WORLD METEOROLOGICAL ORGANIZATION

WORLD WEATHER RESEARCH PROGRAMME

WWRP 2010 – 5

5th WMO SYMPOSIUM ON DATA ASSIMILATION

Melbourne, Australia, 5 - 9 October 2009

WMO/TD-No.1549

Foreword

Improving the combination of observations and dynamical models by data assimilation systems has underpinned many advances in our understanding of the natural environment, and forecasting ability. These improvements, coupled with the development of ever more powerful computers and more sophisticated communication systems such as the internet and the World Wide Web have also heightened expectations. As a result, society is looking for further significant benefits from applications of meteorology, oceanography and hydrology.

It is therefore essential that this community continue to meet and plan the research and development of data assimilation: its fundamental theory and its application to meteorology, oceanography, hydrology and related fields. The use of data assimilation within areas such as modelling chemical species, coupled systems and the land surface raises many new issues. These questions come on top of those associated with continued efforts to meet the expectations from more established atmospheric and oceanic applications.

Since the WMO accepted the challenge to oversee the development of Data Assimilation there has been tremendous developments in the relevant areas of science, both research and operational. This series of meetings from the first in Clermont-Ferrand (1990) followed by Tokyo (1995), Quebec City (1999), Prague (2005) and now Melbourne, have been an important part of showcasing these developments and reporting on fruitful directions for research to meet the increasing demands.

The presentations at this meeting show that despite the success of existing systems, new developments are still needed to bridge the gap between current and desired levels of performance. The number of presentations on new techniques, novel combinations of established techniques and enhanced use of more elaborate observing systems highlights the dynamism within data assimilation theory and application.

International Scientific Organizing Committee.

Andrew Lorenc, Met Office, United Kingdom (Chair)

Table of Contents Listed alphabetically by first author; presenting author in bold An Index of presenting authors is located at end of the Short Abstracts document

Page No.

REFINEMENT OF SIMULATIONS OF DEEP-WATER TSUNAMI PROPAGATION THROUGH THE 1 USE OF OBSERVATIONS Stewart Allen, Diana Greenslade

A COMPARISON OF THE REPRESENTATION OF THE MAIN MODES OF OCEAN CLIMATE 1 VARIABILITY BY TWO STATE-OF-THE-ART OCEAN RE-ANALYSES O. Alves, Y.Yin, M.Balmaseda, P. Oke

MODELLING EQUATORIAL PACIFIC SALINITY FIELDS USING PEODAS 2 O. Alves, R. Wedd, Y. Yin, P. Oke

CAN OCEAN DATA ASSIMILATION IMPROVE TROPICAL CYCLONE FORECASTS? 2 Isabel Andreu-Burillo, Gary Brassington, Peter Oke, Paul Sandery, Justin Freeman, Helen Beggs

NEIGHBORING ENSEMBLE VARIATIONAL ASSIMILATION METHOD TO INCORPORATE 2 MICROWAVE RADIOMETER DATA INTO A CLOUD-RESOLVING MODEL Kazumasa Aonashi, and Hisaki Eito

PRELIMINARY RESULTS OF ASSIMILATION AIRS RADIANCES WITH LOCAL ENSEMBLE 3 TRANSFORM KALMAN FILTER FOR THE CPTEC/INPE GLOBAL MODEL José Antonio Aravéquia, Solange Solange Silva De Souza, José Paulo Bonatti, Dirceu Luiz Herdies, Paulo Kubota

ASSIMILATING RETRIEVALS OF CHEMICAL CONSTITUENTS IN CAM-CHEM AND WRF- 3 CHEM USING AN ENSEMBLE ADJUSTMENT KALMAN FILTER APPROACH Avelino F. Arellano (Oral), David P. Edwards

DEVELOPMENT OF A CLOUD ANALYSIS SYSTEM 4 Thomas Auligne

THE “BACK AND FORTH NUDGING” ALGORITHM FOR OCEANOGRAPHIC DATA 4 ASSIMILATION Didier Auroux, Jacques Blum

RECENT PROGRESS IN HYBRID 4D-VARIATIONAL/ENSEMBLE DATA ASSIMILATION 5 Dale Barker (Oral), Adam Clayton, Andrew Lorenc, Neill Bowler

REAL-TIME SKIN SEA SURFACE TEMPERATURE ANALYSES FOR QUALITY CONTROL OF 5 DATA ASSIMILATED INTO NWP MODELS Helen Beggs, Chelle Gentemann, Peter Steinle

AEROSOL ANALYSIS AND FORECAST IN THE ECMWF INTEGRATED FORECAST SYSTEM 5 Angela Benedetti, Jean-Jacques Morcrette

CONSISTENT OPERATIONAL ENSEMBLE VARIATIONAL ASSIMILATION 6 Loïk Berre (Oral), Gérald Desroziers, Laure Raynaud, Rémi Montroty, Olivier Pannekoucke

THE TOPAZ ICE-OCEAN DATA ASSIMILATION SYSTEM 6 Laurent Bertino (Oral), Francois Counillon

VARIATIONAL ASSIMILATION WITH A THREE LEVEL ATMOSPHERIC MODEL 7 Kaustubha Bhattacharya

DATA ASSIMILATION USING MODULATED ENSEMBLES 7 Craig H. Bishop, Daniel Hodyss

i Page No.

HYBRIDIZATION OF THE 4D-VAR WITH A SEEK* SMOOTHER IN VIEW OF OCEANIC 7 APPLICATIONS Monika Krysta, Eric Blayo, Emmanuel Cosme, Jacques Verron

TARGETING OF OBSERVATIONS FOR RADIONUCLIDES ACCIDENTAL RELEASE 8 MONITORING Rachid Abida, Marc Bocquet

CHOOSING THE GEOMETRY OF CONTROL SPACE FOR AN OPTIMAL ASSIMILATION OF 8 OBSERVATIONS Marc Bocquet

FINE-SCALE VERSUS LARGE-SCALE ATMOSPHERIC DATA ASSIMILATION 9 François Bouttier (Invited)

OPERATIONAL OCEAN DATA ASSIMILATION FOR THE BLUELINK OCEAN FORECASTING 9 SYSTEM Gary B. Brassington, Tim Pugh, Peter R. Oke, Justin Freeman, Xinmei Huang, Graham Warren

ADJUSTMENT OF OCEAN MODEL INITIAL CONDITIONS AND ATMOSPHERIC FORCING 100 FROM OCEAN DATA ASSIMILATION IN THE CALIFORNIA CURRENT SYSTEM. Grégoire Broquet, , Andrew M. Moore, Christopher A. Edwards, Hernan G. Arango and Brian S. Powell

USE OF ENSEMBLE ASSIMILATION TO REPRESENT FLOW-DEPENDENCE IN THE AROME 9 DATA ASSIMILATION SYSTEM Pierre Brousseau, Gérald Desroziers and Loïk Berre

INTERCOMPARISON OF VARIATIONAL AND ENSEMBLE KALMAN FILTER DATA 10 ASSIMILATION APPROACHES IN THE CONTEXT OF GLOBAL DETERMINISTIC NWP Mark Buehner (Oral), P.L. Houtekamer, Herschel Mitchell, Cecilien Charette, Bin He

VERTICAL COVARIANCE LOCALIZATION FOR SATELLITE RADIANCES IN ENSEMBLE 10 KALMAN FILTERS William F. Campbell, Craig H. Bishop and Daniel Hodyss

THE BALANCE CHARACTERISTICS OF SHORT-TERM FORECAST ERRORS ESTIMATED 11 FROM AN ENSEMBLE KALMAN FILTER Jean-François Caron, Luc Fillion

THREE-DIMENSIONAL VARIATIONAL DATA ASSIMILATION IN THE GULF OF ST. 11 LAWRENCE COUPLED ICE-OCEAN MODEL Alain Caya, Mark Buehner, and Tom Carrieres

IMPACT OF USING 4D-VAR ASSIMILATION OF SSM/I DATA OVER AUSTRALIAN REGION 11 Mohar Chattopadhyay, Peter Steinle, Yi Xiao, John Le Marshall, Tan Le, Chris Tingwell

ASSIMILATION OF OPTICAL REMOTE SENSING DATA INTO COASTAL AQUATIC 12 BIOGEOCHEMICAL MODELS Nagur Cherukuru (Oral), Barbara Robson, Vittorio Brando, Arnold Dekker

A CLSMDAS USING FY2C PRECIPITATION AND AMSR-E SOIL MOISTURE DATA BASED ON 13 CLM3 AND ENKF Shi Chunxiang, Xie Zhenghui, Tian Xiangjun, Qian Hui, Liang Miaoling

INFRARED REMOTE SENSING OF ATMOSPHERIC COMPOSITION AND AIR QUALITY: 13 TOWARDS OPERATIONAL APPLICATIONS Cathy Clerbaux (Oral), Maya George, Juliette Hadji-Lazaro, Anne Boynard, Matthieu Pommier, Claire Scannell, Pierre-François Coheur, Daniel Hurtmans, Lieven Clarisse

ii Page No.

THE PRINCIPLE OF ENERGETIC CONSISTENCY 14 STEPHEN E. COHN (ORAL)

FIRST RESULTS OF THE ASSIMILATION OF OZONE TROPOSPHERIC COLUMNS PROVIDED 14 BY THE IASI INSTRUMENT TO ASSESS AIR QUALITY WITH A CHEMICAL TRANSPORT MODEL - CHIMERE AT A CONTINENTAL SCALE Adriana Coman, Gilles Foret, Maxime Eremenko, Anne Boynard, Gaelle Dufour, Matthias Beekmann

IMPLEMENTATION OF A REDUCED RANK SMOOTHER FOR HIGH RESOLUTION 15 OCEANOGRAPHY Emmanuel Cosme, Jean-Michel Brankart, Pierre Brasseur, Jacques Verron

COMPARISON BETWEEN SEQUENTIAL ASSIMILATION AND KRIGEAGE FOR SATELLITE 15 DATA INTERPOLATION Charles Cot, Alain Hauchecorne, Slimane Bekki, David Cugnet

ENHANCING ADAPTIVE FILTERING APPROACHES FOR LAND DATA ASSIMILATION 16 SYSTEMS Wade T. Crow (Oral)

LAND DATA ASSIMILATION ACTIVITIES IN PREPARATION OF THE NASA SOIL MOISTURE 16 ACTIVE PASSIVE (SMAP) MISSION Wade T. Crow, Rolf. H. Reichle

ASSIMILATION OF GPS RADIO OCCULTATION OBSERVATIONS AT NOAA/NCEP 17 L. Cucurull (Oral) and J. Derber

FINE SCALE SNOW ANALYSES IMPROVEMENT THROUGH COARSE SCALE SNOW WATER 17 EQUIVALENT ASSIMILATION Gabriëlle J.M. De Lannoy, Rolf H. Reichle, Paul R. Houser, Kristi Arsenault, Niko E.C. Verhoest, Valentijn R.N. Pauwels

ASSESSMENT OF COASTAL OCEAN OBSERVATIONAL NETWORKS BY ENSEMBLE-BASED 18 REPRESENTER SPECTRAL ANALYSIS Matthieu Le Henaff, Pierre De Mey

REPRESENTATION OF CLIMATE SIGNALS IN REANALYSIS 18 Dick Dee (Invited)

ENSEMBLE-BASED DATA ASSIMILATION FOR WIND ENERGY PREDICTIONS AT FINE 19 SCALES Luca Delle Monache, Julie Lundquist

DATA ASSIMILATION OF REMOTE SENSING INFORMATION FROM SATELLITE AND RADAR 19 DATA John Derber (Invited), Lidia Cucurull, Banghua Yan, Paul VanDelst, Mingjing Tong, David Parrish and Shun Liu

A POSTERIORI DIAGNOSTICS IN AN ENSEMBLE VARIATIONAL ASSIMILATION 20 Gérald Desroziers, Loïk Berre, Vincent Chabot, Bernard Chapnik

ENSEMBLE KALMAN FILTER ASSIMILATION OF ATMOSPHERIC CHEMICAL CONSTITUENTS 20 DATA WITH A MRI CHEMISTRY-CLIMATE MODEL: OSS EXPERIMENTS Makoto Deushi, Tsuyoshi T. Sekiyama, and Kiyotaka Shibata

ERROR COVARIANCES VIA HESSIAN IN VARIATIONAL DATA ASSIMILATION 21 F.-X. Le Dimet, I. Gejadze and V. Shutyaev

AN APPLICATION OF SEQUENTIAL VARIATIONAL ALGORITHM 21 Srdjan Dobricic (Oral)

iii Page No.

DATA ASSIMILATION IN OPEN OCEAN AND SHELF AREAS OF THE MEDITERRANEAN SEA 22 Nadia Pinardi, Srdjan Dobricic, Mario Adani, Daniele Pettenuzzo, Jenny Nilsson, Alessandro Bonazzi, Marina Tonani

ON THE ASSIMILATION OF ARGO FLOAT AND SURFACE DRIFTER TRAJECTORIES INTO 22 THE MEDITERRANEAN FORECASTING SYSTEM Jenny A.U. Nilsson, Srdjan Dobricic, Nadia Pinardi

ASSIMILATION OF MODIS SNOW COVER DATA INTO THE LIS SAC-HT/SNOW17 MODEL 23 OVER THE CONTINENTAL UNITED STATES (CONUS) Jiarui Dong, Mike Ek, Pedro Restrepo, Dongjun Seo, Christa Peters-Lidard, Brian Cosgrove, Victor Koren

THE ERROR CHARACTERISTICS OF SIMULATED MICROWAVE SATELLITE OBSERVATION 23 IN CLOUDY AND RAINY AREA Peiming Dong, ShuoSong Liu, Jishan Xue

NON-LINEAR EXTENSIONS OF THE SEEK FILTER FOR DATA ASSIMILATION AND 24 PARAMETER ESTIMATION INTO COUPLED PHYSICAL-BIOGEOCHEMICAL MODELS OF THE OCEAN Maeva Doron (Oral), David Beal,, Jean-Michel Brankart, Pierre Brasseur

A COMPARISON OF SOIL MOISTURE ANALYSES FROM THE EKF ASSIMILATION OF 24 NEARSURFACE SOIL MOISTURE AND SCREEN-LEVEL TEMPERATURE AND HUMIDITY Clara Draper, Jean-François Mahfouf, Jeffrey Walker, Peter Steinle

USING SMOS OBSERVATIONS IN ECMWF’S LAND SURFACE ANALYSIS SYSTEM 25 Matthias Drusch (Oral), Patricia De Rosnay, Joaquin Munoz-Sabater, Gianpaolo Balsamo

FUTURE SATELLITE DATA PRODUCTS SUITABLE FOR LAND SURFACE ANALYSES 25 Matthias Drusch, Mark Drinkwater

THE TELECONNECTION BETWEEN SEA SURFACE TEMPERATURE ANALYSIS FROM IN 26 SITU DATA AT EAST MOLE, LAGOS AND GLOBAL WARMING Ediang, Okuku Archibong, Ediang, Aniekan Archibong

UNDERSTANDING OCEAN SURGES AND POSSIBLE SIGNALS OVER THE NIGERIAN COAST 26 Ediang, Okuku Archibong, Ediang, Aniekan Archibong

CHEMICAL DATA ASSIMILATION WITH MULTISCALE EMISSION INVERSION. 26 Hendrik Elbern (Invited), Achim Strunk

A SCALE-BASED DISTORTION METRIC FOR MESOSCALE WEATHER VERIFICATION 27 Chermelle Engel, Todd Lane

PRECURSORY SIGNALS OF SIGNIFICANT WEATHER EVENTS FOUND IN ENSEMBLE 27 REANALYSIS ALERA Takeshi Enomoto (Oral), Miki Hattori, Takemasa Miyoshi and Shozo Yamane

REDUCED ARCTIC SEA ICE HINDERS ACCURATE CLIMATE MONITORING - IMPACT OF 28 DEPLETED ARCTIC DRIFTING BUOY NETWORK Jun Inoue, Takeshi Enomoto, Takemasa Miyoshi, Shozo Yamane

CHANGES TO THE GLOBAL OBSERVING SYSTEM – EVOLUTION OR DESIGN? 28 John Eyre (Invited)

ANTARCTIC LACUSTRINE ENVIRONMENT AS A RESULT OF CLIMATE CHANGE AND 29 HUMAN IMPACT Irina Federova, Tatiana Potapova, Maria Romanovskaya

iv Page No.

AN INVESTIGATION OF MODEL ERROR IN A QUASI-GEOSTROPHIC, WEAK-CONSTRAINT 29 4D-VAR ANALYSIS SYSTEM MICHAEL FISHER (ORAL)

ASSIMILATION OF MODIS SNOW COVER DATA AND AMSR-E SNOW WATER EQUIVALENT 30 DATA INTO SNOWMODEL Steven J. Fletcher, Glen E. Liston, Christopher A. Hiemstra and Steve D. Miller

POSITIONAL ERROR IN THE BOUNDARY LAYER CAPPING INVERSION 30 Alison Fowler, Ross Bannister, John Eyre

DATA ASSIMILATION IN MORPHODYNAMICAL MODELS 30 Ivan D. García Triana, Ghada El Serafy, and Arnold W. Heemink

ALL-SKY ASSIMILATION OF MICROWAVE OBSERVATIONS SENSITIVE TO WATER VAPOUR, 31 CLOUD AND RAIN Alan Geer (Oral), Peter Bauer, Philippe Lopez And Deborah Salmond

COMPARISON OF OBSERVATION IMPACTS IN TWO FORECAST SYSTEMS USING ADJOINT 31 METHODS Ronald Gelaro (Oral), Rolf Langland, and Ricardo Todling

AMSR-E PASSIVE MICROWAVE SOIL MOISTURE AND DYNAMIC OPEN WATER FRACTION 32 Ben Gouweleeuw, Albert Van Dijk, Juan Pablo Guerschman, Peter Dyce, Manfred Owe, Richard De Jeu

NETWORK DESIGN AND ASSESSMENT FOR A TSUNAMI OBSERVING SYSTEM 32 Diana Greenslade, Jane Warne

ENKF LOCALIZATION TECHNIQUES AND BALANCE 33 Steven Greybush, Eugenia Kalnay, Kayo Ide, Takemasa Miyoshi

IMPACT OF ADVANCED SOUNDER RADIANCES IN THE FRENCH NUMERICAL WEATHER 33 PREDICTION MODELS Vincent Guidard, Nadia Fourrie, Thomas Pangaud, Florence Rabier

THE CONCORDIASI FIELD CAMPAIGN OVER ANTARCTICA 34 Florence Rabier, Aurélie Bouchard, Eric Brun, Alexis Doerenbecher, Stéphanie Guedj, Vincent Guidard, Fatima Karbou, Vincent-Henri Peuch, Laaziz el Amraoui, Dominique Puech,Christophe Genthon, Ghislain Picard, Michael Town, Albert Hertzog, François Vial, Philippe Cocquerez, Stephen A. Cohn, Terry Hock, Jack Fox, Hal Cole, David Parsons, Jordan Powers, Keith Romberg, Joseph Van andel, Terry Deshler, Jennifer Mercer, Jennifer Haase, Linnea Avallone, Lars Kalnajs, C. Roberto Mechoso, Andrew Tangborn, Andrea Pellegrini, Yves Frenot, Jean-Noël Thepaut, Anthony McNally, Peter Steinle

CHEMICAL SOURCE BACKTRACKING IN TURBULENT BOUNDARY LAYER (TBL) 34 Ajith Gunatilaka, Alex Skvortsov, Branko Ristic, Mark Morelande, Dinesh Pitaliadda, Ralph Gailis

DEVELOPMENT OF A REGIONAL OCEAN REANALYSIS SYSTEM IN THE CHINA SEAS 35 Guijun Han, Wei Li, Xuefeng Zhang, Dong Li, Zhongjie He, Xidong Wang, Xinrong Wu, Jirui Ma

RECENT DEVELOPMENTS IN DATA ASSIMILATION OF CHINESE NEW GFS 35 Wei Han, Jishan Xue, Zhaorong Zhuang, Yan Liu, Xueshun Shen

THE CHOICE OF THE “BEST” DATA ASSIMILATION ALGORITHM FOR SUBSURFACE 36 CHARACTERIZATION Remus Hanea,, Justyna Przybysz-Jarnut, Arnold Heemink

THE MOISTURE BUDGET OVER AMAZON REGION DURING THE MINI-BARCA CAMPAIGN 36 Dirceu l. Herdies, Luiz F. Sapucci, Luis G. G. Goncalves, João G. Mattos, Jose A. Aravéquia and Saulo B. Costa

v Page No.

INVERSE ESTIMATION OF EMPIRICAL PARAMETER IN A CIRCULATION MODEL FOR THE 37 EAST ASIAN MARGINAL SEAS Naoki Hirose

A CHEMICAL DATA ASSIMILATION SYSTEM FOR SOUTH AMERICA USING THE CCATT- 37 BRAMS ATMOSPHERIC MODEL TO ACCESS THE IMPACT OF FIRE EMISSIONS Judith J. Hoelzemann, Karla M. Longo, Hendrik Elbern, Saulo R. Freitas

DATA ASSIMILATION IN EARLY PHASE OF RADIATION ACCIDENT USING PARTICLE FILTER 38 Radek Hofman, Václav Šmídl, Petr Pecha

SIMULATION OF RANDOM 3-D TRAJECTORIES OF THE TOXIC PLUME SPREADING OVER 38 THE TERRAIN Petr Pecha, Radek Hofman

PARTICLE KALMAN FILTERING: A NONLINEAR FRAMEWORK FOR ENSEMBLE KALMAN 100 FILTERS Ibrahim Hoteit, Dinh-Tuan Pham

A MITGCM/DART OCEAN ANALYSIS AND PREDICTION SYSTEM WITH APPLICATION TO 100 THE GULF OF MEXICO Ibrahim Hoteit, Tim Hoar, Nancy Collins, Jeffrey Anderson, Bruce Cornuelle, Armin Kohl and Patrick Heimbach

THE 2009 WRFDA OVERVIEW 39 Xiang-Yu Huang (Oral)

ENSEMBLE DATA ASSIMILATION AT ECMWF 39 Lars Isaksen (Oral)

DEVELOPMENT OF A 4-DIMENSIONAL VARIATIONAL COUPLED DATA ASSIMILATION 39 SYSTEM FOR ENHANCED ANALYSIS AND PREDICTION OF SEASONAL TO INTERANNUAL VARIATIONS Nozomi Sugiura, Yoichi Ishikawa (Oral), Toshiyuki Awaji,, Shuhei Masuda, Hiromichi Igarashi, Takahiro Toyoda, Yuji Sasaki, Yoshihisa Hiyoshi

IMPACT OF 4D-VAR ASSIMILATION PRODUCTS ON BIO-GEOCHEMICAL SIMULATION 40 Yoichi Ishikawa, Toshiyuki Awaji, Hiromichi Igarashi, Shuhei Masuda, Nozomi Sugiura, Takahiro Toyoda, Yuji Sasaki

ESTIMATES OF AIR-SEA FLUXES IN A TROPICAL CYCLONE USING AN ADJOINT METHOD 40 Kosuke Ito, Yoichi Ishikawa, And Toshiyuki Awaji

COMPARISONS OF BREWER-DOBSON CIRCULATIONS DIAGNOSED FROM REANALYSIS 41 Toshiki Iwasaki (Oral), Hisashi Hamada And Kazuyuki Miyazaki

IMPACT ASSESSMENT OF DATA ASSIMILATION ON FINE SCALE AIR DISPERSION FOR A 41 COMPLEX TERRAIN Jana R, Indumati S, Shrivastava R, Oza R.B, Puranik V.D, Kushwaha H.S.

OBSERVATIONAL ERROR COVARIANCE SPECIFICATION IN ENSEMBLE BASED KALMAN 42 FILTER ALGORITHMS Tijana Janjic, Alberta Albertella, Sergey Skachko , Jens Schroeter , Reiner Rummel

A SOIL MOISTURE ASSIMILATION SCHEME BASED ON THE ENSEMBLE KALMAN FILTER 42 USING MICROWAVE BRIGHTNESS TEMPERATURE Binghao Jia, , Zhenghui Xie, Xiangjun Tian, Chunxiang Shi

4D-VAR AND ENKF INTERCOMPARISONS 43 Eugenia Kalnay (Invited)

vi Page No.

APPLICATION OF SINGULAR VECTOR ANALYSIS TO THE KUROSHIO LARGE MEANDER 43 Yosuke Fujii, Masafumi Kamachi (Oral), Norihisa Usui, Hiroyuki Tsujino, and Hideyuki Nakano

CLOUD RESOLVING 4DVAR EXPERIMENT OF A LOCAL HEAVY RAINFALL EVENT USING 44 GPS SLANT DELAY DATA Takuya Kawabata, Yoshinori Shoji, Hiromu Seko Kazuo Saito

SENSITIVITY OF ENSEMBLE FORECASTS TO ENSEMBLE SIZE IN ENSEMBLE TRANSFORM 101 KALMAN FILTER Jun Kyung Kay, Hyun Mee Kim, Young-Youn Park, Joohyung Son, Seonok Moon, Hee-Dong Yoo

USE OF SEVIRI RADIANCES IN THE MET OFFICE MESOSCALE MODELS 44 Graeme Kelly, Robert Tubbs, Pete Francis

AN OBSERVATION OPERATOR FOR THE VARIATIONAL ASSIMILATION OF VORTEX 44 POSITION AND INTENSITY Jeffrey D. Kepert

CHANGE-OF-VARIABLE IN AN ENSEMBLE KALMAN FILTER 45 Jeffrey D. Kepert (Oral)

SNOW RADIANCE ASSIMILATION: CASE STUDIES USING THE COLD LAND PROCESSES 45 EXPERIMENT-1 Edward Kim (Oral), Michael Durand, Steven Margulis and Ally Toure

CALCULATING ANALYSIS SENSITIVITY FOR THE NCEP GLOBAL DATA ASSIMILATION 46 SYSTEM Daryl T. Kleist and Kayo Ide

TECHNIQUE OF ADAPTIVE OBSERVATIONS PLANNING BASED ON ENSEMBLE KALMAN 46 FILTER Ekaterina G. Klimova

JRA-55: JAPANESE 55-YEAR REANALYSIS PROJECT - STATUS AND PLAN 46 Ayataka Ebita, Shinya Kobayashi (Invited), Yukinari Ota, Masami Moriya, Ryouji Kumabe, Kiyotoshi Takahashi and Kazutoshi Onogi

STATE AND PARAMETER ESTIMATION FOR A COUPLED OCEAN--ATMOSPHERE MODEL 47 Dmitri Kondrashov, Michael Ghil, Ichiro Fukumori, Ge Peng

FINDING SOURCES OF BIAS ERROR IN FORECAST MODELS: A FRAMEWORK 47 S. Lakshmivarahan and John M. Lewis

ASSIMILATION OF CHLOROPHYLL DATA INTO FOAM-HADOCC, A COUPLED OCEAN 48 PHYSICAL AND BIOLOGICAL MODEL Daniel Lea, Rosa Barciela, Karen Edwards, David Ford, Matthew Martin

MODEL AND OBSERVATION BIAS CORRECTION IN ALTIMETER OCEAN DATA 48 ASSIMILATION IN FOAM Daniel Lea (Oral), Matthew Martin, Keith Haines

ASSIMILATION OF AMSR-E IN THE ACCESS LIMITED AREA NWP MODEL 48 Jin Lee, Peter Steinle, Clara Draper

SATELLITE DATA ASSIMILATION 49 John Le Marshall (Invited), James Jung, Lars-Peter Riishojgaard ,Stephen Lord, John Derber, Rolf Seecamp

OZONE AND UV INDEX FORECAST 49 Lilia Lemus-Deschamps, Mohar Chattopadhyay, Xiao Yi, Peter Steinle, Asri Sulaiman, Tan Le

vii Page No.

DEVELOPMENT OF DATA ASSIMILATION FOR 1.5KM NWP NOWCASTING SYSTEM 50 Zhihong Li, Susan P Ballard, David Simonin, Cristina Charlton-Perez, Nicolas Gaussiat, Helen Buttery, Graeme Kelly, Robert Tubbs, Catherine Gaffard, Owen Cox, Mark Dixon, Humphrey Lean, Peter Clark

A COMMON SOFTWARE FOR NONLINEAR AND NON-GAUSSIAN LAND DATA 50 ASSIMILATION Xin Li, Liangxu Wang, Xujun Han

APPLICATION OF THE MULTI-GRID METHOD TO THE 2-DIMENSIONAL DOPPLER RADAR 50 RADIAL VELOCITY DATA ASSIMILATION Wei Li, Yuanfu Xie, Shiow-Ming Deng

ASSIMILATION OF SEVIRI SATELLITE RADIANCES IN HIRLAM 4D-VAR 51 M. Stengel, M. Lindskog, P. Undén, P. Dahlgren, N. Gustafsson

AN ENSEMBLE-BASED FOUR DIMENSIONAL VARIATIONAL DATA ASSIMILATION SCHEME 51 Chengsi Liu, Qingnong Xiao, and Bin Wang

USE AND IMPACT OF COSMIC/GPS RADIO OCCULTATION DATA IN GRAPES GLOBAL 52 DATA ASSIMILATION SYSTEM Yan Liu, Jishan Xue

SIMULATIONS OF REMOTELY-SENSED SURFACE SOIL MOISTURE ASSIMILATIONS FOR 52 FUTURE EARTH OBSERVATION MISSIONS Homero F. Lozza

TRADE-OFFS BETWEEN MEASUREMENT ACCURACY AND RESOLUTIONS IN 53 CONFIGURING PHASED-ARRAY RADAR VELOCITY SCANS FOR ENSEMBLE-BASED STORM-SCALE DATA ASSIMILATION Huijuan Lu, Qin Xu

A COMBINED FILTERING AND ERROR PREDICTION PROCEDURE FOR DATA ASSIMILATION 53 IN HYDROLOGICAL AND HYDRODYNAMIC OFRECASTING SYSTEMS Henrik Madsen and Jacob Tornfeldt Sorensen

RECENT DEVELOPMENTS IN LAND DATA ASSIMILATION FOR NUMERICAL WEATHER 54 PREDICTION Jean-François Mahfouf, Gianpaolo Balsamo

HOW IMPORTANT IS TO USE DIAGNOSED BACKGROUND ERROR COVARIANCES FOR THE 54 ATMOSPHERIC OZONE ANALYSIS? Sebastien Massart, Andrea Piacentini And Olivier Pannekoucke

ENSEMBLE KALMAN FILTERING FOR ASSIMILATION OF UPPER ATMOSPHERIC 54 OBSERVATIONS Tomoko Matsuo, Jeffrey L. Anderson

IMPROVING THE PREDICTION OF INFLOWS TO LAKE TAUPO 55 Deborah Maxwell, Bethanna Jackson, James McGregor

CHALLENGES FOR LAND DATA ASSIMILATION 55 Prof. Dara Entekhabi, Prof. Dennis McLaughlin

CONVERGENCE AND STABILITY OF ESTIMATED ERROR VARIANCES DERIVED FROM 55 ASSIMILATION RESIDUALS IN OBSERVATION SPACE Richard Ménard and Yan Yan

DATA ASSIMILATION EXPERIMENTS WITH L1-NORM AND RELATED 56 LAPLACE DISTRIBUTED ERRORS Richard Ménard

viii Page No.

INHOMOGENEOUS BACKGROUND ERROR MODELING AND ESTIMATION OVER 56 ANTARCTICA Yann Michel

REPRESENTATION OF CORRELATION FUNCTIONS USING A ONE-DIMENSIONAL IMPLICIT 56 DIFFUSION EQUATION, WITH APPLICATION TO VARIATIONAL OCEAN DATA ASSIMILATION Isabelle Mirouze, Anthony Weaver

A MODIFIED KALMAN FILTER FOR VARIANCE CONSTRAINT 57 Lewis Mitchell, Georg Gottwald, Sebastian Reich

PERFORMANCE OF A LOCAL ENSEMBLE TRANSFORM KALMAN FILTER DATA 57 ASSIMILATION SYSTEM FOR THE ANALYSIS OF THE ATMOSPHERIC CIRCULATION AND THE DISTRIBUTION OF LONG-LIVED TRACERS Kazuyuki Miyazaki

ESTIMATION OF OBSERVATION ERROR CORRELATION AND THE TREATMENT IN 58 ENSEMBLE KALMAN FILTER Takemasa Miyoshi (Oral), Eugenia Kalnay, and Hong Li

ENSEMBLE DATA ASSIMILATION FOR IDEALIZED CALIFORNIA CURRENT SYSTEM WITH 58 ROMS-LETKF Takemasa Miyoshi, Kayo Ide, Jim Mcwilliams, Gene Li, Yusuke Uchiyama, Eugenia Kalnay

DATA ASSIMILATION EXPERIMENTS FOR AMMA, USING RADIOSONDE OBSERVATIONS 59 AND SATELLITE OBSERVATIONS OVER LAND F. Rabier, C. Faccani, N. Fourrié, F. Karbou, J-P. Lafore, P. Moll, M. Nuret, J-L. Redelsperger

USE OF HETEROGENEOUS BACKGROUND ERROR COVARIANCE MATRICES AT 59 MESOSCALE Thibaut Montmerle and Loïk Berre

THE REGIONAL OCEAN MODELING SYSTEM (ROMS) 4D-VAR ASSIMILATION SYSTEMS 60 APPLIED TO THE CALIFORNIA CURRENT SYSTEM Andrew Moore (Oral), Hernan Arango, Gregoire Broquet, Chris Edwards, Brian Powell, Milena Veneziani And Javier Zavala-Garay

PROPAGATION OF THE IMPACT SIGNAL OF THE ADDITIONALLY-ASSIMILATED 60 OBSERVATIONS OVER THE INDIAN OCEAN THROUGH TROPICAL WAVES Qoosaku Moteki, Kunio Yoneyama, Ryuichi Shirooka, Masaki Katsumata, Masanori Yoshizaki, Takeshi Enomoto, Takemasa Miyoshi, Shozo Yamane

MERGING PARTICLE FILTER FOR HIGH-DIMENSIONAL NONLINEAR PROBLEMS 60 Shin’ya. Nakano, Genta Ueno,, and Tomoyuki Higuchi

COMPARATIVE STUDY FOR THE ENVIRONMENTAL WATER QUALITY ASSESSMENT IN 61 TIRUCHIRAPPALLI, INDIA Natarajan Venkat Kumar, Subbarayan Saravanan , Subbarayan Sathiyamurthi

SEIK - THE UNKNOWN ENSEMBLE KALMAN FILTER 61 Lars Nerger, Wolfgang Hiller, Jens Schröter

SOME NEW APPLICATIONS OF OBSERVING SYSTEM SIMULATION EXPERIMENTS 62 Yulia Nezlin, Yves Rochon, Matt Reszka and Saroja Polavarapu

CAUSES OF ENKF DIVERGENCE WITH ATMOSPHERIC MODELS 62 Gene-Hua Crystal Ng, Dennis McLaughlin, Dara Entekhabi

A NEW MOIST CONTROL VARIABLE FOR THE MET OFFICE'S VARIATIONAL ASSIMILATION 62 SYSTEM Keith Ngan, N Bruce Ingleby, Richard Renshaw, David R Jackson, Andrew C Lorenc

ix Page No.

CONDITIONING AND PRECONDITIONING OF THE 4-D VARIATIONAL DATA ASSIMILATION 63 PROBLEM S.A. Haben, A.S. Lawless, N.K. Nichols

SIMULTANEOUS ESTIMATION OF LAND SURFACE MODEL STATES AND PARAMETERS 63 USING A CONSTRAINT ENSEMBLE KALMAN FILTER Suping Nie, Jiang Zhu, Yong Luo

EFFECTIVENESS OF DRIFTER DATA ASSIMILATION IN IMPROVING HINDCAST OF MESO- 63 SCALE VARIABILITY IN KUROSHIO EXTENSION REGION Kei Nishina, Yoichi Ishikawa, Toshiyuki Awaji, Kosuke Ito

EFFECTS OF GAIN SPECIFICATION AND COVARIANCE ESTIMATION USING THE SQUARE 64 ROOT, STATISTICAL DYNAMICAL AND ENSEMBLE KALMAN FILTERS Terence J. O’Kane and Jorgen S. Frederiksen

REGIONAL OCEAN APPLICATIONS OF THE ENKF/ENOI 64 Peter R. Oke (Invited)

HINDCAST OF THE CIRCULATION IN THE CHUKCHI AND EAST SIBERIAN SEAS 64 Gleb Panteleev, Dmitri Nechaev, Andrey Proshutinsky, Takashi Kikuchi , Rebecca Woodgate, Jinlun Zhang

OPTIMIZATION OF MOORING OBSERVATIONS IN NORTHERN BERING SEA 65 Gleb Panteleev, Max Yaremchuk, Dmitri Nechaev

VOLUME BALANCE AND MEAN OCEAN DYNAMICAL TOPOGRAPHY IN THE BERING SEA 65 Gleb Panteleev, Phyllis Stabeno, Dmitri Nechaev, Vladimir Luchin, Motoyoshi Ikeda

MODEL-REDUCED 4D-VAR DATA ASSIMILATION IN ECOLOGICAL MODELING 65 Joanna S. Pelc, Ghada El Serafy, Arnold W. Heemink

DATA ASSIMILATION OF THE GLOBAL OCEAN USING THE LOCAL ENSEMBLE 66 TRANSFORM KALMAN FILTER (LETKF) AND THE MODULAR OCEAN MODEL (MOM2) Steve Penny, Eugenia Kalnay, James Carton, Kayo Ide, Brian Hunt

ENSEMBLE-DERIVED BACKGROUND-ERROR COVARIANCES: EVALUATION IN THE 66 OPERATIONAL MET OFFICE NWP SYSTEM Chiara Piccolo

A COMPARISON OF LAND SURFACE MODEL DATA ASSIMILATION APPROACHES TO 66 IMPROVE HEAT FLUX ESTIMATES FOR NUMERICAL WEATHER PREDICTION Robert Pipunic, Jeffrey Walker, Andrew Western

IMPLEMENTATION OF SINGULAR VECTORS TO DETERMINE ADAPTIVE OBSERVATIONS 67 DESIGN PROVIDED THE EFFICIENT REPRESENTATION OF THE ENSEMBLE PREDICTION SYSTEMS Oleg M. Pokrovsky

ASSIMILATION OF LAND SURFACE SITE AND REMOTELY SENSING DATA IN THE 67 ATMOSPHERE-LAND ENERGY EXCHANGE MODEL Oleg M. Pokrovsky

ROBUST CHARACTERIZATION OF MODEL PHYSICS UNCERTAINTY AND IMPLICATIONS 71 FOR ENSEMBLE-BASED PREDICTION Derek J. Posselt (Oral), Tomislava Vukićević

ENSEMBLE BACKGROUND-ERROR VARIANCES: OBJECTIVE FILTERING AND IMPACT 68 STUDIES Laure Raynaud, Loïk Berre, Gérald Desroziers

x Page No.

ASSIMILATING GEOSTATIONARY SATELLITE MTSAT-1R THERMAL DATA TO CONSTRAIN 68 REGIONAL ESTIMATES OF SURFACE WATER AND ENERGY PARAMETERS Luigi J. Renzullo

MODEL REPRESENTATION ERROR ESTIMATION FOR OCEAN DATA ASSIMILATION 69 James G. Richman and Robert N. Miller

DATA ASSIMILATION IN A SOIL-VEGETATION-ATMOSPHERE TRANSFER MODEL USING A 69 FILTERING FRAMEWORK Marc Ridler

AIRS IMPACT ON TROPICAL CYCLONE REPRESENTATION IN A GLOBAL DATA 70 ASSIMILATION AND FORECASTING SYSTEM Oreste Reale, Lars Peter Riishojgaard, Joel Susskind, William Lau, Genia Brin

THE DEVELOPMENT OF HYPERSPECTRAL INFRARED WATER VAPOR RADIANCE 70 ASSIMILATION TECHNIQUES IN THE NCEP GLOBAL FORECAST SYSTEM Jim Jung, Lars Peter Riishojgaard (Oral), John Le Marshall

TOWARDS JOINT DATA ASSIMILATION FOR A COUPLED ATMOSPHERE-OCEAN SYSTEM 70 Harold Ritchie, Faez Bakalian, Keith Thompson, Jean-Marc Bélanger

MOISTUREMAP: A SOIL MOISTURE MONITORING, PREDICTING AND REPORTING SYSTEM 71 FOR SUSTAINABLE LAND AND WATER MANAGEMENT Christoph Rüdiger, Jeffrey Walker, Damian Barrett, Robert Gurney, Yann Kerr, Edward Kim, John Le Marshall

FOUR-DIMENSIONAL OBSERVATION IMPACT ON THE US NAVY’S ATMOSPHERIC 72 ANALYSES AND FORECASTS: PART 2: CHANNEL SELECTION AND REAL-TIME MONITORING Benjamin Ruston, Rolf Langland, Nancy Baker, Steve Swadley and Tim Hogan

ASYNCHRONOUS DATA ASSIMILATION WITH THE ENKF 72 Pavel Sakov, Geir Evensen, Laurent Bertino, Francois Counillon

ON TWO COMMON LOCALISATION METHODS IN ENKF 73 Pavel Sakov, Laurent Bertino

IMPACT ASSESSMENT OF DOPPLER RADAR RADIAL WIND OBSERVATIONS 73 Kirsti Salonen, Reima Eresmaa, and Heikki Järvinen

UPGRADE OF THE OPERATIONAL MESOSCALE 4D-VAR AT THE JAPAN 73 METEOROLOGICAL AGENCY Yuki Honda, Ken Sawada (Oral)

AEROSOL DATA ASSIMILATION WITH AN ENSEMBLE KALMAN FILTER USING CALIPSO 74 AND GROUND-BASED LIDAR OBSERVATIONS Tsuyoshi Thomas Sekiyama, Taichu Y. Tanaka, Atsushi Shimizu, Takemasa Miyoshi

LOCAL ENSEMBLE TRANSFORM KALMAN FILTER FOR SEMI-LAGRANGIAN BAROTROPIC 74 MODEL OF ATMOSPHERE Anna V. Shlyaeva, Mikhail A. Tolstykh

IMPLEMENTATION AND IMPACT OF SCATTEROMETER AND AMV DATA ASSIMILATION 75 WITH THE ACCESS CODE Sims, Holly, Peter Steinle, Chris Tingwell, John Le Marshall Yi Xiao, Tan Le

THE 1/12º GLOBAL HYCOM NOWCAST/FORECAST SYSTEM 101 Ole Martin Smedstad, J.A. Cummings, E.J. Metzger, H.E. Hurlburt, A.J. Wallcraft, D.S. Franklin, J.F. Shriver, P.G. Thoppil

xi Page No.

PREDICTING SOURCES AND SINKS OF BIO-OPTICAL TRACERS WITH A 4DVAR OCEAN 75 ASSIMILATION SYSTEM Scott R Smith and Igor Shulman

CYCLING THE REPRESENTER METHOD WITH NONLINEAR MODELS 76 Hans E Ngodock, Scott R. Smith and Gregg A. Jacobs

VARIATIONAL DATA ASSIMILATION USING THE NAVY COASTAL OCEAN MODEL 76 Scott R Smith, Hans E. Ngodock, Matthew J. Carrier, Max Yaremchuk

NON-GAUSSIAN AND NONLINEAR DATA ASSIMILATION 77 Chris Snyder (Invited)

REGIONAL MODELING OFF THE BRAZILIAN EASTERN COAST: PRELIMINARY RESULTS OF 77 A OPERATIONAL SYSTEM AIMED ON OCEANIC FORECAST Igor Monteiro, Giovanni Ruggiero, Rafael Piovesan, Hugo Bastos, Ivan Soares, Mauro Cirano, Edmo Campos, Afonso Paiva, Clemente Tanajura, Renato Martins, José Antonio Lima

AN ADAPTIVE APPROACH TO MITIGATE BACKGROUND COVARIANCE LIMITATIONS IN THE 78 ENSEMBLE KALMAN FILTER H. Song, I. Hoteit, B. D. Cornuelle and A. C. Subramanian

GLOBAL OCEANOGRAPHIC VARIATIONAL DATA ASSIMILATION OF IN-SITU 78 OBSERVATIONS AND SPACE-BORNE ALTIMETER DATA FOR REANALYSIS APPLICATIONS Andrea Storto, Srdjan Dobricic, Simona Masina and Pierluigi Di Pietro

ANN BASED DROUGHT FORECASTING FOR CHITTAR RIVER BASIN INDIA – A CASE STUDY 78 Subbarayan Saravanan, Natarajan Venkat Kumar, Subbarayan Sathiyamurthi

IMPLEMENTATION OF THE NONLINEAR FILTERING PROBLEM AND BALANCED DYNAMICS 79 Aneesh Subramanian, Ibrahim Hoteit, Lisa Neef

FUTURE CHANGES IN THE LEEUWIN CURRENT TRANSPORT INFERRED FROM 79 STATISTICAL AND DYNAMICAL DOWNSCALING Chaojiao Sun, Ming Feng, Richard Matear, and Matthew Chamberlain

USE OF LATITUDE DEPENDENT COVARIANCE FOR AUSTRALIAN REGIONAL MODEL DATA 80 ASSIMILATION Xudong Sun, Peter Steinle

SNOW DATA ASSIMILATION FOR WATER BUDGET IN SIBERIAN LENA RIVER BASIN 80 Kazuyoshi Suzuki, Glen E. Liston, Yoshiyuki Fujii, Taikan Oki, Tetsuzo Yasunari

IMPACT OF THE IN-SITU CTD DATA FOR THE ASSIMILATED ESTIMATES IN THE JAPAN SEA 80 Katsumi Takayama, Naoki Hirose, and Tatsuro Watanabe

MODELLING NON-GAUSSIANITY OF BACKGROUND AND OBSERVATIONAL ERRORS BY 81 THE MAXIMUM ENTROPY METHOD Carlos Alberto Pires, Olivier Talagrand and Marc Bocquet

ON THE EXISTENCE OF AN OPTIMAL SUBSPACE DIMENSION FOR 4DVAR 81 Anna Trevisan, Massimo D'isidoro and Olivier Talagrand (Oral)

PREPARING THE ECMWF FORECAST SYSTEM FOR ADM-AEOLUS DOPPLER WIND LIDAR 82 DATA David Tan, Lars Isaksen, Jos De Kloe, Gert-Jan Marseille, Ad Stoffelen, Alain Dabas, Charles Desportes, Christophe Payan, Paul Poli, Dorit Huber, Oliver Reitebuch, Pierre Flamant, Olivier Le Rille, Herbert Nett And Anne-Grete Straume

xii Page No.

ASSIMILATION OF SEA SURFACE TEMPERATURE AND SEA ICE DATA IN THE BIO OCEAN 82 FORECASTING SYSTEM Charles Tang, Yongsheng Wu and Ewa Dunlap

ENSEMBLE DATA ASSIMILATION FOR OZONE FORECAST: UNCERTAINTY IDENTIFICATION 83 AND CONSTRAINT Xiao Tang, Jiang Zhu, Zifa Wang

A COMPARISON OF VARIATIONAL AND ENSEMBLE-BASED DATA ASSIMILATION SYSTEMS 83 FOR REANALYSIS OF SPARSE OBSERVATIONS Jeffrey s. Whitaker, Gilbert P. Compo and Jean-Noël Thépaut (Oral)

FORECASTING MESOSCALE VARIABILITY OF THE NORTH ATLANTIC USING A 83 PHYSICALLY MOTIVATED SCHEME FOR ASSIMILATING ALTIMETER AND ARGO OBSERVATIONS Keith R. Thompson (Oral) and Yimin Liu

REGIONAL AND AUSTRALIAN DATA ASSIMILATION AND NUMERICAL WEATHER 84 PREDICTION IN ACCESS Chris Tingwell

AN APPROACH TO ASSESS OBSERVATION IMPACT BASED ON OBSERVATION-MINUS- 84 FORECAST RESIDUALS RICARDO TODLING

THE GMAO 4DVAR SYSTEM: PRELIMINARY RESULTS 85 Ricardo Todling, Yannick Trémolet

ENSEMBLE DATA ASSIMILATION WITH THE CNMCA REGIONAL FORECASTING SYSTEM 85 Massimo Bonavita, Lucio Torrisi, Francesca Marcucci

DEVELOPMENTS IN 4D-VAR 86 Yannick Trémolet (Invited)

BLENDVAR - A NEW ANALYSIS SCHEME FOR LIMITED ARE MODEL ALADIN/CE 86 Alena Trojakova, Maria Derkova

MODEL-DATA FUSION FOR STATE AND PARAMETER ESTIMATION IN CONTINENTAL- 86 SCALE HYDROLOGICAL MODELLING Cathy Trudinger, Michael Raupach, Peter Briggs, Vanessa Haverd, Edward King, Matt Paget

SPATIAL SATELLITE OBSERVATION-ERROR STATISTICS FOR AMSU-A DATA: ESTIMATION 87 AND IMPLICATIONS FOR DATA ASSIMILATION Vadim Gorin, Mikhail Tsyrulnikov (Oral)

COVARIANCE REGULARIZATION IN INVERSE SPACE 87 Genta Ueno, Takashi Tsuchiya

IMPROVING STRATEGIES WITH CONSTRAINTS REGARDING NON-GAUSSIAN STATISTICS 88 IN MOVE/MRI.COM Norihisa Usui, Shiro Ishizaki, Yosuke Fujii, And Masafumi Kamachi

THE ROLE OF DATA ASSIMILATION IN LARGE-SCALE HYDROLOGICAL MODELLING TO 88 SUPPORT WATER RESOURCES ASSESSMENT IN AUSTRALIA Albert I.J.M. Van Dijk, Luigi J. Renzullo

PARTICLE FILTERING: BEATING THE CURSE OF DIMENSIONALITY 89 Peter Jan Van Leeuwen (Oral)

MULTI-MODEL DATA ASSIMILATION AKA SUPER-ENSEMBLES 89 Luc Vandenbulcke, Fabian Lenartz, Michel Rixen

xiii Page No.

ITERATIVE KALMAN FILTERING 90 Martin Verlaan

EFFICIENT PARAMETERIZATION OF THE OBSERVATION ERROR COVARIANCE MATRIX 90 FOR SQUARE ROOT OR ENSEMBLE KALMAN FILTERS: APPLICATION TO OCEAN ALTIMETRY Jean-Michel Brankart, Clément Ubelmann, Charles-Emmanuel Testut, Emmanuel Cosme, Pierre Brasseur And Jacques Verron

LINKING ALTIMETRY AND OCEAN COLOR: A DATA ASSIMILATION APPROACH USING 91 LYAPUNOV EXPONENTS Jacques Verron, Jean-Michel Brankart, Emmanuel Cosme, Pierre Brasseur and Olivier Titaud

DIRECT ASSIMILATION OF IMAGE SEQUENCES IN NUMERICAL MODELS 91 Arthur Vidard (Oral), Olivier Titaud, Innocent Souopgui, François-Xavier Le Dimet

OBSERVABILITY OF A LARGE CONTROL VECTOR IN A 4D-VAR OCEAN DATA 92 ASSIMILATION Tsuyoshi Wakamatsu, Michael G. G. Foreman,

ON THE INFUENCE OF RANDOM WIND STRESS ERRORS ON THE FOUR-DIMENSIONAL, 92 MIDLATITUDE OCEAN INVERSE PROBLEM Tsuyoshi Wakamatsu, Michael G. G. Foreman, Patrick F. Cummins, Josef Y. Cherniawsky

RECENT ADVANCES IN VARIATIONAL ASSIMILATION FOR GLOBAL OCEAN APPLICATIONS 92 Anthony Weaver (Invited) Kristian Mogensen, Magdalena A. Balmaseda, Matthew Martin and Arthur Vidard

DEVELOPMENTS IN ENSEMBLE DATA ASSIMILATION 93 Jeffrey S. Whitaker (Invited)

ESTIMATION OF FRICTION PARAMETERS AND LAWS IN OCEANIC GRAVITY CURRENTS 93 Achim Wirth, Jacques Verron

FEATURE-BASED ENSEMBLE ESTIMATION FOR RAINFALL APPLICATIONS 93 Rafal Wojcik, Dennis McLaughlin

COMPARISONS OF SOME ENSEMBLE OPTIMAL INTERPOLATION SCHEMES FOR 94 ASSIMILATING ARGO PROFILES INTO A HYBRID COORDINATE OCEAN MODEL Jiping Xie and Jiang Zhu

A SEQUENTIAL HYBRID 4DVAR SYSTEM IMPLEMENTED USING A MULTIGRID TECHNIQUE 94  SPACE AND TIME MULTISCALE ANALYSIS SYSTEM Yuanfu Xie, Steven E. Koch and Steven C Albers,

A DUAL-PASS VARIATIONAL DATA ASSIMILATION FRAMEWORK FOR ESTIMATING SOIL 95 MOISTURE PROFILES FROM AMSR-E MICROWAVE BRIGHTNESS TEMPERATURE Zhenghui Xie, Xiangjun Tian,Aiguo Dai, Chunxiang Shi, Binghao Jia, Feng Chen

FOUR-DIMENSIONAL OBSERVATION IMPACT ON THE US NAVY’S ATMOSPHERIC 95 ANALYSES AND FORECASTS: SYSTEM DEVELOPMENT AND TEST Liang Xu, Rolf Langland, Nancy Baker, Tom Rosmond, Boon Chua

RADAR DATA ASSIMILATION IN GRAPES 95 Jishan Xue (Oral) and Hongya Liu

ENSEMBLE AND VARIATIONAL RADAR DATA ASSIMILATION FOR CONVECTIVE STORM 96 AND HURRICANE PREDICTIONS Ming Xue (Oral)

xiv Page No.

