery Roshydromet Russia

Nolan, Glenn Marine Institute Ireland

Mason, Paul GCOS Steering Committee UK

Nunez, Rodrigo Servicio Hidro. y Oceano. de la Armada Chile

694


O'Mahony, Cathal University College Cork Ireland

Peterssen, Wilhelm GKSS Research Centre Germany

Oduyebo, Ololade Philips Artech Professional Education Centre Russia

Petersson, Sifin EuroGOOS Sweden

Oilier, Gilles European Commission Belgium

Petit de la Villeon, Loic Ifremer France

Ortega, Christian CLS France

Petrova, Elitsa Institute of Fisheries and Aquaculture Bulgaria

Ozsoy, Emin METU Institute of Marine Sciences Turkey

Pichot, Georges MUMM Belgium

Pano, Niko Institute of Hydrometeorology Albania

Piechura, Jan Polish Academy of Sciences Poland

Papadopoulos, Anastasios NCMR Greece

Pinardi, Nadia Bologna University Italy

Papineau, Nicole M6t6o-France France

Pison, Virginie MUMM Belgium

Parrilla, Gregorio Institute Espanol de Oceanografia Spain

Plag, Hans-Peter Norwegian Mapping Authority Norway

Perivoliotis, Leonidas NCMR Greece

Platt, Kate ECO Sense Ltd. UK

Person, Roland Ifremer France

Pleskatchevski, Andrei GKSS Research Center Germany

695

Pouliquen, Sylvie Ifremer France

Reistad, Magnar Norwegian Meteorological Institute Norway

Pratt, Christelle Sopac Fiji Islands

Ribotti, Alberto IMC Italy

Preller, Ruth Naval Research Laboratory, MS USA

Romeiser, Roland University of Hamburg Germany

Prevosto, Marc Ifremer France

Rosen, Dov S. Oceanographic & Limnological Research Israel

Priede, Monty University of Aberdeen UK

Rosenthal, Wolfgang GKSS Research Center Germany

Proctor, Roger Proudman Oceanographic Laboratory UK

Ruffini, Giulio Starlab Barcelona S.L. Spain

Prospathopoulos, Aris NCMR Greece

Ruiten, C.J. van RIKZ The Netherlands

Pullen, Julie Naval Research Laboratory USA

Ruokanen, Lotta FIMR Finland

Raicich, Fabio CNR Italy

Ryder, Peter Environmental Information Services UK

Ratier, Alain Eumetsat Germany

Salihoglu, Ilkay METU Institute of Marine Sciences Turkey

Rees, Jon LEFAS UK

Sanchez-Arcilla, Agustin Catalonic Polytechnic University (UPC) Spain

696


Sandven, Stein Nansen Center Norway

Slabakov, Hristo Institute of Oceanology B AS Bulgaria

Sawkins, Mike WS Envirotech Ltd. UK

Smith, Neville Buireau of Meteorology Australia

Savvidis, Yiannis Thessaloniki Port Authority S.A. Greece

Snidvongs, Anond Southeast Asia Start Regional Center Thailand

Schoonhoven, J.V. RIKZ The Netherlands

Soerensen, Jacob V.T. DHI Water and Environment Denmark

Sebastiao, Paulo Jorge Institute Superior TechniconUETN Portugal

Solan, Martin University of Aberdeen UK

Seina, Ari FIMR Finland

Soomere, Tarmo Marine Systems Institute Estonia

Send, Uwe IFM Kiel Germany

Soukisian, Takvor NCMR Greece

She, Jun Danish Meteorological Institute Denmark

Spyropoulos, Kyriakos Triton Consulting Engineers Greece

Shiganova, Tamara PP Shirshov Institute of Oceanology Russia

Steedman, Raymond WA GOOS Australia

Silvestri, Cecilia APAT Italy

Stel, Jan H. NWO/Council of Earth & Life Sciences The Netherlands

Simspon, Alfred Thomas SOPAC Fiji Islands

Strom, Guro Dahle Norwegian Space Centre Norway

697

Summerhayes, Colin IOC France

Turton, Jonathan Met Office UK

Szaron, Jan SMHI Sweden

Tziavos, Christos NCMR Greece

Szefler, Kazimierz Maritime Institute Poland

Ufermann, Susanne SOC UK

Tennvassas, Tove Kongsberg Spacetec AS Norway

Valdes, Luis Instituto Espanol de Oceanografia Spain

Thanos, Ioannis Martedec S.A. Greece

Vallerga, Silvana CNR and IMC Italy

Theocharis, Alexander NCMR Greece

Walne, Anthony Sir Alistair Hardy Found. Ocean Science UK

Toro, Cesar IOC/UNESCO Columbia

Vargas Yanez, Manuel Instituto Espanol de Oceanografia Spain

Tragou, Elina NCMR Greece

Webb, Allen Oceanroutes UK Ltd. UK

Tromp, Dik RIKZ The Netherlands

Verduin, Jennifer Institute for Hydrobiology and Fisheries Germany

Trotte, Janice IOC/UNESCOmRio GOOS Office Brazil

Verlaan, M. RIKZ The Netherlands

Tsabaris, Christos NCMR Greece

Ververi, Irini Aegean University Greece

698


Westbrook, Guy Marine Institute Ireland

Zarkogiannis, Stergios Aegean University Greece

White, Jonathan Marine Institute Ireland

Zavatarelli, Marco University of Bologna Italy

Violeau, Damien Electricite de France France

Zenetos, Argyro

Vlugt, Tom van der Radac B.V. The Netherlands

Zervakis, Vasilis NCMR Greece

Wyatt, Lucy University of Sheffield UK

Zodiatis, George Oceanography-DFMR Cyprus

Vyazilov, Evgeny Research Institute for Hydro. Inf. Russia

Zoellner, Reinhard Deutscher Wetterdienst Germany

Yunev, Oleg Institute of Biology of the Southern Seas Ukraine Albania Australia Belgium Brazil Bulgaria Canada Chile China Coumbia Croatia Cuba Cyprus Denmark Ecuador Egypt Estonia

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Fiji Islands Finland France Georgia Germany Ghana Greece Ireland Israel Italy Japan Lebanon Malta Norway Palestine Poland

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