DATA ASSIMILATION IN INDIAN AND WESTERN PACIFIC OCEAN 96 Changxiang Yan and Jiang Zhu

RUNNING IN PLACE METHOD WITH LOCAL ENSEMBLE TRANSFORM KALMAN FILTER FOR 97 TYPHOON ASSIMILATION AND PREDICTION Shu-Chih Yang, Eugenia Kalnay

A METHOD OF SUCCESSIVE CORRECTIONS OF THE CONTROL SUBSPACE IN THE 97 REDUCED-ORDER 4DVAR DATA ASSIMILATION Max Yaremchuk, Dmitri Nechaev, Gleb Panteleev

AN ENSEMBLE OCEAN DATA ASSIMILATION SYSTEM FOR SEASONAL PREDICTION 97 Yonghong Yin, Oscar Alves, Peter Oke, Faina Tseitkin

THE ADEQUACY OF EXISTING OBSERVING SYSTEMS MONITORING AMOC AND THE 102 NORTH ATLANTIC CLIMATE S. Zhang, A. Rosati and T. Delworth

A MULTIVARIATE EMPIRICAL ORTHOGONAL FUNCTION BASED SCHEME FOR BALANCED 98 INITIAL ENSEMBLE GENERATION OF AN ENSEMBLE KALMAN FILTERING Fei Zheng And Jiang Zhu

APPLICATION OF ENKF TO ENSO ENSEMBLE PREDICTION WITH AN INTERMEDIATE 98 COUPLED MODEL Fei Zheng, Jiang Zhu, And Rong-Hua Zhang

AN ESTIMATION OF FORECAST ERROR COVARIANCE MATRIX USING MULTIVARIATE 99 INFLATION FOR KALMAN FILTERING DATA ASSIMILATION Xiaogu Zheng

DESIGN OF THE GRAPES ENSEMBLE KALMAN FILTER DATA ASSIMILATION SYSTEM AND 99 ITS TENTATIVE EXPERIMENT Zhaorong Zhuang, Jishan Xue

CLOUD-RESOLVING ENSEMBLE DATA ASSIMILATION 99 Milija Zupanski (Oral), Dusanka Zupanski

xv

ABSTRACTS

Listed alphabetically (presenting author in bold)

REFINEMENT OF SIMULATIONS OF DEEP-WATER TSUNAMI PROPAGATION THROUGH THE USE OF OBSERVATIONS

Stewart ALLEN1 ([email protected]), Diana GREENSLADE1 1Centre for Australian Weather and Climate Research, Bureau of Meteorology

A major component of the Australian Tsunami Warning System (ATWS) is the deployment of a network of tsunameters in the Australian region. These sensors aid not only in the detection and measurement of tsunamis, but can also be used to refine forecasts of tsunami propagation.

This presentation will outline our current research into using tsunameter observations to improve simulations in the ATWS T1.1 tsunami scenario database. This includes:

• Processing of observations into an appropriate form; • Different methods of incorporation into simulations; • Techniques by which any improvements can be measured and verified.

The method will be demonstrated using recent case studies for which tsunameter observations exist. Lastly, issues and avenues of further research will be addressed.

Although this method is initially being designed for use with the T1.1 scenario database, it could also be applied to real-time numerical tsunami simulations following expected increases in computational power and other scientific advances.

A COMPARISON OF THE REPRESENTATION OF THE MAIN MODES OF OCEAN CLIMATE VARIABILITY BY TWO STATE-OF-THE-ART OCEAN RE-ANALYSES

O. ALVES11, Y.YIN1, M.BALMASEDA2 and P. OKE1 Centre for Australian Weather and Climate Research1, ECMWF2

Two state-of-the-art ocean re-analysis that have been carried for dynamical seasonal prediction are compared. The two analysis systems are the ECMWF system-3 ocean data assimilation system and the Australian PEODAS ocean data assimilation system. This study inter-compares a 25 year re-analysis produced using each of these system.

A particular focus of this study is how well the re-analyses represent the main modes of climate variability, including differences in the representation of these modes. The main modes studied are the ocean components of: ENSO, ENSO-Modoki, Indian Ocean dipole and the Madden Julian Oscillation.

Composites of the simulation of the modes of variability for each re-analysis are compared with each other and with a simulation using an ocean model run with no assimilation. The evolution of temperature, salinity and ocean current anomalies associated with each mode in each re-analysis is described, including documentation of major discrepancies.

1

MODELLING EQUATORIAL PACIFIC SALINITY FIELDS USING PEODAS

O. ALVES1, R. WEDD1, Y. YIN1, P. OKE2 Bureau of Meteorolgy1, CSIRO Marine Research2

The increase of observational salinity data in recent years, driven by the Argo floats, has shown a greater variability in equatorial Pacific near-surface salinity fields than was previously thought. The ability of salinity stratification to influence currents and temperature in the region through a reduction of mixed layer-cold water mixing has led to great interest in the potential for salinity data assimilation to improve ENSO modelling.

The POAMA Ensemble Ocean Data Assimilation System (PEODAS) is an extension to the BLUElink Ocean Data Assimilation System (BODAS) that has been optimised for use with the Predictive Ocean Atmosphere Model for Australia (POAMA). PEODAS assimilates salinity and temperature profiles from CTD, XBT and Argo observations using an ensemble-based multivariate optimal interpolation method. Covariances are calculated from the spread of a time-evolving ensemble of POAMA runs with perturbed forcing fields.

The effect of salinity data assimilation on the salinity fields of the equatorial Pacific as modelled by POAMA is presented. Three simulations are compared: simulation with temperature and salinity data assimilation; simulation without salinity data assimilation but with salinity profiles updated from temperature data assimilation according to a T-S relationship; and simulation with no salinity assimilation and no enforced T-S relationship.

CAN OCEAN DATA ASSIMILATION IMPROVE TROPICAL CYCLONE FORECASTS?

Isabel ANDREU-BURILLO1, Gary BRASSINGTON, Peter OKE2, Paul SANDERY, Justin FREEMAN, Helen BEGGS

The BLUElink Ocean Data Assimilation System (BODAS) currently produces analyses within the operational Ocean Model Analysis and Prediction System (OceanMAPS) and the BLUElink ReANalysis (BRAN) multi- year run. A regional version of BODAS has been interfaced to the re-locatable Coupled Limited Area Model (CLAM). CLAM is presently based on a regional version of MOM4, OASIS and an operational limited area atmospheric prediction system for tropical cyclones. The regional ocean model can be nested either to the BLUElink reanalysis, for historical cases, or OceanMAPS, for real-time cases. Focusing on short-range coupled weather forecasting, its main applications are severe weather systems such as tropical cyclones and oceanic mesoscale features as for example the East Australian current. In this work we examine the impact of assimilation of remotely sensed sea level data, in situ observations and satellite SST products on the representation of sea surface temperature, heat content and vertical stratification over the upper ocean layers to a depth constrained by the observing system, which will approximately coincide with the layers relevant to tropical cyclone coupling. In particular, two complementary SST products are assessed in terms of their information content and of their impact on the analysis results, both through innovation values and through comparison with respect to independent observations. This framework will let us explore the processes and scales relevant to tropical cyclones and eventually determine the role of oceanic features in their life cycle.

1CAWCR/BoM (Centre for Australian Weather and Climate Research/Bureau of Meteorology) 2 CAWCR/CSIRO (Centre for Australian Weather and Climate Research/Commonwealth Scientific and Industrial Research Organisation)

NEIGHBORING ENSEMBLE VARIATIONAL ASSIMILATION METHOD TO INCORPORATE MICROWAVE RADIOMETER DATA INTO A CLOUD-RESOLVING MODEL

Kazumasa AONASHI ([email protected]) and Hisaki EITO Organization/ Affiliation: Forecast Research Department Meteorological Research Institute/ JMA

Since Microwave Radiometer (MWR) brightness temperatures (TBs) are sensitive to hydrometers, assimilation of TBs expected to improve the Cloud-Resolving Model (CRM) forecasts. The goal of the present study is to develop a data assimilation system that incorporates the MWR TBs into the CRM developed by Japan Meteorological Agency (JMANHM).

2 First, we estimated the CRM forecast error using Ensemble forecasts with initial perturbations. The results show distinct differences in the forecast error correlation between rainy and rain-free areas. Since we often found large-scale positional errors of rainy areas between the observation and the CRM forecasts, this suggests that use of Ensemble forecast error covariance is not appropriate for data assimilation in these areas.

In order to alleviate this problem, we developed the Ensemble-based assimilation method that used Ensemble forecast error covariance at neighboring points. This method consisted of the selection scheme of the neighboring points and the Ensemble-based variational assimilation scheme. In the selection scheme, we obtained the neighboring points that maximized the product of probability of positional error and the conditional probability of TB observation given by the CRM Ensemble mean. In the assimilation scheme, we assumed that the forecast error covariance can be expressed by Schur product of the Ensemble forecast error covariance and a prescribed correlation functions.

We executed the above method to assimilate the AMSRE TBs for an extra-tropical low case (27th Jan. 2003). The preliminary results show that the selection scheme reduced the large-scale positional error of the cold front and mesoscale precipitation features accompanied with the low.

PRELIMINARY RESULTS OF ASSIMILATION AIRS RADIANCES WITH LOCAL ENSEMBLE TRANSFORM KALMAN FILTER FOR THE CPTEC/INPE GLOBAL MODEL

José Antonio ARAVÉQUIA ([email protected]), Solange Solange Silva de SOUZA, José Paulo BONATTI, Dirceu Luiz HERDIES and Paulo KUBOTA Centro de Previsão de Tempo e Estudos Climáticos - CPTEC, Instituto Nacional de Estudos Espaciais - INPE.

The CPTEC/INPE has operational weather forecast using global circulation model four times a day. Today the CPTEC/INPE's data assimilation system is based on the PSAS algorithm. Looking for its next generation data assimilation system CPTEC/INPE is testing an implementation of The Local Ensemble Transform Kalman Filter (LETKF) developed by the Chaos Group at Department of Atmospheric and Ocean Science of the University of Maryland. The LETKF consist of an 4D implementation of LETKF that assimilates all conventional data and satellite data. It uses the Community Radiative Transfer Model (CRTM) forward model as Observation Operator to be able to assimilate satellite radiances such as AIRS and AMSU-A from the Earth Observing System (Aqua-Terra satellites). In this work we present results of an experiment using real data to the period of January and February 2004, using a lower resolution of the operational model and a small number of channels in the infrared region of the AIRS data.

The first results were taken to test the implementation and have shown to be able to ingest the available data and showed subjective reasonable results.

ASSIMILATING RETRIEVALS OF CHEMICAL CONSTITUENTS IN CAM-CHEM AND WRF-CHEM USING AN ENSEMBLE ADJUSTMENT KALMAN FILTER APPROACH

Avelino F. ARELLANO ([email protected]) and David P. Edwards National Center for Atmospheric Research (NCAR)

The availability of satellite-derived measurements of chemical constituents presents an opportunity to enhance our understanding of atmospheric composition and the processes controlling their distribution. Here, we present assimilation studies of carbon monoxide (CO) as observed from several satellite instruments (e.g. MOPITT, TES, IASI), using an ensemble-based regional-to-global chemical data assimilation (DA) system being developed at NCAR. As a product of incomplete combustion and with a relatively long lifetime of about 1 to 2 months, CO is used in atmospheric chemistry as a tracer of pollution sources and transport, including convection, stratosphere-troposphere exchange and long-range transport. Since its spatio-temporal variability is mainly attributed to transport and emissions, the assimilation of CO measurements into chemical transport models (CTM) facilitates the diagnosis on how well transport and emissions are represented in predictive models. Recent advances in DA techniques provide an additional capability to explore the potential synergies between the state of CO and other atmospheric states, including those that describe transport processes. In this work, we explore this type of synergy by investigating the statistical impact of meteorological observations to CO state, as well as, the statistical impact of CO observations to horizontal wind and other transport variables. We apply a DA system comprising of a community DA software facility called DART (Data Assimilation Research Testbed), a general circulation model with chemistry called CAM-Chem (Community Atmosphere Model), and a regional model with

3 chemistry, called WRF-Chem (Weather Research and Forecasting Model). We assimilate meteorological and/or CO observations in DART/CAM-Chem and also in DART/WRF-Chem using boundary conditions from DART/CAM-Chem analyses. Such a system resembles that of a next-generation regional-to-global numerical weather prediction system with chemistry. Using a 40-80 member ensemble, our initial results show that there are additional constraints on key atmospheric states with the assimilation of these observations.

DEVELOPMENT OF A CLOUD ANALYSIS SYSTEM

Thomas AULIGNE et al. ([email protected]) National Center for Atmospheric Research

The National Center for Atmospheric Research (NCAR) is involved in a new ambitious project to fully characterize the environment and produce a more representative analysis and forecast of the three dimensional cloudy atmosphere under a concept entitled the Atmosphere and Cloud Analysis and Prediction System (ACAPS).

The primary objectives of this development effort are to improve current numerical weather analysis and prediction systems in order to (1) fully exploit the vast amount of satellite data available in the National Polar- orbiting Operational Environmental Satellite System (NPOESS) era; and (2) more accurately analyze and predict cloud-cover conditions.

This presentation will summarize the conclusions and recommendations from reviews and discussions from a panel of experts on cloud analysis. Various topics linked with the assimilation of cloud hydrometeors will be discussed, including (1) advanced high-resolution data assimilation techniques, (2) adjoint coding for cloud processes, (3) current cloud assimilation systems, (4) cloud-prediction capabilities, (5) current and future cloud and precipitation observing systems, (6) the choice of cloud observables, (7) the choice of cloud control variable.

THE “BACK AND FORTH NUDGING” ALGORITHM FOR OCEANOGRAPHIC DATA ASSIMILATION

Didier AUROUX1 ([email protected]) and Jacques BLUM2 1 University of Toulouse, France 2 University of Nice, France

We generalize the so-called “nudging” algorithm in order to identify the initial condition of a dynamical system from experimental observations. The standard nudging algorithm consists in adding to the state equations of a dynamical system a feedback term, which is proportional to the difference between the observation and its equivalent quantity computed by the resolution of the state equations.

The Back and Forth Nudging algorithm consists in solving first the forward nudging equation and then a backward equation, where the feedback term which is added to the state equations has an opposite sign to the one introduced in the forward equation. The initial state of this backward resolution is the final state obtained by the standard nudging method. After resolution of this backward equation, one obtains an estimate of the initial state of the system. We iteratively repeat these forward and backward resolutions (with the feedback terms) until convergence of the algorithm.

This algorithm has been compared to the 4D-VAR algorithm, which also consists in a sequence of forward and backward resolutions. In our algorithm, it is useless to linearize the system and the backward system is not the adjoint equation but the model equation, with an extra feedback term that stabilizes the ill-posed backward resolution.

We have proved on several idealized situations that, provided that the feedback term is large enough as well as the assimilation period, we have convergence of the algorithm to the real initial state.

Numerical results have been performed on Burgers, shallow-water and layered quasi-geostrophic models. Twice less iterations than the 4D-VAR are necessary to obtain the same level of convergence. This algorithm is hence very promising in order to obtain a correct trajectory, with a smaller number of iterations than in a variational method, with a very easy implementation.

4 RECENT PROGRESS IN HYBRID 4D-VARIATIONAL/ENSEMBLE DATA ASSIMILATION

Dale BARKER1 ([email protected]), Adam CLAYTON1, Andrew LORENC1, and Neill BOWLER1 1 Met. Office, Fitzroy Road, Exeter, EX1 3PB, UK

The coupling of ensemble prediction and data assimilation systems via the use of ensemble perturbations to represent flow-dependent forecast error covariances for data assimilation is a topic of significant current research. Hybrid variational/ensemble data assimilation methods attempt to combine the strengths of ensemble-based data assimilation techniques such as the EnKF (i.e. flow-dependence error covariances derived from full nonlinear model integrations) with variational techniques such as 4D-Var (full-rank error covariances, simultaneous treatment of all observations). The presentation will begin with a brief review of the various hybrid variational/ensemble techniques applied to date, followed by results from recent work to define vertical and variable-dependent covariance localization within the WRF model's variational data assimilation scheme. Recent results from efforts to couple the operational 4D-Var data assimilation scheme and the Met. Office Global/Regional Ensemble Prediction System (MOGREPS) via a hybrid 'alpha control variable' approach will be presented. Finally, plans to further develop the hybrid approach and to make increasing use of ensemble forecast information within data assimilation (e.g. via flow-dependent quality control) will be laid out.

REAL-TIME SKIN SEA SURFACE TEMPERATURE ANALYSES FOR QUALITY CONTROL OF DATA ASSIMILATED INTO NWP MODELS

Helen BEGGS(1) ([email protected]), Chelle GENTEMANN(2) and Peter STEINLE(1) (1) Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, Australia (2) Remote Sensing Systems, Santa Rosa, USA

In regions of the ocean experiencing high insolation and low winds, the skin (~10 micron depth) sea surface temperature (SSTskin) can experience daily variation of up to 6 or more Kelvin. The presence of cloud can also result in large cooling of SSTskin measurements from infrared sensors on satellites. Short-term forecasts of SSTskin would be very useful in order to distinguish observations from satellites corrupted by cloud. A regional, hourly, 1/12° resolution, SSTskin analysis (“RAMSSA_skin”) and global, 3-hourly, 1/4° resolution, SSTskin (“GAMSSA_skin”) have been developed at the Bureau of Meteorology (BoM) as part of the BLUElink> Ocean Forecasting Australia Project. Both skin analyses are formed by adding an estimate of DSST (SSTskin – SSTfnd) at that time to the BoM daily operational RAMSSA SSTfnd or GAMSSA SSTfnd analyses, where SSTfnd is the foundation (or pre-dawn) SST. DSST is calculated from a simple algorithm derived from geostationary satellite (SEVIRI) skin SST and AMSR-E surface wind data. For RAMSSA_skin, the inputs to the DSST algorithm are mean hourly, 10 m winds from the BoM ACCESS-R regional NWP 24 hour forecasts. For GAMSSA_skin, the mean 3-hourly, 10 m winds from the BoM ACCESS-G global NWP 24 hour forecasts are used.

Both RAMSSA_skin and GAMSSA_skin have been validated against the 1 km AATSR SSTskin L2P product available from http://ghrsst.nodc.noaa.gov/. For the period 1-31 January 2009, RAMSSA_skin – AATSR SSTskin = 0.14 ± 0.38ºC. For the same period, GAMSSA_skin – AATSR SSTskin = 0.10 ± 0.38ºC. These are encouragingly low errors and indicate that the simple empirical DSST model in conjunction with ACCESS-R and ACCESS-G forecast winds should be useful in predicting diurnal warming in all but the most extreme cases (> 3°C). Late in 2009, the BoM ACCESS NWP data assimilation team will test RAMSSA_skin and GAMSSA_skin skin SST analyses in the new regional and global NWP analysis systems (ACCESS-R and ACCESS-G) for the quality control of satellite data inputs.

Hourly RAMSSA_skin analyses are available over the domain 65ºE to 185ºE, 15ºN to 65ºS, back to 1 October 2008, in netCDF files downloadable from http://godae.bom.gov.au. Likewise, the 3-hourly, global, GAMSSA_skin analyses are available in the same format back to 1 June 2008.

AEROSOL ANALYSIS AND FORECAST IN THE ECMWF INTEGRATED FORECAST SYSTEM

Angela BENEDETTI ([email protected]), Jean-Jacques MORCRETTE European Centre for Medium-Range Weather Forecasts, Reading, UK

This study presents recent developments in aerosol assimilation at the European Centre for Medium-Range Weather Forecasts (ECMWF) under the Global and regional Earth-system Monitoring using Satellite and in- situ data (GEMS) project. The aerosol modelling and analysis system is fully integrated in the operational four-dimensional assimilation suite. Its purpose is to produce near-real time aerosol forecasts and

5 reanalyses of aerosol fields using optical depth data from satellite sensors. The theoretical architecture and practical implementation of the aerosol assimilation will be described. A detailed discussion of the background errors and observations errors for the aerosol fields will be provided. Observation and analysis biases will also be discussed. Results will be presented from the multi-year reanalysis which has been run from 2003 to 2007 using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua and Terra satellites. Examples will range from biomass burning in West Africa to Saharan dust transport over the Atlantic. Independent datasets will be used to show that the GEMS-ECMWF aerosol analysis produces good estimates of aerosol optical depths. Results from the near-real time aerosol assimilation and forecasting system will also be presented with focus on the California fires of summer 2008.

CONSISTENT OPERATIONAL ENSEMBLE VARIATIONAL ASSIMILATION

Loïk BERRE ([email protected]), Gérald DESROZIERS, Laure RAYNAUD, Rémi MONTROTY, and Olivier PANNEKOUCKE Météo-France, CNRM/GAME

While variational assimilation systems (Var) tend to predominate in large NWP centres, one additional approach is to run an ensemble assimilation system based on Kalman filter techniques (e.g. EnKF or ETKF), in order to estimate flow-dependent background error covariances. In this context, although this is rarely evoked, formal and experimental differences between variational and Kalman approaches raise some issues of possible (un)consistence between the two techniques, in particular in the way of simulating analysis errors, and also in terms of balance constraints for instance. Developing and maintaining two different techniques is also heavier than using the same code and techniques for both deterministic and ensemble parts.

An alternative approach, which has not been much explored so far compared e.g. to EnKF/Var and ETKF/Var hybrid techniques, is to employ a consistent variational approach in both deterministic and ensemble assimilation systems. This is the option which has been chosen for the ensemble variational system at Météo-France, in order to provide flow-dependent covariances to 4D-Var. Due to the consistent use of the variational technique in both deterministic and ensemble systems, this approach has been relatively easy to develop and to maintain. This system is running operationally since July 2008, and it is the first operational system of this kind in the NWP community.

The specificities and originalities of this ensemble variational system will thus be presented and discussed. In particular, formal and experimental evidence of the importance of consistence between the deterministic and ensemble assimilation systems will be illustrated. Moreover, instead of the usual Schur filtering of correlations in EnKFs, optimized local space averaging techniques of both variances and correlations are employed to filter out sampling noise effects in ensemble-based estimates. Finally, the use of objective innovation-based estimates will be illustrated, in order to validate ensemble-based covariances and to estimate model error covariances.

THE TOPAZ ICE-OCEAN DATA ASSIMILATION SYSTEM

Laurent BERTINO ([email protected]), Pavel SAKOV and Francois COUNILLON Nansen Environmental and Remote Sensing Center, Thormøhlensgate 47, Bergen 5006, Norway

The TOPAZ system, developed at the Nansen Center and exploited in the operational suite of the Norwegian Meteorological Institute (met.no) uses an Atlantic and Arctic configuration of the Hybrid Coordinate Ocean Model (HYCOM) at a horizontal resolution between 11 km and 16 km, the 3D ocean model is coupled to a sea-ice model and uses the Ensemble Kalman Filter (EnKF) with 100 dynamical members. The system assimilates satellite altimeter data, SST data, sea-ice concentrations, sea-ice drift and temperature and salinity profiles from the Argo profiling floats. TOPAZ constitutes the Arctic forecasting system of the European My-Ocean project and contributes to the GODAE/OceanView project. The presentation describes the on-going data assimilative reanalysis, the data to be assimilated and the distribution of numerical data. The applications will be illustrated with simulated iceberg trajectories and ecosystem modeling.

6

VARIATIONAL ASSIMILATION WITH A THREE LEVEL ATMOSPHERIC MODEL

Kaustubha BHATTACHARYA

The three-level quasi geostrophic model of Marshall and Molteni(1992) has been used to construct a variational assimilation system. The model is one dimensional, spectral with global domain and pressure as vertical coordinate. ‘Cost function’ is defined to be the distance between synthetic observation and the model state – no background is used. The synthetic observations are obtained from a previous model run with climatological standard deviations acting as proxies for observation errors. Gradient of cost function are calculated using adjoint based method. These gradients are used as input to the iterative minimization procedure described by the m1qn3 package, where the model initial state is the control variable.

The model consists of three equations describing the rate of change of the quasi-geostrophic potential vorticity at the three model levels (200, 500 and 800 hPa). This rate is determined by the balance between a climatologically computed source term and two sink terms, one being a jacobian term pertaining to that level only and the other being a dissipation term. The dissipation can be due to Newtonian relaxation of temperature, linear drag on the wind or the horizontal diffusion of vorticity and temperature. The m1qn3 package (Gilbert and Lemarchal, 1989) uses the L-BFGS-B code. It turned out that between 30 to 50 iterations were needed for convergence.

This simple variational assimilation system can be used for further research in a variety of ways. Most obvious extensions are use of real observations and background field. Another use can be to perform experiments for determining limits for long period variational assimilation which is an important practical question.

DATA ASSIMILATION USING MODULATED ENSEMBLES

Craig H. BISHOP ([email protected]), Daniel HODYSS Naval Research Laboratory, Monterey, CA, USA

A 4-dimensional variational ensemble data assimilation (DA) scheme is introduced that (a) finds analysis corrections using a global variational solve, and (b) uses flow adaptive propagating error covariance localization in order to allow for long-time window DA and variations in error correlation length scales. The scheme is based on a new method for inexpensively generating the square root of an adaptively localized global 4-dimensional error covariance model in terms of products or modulations of smoothed ensemble members with themselves and with raw ensemble members. The columns of the square root of this matrix may be interpreted as members of a modulation ensemble. For a 100 member raw ensemble, the modulation ensemble contains one million members. With the help of a global numerical weather prediction (NWP) model we (a) show that this million member ensemble provides a plausible model of 4-dimensional forecast error covariances (b) show that the initial time error covariance model provided by this covariance model is superior to that used by the operational (3D-VAR) DA scheme of the US Navy, (c) compare the performance of adaptive localization to optimally tuned non-adaptive localization, and (d) assess the quality of the statistical Tangent Linear Model (TLM) implied by the 4D adaptively localized covariances.

HYBRIDIZATION OF THE 4D-VAR WITH A SEEK* SMOOTHER IN VIEW OF OCEANIC APPLICATIONS

Monika KRYSTA1,2, Eric BLAYO2 ([email protected]), Emmanuel COSME1, and Jacques VERRON1 1LEGI, CNRS, Grenoble, France 2University of Grenoble and INRIA, Grenoble, France

The role of the background error covariance matrix in spreading observational information is particularly crucial in oceanography since some of the state variables are often not observed. Feasibility requirements in actual applications often result in an additional constraint requiring that the effective analysis increment is confined to a lowdimensional subspace of control. This study tackles the problem of adaptation of the definition of the subspace of control and the associated reduced rank error covariance matrix in order to account for additional information which is being gradually gained in data assimilation procedure.

The problem has been studied in a framework of a hybrid approach to data assimilation. The skeleton of the hybrid is formed by the 4D-Var enriched with an admixture of an equivalent Kalman smoother. The latter provides a recipe for the evolution of the error covariance matrix which is modified at each transition between successive 4D-Var assimilation windows. In the analysis step it is updated according to the quality of the

7 measurements assimilated into the system. Following system's trajectory, the basis spanning the control subspace is, in turn, adjusted in the forecast step. A series of numerical experiments implementing the hybrid method in a shallow water model has been performed. It has been opted for an initialization of the subspace of control with the empirical orthogonal functions arising from different samples of model trajectory. A number of tests have been performed in various configurations of twin experiments and the circumstances where the hybrid outperforms the standard 4D-Var have been identified. The information content in the initial and final subspaces of control has also been assessed. The general conclusion inferred from this study indicates that propagating the background error covariance matrix may result in compensation of its imperfect initialization.

*Singular Evolutive Extended Kalman smoother

TARGETING OF OBSERVATIONS FOR RADIONUCLIDES ACCIDENTAL RELEASE MONITORING

Rachid ABIDA1,2, Marc BOCQUET1,2 ([email protected]) 1Université Paris-Est, CEREA, Joint laboratory École des Ponts ParisTech and EDF R&D, France; 2INRIA, Paris-Rocquencourt Research Centre, France

In the event of an accidental atmospheric release of radionuclides from a nuclear power plant, accurate real- time forecasting of the activity concentrations of radionuclides, is acutely required by the decision makers for the preparation of adequate countermeasures. Yet, the accuracy of the forecasted plume is highly dependent on the source term estimation. Inverse modelling and data assimilation techniques should help in that respect. However the plume can locally be thin and could avoid a significant part of the radiological network surrounding the plant. Deploying mobile measuring stations following the accident could help to improve the source term estimation.

A method is proposed for the sequential reconstruction of the plume, by coupling a sequential data assimilation algorithm based on inverse modelling with an observation targeting strategy. The performance of the sequential assimilation with and without targeting of observations is assessed in a realistic framework. It focuses on the Bugey nuclear power plant (France) and its surroundings within 50 kilometres of the plant. The existing surveillance network is used and realistic observational errors are assumed. The targeting scheme leads to a better estimation of the source term as well as the activity concentrations in the domain. The mobile stations tend to be deployed along plume contours, where activity concentration gradients are important. It is shown that the information carried by the targeted observations is very significant, as compared to the information content of fixed observations. A simple test on the impact of model error from meteorology shows that the targeting strategy is still very useful in a more uncertain context.

Given the present ratio of targeted observations versus fixed observations, the technique should prove more useful in this context than its known counterpart in meteorological forecast.

CHOOSING THE GEOMETRY OF CONTROL SPACE FOR AN OPTIMAL ASSIMILATION OF OBSERVATIONS

Marc BOCQUET ([email protected]) Université Paris-Est, CEREA, Joint laboratory École des Ponts ParisTech and EDF R&D, France; INRIA, Paris-Rocquencourt Research Centre, France

In geophysical data assimilation the observations are shedding light on a control parameter space, through a model, a statistical prior and an optimal combination of these sources of information. This control space can be a set of discrete parameters, or, more often in geophysics, part of the state space which is distributed in space and time. When the control space is continuous, it must be discretised for numerical modelling. This discretisation, which we call a representation of this distributed parameter space, is almost always fixed a priori. Here, and unlike what is usually assumed in geophysical data assimilation, the representation of the control space is considered a degree of freedom on its own. The goal is to show that one could optimise it to perform data assimilation in optimal conditions. Another objective is to define a general methodological framework for multiscale data assimilation.

A possible mathematical framework is then proposed. The optimal representation is chosen over a large dictionary of adaptive grid representations involving several space and time scales. A measure of the reduction of uncertainty is chosen as a simple optimality criterion. In other words, the representation is chosen so that the use of the information content of observations be optimised.

8 The formalism is then applied to inverse modelling in atmospheric chemistry at continental scale, using synthetic and real data. Incidentally, the method sets the right balance between time and space scales in control space for an optimal assimilation.

FINE-SCALE VERSUS LARGE-SCALE ATMOSPHERIC DATA ASSIMILATION

François BOUTTIER ([email protected]) and collaborators Météo-France/CNRM, CNRS/GAME

How scale specific are the current atmospheric data assimilation systems? A review of the main techniques used nowadays will be presented, with a focus on numerical weather prediction systems. The optimal observation selection, background error model, and data assimilation algorithm depend on physical properties of the underlying model. This link will be illustrated by comparing established global numerical weather prediction systems, with some recent high resolution data assimilation systems. It will be argued that most (but not all) features of modern global systems should be included into higher resolution systems, although convective scale applications raise puzzling new challenges.

OPERATIONAL OCEAN DATA ASSIMILATION FOR THE BLUELINK OCEAN FORECASTING SYSTEM

Gary B. BRASSINGTON1,2 ([email protected]), Tim PUGH1,2, Peter R. OKE1,3, Justin FREEMAN1,2, Xinmei HUANG2 and Graham WARREN2 1CAWCR 2Bureau of Meteorology, Melbourne, Australia 3CSIRO

Ocean forecast services have been provided to the Australian public through the Bureau of Meteorology since August 2007. This first generation prediction system was developed through an Australian government research project called BLUElink. This system focuses on mesoscale resolution (so-called eddy resolving) and short-range forecasting (hours to weeks). An ensemble optimal interpolation scheme called BODAS was developed to assimilate the global ocean observing system. Several practical decisions have been chosen in BODAS and the operational forecast system to address key constraints of real-time observing system, the sparseness of the observational data and computational efficiency. We will examine the performance of the operational system over the past two years as well as benchmark this against similar systems during a common inter-comparison period. Particular focus will be given to metrics that highlight the positive impacts and deficiencies of the design on both ocean performance and ocean services. In addition we will highlight future directions and introduce the performance of an upgraded system being developed under a follow-on project BLUElink-2.

USE OF ENSEMBLE ASSIMILATION TO REPRESENT FLOW-DEPENDENCE IN THE AROME DATA ASSIMILATION SYSTEM

Pierre BROUSSEAU ([email protected]), Gérald DESROZIERS and Loïk BERRE METEO-France,CNRM/GAME

AROME (Applications of Research to Operations at MEsoscale) is the new meso-scale weather forecast model of Meteo-France used in operations since December 2008. This system covers the French territory with a high horizontal resolution (2.5 km) in order to improve local event forecasts (heavy precipitation, fog, urban effects, etc).

It runs with a data assimilation system derived from the regional ALADIN-FRANCE 3D-Var scheme, which is running operationally at Meteo-France since the end of 2005. Designed to produce informative analyses at the AROME 2.5km resolution, it assimilates the usual NWP observations plus high-density and high- frequency observations (RADAR measurements among others) using a rapid update cycle and climatologic background-error statistics, appropriated to the scales resolved by AROME.

These statistics share the same multivariate formulation as in ALADIN-FRANCE (Berre 2000), using errors of vorticity, divergence, temperature, surface pressure and humidity, with scale-dependant statistical regressions to represent cross-covariances. They are calculated using an ensemble-based method (Berre et al. 2006).

9 Using ensemble assimilation techniques becomes usual in order to specify flow-dependent error covariances in variational assimilation schemes. Flow-dependent features will be illustrated in the AROME framework. Then the introduction of flow-dependent structure functions into AROME 3D-Var using such techniques will be considered.

INTERCOMPARISON OF VARIATIONAL AND ENSEMBLE KALMAN FILTER DATA ASSIMILATION APPROACHES IN THE CONTEXT OF GLOBAL DETERMINISTIC NWP

Mark BUEHNER ([email protected]), P.L. HOUTEKAMER, Herschel MITCHELL, Cecilien CHARETTE and Bin HE Meteorological Research Division, Environment Canada, Dorval, Canada;

An intercomparison of the Environment Canada variational (3D-Var and 4D-Var) and ensemble Kalman filter (EnKF) data assimilation systems is being conducted in the context of producing global deterministic numerical weather forecasts. Both 3D-Var and 4D-Var experiments are considered that each use either the background error covariances similar to those used operationally, which are nearly static with horizontally homogeneous and isotropic correlations, or flow-dependent covariances based on the EnKF background ensembles. An EnKF experiment, run with the same horizontal resolution as the 4D-Var inner loop, uses the mean of each 96-member analysis ensemble to initialize the higher resolution deterministic forecasts. In addition, the Ensemble-4D-Var approach is evaluated. This approach uses 4D flow-dependent background error covariances estimated from the EnKF ensembles to produce a 4D analysis with the variational data assimilation system, but without the need of tangent-linear or adjoint versions of the forecast model. All experiments assimilate the same full set of meteorological observations and use the same configuration of the forecast model to produce medium-range forecasts.

Results show that use of the 4D-Var analysis, with the background error covariances similar to those used operationally, or the EnKF ensemble mean produces forecasts of comparable quality. A positive impact is obtained from using the EnKF flow-dependent error covariances in the variational systems (gain of ~10 hours at day 5 in southern extra-tropics vs. standard 4D-Var). Finally, three distinct configurations of the variational data assimilation system were compared with each using EnKF background-error covariances: the Ensemble-4D-Var approach produces improved forecast quality relative to 3D-Var, but not better than 4D-Var.

VERTICAL COVARIANCE LOCALIZATION FOR SATELLITE RADIANCES IN ENSEMBLE KALMAN FILTERS

William F. CAMPBELL, Craig H. Bishop and Daniel Hodyss Naval Research Laboratory, Monterey, CA

A widely used observation space covariance localization method is shown to adversely affect satellite radiance assimilation in Ensemble Kalman Filters (EnKFs) when compared to model space covariance localization. The two principal problems are that distance and location are not well defined for integrated measurements, and that neighboring satellite channels typically have broad, overlapping weighting functions, which produce true, nonzero correlations that localization in radiance space can incorrectly eliminate. The limitations of the method are illustrated in a 1D conceptual model, consisting of three vertical levels and a two-channel satellite instrument. A more realistic 1D model is subsequently tested, using the thirty vertical levels from the Navy Operational Global Atmospheric Prediction System (NOGAPS), the Advanced Microwave Sounding Unit A (AMSU-A) weighting functions for channels six through eleven, and the observation error variance and forecast error covariance from the NRL Atmospheric Variational Data Assimilation System (NAVDAS). Analyses from EnKFs using radiance space localization are compared with analyses from unlocalized EnKFs, EnKFs using model space localization, and the optimal analyses using the NAVDAS forecast error covariance as a proxy for the true forecast error covariance. As measured by mean analysis error variance reduction, radiance space localization is inferior to model space localization for every ensemble size and meaningful observation error variance tested. Furthermore, given as many satellite channels as vertical levels, radiance space localization cannot recover the true temperature state with perfect observations, whereas model space localization can.

10

THE BALANCE CHARACTERISTICS OF SHORT-TERM FORECAST ERRORS ESTIMATED FROM AN ENSEMBLE KALMAN FILTER

Jean-François CARON ([email protected]) and Luc FILLION Data Assimilation and Satellite Meteorology Section, Meteorological Research Division, Environment Canada.

Ensemble-based techniques are increasingly used to define flow dependent background error covariances in Variational data assimilation (VAR) systems. Buehner et al. (2008) for instance showed that the forecast skill of a deterministic VAR system can be improved by using background error covariances based on 6h Ensemble Kalman Filter (EnKF) perturbations instead of past lagged forecasts (so-called NMC method; Parrish and Derber, 1992). As was clearly mentioned in Parrish and Derber's scientific paper however, was the benefit of the NMC method, when combined with a global analysis approach, in terms of dynamical balance observed in the resulting analyses. The present study examines whether the same benefit are maintained when using an ensemble-based approach to define error samples. We thus analyze the balance properties of (1) forecast error samples coming from a state-of-the-art operational EnKF and (2) EnKF-based background error covariances. Potential sources of imbalance in EnKF forecast error samples related to the use of the ensemble mean as the reference state and the comparison of ensemble members using different model configuration are carefully examined. Comparison is also made with forecast error samples based on the NMC method (extracted from CMC operational system) using forecast from various lead time (e.g. 48h- 24h, 24h-12h and 12h-6h). Preliminary results indicate that 6h EnKF “forecast errors” (perturbations) as defined by the use of the ensemble mean exhibit less degree of balance then 48h-24h NMC based forecast error samples but show a degree of balance which is typical of a short term forecast. Investigation of the balance properties of the EnKF analysis increments will also be presented at the conference.

THREE-DIMENSIONAL VARIATIONAL DATA ASSIMILATION IN THE GULF OF ST. LAWRENCE COUPLED ICE-OCEAN MODEL

Alain CAYA1 ([email protected]), Mark BUEHNER1, and Tom CARRIERES2 1Meteorological Research Division, Environment Canada, Dorval, Canada; 2Ice and Marine Services, Environment Canada, Ottawa, Canada

A three-dimensional variational data assimilation (3D-Var) system has been developed to provide analyses of the ice-ocean state for a coupled atmosphere-ice-ocean model for numerical weather prediction in the area of the Gulf of St. Lawrence. The study focuses on the impact of sea-ice data assimilation on the prediction of sea-ice condition.

Data assimilation experiments, using various configurations of the 3D-Var, are conducted over a 4-month period during the winter of 2007 and are compared to the direct data insertion approach. In the direct insertion approach, the only data assimilated are the gridded RADARSAT image analyses produced by the Canadian Ice Service. The impact of additionally assimilating ice concentration retrievals from AMSR-E brightness temperatures using the NASA TEAM 2 algorithm is studied. To obtain ocean state analysis increments consistent with the sea ice analysis increments and in preparation to assimilating sea surface temperature data, work is done to estimate background-error covariances for the ice and ocean variables.

IMPACT OF USING 4D-VAR ASSIMILATION OF SSM/I DATA OVER AUSTRALIAN REGION

Mohar CHATTOPADHYAY ([email protected]), Peter STEINLE, Yi XIAO, John Le MARSHALL, Tan LE and Chris TINGWELL Centre for Australia Weather and Climate Research, CAWCR, Bureau of Meteorology, Melbourne, Australia.

Brightness temperatures from the Special Sensor Microwave/Imager (SSM/I) instrument – flown on the polar orbiting DMSP satellites – are used to retrieve atmospheric parameters which include the near surface wind speed over the ocean, column integrated water vapour and liquid and ice water path. These retrieved parameters are part of the observation set analysed by the Met Office 4dVAR data assimilation system which provides the data assimilation system for the Australian Community Climate and Earth System Simulator (ACCESS) NWP suite.

11

Although many SSM/I observations are rejected because of cloud, rain contamination or failure to pass other quality control checks, there are far more observations available to the assimilation system than can actually be analysed. This is mainly because of the computational expense of processing a large number of dense observations in an operational 4dVAR system. To make operational 4dVAR tractable, a common is that the observation errors are uncorrelated. Hence, to use SSM/I observations effectively in 4dVAR they need to be thinned. Thinning of satellite data generally improves the forecasts finding a balance between ignoring too many observations and giving observations too much weight by ignoring correlated observation error. In spite of these caveats, high resolution satellite data can still be useful for successful forecasting of special weather events, such as, tropical cyclones, cold fronts or large convective events.

Different assimilation and model configurations require different thinning distances for each class of satellite observation depending upon the accuracy of the forecast model and the observation operator the satellite footprint, and the formulation of the observation error covariance matrices. In this study we explore the different possibilities of using SSM/I data in the 4dVAR assimilation system to get maximum positive impact on ACCESS NWP forecasts over the Australian region. Preliminary results showed that during large convective events, the model was unable to capture the large scale circulation be simply reducing the thinning distance of SSM/I data in assimilation. However, the model is sensitive towards the data as the amount and the spatial distribution of the precipitation changed considerably when compared with satellite derived rainfall amount and precipitation analysis. The model results will be analysed in further detail and results will be presented in the conference.

ASSIMILATION OF OPTICAL REMOTE SENSING DATA INTO COASTAL AQUATIC BIOGEOCHEMICAL MODELS

Nagur CHERUKURU ([email protected]), Barbara ROBSON, Vittorio BRANDO and Arnold DEKKER CSIRO Land and Water, Canberra, ACT 2601

Fitzroy Estuary and Keppel Bay (FEKB) is a shallow water macro tidal system in the tropical regions of eastern Australia. A biogeochemical model for FEKB which is based on CSIRO Environmental Modelling System (EMS) was previously developed to understand the processes in this ecosystem. The FEKB biogeochemical model was built on a three dimensional hydrodynamic and sediment dynamic model. This model is capable of simulating the transformation and transportation of particulate and dissolved substances that pass through FEKB towards the Great Barrier Reef.

The predictive capability of such models could be influenced by various events. Conventional approaches to correct for such deviations in the modelled estimates use single point measurements and spatial interpolations. Methods using point measurements has proved to be time consuming and, in many cases, impractical and insufficient. In principle, optical remote sensing can provide spatially distributed ocean colour measurements at spatial and temporal scales which could help constrain and improve the modelled parameters.

To achieve the objective of assimilation of optical remote sensing data, we have developed and coupled an inherent optical property based underwater light propagation model with the FEKB biogeochemical model. This coupled model structure facilitates the assimilation of MODIS derived optical parameters into the biogeochemical model.

In this presentation we describe the structure of the coupled optics and biogeochemical model and present a methodology that uses optical remote sensing data to constrain the biogeochemical model behaviour in FEKB.

12

A CLSMDAS USING FY2C PRECIPITATION AND AMSR-E SOIL MOISTURE DATA BASED ON CLM3 AND ENKF

Shi CHUNXIANG1,2, Xie ZHENGHUI2, Tian XIANGJUN2, Qian HUI3, Liang MIAOLING4 1. National Satellite Meteorological Center, CMA, Beijing 100081, China 2. Institute of Atmosphere Physics, Chinese Academy of Science, Beijing 100029, China 3. Key Laboratory for Continental Dynamics of the Ministry of land and Resources of China, Institute of Geology, Chinese Academy of Geological Science，Beijing 100037,China， 4. National Meteorological Center, CMA, Beijing 100081, China.

Soil moisture plays a vital role in land-atmosphere interactions. The purpose of this paper is to develop a CLSMDAS (China Land Soil Moisture Assimilation System) which could assimilate soil moisture from satellite remote sensing data, and then to obtain high temporal and spatial distribution of soil moisture in China.

The CLSMDAS includes three modules, one module is the cumulative precipitation time downscaling method based on geostationary satellite data using cloud precipitation probability as weight and the estimation of spatial and temporal distribution of incident solar radiation applied to estimate atmosphere forcing data of land surface model, another is soil moisture assimilation module based on CLM3 and EnKF, the other is a data analysis module of soil moisture observation.

In this paper, the FY2C precipitation and surface incident solar radiation data set with the one hour time resolution and 10km spatial resolution from 2005 to 2007 are developed as the atmosphere forcing of land surface model. These data sets are validated using surface observation data. It shows the FY2C precipitation and surface incident solar radiation data set is reasonable.

The compare indicated that there are large deviations between the retrieved soil moisture between NASA/AMSR-E soil moisture operational product and the gauge observation in China. It indicates the soil moisture retrieval from microwave satellite sensing could be improved.

Then several assimilation experiments have been tried using the atmosphere forcing data sets generated by the assimilation system in this paper and CLSMDAS and the soil moisture sets retrieved from AMSR-E. The result of an assimilation experiment from June to September in 2006 indicates that the simulation of land surface model and the assimilation could both represent the spatial and temporal distribution of soil moisture well. And the distribution of assimilated soil moisture corresponds well to summer drought in Chongqing and Sichuan in August in 2006 which is the worst since 1949. It also has a good relationship with the drought in September in east of Hubei and south of Guangxi and so on.

INFRARED REMOTE SENSING OF ATMOSPHERIC COMPOSITION AND AIR QUALITY: TOWARDS OPERATIONAL APPLICATIONS

Cathy CLERBAUX ([email protected]), Maya GEORGE, Juliette HADJI-LAZARO, Anne BOYNARD, Matthieu POMMIER, Claire SCANNELL Université Paris 6, LATMOS/IPSL ; CNRS/INSU, Paris, France

Pierre-François COHEUR, Daniel HURTMANS, Lieven CLARISSE Université Libre de Bruxelles, Spectroscopie atmosphérique, Bruxelles, Belgium

Atmospheric remote sensing from satellites is an essential component of the observational strategy deployed to monitor atmospheric pollution and changing composition. In the last decade remote sensors using the thermal infrared spectral range have demonstrated their ability to sound the troposphere and provide global distribution for some of the key atmospheric species. This paper illustrates operational applications from the IASI instrument onboard the European satellite MetOp, which opens new perspectives for various environmental issues.

13

The IASI mission has an unprecedented horizontal resolution and coverage, from which global, regional and local distributions of trace gas concentrations can be derived. After an introduction that highlights the specific capabilities of IASI as compared to former or currently flying missions, this talk will summarize the results we have obtained from the 2008-2009 near real time processing of species, related to air quality monitoring and forecast, to long range monitoring of compounds emitted by fires, and to the early detection of SO2 from volcanic plumes for aviation hazard mitigation. The inversion method employed to retrieve these species is either based on neural network technique or optimal estimation theory. A brief description of the algorithms will be provided. Last but not least, the potential of near real time delivery of atmospheric composition products from IASI to the user community such as the GEMS/MACC consortium for incorporation in their data assimilation system will be discussed.

THE PRINCIPLE OF ENERGETIC CONSISTENCY

Stephen E. COHN ([email protected]) Global Modeling and Assimilation Office NASA Goddard Space Flight Center

A basic result in estimation theory is that the minimum variance estimate of the dynamical state, given the observations, is the conditional mean estimate. This result holds independently of the specifics of any dynamical or observation nonlinearity or stochasticity, requiring only that the probability density function of the state, conditioned on the observations, has two moments. For nonlinear dynamics that conserve a total energy, this general result implies the principle of energetic consistency: if the dynamical variables are taken to be the natural energy variables, then the sum of the total energy of the conditional mean and the trace of the conditional covariance matrix (the total variance) is constant between observations.

Ensemble Kalman filtering methods are designed to approximate the evolution of the conditional mean and covariance matrix. For them the principle of energetic consistency holds independently of ensemble size, even with covariance localization. However, full Kalman filter experiments with advection dynamics have shown that a small amount of numerical dissipation can cause a large, state-dependent loss of total variance, to the detriment of filter performance. The principle of energetic consistency offers a simple way to test whether this spurious loss of variance limits ensemble filter performance in full-blown applications.

The classical second-moment closure (third-moment discard) equations also satisfy the principle of energetic consistency, independently of the rank of the conditional covariance matrix. Low-rank approximation of these equations offers an energetically consistent, computationally viable alternative to ensemble filtering.

Current formulations of long-window, weak-constraint, four-dimensional variational methods are designed to approximate the conditional mode rather than the conditional mean. Thus they neglect the nonlinear bias term in the second-moment closure equation for the conditional mean. The principle of energetic consistency implies that, to precisely the extent that growing modes are important in data assimilation, this term is also important.

FIRST RESULTS OF THE ASSIMILATION OF OZONE TROPOSPHERIC COLUMNS PROVIDED BY THE IASI INSTRUMENT TO ASSESS AIR QUALITY WITH A CHEMICAL TRANSPORT MODEL - CHIMERE AT A CONTINENTAL SCALE

Adriana COMAN1 ([email protected]), Gilles FORET1, Maxime EREMENKO1, Anne BOYNARD12, Gaelle DUFOUR1, Matthias BEEKMANN1 Laboratoires Interuniversitaire des Systèmes Atmosphériques1, CNRS/University Paris XII Laboratoire Atmosphère Milieux Observations Spatiales2, CNRS/University Paris VI

New infrared satellite sensors (TES, IASI) can now deliver information about the ozone concentrations of the free troposphere at daily frequency. In spite of a lowest sensitivity to surface ozone concentrations, Eremenko et al (2008) have shown that the IASI instrument was able to detect photochemical episodes with high accuracy (by comparison to ozone sondes) indicating a genuine potential to improve the model simulations.

We present here our first assimilation results using tropospheric ozone columns, derived from the IASI instrument (LISA-IASI scientific ozone product), and the CHIMERE chemical transport model over the European domain. We use a continental version of the model with a horizontal resolution of 0.5° × 0.5° and 17 vertical layers from the surface up to 200 hPa.

14

Assimilation experiments are performed using an Ensemble Kalman Filter (EnKF). This Monte Carlo approach requires the construction of an ensemble to calculate the covariance matrix error used to propagate innovations given by observations. The construction of this ensemble is based on the perturbations of most uncertain parameters of the model and a local analysis is used in order to avoid spurious covariances from remote areas. The averaging kernels (derived during the retrieval process) which yield the vertical sensitivity of the retrievals are used to project the values from the model space to the observation space.

The improvement in 3D ozone fields is quantified using in situ measurements (surface, vertical profiles). Special attention will be given to evaluate, to which extent the assimilation exercise will allow improvement of surface ozone fields and thus simulation of air quality. This is the first time to our knowledge that IASI ozone observations are assimilated into a regional model in order to improve regional scale 3D ozone fields

IMPLEMENTATION OF A REDUCED RANK SMOOTHER FOR HIGH RESOLUTION OCEANOGRAPHY

Emmanuel COSME ([email protected]), Jean-Michel BRANKART, Pierre BRASSEUR and Jacques VERRON Grenoble University-CNRS/LEGI, Grenoble, France

A specificity of the Kalman Filter lies in that each analysis product contains the information of past, present, but not subsequent observations. However, for some specific problems such as reanalyses, subsequent observations are available and may be advantageously used for the estimation process. Under the same assumptions as for the Kalman Filter, this retrospective estimation is performed by optimal smoothers. Here, the reduced rank smoother formulation of the Singular, Evolutive, Extended Kalman (SEEK, Pham et al, 1998) filter is presented. The processing of the model error in the smoother is particularly stressed.

The SEEK smoother is implemented with an ocean circulation model in a double-gyre, 1/4° configuration, able to represent mid-latitude mesoscale dynamics. Twin experiments are performed: the true fields are drawn from a simulation at a 1/6° resolution, and noised. Then, altimetric satellite tracks and sparse vertical profiles of temperature are extracted to form the observations.

The smoother is efficient in reducing errors, particularly in the regions poorly covered by the observations at the filter analysis time. It results in a significant reduction of the global error: the Root Mean Square error in Sea Surface Height from the filter is further reduced by 20% by the smoother. The actual smoothing of the global error through time is also verified. Finally, two key issues are discussed: the time distance within which observations may be favorably used to correct the state estimates, and the model error parameterization. We conclude that the smoother may be advantageously used with high resolution ocean models in the near future, to provide high quality reanalyses of the ocean circulation.

Pham, D. T., J. Verron, and M. C. Roubaud, A singular evolutive extended Kalman filter for data assimilation in oceanography, J. Marine. Sys., 16 , 323-340, 1998.

COMPARISON BETWEEN SEQUENTIAL ASSIMILATION AND KRIGEAGE FOR SATELLITE DATA INTERPOLATION

Charles COT ([email protected]), Alain HAUCHECORNE, Slimane BEKKI, David CUGNET Latmos (ex Service d’Aéronomie) du CNRS, Institut Pierre-Simon Laplace, BP3, 91371 Verrières-le-Buisson, France

GOMOS (Global Ozone Monitoring by Occultation of Stars) is the first space instrument dedicated to the study of the atmospheric composition by the technique of stellar occultations. The experiment aboard ENVISAT satellite was designed in order to evaluate stratospheric ozone concentration and trend (and other atmospheric minor constituents) over the Earth during the last few years.

Ozone concentration is variable in space and time. Spatial variability may be observed by a sufficient number of occultations and time variability by recording time series. GOMOS measurements are randomly distributed in space and time. A continuous field at grid points evenly spaced is needed to obtain a good estimate of ozone climatology and variability.

15 We present here a comparison between a heavy method consisting in the sequential assimilation of GOMOS data in the chemical transport model MIMOSA with a simple and fast multidimensional interpolation method: krigeage. Two kinds of studies have been made :

-Firstly 3 days of GOMOS data with their associated error bars are krigged at grid points evenly spaced. This field is compared with the results of GOMOS data assimilated in Mimosa. -Secondly a 3D ozone field is generated with Mimosa model. 3 days of GOMOS data are simulated using this 3D field and a Gaussian noise is added to represent measurement errors. Then these simulated data are krigged at evenly spaced grid points and the results are compared with the initial Mimosa field.

Conclusions will be given on the validity and/or superiority of the krigeage method in terms of accuracy and computational efficiency.

ENHANCING ADAPTIVE FILTERING APPROACHES FOR LAND DATA ASSIMILATION SYSTEMS

Wade T. CROW ([email protected]) USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, Maryland, USA

Recent work has presented the initial application of adaptive filtering techniques to land surface data assimilation systems. Such techniques are motivated by our current lack of knowledge concerning the structure of large-scale error in either land surface modeling output or remotely-sensed estimates of land surface water and energy balance variables. Most current adaptive techniques are based on classical whitening approaches in which a lack of temporal auto-correlation within filtering innovations is assumed to be a necessary and sufficient condition for optimal filter performance. However, the application of these approaches to the assimilation of remotely-sensed surface soil moisture has uncovered two serious problems. First, the iterative application of whitening approaches to land surface models leads to extremely slow convergence on optimal error parameters and is therefore not appropriate for satellite data sets with limited temporal heritage. Second, errors in available remotely-sensed soil moisture datasets are commonly too heavily auto-correlated for whitening approaches to function effectively. This presentation will illustrate these problems and develop an alternative methodology which circumvents both limitations. The approach is based on the application of a so-called “triple collocation” technique to independently estimate observational errors in remotely-sensed surface soil moisture. Such approaches estimate error magnitudes in a given geophysical variable by averaging across variations within three independently-obtained estimates of the variable. Here errors in surface soil moisture retrievals obtained from the Advanced Microwave Scanning Radiometer (AMSRE-E) are estimated via triple-collocation and used to constrain optimal modeling errors (by tuning modeling errors until normalized filtering innovations are variance unity). Real data validation results within several heavily-instrumented ground test sites reveal that the procedure leads to faster convergence to optimized error parameters and significantly enhances surface soil moisture estimates relative to existing adaptive filtering approaches.

LAND DATA ASSIMILATION ACTIVITIES IN PREPARATION OF THE NASA SOIL MOISTURE ACTIVE PASSIVE (SMAP) MISSION

Wade T. CROW1 ([email protected]), Rolf. H. REICHLE2 1 USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, Maryland, USA 2 NASA Goddard Space Flight Center/Global Modeling and Assimilation Office, Greenbelt, Maryland, USA

Slated for launch in 2013, the NASA Soil Moisture Active/Passive mission represents a generational advance in our ability to globally observe time and space variations in surface soil moisture fields. The SMAP mission concept is based on the integrated use of L-band active radar and passive radiometry measurements to optimize both the accuracy and resolution of remotely-sensed soil moisture estimates. Data assimilation activities represent a critical linkage between SMAP products and eventual science and operational applications. In particular, SMAP mission plans call for the generation of a dedicated data assimilation product to vertically extrapolate near-surface (0 to 5-cm) soil moisture retrievals to produce deeper, root-zone (0 to 1-m) soil moisture estimates required by most applications. A global, Ensemble Kalman filtering land data assimilation system capable of generating this product is currently under development at the NASA Global Modeling and Data Assimilation Office. This presentation will highlight two specific elements of this development. First, we will summarize recent efforts to quantify the added value of SMAP soil moisture retrievals for global soil moisture monitoring activities. Existing applications already posses access to soil moisture estimates derived from off-line water balance models constrained solely by observed rainfall and meteorological variables.

16 Clarifying the added benefit of assimilating remotely-sensed surface soil moisture retrievals into such systems (relative to this existing baseline) is critical for articulating expected SMAP impacts on key applications. Second, we will describe ongoing efforts to apply adaptive filtering techniques to land surface data assimilation systems. Land surface modeling error arises from a highly diverse set of sources, and failure to adequately characterize either the origin or structure of errors can lead to a significant reduction in the accuracy of analysis products. Consequently, the development and implementation of an effective adaptive filtering system represents an important goal for efforts to effectively assimilate SMAP soil moisture products.

ASSIMILATION OF GPS RADIO OCCULTATION OBSERVATIONS AT NOAA/NCEP

L. CUCURULL and J. DERBER

The COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate) mission launched six small satellites in April 2006, each carrying a GPS radio occultation (RO) receiver. COSMIC constellation provides ~2,500 RO soundings per day, nearly uniformly distributed around the globe in near real time (less than 180 min). GPS RO data are of high accuracy (< 0.5 K in temperature), are minimally affected by aerosols, clouds or precipitation, are independent of radiosonde calibration, and are not expected to have instrument drift and satellite-to-satellite instrument bias. In addition, the data have the same accuracy over land than over ocean. GPS technology is a limb sounding geometry complementary to ground and space nadir viewing instruments.

NOAA/NCEP has been assimilating GPS RO observations from the COSMIC mission into its global operational model since 1 May 2007. The assimilation of these new data type has been shown to provide significant positive impact in model skill, in particular in the Southern Hemisphere, demonstrating that GPS RO is a key component of the global observing system.

The forward operator and associated tangent linear and adjoint models, quality control procedures and observation error characterization associated with the GPS RO observations have been recently updated at NOAA. These changes will be implemented in the operational system in Fall 2009.

During the presentation, updates on the assimilation of GPS RO observations and the impact of GPS RO in the global operational weather forecasts at NOAA will be discussed.

FINE SCALE SNOW ANALYSES IMPROVEMENT THROUGH COARSE SCALE SNOW WATER EQUIVALENT ASSIMILATION

Gabriëlle J.M. DE LANNOY1,2, Rolf H. REICHLE3,4, Paul R. HOUSER2, Kristi ARSENAULT2, Niko E.C. VERHOEST1, Valentijn R.N. PAUWELS1 1 Laboratory of Hydrology and Water Management, Ghent University, Coupure links 653, B-9000 Ghent, Belgium, [email protected] 2 George Mason University & Center for Research on Environment and Water, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705-3106, USA 3 Global Modeling and Assimilation Office (Code 610.1), NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA 4 Goddard Earth Sciences and Technology Center, University of Maryland, Baltimore County, Baltimore, MD 21250, USA

A number of ensemble Kalman filter (EnKF) approaches are explored in the National Aeronautics and Space Administration (NASA) Land Information System (LIS) to assimilate coarse scale (25 km) water equivalent (SWE) observations into fine scale (1 km) model simulations. Both a 1-dimensional (1D) or point filter and a variety of 3-dimensional (3D) or spatial filters are explored in a synthetical study.

Two scenarios are tested after partitioning the coarse observation into a number of finer scale observations, either with or without corresponding observation error scaling. First, the analysis at each fine scale grid point is calculated using the single collocated (partitioned) fine scale observation (1D filter). In a second case, several fine scale observations are used to update each fine scale state, while taking into account the spatial structure in the background error covariance. The full coarse observations are also used in two types of 3D filtering. A first technique updates each fine scale state variable using the overlying coarse observation. A second technique updates each fine scale state variable using the overlying and all surrounding coarse observations in a domain centred on the fine scale analysis point.

17 The 1D filter using partitioned observations without observation error scaling reduces the spatial variability too much, except during very shallow snow pack periods with a very reduced forecast uncertainty. With inclusion of error scaling, the observation impact is reduced and the analysis shows much more of the open loop spatial variability, but the 1D filter operation is non-optimal.

With a realistic spatial background error structure, it is shown that the 3D filters can improve the spatial mean snowpack, but also bring the fine scale subpixel variability much closer to the truth than either the observations or the open loop forecasts alone. The best results are obtained when using a number of coarse observations in a domain centred on each fine scale analysis point (RMSE reduction by 60%, correlation increase from 0.15 to 0.64, when compared to the open loop). Even though the whole study domain is observed, there is a substantial value in including observations from neighbouring areas to enhance the fine scale SWE structure estimation within a 3D filter. The typical assimilation of a single coarse observation to update the underlying fine scale forecasts can bring the overall analyzed field close to the truth in the mean value sense, but it is largely inferior for estimating the fine scale structure. Furthermore, this filtering results in an analysis field with obvious artificial transitions at the coarse observation pixel boundaries.

ASSESSMENT OF COASTAL OCEAN OBSERVATIONAL NETWORKS BY ENSEMBLE-BASED REPRESENTER SPECTRAL ANALYSIS

Matthieu LE HENAFF12, Pierre DE MEY1 ([email protected]) LEGOS, Toulouse1, RSMAS, Miami2

The design of sustained coastal ocean observing systems, and adaptive/targeted field programs, are topics of considerable interest. Critical support of future satellite missions is also of great importance to coastal oceanography at the present time. The development of coastal ocean modelling in the recent years has allowed an improved representation of the associated complex physics. Such models are probably mature enough be used to design observation networks in coastal areas, using objective metrics, based on ideas such as the idea that a “good” network is a network that is able to detect and control model error.

While Observing System Experiments (OSEs) and OSSEs provide an integrated, but methodology- dependent, performance assessment of an observational array, we propose an approach based on the representer matrix spectrum focusing on the capacity of a given array to detect model errors. This can be achieved independently of any data assimilation method, e.g. from stochastic modelling, or as part of an Ensemble Kalman Filter. In our Representer Matrix Spectra (RMSpectrum) method, we combine the prior state error and observation error covariance matrices into a single scaled representer matrix. Its eigenspectrum contains information on which model state error modes a network can detect and potentially constrain, amidst structured observation error background.

The method is applied to a 3D coastal model of the Bay of Biscay, with a focus on mesoscale turbulence errors and wind forcing errors. We illustrate the methodology through performance tests of various in situ and satellite networks. Although the RMSpectrum technique is easily set up and used as a “black box”, the utility of its results is maximised by physical analysis. The technique provides both quantitative (eigenvalues) and qualitative (eigenvectors) tools to study and compare various network options. The qualitative approach is essential to discard possibly inconsistent modes.

REPRESENTATION OF CLIMATE SIGNALS IN REANALYSIS

Dick DEE ([email protected]) European Centre for Medium-Range Weather Forecasts, Reading, UK

The basic idea behind reanalysis is to use a state-of-the-art modeling and data assimilation system to extract maximum information from past observations. The original motivation for doing this at NWP centers was to gain a better understanding of the general circulation, the physical processes involved, and the impact of different observing system components on forecast quality. Since then, reanalysis data produced at several centers have provided a comprehensive, physically coherent record of the global atmosphere spanning several decades, and these data now serve numerous applications in many different fields.

18

Successive generations of reanalyses benefit from progress made in modeling and data assimilation, resulting in increasing accuracy, variety, and usability of reanalysis products. These products, in turn, have become indispensable for research and development at NWP centers and elsewhere, aimed at improving forecasting systems and extending their predictability to seasonal time scales. An important, but often overlooked, benefit of reanalysis is that it drives the improvement of observational data sets required for its production. This includes, for example, the collection and quality control of conventional meteorological observations, the improvement of historic sea-surface temperature and sea-ice data products, and the re- processing and inter-satellite calibration of radiance data.

The role of reanalysis in climate science is clearly growing, as reanalyzed climate data provide an essential resource for the development and improvement of climate prediction models. The use of reanalysis data for studying past climate change is, however, still controversial. Representations of climate signals in reanalysis can be affected by changes in the observing system which modulate the systematic errors in the assimilation. This is a fundamental issue with any data analysis that involves incomplete and conflicting sources of information. Control of systematic error components and provision of meaningful error estimates on reanalysis data presents the ultimate challenge in data assimilation.

ENSEMBLE-BASED DATA ASSIMILATION FOR WIND ENERGY PREDICTIONS AT FINE SCALES

Luca DELLE MONACHE 1, Julie LUNDQUIST 2, 1 National Center for Atmospheric Research, 2 Lawrence Livermore National Laboratory [email protected]

An ensemble-based data assimilation approach is explored to produce analysis and short-term predictions (0-48 h) at fine resolution (3-5 km) for offshore wind energy applications. The prediction system is based on the Weather Research and Forecasting (WRF) Model, coupled with the Data Assimilation Research Testbed (DART) system, to realize ensemble Kalman Filter (EnKF) wind forecasts. The prognostic quantity of interest is the wind in the lower part of the atmospheric boundary layer, and in particular winds at hub height, i.e., approximately 80 m-100 m above sea level. Quantities assimilated include conventional surface and upper-air observations and a high-resolution sea surface temperature satellite data set. Additionally, wind and temperature observations from meteorological towers in the proximity of the wind farm and from individual turbines are assimilated. These observations are often influenced by the turbine itself as well as upwind turbines. Hence, a sensitivity test on different configurations of these observation errors within the EnKF framework is carried out, to understand the impact of those configurations on the filter performance. Data thinning procedures are considered to make the best use of the dense data provided by the several turbines, and the impact on analysis quality and forecast skill of these unconventional observations is explored.

DATA ASSIMILATION OF REMOTE SENSING INFORMATION FROM SATELLITE AND RADAR DATA

John DERBER1 ([email protected]), Lidia CUCURULL2, Banghua YAN2, Paul VANDELST3, Mingjing TONG4, David PARRISH1 and Shun LIU3 DOC/NOAA/NWS/NCEP/EMC1, DOC/NOAA/NESDIS/STAR2, SAIC3, UCAR Visiting Scientist Program4

One of the major changes in data assimilation over the last 20 years has been the ability to incorporate indirect observations into assimilation systems. An indirect observation is any observation not in the form of the analysis variable. While it was possible to include indirect observations in earlier schemes, it was not commonly done and much effort was expended attempting to transform indirect observations into meteorological variables. The inclusion of the forward operator transforming between the analysis variables and the observations first became common with the advent of variational analysis systems. The use of the forward operator allowed much more freedom in the choice of analysis variables and allowed the use of the observations in more raw form.

In this presentation, the advantages and difficulties of the indirect use of observations will be shown with examples using remotes sensing (satellite radiances, Doppler radar and GPS radio occultation). The impact of indirect assimilation of these observations on various components of the assimilation system, such as the use of linear and nonlinear forward models, quality control, and bias correction will also be shown. Future directions in the improvement of the forward models and the resulting impact on the planning and design of future remote sensing programs will also be discussed.

19 A POSTERIORI DIAGNOSTICS IN AN ENSEMBLE VARIATIONAL ASSIMILATION

Gérald DESROZIERS ([email protected]), Loïk BERRE, Vincent CHABOT, and Bernard CHAPNIK Météo-France, CNRM/GAME

Most Numerical NWP centres are now using variational assimilation systems. Those systems still basically rely on estimation theory in which two sources of information, a background and observations, are combined.

The optimality of such large problems is not guaranteed, since they rely on different approximations. It has been shown that a way to measure the optimality of variational schemes is to compare the observed values of the sub-parts of the cost function, at the minimum, to their theoretical statistical expectations, whose expressions can be formally derived.

A part of the sub-optimality of variational schemes is related to the lack of flow-dependence of the background error covariance matrix B. Some NWP centres tend to implement ensemble assimilation systems based on Ensemble Kalman Filter (EnKF) techniques to document the flow-dependence of B. This EnKF system is run besides the variational assimilation scheme that performs the actual assimilation step in the operational suite. Another approach, followed by Météo-France, is to implement an ensemble of perturbed variational assimilations, which mimics the evolution of errors in the weather prediction system more closely.

We show that the theoretical statistical expectations of sub-parts of the cost-function appear to be direct by- products of an ensemble variational assimilation.

Such a posteriori diagnostics can also be used to optimize the statistics of observation but also background errors. In particular, they allow for the variance inflation needed to represent the effect of model error omitted in the ensemble system.

Moreover, the expectations of the sub-parts of the cost function associated with observations are related to the sensitivity of the analysis to the different subsets observations. As a consequence, it is shown that the weights of the different sources of observations in the analysis can be additionally monitored by ensemble diagnostics.

An application in the French operational variational ensemble is shown.

ENSEMBLE KALMAN FILTER ASSIMILATION OF ATMOSPHERIC CHEMICAL CONSTITUENTS DATA WITH A MRI CHEMISTRY-CLIMATE MODEL: OSS EXPERIMENTS

Makoto DEUSHI ([email protected] ), Tsuyoshi T. SEKIYAMA, and Kiyotaka SHIBATA Meteorological Research Institute, Tsukuba, Japan

A ozone data assimilation (DA) system is developed based on a local ensemble transform Kalman filter (LETKF) method which is a kind of the ensemble kalman filter (EnKF) technique. A chemistry-climate model developed at Meteorological Research Institute (MRI-CCM) is used in the ozone DA system. MRI-CCM is designed to simulate the distributions and time-evolutions of ozone and related chemical species over the troposphere and the middle atmosphere comprehensively, and used for the operational prediction of surface UV-B and photochemical oxidant near the surface at the Japan Meteorological Agency. To assess sensitivities of the analysis accuracies with ensemble size and localization scale systematically, perfect model Observation System Simulation Experiments (OSSEs) are performed in the ozone DA system. In addition, other OSSEs are conducted to investigate how the choice of chemical speices which is assimilated as observational data impact on the ozone analysis.

20

ERROR COVARIANCES VIA HESSIAN IN VARIATIONAL DATA ASSIMILATION

F.-X. LE DIMET ([email protected]), I. GEJADZE and V. SHUTYAEV MOISE project (CNRS, INRIA, UJF, INPG); LJK, Université de Grenoble BP 51, 38051 Grenoble Cedex 9, France Department of Civil Engineering, University of Strathclyde, John Anderson Building, 107 Rottenrow, Glasgow, G4 ONG, UK Institute of Numerical Mathematics, Russian Academy of Sciences, 119333 Gubkina 8, Moscow, Russia

The problems of variational data assimilation (DA) are formulated as optimal control problems for a specified cost functional, while unknown parameters of a chosen dynamical model such as initial state, boundary conditions, forcing terms and distributed coefficients are sought. The necessary optimality condition reduces the problem to the optimality system which includes all available information. The optimal solution error can be derived through the errors of the input data using the Hessian of the cost functional. If the errors of the input data (observation, background and model errors) are Gaussian, then the analysis error covariance operator can be approximated by the inverse Hessian of the auxiliary control problem based on the tangent linear model constraints.This approximation is sufficiently accurate far beyond the validity of the tangent linear hypothesis. Here we present the generalization of this result to the case of other model parameters (boundary conditions and distributed coefficients). The algorithm based on the quasi-Newton BFGS method is adapted for constructing the optimal solution error covariance matrix for parameter estimation problems.

We also investigate cases of highly non-linear dynamics, when the inverse Hessian does not properly approximate the analysis error covariance matrix, the latest being computed by the fully non-linear ensemble method with a significant ensemble size. A modification of this method that allows us to obtain sensible approximation of the covariance with a much smaller ensemble size is presented. Finally, we discuss a relationship between the performance of the incremental variational DA in strongly nonlinear cases and the proximity of the inverse Hessian and the analysis error covariance matrix.

AN APPLICATION OF SEQUENTIAL VARIATIONAL ALGORITHM

Srdjan DOBRICIC ([email protected]) CMCC, Bologna, Italy

The sequential variational (SVAR) algorithm splits the minimization of the weak fourdimensional (4DVAR) cost function in which the model errors are not correlated in time. In a 4DVAR cost function spanning the time window with n time steps, SVAR applies 4n cost functions to sequentially find the most likelihood model state vector. In addition it estimates background error covariances corresponding to the end of the time window. Theoretically it has been shown that the optimal state estimate of SVAR is equal to that obtained by the minimization of the whole 4DVAR cost function at once. However, in practice SVAR applies the additional approximation that the matrix of background error covariances may be approximated by a limited number of eigenvetors and corresponding eigenvalues. The number of eigenvectors is limited by the available memory of the computers and, more importantly, by the computational time necessary to estimate the temporal evolution of the background error covariance matrix. Here we will show an algorithm to estimate only the most significant eigenvectors and eigenvalues of the dynamically changing part of the background error covariance matrix. The application of this algorithm makes the computational requirement of the SVAR’s forward sweep similar to that of 4DVAR with the incremental algorithm. On the other hand, theoretically the backward sweep of SVAR may be applied in a computationally very efficient way by a single backward integration of the adjoint of the tangent linear model. However, in an application with a long time window it appears that this approach may be numerically unstable. Therefore, it is suggested to use an alternative solution which is numerically stable, but doubles the computational cost of the SVAR algorithm.

21

DATA ASSIMILATION IN OPEN OCEAN AND SHELF AREAS OF THE MEDITERRANEAN SEA

Nadia PINARDI, Srdjan DOBRICIC, Mario ADANI, Daniele PETTENUZZO, Jenny NILSSON, Alessandro Bonazzi and Marina TONANI Istituto nazionale di Geofisica e Vulcanologia, Bologna, Italy Centro Euro-Mediterraneo per I Cambiamenti Climatici, Bologna, Italy

Since 1999 the Mediterranean Forecasting System (MFS) provides oceanographic analyses and short term forecast for the Mediterranean Sea. Recently the old optimal interpolation (OI) scheme has been substituted by a three-dimensional variational (3DVAR) scheme for the data assimilation. While maintaining some concepts from the old OI scheme, like the use of vertical Eofs, 3DVAR applies several novel solutions to represent background error covariances. It applies the recursive filter in the presence of coastal boundaries, integrates a barotropic model to covariate sea level errors with temperature and salinity errors and damps the velocity divergence along the coasts. These unique properties make the 3DVAR scheme suitable for the data assimilation in the Mediterranean Sea characterised by a high complexity of the coastline and the bottom topography. Furthermore, the application of a variational scheme facilitates the assimilation of new types of observational data sets like the positions of drifting floats or the information from the drifts of underwater gliders. The ongoing development is concentrated on the assimilation of sea surface temperature, the application to regional systems and the ensemble estimation of errors due to the atmospheric forcing.

ON THE ASSIMILATION OF ARGO FLOAT AND SURFACE DRIFTER TRAJECTORIES INTO THE MEDITERRANEAN FORECASTING SYSTEM

Jenny A.U. NILSSON1, Srdjan DOBRICIC2 ([email protected]), Nadia PINARDI3 1Istituto Nazionale di Geofisica e Vulcanologia, 2Centro EuroMediterraneo per I Cambiamenti Climatici, 3Università di Bologna

The Mediterranean Forecasting System has been in operations for nearly a decade, and it is continuously providing analyses on a weekly basis for the region. These forecasts are of great importance as they provide local and basinscale information of the environmental state of the sea, and are also highly useful for tracking oil spill and searchandrescue missions.

The circulation in the interior Mediterranean Sea is to a large extent characterized by mesoscale eddies, which often have proved somewhat difficult to simulate in an adequate manner. Data assimilation is a widely used method to improve the forecast skill of operational models and, in this study, the threedimensional variational (OceanVar) scheme has been extended to include Argo as well as surface drifter trajectories, with the objective to constrain and ameliorate the numerical output primarily in terms of the velocity fields.

The method of implementing the float positions into the cost function is highly unique, since it uses a trajectory model as the observational operator. The modelled float trajectories are obtained by applying the particle advection equation on the 5day period when the Argofloat Is drifting at parking depth (350m). The surface drifter trajectories are assimilated similarly on a daily basis, hereby making the particle advection assumption even more robust.

It is furthermore crucial to establish that the extended Oceanvar scheme does not decrease the forecast/analysis quality of the other output variables (e.g. SLAs, temperature, salinity). Numerical experiments were undertaken and it was concluded that the trajectory assimilation improves the simulation of SLAs and velocity fields based upon analyses, and the forecast skill of the temperature and salinity fields remain at the former quality level.

22

ASSIMILATION OF MODIS SNOW COVER DATA INTO THE LIS SAC-HT/SNOW17 MODEL OVER THE CONTINENTAL UNITED STATES (CONUS)

Jiarui DONG1,2 ([email protected]), Mike EK1, Pedro RESTREPO3, Dongjun SEO3, Christa PETERS- LIDARD4, Brian COSGROVE3, Victor KOREN3 1 NOAA/NCEP/EMC, Camp Springs, MD, USA 2 Science Applications International Corporation (SAIC), MD, USA 3 NOAA/OHD/HL, Silver Spring, MD, USA 4 NASA/GSFC Hydrological Science Branch, Greenbelt, MD, USA

In the western United States, over half of the water supply is derived from mountain snowmelt. In many mid latitude and high altitude regions, the snow delays runoff and provides water in the spring and summer when it is needed most. Therefore, accurate knowledge of snowpack properties is important for short-term weather forecasts, climate change prediction, and hydrologic forecasting.

As both the model predictions and passive microwave snow water equivalent (SWE) observations contain large errors due to land surface complexities and temporally frequent snowmelt processes in the western United States, the 500-m daily MODIS snow cover area (SCA) product has been used in this study as an important constraint on snowpack processes in land surface and hydrological models. The uncertainty in the MODIS SCA product has been assessed over some selected regions, and quality control will be applied to the MODIS SCA product before it is assimilated into the SNOW17 model.

In this study, we assimilate the MODIS derived snow cover fraction (SCF) into the LIS SAC-HT/SNOW17 model operating on the HRAP (Hydrologic Rainfall Analysis Project) grid at 4.7625-km resolution over the entire CONUS. To avoid cloud contamination, we update the snow cover fraction at pixels which feature less than 50% cloud coverage. Because the change in snow cover fraction makes no change to the amount of SWE in the SNOW17 module, we have developed a new scheme to account for the effect of a change in snow cover fraction to total SWE. We select the traditional bisection method to study this inverse problem, and perform a series of tests to assess the assimilation algorithm performance. Multi-year model simulations with and without MODIS SCF assimilation are presented, and evaluated with in-situ SWE observations and stream flow records.

THE ERROR CHARACTERISTICS OF SIMULATED MICROWAVE SATELLITE OBSERVATION IN CLOUDY AND RAINY AREA

Peiming DONG ([email protected]), ShuoSong LIU and Jishan XUE Chinese Academy of Meteorological Sciences

The use of Satellite data contributes greatly to the improvement of the accuracy of numerical weather forecast. However, it is just mainly the clear-sky satellite data that are used in most current data assimilation systems. The module of the radiant effect of water content are being developed in both RTTOV and CRTM, two popular rapid radiant transfer models developed by EUMETSAT Satellite Application Facility on Numerical Weather Prediction (NWP SAF) and Joint Center for Satellite Data Assimilation (JCSDA), respectively, to match the requirement of the use of satellite data affected by cloud and rain. The RTTOV93 and CRTM12 are already implemented in the GRAPES-3Dvar, a three dimensions variational data assimilation system developed in Chinese Academy of Meteorological Sciences.

With the water content output of the regional mesoscale model WRF as input, the error characteristics of simulated microwave satellite observation in cloudy and rainy area are investigated and compared. The improvement of simulation of satellite observation is examined by the consideration of the radiant effect of water content and the physical mechanism of satellite observation. The quantitative statistics and analysis are performed to reveal the characteristics of the bias and the proportion of influence of each kind of water content at each satellite channel. It is expected that these results will benefit the understanding of the error characteristics of simulated microwave satellite observation in cloudy and rainy area and the attempt to use satellite data affected by cloud and rain in numerical forecast.

23 NON-LINEAR EXTENSIONS OF THE SEEK FILTER FOR DATA ASSIMILATION AND PARAMETER ESTIMATION INTO COUPLED PHYSICAL-BIOGEOCHEMICAL MODELS OF THE OCEAN

Maeva DORON1 ([email protected]), David BEAL1,2, Jean-Michel BRANKART1 and Pierre BRASSEUR1 1LEGI, CNRS, BP53, 38041 Grenoble cedex 9 FRANCE 2Present address : EVS, ENS-Lyon, BP7000, 69342 Lyon Cedex 07 FRANCE

Coupled physical-biogeochemical modelling of the ocean is becoming one major field of application for data assimilation, since i) biogeochemical models are still rudimentary with respect to the actual biology in the oceans and rely on imperfect parameterisations, ii) the multi-scale interactions and coupling mechanisms between the physics and the biology are poorly known and, iii) the development of satelliteborne ocean color sensors allow global and regular survey of chlorophyll /a/ concentrations.

In this study, we concentrate on two important sources of errors in a three-dimensional coupled physical- biogeochemical model of the North Atlantic with a 1/4° horizontal resolution: first, errors due to wind and vertical mixing which affect the nutrient supply and the plankton residence in the euphotic layer, and second, errors in the parametrisation of key primary production processes.

To explore independantly the sensitivity of the model to imperfections in the wind forcings or in parameterisations of the biogeochemical processes, two Monte Carlo experiments were performed (using 200 members in 30-day runs) during the spring bloom of 1998. The response of the coupled model exhibits strongly non-linear effects such as thresholds. In order to account for the non-linear correlations between state variables, a nonlinear change of variables is performed that operates separately on every state vector component by mapping their ensemble percentiles on the Gaussian percentiles. It is shown that, with respect to linear estimates, this anamorphosis method is a promising approach to decrease the estimation error due to imperfect wind forcings. In the case of model parameterisation errors, twin experiments using the SEEK filter are conducted to explore the possibility to control of three biogeochemical rate parameters using observations of surface phytoplankton. The results illustrate the importance of a non-linear analysis step in ensemble assimilation methods such as the SEEK filter.

A COMPARISON OF SOIL MOISTURE ANALYSES FROM THE EKF ASSIMILATION OF NEARSURFACE SOIL MOISTURE AND SCREEN-LEVEL TEMPERATURE AND HUMIDITY

Clara DRAPER1 ([email protected]), Jean-François MAHFOUF2, Jeffrey WALKER1, and Peter STEINLE3 (1) Department of Civil and Environmental Engineering, University of Melbourne, Melbourne, Australia (2) Meteo-France/CNRS, CNRM/GAME, GMME/TURBAU (3) Centre for Australian Weather and Climate Research, Melbourne, Australia.

An EKF analysis based on the assimilation of remotely sensed near-surface soil moisture, and screen-level temperature and relative humidity, is described for Météo-France’s Aire Limitée Adaptation Dynamique développement InterNational (ALADIN) numerical weather prediction model. The assimilation of screen-level observations and near-surface soil moisture is compared in an experiment over Europe for 2008. The EKF uses an offline version of ALADIN’s land surface scheme, the Interactions between Surface, Biosphere, and Atmosphere (ISBA) model, with the forcing applied at the first atmospheric model layer (17 m) to enable the assimilation of screen-level atmospheric observations. The assimilated temperature and humidity data are taken from the optimal interpolation scheme used to initialise screen-level variables in ALADIN, and the near-surface soil moisture data are obtained from C-band descending pass Advanced Microwave Scanning Radiometer (AMSR-E) observations, using the VUA-NASA retrieval algorithm. The AMSR-E soil moisture observations are re-scaled to ALADIN’s soil moisture climatology by matching its Cumulative Distribution Function (CDF) to that of the model. There is a strong seasonal cycle in the bias between the AMSR-E and ALADIN soil moisture, necessitating the correction of the AMSR-E seasonal cycle prior to the CDF-matching. The root-zone soil moisture analyses, and subsequent latent and sensible heat forecasts are compared for the assimilation of i) the once daily AMSR-E near-surface soil moisture, ii) the 6-hourly screen-level observations, and iii) a combined assimilation of both data types. Additionally, the soil moisture analyses are evaluated against ground-based observations of soil moisture from the SMOSREX and SMOSMANIA networks in southern France.

24

USING SMOS OBSERVATIONS IN ECMWF’S LAND SURFACE ANALYSIS SYSTEM

Matthias DRUSCH1 ([email protected]), Patricia DE ROSNAY2, Joaquin MUNOZ-SABATER2 and Gianpaolo BALSAMO2 European Space Agency, ESTEC, Noordwijk, The Netherlands1 European Centre for Medium-Range Weather Forecasts, Reading, UK2

The European Space Agency’s third earth explorer SMOS (Soil Moisture and Ocean Salinity) will be launched in September 2009. SMOS has been designed to make global observations of soil moisture and ocean salinity for at least three years. SMOS will demonstrate a completely new measuring technique and carry the first-ever polar-orbiting space borne 2D interferometric radiometer. Within the framework of the SMOS data assimilation study, the European Centre for Medium-range Weather Forecasts (ECMWF) is currently revising the operational land surface analysis system to make optimal use of the SMOS brightness temperature observations.

In the presentation we will give an overview of the SMOS mission and the near real time data product, introduce ECMWF’s revised Kalman filter based land surface analysis system and show first results for the soil moisture analysis. Using screen-level parameters, i.e. 2m temperature and relative humidity, the Kalman filter based system performed better with respect to the vertical distribution of analysis increments. The forward operator transferring the model’s first guess surface fields into top of atmosphere brightness temperatures will be presented and evaluated using L-band observations from the 1973 Skylab mission and re-analysis data. A simple bias-correction scheme based on cumulative distribution function matching will be introduced and the impact of satellite derived surface soil moisture data on the analysed soil moisture fields will be shown using data from the TRMM Microwave Imager. The potential impact on the forecast skill will be discussed.

FUTURE SATELLITE DATA PRODUCTS SUITABLE FOR LAND SURFACE ANALYSES

Matthias DRUSCH ([email protected]) and Mark DRINKWATER European Space Agency, ESTEC, Noordwijk, The Netherlands

The European Space Agency (ESA) is currently developing five new missions called Sentinels specifically for the operational needs of the joint European Commission–ESA GMES programme. Each Sentinel mission is based on a constellation of two satellites to fulfil revisit and coverage requirements to provide robust datasets for GMES Services.

• Sentinel-1 is a polar-orbiting, all-weather, day-and-night radar imaging mission for GMES land and ocean/ice services. The first Sentinel-1 satellite is planned for launch in the 2011-12 timeframe. • Sentinel-2 is a polar-orbiting, multi-spectral high-resolution imaging mission for GMES land monitoring to provide, for example, imagery of vegetation, soil and water cover, inland waterways and coastal areas. Sentinel-2 will also provide information for emergency services. The first Sentinel-2 satellite is planned for launch in the 2012 timeframe. • Sentinel-3 is a multi-instrument mission to determine parameters such as sea-surface topography, sea- and land-surface temperature, ocean colour and land colour with high-end accuracy and reliability. The first Sentinel-3 satellite is planned for launch in the 2012 timeframe.

The presentation will introduce the future level 1 products and level 2 products related to land surface applications and outline potential applications in numerical weather prediction.

As part of the ESA’s Living Planet Programme there are also currently two candidate ESA Earth Explorer Candidate, CoReH2O and BIOMASS, that focus on land surface applications. Their current status in Phase A will be presented together with a summary of the scientific studies and their relevance for weather forecasting.

25

THE TELECONNECTION BETWEEN SEA SURFACE TEMPERATURE ANALYSIS FROM IN SITU DATA AT EAST MOLE, LAGOS AND GLOBAL WARMING

EDIANG, Okuku Archibong, EDIANG, Aniekan Archibong ([email protected]) Nigerian Meteorological Agency, PMB1215 Oshodi Lagos, Nigeria.

Marine weather observers have since 1988 been making sea surface temperature observations at East mole station, about 2 kilometres from the Coast. The station uses the rubber sea – temperature bucket thermometer and makes observations on hourly basis, sea surface temperature has influence on Lagos coastal weather and it is important especially for coastal fishermen, offshore oil and gas industries, shipping vessels, coastal recreational and port handling facilities. Some evidences of global warming in Nigeria have been observed using sea surface temperature (SST) for the period of 1989 – 2007 which statistically analyzed, results shows that the Nigerian coastal waters is warmest in April and Coldest in August. The period 1989 – 2007 mean yearly data of sea surface temperature (SST) show some of the teleconnections with global warming.

The attempt in this paper is however to highlight the features of sea surface temperature over the Lagos coastal waters. Indicating the global warming is evident in the environment of Nigeria Coastal line.

UNDERSTANDING OCEAN SURGES AND POSSIBLE SIGNALS OVER THE NIGERIAN COAST

EDIANG, Okuku Archibong, EDIANG, Aniekan Archibong ([email protected]) Nigerian Meteorological Agency, PMB1215 Oshodi Lagos, Nigeria.

Twenty Seven occurrence of ocean surge events. Over the beach of the Victoria island in Nigeria were recorded between 1994 to 2008 and each with its devastating consequences resulting from the massive flooding and erosion. Statistical analysis and parametric wind-wave model were used to investigate the ocean atmospheric interactions in terms of theirs characteristics, especially before during and after every surge event from 1994-2008.

It revealed that all ocean surges apart from the surge of March 2002 were experienced in summer months of April to October, but more frequent in August. Analysis of the trend and pattern of sea surface temperature variations were carried out with the obtained sea surface temperature data. The mean monthly observations for each year of storm surges for the period 1994 – 2008, excluding 1999, 2003, 2004, 2006 due to lack of data. Were statistically treated to obtain the mean yearly sea surface temperature values and dates of storm surges. Further investigations revealed that the ocean surges are influenced by moderate winds (between 15 – 18kts in the strength on the average) over the retch (Lat. 100S – 200S and Long 00E – 100E). These winds were observed to be generally strongest three to two days before the even. They can generate wave height of about 1.8m and with favourable cross equatorial flow, the swell may reach the coast in about 2 – 4 days and when they coincide with high tide they can inundate the beach. The highest mean wind speed are between 5.8m in 1994 and 4.1m in 2002 and 3.6m in 2005 respectively.

CHEMICAL DATA ASSIMILATION WITH MULTISCALE EMISSION INVERSION

Hendrik ELBERN ([email protected]), Achim STRUNK Rhenish Institute for Envoironmental Research at the University of Cologne

Data assimilation in atmospheric chemistry differ from meteorological assimilation schemes in two ways, at least:

1. initial values are of less importance, and 2. spatial structure functions for forecast error covariances are not sufficiently well approximated by homogeneous and isotropic correlations.

The presentation addresses both items. In the first case, it is the emission rates which are of primary importance for polluted areas, rather than initial values. It will be shown that, as a rule, those parameters are to be considered for optimisation, which control the model evolution and, at the same time, the knowledge of which is poor. This applies mainly to emission rates.

26

Another critical feature of chemistry modelling, which must also be addressed in data assimilation, is the multi-scale nature of chemical regimes: point and line emissions distributed by local flow patterns must be contrasted with intercontinental transport. In this study, nested 4D-var chemical data assimilation improves not only emission rates of emitted observed species, but also of not observed precursors, if measurements of product species like ozone are assimilated.

As the second detail critical for chemistry data assimilation, the prevailing elongated and partly scale bridging patterns of tracers must be considered, as opposed to more circular geopotential structures. The related case dependent construction of the forecast error covariances is described, where the covariance matrix is replaced by a diffusion operator. While the statistical properties of covariances are maintained, suitable choices of the local diffusion coefficients allow for flexible designs of structure functions and reduction of the numerical complexity from quadratic to linear order in space state.

A SCALE-BASED DISTORTION METRIC FOR MESOSCALE WEATHER VERIFICATION

Chermelle ENGEL1,2 ([email protected]) and Todd LANE2 Bureau of Meteorology1, Melbourne University2

Verification of high-resolution mesoscale weather forecasts has become increasingly important in recent years due to increases in model resolution and modelling of mesoscale physical processes. Traditional verification scores such as mean square error or variance have limited use in terms of assessing the value of these types of forecasts, and can actually produce misleading results. In order to get around this problem, new verification measures are currently being developed.

One avenue of verification development has been the use of algorithms based on distortion- or optical-flow. While these methodologies may show promise when applied to test cases such as those from the Spatial Forecast Verification Intercomparison Project, they may encounter less favourable results when applied to meteorological fields with field motion/placement error dependent upon scale.

This talk will address a new type of verification measure combining scale-decomposition and a distortion- or optical-flow based technique to characterize distortion error with scale. The capability of this technique to perform will be assessed using both simple and more complex idealized examples. Comparisons with existing methodologies will be discussed along with links data assimilation.

PRECURSORY SIGNALS OF SIGNIFICANT WEATHER EVENTS FOUND IN ENSEMBLE REANALYSIS ALERA

Takeshi ENOMOTO1 ([email protected]), Miki HATTORI2, Takemasa MIYOSHI3 and Shozo YAMANE4 Earth Simulator Center, Japan Agency for Marine-Earth and Science and Technology1 Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology2 University of Maryland3 Doshisha University4

An experimental ensemble reanalysis dataset was produced with a data assimilation system composed of the atmospheric general circulation model for the Earth Simulator (AFES, Numaguti et al. 1997; Ohfuchi et al. 2004; Enomoto et al. 2008) and the local ensemble transform Kalman filter (LETKF, Hunt et al. 2007) in a collaborative project among the Japan Meteorological Agency, Japan Agency for Marine-Earth Science and Technology and Chiba Institute of Science (Miyoshi et al. 2007, SOLA). Although its duration is limited to about one and a half years from May 2005, this dataset, called ALERA (AFES-LETKF experimental ensemble reanalysis), provides extra information not present in conventional reanalysis datasets: the ensemble spread that represents the analysis error.

The ensemble spread not only provides a measure of quality of the analysis accuracy but also represents dynamical uncertainty of the flow. It is found that the ensemble spread appears to provide precursory signals of some significant weather events. For example, a region with large ensemble spread in the subtropical anticyclone often develops into a tropical cyclone. It is speculated that the ensemble spread becomes large due to differences in timing and intensity of convections among ensemble members.

There are more examples that indicates precursory nature of the ensemble spread. The Somali jet extends eastward during the onset of the Indian summer monsoon. Its leading edge accompanies large ensemble spread of the winds in the lower troposphere. The westerly bursts in the eastern Indian Ocean occur a few

27 days after rapid increase of the ensemble spread. Over Vietnam, the ensemble spread increases prior to the onset of the monsoon westerly or intraseasonal variation of precipitation. In the upper stratosphere, sudden warmings follow the rapid divergence of the temperature there among ensemble members. These findings would suggest that ensemble reanalysis allow us to explore new aspects of atmospheric phenomena.

REDUCED ARCTIC SEA ICE HINDERS ACCURATE CLIMATE MONITORING - IMPACT OF DEPLETED ARCTIC DRIFTING BUOY NETWORK

Jun INOUE1, Takeshi ENOMOTO2 ([email protected]), Takemasa MIYOSHI3, and Shozo YAMANE4 Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology1 Earth Simulator Center, Japan Agency for Marine-Earth and Science and Technology２ Department of Atmospheric and Oceanic Science, University of Marylanｄ3 Department of Environmental Systems Science, Doshisha University４

Arctic drifting buoys, which are deployed over the sea ice and drift with it, are being used to measure meteorological information such as surface pressure and air temperature. Yet, the recent rapid decrease in thick sea ice has limited opportunities for buoy deployments. An area in which buoy deployment is impossible, due to a large expanse of open water area during summer, will not provide data either in summer or in the subsequent winter, leaving large areas without data. This has raised concerns over the deterioration of the accuracy of reanalysis data, highlighting the need for evaluating the impact of buoy observations on the data and for the future management of the buoy network. We investigated the impact of Arctic ice-drifting buoys on an experimental ensemble reanalysis called ‘ALERA’. The ALERA, where the buoy data are assimilated, includes the analysis ensemble mean and spread for each prognostic variable. In the data set, ensemble spreads of surface variables were found to be small only in the regions of densely aggregated buoys. Comparing the ALERA and the data set without the assimilation of surface pressure data observed by the buoys, differences in the ensemble mean and spread between two data sets were locally large, modifying air temperature and winds near the surface. Examining the effect of Arctic-buoy distribution on long-term reanalysis data sets, it was found that the amount of cross-ensemble spreads derived from common reanalysis is very sensitive to the number of buoys. This suggests that data set accuracy might be more vulnerable to deterioration in the near future due to fewer opportunities for buoy deployments over the sea ice; thus the findings of this study provide quantitative evidence of the need for sustainable international efforts to maintain and expand the network of in-situ meteorological observations in the Arctic Ocean.

CHANGES TO THE GLOBAL OBSERVING SYSTEM – EVOLUTION OR DESIGN?

John EYRE ([email protected]) Met Office, UK

The Global Observing System (GOS) of WMO supports a wide range of observational needs of WMO programmes, including those of numerical weather prediction (NWP). Through its Rolling Review of Requirements (RRR) process, the WMO Commission for Basic Systems keeps under review the users’ requirements for observations and the capabilities of present, planned and proposed observing systems to meet these requirements. The RRR process will be described, including the role within it of the new “Vision for the GOS in 2025”.

Among the key sources of information informing the RRR process, in its consideration of the needs of operational NWP, are the impact studies (OSEs and OSSEs) run by data assimilation centres. Through these studies, the impacts of current observations and the potential impacts of future observations are assessed. These results help to inform recommendations on the future of the GOS. Some key findings from recent studies will be presented.

The GOS is a complex system of systems, which is not designed or managed as a unitary system. Changes take place in a continuous, piecemeal and “evolutionary” manner, building on the strengths of existing systems while seeking to take advantage of the potential of new observing technologies. Nevertheless, the evolution of the GOS is expected to take place in response to an international “Vision”, and to this extent the GOS can also be considered to be designed. Moreover, at a lower level, changes to components of the GOS require specific design decisions. Some of these decisions benefit from specific input from NWP impact studies. Examples will be given and, in particular, areas will be highlighted in which particular questions concerning the future direction of the GOS would benefit from specific data assimilation experiments.

28 ANTARCTIC LACUSTRINE ENVIRONMENT AS A RESULT OF CLIMATE CHANGE AND HUMAN IMPACT

Irina FEDEROVA1, ([email protected], [email protected]), Tatiana POTAPOVA2, Maria ROMANOVSKAYA2 1Arctic and Antarctic Research Institute, 2Saint-Petersburg State University

Lakes of Antarctic oases are one of the mark points of climate changes. Antarctic oases – area without ice covering, but their environment totally depend on glacial conditions and sea level re- or transgressions influence. Resent hydrological and hydrochemical regimes of oases’ lakes are the mirror of Quaternary as well as last ten years anthropogenic press.

Investigation of the East Antarctic oasis Schirmaher lakes in summer period 2006-2007 and 2007-2008 allows getting some significant results. In spite of ultra fresh water in most part of oasis lakes the hydrochemical type is chloride-natrium (a relict marine origin type). It means that marine factors – sea level transgressions and transport of salt aerosol from an ocean – determine hydrochemical features of water, for the most part.

At present a glacial edge on Schirmaher oasis steps back and wetting of the area seemingly should be increase, but only 30 % of lakes depressions are with water in a summer period. It shows oasis dehydration. This fact is confirmed by decreasing of seasonal water streams quantity.

Anthropogenic impact for the last time was noticed in some Antarctic lakes also due to biogenic elements high concentration in water of lakes nearby polar stations. Absence of correct water management along with oasis deglaciation could be reasons of unique Antarctic lacustrine ecosystem degradation in future.

Investigation as distant objects as Antarctic lacustrine system and comparison of previous and modern data allow getting environmental information about climate change and human impact of more extended area round south Pole.

AN INVESTIGATION OF MODEL ERROR IN A QUASI-GEOSTROPHIC, WEAK-CONSTRAINT 4D-VAR ANALYSIS SYSTEM

Michael FISHER ([email protected]) European Centre for Medium-Range Weather Forecasts

Weak-constraint 4D-Var with an extended assimilation window may be viewed as a method for solving the Kalman filter equations, in that the solution at the end of the window converges towards that of the Kalman filter as the length of the assimilation window is increased. This was demonstrated for a simple one- dimensional nonlinear system by Fisher et al. (QJRMS, 2005).

In this paper we extend this result to a two-level quasi-geostrophic system.

A common approach in Kalman filtering studies for idealised systems is to perturb the assimilating model by adding an explicit, temporally uncorrelated noise term, with a prescribed spatial covariance structure Q. The Kalman filter is then implemented using this same model-error covariance matrix. In effect, the Kalman filter includes a perfect statistical description of the model error.

An alternative approach to is to make the assimilating model imperfect by perturbing some of its parameters.

We compare idealised weak-constraint 4D-Var analyses for the perfectly-described model error scenario with analyses in which the assimilating quasi-geostrophic model is run with perturbed layer depths. In both cases, the covariance matrix of model error used by the analysis is constructed from a large sample of perturbed and un-perturbed model integrations.

We find that analyses in which the model error is perfectly described by the assumed covariance matrix of model error are much more accurate than can be achieved in the perturbed-parameters case. We believe that this is because the model errors are poorly described by the assumed covariance model. We show that the perturbed-model errors are strongly correlated in time, and are significantly inhomogeneous and anisotropic. We suggest that accounting for the temporal correlation of model error, as well as its spatial inhomogeneity and anisotropy is likely to be important for any real-world, weak-constraint or Kalman filter based analysis system.

29

ASSIMILATION OF MODIS SNOW COVER DATA AND AMSR-E SNOW WATER EQUIVALENT DATA INTO SNOWMODEL

Steven J. FLETCHER ([email protected]), Glen E. LISTON, Christopher A. HIEMSTRA and Steve D. MILLER Cooperative Institute for Research in the Atmosphere, Colorado State University

One of the big challenges in snow modeling involves how best to assimilate available remote sensing data to enhance information content and constrain the uncertainty within snow evolution models. Snow cover and snow water equivalent (SWE) are available from current NASA satellite sensors; (250-500 m) snow-covered area observations at 250-500m resolution are available from MODIS and SWE estimates at 25 km resolution are available from AMSR-E. However, these data are at coarser spatial and temporal resolution than the high-resolution snow models. Our challenge is to combine available snow data from the satellite instruments with a high spatial and temporal resolution snow-evolution model in a way that realistically distributes snow cover and SWE observations over the complex terrain.

In this paper we present a 2D VAR (x y) DA system which uses a high resolution snow evolution model for the system’s modeling component. The system is designed to assimilate both MODIS snow cover and AMSR-E SWE data. Test simulations were performed for the 2006-2007 winter season over two high plains domains: the first is centered on the borders of Colorado, Wyoming and Nebraska (Platte River watershed), and the second is located in southeast Colorado (Arkansas River watershed). The two domains have variable weather, topography, and land cover features that all influence the snow distributions in these areas.

Provisional model runs over the two domains mentioned above, when compared to MODIS snow cover images, have shown the model to have cleared areas of snow when MODIS suggests that they should still be snow covered, hence demonstrating the need to assimilate MODIS into SnowModel to improve the representation of snow cover in the model.

POSITIONAL ERROR IN THE BOUNDARY LAYER CAPPING INVERSION

Alison FOWLER1 ([email protected]), Ross BANNISTER1, John EYRE2 University of Reading1, UK Met Office2

A new method for improving the assimilation of the boundary layer (BL) capping inversions is introduced and tested. The BL capping inversion is important for the correct diagnosis and forecast of stratocumulus clouds. It is often the case that the background accurately represents the structure of the BL capping inversion but gives it the wrong vertical location which is assumed to be correctly diagnosed within the observations. The sharp structure of the inversion means that in conventional data assimilation schemes it is difficult to, in effect, move the background inversion vertically towards the observations without loosing its structure. This problem is made worse by the use of static background error covariances which do not represent the de- correlation in errors between the BL and free tropospheric air. In order to allow for this vertical movement of the background inversion a new control variable is introduced which gives the background inversion height an associated error. This variable is then used to describe the explicit vertical movement of the background inversion and error structure in the analysis which gives an improved fit to the observations.

DATA ASSIMILATION IN MORPHODYNAMICAL MODELS

Ivan D. GARCÍA TRIANA1 ([email protected]), Ghada EL SERAFY12, and Arnold W. HEEMINK1 Delft University of Technology1, Deltares (WL | Delft Hydraulics)2

Due to the nature of nearshore morphodynamics, models must consider several natural processes that interact with each other. In the case of Delft3D, a wave model must provide the necessary boundary conditions for a flow model. The flow model provides the velocity vector field necessary for the assessment of sediments transport which is done with a morphology module. An iterative approach is necessary since changes in bathymetry affect the flow conditions of the system. Since assessing an accurate velocity vector field, especially in the nearshore area, is already a challenge and the interaction between models is troublesome, modeling these processes is complex and extremely expensive.

High uncertainties are found in the driving forces, parameters and input variables. Previous works have shown that near-shore morphodynamical models are especially sensitive to wave parameters. An accurate

30 estimation of these parameters is important for improving forecasting capabilities;hence, the implementation of a data assimilation procedure would be optimal.

Unfortunately, execution times makes the implementation of the ensemble kalman filter prohibitive and the complexity of the overall model makes the construction of an adjoint of its tangent linear approximation unfeasible. To cope with these limitations, a novel model-reduction technique along with a 4DVar data assimilation process is being implemented with the aim of parameter optimization. This approach does not require the adjoint of the tangent linear approximation of the original model. In this paper a twin experiment is presented of a model reduced 4DVar scheme that combines the results of the model with the observations and optimizes the parameters of interest. Two reduced models have been constructed based on principal component analyses: one using the software system Delft3D, the other using a combination of Delft3D and another near-shore morphodynamical models. The first results are very encouraging, but still show a lot of room for improvement.

ALL-SKY ASSIMILATION OF MICROWAVE OBSERVATIONS SENSITIVE TO WATER VAPOUR, CLOUD AND RAIN

Alan GEER ([email protected]), Peter BAUER, Philippe LOPEZ and Deborah SALMOND European Centre for Medium-range Weather Forecasts (ECMWF)

Rain- and cloud-affected microwave imager observations have been assimilated at ECMWF since 2005. Originally, observations were split into “clear” and “cloudy” streams, with the clear observations being used directly in the four-dimensional variational assimilation (4D-Var) and the cloudy observations passing through an initial 1D-Var step, after which a total column water vapour (TCWV) pseudo-observation was assimilated into 4D-Var. A new “all-sky” system, introduced operationally in March 2009, assimilates all microwave imager observations, whether clear, cloudy or rainy, directly in 4DVar. The radiative effect of clouds and precipitation are simulated where necessary, and for the first time observational information on hydrometeors can be fed directly into the analysed fields. An advantage of the new approach is that the first guess can be corrected where the model is cloudy and the observations are clear. While the all-sky system shows roughly the same forecast performance as the old one, it gives a starting point from which to really begin improving the cloud and rain analysis. One of the areas that still needs work is the treatment of cloud in the 4D-Var minimisation. At the moment, cloud is not included in the control vector, which means it is difficult to create cloud at the beginning of the assimilation window. Only later in the window can the model, when needed, create cloud to match the observations. Another area that we are working on is the bias correction, which needs to be able to deal with synoptically-dependent biases in the ECMWF cloud and rain fields.

COMPARISON OF OBSERVATION IMPACTS IN TWO FORECAST SYSTEMS USING ADJOINT METHODS

Ronald GELARO1 ([email protected]), Rolf LANGLAND2, and Ricardo TODLING1 NASA Global Modeling and Assimilation Office1, Naval Research Laboratory2

An experiment is being conducted to compare directly the impact of all assimilated observations on short- range forecast errors in different operational forecast systems. We use the adjoint-based method developed by Langland and Baker (2004), which allows these impacts to be efficiently calculated. This presentation describes preliminary results for a “baseline” set of observations, including both satellite radiances and conventional observations, used by the Navy/NOGAPS and NASA/GEOS-5 forecast systems for the month of January 2007. In each system, about 65% of the total reduction in 24-h forecast error is provided by satellite observations, although the impact of rawinsonde, aircraft, land, and ship-based observations remains significant. Only a small majority (50-55%) of all observations assimilated improves the forecast, while the rest degrade it. It is found that most of the total forecast error reduction comes from observations with moderate-size innovations providing small to moderate impacts, not from outliers with very large positive or negative innovations. In a global context, the relative impacts of the major observation types are fairly similar in each system, although regional differences in observation impact can be significant. Of particular interest is the fact that while satellite radiances have a large positive impact overall, they degrade the forecast in certain locations common to both systems, especially over land and ice surfaces. Ongoing comparisons of this type, with results expected from other operational centers, should lead to more robust conclusions about the impacts of the various components of the observing system as well as about the strengths and weaknesses of the methodologies used to assimilate them.

31

AMSR-E PASSIVE MICROWAVE SOIL MOISTURE AND DYNAMIC OPEN WATER FRACTION

Ben GOUWELEEUW1 ([email protected]), Albert VAN DIJK1, Juan Pablo GUERSCHMAN1, Peter DYCE1, Manfred OWE2 and Richard DE JEU3 1CSIRO Land and Water, Canberra ACT 2601, Australia 2NASA/Goddard Space Flight Center, Greenbelt, 20771 MD, USA 3Vrije Universiteit, Amsterdam 1081 HV, The Netherlands

Low-frequency Passive Microwave (PM) data has the potential to improve Soil Moisture (SM) fields operationally produced by Land Surface Models (LSMs) and Numerical Weather Prediction (NWP) models. Conditions under which SM cannot be accurately retrieved from PM sensors include precipitating clouds, dense vegetation, snow cover, frozen soil and surface water. Typically, quality control masks are provided to screen data affected by these conditions. While most of these masks are dynamic and can be derived from ancillary data, the mask for open water is generally static and refers to coastal areas and large continental lakes only. Due to the high dielectric constant of water, however, even a small sub-pixel fraction of open water, may result in a non-negligible SM overestimation.

The NASA-VU Amsterdam Land Surface Parameter Retrieval Model (LSPRM) has demonstrated skill for providing independent estimates of land surface parameters, such as SM, Land Surface Temperature (LST) and vegetation Optical Depth (OD). Satellite retrievals of these parameters may be combined with simulated and observed data in an assimilation scheme in order to generate the best possible data fields. These data may then be used to initialize NWP models and aid in continuous bias correction. Prior to embarking on such data assimilation efforts using LSPRM, model output from the Land Information System (LIS) LSMs developed at NASA/GSFC is compared to PMSM and station data for the Mesonet observational grid in Oklahoma, USA.

Analysis reveals AMSR-E PMSM over South-East Oklahoma demonstrates a distinct seasonality not observed in ground-observed or LSM simulated SM. PMSM overestimates SM in the cold half of the year, which appears to be correlated with the dynamics of an open water fraction cover, computed as the Open Water Index (OWI) from 1 km 16-day composite MODIS reflectance data. The results indicate regional variations in the fraction of open water have a profound effect on global AMSR-E PMSM, which is currently not accounted for.

NETWORK DESIGN AND ASSESSMENT FOR A TSUNAMI OBSERVING SYSTEM

Diana GREENSLADE1 ([email protected]) and Jane WARNE2 1Centre for Australian Weather and Climate Research, Australian Bureau of Meteorology 2Observations and Engineering Branch, Australian Bureau of Meteorology

A critical component of any tsunami warning service is the confidence that all tsunamis will be detected. Tsunamis are best detected through variability in sea-level and this is provided by coastal tide gauges and deep-water tsunameters. In designing the sea-level observing network for Australia, many potential regional tsunami source zones need to be considered, such as the Makran trench in the north-west Indian Ocean, the Puysegur trench south of New Zealand, a number of subduction zones in the south-west Pacific Ocean and, of course, the Sunda trench along the north-eastern boundary of the Indian Ocean. The network design is based on a series of zones that are related to specific sections of the subduction zones surrounding Australia. To evaluate the design, the Joint Australian Tsunami Warning Centre’s tsunami scenario database (T1.1) was used to determine the travel time from all sources to all measurement sites and a number of forecast or land-fall points. A variety of network configurations were tested to determine if they provided adequate warning. This test can also determine the level of redundancy and whether a particular measurement site provides value to the network. From these results a priority can be assigned to each measurement site which can be used to ensure the operational health and effective management of the network.

32

ENKF LOCALIZATION TECHNIQUES AND BALANCE

Steven GREYBUSH1 ([email protected]), Eugenia KALNAY1, Kayo IDE1, and Takemasa MIYOSHI1 1 University of Maryland, College Park, Maryland, USA

One of the strengths of data assimilation with the Ensemble Kalman Filter (EnKF) is the ability of estimates of the model covariance to evolve with time, using the flow-dependent information inherent in an ensemble of model runs (Kalnay, 2003). Spatial localization modifies the covariance matrices to reduce the influence of distant regions (Houtekamer and Mitchell, 2001), removing spurious long distance correlations (Anderson, 2007). In addition to allowing efficient parallel implementation, this takes advantage of the ensemble's lower dimensionality in local regions (Hunt et. al., 2007). There are two primary methods for localization. In B- localization, the model covariance matrix elements are reduced by a Schur product (Hamill et al., 2001) or modification function (Bishop and Hodyss, 2007) so that grid points that are far apart show no statistical relationship. In R-localization, the observation covariance matrix is multiplied by a distance-dependent function, so that far away observations are considered to have infinite error (Hunt et al. 2007; Miyoshi 2005).

Successful NWP depends upon well-balanced initial conditions. Lorenc (2003) and Kepert (2006) note that localization can disrupt the relationship between the height gradient and the wind speed of the analysis increments, resulting in an analysis that can be significantly ageostrophic. This study compares the accuracy (RMSE) and geostrophic balance of EnKF analyses using no localization, B-localization, and R- localization. The investigation begins with a simple one-dimensional balanced waveform using the shallow water equations. Results suggest a tradeoff between balance and accuracy, with differing optimal localization length scales for B-localization and R-localization. We are expanding the comparison of B- and R- localizations by including a global general circulation model, SPEEDY (Molteni, 2003). Additionally, a discussion of various metrics of imbalance, including the ageostrophic wind, second derivative of surface pressure, and comparison with a digitially filtered field (Lynch and Huang, 1992) will be included.

IMPACT OF ADVANCED SOUNDER RADIANCES IN THE FRENCH NUMERICAL WEATHER PREDICTION MODELS

Vincent GUIDARD1 ([email protected]), Nadia FOURRIE1, Thomas PANGAUD1, Florence RABIER1 1 Météo-France and CNRS, CNRM-GAME

The aim of this presentation is to describe the developments performed at Meteo-France to assimilate the IASI and AIRS radiances for clear and cloudy observation conditions.

Currently, 54 AIRS channels and 64 IASI channels are assimilated in operations, both in the global model ARPEGE 4D-Var and in the limited-area model ALADIN 3D-Var. They both provide information on temperature mainly from 50 hPa down to 650 hPa. Clouds are detected using the McNally and Watts (2003) method, which flags clear or cloudy each channel in a profile. Data are bias corrected with an adaptative variational method (namely VarBC).

Cloud affected radiances used to be rejected from the assimilation (90% of total observations). The under- exploitation of these sounding instruments and the fact that sensitive regions are often cloudy, motivated our research efforts to assimilate AIRS and IASI cloudy radiances. The assimilation of AIRS radiances affected by low clouds inside the variational assimilation scheme has been implemented in the operational configuration. Cloud-top pressure and net emissivity are calculated offline by a CO2-Slicing algorithm. These cloud parameters are then provided to the radiative transfer model to simulate cloudy radiances from the background. Impact on the forecast of assimilating AIRS cloud-affected radiances is significantly positive, especially for long-term forecasts.

The CO2-slicing approach used to assimilate AIRS cloudy radiances is currently extended and adapted to IASI data. The impact of the additional cloudy IASI radiances will be studied with global forecast scores and through impact studies on Atlantic storms of January 2009. The assimilation of IASI radiances is also extended to some water vapour channel and is currently evaluated. First results showed a positive impact on the forecasts.

Similar approach will be evaluated to assimilate cloud-affected SEVIRI radiances in LAM ALADIN on top of clear ones, using observation files provide information to derive cloud parameters.

33

THE CONCORDIASI FIELD CAMPAIGN OVER ANTARCTICA

Florence RABIER1, Aurélie BOUCHARD1, Eric BRUN1, Alexis DOERENBECHER1, Stéphanie GUEDJ1, Vincent GUIDARD1 ([email protected]), Fatima KARBOU1, Vincent-Henri PEUCH1, Laaziz EL AMRAOUI1, Dominique PUECH1,Christophe GENTHON2, Ghislain PICARD2, Michael TOWN2, Albert HERTZOG3, François VIAL3, Philippe COCQUEREZ4, Stephen A. COHN5, Terry HOCK5, Jack FOX5, Hal COLE5, David PARSONS5, Jordan POWERS5, Keith ROMBERG5, Joseph VAN ANDEL5, Terry DESHLER6, Jennifer MERCER6, Jennifer HAASE7, Linnea AVALLONE8, Lars KALNAJS8, C. Roberto MECHOSO9, Andrew TANGBORN10, Andrea PELLEGRINI11, Yves FRENOT12, Jean-Noël THEPAUT 13, Anthony McNALLY13, Peter STEINLE 14

1CNRM/GAME (Météo-France and CNRS), Toulouse, France 2LGGE, Grenoble, France 3LMD, Paris, France 4CNES, Toulouse, France 5NCAR, Boulder, Colorado 6University of Wyoming, Laramie, Wyoming 7Purdue University, West Lafayette, Indiana 8University of Colorado, Boulder, Colorado 9UCLA, Los Angeles, California 10Global Modeling and Assimilation Office, Washington DC 11PNRA, Roma, Italy 12IPEV, Brest, France 13ECMWF, Reading, United-Kingdon 14CAWCR, Melbourne, Australia

Concordiasi is a field campaign, part of the IPY-THORPEX cluster, which takes place in Antarctica in order to validate satellite data assimilation at high latitudes. Our focus of interest is the hyperspectral IASI sensor on the European MetOp platform. The campaign is made up of two parts, each one during austral spring. In spring 2008, additional sondes have been launched at Dumont d’Urville and DomeC, measuring the atmospheric profiles up to 20km and above, synchronised with the MetOp track over these stations.

Many difficulties are associated with data assimilation over high latitudes, mainly due to the estimation of the emissivity for cold areas and to the cloud detection, important for infrared measurements. To improve our understanding of such parameters, the radiosoundings obtained after the first part of the campaign are compared to the output of the meteorological model of Météo-France. Before studying precisely the satellite retrievals based on the model fields at these stations, adjustment of the model for Antarctica has been done. The model has been tuned in order to increase the horizontal resolution for these latitudes. Moreover, developments on the calculation of microwave emissivity and the assimilation of infrared satellite data over high latitudes have lead to some analysis and forecast improvements, shown as a better fit to radiosoundings.

The second part of the campaign is planned for the 2010 austral spring. Stratospheric balloons will bring complementary information for satellite data assimilation but also on chemical and stratospheric dynamical processes. Six of these balloons will carry GPS receivers and in-situ instruments measuring temperature, pressure, ozone, and particles. All the balloons are capable of releasing dropsondes on demand, for measuring atmospheric parameters. The strategy for their release will depend on the MetOp track on the one hand, and on sensitive areas on another hand.

CHEMICAL SOURCE BACKTRACKING IN TURBULENT BOUNDARY LAYER (TBL)

Ajith GUNATILAKA1 ([email protected] ), Alex SKVORTSOV1, Branko RISTIC2, Mark MORELANDE3, Dinesh PITALIADDA1 and Ralph GAILIS1 1 2 Defence Science and Technology Organisation, HPP Division , ISR Division 506 Lorimer Street, Fishermans Bend, VIC 3207, Australia, The University of Melbourne, Melbourne Systems Lab, EEE Dept., Parkville, VIC 3052, Australia

The problem of chemical source backtracking is of great interest in application to ecological monitoring and defence systems. In the current paper we propose a framework to estimate the strength (emission rate) and the location of a chemical source which is continuously releasing a contaminant into the atmosphere. A network of spatially distributed chemical detectors is used to measure the concentration of contaminants at regular intervals and report to a fusion centre. Source parameters are estimated using a sequential

34 Bayesian framework, with the posterior expectation approximated using Monte Carlo integration. A progressive correction approach is used to deal with problems caused due to the prior distribution being much more diffused compared to the likelihood.

The statistical performance of the estimation algorithm is analysed using a synthetic dataset. This dataset was generated by using a well-known analytic solution of the turbulent diffusion equation for the mean concentration and a probability density function for the fluctuating part [1-4]. The robustness of the estimation algorithm in the presence of imperfectly known environmental parameters is investigated. We also discuss the performance of the algorithm when applied to real experimental concentration data collected in a water channel experiment which mimicked atmospheric TBL flow over an urban canopy.

References: A. Gunatilaka, B. Ristic, A. Skvortsov, M. Morelande, Parameter Estimation of a Continuous Chemical Plume Source, 11th Int. Conf. Info. Fusion, 2008 A. Gunatilaka, A. Skvortsov, and B. Ristic, “A synthetic environment for validating CB data fusion algorithms”, poster presentation, LWC, 2008 V. Bisignanesi and M. S. Borgas, Models for integrated pest management with chemicals in atmospheric surface layers, ECOMOD, 2007 A. T. Skvortsov, P. D. Dawson, M. D. Roberts and R. M. Gailis, “Modelling of flow and tracer dispersion over complex urban terrain in the atmospheric boundary layer”, WSEAS Trans. Fluid Mechanics, 2008

DEVELOPMENT OF A REGIONAL OCEAN REANALYSIS SYSTEM IN THE CHINA SEAS

Guijun HAN1 ([email protected]), Wei LI1, Xuefeng ZHANG1, Dong LI1, Zhongjie HE1, Xidong WANG1, Xinrong WU1 and Jirui MA1 National Marine Data and Information Service, Tianjin 300171, China1

A regional ocean reanalysis system in the China seas and the adjacent sea area has been developed recently. The regional ocean model used is a parallel version of Princeton Ocean Model with generalized coordinate system (POMgcs) with a domain covering an area extending from 10˚S to 52˚N in latitude and from 99˚E to 150˚E in longitude. A global version of the MIT general circulation model (MITgcm) is employed to provide open boundary conditions for the regional ocean model. A sequential three-dimensional variational (3DVAR) analysis scheme has been designed and implemented in both the regional and global model, using a multi-grid framework. Such sequential 3DVAR analysis scheme can be performed in three dimensional spaces which is totally different from the traditional 3DVAR which is performed on each model level with the vertical correlations ignored. This sequential 3DVAR analysis scheme can retrieve resolvable information from longer to shorter wavelengths for a given observation network and yield multi-scale, inhomogeneous analysis. The ocean model is forced by National Centers for Environmental Prediction (NCEP) reanalysis surface wind stress (combining QuikSCAT observing wind fields), heat, and water flux. By assimilating the oceanic observation data into the model, including satellite remote sensing sea surface temperature (SST), altimetry sea surface height (SSH), temperature and salinity profiles taken from Argo and World Ocean Database 2005 (WOD05) maintained by National Oceanographic Data Center (NODC), the reanalysis fields of sea surface height, temperature, salinity and current in the China seas and the adjacent sea area are produced which spans 20 years from 1986 to 2005.

RECENT DEVELOPMENTS IN DATA ASSIMILATION OF CHINESE NEW GFS

Wei HAN ([email protected]), Jishan XUE, Zhaorong ZHUANG, Yan LIU and Xueshun SHEN Chinese Academy of Meteorological Sciences, Beijing 100086,China

The brief description and results of pre-operational trials of Chinese new generation global forecast system (GFS) are presented in this paper. The new GFS is based on the global version of the unified NWP system GRAPES (Global/Regional Assimilation PrEdiction System) developed recently. The observational data assimilated include conventional rawinsondes and surface synoptic reports, and a variety of unconventional data such as ATOVS from NOAA series satellites and AMVs from geostationary satellites and polar satellites. One year pre-operational experiments of cyclic assimilation with 6 hour time window and 10 day forecasts initiating at 12 UTC every day have been conducted by use of archived data from Dec 1st 2006 through Nov 30th 2007. The predictions are verified not only with GRAPES analyses but with NCEP analyses as well to assess the performance of the whole GFS system. Limited by the computer resources available the spatial resolution of the experimental system is set to 1deg lat/long which is lower than the resolution of current operational system. Even with this low resolution, improvements of analyses and forecasts are evident comparing with the old system, especially in the southern hemisphere.

35

The impact tests of different observational systems on the analysis and forecast show that the assimilation of satellite data plays the main role in the improvement of assimilation and forecasts. However there are still obvious differences between current GFS and NCEP analyses resulting in the degradation of forecasts of the former. Further studies show that the difference in the usage of the observational data in some specific regions is a main factor responsible to the difference in the assimilations of the two systems, implying the necessity of optimization of the data screening and quality control algorithms.

Some preliminary results from assimilation of FY3A microwave radiances in GRAPES GFS will also be presented.

THE CHOICE OF THE “BEST” DATA ASSIMILATION ALGORITHM FOR SUBSURFACE CHARACTERIZATION

Remus HANEA1,2([email protected]) ([email protected]), Justyna PRZYBYSZ-JARNUT2 and Arnold HEEMINK2 TNO Knowledge for Business1, Delft University of Technology2

History matching (data assimilation) is the act of adjusting a reservoir model until its simulated production response closely reproduces the past behaviour of the reservoir. Once a reservoir model has been history matched, it can be used to simulate future reservoir behaviour with a higher degree of confidence, particularly if the adjustments are constrained by known geological properties of the reservoir (subsurface characterization).

The accuracy of the history matching process depends on the quality of the reservoir model (prior information) and the quality and quantity of measured data, typically production data or pressures. Manually adjusting reservoir parameters results in trial and error solutions: it is impossible to use all information; it is not suitable for complex reservoir models; it is time consuming and it provides suboptimal results.

Consequently, quantification of uncertainty in the description of the reservoir is a key-issue. This requires a proper understanding of the main uncertainty drivers. One solution for this problem would be to apply methods that allow for multiple matched reservoir models (realizations) and are able to deal with the uncertainty in an optimal fashion. Many of these methods have been studied and applied to history matching problems.

The main goal of this article is to shows that there is no “best candidate” for history matching (or data assimilation) in general (ensemble methods or variational methods); a customized choice will have to be made for a specific application based on a proper understanding of all these methods and taking into account the complexity of the problem, the quantity and quality data available, the quality of the prior knowledge (e.g. of the geology) and the knowledge about the uncertainties. So, more effort should be dedicated to a very good understanding of the application in hand, which finally leads to an arbitrary choice for the data assimilation approach that solves the inverse problem.

Examples with a simple 2D reservoir and a more realistic 3D reservoir are presented where different data assimilation methods were applied to prove these conclusions.

THE MOISTURE BUDGET OVER AMAZON REGION DURING THE MINI-BARCA CAMPAIGN

Dirceu L. HERDIES1 ([email protected]), Luiz F. SAPUCCI1, Luis G. G. GONCALVES2, João G. MATTOS1, Jose A. ARAVÉQUIA1 and Saulo B. COSTA1 1Centro de Previsão e Estudos Climáticos (CPTEC), Instituto Nacional de Pesquisas Espaciais (INPE), Cachoeira Paulista – SP, Brasil 2Hydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt – MD, USA

One of the largest problems in the study of the patterns of atmospheric circulation on South America is associated to the lack of observational data on that area, especially on the Amazonian region that dominates great part of the tropical section of South America. The combined use of observations and modeling is a powerful tool for the understanding of those patterns at global level and more precisely on the study area, due to the lack of data. Several efforts have been made to soften the actual situation, with the accomplishment of several field experiments. Among these experiments, one of the most important is the one of LBA and most recent Mini-BARCA/LBA, that occurred during June and November of 2008, a multidisciplinary experiment that involve several national and international Institutions in the objective

36 common of best understand the biogeochemical cycle of the Amazonian. The general objective of this study was the generation of a high resolution reanalysis (20 km) for the whole area of South America using all the collected data from the field experiment Mini-BARCA/LBA and including all the conventional and satellite dataset available during this period in the procedure of assimilation of data, with the specific objective to assess the impact of these observations on analyses in the Amazon region. The combined use of all these observation and modeling was very important for understanding the moisture budget over this region. The comparison between the analyses that include the extra data from the Mini-BARCA/LBA experiment shows the importance of regular observations over the Amazon basin. All of the dataset used in this study are available to the community at LBA webpage to download and use in additional validation/evaluation studies in the future.

INVERSE ESTIMATION OF EMPIRICAL PARAMETER IN A CIRCULATION MODEL FOR THE EAST ASIAN MARGINAL SEAS

Naoki HIROSE ([email protected]) Research Institute for Applied Mechanics, Kyushu University, Kyushu University, Japan.

A set of empirical parameters used in an ocean circulation model is calibrated by using Green’s function with constraint of a number of in-situ temperature, salinity and velocity measurement (Menemenlis et al., MWR, 2005). The optimized parameter reduces the cost function by ~18% compared to the worst case. The inverse estimation suggests that the surface wind stress should be reduced by ~25% to drive the ocean circulation properly. The missing part of the momentum energy may be dissipated into unresolved subgridscale variabilities of the upper ocean such as surface waves, turbulent mixing, or fine-scale internal waves. Minor river discharges can be represented by the precipitation over land within 80-85 km from the coastline. The horizontal viscosity and diffusion coefficients are increased to intensify northward heat transport in the Japan/East Sea. It is also reasonable for the calibrated parameter of bottom drag and bulk transfer coefficients to meet the traditional values.

A regional prediction system downscaled from 1/4°x1/5° to 1/12°x1/15° grid spacing is constructed based on the calibrated model. The z-coordinate ocean models are sequentially updated to realistic state by assimilating sea surface temperature and height data with a nudging method (Manda et al., JAOT, 2005) and a reduced-order Kalman filter (Hirose et al., JO, 2007), respectively. The high-resolution realistic estimates are crucial to the studies of regional oceanography, air-sea interaction and coastal fishery.

A CHEMICAL DATA ASSIMILATION SYSTEM FOR SOUTH AMERICA USING THE CCATT-BRAMS ATMOSPHERIC MODEL TO ACCESS THE IMPACT OF FIRE EMISSIONS

Judith J. HOELZEMANN1,2 ([email protected]), Karla M. LONGO,3, Hendrik ELBERN4,5, Saulo R. FREITAS1

1 CPTEC / INPE - Center for Weather Forecast and Climate Studies at the Brazilian National Institute for Space Research, Cachoeira Paulista, Brazil 2 CCST / INPE – Center for Earth System Sciences at the Brazilian National Institute for Space Research, São José dos Campos, Brazil 3 CEA / INPE - General Coordination of Spatial and Atmospheric Sciences at the Brazilian National Institute for Space Research, São José dos Campos, Brazil 4 RIU - Rhenish Institute for Environmental Research at the University of Cologne, Cologne, Germany 5 now also at Institute for Chemistry and Dynamics of the Geosphere II, Research Center Juelich, Germany

An aerosol and trace gas data assimilation system for South America is presented that consists of a three dimensional variational assimilation technique (3D-VAR) with a diffusion approach to account for background error covariances developed by the Rhenish Institute for Environmental Research (RIU) at the University of Cologne in Germany and the Coupled Chemistry-Aerosol-Tracer Transport model coupled to the Brazilian developments on the Regional Atmospheric Modeling System (CCATT-BRAMS) that is being developed at the Brazilian National Institute for Space Research (INPE).

37 The system assimilates Aerosol Optical Depth (AOD) of the Moderate Resolution Imaging Spectroradiometer (MODIS), and from the ground based AERONET (AERosol Robotic NETwork) and is currently being prepared for assimilating data from aircraft measurement campaigns and chemical trace gases. Inhomogeneous and anisotropic areas of influence for AERONET sites are included in the background error covariance matrix to exploit available observations as efficient as possible. Correlations were calculated by climatological statistics of MODIS and AERONET AOD during the burning season. The aim is to objectively improve numerical transport simulations of South American air pollution sources, mainly from fires, and their dispersion in the atmosphere over South America and the South Atlantic for operational chemical weather forecast and research purposes. Calculation of areas of influence and evaluation results of this assimilation system will be presented.

DATA ASSIMILATION IN EARLY PHASE OF RADIATION ACCIDENT USING PARTICLE FILTER

Radek HOFMAN ([email protected]), Václav ŠMÍDL, and Petr PECHA Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic

Exploitation of the data assimilation methodology in the field of radiation protection is studied. When radioactive pollutants are released into the atmosphere, a radioactive plume is passing over the terrain. The released radioactive material causes pathwayspecific irradiation which has detrimental effects on population health. In order to ensure efficiency of introduced countermeasures, it is necessary to predict spatial and temporal distribution of the aerial pollution and material already deposited on the ground. The predictions are made by the means of numerical dispersion models with many inputs. A group of the most significant input parameters affecting the dispersion process was selected using available sensitivity and uncertainty studies performed on dispersion models. Exact values of these parameters are uncertain due to the stochastic nature of atmospheric dispersion, hence the parameters are modeled as random quantities.

Data assimilation is the optimal way how to exploit information from both the measured data and expert- selected prior knowledge to obtain reliable estimates of the input parameters. Early identification of the parameters is essential for reduction of uncertainty of the radiation situation predictions. In this paper, sampling-importance-resampling algorithm (particle filter) is used to evaluate posterior distribution of estimated parameters and improve their estimates on-line as the plume is passing over the stationary measuring sites. The algorithm is tested on two scenarios. First, data assimilation of a simple Gaussian puff model is studied. Since it is a basic statistical approximation of the solution of the three dimensional advection-diffusion equation, its simplicity and transparency allow for better insight. Second, similar scenario with more advanced medium-range plume model with more estimated parameters is studied to assess scaling of performance of the approach. In the first scenario, topology of the simulated measurements is identical to the real topology of the Czech National radiation protection network maintained by the responsible authorities.

SIMULATION OF RANDOM 3-D TRAJECTORIES OF THE TOXIC PLUME SPREADING OVER THE TERRAIN

Petr PECHA, and Radek HOFMAN ([email protected] ) Institute of Information Theory and Automation, AV CR, v.v.i., Prague, Czech Republic

An efficient software tool for purposes of simulation of random evolution of the concentration distribution of toxic admixtures originally discharged into the atmosphere is presented. The main goal of the development is its application as a pivot algorithm of the multiple recalled root of the Sampling-Importance-Resampling procedure for online Bayesian tracking of the plume trajectory progress. A certain variant (e.g. variance reduction by marginalisation) of Particle Filter originating from common sequential Monte Carlo method with adaptive resampling is applied consequently in joint analysis for simulation of the posterior distribution of the system state (e.g. time and spatial distribution of the pollution concentration). The 3-D trajectories represent the “particles”, and during the resampling, those particles having small weights with regard to the measurements are eliminated.

The ensemble of 3-D trajectory realisations offers good basis for uncertainty analysis and studies of sensitivity. These analyses should involve uncertainties due to stochastic character of input data, insufficient description of real physical processes by parametrisation, incomplete knowledge of submodel parameters, uncertain release scenario, simplifications in computational procedure etc. It facilitates to follow the recent trends in risk assessment methodology insisting in transition from deterministic procedures to probabilistic approach which enables to generate more informative probabilistic answers on assessment questions. Limited number of the most important random model parameters is selected for parametrisation of the 3-D trajectories.

38

The environmental model of pollution transport itself is based on segmented plume-puff modification of the classical Gaussian approach. Our poster illustrates the results related both to the probability approach of consequence assessment and generation of inputs inevitable for assimilation (prior physical knowledge included in the background fields and model error covariance structure). Real scenario of radioactivity dissemination analysed here demonstrates the complexity of the problem requiring a good degree of understanding and ad hoc developments.

THE 2009 WRFDA OVERVIEW

Xiang-Yu HUANG National Center for Atmospheric Research, Colorado, USA

WRFDA - the Weather Research and Forecasting (WRF) Data Assimilation system, developed at the National Center for Atmospheric Research, is an advanced data assimilation system, which provides state- of-art 3D/4D variational (3D/4D-Var) and hybrid varational/ensemble techniques. It is a component of the WRF modeling system and under the WRF software framework.

WRFDA has become the analysis component of several operational data assimilation systems in the world. It has also found its applications in the research community. An overview of the system and its applications will be given at the symposium.

ENSEMBLE DATA ASSIMILATION AT ECMWF

Lars ISAKSEN ([email protected]) European Centre for Medium–Range Weather Forecasts, Reading, UK

Ensemble Data Assimilation (EnDA) is a method that uses an ensemble of 4D-Var analyses with perturbed observations and sea surface temperature fields. The EnDA system also includes a stochastic representation of model error. The EnDA system has recently been developed and implemented at ECMWF. It is used to improve the representation of initial uncertainty in the Ensemble Prediction System (EPS) and to quantify analysis uncertainty. Research is ongoing to use EnDA spread to introduce flow dependent background error in the deterministic 4D-Var system. We will describe the EnDA method and show how the performance depends on model and analysis resolution, representation of stochastic model errors and ensemble size. The ability to estimate analysis uncertainty will also be evaluated and discussed. The EnDA method has many applications. We will show how it is used to estimate static and flow-dependent background error, including advanced estimation of humidity background error. The benefit of using the EnDA method in the EPS will be shown. It will also be described how EnDA can be used as an alternative to an OSSE to evaluate the potential impact of ADM-Aeolus Doppler wind lidar measurements. Finally we will discuss the links between Variational Data Assimilation with an EnDA component and Ensemble Kalman Filter methods.

DEVELOPMENT OF A 4-DIMENSIONAL VARIATIONAL COUPLED DATA ASSIMILATION SYSTEM FOR ENHANCED ANALYSIS AND PREDICTION OF SEASONAL TO INTERANNUAL VARIATIONS

Nozomi SUGIURA1, Yoichi ISHIKAWA2 ([email protected], Toshiyuki AWAJI1,2, Shuhei MASUDA3, Hiromichi IGARASHI1, Takahiro TOYODA3, Yuji SASAKI1 and Yoshihisa HIYOSHI1 1:Data Management and Engineering Department, Data Research Center for Marine-Earth Sciences, JAMSTEC, Yokohama, Japan 2:Department of Geophysics, Kyoto University, Kyoto, Japan 3:Ocean Climate Change Research Program, Institute of Global Change Research, JAMSTEC, Yokohama, Japan

A four-dimensional variational (4D-VAR) data assimilation system using a coupled ocean-atmosphere global model has been successfully developed with the aim of better defining the dynamical states of global climate on seasonal to interannual scales. The application to the state estimation of climate processes during the 1990s shows, in particular, that the representations of structures associated with several key events in the tropical Pacific and Indian Ocean sector are significantly improved. This fact suggests that our 4D-VAR coupled data assimilation (CDA) approach has the potential to correct the initial location of the model climate attractor based on observational data. In addition, the coupling parameters that control the air-sea exchange fluxes of mass, momentum and heat become well adjusted.

39

Such an initialization using the 4D-VAR coupled assimilation approach allows us to make a roughly 1.5-year lead-time prediction of the 1997-1998 El Niño event. These results demonstrate that our 4D-VAR CDA system has the ability to enhance forecast potential for seasonal to interannual phenomena. Further, our group is struggling to break through a physical barrier to prediction, the so-called spring-barrier, to realize higher prediction skill with optimized bulk adjustment factors.

IMPACT OF 4D-VAR ASSIMILATION PRODUCTS ON BIO-GEOCHEMICAL SIMULATION

Yoichi ISHIKAWA1 ([email protected]), Toshiyuki AWAJI1,2, Hiromichi IGARASHI2, Shuhei MASUDA2, Nozomi SUGIURA2, Takahiro TOYODA2, Yuji SASAKI2 1Graduate School of Science, Kyoto University, Kyoto, Japan 2 Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Accurate descrtipitions of bio-geochemical fields and their variabilities are quite important for the better monitoring of marine environment and its prediction. Toward this goal, the marine ecosystem modeling has gathered inreasing interest in the past decade and has been energetically developed to the level capable of representing the basic bio-geochemical processes in actual oceans. Recent studies pointed out that realistic representation of the oceanic physical field is one of the critical issues in simulating realistic bio-geochemical processes. For this purpose, the optimal synthesis of observational data and ocean general circulation models (OGCMs) by an adjoint method has the advantage of creating dynamically consistent reanalysis datasets.

In this study, as a first step toward an innovative assimilation system that can integrate both physical and bio-geochemical data using the adjoint approach, we take up the challenge of coupling the lower trophic level ecological model, “NEMURO” (Kishi et al., 2007), with an OGCM to estimate 3-dimensional structures of biological and geochemical fields. In doing so, we used the reanalysis dataset derived from a 4- dimensional variational ocean data assimilation experiment (Masuda et al. 2006) as the background physical data. The assimilated elements in their experiment are mainly in-situ temperature and salinity data, OISST values, and sea-surface dynamic-height anomaly data. The OGCM covers a global ocean with a horizontal resolution of 1 degree in both latitude and longitude, with 36 vertical levels.

Using the ecological parameters in the northern North Pacific, the coupling experiments were performed for two cases in which the reanalysis dataset is used or not as the background physical data, respectively. The comparison between these two cases was made focusing on plankton biomasses and those variabilities. The result showed the significant difference in the biological and geochemical field and suggested the efficiency of the reanalysis dataset for the ecological state estimation.

ESTIMATES OF AIR-SEA FLUXES IN A TROPICAL CYCLONE USING AN ADJOINT METHOD

Kosuke ITO ([email protected] ), Yoichi ISHIKAWA, and Toshiyuki AWAJI Kyoto University

The adjoint method, which combines observations with a dynamical model using a variational approach, is one of the powerful candidates to improve physical parameter values as well as initial states suitable for real processes. Here, we illustrate the improved estimates of air-sea fluxes in a tropical cyclone as an example.

The transfer of momentum and heat between the atmosphere and the ocean is a crucial subject in tropical cyclone intensity prediction [Emanuel, 1986]. The behavior of drag coefficient Cd and heat coefficient Ck are actually determined on the basis of the extrapolations from the measurements in weak-to-moderate wind regimes. However, there is no consensus on the quantitative estimates of these coefficients for high-wind regime. The uncertainty causes the errors in the hurricane modeling. The adjoint method is one of the powerful candidates for reducing such uncertainty.

As a first step toward this goal, we employ a simple atmosphere-ocean coupled model, and perform an identical twin experiment. The atmospheric model is a nonhydrostatic, axisymmetric tropical cyclone model originally made by Rotunno and Emanuel (1987). The model is coupled to the one-dimensional ocean model developed by Schade (1999). A “true” case with the particular setting of the Cd,true and Ck,true is generated by the numerical integration of this model. The pseudo “observations” are generated by adding Gaussian noise to dynamical variables in the true run. The coefficients in the simulation run are set to the Cd,sim and Ck,sim, and the observations are incorporated through the adjoint method.

40 As long as observations are sufficiently assimilated, our result shows that the air-sea exchange coefficients are successfully corrected toward the “true” values. In the presentation, the impacts on the tropical cyclone intensity prediction and the feasibility of this system will be discussed in terms of the recent observational projects.

COMPARISONS OF BREWER-DOBSON CIRCULATIONS DIAGNOSED FROM REANALYSIS

Toshiki IWASAKI1 ([email protected]), Hisashi HAMADA1 and Kazuyuki MIYAZAKI2 Tohoku University1, Japan Agency for Marine-Earth Science and Technology2

A comparison is made of the stratospheric mean-meridional circulations, Brewer-Dobson (B-D) circulation, diagnosed with mass-weighted isentropic zonal means (MIM) from the reanalyses, JRA-25, ERA-40, ERA- Interim, NCEP/NCAR and NCEP/DOE.

The reanalyses coincidently exibit the climatological mean seasonality of B-D circulation, although they have considerable discrepancy of the magnitude of mean-meridional mass flux particularly in low-latitudes. In the northern hemisphere, meridional overturning circulation at 100 hPa is maximal in winter. On the other hand, in the southern hemisphere, it is maximal in fall and its value is much smaller than the southern hemispheric one. The interhemispheric difference may be understood in terms of planetary wave activities.

Interannual variability of B-D circulation in winter is coincident among the reanalyses, probably because wave-mean flow interactions of planetary waves are reasonably presented enough to drive mean-meridional flows in the data assimilation processes.

Yearly trends are not reliably observed, because they are small compared with large interannual variability and large inconsistency among the reanalyses. Further improvement of data assimilation system is desired to study climate change in the B-D circulation.

It should be noted that zonal mean vertical velocity is too noisy to identify the spatiotemporal variability of downward branch of B-D circulation, except for JRA-25 and ERA-Interim. In case of ERA-Interim, 4D-Var seems to be effective to suppress noises of vertical velocity and analyze realistic thermodynamic-dynamic equilibrium states.

IMPACT ASSESSMENT OF DATA ASSIMILATION ON FINE SCALE AIR DISPERSION FOR A COMPLEX TERRAIN

R JANA1 ([email protected]), S INDUMATI1, R SHRIVASTAVA1, R.B. OZA1, V.D. PURANIK1, H.S. KUSHWAHA2 1 Environmental Assessment Division 1,2 Health, Safety & Environment Group Bhabha Atomic Research Centre Mumbai-400076, India

The local land surface plays a crucial role in real-time monitoring and forecasting of ground level concentration (GLC) of pollutant. In view of this, the present study is conceded out to assess the impact of high resolution digital elevation model (DEM) on air dispersion. The influence of DEM, meteorological and micrometeorological conditions is compared through the distribution of GLC of SF6 as tracer gas. A continuous elevated point release of SF6 from a stack is assumed at a constant rate for a week. To study such impact, CALMET/CALPUF model, a regulatory model of Environmental Protection Agency (EPA), USA is used as it is suitable for a complex terrain. CALMET generates terrain following meteorological parameters in a spatial domain whereas CALPUFF produces GLC of SF6 in the present study, a Lagrengian, non-steady state puff model. The study is carried out at three levels.

The area undertaken is a complex terrain of India dominated by hilly forest with a reservoir. The domain of the study is 12 km × 12 km centering a 50 m stack. Hypothetically, atmospheric and land surface conditions are set at different level of the study. For this, a prognostic meteorological model called as part-1 of The Air Pollution Model (TAPM) developed by CSIRO, Australia is used to generate nudged wind, temperature and pressure profile for the study area. Nudging effect found satisfactory for which locally measured wind speed and direction data at several heights are utilized. Derived meteorological condition as data profile is implemented on CALMET at initial guess using TAPM. Finally, the study is carried over to capture the influence of fine scale resolution of DEM and major land surface heterogeneity on GLC of SF6.

41 The variation in wind field, micrometeorological parameters, and hence in GLC of tracer gas is compared at hypothetical receptor points. The variation in distribution of GLC with the progress of the hours is one fold to several folds at various receptor points due to the variation of spatial resolution of DEM. Distribution of SF6 puffs is effectively influenced by kinematics and slope flow due to the higher resolution geometry of the terrain.

OBSERVATIONAL ERROR COVARIANCE SPECIFICATION IN ENSEMBLE BASED KALMAN FILTER ALGORITHMS

Tijana JANJIC1 ([email protected]), Alberta ALBERTELLA, Sergey SKACHKO 2, Jens SCHROETER 1, Reiner RUMMEL (1) Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany (2) Department of Earth Sciences, University of Quebec in Montreal, Montreal,Canada (3) Institute for Astronomical und Physical Geodesy (IAPG), TU Munich,Germany

The study focuses on the effects of different observational error covariance structures on the assimilation in ensemble based Kalman filter with domain localization. With the domain localization methods, disjoint domains in the physical space are considered as domains on which the analysis is performed. Therefore, for each subdomain an analysis step is performed independently using observations not necessarily belonging only to that subdomain. Results of the analysis local steps are pasted together and then the global forecast step is performed.

Because the assimilation is performed independently in each local region, the smoothness of the analysis fields are of concern with domain localization methods. In order to resolve the problem with smoothness of analysis fields, the method of observational error covariance localization was proposed by Hunt et al. 2007. This method modifies the structure of the observational error covariance matrix for the subdomain depending on the distance of observation to the analysis point. The most widely used correlation structure is 5-th order piecewise rational function. We investigate use of different correlation structures together with this method. In particular for the case when the observational error covariance that would be the most appropriate for the observations contains also negative values.

Comparisons are done for estimation of ocean circulation via assimilation of satellite measurements of dynamical ocean topography (DOT) into the global finite-element ocean model (FEOM). The DOT data are derived from a complex analysis of multi-mission altimetry data combined with a referenced earth geoid. We are using domain localized SEIK algorithm with observational error covariance localization and different correlation models for localization. The assimilation results are compared in spectral space too. The effects on non-observed fields are considered, as well as the impact on consistency when comparing steric height changes induced by assimilation of dynamical ocean topography data.

A SOIL MOISTURE ASSIMILATION SCHEME BASED ON THE ENSEMBLE KALMAN FILTER USING MICROWAVE BRIGHTNESS TEMPERATURE

Binghao JIA1, 2, Zhenghui XIE1 ([email protected]), Xiangjun TIAN1, and Chunxiang SHI3 1 ICCES/LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 2 Graduate University of the Chinese Academy of Sciences, Beijing, 100049 3 National Satellite Meteorological Center, China Meteorological Administration, Beijing, 100081

This study presents a soil moisture assimilation scheme, which could assimilate microwave brightness temperature directly, based on the ensemble Kalman filter and the shuffled complex evolution method (SCE- UA). It uses the soil water model of the land surface model CLM3.0 as the forecast operator, and a radiative transfer model (RTM) as the observation operator in the assimilation system. The assimilation scheme is implemented in two phases: the parameter calibration phase and the pure soil moisture assimilation phase. The vegetation optical thickness and surface roughness parameters in the RTM are calibrated by SCE-UA method and the optimal parameters are used as the final model parameters of the observation operator in the assimilation phase. The ideal experiments with synthetic data indicate that this scheme could improve the simulation of soil moisture at the surface layer significantly. Furthermore, the estimation of soil moisture in the deeper layers could be also improved to a certain extent. The real assimilation experiments with AMSR-E brightness temperature at 10.65GHz (vertical polarization) show that the root mean square error (RMSE) of soil moisture in the top layer (0-10cm) by assimilation is 0.03355 m3/m3, which is reduced by 33.6% compared with that by simulation (0.05052 m3/m3).

42 The mean RMSE by assimilation for the deeper layers (10-50cm) is also reduced by 20.9%. All these experiments demonstrate the reasonability of the assimilation scheme developed in this study.

Key words: land data assimilation, soil moisture, ensemble Kalman filter, SCE-UA method, radiative transfer model, AMSR-E

4D-VAR AND ENKF INTERCOMPARISONS

Eugenia KALNAY

In November 2008 a very successful Workshop allowing in-depth discussions of the advantages and disadvantages of the two leading advanced methods for data assimilation was held in Buenos Aires. There were sessions devoted to estimation of the background error, model error, nonlinearities and non- Gaussianities, computational issues, validation, and synergies, each followed by discussions. In general there was not a single issue in which either 4D-Var or EnKF came out as clear losers either in the presentations or in the discussions. Perhaps the most surprising talk was by Mark Buehner, from Environment Canada, which showed that in a careful comparison of their operational 4D-Var and EnKF systems, the EnKF results were at least as good as those from 4D-Var. Moreover, a hybrid system using 4D- Var with the background error covariance from EnKF was even better in the Southern Hemisphere.

I will review the current status and show how new techniques originally developed for variational systems can be adapted to EnKF with additional advantages associated with the fact that ensemble perturbations are computed nonlinearly.

APPLICATION OF SINGULAR VECTOR ANALYSIS TO THE KUROSHIO LARGE MEANDER

Yosuke FUJII1, Masafumi KAMACHI1 ([email protected]), Norihisa USUI1, Hiroyuki TSUJINO1, and Hideyuki NAKANO1 Meteorological Research Institute / Japan Meteorological Agency1

Singular vector analysis is one of promising tools to identify a key region important for predicting a certain target phenomenon. It is also useful for designing an efficient observing system.

Singular vector analysis is applied to the formation process of the Kuroshio large meander south of Japan. Meteorological Research Institute Community Ocean Model (MRI.COM) and its tangent linear and adjoint codes are employed for the analysis. The largest singular vector subtracted in the analysis represents a perturbation intensifying an anticyclonic eddy approaching the Kuroshio path southeast of Kyushu (southwestern part of Japan) from east. This eddy forces cold advection crossing the Kuroshio from the coastal to offshore sides and induces downwelling in its northern part. In the deep layer (deeper than 1500m). Then an anticyclonic eddy is generated by the downwelling. This anticyclonic eddy in the deep layer causes baroclinic instability together with a small meander of the Kuroshio in the upper layer. The instability promotes the growth of the small meander to the Kuroshio large meander. Addition of the perturbation represented by the singular vector to the original initial state intensifies the process above, resulting in a more development of the large meander than the original state two month later.

This analysis clarifies the physical process of the large meander formation. It also indicates that, to properly predict the large meander, a forecast model must be well constrained by data assimilation around southeast of Kyushu. This implies that additional observations in that region are likely to benefit the forecast of the variability of the Kuroshio Current.

43

CLOUD RESOLVING 4DVAR EXPERIMENT OF A LOCAL HEAVY RAINFALL EVENT USING GPS SLANT DELAY DATA

Takuya KAWABATA ([email protected]), Yoshinori SHOJI, Hiromu SEKO and Kazuo SAITO Meteorological Research Institute / Japan Meteorological Agency

A local heavy rainfall event with a horizontal scale of about 10 km occurred on 5 August 2008 at Toshima, Tokyo (Toshima heavy rainfall). This event was caused by cumulonimbi which generated around Tokyo bay and four drainage workers at a construction site were killed by an abrupt freshet. A cloud-resolving nonhydrostatic 4-dimensional variational assimilation system (NHM-4DVAR; Kawabata et al. 2008) was applied to this heavy rainfall event. Doppler radar radial winds, GPS precipitable water vapor (GPS-PWV), surface winds and temperatures were assimilated and 10-minutes assimilation window was adopted in this assimilation experiment.

In addition to above data, a new assimilation method on GPS slant delay data (GPS-SD) was developed. The GPS-PWV has only zenith information on the observation site. On the other hand, the GPS-SD has vertical and horizontal information of water vapor distribution. These characteristics are advantageous to reproduction of small scale cumulonimbus, especially, for a high resolution assimilation system.

In the observation operator, lays from GPS satellites to observation sites are assumed along straight lines. Delays in a grid box are calculated by the multiplication of refractivity and the pass length in the grid box. Statistics of the departure values (forecast - observation) show a Gaussian shaped distribution. In the view of the radar reflectivity, both results of assimilation experiments with the GPS-PWV and with the GPS-SD, well and similarly reproduced the cumulonimbus which caused the Toshima heavy rainfall, while vertical distributions of water vapor were slightly different. Assimilation method of the GPS-SD works as well as one of the GPS-PWV. Since differences between the two methods were not large, relative advantages of the GPS-SD assimilation method are not clear on this development stage, they should be further investigated.

USE OF SEVIRI RADIANCES IN THE MET OFFICE MESOSCALE MODELS

Graeme KELLY, Robert TUBBS and Pete FRANCIS

The aim of this UK Met Office project is to assimilate, in 3D-Var/4D-Var, radiances from the SEVIRI instrument on board Meteosat. Until now, these data have only been used indirectly in NWP, using cloud and rainfall estimates from an ageing nowcasting system (“Nimrod”). This study began by developing the infrastructure to monitor and calculate SEVIRI radiance bias corrections. Then assimilation trials, using the clearest SEVIRI data, were run using both the North Atlantic Europe and UK 4km versions of the Met Office’s Unified Model. The success of these trials led to clear SEVIRI radiances being included in the latest operational versions of these models and they are also being included in the new (1.5 km) version of the mesoscale system. Recent work using improved cloud-detection over land has lead to increased use of lower sensing SEVIRI channels over selected land surfaces. Finally, looking towards the future, simulations of cloudy SEVIRI radiances have been carried out using the model guess and comparisons with the observations suggest it should be viable to assimilate cloudy SEVIRI radiances directly in 4D-Var. Some examples of cloudy radiance assimilation will be discussed.

AN OBSERVATION OPERATOR FOR THE VARIATIONAL ASSIMILATION OF VORTEX POSITION AND INTENSITY

Jeffrey D. KEPERT ([email protected]) Centre for Australian Weather and Climate Research, Melbourne, Australia

Tropical cyclone forecasters produce manual estimates of vortex position and intensity every few hours, based mainly on satellite imagery interpretation with assistance from radar, scatterometer and aircraft reconnaissance when available. These estimates are currently either ignored by assimilation systems, or used only for the generation of synthetic observations. Given the high impact of tropical cyclones and the limitations of conventional data within the cyclone core, neither approach is entirely satisfactory. To directly assimilate these data, an observation operator is required to estimate the vortex position and intensity from the background field. If the assimilation system is variational, this operator must be differentiable, since its tangent linear and adjoint are also required. Thus algorithms which search directly for the lowest pressure or maximum vorticity are unsuitable.

44 A differentiable observation operator for vortex position and intensity will be presented, that may be suitable for variational assimilation of such data. The operator is iterative but usually converges in a few steps. Each iteration is differentiable, so calculating the derivative of several iterations is a straightforward application of the chain rule. Variational assimilation of vortex position using the operator will be demonstrated in an idealised setting. It is anticipated that the operator may also be useful in adjoint sensitivity studies.

CHANGE-OF-VARIABLE IN AN ENSEMBLE KALMAN FILTER

Jeffrey D. KEPERT ([email protected]) Centre for Australian Weather and Climate Research, Melbourne, Australia

The atmospheric (or oceanic) state can be represented in multiple ways. The choice of variables has long been recognised as crucial to the successful implementation of variational assimilation systems. The transformation of velocity to streamfunction and velocity potential and the replacement of total mass by its balanced and unbalanced components have proven to be highly beneficial in variational assimilation, both for formulating the background error model and for numerical efficiency.

Such transformations are readily incorporated into an EnKF. It has recently been shown by the author that calculating the covariance localisation in streamfunction-velocity potential space reduces the dynamical imbalance introduced by the analysis, and leads to a more accurate system. Here, that work is extended to include a mass transformation also. In addition, the question of whether some choices of analysis variable are better than others is considered. The EnKF analysis can be regarded as including a linear regression from the observations to the analysis space, so it could reasonably be supposed that the choice of analysis variable will affect the success of this regression - for example, a nearly linear relationship should perform better than a highly nonlinear one. Results will be presented and analysed from a simple test system that explores this question. The important question of dynamical balance will be considered, along with system accuracy.

SNOW RADIANCE ASSIMILATION: CASE STUDIES USING THE COLD LAND PROCESSES EXPERIMENT-1

Edward KIM1 ([email protected]), Michael DURAND2, Steven MARGULIS3 and Ally TOURE4 1NASA Goddard Space Flight Center, 2Byrd Polar Research Center, 3 University of California Los Angeles, 4University of Sherbrooke

Snow is an important water resource for many parts of the world—it has been estimated that one-sixth of the global population lives in areas where streamflow is dominated by snowmelt runoff. Satellite remote sensing via passive microwave (PM) observations of the Earth have been made from space since the 1970’s, and satellite PM retrievals of snow parameters have a long heritage. So far, they have been generated primarily by regression-based “inversion” methods based on snapshots in time. Direct assimilation of microwave radiances into physical land surface models can provide a more physically-consistent retrieval framework, potentially avoiding errors associated with assimilation of products from regression/inversion methods. Radiance assimilation (RA) has been used for years for atmospheric profile retrievals by the operational weather forecasting community with great success. This paper synthesizes the results from a recent set of studies to explore the utility of RA for snow.

The studies have used field measurements of PM radiances and in situ snow ground truth from the Cold Land Processes Experiment-1, a NASA snow field campaign in Colorado, USA in 2002-2003. A radiance assimilation scheme for snow requires a snowpack model (SM) coupled to a radiative transfer model (RTM). We report a detailed evaluation of the accuracy and sensitivity of the RTM itself, as well as the RTM accuracy when driven by two different SMs, which are in turn forced by measured in situ weather. The two SMs represent simple and sophisticated snow models in order to evaluate the stratigraphic fidelity required to achieve benefits from RA. Results from simple radiance assimilation runs using an ensemble Kalman filter demonstrating the promise of RA for snow are presented and discussed.

45

CALCULATING ANALYSIS SENSITIVITY FOR THE NCEP GLOBAL DATA ASSIMILATION SYSTEM

Daryl T. KLEIST123 ([email protected]) and Kayo IDE3 NOAA National Centers for Environmental Prediction1, Science Applications International Corporation2, University of Maryland-College Park3

The analysis sensitivity (or influence matrix) quantifies the information content for a given set of observations on an analysis and provides diagnostics on the influence of individual observations. This can be done in the context of variational data assimilation (Cardinali et al. 2004) or within the framework of an Ensemble Kalman Filter (EnKF, Liu et al. 2009). Larger analysis sensitivity indicates more influence of observations and hence less influence of background.

In this paper, the analysis sensitivity will be shown for two low-resolution experimental versions of the NCEP global data assimilation system using a reduced set of operationally available observations. The experimental versions utilize the Local Ensemble Transform Kalman Filter (LETKF) and 3DVAR (as in Zhu and Gelaro 2008) respectively. The analysis sensitivities calculated for the two systems will be compared. Finally, the implications for operational feasibility and usefulness will be discussed.

TECHNIQUE OF ADAPTIVE OBSERVATIONS PLANNING BASED ON ENSEMBLE KALMAN FILTER

Ekaterina G. KLIMOVA ([email protected]) Institute of Computational Technologies, Russian Academy of Sciences, Novosibirsk, Russia Siberian Hydrometeorological Research Institute (SibHRI), Novosibirsk, Russia

The Kalman filter algorithm is one of the most popular approaches to solving the problem of data assimilation. To obtain an optimal estimate of the state of the atmosphere at a given time by using observational data and a prognostic model, which is generally nonlinear, the equation for the conditional mean is solved. This problem is intractable in the general form. There exist simplified variants based on linearization with respect to a basic state (extended Kalman filter) or on expansion into a power series in terms of the estimation error (second order truncated filters).

Ensemble filtering is a major approach to the use of Kalman filtering for data assimilation. The ensemble approach allows one to calculate the estimation error covariance matrices for nonlinear prognostic models. The ensemble Kalman filter, like the conventional Kalman filter, is an algorithm difficult to implement technically, since it needs operations with high-order matrices.

In the report, an efficient algorithm of observational data assimilation for nonlinear models with an ensemble of predictions (the ensemble π-algorithm) is proposed. In operations count, the ensemble π-algorithm is close to the Local Ensemble Transform Kalman Filter (LETKF). However, the formulas for the ensemble π- algorithm are different from LETKF formulas and obtained in a different way.

At realization of an environment monitoring huge data files of observations are processed. At the same time some areas remains badly covered by an observational network. In this connection, problem of an estimation of areas in which realization of additional measurements (adaptive observations) for improvement of the analysis and the forecast quality is required. In the report the algorithm of an estimation of such areas on a basis of ensemble π-algorithm is offered. Results of numerical experiments with barotropic quasi geostrophic model are presented.

JRA-55: JAPANESE 55-YEAR REANALYSIS PROJECT - STATUS AND PLAN

Ayataka EBITA, Shinya KOBAYASHI ([email protected]), Yukinari OTA, Masami MORIYA, Ryouji KUMABE, Kiyotoshi TAKAHASHI and Kazutoshi ONOGI Japan Meteorological Agency

Japan Meteorological Agency (JMA) started the second Japanese atmospheric reanalysis project JRA-55. It will cover 55 years, extending back in 1958, when the global radiosonde network was established. It aims at providing the comprehensive atmospheric dataset that is suitable for studies of climate change and multi- decadal variability, by producing a more time-consistent dataset for a longer period than JRA-25.

46 The JRA-55 data assimilation system will be based on JMA’s latest numerical weather prediction (NWP) system. It incorporates many improvements introduced into the operational NWP system since the time of JRA-25 production. Among those are 1) improved data assimilation scheme (from 3D-Var to 4D-Var); 2) increased model resolution (from T106L40 to TL319L60); 3) variational bias correction for satellite radiances; 4) new radiation scheme; 5) updated dynamical and physical processes. It also incorporates reanalysis specific modifications, such as 6) use of historical records of greenhouse gases; 7) updated 3-dimentional daily ozone data; 8) quality control information drawn from experiences in previous reanalyses. These improvements are expected to solve, partly at least, some of the known problems in the JRA-25 dataset, such as cold biases in the lower stratospheric temperature and unnatural jumps in time series of mean temperature with changes of satellite data.

JMA also has a plan to downscale JRA-55 products by using a Japanese regional forecast model for the 55 years to help generate a detailed climatology database to diagnose local climate. In addition, JRA-55 is expected to provide a basis for many meteorological applications such as development and verification of seasonal forecast models, ocean research, atmospheric composition research, and so on.

JRA-55 is now in the preparation phase, and production is planed to start in 2009 and expected to complete in 2013. Details of the plan will be presented at the meeting.

STATE AND PARAMETER ESTIMATION FOR A COUPLED OCEAN--ATMOSPHERE MODEL

Dmitri KONDRASHOV1 ([email protected]), Michael GHIL1, Ichiro FUKUMORI2, Ge PENG2 1Department of Atmospheric and Oceanic Sciences and Institute of Geophysics and Planetary Physics, University of California, Los Angeles, U.S.A. 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA

We apply parameter estimation within data assimilation framework of a large, coupled ocean–atmosphere general circulation model (GCM). This GCM includes ocean component of CM2 model from Geophysical Fluid Dynamics Laboratory (GFDL), coupled to atmospheric Quasi-equilibrium Tropical Circulation Model (QTCM) from UCLA. State estimation of CM2 is based on the partitioned Kalman filter (KF) approach. State- parameter cross-covariance is a key element for parameter estimation from the observed state. It is estimated by using model linearization via numerical perturbations, coupled with Ricatti equation for propagating augmented error covariance matrix & partitioned approximation of the KF. We use twin model experiments to test our parameter estimation framework for bulk coefficients in parameterizations of turbulent air-ocean fluxes.

FINDING SOURCES OF BIAS ERROR IN FORECAST MODELS: A FRAMEWORK

S. LAKSHMIVARAHAN ([email protected]) and John M. LEWIS ([email protected]) University of Oklahoma and National Severe Storms Laboratory

A mathematical method has been developed to identify the sources of bias error in dynamical systems that can be represented by a set of coupled ordinary differential equations. The basic tenet is that the forecast bias can be represented by a first-order Taylor series in the elements of the control vector – expressed in the form of sensitivity of model output to the initial condition, boundary condition and the parameters. The bias estimation problem is recast as an inverse problem using the least squares framework. It can be shown that there is a close connection between this (forward) sensitivity based approach and the standard 4-D VAR method based on the adoint method.

A general framework is constructed and applied to the Lagrangian mixed layer model that is applicable to the cold-air outflow phase of the return flow over the Gulf of Mexico. Observations for a particular return-flow case are used to establish the idealized control vector and forecast. Bias is then added to this control vector and numerical experiments are executed to test the viability of the methodology. Principal results of the experiments are: (1) in all experiments bias correction is of the correct sign and magnitude close to the ideal, and (2) the accuracy of the estimates improve with an iterative version of the algorithm.

47

ASSIMILATION OF CHLOROPHYLL DATA INTO FOAM-HADOCC, A COUPLED OCEAN PHYSICAL AND BIOLOGICAL MODEL

Daniel LEA1 ([email protected]), Rosa BARCIELA1, Karen EDWARDS1, David FORD1 and Matthew MARTIN1. Met Office, UK1.

We implement a chlorophyll assimilation scheme using processed ocean colour data provided by Globcolour. The data is assimilated into a coupled physical and biological model based on the FOAM (Forecast Ocean Assimilation Model) and HadOCC (Hadley Centre Ocean Carbon Cycle) models. The physical model assimilates various data types including sea surface temperature and in-situ profile data using an optimal interpolation-type scheme. The chlorophyll data is assimilated by calculating 2D chlorophyll increments which are balanced with changes in the nutrients, zooplankton, phytoplankton, detritus, pCO2 and alkalinity using the Hemmings (2008) scheme.

The results of 2 year hindcasts of the system with and without chlorophyll assimilation are compared. A demonstration pre-operational version of this chlorophyll data assimilation system will also be examined.

MODEL AND OBSERVATION BIAS CORRECTION IN ALTIMETER OCEAN DATA ASSIMILATION IN FOAM

Daniel LEA1 ([email protected]), Matthew MARTIN1 and Keith HAINES2 Met Office, UK1. ESSC, Reading University, UK2.

We have implemented a combined online model and observation altimeter bias correction system in the UK Met Office FOAM (Forecasting Ocean Assimilation Model)-NEMO optimal interpolation-type ocean data assimilation system. The observation bias scheme is designed to estimate the error in the mean dynamic topography (MDT) that must be used in altimeter data assimilation. The MDT is added to the altimeter data supplied as sea-level anomalies giving the absolute sea surface height. The bias scheme separately estimates the remaining model bias in the model sea surface height field. The final unbiased estimate of the absolute dynamic topography is assimilated into the FOAM model by adjusting the subsurface density field using the Cooper and Haines (1996) scheme. A large scale barotropic adjustment to the model free surface is also made.

Various diagnostics including the observation minus background statistics show that both the model and observation bias schemes improve the assimilation results. Combining the schemes provides better results than either alone. We compare results from the ¼ degree resolution global model and 1/12 degree regional area models of the North Atlantic, Mediterranean and Indian Ocean.

ASSIMILATION OF AMSR-E IN THE ACCESS LIMITED AREA NWP MODEL

Jin LEE 1 ([email protected]), Peter STEINLE 2 and Clara DRAPER 3 Bureau of Meteorology 2,1 , Melbourne University 3

Appropriate specification of soil moisture has been a long standing issue for NWP forecasts over Australia. The highly variable rainfall and high levels of insolation in the interior lead to highly variable latent and sensible heat fluxes over the continent. Remotely-sensed soil moisture observations from satellites have however been available for some time from instruments such as AMSR-E. As part of the Australian Bureau of Meteorology’s introduction of a new operational limited area NWP model, experiments have been conducted using soil moisture retrievals from the passive C-band instrument, AMSR-E.

The Bureau’s new NWP system is based on the UK Met Office 4dVAR and Unified Model, and is part of the development of the Australian Community Climate and Earth System Simulator (ACCESS). The ACCESS system currently uses a soil moisture nudging scheme to initialise its soil moisture. It is an indirect method relying on a coupling between soil moisture and screen-level temperature and humidity.

The effects of the assimilation of remotely sensed soil moisture observations from the night time AMSR-E overpass on the model’s near-surface fields will be presented.

48 SATELLITE DATA ASSIMILATION

1,2 John LE MARSHALL ([email protected]), James JUNG3, Lars-Peter RIISHOJGAARD3 ,Stephen LORD3, John DERBER3, and Rolf SEECAMP1 1 Bureau of Meteorology, Melbourne, Australia , Centre for Australian Weather and Climate Research, Melbourne, Australia 2,Joint Center for Satellite Data Assimilation, NOAA Science Center, Camp Springs, 3 MD

During this decade, planned satellite missions will result in a five order of magnitude increase in the volume of data available for use by the operational and research weather, ocean and climate communities. These data will exhibit accuracies and spatial, spectral and temporal resolutions never before achieved. Here are described recent advances in satellite data assimilation which will help ensure that full benefit is derived from the considerable investment in these space-based missions. Results from data assimilation experiments which indicate the utility of current data types are summarised. They show the total and individual, satellite instrument contribution to the predictability associated with the Global Observing System. Results showing the impact of new instruments such as the Atmospheric Infrared Sounder (AIRS), the Infrared Atmospheric Sounding Interferometer (IASI), the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager/Sounder (SSMIS), WindSat, the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Climate (COSMIC) Global Positioning System (GPS) based radio-occultation systems are noted. Assimilation methods are summarised and areas for development to ensure that these and future instruments are fully utilized are noted. These areas include work towards the full use of ultraspectral sounding data over land and in the estimation of atmospheric moisture. They also include activity directed towards better utilization of atmospheric motion vectors. Key future instruments including some to be flown by NOAA, NASA, ESA and EUMETSAT are listed and their contribution to analysis, predictability and the understanding of earth system processes are described. These include instruments which will enhance the ultraspectral infrared and microwave sensing of the earth and its atmosphere, instruments providing the ability to monitor soil moisture and also to provide global wind observations. In addition missions measuring carbon dioxide, aerosols, clouds and ocean colour will be noted as will be the opportunity of measuring surface pressure from space.

OZONE AND UV INDEX FORECAST

Lilia LEMUS-DESCHAMPS1 ([email protected]), Mohar CHATTOPADHYAY2, Xiao YI2, Peter STEINLE2, Asri SULAIMAN2 and Tan LE2 Weather and Environmental Prediction (WEP), CAWCR, Bureau of Meteorology, Victoria, Australia1, Australian Community Climate Earth System Simulator (ACCESS), CAWCR, Bureau of Meteorology, Victoria, Australia2

To alert Australians of possible high levels of ultraviolet radiation (UV) the Bureau of Meteorology issues daily UV Index forecast. The UV Index forecast is calculated in the Ozone and UV Index forecast System using meteorological fields from the global numerical weather forecast model, a two-stream delta-Eddington radiation code, and an ozone assimilation/forecast scheme.

With the implementation of the ACCESS modelling system, the interface of the current forecast system to predict the ozone and UV fields within the new framework has been implemented. The new approach is based on the UK Met Office development and includes assimilation of satellite radiances, 3d-Var and tracer advection. Ozone analysis and forecast fields, as well as the meteorological fields from the global forecast model, ACCESS-G, are input to the UV radiation code. The day-and-time of the year, latitude, longitude, altitude, surface-albedo, ozone absorption dependence on temperature, Rayleigh scattering, solar irradiance, aerosols and clouds are also taken into account in the radiation code.

The new Ozone analysis and forecast (ACCESS-O3) has been running in near-real-time since late April 2009. The seven day forecast of ozone and meteorological fields have been used to calculate the UV Index forecast. Here we present some of the results, summarize the current status of the new Ozone and UV forecast system and discuss future development.

49

DEVELOPMENT OF DATA ASSIMILATION FOR 1.5KM NWP NOWCASTING SYSTEM

Zhihong LI ([email protected]), Susan P BALLARD, David SIMONIN, Cristina CHARLTON- PEREZ, Nicolas GAUSSIAT, Helen BUTTERY, Graeme KELLY, Robert TUBBS, Catherine GAFFARD, Owen COX, Mark DIXON, Humphrey LEAN and Peter CLARK Met Office, JCMM, University of Reading, Reading, UK

A 1.5km resolution forecast system producing 36 hour forecasts should be operational over the UK using 3D-VAR in the coming year. In parallel a prototype NWP nowcasting system is being developed to produce hourly 7 hour forecasts for southern England, also at 1.5km resolution. These latter forecasts need to be produced rapidly and to match the observations as closely as possible in the early hours of the forecast so are more challenging for data assimilation.

An hourly 3D-VAR analysis and forecast system has been run over a limited number of cases of summer rain and convection using conventional data. Research and development is under way to investigate use of 4D-VAR assimilation and to exploit novel observations in both 3D-VAR and 4D-VAR such as radar doppler winds, radar reflectivity, surface rain rates, satellite imagery data, ceilometer, cloud radar and radiometer data as well as more frequent conventional observations. Research is also investigating the background errors, balances and control variables required for use in convective scale data assimilation.

This paper will present plans and initial results from this work planned to deliver a prototype system by 2012.

A COMMON SOFTWARE FOR NONLINEAR AND NON-GAUSSIAN LAND DATA ASSIMILATION

Xin LI ([email protected]), Liangxu WANG, Xujun HAN Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China

A common software for land data assimilation system is being developed with parallel and distributed computing technologies, which is capable of using for nonlinear and non-Gaussian data assimilation. The goal of the software is a generic, fast, robust, high performance, easy-to-use, multi-source remote sensing data assimilation system. The software is implemented mainly with C++ programming language and used mixed-language programming with C and Fortran. To achieve the high performance goal, OPENMP and OPENMPI technologies are used. The software includes common methods of data assimilation, a general observation operator and a general model operator. For common methods of data assimilation, a variety of non-linear Kalman filter (Unscented Kalman filter, Ensemble Kalman Filter, extended Kalman filter, central difference Kalman filter) and variants of particle filters (sequence of importance resampling particle filter, unscented particle filter) are included. For the general observation operator, based on some existing sophisticated radiative transfer models, the forward models between land surface variables (soil moisture, evapotranspiration, soil temperature, reflectance, snow cover, snow depth, and ecosystem productivity) and remote sensing observations from different satellite sensors are developed. The general model operator includes the distributed hydrological models (Variable Infiltration Capacity model (VIC), and GEOtop), the Lund-Potsdam-Jena Dynamic Global Vegetation Model, Common Land Model, Simple Biosphere Model 2 (SiB2), and Simultaneous Heat and Water Model (SHAW).

APPLICATION OF THE MULTI-GRID METHOD TO THE 2-DIMENSIONAL DOPPLER RADAR RADIAL VELOCITY DATA ASSIMILATION

Wei LI1, 3 ([email protected]), Yuanfu XIE2, Shiow-Ming DENG4 1College of Physical and Environmental Oceanography, Ocean University of China, Qingdao 266003, China 2NOAA Earth System Research Laboratory, Boulder, CO, USA 3National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171, China 4Central Weather Bureau, Taiwan, China

In recent years, Earth System Research Laboratory (ESRL) of National Oceanic and Atmospheric Administration (NOAA) has developed a Space and Time Mesoscale Analysis System (STMAS) which currently is a sequential three-dimensional variational data assimilation (3DVAR) system and is developing into a sequential four-dimensional variational analysis (4DVAR) in the near future. It is implemented by using a multi-grid method based on a variational approach to generate grid analyses. This study is to test how STMAS deals with two-dimensional (2D) Doppler radar radial velocity and to what degree the 2D Doppler radar radial velocity can improve the conventional (in situ) observation analysis.

50

Two idealized experiments and one experiment with real Doppler radar radial velocity data, handled by STMAS, demonstrated significant improvement of the conventional observation analysis. Because the radar radial wind data can provide additional wind information (even it is incomplete, e.g., missing tangential wind vector), the analyses by assimilating both radial wind data and conventional data showed better results than those by assimilating only conventional data. Especially in the case of sparse conventional data, radar radial wind data can provide significant information and improve the analyses considerably.

ASSIMILATION OF SEVIRI SATELLITE RADIANCES IN HIRLAM 4D-VAR

M. STENGEL, M. LINDSKOG ([email protected]), P. UNDÉN, P. DAHLGREN, N. GUSTAFSSON SMHI, Swedish Meteorological and Hydrological Institute

The High Resolution Limited Area Model (HIRLAM) utilizes a 4-dimensional variational data assimilation (4D- Var) system. This 4D-Var system has been prepared to handle satellite radiances from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), on-board the Meteosat satellites. Cloud-unaffected radiances, which were identified in regions with clear-sky and low-level cloud conditions, were used to conduct extended assimilation and forecast experiments. The results of these experiments, revealing a positive impact of SEVIRI radiances on forecast quality, are presented. In addition to cloud-unaffected radiances, the HIRLAM assimilation system is capable of using cloud-affected radiances. This was achieved by introducing a simplified moist physics scheme (developed at ECMWF) as a part of the observation operator. The functionality of this operator is demonstrated and the results of a case study are shown.

AN ENSEMBLE-BASED FOUR DIMENSIONAL VARIATIONAL DATA ASSIMILATION SCHEME

Chengsi LIU123 ([email protected]), Qingnong XIAO2, and Bin WANG3

NMC, China Meteorological Administration, Beijing, China 1 ESSL/MMM, National Center for Atmospheric Research, Boulder, CO2 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China3

Applying flow-dependent background error covariance (B matrix) in variational data assimilation is an interesting research topic in recent years. In this paper, we designed an ensemble-based four-dimensional variational (En4D-Var) algorithm, which uses the flow-dependent background error covariance matrix constructed by ensemble forecasts. A great advantage of the designed En4D-Var over standard 4D-Var is that the tangent linear and adjoint models can be avoided. In addition, it can be easily incorporated into variational data assimilation systems that have already existed in operational centers and research community.

A one-dimensional shallow water model was used for preliminary tests of the En4D-Var scheme. We made comparison of the results from En4D-Var with those from other data assimilation schemes, e.g. 4D-Var, 3D- Var and EnKF. The results show that the En4D-Var has a comparable analysis to the widely-used variational or ensemble data assimilation schemes.

In addition, experiments were carried out to test sensitivities of EnKF and En4D-Var. The experiments indicated that En4D-Var obtained reasonably sound analysis even with larger observation error, higher observation frequency and more unbalanced background field.

The EOF decomposed correlation function operator and analysis time tuning are formulated to reduce the impact of sampling errors in En4DVar. With the Advanced Research WRF (Weather Research and Forecasting) model - ARW, we design Observing System Simulation Experiments (OSSEs) and examine performances in real-dimension data assimilation. It is indicated that the designed En4DVar localization techniques can effectively alleviate the impacts of sampling errors upon analysis. Most forecast errors and biases in ARW are reduced by En4DVar comparing with the result from control experiment. To compare the ensemble-based sequential algorithm with the ensemble-based retrospective algorithm, we carry out En3DVar cycling experiments. The experimental results suggest the ensemble-based retrospective assimilation, En4DVar, has better capability than the ensemble-based sequential algorithm such as En3DVar cycling approach.

51 USE AND IMPACT OF COSMIC/GPS RADIO OCCULTATION DATA IN GRAPES GLOBAL DATA ASSIMILATION SYSTEM

Yan LIU ([email protected]) and Jishan XUE Research Center for Numerical Meteorological Prediction,Chinese Academy of Meteorological Sciences, No.46 South Zhongguancun Street，Haidian District, Beijing 100081, China

Compared to previous GPS mission, more than 60% of GPS Radio Occultation (RO) soundings from COSMIC （ Constellation Observation System for Meteorology, Ionosphere and Climate, COSMIC）mission launched on 14 April 2006 can penetrate below 1Km over the tropics and the magnitude of negative refractivity bias is substantially reduced. Therefore, a new data selection procedure is studied in preoperational GRAPES 3DVAR data assimilation on that allows the uses of more RO data and improves the water vapor analysis in the lower troposphere. At the mean time, forecast impact experiments of one month are conducted to further assess the potential impact of COSMIC RO measurements on short- and medium-range forecast after using new quality control scheme, in addition to the conventional and satellite data used in the present assimilation system. Results show that the COSMIC measurements provide good temperature information not only in the upper troposphere and lower stratosphere but also in the lower troposphere, particularly in the southern hemisphere, which produce a clear improvement in the RMS and Bias analysis fit to NCEP analysis. The accuracy of analyzed water vapor is also improved, as verified against independent radiosonding and dropsonde observations that had not been used in these experiments. The wet bias of the assimilation system in the tropical ocean is reduced after assimilating more RO observations below 4Km altitudes. The new data selection procedure also is shown to have a positive impact on short- and medium-range forecast.

SIMULATIONS OF REMOTELY-SENSED SURFACE SOIL MOISTURE ASSIMILATIONS FOR FUTURE EARTH OBSERVATION MISSIONS

Homero F. LOZZA ([email protected]) National Commission for Space Research -CONAE – Av. Paseo Colón 751, Buenos Aires, Argentina (1063), National Institute for Water -INA- AU Ezeiza-Cañuelas, Tramo J. Newbery km:1.620, Ezeiza, Buenos Aires, Argentina (1804)

The 2010-decade will be witness to a rise in earth observation satellites with microwave instruments on board. Uses and applications for their collected data have been addressed in many investigations, both for passive and active (radar) microwave sensors. Several scientists agree on that soil moisture is well suited for microwave remote sensing. In addition, soil moisture is a key variable for agronomy, hydrology and meteorology whose natural spatial and temporal fluctuations appeared impossible to be coped without the aid of space technology.

We concentrate on applications of L-band Synthetic Aperture Radar (SAR) data that will be captured by the SAOCOM satellite platforms from 2012 onwards. SAOCOM satellites are designed by the National Commission for Space Research (CONAE) for monitoring soil moisture in the Argentine Pampas Region, to enhance hydrologic emergency management and to support decision making in agriculture.

Soil moisture profiles will be informed after assimilation of remotely-sensed surface soil moisture into a soil water dynamic model. We have performed simulations to guide the final application designs. We applied Richards equation to describe soil moisture dynamics whose local character ensures compatibility with SAOCOM resolution of 100 meters. Model calibration was performed with in-situ soil water content measurements at an experimental plot within the interest region using time domain reflectometry (TDR) techniques. An independent data set of in-situ surface soil moisture measurements was collected to mimic remote soil moisture retrievals. This second data set fed an Extended Kalman Filter (EKF) for assimilation purposes. We found that surface soil moisture assimilation corrects some fails on modeled profiles due to poorly calibrated parameters and misconceived input variables such as dew. We noted that the magnitude of the corrections depends on the satellite revisit time an inter-storm duration for each selected site.

52

TRADE-OFFS BETWEEN MEASUREMENT ACCURACY AND RESOLUTIONS IN CONFIGURING PHASED-ARRAY RADAR VELOCITY SCANS FOR ENSEMBLE-BASED STORM-SCALE DATA ASSIMILATION

Huijuan LU1,2 ([email protected]) and Qin Xu3 1 Research Center for Numerical Prediction, State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China 2 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, USA 3 NOAA/National Severe Storms Laboratory, Norman, Oklahoma, USA

Assimilation experiments are carried out with simulated radar radial-velocity observations to examine the impacts of observation accuracy and resolutions on storm-scale wind assimilation with an ensemble square root filter (EnSRF) on a storm-resolving grid (∆x = 2 km). In this EnSRF, the background covariance is estimated from an ensemble of 40 imperfect-model predictions. The observation error includes both measurement error and representativeness error, and the error variance is estimated from the simulated observations against the simulated “truth”. The results show that the analysis is not significantly improved when the measurement error is overly reduced (from 4 to 1 m s-1) and becomes smaller the representativeness error. The analysis can be improved by properly coarsening the observation resolution (to 2 km in the radial direction) with an increase in measurement accuracy and further improved by properly enhancing the temporal resolution of radar volume scans (from every 5 to 2 or 1 min) with a decrease in measurement accuracy. There can be an optimal balance or trade-off between measurement accuracy and resolutions (in space and time) for configuring radar scans, especially phased-array radar scans, to improve storm-scale radar wind analysis and assimilation.

A COMBINED FILTERING AND ERROR PREDICTION PROCEDURE FOR DATA ASSIMILATION IN HYDROLOGICAL AND HYDRODYNAMIC OFRECASTING SYSTEMS

Henrik MADSEN and Jacob Tornfeldt SORENSEN DHI, Agern Allé 5, DK-2970 Horsholm, Denmark

Data assimilation procedures based on the Kalman filter (or various approximations of the Kalman filter) are widely applied in operational hydrological and hydrodynamic forecasting systems. In its basic implementation the Kalman filter provides an improved estimate of the state of the modeled system at the time of forecast, which is then used as initial conditions in a normal model forecast simulation. Thus, the forecast skill of the Kalman filter is limited to a time horizon where the improved initial conditions are washed out of the system. However, often model forecast errors show some systematic behaviour (and are not just white noise), and prodiction of thse forecast errors can then be utilized in the forecast period to improve the forecast skill.

In this paper a hybrid data assimilation procedure is presented that combined Kalman filtering with forecast error prediction. Standard time series modeling tools such as linear auto-regressive, moving average (ARMA) and artifical neural networks (ANN) can be used to estimate the forecast errors. These estimates are then applied in the forecast period for state updating using the Kalman filter. An adaptive estimation procedure is implemented where the error forecast model is updated in the light of new data when a new model forecast is to be issued. This allows the error forecast model to adapt to the prevailing conditions at the time of forecast,t accounting for any termporal variations in the structure of the model errors.

The performance of the combined filtering and error prediction procedure is demonstrated with a hydrological forecasting system for the Seine River and a hydrodynamic water level forecasting system for the Venice Lagoon.

53 RECENT DEVELOPMENTS IN LAND DATA ASSIMILATION FOR NUMERICAL WEATHER PREDICTION

Jean-François MAHFOUF1 and Gianpaolo BALSAMO 2 1 CNRM/GAME - M´et´eo-France/CNRS (Toulouse, France), [email protected] 2 ECMWF (Reading, United Kingdom)

This paper provides a review of techniques developed for initialising the prognostic variables of land surface parametrizations in numerical weather prediction models. The importance of soil moisture initialisation is emphasized since the summertime evolution of the planetary boundary layer is very sensitive to its specification and the associated time scales are much larger that those of medium range forecasts. Similarly, snow cover errors can have impact of several degrees on winter low-level atmospheric temperature forecast. Current operational methods are described and illustrated on few examples. More advanced techniques, based on Kalman filters, developed for the assimilation of microwave satellite radiances and/or derived soil moisture are also presented. Finally, various methods for the analysis of other slowly varying components at the surface : albedo and leaf area index are summarized.

HOW IMPORTANT IS TO USE DIAGNOSED BACKGROUND ERROR COVARIANCES FOR THE ATMOSPHERIC OZONE ANALYSIS?

Sebastien MASSART1 ([email protected]), Andrea PIACENTINI1 and Olivier PANNEKOUCKE2 URA CNRS/CERFACS No. 1875 1, Météo-France CNRM-GAME 2

Valentina is a modular variational data assimilation chain developed at CERFACS. When coupled to the Météo-France Mocage Chemistry Transport Model (CTM) it provides analyses of several atmospheric constituents (ozone, carbon monoxide, etc.). As for every assimilation system, the background error covariance matrix is a key component of Valentina. A special effort was recently made to introduce non- homogeneous background error correlation length-scales in the Valentina correlation operator. These length- scales are statistically derived based on an ensemble of assimilation experiments. This ensemble of experiments is also used to provide an estimation of the background error variance.

This study illustrates the effect of using diagnosed correlation length-scales and background error variance instead of simple parameterizations. Our results are based on stratospheric and upper tropospheric ozone analyses realized during a five months period that covers the formation of the South Pole ozone hole. The assimilated data are provided by the Microwave Limb Sounder (MLS) onboard Aura satellite.

Our methodology consists in comparing four experiments, which differ only by the modelling of the background error covariance matrix. The four experiments can be characterized as follows:

1. homogeneous length-scales and error standard deviation proportional to the background; 2. same as 1. but with diagnosed length-scales; 3. same as 1. but with diagnosed standard deviation; 4. diagnosed length-scales and standard deviation.

The criterion used to assess the improvements of the ozone analysis determined by the newly used of diagnosed length-scales and standard deviation is based on the evaluation of the bias and the standard deviation with independent data.

ENSEMBLE KALMAN FILTERING FOR ASSIMILATION OF UPPER ATMOSPHERIC OBSERVATIONS

Tomoko MATSUO12 ([email protected]), Jeffrey L. ANDERSON3 University of Colorado at Boulder1, Space Weather Prediction Center2, National Center for Atmospheric Research3

The density of the Earth’s thermosphere is so tenuous that it lends itself to control by weak external drivers from above and below; nonetheless, it is enough to exert significant drag on orbiting spacecrafts, motivating numerous observational and modeling efforts since the dawn of space exploration. While thermospheric observations remain relatively limited, the recent availability of global observations of ionospheric parameters such as electron density, especially from GPS receivers on low Earth orbiting platforms, has motivated a number of attempts to assimilate ionospheric data. In this paper we examine the abilities of ensemble Kalman filter with a general circulation model of a coupled thermosphere and ionosphere system. We assess the assimilation of thermospheric observations in conjunction with constraints from ionospheric observations and using advanced ensemble techniques such as adaptive covariance inflation and localization of covariance.

54

IMPROVING THE PREDICTION OF INFLOWS TO LAKE TAUPO

Deborah MAXWELL ([email protected]), Bethanna JACKSON, James MCGREGOR 1Victoria University of Wellington, New Zealand

Lake Taupo, located in the central North Island, is New Zealand’s largest lake. It is the effective source of the Waikato River, along which the Waikato Power Scheme has been developed. This hydro scheme consists of eight dams and nine power stations and generates approximately 10% of New Zealand’s electricity. Lake Taupo holds 93% of the total storage for the scheme. Due to resource management constraints, outflows from Lake Taupo are managed to mimic natural lake level fluctuations. Consequently, it is the amount and timing of inflows to the lake that effectively determines the volume of water available to the scheme for power generation.

A semi-distributed physically-based conceptual rainfall-runoff model has been developed for the Lake Taupo catchment to predict inflows to the lake. Monte Carlo simulations are used to identify appropriate model structures and for initial parameter calibration. The Ensemble Kalman Filter is used to further improve model output accuracy. As well as providing state correction, information obtained from state and output innovations are used to improve the model structure and parameter sets.

Illustrative results are presented for two sub-catchments of Lake Taupo – the Tauranga-Taupo River (199km2) and Whareroa River (58km2). The approach performs well in both catchments despite differences in size, geology and land cover. Using the Ensemble Kalman Filter as part of the model development has further improved model output by reducing uncertainty and improving the accuracy and reliability of results.

CHALLENGES FOR LAND DATA ASSIMILATION

Prof. Dara ENTEKHABI, Prof. Dennis MCLAUGHLIN Massachusetts Institute of Technology, Cambridge, MA, USA.

Data assimilation for land surface applications raises issues that are somewhat different than those encountered in atmospheric and oceanic applications. Some of the differences are revealed in systems features such as dynamical behavior, spatial organization, variability (and uncertainty) in forcing, and the credibility of Gaussian assumptions. Such features both motivate and constrain the science objectives that drive research in hydrologic data assimilation. Many of these objectives require accurate estimation of the land surface water and energy fluxes that regulate global biogeochemical cycles, weather, and climate. Hydrologic fluxes are strongly impacted by human activities but are difficult to measure directly, especially over extensive areas. Recent advances in remote sensing provide new possibilities for estimating fluxes such as evaporation and recharge from measurements of land surface states such as soil temperature and moisture. We use several examples to show how the estimation process depends on assumptions made in land surface models that relate fluxes and states. Assimilation of data from new satellite missions should provide the information needed to improve these models and to obtain more accurate flux estimates -- for better understanding, better resource management, and better forecasts.

CONVERGENCE AND STABILITY OF ESTIMATED ERROR VARIANCES DERIVED FROM ASSIMILATION RESIDUALS IN OBSERVATION SPACE

Richard MÉNARD ([email protected]) and Yan YAN Air Quality Research Division, Environment Canada

The estimation of error statistics using residuals in observation space, such as OmF, AmF and OmA, has received lately considerable attention. This approach has been used to derive statistics of meteorological and chemical variables using a global coupled chemistry-dynamics model based on the Canadian operational numerical weather prediction model and variational assimilation system, and for the purpose of online adaptive estimation of error statistics.

The convergence of observation (or forecast) error variance of the iterated scheme usually occurs but can lead to incorrect true values when the prescribed model (or forecast) error variance is different from the true value. An analysis of the convergence is carried out for both, a scalar case and 1D spatially correlated case supporting the experimental results and providing analytical expressions of convergent values, criteria of convergence and convergence rates. When both observation and forecast error variances are iterated and

55 estimated simultaneously, the simple models indicates that the scheme is not converging in all cases whether or not observation and forecast error correlation lengths scales are different. This is explained by the fact that AmF, OmA are OmF are not independent information and that the system of coupled prescribed-estimated error covariance equations is rank deficient. Experimental results with meteorological variables where the error variance of only one observation type is varied while the forecast error also varies in the iterative scheme shows not clear convergence. However, for a single long-lived chemical constituent observed by a single instrument, the scheme clearly doesn’t converge and in some cases diverge.

DATA ASSIMILATION EXPERIMENTS WITH L1-NORM AND RELATED LAPLACE DISTRIBUTED ERRORS

Richard MÉNARD ([email protected]) Air Quality Research Division, Environment Canada

In a number of environmental data assimilation applications (e.g. atmospheric constituents) the innovation distribution displays a Laplace (or double exponential) distribution which is peaked at the origin and displays long tails. The Laplace distribution plays an important role in probability. Similarly to Gaussian distribution that maximizes entropy with a given mean in an L2-norm, a Laplace distribution maximizes entropy with a given mean in an L1-norm. The maximum likelihood estimate is the median, and is thus a robust estimator displaying little sensitivity to outliers, which is appealing to nearly all data assimilation applications.

One difficulty with this problem is that there exists several multivariate formulation of the Laplace distribution in multi-dimensions, such as the vector of independent univariate Laplace, the elliptical contoured distribution and the Bessel’s function representation. The state estimation for each of these distributions are discussed either from either a variational point of view (using a conjugate gradient solver) or with an explicit formulation of the estimator. These different algorithms are then applied to a specific example of analysis of surface air quality.

If time permits, issues related to large-scale analysis problems and fast inversion of double exponential covariance functions will also be presented.

INHOMOGENEOUS BACKGROUND ERROR MODELING AND ESTIMATION OVER ANTARCTICA

Yann MICHEL NCAR/MMM

Variational data assimilation use a modeling of the background error statistical properties to achieve efficiency in geophysical problems with very large dimensions. For example, balance features of forecast errors are generally incorporated in the background constraint to model the statistical correlation between variables. The spectral approach may be used to achieve nonseparability, in terms of both vertical variability of horizontal correlations and dependence of vertical correlations with horizontal scale. In contrary, the gridpoint approach used in WRFVAR may be used to relax the homogeneity assumption, and would allow to easily introduce some flowdependence in the balance, in the horizontal correlation or in the error standard deviation.

We show recent developments that allow to incorporate this flowdependence within the background error modeling. High order, computational efficient inhomogeneous recursive filters have been introduced in WRFVAR to model varying lengthscales. A economical estimate of these gridpoint lengthscales is also described. We highlight the importance of climatological flow dependence for the Antarctic domain, where striking differences appear over the continental area with respect to the overseas storm track, for the error standard deviations, the balance as well as the lengthscales.

REPRESENTATION OF CORRELATION FUNCTIONS USING A ONE-DIMENSIONAL IMPLICIT DIFFUSION EQUATION, WITH APPLICATION TO VARIATIONAL OCEAN DATA ASSIMILATION

Isabelle MIROUZE1, 2 ([email protected]), Anthony WEAVER1 CERFACS, Toulouse, France1, CNRS-IMT, Toulouse, France2

A method for representing correlation functions using a one-dimensional (1D) implicit diffusion equation is described. Application of an M-step 1D implicit diffusion operator to a given field is shown to be equivalent to convolving that field with an Mth order auto-regressive (AR) function. Expressions for both the length scale of

56 the AR function and the normalization factor required to generate unit amplitude (correlation) AR functions are given in terms of the parameters of the diffusion model. A straightforward extension to the diffusion model is described which allows it to produce correlation structures that are unaffected by solid boundaries. Spatial variations in the length scale can be accounted for using inhomogeneous diffusion coefficients. An important aspect of the inhomogeneous problem is the estimation of the normalization factors which are no longer constant. Here an approximate expression for the local normalization factor is proposed as an alternative to estimates produced using expensive algorithms such as randomization. A product of 1D implicit diffusion operators has been used to construct a threedimensional (3D) background-error correlation model in a variational data assimilation system for a global configuration of the NEMO ocean model. Aspects of the numerical implementation and performance are discussed and compared to an existing 3D correlation model based on an explicit diffusion operator. Examples of the 3D correlation structures generated by the implicit diffusion model are presented.

A MODIFIED KALMAN FILTER FOR VARIANCE CONSTRAINT

Lewis MITCHELL1 ([email protected]), Georg GOTTWALD1, Sebastian REICH2 University of Sydney1, Universität Potsdam2

We study the problem of data assimilation in situations where there is only a sparse observational network available, with a model state containing large unobserved regions. This is a typical situation, for example when incorporating the mesosphere into a numerical model of the atmosphere, for which there are virtually no observations available.

We present a way in which the well known Kalman filter may be modified in order to incorporate statistical information about the unobserved components of the system. We derive this variance constraining Kalman filter (VCKF) as a variational problem with a weak constraint, and apply it to some well-known low- dimensional systems. By performing twin experiments and comparing with the ensemble transform Kalman filter (ETKF), we find that including information on unobserved components can improve the skill of the analysis, and lead to greater numerical stability.

We illustrate our results for the toy Lorenz-63, -86 and -96 models, and discuss applications to higher- dimensional systems and more complex slowfast systems.

PERFORMANCE OF A LOCAL ENSEMBLE TRANSFORM KALMAN FILTER DATA ASSIMILATION SYSTEM FOR THE ANALYSIS OF THE ATMOSPHERIC CIRCULATION AND THE DISTRIBUTION OF LONG-LIVED TRACERS

Kazuyuki MIYAZAKI ([email protected]) Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

A data assimilation system for the analysis of atmospheric circulation and long-lived tracer distributions in the troposphere and stratosphere has been developed and tested by using a local ensemble transform Kalman filter (LETKF), which has been applied to assimilate both meteorological fields and long-lived tracer concentrations into a general circulation model and an atmospheric transport model. Assimilated meteorological fields are used for driving the atmospheric transport model. The performance of the LETKF data assimilation system is assessed under idealized conditions by assuming that the forecast models provide a perfect representation of atmospheric conditions. The LETKF meteorological analysis facilitates the study of atmospheric transport characteristics and provides high-quality tracer transport simulations, reflecting its flow-dependent and physically well-balanced analysis. In particular, eddy mixing features are better analyzed by LETKF than by an analysis that employs a conventional assimilation scheme (i.e., nudging technique). The conventional analysis causes excessive tracer eddy dispersions, which were commonly observed in previous studies using 3D-VAR analyses. Further improvement in tracer analysis can be achieved by assimilating the tracer concentration within the LETKF. The assimilation of tracer concentration effectively reduces the tracer background error caused by initial distribution and surface flux errors. Tracer analysis can also be improved by considering the covariance with wind fields in a background error matrix of LETKF, in which wind observation directly impacts the tracer states. The sensitivity of the tracer analysis to assimilation parameters and model errors is discussed in order to obtain an optimal data assimilation framework for long-lived tracers.

57

ESTIMATION OF OBSERVATION ERROR CORRELATION AND THE TREATMENT IN ENSEMBLE KALMAN FILTER

Takemasa MIYOSHI1,2 ([email protected]), Eugenia KALNAY1, and Hong LI3 1 University of Maryland, College Park, Maryland, USA 2 University of California, Los Angeles, California, USA 3 Shanghai Typhoon Institute, Shanghai, China

The treatment of error-correlated observations is an important problem in practice, but usually the observation error covariance matrix is assumed to be diagonal for simplicity and computational reasons. The observation error variance can be estimated by innovation statistics (Hollingsworth and Lonnberg 1986), but it is generally difficult to estimate observation error correlations. Although most observations are made independently and those errors are assumed to be uncorrelated, some important observations including satellite-retrieved temperature profiles, atmospheric motion vectors (AMVs), and sea-surface winds by satellite scatterometers, are considered to have significantly correlated errors. The correlated errors are typically treated by increasing the observation error variance in data assimilation. Error-correlated observations have less information content than observations without error correlations, which is approximated by increasing observation error variance but still assuming no error correlation. This, which we call “method A”, is not equivalent to considering the error correlation explicitly in data assimilation, which we call “method B”.

One of the main purposes of this study is to see the difference between methods A and B. Another main issue addressed in this study is the capability of estimating the observation error correlations. For these investigations, we perform experiments using an ensemble Kalman filter (EnKF) applied to the Lorenz-96 model with 40 variables. Here, we simulate observations with correlated errors. First, we apply the method of adaptive estimation of the covariance inflation and observation error variance by Li et al. (2009) to see how the method works with error-correlated observations. Then, we generalize the method to estimate the observation error correlations explicitly. The comparison between the two experiments addresses how much impact the explicit consideration of correlated errors has in EnKF data assimilation.

ENSEMBLE DATA ASSIMILATION FOR IDEALIZED CALIFORNIA CURRENT SYSTEM WITH ROMS- LETKF

Takemasa MIYOSHI1,2 ([email protected]), Kayo IDE1,2, Jim MCWILLIAMS2, Gene LI3, Yusuke UCHIYAMA2, and Eugenia KALNAY1 1 University of Maryland, College Park, Maryland, USA 2 University of California, Los Angeles, California, USA 3 Jet Propulsion Laboratory, Pasadena, California, USA

A local ensemble transform Kalman filter (LETKF) is applied to the Regional Ocean Modeling System (ROMS) with the idealized California coast setup at a 7-km resolution (ICC6 configuration by Capet et al. 2008); data assimilation experiments are performed with simulated observations under the perfect model assumption. First, bred vectors (BVs) are computed to understand better the dynamical nature of the evolving model and to have implications to the observing network design. The small scale instabilities shown by BVs near the eastern coastal boundary imply that denser observations are desirable in those regions. The BVs indicate larger scales in the offshore area farther than about 200 km from the coast, suggesting sparser observations would suffice there. The vertical structure suggests high error correlation in surface mixing layer which suddenly changes near the thermocline depth. BV has stronger signal at the thermocline, which becomes smaller in deeper levels. Thus, more independent observations are desirable near the thermocline.

A forecast/assimilation cycle experiment with a regular observing network is performed to ensure that the LETKF works appropriately with ROMS. Then, several ROMS-LETKF cycle experiments are performed with different observing networks including simulated real observations such as sea-surface temperature (SST) and height (SSH) from satellites, surface current by ocean radars located near the coast, and some sporadic ocean gliders to observe vertical profiles as deep as 1000 m. These observations are major sources of the operational three-dimensional variational (3D-Var) data assimilation system for California coast ocean prediction by Jet Propulsion Laboratory (JPL). The impact of these observations is investigated with the idealized data assimilation experiments.

58

DATA ASSIMILATION EXPERIMENTS FOR AMMA, USING RADIOSONDE OBSERVATIONS AND SATELLITE OBSERVATIONS OVER LAND

F. RABIER, C. FACCANI, N. FOURRIÉ, F. KARBOU, J-P. LAFORE, P. MOLL ([email protected]), M. NURET, J-L. REDELSPERGER CNRM-GAME, Météo-France and CNRS - Toulouse – France A. AGUSTI-PANAREDA ECMWF, Shinfield Park, Reading RG29AX, UK F. HDIDOU Direction de la Météorologie Nationale - Casablanca – Morocco O. BOCK Laboratoire de Recherche en Géodésie - IGN – France

Additional data from soundings recorded during the 2006 AMMA campaign have been assimilated into the Numerical Weather Prediction ARPEGE system, with and without a bias correction for relative humidity. Other assimilation experiments have used soundings which were received operationally at the time, or from a degraded pre-AMMA radiosonde network. The impact of different scenarii on the analysis and forecast over western Africa has been evaluated. For an experiment using all data together with a bias correction, the humidity analysis and the daily and monthly averaged precipitation are improved. The impact of additional radiosonde observations is found to propagate downstream with a positive impact on the forecast performance over Europe at the two and three-day forecast range. Whereas radiosonde observations have shown to be very relevant, satellite microwave data provide another and important source of information. These are more easily used over sea than over land and in-house developments have been necessary to advance the use of these data over the continents. Data assimilation experiments using for the first time ever AMSU-B humidity observations over land have emphasized strong drying and moistening features over Western Africa, which is consistent with results obtained with the enhanced radiosonde network. The drying or moistening of the atmosphere has been successfully evaluated using independent humidity measurements.

As a consequence, the African Monsoon appears to be better organized with a stronger Inter-Tropical Convergence Zone. Both series of data assimilation experiments have shown that additional data over the African continent, either in situ or satellite-based, if carefully processed, can help to improve the description and the prediction of the monsoon. The positive impact can also propagate in time during the forecast and affect Europe a few days later.

USE OF HETEROGENEOUS BACKGROUND ERROR COVARIANCE MATRICES AT MESOSCALE

Thibaut MONTMERLE ([email protected]) and Loïk BERRE CNRM-GAME (Météo-France/CNRS)

This study focuses on the feasibility of a simultaneous use of different background error covariance matrices at convective scale. The context of this work is the assimilation of observation linked to precipitations (radar reflectivity, pseudo-observations deduced from structure matching) in the AROME model, which is running operationally at Météo-France since December 2008 and which covers the French territory with a 2.5 km horizontal resolution. This system is based on cycled 3Dvar analyses that are performed 8 times per day, using multivariate background error statistics deduced from an ensemble-based method. Knowing that these statistics are unable to generate increments that preserve the characteristics of precipitating structures like intensities or shape, research are ongoing to use specific statistics in clear air and precipitating areas.

At first, such statistics have been computed for 17 precipitating cases using an ensemble of AROME forecasts coupled with an ensemble of ALADIN forecasts starting from analyses that consider perturbed observations. Results obtained from 3h forecasts differences performed separately for clear air and precipitating columns will be discussed. Convection and microphysical processes, which are explicitly resolved in AROME, explain in particular the large discrepancies in correlation lengths, error variances and in the coupling between humidity, temperature and divergence errors.

Theses results argue in favor of including an heterogeneous background error covariance matrix in AROME incremental 3Dvar. This consists in expressing the analysis increment as the sum of two terms, one for precipitating and one for non-precipitating areas, making use of a mask deduced from radar observations. This implies to double the control variable and the gradient of the cost function. Impacts on increments for single observation experiments will be shown at first. Then results on real case that consider radar reflectivity in the analyses will be discussed.

59

THE REGIONAL OCEAN MODELING SYSTEM (ROMS) 4D-VAR ASSIMILATION SYSTEMS APPLIED TO THE CALIFORNIA CURRENT SYSTEM

Andrew MOORE1 ([email protected]), Hernan ARANGO2, Gregoire BROQUET1, Chris EDWARDS1, Brian POWELL3, Milena VENEZIANI1 and Javier ZAVALA-GARAY2 University of California Santa Cruz1, Rutgers University2, University of Hawaii3

Three 4D-Var assimilation systems have been developed for the Regional Ocean Modeling System (ROMS): an incremental strong constraint system (IS4D-Var); a Physical-space Statistical Analysis System (4D- PSAS); and an indirect representer-based system (R4D-Var). 4D-PSAS and R4D-Var can be used to compute circulation estimates subject to either the strong or weak constraint. The control variables consist of the model initial conditions; surface fluxes of momentum, heat, and freshwater; the open boundary conditions; and corrections for model error in the weak constraint case. In the case of IS4D-Var, a search is performed for the best circulation estimate in the full space of the model, while in the case of 4D-PSAS and R4D-Var the search is restricted to that part of state-space spanned by the observations. A comparison of a 4 year sequence of analysis-hindcast cycles using IS4D-Var, 4D-PSAS and R4D-Var for strong and weak constraint assimilations will be presented for the mesoscale circulations that characterize the California Current System (CCS). In addition, the adjoint of 4D-PSAS and R4D-Var are used to quantify the impact of individual observations on specific aspects of the CCS circulation during an analysis-hindcast cycle. The relative performance, advantages, disadvantages, and challenges of each 4D-Var approach used in ROMS will be discussed. ROMS is an open source community ocean model, and all three data assimilation systems are freely available. ROMS is the only community ocean model for which all of the aforementioned 4D-Var platforms are available.

PROPAGATION OF THE IMPACT SIGNAL OF THE ADDITIONALLY-ASSIMILATED OBSERVATIONS OVER THE INDIAN OCEAN THROUGH TROPICAL WAVES

Qoosaku MOTEKI1 ([email protected]), Kunio YONEYAMA1, Ryuichi SHIROOKA1, Masaki KATSUMATA1, Masanori YOSHIZAKI1, Takeshi ENOMOTO2, Takemasa MIYOSHI3, Shozo YAMANE4 1Research Institute for Global Change (RIGC), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan; 2Earth Simulator Center, JAMSTEC, Yokohama, Japan; 3University of Maryland, College Park, Maryland, USA 4Doshisha University, Kyotanabe , Japan

The propagation of the impact signal of the additionally-assimilated radiosondes during MISMO (Mirai Indian ocean cruise for the Study of the MJO-convection Onset in 2006) in an objective analysis “ALERA” was investigated. ALERA was produced by an ensemble Kalman filter, so that the analysis ensemble spread is available in addition to the analysis itself. The difference between the analyses with and without the additional radiosondes spreads out quickly on the entire globe, so that it is difficult to find the observation impact signal. However, with the ensemble spread indicating analysis errors, a statistical significance test can be performed to extract a meaningful impact signal. Using the 6-hourly fields of the impact signal, we examined the propagation associated with tropical waves. Focusing on the eastward propagating signal, its speed was found to be 15-20 m/s, which is consistent with that of Kelvin waves. Remarkable signals in the vicinity of tropical cyclones generating over the tropical western Pacific coincided when Kelvin waves passed to the south of the tropical cyclones. The signal was clearly weakened when the additional radiosondes only during the passage of the Kelvin waves were excluded. The results imply that the information added by the additional radiosondes affects tropical cyclones through the Kelvin waves. In the previous statistical composite analyses, the influence of Kelvin waves on tropical cyclones was limited because of weak statistical signals. The result of this study strongly supports that Kelvin waves affect tropical cyclones.

MERGING PARTICLE FILTER FOR HIGH-DIMENSIONAL NONLINEAR PROBLEMS

Shin’ya. NAKANO1 ([email protected]), Genta UENO1,2, and Tomoyuki HIGUCHI1,2 1. The Institute of Statistical Mathematics 2. Japan Science and Technology Agency

Most of sequential data assimilation techniques are based on Bayesian approach. The information of observations is incorporated into the system model by considering a posterior probability density function (PDF) derived from a prior PDF, where the prior PDF is obtained using past data and a system model.

60 The particle filter (PF) is one of such sequential data assimilation algorithms. The PF approximates PDFs at each time step by an ensemble of a large number of particles. An estimation of a posterior PDF is obtained by resampling with replacement from a prior ensemble. Since the PF does not require assumptions of linearity or Gaussianness, it is applicable to general nonlinear problems including cases with nonlinear observations which other algorithms such as the ensemble Kalman filter do not provide good estimation.

However, the PF often encounters a problem called `degeneration'. As resampling procedures are applied recursively, most of the particles are replaced by some particular particles, and the posterior PDF is eventually represented by only a few of the particles among the members of the initial ensemble. This reduces the validity of ensemble approximation.

To overcome the degeneration problem, we devised another sequential data assimilation algorithm, the merging particle filter (MPF), on the basis of the PF. The difference between the MPF and the PF is that the filtering procedure in the MPF is performed by merging several particles sampled from a prior ensemble, which is rather similar to the genetic algorithm. The merging procedure is performed so that the first and second moments of a posterior PDF is preserved. Like the PF, the MPF is applicable even to cases with nonlinear observations. In comparison with the PF, the MPF requires far fewer particles and thus it remarkably reduces computational cost.

COMPARATIVE STUDY FOR THE ENVIRONMENTAL WATER QUALITY ASSESSMENT IN TIRUCHIRAPPALLI, INDIA

NATARAJAN Venkat Kumar*1, SUBBARAYAN Saravanan‡1 , SUBBARAYAN Sathiyamurthi2 *1 Research Scholar, Department of Civil Engineering, National Institute of Technology, Tiruchirapalli -620015.India.E-mail:[email protected] ‡1 Lecturer, Department of Civil Engineering, National Institute of Technology, Tiruchirapalli – 620015. India. 2Lecturer,Department of Soil Science and Agriculture Chemistry, Annamalai University, Chidambram –608002

Environmental quality assessment is essential for urban development. Rapid urbanization has made it all the more essential now than before. But there is dearth of appropriate techniques to assess urban environment quality (UEQA). Here is a technique that is feasible, flexible and valid. Environmental information for UEQE assessment is broken into smaller components or indicators. Water quality, topography, slope and aspects, vegetation as demography of the study and land use have been evacuated as per their contribution towards urban environmental pollution (UEP). The implementation process of fuzzy multi-criteria evaluation in GIS through fuzzy inference network includes three phases. Firstly, ever bottom indicator of each component is overlaid based on fuzzy operation, also called intermediate hypothesis. Finally, the final hypothesis performing the fuzzy overlay operation of environment pollution and physical environment component to get the final quality map. ‘GAMMA’ operator has been used while applying FUZZY LOGIC technique to obtain the final map. Fuzzy Algebric sum and Fuzzy OR are used as intermediate hypothesis. The final criteria map by Boolean theory has been processed by using “Arithmetic Sum”. The results so obtained have been classified under four categories of pollution. Ambiguity resolution is more in fuzzy approach because of the continuous range of values whereas in Boolean approach, it is a discrete integer value. This clearly suggests that fuzzy approach will give more information about the pollution level than the Boolean approach which is also evident from the higher percentage of area obtained by fuzzy approach in critical class.

SEIK - THE UNKNOWN ENSEMBLE KALMAN FILTER

Lars NERGER ([email protected]), Wolfgang HILLER, Jens SCHRÖTER Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany

The SEIK filter (Singular "Evolutive" Interpolated Kalman filter) has been introduced in 1998 by D.T. Pham as a variant of the SEEK filter, which is a reduced-rank approximation of the Extended Kalman Filter. In recent years, it has been shown that the SEIK filter is an ensemble-based Kalman filter that uses a factorization rather than the square root of the state error covariance matrix. Unfortunately, the existence of the SEIK filter as an ensemble-based Kalman filter with similar efficiency as the ensemble square-root Kalman filters introduced later, appears to be widely unknown and the SEIK filter is typically omitted in reviews about ensemble-based Kalman filters. To raise the attention about the SEIK filter as a very efficient ensemble- based Kalman filter, we review the filter algorithm and compare it with ensemble square-root Kalman filter algorithms. For a practical comparison we discuss results from twin experiments in which the SEIK filter and the Ensemble Transformation Kalman filter (ETKF) are applied to assimilate sea level anomaly data into the finite-element ocean model FEOM.

61

SOME NEW APPLICATIONS OF OBSERVING SYSTEM SIMULATION EXPERIMENTS

Yulia NEZLIN1 ([email protected]), Yves ROCHON2, Matt RESZKA2 and Saroja POLAVARAPU2 1University of Toronto, Toronto, Canada 2Environment Canada, Toronto, Canada

Observing System Simulation Experiments (OSSEs) are occasionally used in data assimilation to evaluate the impact of new measurement types on current assimilation systems. Some different applications of OSSEs, in which observations are set as consistent with the model used in assimilation will be discussed. Simulations were conducted with the Canadian Middle Atmosphere Model Data Assimilation System (CMAM-DAS), which uses a 3-D variational assimilation scheme. The resulting forecasts were then examined in the following ways. The error contributions of different components of the assimilation system on forecast error levels were investigated. Secondly, the minimal 6 h forecast error level in the system with a “perfect” model and with “nearly perfect” observations (perturbed by random noise below the precision of observation errors) was identified. The dependence of this minimal error level on variability of short time scales in the model will be discussed.

Finally, scale dependent limits on CMAM’s potential ability to predict the mesosphere were found using this methodology.

CAUSES OF ENKF DIVERGENCE WITH ATMOSPHERIC MODELS

Gene-Hua Crystal NG ([email protected]), Dennis MCLAUGHLIN, Dara ENTEKHABI Massachusetts Institute of Technology

Two complications in ensemble data assimilation with atmospheric models are nonlinear dynamics and errors caused by finite sample sizes. While past studies tend to focus on the issues separately, this work explores the interplay between the two. In the Ensemble Kalman Filter (EnKF), covariance uncertainty due to finite sample size is known to result in overestimation of mean squared error of the ensemble mean and underestimation of the ensemble variance during the analysis step. For perfect model scenarios, this combination has the clear potential for filter divergence (i.e. over-confidence around an incorrect state), and this is commonly observed in practice. While schemes such as localization and variance inflation help prevent divergence, their heuristic modification of the covariance adversely affects filter optimality. Better understanding of the divergence problem can lead to improved remedies. In our work, we quantify how covariance error from sampling and nonlinear dynamics during the forecast step together contribute to divergence over a number of assimilation cycles. Through experiments with nonlinear atmospheric models, we find that two experimental regimes exist for how covariance error affects estimates. When there is little information about the initial condition, and the ensemble represents nonlinear climatology, filter performance is highly susceptible to very small covariance errors. Very large ensembles (much larger than the state dimension) then become necessary. Once the ensemble focuses around the true states, the filter becomes less sensitive, especially if the ensemble includes perturbation directions with greatest error growth. Overall, the impact of covariance error increases with sparser observations. These results have implications for improved robust, reduced rank approaches for atmospheric applications.

A NEW MOIST CONTROL VARIABLE FOR THE MET OFFICE'S VARIATIONAL ASSIMILATION SYSTEM

Keith NGAN ([email protected]), N Bruce INGLEBY, Richard RENSHAW, David R JACKSON, Andrew C LORENC Met Office

Assimilation of humidity has been recognised as a weakness of the Met Office's 4DVAR system. Particular issues include an upper tropospheric dry bias and the assimilation of stratocumulus. Such issues may be addressed via the introduction of a new moist control variable, which is currently being developed at the Met Office, following work by Hólm and co-workers at ECMWF.

The theoretical basis for the new moist control variable, which uses conditional statistics defined with respect to a symmetrised reference field, will be reviewed and differences in our specific implementation given. The extent to which asymmetry, bias, and non-Gaussianity are alleviated will be discussed. Preliminary results from short trials will be presented and comparisons made with the previous control variable.

62 CONDITIONING AND PRECONDITIONING OF THE 4-D VARIATIONAL DATA ASSIMILATION PROBLEM

S.A. HABEN1, A.S. LAWLESS1, N.K. NICHOLS1 ([email protected]) University of Reading

The four-dimensional variational data assimilation problem (4-DVar) has been found to be very ill- conditioned; that is, the solution is sensitive to small errors and the problem is hard to solve accurately. Moreover, standard optimization procedures for solving the problem are slow to converge. We examine the causes for this ill-conditioning and demonstrate how the conditioning behaves as a function of the length- scales in the background error covariance matrices. We also examine the effect of observations on the conditioning. It is shown that the further apart the observations, the better the conditioning of the problem. Preconditioning of the system by the square root of the background covariance matrix is shown, theoretically and in practice, to improve the conditioning of the problem significantly. Different choices of the correlation structures used to describe the background error are also examined and it is shown how these choices affect the conditioning of the system.

SIMULTANEOUS ESTIMATION OF LAND SURFACE MODEL STATES AND PARAMETERS USING A CONSTRAINT ENSEMBLE KALMAN FILTER

Suping NIE12 ([email protected]), Jiang ZHU2, Yong LUO1 National Climate Center, China Meteorological Administration1, Institute of Atmospheric Physics, Chinese Academy of Sciences2

The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, initial soil moisture condition and atmospheric forcing. The basic idea is the state augmentation technique which considers model parameters as parts of model states beside conventional state variables. As some applications show that this approach is problematic if multiple model parameters are estimated simultaneously, a new constraint multi-parameter estimation method is developed to overcome the decline of estimation performance. The constraint is carried out using some empirical relationships among parameters to generate the multi-parameter perturbations in assimilation processes. Using a series of idealized experiments, model generated surface soil moisture observations are assimilated to estimate soil moisture state and three important hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in a physically-based land surface model. The constraint estimation of single imperfect parameter is as successful as standard (non-constraint) estimation results with estimated mean values converging to their true values and the root mean squared errors (RMSE) of soil moisture lower than that of non-parameter-estimation benchmark experiments obviously. Comparing to significant degeneracy of performance in standard multi-parameter estimation, the performance of this constraint multi-parameter estimation is as good as that of single- parameter case even observations are available sparsely in time. Furthermore, results also show that this method is applicable for all soil types of the model and the advantage of it can be displayed better with a proper temporal-sparse assimilation interval.

EFFECTIVENESS OF DRIFTER DATA ASSIMILATION IN IMPROVING HINDCAST OF MESO-SCALE VARIABILITY IN KUROSHIO EXTENSION REGION

Kei NISHINA1 ([email protected]), Yoichi ISHIKAWA1, Toshiyuki AWAJI1, Kosuke ITO1 Kyoto University1

Our goal is to make a more realistic hindcast of the energetic Kuroshio Extension Region (hereafter KER) in the North Pacific focusing on meso-scale variability which plays an important role in heat transport, watermass formation, and ecosystem. Since surface drifters have the potential to provide velocity information over wide areas in swift current regions, we have performed simultaneous assimilation of drifter-derived velocity data into our high-resolution 4D-VAR data assimilation system together with satellite and in-situ hydrographic data, and investigated the effectiveness.

Experimental results with or without assimilation of drifter data (hereafter Exp Drf and Exp NoDrf cases) have been compared for the period during Aug-Oct 2005 when a relatively large number of drifters were located in the KER. As a result, it is found that the velocity field obtained in Exp Drf is better fit with observed one than that in Exp NoDrf. For example, meso-scale features such as Kuroshio Extension meandering jet and associated eddies are effectively corrected particularly in October. Our examination suggests that this

63 improvement in Exp Drf is attributed mainly to the dynamical adjustment of the zonal meandering jet to the assimilated drifter information input in the KER via the westward propagation of the first mode baroclinic Rossby waves.

Moreover, Exp Drf provided more realistic life cycles of meso-scale eddies and streamers which are key factors for the inter-gyre exchange between subtropical and subarctic gyres. Thus, the corrected meso-scale events lead to the improved estimate of cross-frontal heat and material transport in the KER. In addition, thanks to the advantage of our 4D-VAR simultaneous assimilation of surface drifter data, vertical transmissions of information obtained by drifters and significant improvement with fewer drifter data are achieved. For these improvements, the product by our assimilation system becomes useful for understanding the complicated dynamical processes inherent in the KER.

EFFECTS OF GAIN SPECIFICATION AND COVARIANCE ESTIMATION USING THE SQUARE ROOT, STATISTICAL DYNAMICAL AND ENSEMBLE KALMAN FILTERS

Terence J. O’KANE ([email protected]) and Jorgen S. FREDERIKSEN Center for Australian Climate & Weather Research, CSIRO, Australia

We examine the combined role of sampling error and various specifications of the Kalman gain, from homogeneous and isotropic, homogeneous but anisotropic through to fully inhomogeneous and anisotropic. We consider covariance estimation and forecast error growth during periods of strongly nonlinear large scale atmospheric regime transitions through comparison of both stochastic and deterministic variants of the ensemble Kalman filter and the statistical dynamical Kalman filter. This work extends the earlier paper of O’Kane & Frederiksen (Entropy; 2008) to consider the full matrix specification of the gain as compared to spectrally diagonal cases and is in broad agreement with the simple variational study by Vidard et al (Tellus; 2003). We find that even for very large sample sizes a homogeneous anisotropic gain out performs the full inhomogeneous gain and in particular for methods that employ perturbed observations. We also examine new methods of combined spectral-grid point covariance localization.

REGIONAL OCEAN APPLICATIONS OF THE ENKF/ENOI

Peter R. OKE ([email protected]) Center for Australian Climate & Weather Research, CSIRO, Australia

One perspective of ensemble data assimilation is that the sub-space of the ensemble is all that matters. The ensemble's sub-space defines the possible states into which the model updates can be projected. The update at any given time depends on the model-data misfits and their projection onto the ensemble's sub- space. Therefore is the time-dependence of an ensemble really important? Or is the rank of the ensemble's sub-space the critical factor? Given a finite computational resource, one may choose to perform ensemble data assimilation with a huge stationary ensemble, or a small time-varying ensemble. Can a data assimilation system with a huge stationary ensemble out-perform a system with a small time-varying ensemble? What is the best strategy? Drawing from a series of oceanographic applications of an ensemble data assimilation system, a case will be made for EnOI over its extravagant big brother, the ensemble Kalman Filter.

HINDCAST OF THE CIRCULATION IN THE CHUKCHI AND EAST SIBERIAN SEAS

Gleb PANTELEEV1 ([email protected]), Dmitri NECHAEV2, Andrey PROSHUTINSKY3, Takashi KIKUCHI4 , Rebecca WOODGATE5, Jinlun ZHANG5 1International Arctic Research Center, University of Alaska 2Department of Marine Science, University of Southern Mississippi 3Woods Hole Oceanographic Institute, 4JAMSTEC, Japan, 5Aplied Physical Laboratory, University of Washington,

In order to reconstruct the circulation in the Arctic Ocean from available observations we are developing an efficient data assimilation system involving application of several data assimilation approaches. In a preliminary effort, the data assimilation system was used to reconstruct circulation in the Chukchi Sea during 1990-1991 and circulation in the East Siberian Sea during the fall of 1994. The reconstructed circulation in the Chukchi Sea is in good agreement with observations. The obtained velocity, temperature and salinity fields are used to estimate volume, heat, and salt transports and to analyze some specific features of the Chukchi Sea circulation. The estimates of the circulation in the East Siberian Sea are used to quantify the flow through the Long Strait and to derive the non-stationary reference sea surface height for the Chukchi and East Siberian seas.

64

OPTIMIZATION OF MOORING OBSERVATIONS IN NORTHERN BERING SEA

Gleb PANTELEEV1 ([email protected]), Max YAREMCHUK2, Dmitri NECHAEV3 1International Arctic Research Center, University of Alaska 2Department of Physics, University of New Orleans 3Department of Marine Science, University of Southern Mississippi

The problem of the optimal sampling strategy for moored current velocity observations in the Northern Bering Sea is addressed. We analyze dynamically induced correlations in the North Bering Sea currents and conduct their sensitivity analysis to optimize positions of a limited number of moorings. Optimization of the sampling strategy is performed with respect to robustness of the reconstruction of the North Bering Sea circulation with a particular emphasis on the accurate monitoring of the mean Bering Strait transport. Computations reveal four major regions in the North Bering Sea basin that are highly correlated with the Bering Strait transport. Apart from the regions within the Bering Strait itself, they include the Anadyr Strait and a region 100 km south of the Cape of Prince of Wales. Results of the sensitivity analysis are tested within the framework of twin data experiments for the examples of the quasi-stationary and oscillatory background circulations.

VOLUME BALANCE AND MEAN OCEAN DYNAMICAL TOPOGRAPHY IN THE BERING SEA

Gleb PANTELEEV1 ([email protected]), Phyllis STABENO2, Dmitri NECHAEV3, Vladimir LUCHIN4, Motoyoshi IKEDA5 1International Arctic Research Center, University of Alaska 2Pacific Marine Environmental Laboratory, NOAA, 3Department of Marine Science, University of Southern Mississippi 4Il’ichev Oceanographic Institute,Far Eastern Branch of RAS 5University of Sapporo, [email protected]

We present the results of the multiyear efforts on the development of 4Dvar data assimilation system in the Bering Sea. The presented result include the estimate of the Bering Sea volume balance as a variational inverse of the hydrographic (temperature, salinity and velocity) and atmospheric climatologies. The optimized transports through the Kamchatka Strait, Near Strait, Amchitka and Amukta passes are -28, 13, 6, and 3.5 Sv respectively. These transports are significantly higher than the conventional climatological estimates but agree well with the recent transports calculations based on direct velocity measurements. Posterior error analysis and satellite sea surface height observations indicate high interannual and seasonal variability of the transports through the Aleutian passes. It was found, that the changes in the Kamchatka strait transport are controlled by variability of the Near strait inflow and by Alaska Stream transport. Another important result of this study is the estimate of the mean climatological sea surface height (SSH) distribution that can be used as a reference SSH for the satellite altimetry data in the Bering Sea region. Several numerical experiments reveal that the combination of the obtained reference SSH with satellite altimetry anomaly observations results in a realistic reconstruction of the Amukta pass circulation.

MODEL-REDUCED 4D-VAR DATA ASSIMILATION IN ECOLOGICAL MODELING

Joanna S. PELC1,2 ([email protected]), Ghada EL SERAFY2, and Arnold W. HEEMINK1 Delft University of Technology1, Deltares (WL | Delft Hydraulics), The Netherlands2

Phytoplankton blooms, also called algal blooms, are important factors in ecological quality modeling. In order to model algal blooms, algal biomass has to be specified. This is done by measuring the concentration of chlorophyll-a, which provides a reasonable estimate of algal biomass. To model chlorophylla concentration the generic ecological model (BLOOM/GEM) (Blauw et al., Hydrobiologia, 2008) is used. The model was developed to simulate nutrients cycle, primary production and ecosystem functioning. Moreover, it consists of detailed underlying hydrodynamics, suspended sediment and river loads, which are required for ecological modeling. A 2D version of this model is used to simulate chlorophyll-a concentration in the southern North Sea. The ecological model consists of 30 state variables and more then 400 parameters, and it is defined on 8710 grid cells. Since many of the parameters are highly uncertain, the main task of this work is to update the parameters, such that better model predictions are obtained. Based on the sensitivity analysis 20 parameters have been chosen as the main source of uncertainty (Salacinska et al., Ecological Modeling, 2009). In this paper, based on a number of simulations of the original model, proper orthogonal decomposition (POD) is used to obtain a reduced model (Vermeulen and Heemink, MWR, 2006). This reduced model carries out the most important information with respect to relation between the parameters and the dynamics of the model. Finally model-reduced 4D variational data assimilation is performed to

65 estimate the parameters. Since model-reduced 4D-Var is performed in the reduced space, the implementation of the adjoint of the tangent linear approximation of the original model is not required. The POD-reduced model technique is explained, and corresponding results are presented. The first results of model-reduced 4D-Var are illustrated. For data assimilation purposes MERIS satellite data is used, and in- situ data is used for results validation.

DATA ASSIMILATION OF THE GLOBAL OCEAN USING THE LOCAL ENSEMBLE TRANSFORM KALMAN FILTER (LETKF) AND THE MODULAR OCEAN MODEL (MOM2)

Steve PENNY ([email protected]), Eugenia KALNAY, James CARTON, Kayo IDE, Brian HUNT University of Maryland, College Park

The Local Ensemble Transform Kalman Filter (LETKF) of Hunt et al is applied to the Modular Ocean Model (MOM2) of the Geophysical Fluid Dynamics Laboratory (GFDL). Modifications were made to account for error in the forcing fields, unique properties and dispersion of oceanic observation data, and the slower dynamics of the system compared with atmospheric applications. A reanalysis was performed for a selected time period using historical XBT and SST observation data. In addition, verification tests were performed with simulated data and control nature runs. Results for a short reanalysis period and comparisons with the Simple Ocean Data Assimialtion (SODA) of Carton et al will be presented.

ENSEMBLE-DERIVED BACKGROUND-ERROR COVARIANCES: EVALUATION IN THE OPERATIONAL MET OFFICE NWP SYSTEM

Chiara PICCOLO ([email protected]) Met Oce, FitzRoy Road Exeter EX1 3PB, United Kingdom

The background error covariance is a key element in any data assimilation system. In current numerical weather prediction systems, the estimation of the covariance matrices of the background error is difficult because the lack of information on the statistical properties of the background error and because the dimension of these matrices is of the order of 107 x 107.

In the current Met Office system the background covariance matrix is calculated using the so-called NMC method. In this study we evaluate alternative approaches to generate the background error covariances for atmospheric data assimilation. We compare covariances using lagged forecast errors (NMC) with those generated using ensemble short-term forecast errors, both a proxy of background errors. Here we use two sets of training data to compute the ensemble-derived background error statistics: (i) the Met Office Global and Regional Ensemble Prediction System (MOGREPS), where the ensemble initial perturbations are generated by the Ensemble Transform Kalman Filter (ETKF) with an additional contribution generated by a Stochastic Physics scheme to sample model uncertainties and (ii) the ECMWF Ensemble of Analysis, where the analysis-forecast system is run several times for the same period with randomly-perturbed observations. Using the ensemble-derived background error covariances estimated from the two training sets, we present results from operational Met Office 4DVAR trials in addition to verification statistics calculated from a month of analysis-forecast experiments.

A COMPARISON OF LAND SURFACE MODEL DATA ASSIMILATION APPROACHES TO IMPROVE HEAT FLUX ESTIMATES FOR NUMERICAL WEATHER PREDICTION

Robert PIPUNIC1 ([email protected]), Jeffrey WALKER1 and Andrew WESTERN1 1Department of Civil and Environmental Engineering, The University of Melbourne, Australia

Land Surface Models (LSMs) provide estimates of latent (LE) and sensible (H) heat flux feedbacks to the atmosphere which are required for initialising numerical weather prediction models. Raw LSM predictions are typically uncertain due to approximations of complex physical processes in these models in addition to errors associated with parameterisation, forcing data and prescribing initial state values. Data assimilation is a proven technique for improving the quality of predictions by incorporating observed data to make model corrections through time. A variety of spatially explicit data is available from different remote sensing platforms which are potentially suitable for LSM data assimilation in a numerical weather prediction context. Much of the past LSM data assimilation research has focussed on assimilating surface soil moisture observations, with the assumption that improving a model’s soil moisture representation can improve the related heat flux predictions. Skin temperature assimilation can also make improvements by impacting on a model’s energy balance. The potential of achieving optimal improvements to predicted LE and H through

66 assimilating instantaneous LE and H observations has received limited attention in the literature, possibly due to remotely sensed LE and H observations being an emerging product. This work compares a number of LSM data assimilation approaches where observations of surface soil moisture, skin temperature, and LE and H were assimilated individually and in combination. The assimilation was applied to the CABLE model which was recently implemented as the new LSM for Australia’s weather prediction system. CABLE was used offline and the experimental results compared to assess what impact the different approaches had on the state variables and heat fluxes. The experiments include a synthetic-twin study, a one-dimensional assimilation study using real field data and an example of remote sensing data assimilation focussing on an area in the Murray Darling Basin in south east Australia.

IMPLEMENTATION OF SINGULAR VECTORS TO DETERMINE ADAPTIVE OBSERVATIONS DESIGN PROVIDED THE EFFICIENT REPRESENTATION OF THE ENSEMBLE PREDICTION SYSTEMS

Oleg M. POKROVSKY ([email protected]) Main Geophysical Observatory, St. Petersburg, 194021, Russian Federation

The global general circulation model (GCM) based on a system of physically full equations in spectral form, which was developed in the Main Geophysical Observatory (Russia), was used in the ensemble NWP. The ensemble prediction system (EPS) was constructed by means of the singular vectors (SVs) set. It was found that a set of the first five SVs comprises a basis provided a most rapid growth of any perturbations in initial fields. A cost function and relevant optimization method were developed to determine an optimal design for the additional adaptive observations with account for the operational networks and the satellite remote sensing system. Thus this cost function was stated as depended on the adaptive subsystem configuration. We proved that an efficiency of the adaptive network design is closely related to the root mean square minimization of the error fields responded to principal singular vector representation related to main meteorological variables by means of experiments with GCM. There were considered three spatial resolution grades responded to wave numbers 21, 42 and 63. It was found that increasing in the SV spatial resolution permits to enhance the time and locality determination for adaptive measurements. The denial experiments with EPS targeted to the Northern Asia demonstrated that the major information content of the adaptive additional observations might be inferred from data distributed along Arctic and Pacific Ocean coasts. It was found also that the most important meteorological parameters have largest variance just in these regions. It might be explained by the fact that principal SVs attain maximum values also in these areas. We found the impact areas for above adaptive observations. Particularly, we found impact areas in the 1-7 days weather forecast. These are the Northern Pacific, Alaska and Western Canada. It was found that, when West Pacific oscillation index attains the negative magnitudes the adaptive measurements at Taimyr, Kamchatka and Chukotka peninsulas provide some important pieces of data for NWP over Polar Canada, North-East China, North Japan and North-Western part of North America for 3-5 days.

ASSIMILATION OF LAND SURFACE SITE AND REMOTELY SENSING DATA IN THE ATMOSPHERE- LAND ENERGY EXCHANGE MODEL

Oleg M. POKROVSKY ([email protected]) Main Geophysical Observatory, St. Petersburg, 194021, Russian Federation

The regional model was developed for joint assimilation of the land surface site operational data on components of the radiation and heat balances provided by Russian meteorological network and remotely sensing data concerned to the surface short wave (SW) radiation budget, outgoing long wave (LW) radiation and evaporation rate. Statistical fuzzy set model was used to reveal the essential relationships between various components of radiation and heat budget (global SW irradiance, land temperature and moisture, outgoing LW radiation, evaporation rate, latent heat) and to classify diurnal cycles of above variables. The amplitude of diurnal cycle has been found to be a good indicator of surface flux partitioning into various fuzzy classes. Neural network module provides a simulation of non-linear implicit linkages existed between different model variables and describes its temporal evolving. Fuzzy-neural model was trained by ground-based site measurement data sets. This model requires brightness temperature and radiance flux measurements, at least, at three times during a day: morning, noon and afternoon hours. Input related links are presented by simultaneous AVHRR/NOAA satellite measurement data in the solar radiation channels and infrared atmospheric window. Output variables are the SW radiation and heat balance components: global irradiance, land temperature and moisture, air sensible and latent heating, heat flux into the ground. Last component is related with soil temperature at standard depth levels: 0, 5, 10, 15, 20 cm. This approach provides most accurate estimates of ground flux components (10-20%). It turned out that air sensible and latent heating fluxes could be estimated with 40-70% relative accuracy. Our study has revealed that additional information on the wind speed and soil humidity strongly impacts on accuracy of the air sensible and latent heating fluxes.

67

ENSEMBLE BACKGROUND-ERROR VARIANCES: OBJECTIVE FILTERING AND IMPACT STUDIES

Laure RAYNAUD ([email protected]), Loïk BERRE, Gérald DESROZIERS CNRM/GAME, Météo-France/CNRS, Toulouse, France

Background-error variances are key components of any data assimilation scheme, since they determine the respective weights of the background and the observations in the analysed state.

The use of an ensemble of perturbed assimilations has been widely studied recently, and it appears to be a suitable framework for the estimation of either climatological or flow-dependent variances.

However, random errors in the variance estimation arise from the finite ensemble size, through the associated sampling noise. To fully take advantage of the information given by the ensemble, some filtering tools must be implemented.

Spatial properties of the noise are first investigated. It is shown that the spatial structures of the noise and the background-error are closely connected.

An objective filtering procedure, which relies on an estimate of spectral signal/noise ratios, is then introduced.

The robustness and the relevance of the proposed filter are examined in an application to an ensemble of Météo-France Arpège forecasts. The filter shows ability to remove the noise, while preserving the useful signal. Resulting filtered variance maps are accurate (with a residual estimation error variance around 10%) and closely linked to the meteorological situation.

The usefulness of this filtering is next assessed through impact studies. Results indicate rather positive impacts, which are well-pronounced for wind forecasts.

The impact of flow-dependent variances (compared to climatological ones) is also examined in the French 4D-Var scheme. It is shown that humidity variances have a positive global impact, especially above 50hPa, while surface pressure variances give a sizeable local contribution to reducing position errors of lows.

Finally, flow-dependent information is particularly relevant in extreme weather events. This is illustrated on a severe storm over France, for which using ensemble variances lead to dramatic improvements in the prediction of both position and intensity.

ASSIMILATING GEOSTATIONARY SATELLITE MTSAT-1R THERMAL DATA TO CONSTRAIN REGIONAL ESTIMATES OF SURFACE WATER AND ENERGY PARAMETERS

Luigi J. RENZULLO ([email protected]) CSIRO Land and Water

The ability to accurately map the flux and stores of water in the landscape is an essential part of water resources assessment, with implications for water management and the forecasting of future availability. Land surface models (LSMs) can provide estimates of soil moisture and evaporative fluxes for large parts of the continent but model constraints are limited to data from a small number of scattered in-situ monitoring sites, and are therefore subject to credulity in those ungauged areas of the country.

Another source of LSM constraint is provided by the indirect measurements of remote sensing systems. An advantage of satellite-based remote sensing data is the spatial coverage (~continental) and observation frequency (< daily). A difficulty is that, with the exception of some inferred image products, rarely is the LSM output of interest matched exactly by an equivalent remotely-sensed observation. Therefore, if these data are to be of use, it is necessary to either develop retrieval schemes that derive as accurately as possible from the satellite observations the land surface variables that correspond to model output, or develop appropriate observational models that relate the LSM states/variables to the remotely-sensed observations.

68 This paper describes how the geostationary MTSAT-1R satellite’s thermal observations may be used to constrain estimates of water and energy flux parameters. The first part of the paper is devoted to the retrieval of land surface temperature (LST) estimates from the MTSAT-1R thermal brightness temperature observations. The second part focuses on the assimilation of the derived LST images in a simple coupled water-energy balance model to provide estimates of surface energy flux for a number of study sites, and points towards how this can be extended to the continent. Estimates of evaporative fluxes are compared with a selection of ground-based measurements and satellite-derived products.

MODEL REPRESENTATION ERROR ESTIMATION FOR OCEAN DATA ASSIMILATION

James G. RICHMAN1 ([email protected]) and Robert N. MILLER2 1 Ocean Dynamics and Prediction Branch, Code 7323, Naval Research Laboratory, Stennis Space Center, MS 395291 2 College of Oceanic and Atmospheric Sciences, Oregon State University, Corvallis, OR 973312

The difference between observations and a model simulation can be decomposed int instrumental error, model forecast error and representation error. A major challenge for data assimilation is the accurate characterization of these errors. We have developed a technique which identifies the information content of a model. We use a long simulation ot formulate a basis function for a reduced state space of the model using our metric for the information content. The projection of a sequence of model-data misfits can be used to estimate the model forecast errors, which is analogous to the estimate obtained by ensemble methods. The remainder of the misfits can be assigned to the model representation error and instrument error. We test our construction using a coarse resolution ocean general circulation odel and satellite observations of sea surface height and temperature. On construction of the representation error differs from the recent technique presented by Oke and Sakov (2008) by including error of unrepresented physics of the model. We generate a monte carlo realization from our representation error maps. We find that the probability density function of our monte carlo representation and optimal interpolation analysis increments are indistinguishable from the probability density function of the actual model-data misfits.

DATA ASSIMILATION IN A SOIL-VEGETATION-ATMOSPHERE TRANSFER MODEL USING A FILTERING FRAMEWORK

Marc RIDLER

It remains a crucial challenge to adequately address uncertainty associated with hydrological predictions. The key to solving this challenge is to understand, quantify and reduce uncertainty in a systematic and cohesive manner. Although data assimilation techniques are emerging to tackle hydrological uncertainty, no well-accepted guidelines exist to implement these principles.

The use of a filtering data assimilation framework is explored using the distributed, integrated hydrological modeling system MIKE SHE which is coupled with an advanced soil-vegetation-atmosphere transfer (SVAT) model that allows direct input from satellite remote-sensing data. The SVAT model is driven using precipitation, radiation and air temperature from the Meteosat Second Generation (MSG) satellite, along with leaf area index and albedo products from the Moderate Resolution Imaging Spectroradiometer (MODIS). Measurements of Land surface temperature (LST) available from MODIS are assimilated based on run time to improve model predictions.

Our goal is to validate our framework using on-site hydrological measurements in Western-Africa. The African Monsoon Multidisciplinary Analysis (AMMA) is an international project with the aim to improve knowledge and understanding of the West-African monsoon. In particular, the mesoscale convective systems which bring most of the rainfall over this area can be studied in detail as it corresponds also to the scale of many catchments. Using the on-site data, the data assimilation framework will be validated and tweaked to ensure greater confidence in automated remote-sensing based models.

69 AIRS IMPACT ON TROPICAL CYCLONE REPRESENTATION IN A GLOBAL DATA ASSIMILATION AND FORECASTING SYSTEM

Oreste REALE1, Lars Peter RIISHOJGAARD2 ([email protected]), Joel SUSSKIND3, William LAU3 and Genia BRIN4 University of Maryland Baltimore County1 Joint Center for Satellite Data Assimilation2 NASA Goddard Space Flight Center3 SAIC4

Two sets of data assimilation experiments covering boreal spring and boreal summer conditions respectively were carried out with the NASA GEOS-5 Data Assimilation and Forecasting System. The periods chosen are April-May 2008 (to overlap with the catastrophic cyclone Nargis which hit Myanmar) and August-September 2006. In each period, three data assimilation experiments were performed: a control run (in which all conventional and satellite data used by operational centers are assimilated, with the exclusion of AIRS), an experiment with AIRS clear-sky radiances added on top of the observations used in the control run) and an experiment in which quality-controlled AIRS temperature retrievals obtained under partial cloudy cover are ingested on top of the control observations. In all experiments, 5-day forecasts were issued once per day, and the experiments were evaluated primarily based on global forecast skill. In both boreal spring and boreal summer conditions the assimilation of AIRS cloudy retrievals are found to lead to superior forecast skill.

In addition, an assessment of the analysis of tropical cyclones is performed over the Indian Ocean and in the Atlantic. It is shown that AIRS cloudy retrievals always improve the tropical cyclone representation in the analysis, creating more confined and deeper circulations. This was noted for cyclone Nargis in the Indian Ocean, as well as for several tropical systems in the Atlantic Ocean. The fundamental causes of this improvement will be discussed in detail in the presentation.

THE DEVELOPMENT OF HYPERSPECTRAL INFRARED WATER VAPOR RADIANCE ASSIMILATION TECHNIQUES IN THE NCEP GLOBAL FORECAST SYSTEM

Jim JUNG1, Lars Peter RIISHOJGAARD1 ([email protected]), John LE MARSHALL2 Joint Center for Satellite Data Assimilation1 Bureau of Meteorology2

Work focused on improving the techniques used to assimilate the AIRS and IASI water vapor channels into operational NWP systems is presented. Several problems must be resolved when using these channels. In general, the non-linearity of the water vapor channels makes them difficult to assimilate. This problem is further accentuated by the fact that the background water vapor field is not well simulated by most global forecast systems, including the NCEP Global Forecast System used here. The issues of supersaturation and small negative moisture values in the model background field are being addressed. Initial experiments have been performed to address the non-linearity of some of the water vapor channels proposed to be assimilated in the GFS, with some success. Techniques to reduce the effects of supersaturation and the negative moisture values have been developed and are being tested. The impact of the background error variances and structure functions for the pseudo-relative humidity in the stratosphere will be discussed. It will be shown that a cold temperature bias and lower heights in the stratosphere results from assimilation of the hyperspectral water vapor channel. The current status of diagnosing and if possible eliminating the causes of this bias will be presented.

TOWARDS JOINT DATA ASSIMILATION FOR A COUPLED ATMOSPHERE-OCEAN SYSTEM

Harold RITCHIE1,2 ([email protected]), Faez BAKALIAN2, Keith THOMPSON2, and Jean-Marc BÉLANGER3 1 Meteorological Research Division, Environment Canada, Dartmouth, NS, Canada 2 Department of Oceanography, Dalhousie University, Halifax, NS, Canada 3 Meteorological Research Division, Environment Canada, Dorval, QC, Canada

A simple state space model representation of the coupled atmosphere-ocean system has been employed to critically examine the advantages and disadvantages of single, independent, joint, and iterative assimilation based on Kalman Filtering. In single-medium assimilation, data is incorporated into one medium only while holding the other medium fixed except for occasional flux adjustments. In independent assimilation, data is assimilated into each of the respective media and the information is communicated between the media only through the coupling terms.

70 In joint assimilation, the data is assimilated into each of the respective media but the information is immediately transferred to the other medium through the Kalman Filter and both media are updated simultaneously at each assimilation stage. In iterative assimilation, which is a hybrid between independent and joint assimilation, on the first iteration the data is assimilated into one medium only while the other is held fixed and on the next iteration, the order of assimilation is reversed. This was carried out until steady state convergence was obtained. The background error variances in each case were evaluated using the NMC method and the model forecasts compared and contrasted for independent, joint, and iterative assimilation. This research is being conducted for use in a coupled system consisting of the Environment Canada GEM atmosphere model and the Mercator-Océan NEMO (France) data assimilation and forecast systems. The status of the GEM-NEMO system will also be presented.

ROBUST CHARACTERIZATION OF MODEL PHYSICS UNCERTAINTY AND IMPLICATIONS FOR ENSEMBLE-BASED PREDICTION

Derek J. POSSELT1 ([email protected]) and Tomislava VUKIĆEVIĆ2 1Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan 2ATOC and CIRES, University of Colorado

Model physics (e.g., microphysics, convection, and radiation) schemes represent an important source of uncertainty in numerical models that range in scale from large eddy simulation to general circulation models. Much of this uncertainty is associated with specification of parameters that control the rates and/or characteristics of physical processes. In contrast to errors in forecast initial conditions, the characteristics of model physics uncertainty are not well understood, hence model physics error is not included in most operational data assimilation systems. As ensembles of simulations are increasingly used in data assimilation and probabilistic forecasting, it is desirable to perturb both initial conditions and model physics parameters. To do so properly requires knowledge of which parameters have the greatest effect on model results, as well as the characteristics of the relationship between model output and changes to parameters.

In this paper, we map the functional relationship between model parameters and observations using a Markov chain Monte Carlo (MCMC) algorithm. We examine cloud microphysics and radiation packages from a cloud resolving model that are similar to schemes used in modern regional and general circulation models, and demonstrate how the joint probability distribution returned from MCMC can be used to

• map the functional relationship between changes in model physics parameters and changes in model output, • identify which parameters have the most significant effect on various model output fields, • describe the nature of nonlinearity in the parameter-state relationship, and • explore how changes in the characteristics of observations affect the model state.

The results of the MCMC-based inversion shed light on the reasons behind the loss in “parameter identifiability” noted in previous studies, and suggest ways in which nonuniqueness can be avoided in the construction of an ensemble based data assimilation scheme that includes model physics parameters as control variables.

MOISTUREMAP: A SOIL MOISTURE MONITORING, PREDICTING AND REPORTING SYSTEM FOR SUSTAINABLE LAND AND WATER MANAGEMENT

Christoph RÜDIGER1 ([email protected]), Jeffrey WALKER1, Damian BARRETT2, Robert GURNEY3, Yann KERR4, Edward KIM5, John LE MARSHALL6 1Dept. of Civil and Environmental Engineering, The University of Melbourne, Melbourne, Australia 2Sustainable Minerals Institute, The University of Queensland, Brisbane, Australia 3NERC Environmental Systems Science Center, University of Reading, Reading, United Kingdom 4Biospheric Processes, CESBIO, Toulouse, France 5Hydrospheric and Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, United States 6Bureau of Meteorology Research Center, Bureau of Meteorology, Australia

A modelling framework called MoistureMap is being developed for reliable monitoring and prediction of the soil moisture status at 1km resolution, with the Murrumbidgee River catchment as a demonstration test-bed. This high-resolution soil moisture information will be derived from land surface model predictions, constrained mainly with soil moisture data from the Soil Moisture and Ocean Salinity (SMOS) satellite, but with the option of using other remotely sensed information such as thermal data from MTSAT-1R. SMOS is a European Space Agency mission scheduled for launch later this year and is the first-ever dedicated

71 microwave satellite for soil moisture measurement. The Murrumbidgee River catchment is an 80,000km2 watershed located in south-eastern Australia, and has been selected for this demonstration study due to its large diversity in climatic, topographic and land cover characteristics. Moreover, it has been instrumented and monitored for soil moisture and supporting data for more than 7 years, and will be the focus of an extensive SMOS validation experiment immediately following SMOS launch, which will also provide validated surface soil moisture observations at 1km resolution. Consequently the existing in-situ network of profile soil moisture monitoring sites and planned airborne campaigns for surface soil moisture mean that the surface and root zone soil moisture information from MoistureMap will be thoroughly tested.

MoistureMap will use the CSIRO Atmosphere Biosphere Land Exchange (CABLE) land surface model, which is ideally suited to the assimilation requirements of this modelling system, due to its prediction of surface and root zone soil moisture together with surface soil and vegetation temperature, which are necessary for radiance and thermal data assimilation. This presentation presents the first results from an ensemble Kalman filter assimilation of soil moisture observations, with performance assessed against in-situ station observations and compared with predictions from the same model simulation without assimilation.

FOUR-DIMENSIONAL OBSERVATION IMPACT ON THE US NAVY’S ATMOSPHERIC ANALYSES AND FORECASTS: PART 2: CHANNEL SELECTION AND REAL-TIME MONITORING

Benjamin RUSTON ([email protected]), Rolf LANGLAND, Nancy BAKER, Steve SWADLEY and Tim HOGAN Marine Meteorology Division, Naval Research Laboratory

Use of satellite data in NWP has continued to grow with the advent of hyperspectral sounders. Determination of whether a channel is beneficial or non-beneficial is aided by observation impact technique described in Langland and Baker (2004). In this approach, the adjoint of the forecast model and the data assimilation system are used with a forecast error metric (here, a moist static energy error norm) to assess whether subsets of observations improve or degrade the NWP forecast. The U.S. Navy’s 4D-Var data assimilation system, NAVDAS-AR1, has been assimilating the AIRS and IASI sensors, along with AMSU-A and SSMIS microwave sensors, since the summer 2008. We have been able to increase forecast impact (skill) in both northern and southern hemispheres through careful modifications to the channel selection and quality control guided by the observation impact assessed through the adjoints of NOGAPS2 and NAVDAS- AR. We will present examples illustrating the use of observation impact, and the consistency between reduction of moist static energy error norm and the traditional NWP forecast metrics. We will also illustrate how the observation impact can be combined with our standard suite of satellite monitoring tools to allow continual channel refinement and detection of meteorological events such as stratospheric warmings. These meteorological events may be missed (i.e., not predicted) by the NWP model. In this case, the observation impact for the higher-peaking sounding channel radiances becomes non-beneficial, even though the observation quality remains high. Detection of these events is important to properly re-balance the background and radiance observation error variances, thereby mitigating any destructive forecast impact. Lastly, the NOGAPS model top for NAVDAS-AR was recently extended to 0.01 hPa, allowing assimilation of additional channels in both the IR and microwave spectrum. We will present the strategy used to determine which new channels were selected for assimilation.

ASYNCHRONOUS DATA ASSIMILATION WITH THE ENKF

Pavel SAKOV1 ([email protected]), Geir EVENSEN1,2, Laurent BERTINO1 and Francois COUNILLON1 1Nansen Environmental and Remote Sensing Center, Thormøhlensgate 47, Bergen 5006, Norway; 2Statoil Research Center, Sandsliveien 90, Bergen 5020, Norway

We consider the problem of assimilation of asynchronous observations, or four-dimensional data assimilation, with the ensemble Kalman filter (EnKF). It is shown that for a system with linear dynamics the ensemble Kalman smoother (EnKS) provides a simple and efficient solution for the problem: one just needs to use the ensemble observations from the time of observation during the update, for each assimilated observation. This recipe can be used for assimilating both past and future data; in the context of assimilating generic asynchronous observations we refer to it as the asynchronous EnKF, or AEnKF. The AEnKF is essentially equivalent to the four-dimensional variational data assimilation (4D-Var). It requires only one forward integration of the system to obtain and store the data necessary for the analysis, and therefore is

1 NRL Atmospheric Variational Data Assimilation System – Accelerated Representer 2 Navy Operational Global Atmospheric Data Assimilation System

72 feasible for large-scale applications. Unlike 4D-Var, the AEnKF requires no tangent linear or adjoint model. Operational use of AEnKF in TOPAZ is discussed. Also, results of numerical experiments with AEnKF that illustrate some aspects of its functionality are presented.

ON TWO COMMON LOCALISATION METHODS IN ENKF

Pavel SAKOV ([email protected]) and Laurent BERTINO Nansen Environmental and Remote Sensing Center, Thormøhlensgate 47, Bergen 5006, Norway

In this work we investigate relation between two common localisation methods in EnKF: covariance localisation and local analysis. Covariance localisation involves modification of the update equations by replacing the state error covariance by its elementwise product with some distance-dependent correlation matrix. Local analysis involves building a local window around each updated state vector element and using an approximation of the state vector within this window in the update. We show that

- both methods can be formulated in terms of ensemble tapering; - they lead to different results; - in the case of weak data assimilation they become nearly equivalent.

Details of both formulations are discussed, including the issues of consistency and numerical effectiveness.

IMPACT ASSESSMENT OF DOPPLER RADAR RADIAL WIND OBSERVATIONS

Kirsti SALONEN1 ([email protected]), Reima ERESMAA1, and Heikki JÄRVINEN1 Finnish Meteorological Institute1

This presentation discusses the pre-operational development for data assimilation of Doppler radar radial wind observations in the High Resolution Limited Area Model (HIRLAM). The HIRLAM variational data assimilation system includes all the needed tools for exploitation of radial wind observations. Raw observations are processed to spatial averages, superobservations, before they enter the data assimilation system. Superobservation generation decreases random and representativity errors of the raw data. The radar radial wind observation operator implemented in the HIRLAM reference code takes into account the radar measurement geometry, the broadening of the radar pulse volume, and the bending of the radar pulse path.

A measurement task designed especially for the radial wind data assimilation purposes has been implemented to the Finnish Meteorological Institute radar network. Observation quality monitoring indicates that the main error sources for the radial wind observations are ground clutter and velocity ambiguity. The HIRLAM quality control procedures are able to detect and reject most of these erroneous observations. Impact studies show encouraging results. Surface verification indicates that the use of radar wind observations has a positive impact on 10 m wind forecasts. Upper air verification shows positive impact on wind and temperature forecasts at the 925 – 700 hPa levels.

UPGRADE OF THE OPERATIONAL MESOSCALE 4D-VAR AT THE JAPAN METEOROLOGICAL AGENCY

Yuki HONDA and Ken SAWADA ([email protected]) Numerical Prediction Division, Japan Meteorological Agency

For disaster prevention and aviation forecast, the Japan Meteorological Agency (JMA) has been operated a mesoscale numerical weather prediction system known as the Mesoscale Model (MSM). In April 2009, its operational mesoscale analysis system based on a hydrostatic spectral model (Meso 4D-Var) was replaced with a new 4D-Var system based on the JMA non-hydrostatic model (NHM). This new 4D-Var is called JNoVA. By this upgrade, MSM has become a consistent system in the sense both analysis and forecast are based on the non-hydrostatic dynamics. In this upgrade, main differences of two systems are as follows. First, as a time integration operator, (simplified) NHM is used instead of the hydrostatic spectral model. Besides, the latest operational NHM is adopted as an outer model. Second, the resolution is increased. The horizontal resolution of analysis is raised from 10 km to 5 km, and that of the inner model is changed from 20 km to 15 km. Third, the detailed configurations of data assimilation system are changed. Especially, the assimilation window is shortened from 6 hours to 3 hours. Additionally, several other careful adjustments are made, including the recalculation of the background error covariance matrix.

73 To compare the performance of JNoVA with that of Meso 4D-Var, twin experiments are done under almost the same conditions as the operational system in summer (2006/7/16 – 8/31) and in winter (2007/12/23 – 2008/1/23). The quantitative precipitation forecast of JNoVA is better than that of Meso 4D-Var for all thresholds according to the equitable threat score of three-hourly accumulated precipitation forecasts. For upper-air and surface verifications, JNoVA gets even or better than Meso 4D-Var. A remarkable improvement for individual forecasts is seen in the case of typhoon Wukong in 2006. In general, it is confirmed that JNoVA brings an improvement of the forecast scores.

AEROSOL DATA ASSIMILATION WITH AN ENSEMBLE KALMAN FILTER USING CALIPSO AND GROUND-BASED LIDAR OBSERVATIONS

Tsuyoshi Thomas SEKIYAMA1 ([email protected]), Taichu Y. TANAKA1, Atsushi SHIMIZU2, Takemasa MIYOSHI3 Meteorological Research Institute, Tsukuba, Japan1; National Institute for Environmental Studies, Tsukuba, Japan2; University of Maryland, USA3

We have developed an advanced data assimilation system for dust, sulfate, and seasalt aerosols with a global chemistry transport model and a four-dimensional ensemble Kalman filter (4D-EnKF). Aerosol observations used for this data assimilation were derived from two sources:

1) The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) operated by NASA, 2) The ground-based East Asia lidar network managed by the National Institute for Environmental Studies of Japan (NIES).

These aerosol observations were ingested in this data assimilation system where they were processed with an ad hoc observational operator specifically designed for attenuated backscatter measurements by the lidar.

One-month data assimilation cycle experiments for dust (partitioned into 10 size bins), sulfate, and sea-salt (partitioned into 10 size bins) aerosols were performed in May 2007 using CALIPSO data and/or NIES lidar network data. These experiment results were compared with each other, and validated by other independent observations such as weather reports of aeolian dust events.

Detailed four-dimensional structures of aerosol outflows from source regions over continents and oceans for various particle types and sizes were well reproduced in these experiments with CALIPSO data and/or NIES lidar network data. The intensity of dust emission at each grid point was also rationally corrected by the 4D- EnKF. The Level 1B data of CALIPSO, namely attenuated backscattering coefficients, were successfully assimilated for the first time, to the best of the authors’ knowledge. These results are valuable for the comprehensive analysis of aerosol behavior as well as aerosol forecasting.

LOCAL ENSEMBLE TRANSFORM KALMAN FILTER FOR SEMI-LAGRANGIAN BAROTROPIC MODEL OF ATMOSPHERE

Anna V. SHLYAEVA1 ([email protected]) and Mikhail A. TOLSTYKH1,2 1 Hydrometeorological Research Center of Russia, Moscow, Russia 2 Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia

Local ensemble transform Kalman filter (LETKF, Hunt et al., 2007) data assimilation system is implemented for the global semi-Lagrangian barotropic model of atmosphere with the orography and external forcing. The resolution of the model is 1.5 degrees in longitude and latitude. The purpose of the forcing is to introduce an instability into the system. The forcing is chosen so that the mean state of the system is close to the mean state of the actual atmosphere according to the NCEP/NCAR reanalysis. It’s calculation is carried out by averaging the field of the vertical component of absolute vorticity at 300mb for the 5 days around the date of analysis at every analysis step.

The only analysis variable is the vertical component of absolute vorticity. Pseudoobservations are generated at random grid points and assimilated every 6 hours. The observations have equal weight within influence distance from the grid point, beyond which the weight of the observations decreases exponentially with predefined e-folding distance. The procedure of simultaneous estimation of temporally varying adaptive covariance inflation and observation error covariance (Li et al., 2009) is applied. The LETKF was found to be stable for this problem for at least 5 months of assimilation.

74 Multiple experiments were carried out to study the filter behavior under different initial conditions including:

· external forcing generation strategy; · observations positions generation strategy and observations count; · values of influence and e-folding distances; · start value of the observation error covariance; · use of random rotations; · ensemble members count.

The ongoing research is aimed on the simultaneous estimation of spatially (not only temporally) varying adaptive covariance inflation and observation error covariance. It is planned to apply this implementation of the LETKF to a more realistic atmospheric model.

IMPLEMENTATION AND IMPACT OF SCATTEROMETER AND AMV DATA ASSIMILATION WITH THE ACCESS CODE

Holly SIMS ([email protected]), Peter STEINLE, Chris TINGWELL, John LE MARSHALL Yi XIAO, Tan LE Centre for Australian Weather and Climate Research, Bureau of Meteorology, GPO Box 1289K Melbourne, VIC 3001, Australia

The Australian Community Climate Earth System Simulator (ACCESS) has recently been implemented as the Australian Bureau of Meteorology’s NWP system. ACCESS has been developed at the Centre for Australian Weather and Climate Research (CAWCR), and the ACCESS NWP suites are based on the UK Met Office Unified Model and 4dVAR assimilation system.

As part of the tailoring of this system to meet local need experiments have been conducted to optimize the use of Atmospheric motion vectors (AMVs) and Scatterometer data within ACCESS 4dVAR

AMVs from four data providers are assimilated in ACCESS: GOES, EUMETSAT, JMA and AMVs calculated locally at the Australian Bureau of Meteorology over the Australian region. Impact experiments using the AMV data from all providers have been conducted using the global ACCESS NWP system, to provide the ideal spatial and temporal thinning as well as the optimal initial quality control for this system. The locally produced AMVs are of particular significance to the ACCESS regional, limited area suites. The accurate positioning of important meteorological features can be improved through well tuned data assimilation of AMVs. Experiments are also conducted to determine the optimal data assimilation of locally produced AMVs.

QUIKSCAT and ASCAT data are also assimilated in ACCESS. , giving surface wind information over sea. Scatterometer data is of particular importance for the Australian region due to relative scarcity of other surface measurements. Experiments are currently being conducted to tune the scatterometer inclusion swath. Other experiments will be conducted in order to determine optimal quality control and assimilation of QUIKSCAT and ASCAT data.

PREDICTING SOURCES AND SINKS OF BIO-OPTICAL TRACERS WITH A 4DVAR OCEAN ASSIMILATION SYSTEM

Scott R SMITH ([email protected]) and Igor SHULMAN Naval Research Laboratory, Code 7320, Bldg 1009, Stennis Space Center, MS 39529, USA

A 4DVAR ocean assimilation system has been constructed at NRL which employs the weak-constraint representer method. This assimilation system is based on the Navy Coastal Ocean Model (NCOM), which is a free-surface ocean model that is based on the primitive equations and the hydrostatic, Boussinesq, and incompressible approximations. NCOM uses sigma coordinates for the upper layers, z-level coordinates for the lower layers, and has the capability of easily incorporating additional tracer fields (that are in addition to temperature and salinity) into the dynamics.

In the summer of 2008, a large field experiment was conducted in the Monterey Bay (off the coast of California) and a large array of Bio-Optical data was collected from Slocum Gliders, Scanfish, moorings, water samples and many remote sensing satellites. Certain fields of Bio-optical data (such as spectral particle backscattering) will be assimilated into the NCOM-4DVAR system as an additional tracer. In these 4DVAR assimilation experiments, the source/sink terms of these additional tracers will be treated as a weak constraint variable and will be corrected for. These predicted source/sink solutions will assist in determining

75 when, where, and by how much the particular Bio-Optic property does not satisfy the tracer physics of the ocean model.

CYCLING THE REPRESENTER METHOD WITH NONLINEAR MODELS

Hans E NGODOCK, Scott R. SMITH and Gregg A. JACOBS The Naval Research Laboratory, Stennis Space Center, Mississippi (USA)

Variational data assimilation with nonlinear models requires tangent linearization, which may be sufficiently accurate only for relatively short time scales. However, for time intervals beyond the scales of nonlinear event development, the tangent linearization cannot be expected to be sufficiently accurate. The representer method would, therefore, not be able to yield a reliable and accurate assimilation solution. However, the method can be implemented for successive cycles in order to solve the entire nonlinear problem. By cycling the representer method, it is possible to reduce the assimilation problem into intervals in which the linear theory is able to perform accurately. For each cycle, the background needed for the tangent linearization is computed by propagating the nonlinear dynamics using the final solution to the linearized assimilation problem from the previous cycle as the initial conditions. This study demonstrates that by cycling the representer method, the tangent linearization is sufficiently accurate once adequate assimilation accuracy is achieved in the early cycles. The outer loops that are usually required to contend with the linear assimilation of a nonlinear problem are not required beyond the early cycles, because the tangent linear model is sufficiently accurate at this point. The combination of cycling the representer method and limiting the outer loops to one significantly lowers the cost of the overall assimilation problem. In addition, this study shows that weak constraint assimilation is capable of extending the assimilation period beyond the time range of the accuracy of the tangent linear model. That is, the weak constraint assimilation can correct the inaccuracies of the tangent linear model and clearly outperform the strong constraint method. Examples using the Lorenz attractor, the Lorenz 40-component model, a 1.5-layer reduced gravity and the NAVY coastal ocean model will be presented.

VARIATIONAL DATA ASSIMILATION USING THE NAVY COASTAL OCEAN MODEL

Scott R SMITH1, Hans E. NGODOCK1 ([email protected]), Matthew J. CARRIER1, Max YAREMCHUK2 1Naval Research Laboratory, Stennis Space Center, MS (USA) 2University of New Orleans, LA (USA)

As a replacement to its optimal interpolation based operational assimilation method, a 3DVAR system was recently developed for assimilation of ocean observation into the Navy Coastal Ocean model (NCOM). NCOM uses a hybrid vertical coordinate combining sigma layers in the upper ocean and z-levels below, with a relocatable capability that allows the user to set up a regional nested domain from the global simulation that runs routinely. The 3DVAR system is implemented with two formulations of the background error covariance. One formulation is based on the use of empirical orthogonal functions (EOFs) of the model trajectory. The other formulation uses the correlation function modeled by the generalized diffusion equation with cross-correlations between model state variables imposed by a balance operator. Both covariance formulations are used in twin and real data experiments in the Monterey Bay. Also assimilation with real observations are carried out with a high resolution regional model around Hawaii. Observations include along-track sea surface height satellite altimetry, satellite sea surface temperature and vertical profiles of temperature and salinity from a fleet of gliders and ARGO floats, synthetic profiles from the NAVY modular data assimilation system (MODAS). Results will be presented.

76

NON-GAUSSIAN AND NONLINEAR DATA ASSIMILATION

Chris SNYDER ([email protected]) National Center for Atmospheric Research, Boulder CO, USA

Non-Gaussian distributions arise frequently in data assimilation for geophysical systems owing to the nonlinearity of the physical systems and some observations, as well as the importance of positive-definite quantities, such as constituents. Existing assimilation schemes of course deal with certain, limited non- Gaussian effects using techniques tailored to the specific problem. For example, when observations have occasional gross errors, the observation errors can be modelled as having a long-tailed, non-Gaussian distribution and the resulting non-quadratic cost functions in variational approaches can be handled by an outer loop in the minimization.

It is more challenging to develop assimilation algorithms that are effective for non-Gaussian problems in general. Three-dimensional and four-dimensional variational schemes compute the mode of the posterior pdf, which may be non-Gaussian, but they assume a Gaussian distribution for the prior or background. The ensemble Kalman filter accounts for nonlinearity in the forecast step but uses a linear analysis step—in essence, a Monte-Carlo approximation to the best linear unbiased estimator—that is suboptimal except for Gaussian problems. Nevertheless, the ensemble Kalman filter outperforms a truly Gaussian method that replaces the prior ensemble with a draw from a Gaussian with the same mean and covariance.

Particle filters are a sequential Monte-Carlo implementation of Bayes rule. They are fully nonlinear and non- Gaussian, since they treat the underlying probability distributions non-parametrically, and they produce an approximation to a random sample from the posterior pdf. Existing algorithms, however, require sample (or ensemble) sizes that scale exponentially with the problem size and are thus are not feasible for most high- dimensional systems. Importance sampling with a good choice of proposal density can reduce the necessary sample size for a given problem but does not change the exponential scaling as the problem size increases. Novel enhancements will be needed before particle filters are applicable to most geophysical systems; these may come by borrowing ideas, such as the spatially local character of the update, from the ensemble Kalman filter.

REGIONAL MODELING OFF THE BRAZILIAN EASTERN COAST: PRELIMINARY RESULTS OF A OPERATIONAL SYSTEM AIMED ON OCEANIC FORECAST.

Igor MONTEIRO1, Giovanni RUGGIERO1, Rafael PIOVESAN1, Hugo BASTOS1, Ivan SOARES1 ([email protected]), Mauro CIRANO2, Edmo CAMPOS4, Afonso PAIVA5, Clemente TANAJURA2, Renato MARTINS5, José Antonio LIMA5. Universidade Federal do Rio Grande Universidade Federal da Bahia Universidade Federal do Rio de Janeiro Universidade de São Paulo Centro de Pesquisas e Desenvolvimento Leopoldo Américo Miguez de Mello (Cenpes), Petrobrás.

A hierarchy of ocean models is being used in the South Atlantic Ocean as part of the efforts of a Multi Institutional group, named REMO, whose main objective is to implement an operational system aimed on oceanic forecast. The group has implemented the model HYCOM (Hybrid Coordinate Ocean Model) in two domains. One domain spans the Atlantic from latitude 10ºN to latitude 70ºS and from longitude 70ºW to longitude 21ºE and the other, which is nested on the former, spans the tropical region from latitude 8ºN to latitude 45ºS and from longitude 68ºW to longitude 18ºW, including the entire Brazilian coastline. Grid resolutions are ¼ of degree on the former and 1/12 on the latter and both grids are making use of the Cooper & Hynes method to incorporate SSH (Sea Surface Height) data. The operational system also includes regional implementations of the model POM (Princeton Ocean Model) and the model ROMS (Rutgers Ocean Modeling System) nested on the 1/12 HYCOM grid. The study here presented concerns the results obtained with the POM model. In this regional implementation of POM we use an assimilation method where the analysis temperature and salinity fields are computed from SSH data, based on correlations between the SSH anomalies and sub-surface temperature and salinity anomalies. Long term simulations (10 years) were carried out in order to obtain the correlation coefficients and twin experiments (with and without assimilation) were carried out to evaluate the impact of the assimilation method. The results obtained in the simulation which have considered the assimilation method were closer to observations and the error computation suggest that the use of assimilation has reduced mesoscale features which develop from uncertainties in initial conditions.

77

AN ADAPTIVE APPROACH TO MITIGATE BACKGROUND COVARIANCE LIMITATIONS IN THE ENSEMBLE KALMAN FILTER

H. SONG, I. HOTEIT, B. D. CORNUELLE, A. C. SUBRAMANIAN

A new approach is proposed to address the background covariance limitations arising from under-sampled ensembles and unaccounted model errors in the ensemble Kalman filter (EnKF). The method enhances the representativeness of the EnKF ensemble by augmenting it with new ensemble members chosen adaptively to add missing information that prevents the EnKF from fully fitting the data to the ensemble. The vectors to be added are obtained by back-projecting the residuals of the analysis/data differences onto the state space and using a stationary background covariance to transform them. The simplest version of the method amounts to using optimal interpolation (OI) on the analysis residuals to construct a new ensemble member. The approach is tested with the error covariance localization and covariance inflation factor implemented Lorenz-96 model. Assimilation experiments suggest that the new adaptive scheme significantly improves the EnKF behavior with small size ensembles and model deficiencies. The new adaptive approach can be easily implemented in any existing EnKF-based assimilation system. The selection of the new ensemble members is performed independently from the analysis step of the EnKF, and therefore no changes are needed in the EnKF algorithm.

GLOBAL OCEANOGRAPHIC VARIATIONAL DATA ASSIMILATION OF IN-SITU OBSERVATIONS AND SPACE-BORNE ALTIMETER DATA FOR REANALYSIS APPLICATIONS

Andrea STORTO1 ([email protected]), Srdjan DOBRICIC2, Simona MASINA3 and Pierluigi DI PIETRO1 1Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy 2 Centro Euro-Mediterraneo per i Cambiamenti Climatici, Bologna, Italy 3Centro Euro-Mediterraneo per i Cambiamenti Climatic, and Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy

In the framework of MyOcean and others national and international projects, we have developed a global three-dimensional variational data assimilation system for analysing the state of the global ocean within the last two decades. In the ocean data assimilation the representation of vertical background covariances consists of applying column-independent multivariate vertical EOFs of temperature and salinity. Different methods for deriving the vertical EOFs (e.g. from interannual ensemble simulations) are explored. Horizontal covariances are computed by means of a recursive filter, whose vertically-varying correlation radius is uniform on the horizontal over dynamically homogeneous macro-regions. The quality-checked observing network includes temperature and salinity profiles from bathytermographs, sea station reports, buoys and ARGO floats and along-track sea-level anomaly (SLA) observations from T/P, ERS-1 and -2, GFO, Envisat and Jason-1, during their respective nominal operations period. Sea-level anomaly data are assimilated through local hydrostatic adjustments, for the areas where a “level of no-motion” is applicable. The measured sea-level displacement is split into thermosteric and halosteric contributions by means of the adjoint of the sea-level operator, preserving the conservation of the water column properties. The corrections are therefore driven by the vertical structure of the EOFs. The data assimilation system runs with a general circulation model at both coarse and eddy-permitting resolution forced with the same atmospheric fluxes. One of our objectives is the assessment of the impact of altimeter data assimilation on different spatial scales.

ANN BASED DROUGHT FORECASTING FOR CHITTAR RIVER BASIN INDIA – A CASE STUDY

SUBBARAYAN Saravanan1, NATARAJAN Venkat Kumar2, SUBBARAYAN Sathiyamurthi3 1Lecturer, Department of Civil Engineering, National Instutute of Technology, Tiruchirapalli – 620015. India. E-mail: [email protected] 2Research Scholar, Department of Civil Engineering, National Institute of Technology, Tiruchirapalli – 620015. India. E-mail: [email protected] 3Lecturer, Deparmtent of Soil Science and Agriculture Chemistry, Annamalai University, Chidambram – 608002

Drought is a disastrous natural phenomenon that has significant impact on natural environments and human lives. Drought forecasting plays an important role in the control and management of water resources systems. This study evaluates the suitability of artificial neural network based techniques for spatiotemporal monthly drought mapping using the Standardized Precipitation Index (SPI). These techniques use artifical neural networks (ANNs) and account for possible non-linear orographic effects at different spatial scales and allow for regionally and seasonally varying relief-climate relationships. With the monthly historical rainfall

78 data in the period of 1943-2003 form 26 rainfall monitor stations in Chittar River sub basis, India have been used for the estimation of Standardized Pricipitation Index (SPI) for multiple time scales. The six month SPI is representative of hydrological drought for the present study area and this spatial and temporal validity of the interpolation techniques were checked using supervised split sample test. Seventy percent (70%) of the database was used for the development of the techniques and (30%) of the remaining data were used for the spatial and temporal validation of the methodology. In the training step, the neural network gives 96.6% of accuracy and 93.9% of accuracy in the testing step. The results showed that the proposed technique gave satisfactory spatiotemporal interpolation results in the present study basin and could be used for drought assessment and monitoring.

IMPLEMENTATION OF THE NONLINEAR FILTERING PROBLEM AND BALANCED DYNAMICS

Aneesh SUBRAMANIAN1 ([email protected]), Ibrahim HOTEIT2 and Lisa NEEF3 1 Scripps Institution of Oceanography, la Jolla, CA, USA 2 King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia 3 The Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands

We investigate the role of the linear analysis step of the ensemble Kalman filters (EnKF) in exciting spurious gravity waves in atmospheric motion models. This is achieved through the comparison of the behaviors of an EnKF and a fully nonlinear particle-based filter (NlF) with a simple model of balanced dynamics. The filters have very similar forecast step but their analysis steps are different. More specifically, the analysis step of the NlF generalizes the optimality of the EnKF analysis to non-Gaussian distributions. The model admits a chaotic vortical mode coupled to a comparatively fast gravity wave mode. It can be initialized such that it evolves on a so-called slow manifold, where the fast motion is suppressed. To determine how well the nonlinear analysis step preserves dynamical balance in the solution, identical twin assimilation experiments are performed, wherein the true state is balanced, but the observational errors project onto all degrees of freedom, including the fast modes. The impact of different types of simplifications that can be applied to the nonlinear analysis step in order to reduce the computational burden of the NLF is also studied and discussed.

FUTURE CHANGES IN THE LEEUWIN CURRENT TRANSPORT INFERRED FROM STATISTICAL AND DYNAMICAL DOWNSCALING

Chaojiao SUN1 ([email protected]), Ming FENG1, Richard MATEAR2, and Matthew CHAMBERLAIN2 1CSIRO Marine and Atmospheric Research, Floreat, Western Australia, Australia 2CSIRO Marine and Atmospheric Research, Hobart, Tasmania, Australia

The Leeuwin Current is an anomalous eastern boundary current in the Indian Ocean, primarily driven by a large-scale meridional pressure gradient due to the Indonesian Throughflow. It flows poleward to bring warm and fresh water down to the west coast of Australia, responsible for the balmy weather and less productive coastal environment. The scale and pattern of this pressure gradient is well simulated by the IPCC AR4 climate models. In this study, the dynamic height gradient from climate model outputs is used as a proxy to estimate the Leeuwin Current transport, using correlations derived from high-resolution ocean model simulations in the current climate by the Ocean Forecasting Australian Model (OFAM), the same model used by the BlueLink reanalysis. Four climate model outputs are evaluated, including the GFDL CM2.1, MPI- ECHAM5, UKMO-Hadcm3, and CSIRO Mk3.5. Simulations of the current climate by these models are compared with atmospheric reanalysis products to assess their ability to reproduce current climate.

The results from the statistical downscaling are compared with those from dynamical downscaling, which uses the CSIRO climate model Mk3.5 to force OFAM in 2060s. Climate anomaly (2060s minus 1990s) from Mk3.5 is used to introduce climate change signals. It is recognized that climate models tend to have biases and drifts, and this anomaly approach may mitigate these artefacts from climate models. Several sensitivity experiments were carried out. One uses ten-year climatology from 1990 to 1999 from ERA40 as the “base climate”; the other uses 1995 ERA40 daily forcing. The results show that the downscaling results are quite sensitive to the choice of the base climate forcing, and it is proposed that nudging or restoring be applied to the dynamical downscaling to improve convergence to the large-scale circulation patterns simulated by the climate model (base climate + climate anomaly).

79

USE OF LATITUDE DEPENDENT COVARIANCE FOR AUSTRALIAN REGIONAL MODEL DATA ASSIMILATION

XUDONG SUN ([email protected]) and Peter Steinle The Centre for Australian Weather and Climate Research, Melbourne Australia

The Australian Bureau of Meteorology is introducing a new NWP suite (ACCESS) based on the UK Met Office 4D-Var (Rawlins et al. 2007). One of the issues faced within ACCESS is the latitudinal extent of the regional domain - from the Antarctic coast to 10oN. For limited area domains, this modelling system only supported constant background error covariances across the domain. Such a constraint was not considered to be appropriate for the Australian regional domain.

To reflect the atmospheric statistical differences of tropical, continental and polar region, the latitude dependent background covariance is therefore derived and applied to the data assimilation system. This new covariance statistics is similar to that of global 4D-Var data assimilation used in UK global system. Its basic procedure involves using the NMC method to calculate its vertical covariance at every 5 latitude degree. The variable matrix transformation can then be derived to include the latitudinal variation.

To demonstrate the overall performance of using new latitude dependent background covariance statistics to the regional forecast, two verification methods are used to assess the NWP performance: the observation validation and model grid validation. Both results show some improvement for its skill score and RMS errors; particularly in the upper atmosphere and near the inversion layer. The verification results show no improvement over 2-day or longer forecasting time scale; although this is probably due to the lateral boundary conditions becoming the dominant influence on forecast error.

Further investigation is proposed for advancing our research in this area. That is: the covariance statistics will be generated from regional model so that more localised and high density data can be used, and reflect more accurately reflect correlation statistics with the region.

SNOW DATA ASSIMILATION FOR WATER BUDGET IN SIBERIAN LENA RIVER BASIN

Kazuyoshi SUZUKI1, Glen E. LISTON2, Yoshiyuki FUJII3, Taikan OKI4,1, Tetsuzo YASUNARI5,1 1RIGC/ JAMSTEC, 2CIRA/ Colorado State University, 3NIPR, 4IIS/ The University of Tokyo, 5HyARC/ Nagoya University

Freshwater flux in the river from the pan-arctic continents is one of important components for ecosystems and circulation in Arctic Ocean. In order to understand freshwater flux in the river, surface data set in the large river base was too sparse and limited. Recently, data assimilation technique has been developing to fill the data gap in the large-scale basin or continent. Here, we applied snow data assimilation system (SnowAssim, Liston and Hiemstra, 2008) to get better understanding of water cycle in the Lena River. National Polar Research Institute of Japan had carried out field campaign on snow observation within and around the Lena River basin during 3 winters of 1997-1998, 1998-1999, and 1999-2000. This data was used for snow data assimilation with base-line meteorological data set (BMDS) version 4. In this presentation, we will show the results and effectiveness of snow data assimilation on water budget in the Lena River basin.

IMPACT OF THE IN-SITU CTD DATA FOR THE ASSIMILATED ESTIMATES IN THE JAPAN SEA

Katsumi TAKAYAMA1 ([email protected]), Naoki HIROSE2, and Tatsuro WATANABE1 1 Japan Sea National Fisheries Research Institute, Fisheries Research Agency, Japan. 2 Research Institute for Applied Mechanics, Kyushu University, Kyushu University, Japan.

High variabilities of the water temperature, salinity and velocity in the Japan Sea largely control for the fisheries environment. To present the hindcast and forecast estimates at high accuracy in the Japan Sea, altimeter sea level data have been assimilated into the RIAM Ocean Model by using an approximate Kalman filter (Hirose et al., 2007). Furthermore, assimilation of the in-situ CTD temperature and salinity profiles are expected to improve the estimates. We investigate the impact of the Japanese CTD profiles for the assimilated results as well as the altimeter data. By assimilating in-situ CTD profiles, the model can represent the mesoscale structures in the southern part of the Japan Sea, especially in the Japanese coastal region. The in-situ temperature and salinity data support to capture the mesoscale structures that have been difficult to be measured by the altimeters. The dynamical gain matrix in the Kalman filter allows the correction of the subsurface temperature widely in the Tsushima Warm Current area, even if with a few CTD castings.

80 The in-situ CTD profiles contribute to the accurate state estimation in the Japan Sea as evidenced by the smaller RMS and higher correlations to various observations. This hindcast and forecast system, which is called as the JADE (JApan sea Data assimilation Experiment), is now available for the Japanese fisheries environmental research.

MODELLING NON-GAUSSIANITY OF BACKGROUND AND OBSERVATIONAL ERRORS BY THE MAXIMUM ENTROPY METHOD

Carlos Alberto PIRES(1), Olivier TALAGRAND(2) and Marc BOCQUET(3,4) (1) Instituto Dom Luis, University of Lisbon, Portugal ([email protected]) (2) Laboratoire de Météotorologie Dynamique, École Normale Supérieure, Paris, France ([email protected]) (3) Université Paris-Est, CEREA, Joint Laboratory, École des Ponts ParisTech and EDF R&D, Champs-sur- Marne, France (4) INRIA, Paris-Rocquencourt Research Center, France ([email protected])

The Best Linear Unbiased Estimator (BLUE) has widely been used in atmospheric-oceanic data assimilation. However, when data errors have non-Gaussian pdfs, the BLUE differs from the absolute Minimum Variance Unbiased Estimator (MVUE), minimizing the mean square analysis error. The non-Gaussianity of errors can be due to the statistical skewness and positiveness of some physical observables (e.g. moisture, chemical species) or due to the nonlinearity of the data assimilation models and observation operators acting on Gaussian errors. Non-Gaussianity of assimilated data errors can be justified from a priori hypotheses or inferred from statistical diagnostics of innovations (observation minus background). Following this rationale, we compute measures of innovation non-Gaussianity, namely its skewness and kurtosis, relating it to: a) the non-Gaussianity of the individual error themselves, b) the correlation between nonlinear functions of errors, and c) the heteroscedasticity of errors within diagnostic samples. Those relationships impose bounds for skewness and kurtosis of errors which are critically dependent on the error variances, thus leading to a necessary tuning of error variances in order to accomplish consistency with innovations. We evaluate the sub-optimality of the BLUE as compared to the MVUE, in terms of excess of error variance, under the presence of non-Gaussian errors. The error pdfs are obtained by the maximum entropy method constrained by error moments up to fourth order, from which the Bayesian probability density function and the MVUE are computed. The impact is higher for skewed extreme innovations and grows in average with the skewness of data errors, especially if those skewnesses have the same sign. Application has been performed to the quality-accepted ECMWF innovations of brightness temperatures of a set of High Resolution Infrared Sounder channels. In this context, the MVUE has led in some extreme cases to a potential reduction of 20- 60% error variance as compared to the BLUE.

ON THE EXISTENCE OF AN OPTIMAL SUBSPACE DIMENSION FOR 4DVAR

Anna TREVISAN(1), Massimo D'ISIDORO(1) and Olivier TALAGRAND(2) ([email protected]) (1) Institute of Atmospheric Science And Climate, ISAC-CNR, Via Gobetti 101, 40129 Bologna - Italy (2)Laboratoire de Météorologie Dynamique, Ecole Normale Supérieure, Paris, France

The nonlinear stability properties of a chaotic system are exploited to formulate a reduced subspace 4- dimensional assimilation algorithm, 4DVar-AUS (Assimilation in the Unstable Subspace). The key result is the existence of an optimal subspace dimension for the assimilation that is directly related to the unstable subspace dimension. Theoretical arguments suggest that the optimal subspace dimension is equal to N++1, where N+ is the number of positive Lyapunov exponents. In support of the theory, numerical experiments are performed in a simple model with a variable number of positive exponents: the results show that, in the presence of observational error, the confinement of the assimilation increment in the unstable subspace of the system reduces the RMS analysis error with respect to standard 4DVar. The standard 4DVar solution, while being closer to the observations, is further away from the truth. The explanation of this result is that, assimilating in the unstable subspace, errors in the stable directions are naturally damped: because of observational error, assimilating the the whole space otherwise prevents this decay. In agreement with this interpretation, if observations are perfect standard 4DVar gives the best results.

81 PREPARING THE ECMWF FORECAST SYSTEM FOR ADM-AEOLUS DOPPLER WIND LIDAR DATA

David TAN1 ([email protected]), Lars ISAKSEN1, Jos DE KLOE2, Gert-Jan MARSEILLE2, Ad STOFFELEN2, Alain DABAS3, Charles DESPORTES3, Christophe PAYAN3, Paul POLI3,1, Dorit HUBER4, Oliver REITEBUCH5, Pierre FLAMANT6, Olivier LE RILLE7, Herbert NETT7 and Anne-Grete STRAUME7 1European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK 2Royal Dutch Meteorological Institute (KNMI), De Bilt, The Netherlands 3Meteo France, Toulouse, France 4DorIT, Munich, Germany 5DLR, Oberpfaffenhofen, Germany 6LMD/IPSL, Paris, France 7ESA/ESTEC, Noordwijk, The Netherlands

With a launch anticipated in 2011, ESA’s mission ADM-Aeolus aims at providing accurate wind profiles from space, meeting needs of user communities in operational numerical weather prediction (NWP) and more general atmospheric science. The mission involves flying the first spaceborne Doppler wind lidar on a dedicated platform in sun-synchronous low earth orbit (the lidar, an active instrument operating at an ultraviolet wavelength, 355 nm, altitude around 400 km, expected lifetime 3 years). The expectation is that the Aeolus mission will enable NWP centres to improve analysis and forecast products, thereby strengthening the case for future operational wind-profiling missions.

This paper describes the preparations for routine monitoring and assimilation of Aeolus data within the operational forecast system at the European Centre for Medium-Range Weather Forecasts (ECMWF). The emphasis will be on those aspects that could serve as an example of the (relatively modest) effort required by other NWP centres wishing to receive, process and assimilate Aeolus data.

A substantial part of the preparation has been conducted in a collaborative project on the scientific and technical development of wind retrieval algorithms that constitute the ADM-Aeolus Level-2B processor, which produces the meteorologically representative wind observations considered most suitable for assimilation. Using Eumetsat’s NWP-SAF approach, ESA and ECMWF are making the processor source code available to the meteorological community for use in a general scientific environment or integrated within a data assimilation system. The code may be implemented as either a standalone executable or as a callable subroutine. The approach provides maximum flexibility for NWP centres to customize the wind retrievals to their own needs. It also gives the opportunity for the wider research community to participate in algorithm improvement. This paper describes the main elements of the processing package, its validation, and how the meteorological community can access the software.

ASSIMILATION OF SEA SURFACE TEMPERATURE AND SEA ICE DATA IN THE BIO OCEAN FORECASTING SYSTEM

Charles TANG ([email protected]), Yongsheng WU and Ewa DUNLAP Bedford Institute of Oceanography, Fisheries and Oceans Canada Dartmouth, Nova Scotia, Canada.

Efficient methods have been developed to assimilate daily real-time sea surface temperature (SST) and sea ice data into Bedford Institute of Oceanography’s (BIO) ice-ocean forecasting system. For SST, a flux correction method is used in which an adjustment to the model heat flux is made according to the optimal interpolation theory. The magnitude of the adjustment depends on the vertical mixing depth of temperature, the correlation time scale of the data, the time interval of the time series, and the model and data errors. The vertical mixing depth is scaled by the temperature diffusivity. The ratio of the model and data errors is treated as a free parameter. To evaluate the performance and sensitivity of the assimilation method, the results from assimilated and non-assimilated model runs are compared to an independent in situ data set for eastern Canadian shelves. The comparisons show that the data assimilation has improved the model SST significantly. It reduces the root mean square difference between the model SST and the ship data by up to 40% in spring and 48% in fall. Sensitivity studies are carried by changing the assimilation parameters. The results show that the assimilated SST is more stable in spring than in fall. For ice concentration data, an insertion-nudging method has been developed. Nudging of the daily ice concentration data with a given restoring time scale is applied to the model within a Gaussian time window of a prescribed width around the time of the ice data. The time scales are determined empirically. A sensitivity study shows the results are not overly sensitive to the time scales. The assimilation improves the prediction of ice edge position of the Labrador pack ice significantly.

82 ENSEMBLE DATA ASSIMILATION FOR OZONE FORECAST: UNCERTAINTY IDENTIFICATION AND CONSTRAINT

Xiao TANG1 ([email protected]), Jiang ZHU1, Zifa WANG1 1Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

This study develops an ensemble data assimilation system to improve the ozone forecast based on a Nested Air Quality Prediction Modeling System. The ensemble system is composed of Monte Carlo uncertainty analysis module and Ensemble Kalman Filter data assimilation module. Monte Carlo uncertainty analysis is employed to identify the main sources of uncertainty in ozone simulation. Ensemble Kalman Filter is then used to constrain initial conditions as well as other important uncertainty sources with observed surface ozone concentration. The performance of this system is assessed for urban ozone forecast at Beijing during the 2008 Olympic Summer Games, where a number of measures for reducing pollutants emissions are conducted at Beijing and surrounding area. The results shows that the most influential uncertainty sources to the ozone simulation in Beijing is the local precursor emissions, NO2 photolysis coefficient, wind direction, precursor emissions from outside of Beijing and vertical diffusion coefficient during the 2008 Olympic Summer Games. Forecast skills of different constraining designs for uncertainty sources are compared. Significant improvements can be achieved for urban ozone forecast by jointly constraining initial conditions and local NOx and VOC emissions.

A COMPARISON OF VARIATIONAL AND ENSEMBLE-BASED DATA ASSIMILATION SYSTEMS FOR REANALYSIS OF SPARSE OBSERVATIONS

Jeffrey S. WHITAKER1, Gilbert P. COMPO2 and Jean-Noël THÉPAUT3 ([email protected]) NOAA Earth System Research Laboratory, Boulder CO1, Climate Diagnostics Center/CIRES, University of Colorado, Boulder, CO2 , European Centre for Medium Range Weather Forecasts, Reading, UK3

Historical reanalyses are facing the challenge of producing a spatially and temporally homogeneous picture of the atmosphere, using observing networks which are often quite sparse. For example, prior to the advent of radiosondes in the 1940’s, there were only a few hundred to a few thousand surface meteorological observing stations around the world. Consequently, for historical reanalyses it is crucial to use a data assimilation method which is able to ‘spread out’ in a dynamically consistent way the meteorological information contained in the observations into unobserved regions.

Observing networks for historical reanalysis are also quite inhomogeneous in time, varying from a few hundred synoptic surface observations in the early 20th century, to millions of surface, upper-air and remotely-sensed observations in the late 20th century. Therefore, assimilation methods need to be robust to the dramatically changing observing networks and handle the associate analysis uncertainty variations.

This study examines how different data assimilation systems address these requirements. This is done by performing an observing system experiment, decimating the observations used for operational NWP in January and February 2005. The reduced set of observations representative of the 1930’s surface network coverage, are assimilated into a three-dimensional variational (3D-Var), a four-dimensional variational (4D- Var) and an ensemble data assimilation (EnsDA) system. The accuracy of the resulting analyses is assessed by comparing to operational NWP analyses (in this context the best proxy for the truth). It is found that the 4D-Var and EnsDA systems produce analyses of comparable quality (with a slight advantage in favour of the 4D-Var system), and both are much more accurate than the analyses produced by the 3D-Var system. The EnsDA system also provides useful estimates of analysis error, which are not directly available from the 4D- Var system.

FORECASTING MESOSCALE VARIABILITY OF THE NORTH ATLANTIC USING A PHYSICALLY MOTIVATED SCHEME FOR ASSIMILATING ALTIMETER AND ARGO OBSERVATIONS

Keith R. THOMPSON1 ([email protected]) and Yimin LIU1 1Department of Oceanography, Dalhousie University, Halifax, NS, Canada

A computationally efficient scheme is described for assimilating sea level measured by altimeters and vertical profiles of temperature and salinity measured by Argo floats. The scheme is based on a transformation of temperature, salinity and sea level into a set of physically meaningful variables for which it is easier to specify spatial covariance functions. The transformation allows for local water mass conservation and produces background error covariance structures that are non-separable functions of space. The scheme also allows for (i) sequential correction of temperature and salinity biases, and (ii) online estimation of background error covariance parameters, thereby giving the scheme additional robustness and flexibility.

83 To test the effectiveness of the scheme it is used to make a sequence of 1 to 60 day historical forecasts of the North Atlantic using an eddy permitting ocean model. It is shown that the scheme has useful skill out to 20 days over much of the North Atlantic. More recent results from a pre-operational version of the system, built on a 1/6 degree ocean model based on the NEMO (Nucleus for European Modelling of the Ocean) code one-way coupled to an atmospheric forecast model, will be described and evaluated.

REGIONAL AND AUSTRALIAN DATA ASSIMILATION AND NUMERICAL WEATHER PREDICTION IN ACCESS

Chris TINGWELL ([email protected]), on behalf of the ACCESS research group Centre for Australian Weather and Climate Research (CAWCR), a partnership between CSIRO and the Bureau of Meteorology, Melbourne, Australia

The Australian Community Climate and Earth System Simulator (ACCESS) will provide the Australian Bureau of Meteorology with a suite of NWP systems that incorporate data assimilation and forecast model components developed by the UK Met Office and adapted for local use by CAWCR staff. In the initial operational implementation of the ACCESS NWP suite each component of the Bureau's legacy NWP systems will have an ACCESS replacement. In particular, the regional forecasting products generated by the Bureau's LAPS and MesoLAPS systems will now by provided by ACCESS systems of similar horizontal domain and resolution.

The ACCESS regional system, ACCESS-R, features 4D-Var assimilation of conventional and remotely sensed observational data including satellite data not available previously: most importantly, radiance data from the hyper-spectral infrared sounder AIRS and moisture-sensitive microwave data from the SSM/I instrument; data from the IASI instrument will be available soon. The Met Office Unified Model provides the ACCESS-R forecast component. As well as providing its own forecast products, ACCESS-R provides the nesting conditions for the higher resolution ACCESS Australian system (ACCESS-A), which also features 4D-Var assimilation and UM forecasts.

Much work has been devoted to the data assimilation component of ACCESS for the Australian region, with the success of this work being judged by comparisons of the system forecast skill with that of the bureau's current operational systems. A particular focus of current work has been the optimal use of satellite data: the ability to make use of current and future sensors is seen as critical to maintaining ACCESS as a state-of-the- art NWP system

Here we will give an overview of ACCESS with an emphasis on the higher resolution systems, discuss some of the issues encountered readying it for operational use in the Bureau and show some results indicating the forecast skill it will provide.

AN APPROACH TO ASSESS OBSERVATION IMPACT BASED ON OBSERVATION-MINUS-FORECAST RESIDUALS

Ricardo TODLING ([email protected]) NASA Global Modeling and Assimilation Office

Langland and Baker (2004) introduced an approach to assess the impact of observations on the forecasts. In that, a state-space aspect of the forecast is defined and a procedure is derived that relates changes in the aspect with changes in the initial conditions associated with the assimilation of observations, ultimately providing information about the impact of individual observations on the forecast. However instructive, this approach has its limitations. The typical choice of forecast aspect employed thus far in related works is rather arbitrary and leads to an incomplete assessment of the observing system. Furthermore, the state-space forecast aspect requires availability of a verification state that should ideally be uncorrelated with the forecast but in practice is not. Lastly, the approach involves the adjoint operator of the entire data assimilation system and as such it is constrained by the validity of this operator. In this article, we introduce an observation-space metric that allows us to infer observation impact on the forecast without the limitations just mentioned, as long as the observing system is relatively homogeneous in time. Specifically, using observation-minus- forecast residuals leads to an approach with the following advantages: (i) it suggests a rather natural choice of forecast aspect, directly linked to the analysis system and providing full assessment of the observations; (ii) it naturally avoids introducing undesirable correlations in the forecast aspect by verifying against the observations; and (iii) it does not involve linearization and use of adjoints, therefore being applicable to any length of forecast.

84 The observation-space approach has the additional advantage of being nearly cost-free and very simple to implement. The state- and observation-space approaches might be complementary to some degree, but their limitation sand complexities are substantially different. Illustrations are given using the NASA GEOS-5 data assimilation system.

References: Langland, R.H. and N.L. Baker, 2004: Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus, 56a, 189-201.

THE GMAO 4DVAR SYSTEM: PRELIMINARY RESULTS

Ricardo TODLING ([email protected]), Yannick TRÉMOLET NASA Global Modeling and Assimilation Office

The Goddard Earth Observing System (GEOS-5) Data Assimilation System is now configurable to run strong constraint 4DVAR. The system combines the 4DVAR-capable Gridpoint Statistical Interpolation analysis with the GEOS-5 general circulation model and an early version of the tangent linear and adjoint models of the finite-volume hydrodynamics with simplified physics. The GMAO 4DVAR uses a Lanczos-based conjugate gradient algorithm in a nested resolution inner-loop setting. A range of tests and experiments will have been carried out by the time of the Symposium. Results on balance-related issues, resolution configuration, and choice of assimilation time window will be shown. A comparison with the GMAO 3DVAR and a discussion of future directions of development will also be presented.

ENSEMBLE DATA ASSIMILATION WITH THE CNMCA REGIONAL FORECASTING SYSTEM

Massimo BONAVITA1, Lucio TORRISI ([email protected]) and Francesca MARCUCCI CNMCA, National Meteorological Service, Italian Air Force, Italy 1. Current affiliation: ECMWF, Reading ([email protected])

The ensemble Kalman filter (EnKF) is likely to become the algorithm of choice for the next generation of meteorological and oceanographic data assimilation systems. In this work we present results from real-data assimilation experiments using CNMCA regional NWP forecasting system and compare them to the currently operational variational based analysis. The set of observations used is the same as the one ingested in the operational data stream, with the exception of satellite radiances and scatterometer winds. Results show that the EnKF-based assimilation cycle is capable of producing analysis and forecasts of consistently superior skill than CNMCA operational 3DVar.

One of the most important issues in EnKF implementations lies in the filter tendency to become under- dispersive for practical ensemble sizes. To combat this problem a number of different parameterizations of the background error unaccounted for in the assimilation cycle have been proposed. In the CNMCA system a combination of multiplicative and additive background covariance inflation has been found to give best results and to avoid filter divergence in extended assimilation trials. The additive component has been implemented through the use of scaled forecast differences.

Following suggestions that ensemble square-root filters can violate the gaussianity assumption when used with nonlinear prognostic models, the statistical distribution of the forecast and analysis ensembles has been studied. No sign of the ensemble collapsing onto one or a few model states has been found and the forecast and analysis ensembles appear to stay remarkably close to the assumed Gaussian distribution.

85

DEVELOPMENTS IN 4D-VAR

Yannick TRÉMOLET ([email protected]) European Centre for Medium-Range Weather Forecasts

In 4D-Var, the forecast model is used to propagate the analysis increment within the assimilation window to the time of observations. Usually, this model is assumed perfect, or, at least, it is assumed that the errors due to the model can be neglected compared to other errors in the system. As most aspects of the data assimilation system have improved over the years, and as the assimilation window might become longer in the future, this assumption becomes less realistic. Weak constraints 4D-Var provides the general framework for accounting for model error. We will present two of formulations of weak constraints 4D-Var. The first formulation, which will become operational at ECMWF in 2009, aims at capturing the systematic model error in the stratosphere. This is achieved through the use of a model error forcing term which is kept constant over the length of the assimilation window. Experimentation showed that the error captured by the model error term was not always model error. For example, a feedback effect between the model error term and the Jb balance term of the 4D-Var cost function in the stratosphere was noticed. Results obtained with a model error forcing term in the ECMWF operational 4D-Var system will be presented. The other formulation is more general and comprises a four dimensional state control variable. In principle, this allows for parallelism in the 4D-Var algorithm in the time dimension of the assimilation window which would be essential for computational efficiency with long assimilation windows. However, the nature of the optimization problem in this implementation of weak constraints 4D-Var is more challenging and new preconditioning techniques will have to be implemented. An overview of the expected advantages, challenges and possible approaches towards a fully weak constraints 4D-Var system will be presented.

BLENDVAR - A NEW ANALYSIS SCHEME FOR LIMITED ARE MODEL ALADIN/CE

Alena TROJAKOVA1 ([email protected]) and Maria DERKOVA2 Czech Hydrometeorological Institute1, Slovak Hydrometeorological Institute2

A limited area model (LAM) ALADIN is based on the global numerical weather prediction system IFS/ARPEGE and is being developed in the frame of the international cooperation of 15 Euro-Mediterranean countries. Especially for the LAM applied over smaller domains, an initial state for the numerical integration is usually obtained via more-or-less sophisticated interpolation method. The known drawback of such a procedure called the spin-up effect is not negligible in the early hours of the forecast. A natural direction then is an implementation of local data assimilation procedure, which is able to provide better initial state in order to improve short-range weather prediction.

Current operational implementation of the ALADIN data assimilation system in Czech Republic (ALADIN/CE) comprises digital filter blending and a local surface analysis based on optimum interpolation using SYNOP observation. Blending is a technique allowing to obtaining a more exact initial state by a combination of large scale information coming from the driving model ARPEGE 4D-VAR analysis with small scale features resolved by the high resolution ALADIN/CE model guess.

The new scheme so called BlendVAR consists of adding a 3D-VAR analysis on the top of digital filter blending. ALADIN 3D-VAR relies on IFS/ARPEGE incremental formulation introduced in global assimilation (Courtier et al. 1991). Detailed description of BlendVAR implementation and its tuning, together with the first evaluation of the system’s performance will be presented.

MODEL-DATA FUSION FOR STATE AND PARAMETER ESTIMATION IN CONTINENTAL-SCALE HYDROLOGICAL MODELLING

Cathy TRUDINGER ([email protected]), Michael RAUPACH, Peter BRIGGS, Vanessa HAVERD, Edward KING and Matt PAGET CSIRO Marine and Atmospheric Research

Model-data fusion involves combining a model and data in an optimal way, and includes both parameter estimation and data assimilation. Here we explore model-data fusion applied to a simple water balance model called WaterDyn developed for the Australian Water Availability project (AWAP). The aim of AWAP is to monitor the state and trend of the terrestrial water balance of the Australian continent, at 5 km resolution in the past (1900 to present) and in near real-time. AWAP involves an operational system providing weekly updates to calculated water stores and fluxes (http://www.csiro.au/awap).

86

We explore the application of different model-data fusion methods to provide best estimates of the water stores and fluxes and their uncertainties. We focus first on model calibration, using two different methods for parameter estimation – the Levenberq-Marquardt method and a Genetic Algorithm. We then use the Ensemble Kalman filter for state estimation as well as for state and parameter estimation. We initially use monthly streamflow observations, but are also working towards assimilating other observations like land surface temperature and vegetation greenness that will contain more spatial information.

Some issues we will address include:

• Equifinality, where two or more parameters have a similar effect on model outputs so can be difficult to distinguish. This often occurs in model calibration, although is not always recognised. In our case, calibration with monthly streamflow causes significant equifinality. This leads to minimal additional uncertainty in the water stores, but is more important for some of the fluxes.

• Assimilation of time-averaged observations with the Kalman filter. Our model uses a daily timestep, so assimilating monthly averaged streamflow observations with the Ensemble Kalman filter requires special attention. Using modifications to an existing method, we are able to retain daily variability in the model while improving longer timescale behaviour by assimilating monthly streamflow.

SPATIAL SATELLITE OBSERVATION-ERROR STATISTICS FOR AMSU-A DATA: ESTIMATION AND IMPLICATIONS FOR DATA ASSIMILATION

Vadim GORIN AND Mikhail TSYRULNIKOV ([email protected]) Hydrometeorological Research Centre of Russia

The first goal of this study is to objectively estimate the satellite spatial observation-error statistics for microwave AMSU-A observations known to be one of the most influential sources of observational information for numerical weather prediction. The second goal is to investigate the importance of correct spatial observation-error covariances for practical data assimilation.

The basic methodology is to estimate spatial auto- and cross-channel covariances from a set of collocated satellite-radiosonde pairs. The satellite observation error is modeled as a sum of a 'white' component (that has neither spatial/inter-channel nor other correlations) and a correlated component. The partition of the satellite error variance in the 'white' and correlated components is accomplished by extrapolation of the satellite-minus-forecast covariances to zero distance.

We found that:

1. AMSU-A channels 6-9 horizontal correlations are comparable in length scale to background-error covariances (i.e. are quite broad). 2. Inter-channel covariances as functions of horizontal separation are also large. 3. A substantial cross-correlation between the AMSU-A observation error and the background exists.

In a 3D-Var analysis with simulated data, we demonstrate that neglect of the error covariances we estimated can significantly reduce the efficiency of data assimilation. Real-data experiments confirm the importance of correct specification of satellite error statistics.

COVARIANCE REGULARIZATION IN INVERSE SPACE

1,2 1 Genta UENO ([email protected] ) and Takashi TSUCHIYA 1 2 The Institute of Statistical Mathematics , Japan Science and Technology Agency

In data assimilation, covariance matrices are introduced in order to prescribe the weights of the initial state, model dynamics, and observation, and suitable specification of the covariances is known to be essential for obtaining sensible state estimates. The covariance matrices are specified by sample covariances and are converted according to an assumed covariance structure. Modeling of the covariance structure consists of the regularization of a sample covariance and the constraint of a dynamic relationship. Regularization is required for converting the singular sample covariance into a non-singular sample covariance, removing spurious correlation between variables at distant points, and reducing the required number of parameters that specify the covariances. In previous studies, regularization of sample covariances has been carried out

87 in physical (grid) space, spectral space, and wavelet space. We herein propose a method for covariance regularization in inverse space, in which we use the covariance selection model (the Gaussian graphical model). For each variable, we assume neighboring variables, i.e., a targeted variable is directly related to its neighbors and is conditionally independent of the non-neighboring variables. Conditional independence is expressed by specifying zero elements in the inverse covariance matrix. The non-zero elements are estimated numerically by the maximum likelihood using Newton's method. Appropriate neighbors can be selected with the AIC or BIC information criteria. We address some techniques for implementation when the covariance matrix has a large dimension. We present an illustrative example using a simple 3x3 matrix and an application to a sample covariance obtained from sea surface height (SSH) observations.

IMPROVING STRATEGIES WITH CONSTRAINTS REGARDING NON-GAUSSIAN STATISTICS IN MOVE/MRI.COM

Norihisa USUI1 ([email protected]), Shiro ISHIZAKI2, Yosuke FUJII1, and Masafumi KAMACHI1 1Meteorological Research Institute / Japan Meteorological Agency 2Japan Meteorological Agency

The ocean data assimilation and prediction system, MOVE/MRI.COM, has been developed in Meteorological Research Institute of Japan Meteorological Agency (JMA), and has been used for the operation in JMA since March 2008. The system is composed of the ocean general circulation model (MRI.COM) and the data assimilation system with a variational analysis scheme (MOVE).

The Kuroshio-Oyashio transition (K-O) area east of Japan is a confluence of the two western boundary currents of the wind-driven subtropical and subarctic gyres. In this area, there are complicated frontal structures, which make difficult to give a realistic reproduction of the ocean state in data assimilation. MOVE/MRI.COM well reproduces characteristic oceanic structures such as the Kuroshio extension front, the Oyashio coastal branch, and mesoscale eddies. We, however, find some issues in the K-O region. In some situations, the assimilated Oyashio water shows too cold temperature. In addition, the assimilated field tends to overestimate the area of the Oyashio water in summer. These issues arise from a non-Gaussian distribution of temperature in the K-O region, not assumed in the standard variational scheme. A probability density function (PDF) for observed temperature in the K-O region shows double peaks corresponding to the Kuroshio and the Oyashio waters. One more characteristic of the PDF is a non-symmetric shape of the PDF peak for the Oyashio water, steep (broad) in the lower (higher) side of the peak. On the contrary, the assimilated temperature PDF exhibits too smoothed shape and cannot resolve the observed double peaks. In order to resolve the observed temperature PDF, we add two constraints to the cost function. One constraint is to prevent the too cold Oyashio water. Another one is to reproduce the observed PDF with double peaks. The assimilated temperature PDF using these constraints is effectively improved, while careful tuning is needed.

THE ROLE OF DATA ASSIMILATION IN LARGE-SCALE HYDROLOGICAL MODELLING TO SUPPORT WATER RESOURCES ASSESSMENT IN AUSTRALIA

Albert I.J.M. VAN DIJK ([email protected]) and Luigi J. RENZULLO CSIRO, Canberra; Bureau of Meteorology – CSIRO Water for a Healthy Country Flagship water Information R&D alliance

Prolonged drought in eastern Australia has exposed the lack of water balance information required for timely response and adaptive water resources management. The Australian Bureau of Meteorology has recently been delegated a legislative mandate and resources to develop a range of water information services. CSIRO and the Bureau are jointly developing the model-data systems to underpin these services. A first, proof-of-concept Australian water resources assessment system for the assessment of past and current water resources availability has been developed. It includes features of land surface schemes but combines this with additional model components derived from river and groundwater resource models and associated observations, as well as some new eco-hydrological modelling theory. The system is designed with an emphasis on finding the optimum trade-off between process detail and parameterisation uncertainty to allow assimilation of a wide range of observations. Stream flow gauging and flux tower evapotranspiration data as well as satellite observations of greenness were used in model development, initial parameter estimation and cross-validation. Forcing data for the current system includes gridded daily rainfall and near-surface meteorology (derived by blending station data and satellite observations) and satellite observations land cover. Model-data fusion strategies that were tested include both sequential and non-sequential methods to adjust state, inputs, outputs or parameters. Testing demonstrated that satellite observations can improve water balance estimation in complementary ways. The scale of the system requires pragmatic consideration

88 of the trade-off between accuracy gains and the computational demands of alternative methods and observations. Remotely sensed vegetation vigour (greenness) showed particularly suitable to assimilation as it provides a strong indication of water availability in much of Australia. Active and passive microwave satellite observations of soil moisture and GRACE satellite gravity measurements have been used for system evaluation and generally showed very satisfactory agreement. These observations are guiding further system development and will also be considered for formal assimilation.

PARTICLE FILTERING: BEATING THE CURSE OF DIMENSIONALITY

Peter Jan VAN LEEUWEN ([email protected]) Department of Meteorology, University of Reading

Particle filtering in large-dimensional systems has been hampered by the ‘curse of dimensionality’. In the context of particle filtering this means so much as the enormous increase in ensemble size when the dimension of the system increases. The problem is related to the likelihood: the probability of the observation given a certain model state, or particle, in Bayes theorem. The likelihood appears as relative weights in particle filtering, and when the dimension of the system is large the weights vary enormously, effectively reducing the number of relevant particles to one.

However, when Bayes theorem is formulated in terms of transition densities and a proposal density is used, it becomes clear that the likelihood can be cancelled completely by an effective choice of the transition proposal density. This choice of transition proposal density translates simply to a certain choice of the model forcing. In fact, an infinite number of choices for the model forcing exist that avoid the curse of dimensionality. Interestingly, the error covariance of the model forcing gives a natural localization, that is crucial in present-day Ensemble Kalman filters. A whole new field of particle filtering seems to emerge.

The difficulty now becomes which one to choose. The curse of dimensionality can be avoided by giving each particle equal weight, but we want the particles to be close to the observations too. Bayes theorem gives us no direct way to construct these particles. However, the constraint on the model forcing that avoids the curse of dimensionality hinds at a certain shape of this forcing that is exploited in this talk. In a few relative low dimensional systems a dramatic reduction in ensemble size is found. Results on an ocean model with half a million degrees of freedom will be presented.

MULTI-MODEL DATA ASSIMILATION AKA SUPER-ENSEMBLES

Luc VANDENBULCKE1 ([email protected]), Fabian LENARTZ1, Michel RIXEN2 Université de Liège1, NATO Undersea Research Center2

When different, concurrent models are running at the same time, for the same area, it is possible to obtain a unique forecast with higher skill than any of those individual models. This technique is known as multi-model forecast or super-ensemble (SE). The simplest example, long known to improve forecasting skills, is the mean of the available models.

However, when observations are available, it is possible to obtain a linear combination of the models, which minimizes departure from past observations, and with higher skill in forecasting mode (compared to the ensemble mean). The SE forecast can be further improved by adding a constant model (i.e. unbiasing) or by removing colinearities between models (e.g. by principal component analysis).

The weights of the linear combination can be updated dynamically whenever observations become available, by using a Kalman filter. The latter is used in an unusual way, as the state vector contains the weights of the combination, and the model forecasts are stored in the observation operator. If one supposes that the weights of the combination do not have a Gaussian pdf, a particle filter can also be used. These dynamical methods alleviate the need of a priori fixing the training length.

We present 3 applications of these recent SE techniques. The first one concerns coastal temperature forecasts in the Adriatic Sea; observations from a CTD chain, and models, all were available in near real- time (DART06 campaign). The second application concerns surface drift both in the Adriatic (DART06) and Ligurian Sea (MREA07). The Kalman filter used to evolve the weights of the combination is modified to use complex numbers (ACEKF filter). Finally, the most recent application tries to directly combine multivariate 3D models, using multiple sources of observations (CalVal08 campaign). In all applications, we show a significant increase of forecasting skill, particularly with the dynamical methods.

89

ITERATIVE KALMAN FILTERING

Martin VERLAAN ([email protected]) Deltares

A common approach for 4D-VAR is to linearize the problem around a reference solution and optimize this linear problem, with an outer loop that updates the reference trajecory. This approach, known as incremental 4D-VAR, is conceptually similar to the iterated kalman filter and iterated kalman smoother proposed by Jazwinski already in 1970.

Instead of appying the Kalman filter to the forward tangent model, as proposed by Jazwinksi, in this study an Ensemble Kalman filter (EnKF) or Ensemble Kalman Smoother (EnKS) will be used for the inner iteration. It has been shown by various authors that for a linear model the EnKS is equivalent to 4D-VAR, if both are defined consistently. Thus for a linear model this iterative EnKS approach converges in one step and is equivalent to one inner iteration of incremental 4D-VAR.

For a nonlinear model the inner iterations based on a tangent model or based on an ensemble are not exactly equivalent. On the other hand, a suboptimal inner-iteration mainly slows down the convergence process and does not necessarily result in a suboptimal estimate. Also, the local gradient computed with the tangent model is not necessarily very representative for changes of the costfunction with finite steps of realistic sizes.

In this study the iterative Kalman filter is be developed with an EnKF or EnKS for the inner iterations. Next, these new algorithms are applied to a number of simple testcases and the results are compared with various other algorithms.

EFFICIENT PARAMETERIZATION OF THE OBSERVATION ERROR COVARIANCE MATRIX FOR SQUARE ROOT OR ENSEMBLE KALMAN FILTERS: APPLICATION TO OCEAN ALTIMETRY

Jean-Michel BRANKART, Clément UBELMANN, Charles-Emmanuel TESTUT, Emmanuel COSME, Pierre BRASSEUR and Jacques VERRON ([email protected]) LEGI/CNRS-Grenoble University, Grenoble, France

In the Kalman filter standard algorithm, the computational complexity of the observational update is proportional to the cube of the number y of observations (leading behaviour for large y In realistic atmospheric or oceanic applications, involving an increasing quantity of available observations, this often leads to a prohibitive cost and to the necessity of simplifying the problem by aggregating or dropping observations. If the filter error covariance matrices are in square root form (as in square root or ensemble Kalman filters), the standard algorithm can be transformed to be linear in y providing that the observation error covariance matrix is diagonal. It is an important drawback of this transformed algorithm often leading to assume uncorrelated observation errors for the sake of numerical efficiency. In this paper, it is shown that the linearity of the transformed algorithm in y can be preserved for other forms of the observation error covariance matrix. In particular, quite general correlation structures (with analytic asymptotic expression) can be simulated by adding gradient observations to the original observation vector.

Errors on ocean altimetric observations are spatially correlated. Parameterizing these correlations adequately can directly improve the quality of the observational update and the accuracy of the associated error estimates. In this paper, the example of the North Brazil current circulation is used to demonstrate the importance of this effect and to show that the efficient parameterization that we propose for the observation error correlations is appropriate to take it into account. Adding explicit gradient observations also receives a physical justification. This parameterization is thus proved to be useful to ocean data assimilation systems that are based on square root or ensemble Kalman filters as soon as the number of observations becomes penalizing, and if a sophisticated parameterization of the observation error correlations is required.

90

LINKING ALTIMETRY AND OCEAN COLOR: A DATA ASSIMILATION APPROACH USING LYAPUNOV EXPONENTS

Jacques VERRON ([email protected]), Jean-Michel BRANKART, Emmanuel COSME, Pierre BRASSEUR and Olivier TITAUD LEGI/CNRS-Grenoble University, Grenoble, France

Altimetry has shown the ubiquitous presence of mesoscale eddies in the world ocean. Ocean color images reveal even more complex and smaller scale features known as submesoscales. Mesoscale dynamics are thought to be a key ingredient of the ocean dynamics, while submesoscales are more and more viewed as key elements of the biogeochemical behavior of the ocean. But these two types of scales are in any case real features of the ocean, jointly and interactively contributing to the physico-biogeochemical behavior of the ocean. They are simply two different "images" of the same system and possibly two complementary sources of informations.

In the present work, we propose a data assimilation methodology to jointly use ocean colour and altimetric observations as complementary sources of information on the ocean.

Because the submesoscales are not generally resolved by current ocean models or simply because ocean color images do not consider physical variables, it is necessary to use an intermediate way to make the different sources of information communicating together. Here we choose the Lyapunov exponents (FSLE) introduced in particular in this context by D'Ovidio et al. (2004) as intermediate quantity to characterize the information about the submesoscale tracer patterns that is contained in the mesoscale velocity fields and that was also shown to faithfully characterize ocean color images.

In this work, we demonstrate, in the particular case of FSLE, that the data assimilation procedure is feasible for such mixed data type. We aim at proving that a proxy such as the FSLE is relevant to establish the informational bridge between altimetric and ocean color data. The conceptual framework proposed in this study is likely applicable to other types of data.

DIRECT ASSIMILATION OF IMAGE SEQUENCES IN NUMERICAL MODELS

Arthur VIDARD1,2 ([email protected]), Olivier TITAUD3, Innocent SOUOPGUI1,2, François-Xavier LE DIMET2 INRIA Grenoble-Alpes1, Laboratoire Jean Kuntzmann2, Laboratoire des Ecoulements Géophysiques et Industriels3, Grenoble, France

During the last two decades about thirty satellites were launched to improve the knowledge of the atmosphere and of the oceans. They continuously provide a huge amount of data that are still underused by numerical forecast systems. In particular a significant amount of photographic images is available on which the dynamical evolution of some meteorological or oceanic features (such as eddies, fronts, …) that a human vision may easily detect is not optimally taken into account in realistic applications. Attempt to perform Image Assimilation have been done using pseudoobservation techniques: they provide some apparent velocity fields, which are assimilated as classical Eulerian velocity observations. However these measurements are obtained by some external procedures that are decoupled with the considered dynamical system and may therefore contain significant errors. Here, we suggest a more consistent approach where image sequences are considered as Lagrangian observations and are directly incorporated into the Optimality System in a Variational Data assimilation framework. The observations space, its associated distance and the observation operator are described and numerical results are shown using real images of a laboratory experiment.

OBSERVABILITY OF A LARGE CONTROL VECTOR IN A 4D-VAR OCEAN DATA ASSIMILATION

Tsuyoshi WAKAMATSU1,2 ([email protected]), Michael G. G. FOREMAN1,2 1. Institute of Ocean Sciences, Fisheries and Oceans Canada 2. School of Earth and Ocean Sciences, University of Victoria

A control vector of typical ocean data assimilation system consists of initial values, external forcing and model error. Due to sparseness of data and measurement errors, the retrieval of such large control vector is ill-posed inverse problem. In this presentation, we will address to what extent we can "observe" these control parameters given limited number of data with error using singular value decomposition (SVD) of observability

91 matrix in a 4D-Var system. The algorithm to compute the SVD of observability matrix using tangent linear and adjoint models is derived. The usefulness of the SVD approach in regulating the optimal estimation of a control vector is demonstrated using a quasi-geostrophic ocean circulation model.

ON THE INFUENCE OF RANDOM WIND STRESS ERRORS ON THE FOUR-DIMENSIONAL, MIDLATITUDE OCEAN INVERSE PROBLEM

Tsuyoshi WAKAMATSU1,2 ([email protected]), Michael G. G. FOREMAN1,2 Patrick F. CUMMINS1, Josef Y. CHERNIAWSKY1 1. Institute of Ocean Sciences, Fisheries and Oceans Canada 2. School of Earth and Ocean Sciences, University of Victoria

The effects of the parameterized wind stress error covariance function on the a priori error covariance of an ocean general circulation model (OGCM) are examined. These effects are diagnosed by computing the projection of the a priori model state error covariance matrix to sea surface height (SSH). The sensitivities of the a priori error covariance to the wind stress curl error are inferred from the a priori SSH error covariance and are shown to differ between the subpolar and subtropical gyres because of different contributions from barotropic and baroclinic ocean dynamics. The spatial structure of the SSH error covariance due to the wind stress error indicates that the a priori model state error is determined indirectly by the wind stress curl error. The impact of this sensitivity on the solution of a four-dimensional inverse problem is inferred.

RECENT ADVANCES IN VARIATIONAL ASSIMILATION FOR GLOBAL OCEAN APPLICATIONS

Anthony WEAVER1 ([email protected]) Kristian MOGENSEN2, Magdalena A. BALMASEDA2, Matthew MARTIN3 and Arthur VIDARD4 CERFACS, Toulouse, France1, ECMWF, Reading, UK2, Met Office, Exeter, UK3, INRIA/LJK, Grenoble, France4

In recent years there has been significant progress made by different ocean groups to develop variational data assimilation systems for a variety of models and applications. This presentation describes a variational assimilation system that has been developed for research and operational applications with the NEMO (Nucleus for Modelling of the Ocean) model. The system is based on an incremental formulation and currently supports 3D-Var (FGAT). It has a multivariate backgrounderror formulation that includes relationships between temperature and salinity, geostrophic adjustment of horizontal velocities, and projection of sea level information onto vertical density profiles. It is able to assimilate observations from sub- surface profiles of temperature and salinity, along-track sea-level anomaly data from satellite altimeters, and sea-surface temperature data, and employs an online, automatic system for quality control of real-time observations. An on-line model bias correction algorithm has also been implemented. Results from a 1o global ocean reanalysis produced with the system will be presented. An overview of ongoing and planned scientific and technical developments to the system will also be given, which include improvements to the background-error covariance model and minimization algorithm, extensions to allow for 4D-Var, and applications to higher resolution configurations.

DEVELOPMENTS IN ENSEMBLE DATA ASSIMILATION

Jeffrey S. WHITAKER ([email protected]) NOAA Earth System Research Laboratory

The field of Ensemble Kalman Filter data assimilation has been developing rapidly over the last decade. I will review the basics of the technique, including similarities and differences with four-dimensional variational techniques, with an emphasis on atmospheric applications. Examples of current state-of-the-art systems for global numerical weather prediction and climate reanalysis will be presented. I will then examine the main factors limiting further progress, including issues related to ‘balance’, sampling error and model error.

92

ESTIMATION OF FRICTION PARAMETERS AND LAWS IN OCEANIC GRAVITY CURRENTS

Achim WIRTH ([email protected]), Jacques VERRON (MEOM / LEGI Grenoble, CNRS, France)

The realism of the numerical modelling of the ocean dynamics depends on the capability of the numerical models to correctly represent the important processes, at large and also at small scales. The dynamics of gravity currents was identified as a key process governing the the strength of the thermohaline circulation and its heat transport from low to high latitudes. The dynamics of oceanic gravity currents is governed by small scale turbulent processes which will not be explicitly resolved by ocean global circulation models in the foreseeable future and which can not be determined analytically. In this context data assimilation can be a powerful tool to estimate the small scale turbulent uxes.

A 1.5 dimensional, 1.5 layer shallow water model and an ensemble Kalman filter are used to evaluate the feasibility of estimating friction parameters and determining friction laws of oceanic gravity currents. The two friction laws implemented are a linear Rayleigh friction and a quadratic drag law. We demonstrate that the assimilation procedure rapidly estimates the total frictional force whereas the distinction between the two laws is evolving on a slower time scale. We also demonstrate that, parameter estimation can in this way choose between different parametrisations and help to discriminate between physical laws of nature by estimating the coeffcients presented in such parametrisations.

We then show, that the same shallow water model allows to estimate the bulk friction laws and parameters in high resolution numerical simulations of gravity current dynamics, demonstrating that data assimilation is also a powerful tool in systematically connecting models within a hierarchical model structure.

FEATURE-BASED ENSEMBLE ESTIMATION FOR RAINFALL APPLICATIONS

Rafal WOJCIK ([email protected]), and Dennis McLAUGHLIN Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, USA

This talk outlines our recent efforts to develop practical nonlinear Bayesian ensemble update methods for assimilation of rainfall observations. A key objective of this work is to produce conditional rainfall replicates that retain the distinctive aspects of real-world features of rainfall observations (e.g. cluster size distributions, stationary densities etc.) Our work to date has focused on non-linear alternatives to ensemble Kalman filtering. This talk examines results obtained with variants on the concept of importance sampling, which ranks the likelihood of proposed rainfall features so that the most probable outcomes can be identified. The likelihood functions used in this approach are modeled by an exponential class of probability densities centered around observations. As an example we show a solution to a static rainfall data assimilation problem. However, these results can easily be generalized to sequential problems suggesting possible directions for future research.

COMPARISONS OF SOME ENSEMBLE OPTIMAL INTERPOLATION SCHEMES FOR ASSIMILATING ARGO PROFILES INTO A HYBRID COORDINATE OCEAN MODEL

Jiping XIE1 ([email protected]) and Jiang ZHU2 ICCES, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 10029, China1, LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 10029, China2

To develop a capable ensemble-based scheme for operationally assimilating Argo profile observations into a hybrid coordinate ocean model (HYCOM), we compared some different schemes based on the ensemble optimal interpolation (EnOI) method, which can be divided into two different kinds. The first kind is straightforward, i.e., the observations are vertical temperature (T) and salinity (S) measurements from Argo floats. In the modified schemes, based on Thacker and Esenkov (2002), the Argo profiles are first converted to the “observations” of layer thicknesses and they are assimilated to adjust the model layer thickness and model velocity fields. Further the T (or S) profiles are assimilated to adjust the model layer temperatures (or salinities). Here the observation operator is based on the previously adjusted layer thicknesses. At last under the mixing layer, the model layer salinity (or temperature) is further derived from the equation of seawater state.

Based on these two kinds of EnOI schemes, we design the six assimilation experiments due to the different definitions of the observational space and the localization. In the experiment of EXP1A, the observations are

93 directly from the Argo T and S measurements while in another scheme the observations are interpolated to model layers as in the EXP1B. Other four experiments of EXP2T, EXP2S, EXP2Tv, and EXP2Sv implement the modified schemes. Their differences are which T and S is diagnosed from the equation of seawater state, or whether a vertical localization is applied. All the six schemes were used to assimilate Argo profiles into HYCOM in the Pacific for a four-year period (Jan., 2004-Dec., 2007). A large amount of Argo profiles were withheld to validate the assimilation results. The results show the significant improvement by the modified schemes. The best setup of the modified scheme is to diagnose the temperature and to apply the vertical localization.

A SEQUENTIAL HYBRID 4DVAR SYSTEM IMPLEMENTED USING A MULTIGRID TECHNIQUE  SPACE AND TIME MULTISCALE ANALYSIS SYSTEM

Yuanfu XIE1 ([email protected]), Steven E. KOCH1 and Steven C., ALBERS1,2 Global Systems Division, Earth System Research Laboratory National Oceanic and Atmospheric Administration1 Cooperative Institute for Atmospheric Research Colorado State University2

A sequential variational data assimilation system is developed at Global Systems Division (GSD), Earth System Research Laboratory and it is called Space and Time Multiscale Analysis System (STMAS). It generalizes a single 3DVAR or 4DVAR analysis to provide a anisotropic and multiscale analysis for meteorological data assimilation overcoming the Gaussian error distribution assumption and dependence of accurate error covariance. This method divides data assimilation into two steps, 1) using variational analysis to obtain resolvable information from the observation network and model background; 2) applying statistical analysis of the standard 3DVAR/4DVAR or ensemble Kalman filter on smaller scales that cannot be resolved by the previous step. Taking advantage of the two steps of analysis, the sequential variational analysis system does not handle a full covariance matrix like the current statistical/variational analysis but deal with a narrowly banded covariance. A multigrid technique is implemented in STMAS. The coarser grids of STMAS will be used to extract the resolvable scales of the analysis fields from long to short scales. When it reaches the finest level where observation network cannot provide finer scale information, STMAS naturally becomes a standard 3DVAR or 4DVAR data assimilation system or a hybrid method combining ensemble Kalman filter technique. In this presentation, we will demonstrate the capability of analyzing the resolvable scales of the observation data, from conventional observation dataset and radar radial wind observations for severe storms, such as hurricanes.

A DUAL-PASS VARIATIONAL DATA ASSIMILATION FRAMEWORK FOR ESTIMATING SOIL MOISTURE PROFILES FROM AMSR-E MICROWAVE BRIGHTNESS TEMPERATURE

Zhenghui XIE1 ([email protected]), Xiangjun TIAN1,Aiguo DAI2, Chunxiang SHI3, Binghao JIA1 and Feng CHEN1 1.Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China ([email protected]; [email protected]; [email protected]; [email protected] ) 2. National Center for Atmospheric Center, Boulder, USA (email: [email protected] ) 3. National Satellite Meteorological Center, China Meteorological Administration, Beijing, China ([email protected] )

To overcome the difficulties in determining the optimal parameters needed for a radiative transfer model (RTM), which acts as the observational operator in a land data assimilation system, we have designed a dual-pass assimilation framework to optimize the model state (volumetric soil moisture content) and model parameters simultaneously using the gridded Advanced Microwave Scanning Radiometer – EOS (AMSR-E) satellite brightness temperature data. This algorithm embeds a dual-pass (the state assimilation pass and the parameter optimization pass) optimization technique based on an ensemble-based four-dimensional variational assimilation method and a shuffled complex evolution approach (SCE-UA). The SCE-UA method simultaneously optimizes both model states and parameters using observational information, thereby leading to improved simulations. The RTM is used to estimate brightness temperature from surface temperature and soil moisture. This algorithm is implemented differently in two phases: the parameter calibration phase and the pure assimilation phase. Both passes are conducted in each assimilation time window during the parameter calibration phase. However, only the state assimilation pass is performed in the pure assimilation phase after the parameters are determined during the parameter calibration phase. The parameter optimization pass is turned off in the pure assimilation phase to reduce computational costs. Several experiments conducted using this framework coupled partially with a land surface model (the NCAR CLM3) show that volumetric soil moisture content can be significantly improved to be comparable with in-situ

94 observations by assimilating only daily satellite brightness temperature. Furthermore, the improvement in surface soil moisture also propagates to lower layers where no observations are available.

FOUR-DIMENSIONAL OBSERVATION IMPACT ON THE US NAVY’S ATMOSPHERIC ANALYSES AND FORECASTS: SYSTEM DEVELOPMENT AND TEST

Liang XU1 ([email protected]), Rolf LANGLAND1, Nancy BAKER1, Tom ROSMOND2, and Boon CHUA2 1 Naval Research Laboratory, Monterey, CA 93943, USA 2 Science Applications International Corporation, Monterey, CA 93940, USA

Effective monitoring and assessing of the impact of huge amount of observations on the atmospheric analyses and forecasts is critical to the daily operations at the operational NWP centers around the world. In recent years, various adjoint techniques have been developed and used to assess the impact of the observations. To use the adjoint based monitoring techniques, it is necessary to have the adjoint of the forecast model and the adjoint of the data assimilation system. An 3D-Var based adjoint system has been successfully used at the US Navy on daily base in the past several years to assess the impact of various observations on the US Navy’s short term global forecasts. With the pending upgrade of the US Navy’s current 3D-Var to an 4D-Var operational data assimilation system, we have developed and tested the adjoint of the 4D-Var data assimilation system to be used in monitoring the four-dimensional observation impact. In this presentation, we mainly focus on the formulation, implementation, and validation of the adjoint of the 4D- Var data assimilation system. We will present examples that highlight the capability of the adjoint system.

RADAR DATA ASSIMILATION IN GRAPES

Jishan XUE ([email protected]) and Hongya LIU Chinese Academy of Meteorological Sciences,Beijing 100081,China

A Doppler weather radar data assimilation scheme with three dimensional variational algorithm is proposed and tested with real observational data during the summer monsoon season in the eastern Asia. The observational operators describing the physical connection between the fundamental observational elements, i.e. the radial velocity and the intensity of reflectivity, and the model state variables are set up based on the radar equations commonly used in literatures. Two approaches dealing with the vertical velocity, which is crucial for meso scale weather system and appears in the observational operators, are compared. One of them introduces the Richardson’s equation to compute diagnostically the vertical velocity with horizontal wind, temperature, pressure and its tendency. The other one takes the vertical velocity as one of the state variables and retrieved from the reflectivity using statistical algorithm. It is found that the vertical velocity derived by the former is usually too weak so that the relevant meso-scale system is incorrectly depressed in both the analysis and prediction. It is also found that the diabatic term in the Richardson’s equation should not be neglected. The results of assimilation and prediction experiments with this scheme using operational radar data for a torrential rain event in the Meiyu season and a Typhoon case are analyzed in detail. Both cases show the positive contribution to relief Spin-up phenomenon and improvements of prediction of precipitation caused by meso-β convective systems. However the analysis of hydrometeors needs further verification and the vacancy of data due to the uneven distribution of radars remains a big challenge for the operational application of our scheme.

ENSEMBLE AND VARIATIONAL RADAR DATA ASSIMILATION FOR CONVECTIVE STORM AND HURRICANE PREDICTIONS

Ming XUE ([email protected]) Center for Analysis and Prediction of Storms and School of Meteorology University of Oklahoma, Norman Oklahoma, 73072, USA

Explicit prediction of convective storms dates back to the vision of Douglas Lilly and his colleagues, as put forth in his essay "Numerical prediction of thunderstorms - Has its time come?", published almost twenty years ago (Lilly 1990, QJRMS). Active research on convective-scale data assimilation (DA) and numerical weather prediction (NWP) has been going since then and has intensified in more recent years with the availability of more powerful computers, the development of new data assimilation methodologies, easier access to operational and experimental Doppler radar data as well as the development and availability of improved nonhydrostatic prediction models.

95 In this presentation, we will report on recent research results applying the 3DVAR and ensemble Kalman filter methods to the assimilation of Doppler radar data and other high-resolution observations at convective- resolving (~1 km) resolutions, for domains up to the size of continental United States. The performance and impact of these systems and data on the prediction of mesoscale convective systems down to tornado scales will be evaluated. The impact of coastal ground-based as well as airborne Doppler radar data on hurricane prediction will also be briefly discussed.

DATA ASSIMILATION IN INDIAN AND WESTERN PACIFIC OCEAN

Changxiang YAN and Jiang Zhu Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

A multi-variable data assimilation system in Indian and western Pacific ocean is developed. The ensemble- based optimal interpolation method (ENOI) is used in this assimilation system. The daily anomalies from the long-time model integration are used to produce the background error convariance matrix. The effect of different sampling of ensembles on the distribution of background errors is investigated. It is proved that the ensemble with seasonal update reflects the flow-dependence of background errors more reasonably than the static ensemble.

The assimilated observations include sea surface temperature (SST), temperature and salinity profiles from ARGO, XBT, CTD, TAO and other observed sources, and altimetry data. The quality control is done to discard the problematic data due to the fall-rate of XBT and the drift of pressure sensors of ARGO. The super-obing and thinning methods are used to eliminate redundant observation information or the subgrid- scale information that can not be resolved by the model. At the same time, they may save effectively the computational resources.

The hybrid-coordinate HYCOM model is used to assimilate the above observations via ENOI. Since HYCOM is a isopycnal model, the layer thickness estimated by temperature and salinity observations is assimilated. And the dynamical consistence is validated when the layer thickness information is inserted. The localization is also considered in ENOI. Additionally, the climatological bias correction is firstly done before the assimilation. This improves the model results to a certain extent. And then, the assimilation experiment in the Indian and western Pacific is carried out. The preliminary results are demonstrated and compared with observations.

RUNNING IN PLACE METHOD WITH LOCAL ENSEMBLE TRANSFORM KALMAN FILTER FOR TYPHOON ASSIMILATION AND PREDICTION

Shu-Chih YANG1 and Eugenia KALNAY2 1 Department of Atmospheric Sciences, National Central University, Taiwan 2 Department of Atmospheric and Oceanic Science, University of Maryland, USA

Compared to the routinely performed global assimilation system, the meso-scale assimilation starts only when a meso-scale event potentially occurs and the meso-small scale observations (e.g. Radar reflectivity) are only available after the events are triggered. When the initial ensemble is far away from nature (e.g., cold start), the most likely state is “unlikely” to happen with such limited observations. Therefore, EnKF needs a long spin-up time to reach a satisfactory accuracy and the prediction skill is limited during the spin-up period.

In this study, the running-in-place method (Kalnay and Yang, 2008) is applied to reduce the spin-up and improve the analysis ensemble derived from the Local Ensemble Transform Kalman Filter (LETKF) implemented in the Weather Research Forecasting (WRF) model. The WRF-LETKF system is established to perform the meso-scale assimilation for typhoon prediction. Also, the initial ensemble for the first assimilation cycle is provided by the NCEP reanalysis data and the perturbations are constructed based on the 3D-Var background error covariance. From the OSSE experiments, the standard LETKF analysis from the first assimilation cycle describes a less intense typhoon compared to the nature run. The error in the initial condition results in the deviation of the forecast track from the truth. Our results show that the RIP with LETKF is particularly helpful to adjust the dynamical state. With a dense observation distribution, the RIP significantly improves the accuracy of the analysis ensemble, including the intensity of the typhoon. This implies that RIP has the potential to better use the radar data or to improve the typhoon intensity with the vortex bogus scheme.

96 A METHOD OF SUCCESSIVE CORRECTIONS OF THE CONTROL SUBSPACE IN THE REDUCED- ORDER 4DVAR DATA ASSIMILATION

Max YAREMCHUK1 ([email protected]), Dmitri NECHAEV2 and Gleb PANTELEEV3 1Department of Physics, University of New Orleans 2Department of Marine Science, University of Southern Mississippi 3International Arctic Research Center, University of Alaska

A version of the reduced control space four-dimensional variational method (R4dVar) of data assimilation into numerical models is proposed. In contrast to the conventional 4dVar schemes, the method does not require development of the tangent linear and adjoint codes for implementation. The proposed R4dVar technique is based on minimization of the cost function in a sequence of low-dimensional subspaces of the control space. Performance of the method is demonstrated in a series of twin-data assimilation experiments into a non-linear quasigeostrophic model utilized as a strong constraint. When the adjoint code is stable R4dVar's convergence rate is comparable to that of the standard 4dVar algorithm. In the presence of strong instabilities in the direct model, R4dVar works better than 4dVar whose performance is deteriorated due to the breakdown of the tangent linear approximation. Comparison of the 4dVar and R4dVar also shows that R4dVar becomes advantageous when observations are sparse and noisy.

AN ENSEMBLE OCEAN DATA ASSIMILATION SYSTEM FOR SEASONAL PREDICTION

Yonghong YIN ([email protected]), Oscar ALVES, Peter OKE , and Faina TSEITKIN Centre for Australian Weather and Climate Research(CAWCR) A joint centre of the Bureau of Meteorology and CSIRO

A new Ensemble Ocean Data Assimilation System (PEODAS) has been developed for the seasonal prediction system, POAMA (Predictive Ocean Atmosphere Model for Australia). Based on a 3-dimentional multivariate ensemble optimal interpolation (EnOI) technique, PEODAS uses a pseudo-EnKF approach, where an ensemble of forecasts are integrated and used to estimate the system's time-dependent background error covariances. At each assimilation cycle a central analysis is computed and the ensemble is compressed and nudged around that analysis. The system errors are assumed to form from both forcing and model errors. The ensemble is constructed with the Australian Community Ocean Model (ACOM2), where each member is forced by differently perturbed forcing fields over sequential 3-day intervals and scaled ocean perturbations are added gradually to the ensemble members each day. The forcing perturbations are constructed as random combinations of eigenvectors of differences between NCEP and ERA40 reanalysis products, where temporal correlation between perturbations is enforced. Ocean perturbations are randomly sampled from a set of intra-seasonal anomalies from a long model run.

The ensemble-based covariances generated by PEODAS produce dynamically balanced analysis increments. The time-dependency of the ensemble-based covariances yields spatial structures that are quite different for different dynamical regimes - for example during El Nino and La Nina conditions. A 27-year ocean reanalysis (1980-2006) has been produced by assimilating in situ temperature and salinity profiles from the ENACT quality controlled data set. The performance of PEODAS is evaluated through a series of comparisons with both assimilated and independent observations. PEODAS re-analysis shows better fitting of data in most areas, compared to not assimilating data and the old POAMA assimilation scheme. The equatorial currents in the tropical Pacific Ocean are closer to the observation than its predecessor. PEODAS demonstrates a quantitative improvement in skill and will form the basis of Australia's next operational seasonal prediction system.

A MULTIVARIATE EMPIRICAL ORTHOGONAL FUNCTION BASED SCHEME FOR BALANCED INITIAL ENSEMBLE GENERATION OF AN ENSEMBLE KALMAN FILTERING

Fei ZHENG1 ([email protected]) and Jiang ZHU2 1 International Center for Climate and Environment Science (ICCES), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; 2 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Initial ensemble perturbations for an ensemble data assimilation system are expected to be able to reasonably sample model uncertainty at the analysis time to further reduce analysis uncertainty. Therefore, a careful choice of initial ensemble perturbation method that dynamically cycles ensemble perturbations is required for the optimal performance the ensemble data assimilation system. Based on the multivariate

97 empirical orthogonal function (MEOF) method, and through carefully analyze the balanced relationships between different model variables, a new ensemble initialization scheme is developed to generate balanced initial perturbations for ensemble Kalman filter (EnKF) data assimilation, and is applied in the assimilation experiments of a global spectral atmospheric model with real observations.

The proposed perturbation method is compared to the commonly used method of spatial-correlated random perturbations. The comparison results show that the model uncertainties before the first analysis time, which are forecasted from the new ensemble initial fields, maintain in a much more reasonable spread and more accurate forecast error covariance than those from the random perturbed initial fields. And the analysis results are further improved by the new ensemble initialization scheme due to the more reasonable background information. Also, a 10-day continuous assimilation experiment shows that the ensemble spreads for each model variables are still retaining in reasonable ranges without considering additional perturbations or inflations during the assimilation cycles, while the ensemble spreads from the random perturbed initialization scheme decrease and collapse rapidly.

APPLICATION OF ENKF TO ENSO ENSEMBLE PREDICTION WITH AN INTERMEDIATE COUPLED MODEL

Fei ZHENG1 ([email protected]), Jiang ZHU2, and Rong-Hua ZHANG3 1 International Center for Climate and Environment Science (ICCES), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; 2 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; 3 Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, Maryland, USA

Based on an intermediate coupled model (ICM), and by using the EnKF data assimilation approach for generating the initial ensemble conditions, a probabilistic ENSO ensemble prediction system (EPS) has been developed. First, a linear, first-order Markov-Chain SST anomaly error model is developed and embedded into the EPS to provide the model-error perturbations during the ensemble assimilation process. This approach can effectively prevent the filter-divergence and make the EnKF successfully assimilate the SST data into the model and provide reasonable ensemble initial conditions. The 120-yr hindcast results show that the deterministic skill of the EPS is distinctly greater than that of a single deterministic forecast over the 12-month prediction period. Second, to make up the insufficiency of only SST data assimilation, and to assimilate more observational data into the EPS, a balanced stochastic model-error model is proposed based on the multivariate empirical orthogonal function (MEOF) method. The assimilation comparison results show that it is necessary to develop balanced, multivariate model-error models in order to successfully assimilate both SST and SL observations, and the hindcast results also demonstrate that the balanced model errors can yield more balanced and accurate initial conditions that lead to improved predictions of ENSO events. Finally, due to the poor roles of the statistical atmospheric model in some ENSO cases, a coupled initialization scheme is preliminarily developed to be capable of assimilating the atmospheric data. The developed initialization scheme can make the EPS successfully predict the 2007 La Nina event before 12 months.

AN ESTIMATION OF FORECAST ERROR COVARIANCE MATRIX USING MULTIVARIATE INFLATION FOR KALMAN FILTERING DATA ASSIMILATION

Xiaogu ZHENG College of Global Change and Earth System, Beijing Normal University

An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing –2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics is not known. A simple nonlinear model (Burgers’ equation model) is used to demonstrate the efficacy of the proposed approach.

98

DESIGN OF THE GRAPES ENSEMBLE KALMAN FILTER DATA ASSIMILATION SYSTEM AND ITS TENTATIVE EXPERIMENT

Zhaorong ZHUANG 1,2 ([email protected]), Jishan XUE 1，2 1 Center for Numerical Prediction and Research,Chinese Academy of Meteorological Sciences 2 State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences

Data assimilation is important for the improvement of numerical weather prediction. With the augment of observation data and the improvement of observation quality, the advancement and development of data assimilation technique rely on the description of the uncertain information of background and observation. The description of background error covariance is more accurate from the functional form in 3D-Var to the implicated evolution at the assimilation time interval in 4D-Var, and to the predicting in the Kalman filter. So the actual background error covariance is the key to success of data assimilation technique. The ensemble Kalman filter (EnKF) can obtain the flow-dependent background error covariance by statistic of the ensemble samples so that it is becoming a new research focus in the current data assimilation field. EnKF method, which is also an approach to data assimilation method for strong nonlinear weather process, is a promising way to the GRAPES data assimilation system since it can provide initial fields for ensemble prediction and can be applied to the study of target observation. In this paper, a practical GRAPES ensemble Kalman filter data assimilation system at pressure level and sigma level is established. Through the ideal and actual observation experiments, EnKF system has been verified and can be practically applied in the forecast.

CLOUD-RESOLVING ENSEMBLE DATA ASSIMILATION

Milija ZUPANSKI, ([email protected]), Dusanka ZUPANSKI CIRA/Colorado State University

Cloud microphysics represents one of the most challenging obstacles for development of data assimilation, for both theoretical and practical reasons. From theoretical point of view, the difficulty originates from nonlinearity of cloud processes and observation operators for cloud observations. In addition cloud processes are often represented by non-differentiable operators, which further complicates their proper assimilation. From practical point of view, modeling of cloud processes requires fine spatio-temporal resolution, dramatically increasing the dimension of the control variable and consequently making algorithmic development more difficult.On the other hand, clouds are at the crossroads of weather, climate and hydrology. As such, their proper representation can have a dramatic positive impact in all of these disciplines, prompting a considerable need for mastering data assimilation with cloud microphysics. In this work we address the problem of assimilation of cloud microphysical variables in ensemble data assimilation. We discuss theoretical and practical issues of assimilation of cloud microphysics and offer means for resolving these issues. In particular we focus on using the Maximum Likelihood Ensemble Filter (MLEF), developed with components of both ensemble and variational data assimilation methods. We show recent MLEF results with the Weather Research and Forecasting (WRF) model in applications to intensive precipitation, severe weather and tropical cyclones.

Our results indicate that inclusion of cloud microphysical variables and cloudy satellite radiance observations in data assimilation is beneficial, and most likely necessary for proper analysis and prediction of clouds. The relevance of cloud microphysical variables in cloud-scale data assimilation is also confirmed by information content analysis of assimilated observations, which indicates an increased information content of cloud- sensitive observations.

99

ADJUSTMENT OF OCEAN MODEL INITIAL CONDITIONS AND ATMOSPHERIC FORCING FROM OCEAN DATA ASSIMILATION IN THE CALIFORNIA CURRENT SYSTEM.

Grégoire BROQUET1 ([email protected]), Andrew M. MOORE1, Christopher A. EDWARDS1, Hernan G. ARANGO2 and Brian S. POWELL3 1University of California, Santa Cruz, USA, 2Rutgers University, Camden, USA, 3University of Hawaii, Manoa, USA

The Incremental Strong constraint 4D Variational (IS4DVAR) data assimilation system of the Regional Ocean Model System (ROMS) is used to study the controllability of a realistic, high resolution configuration of the California Current System (CCS). This model is forced with regional COAMPS (Coupled Ocean / Atmosphere Mesoscale Prediction System) atmospheric data. Satellite derived surface observations along with in situ observations are assimilated to adjust sequentially the assimilation windows initial conditions and atmospheric surface forcing (wind stress, heat and freshwater fluxes). Data assimilation twin experiments reveal the ability of the system to reconstruct the reference ocean circulation as well as the reference forcing data from perturbed ones using a realistic ensemble of ocean observations. Real data assimilation experiments are conducted using sea level anomaly data from AVISO, a blended satellite surface temperature product from the CoastWatch/NOAAFisheries, and an ensemble of various in situ temperature and salinity datasets (including California Cooperative Oceanic Fisheries Investigations, CalCOFI, and Global Ocean Ecosystem Dynamics, GLOBEC, data). The realistic experiments yield results similar to those from the twin experiments. The adjustment of initial condition and surface forcing are both shown to improve significantly many characteristics of model dynamics. Trends in the adjustments of surface forcing and the relative influence of the initial condition and of the surface forcing are also analyzed.

PARTICLE KALMAN FILTERING: A NONLINEAR FRAMEWORK FOR ENSEMBLE KALMAN FILTERS

Ibrahim HOTEIT1 ([email protected]) and Dinh-Tuan PHAM2 1 King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia 2 Centre National de la Recherche Scientifique (CNRS), Grenoble, France

This contribution presents a discrete solution of the optimal nonlinear filter that generalizes the optimality of the ensemble Kalman filter (EnKF) methods to nonlinear systems. The approach is based on a Gaussian mixture representation of the state probability distribution function (pdf). This results in a new particle-type filter in which the standard (weight-type) particle filter correction is complemented by a Kalman-type correction for each particle using the associated covariance matrix in the Gaussian mixture. This filter is referred to as the particle Kalman filter (PKF). The optimal solution of the nonlinear filtering problem is then obtained as the weighted average of an ensemble of Kalman filters operating in parallel. The Kalman-type correction reduces the risk of ensemble collapse, which enables the filter to efficiently operate with fewer particles than the particle filter. We first show how the different EnKF methods can be derived as simplified variants of the PKF. We argue that the (deterministic) Square-Root EnKFs are Gaussian-based filters while the traditional (stochastic) EnKF propagates an approximation of the non-Gaussian pdf of the state. We also discuss approaches to alleviate the computational burden of the PKF. Based on low-rank Gaussian mixture covariance matrices, a new filter running a weighted-ensemble of EnKFs is then introduced. Results of numerical experiments with the strongly nonlinear Lorenz-96 model are presented and discussed.

A MITGCM/DART OCEAN ANALYSIS AND PREDICTION SYSTEM WITH APPLICATION TO THE GULF OF MEXICO

Ibrahim HOTEIT1 ([email protected]), Tim HOAR, Nancy COLLINS, Jeffrey ANDERSON2, Bruce CORNUELLE3, Armin KOHL4 and Patrick HEIMBACH5 1 King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia 2 National Center of Atmospheric Research (NCAR), Boulder, CO, USA 3 Scripps Institution of Oceanography, La Jolla, CA, USA 4 Institute of Physical Oceanography, University of Hamburg, Germany 5 Massachusetts Institute of Technology (MIT), Boston, MA, USA

The ECCO system is a new generation of ocean assimilation systems based on the Massachusetts Institute of Technology general circulation model (MITgcm) and its adjoint. The system has been used to produce the first global 1 degree ocean state estimates. It is now also used for regional and coastal MITgcm applications. To improve the predictive capabilities of the ECCO system, the Data Assimilation Research Testbed (DART), which is an ensemble Kalman filter EnKF)-based data assimilation package, has been recently integrated to

100 the ECCO system. DART is a software facility employing different EnKFs and advanced inflation/localization schemes. It has been developed at the National Center of Atmospheric Research (NCAR) and is now used for different operational weather forecasting problems. This contribution describes the integration of DART and the MITgcm, and discusses how this ensemble-based system can complement the existing adjoint- based assimilation system. An example of a 1/10 degree MITgcm/DART application for predicting the evolution of the loop current in the Gulf of Mexico is presented.

SENSITIVITY OF ENSEMBLE FORECASTS TO ENSEMBLE SIZE IN ENSEMBLE TRANSFORM KALMAN FILTER

Jun Kyung KAY1([email protected]), Hyun Mee KIM1, Young-Youn PARK2, Joohyung SON2, Seonok MOON1, Hee-Dong YOO2 1 Atmospheric Predictability and Data Assimilation Laboratory, Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea, 2 Korea Meteorological Administration, Seoul, Republic of Korea

Korea Meteorological Administration (KMA) is preparing to operate the Unified Model (UM) and related pre/post processing system imported from United Kingdom Met Office as next generation numerical weather prediction system from 2010. In this system, Met Office Global and Regional Ensemble Prediction System (MOGREPS) is included. MOGREPS consists of global and regional ensemble system and produces initial ensemble perturbations using Ensemble Transform Kalman Filter (ETKF). ETKF quantifies errors in the analysis, does not need to update the analysis using ensemble mean, and calculates analysis perturbations fast. In ETKF, analysis perturbations in each cycle are calculated by linear combinations of forecast perturbations from the previous cycle. The global ensemble provides initial and boundary condition perturbations for the regional ensemble in MOGREPS. Currently, the number of ensemble members in MOGREPS is 24 (1 for control analysis and 23 for perturbed analysis). Before operating the UM and MOGREPS system, sensitivity experiments are planned to see the impact of ensemble member size on medium-range stochastic forecasts. In this study, sensitivities of stochastic characteristics in initial perturbations and subsequent ensemble forecasts are investigated by changing the number of the initial ensemble member and that of the subsequent forecasting ensemble member. By investigating the quality of the initial condition perturbations and the performance of the ensemble forecasts depending on the ensemble size, the optimal ensemble member configuration will be suggested for the operational ensemble forecasts in KMA.

THE 1/12º GLOBAL HYCOM NOWCAST/FORECAST SYSTEM

Ole Martin SMEDSTAD1 ([email protected]), J.A. CUMMINGS2, E.J. METZGER2, H.E. HURLBURT2, A.J. WALLCRAFT2, D.S. FRANKLIN1, J.F. SHRIVER2, P.G. THOPPIL1 1 QinetiQ North America, 2 Naval Research Laboratory

The 1/12º global HYbrid Coordinate Ocean Model (HYCOM) has been running dailysince 22 December 2006. With ~7 km mid-latitude resolution (3-4 km near the poles), the system depicts the location of mesoscale features such as oceanic eddies and fronts andprovide the three dimensional ocean temperature, salinity and current structure. A modelof this size has to use an efficient assimilation scheme in order to run within the timeconstraints of an operational center. The Navy Coupled Ocean Data Assimilation(NCODA) system is used to assimilate available observations. An important componentof the NCODA system is the quality control of the observations. NCODA uses a multivariate optimal interpolation scheme (MVOI) that assimilates surface observationsfrom satellite altimeter tracks and available SST data. NCODA also assimilates in situobservations, including profile data from BT's and Argo floats. Results from a hindcast used to spin up the model to real time as well as results from the real time system will be shown. Independent observations are used whenever possible in the evaluation of theassimilation system performance. The prediction system provides boundary conditions for higher resolution coastal models. An accurate representation of the oceanographicfields at the open boundaries of a coastal model is important for a successful coastalocean prediction system. Results from the global system can be viewed on the HYCOMweb page http://www.hycom.org. The model output can also be accessed through this web page.

101

THE ADEQUACY OF EXISTING OBSERVING SYSTEMS MONITORING AMOC AND THE NORTH ATLANTIC CLIMATE

Shaoqing ZHANG, A. ROSATI and T. DELWORTH Geophysical Fluid Dynamics Laboratory, Princeton University [email protected]

The adequacy of existing observing systems to monitor the Atlantic meridional overturning circulation (AMOC) and the North Atlantic climate has been assessed by assimilating ``observations'' drawn from an IPCC AR4 model simulation based on various existing observing systems into the coupled model ensemble. Instantaneously blending observed data into all coupled components, the fully-coupled assimilation produces a mostly balanced and coherent estimate for the North Atlantic climate system which is coupled by the North Atlantic Oscillation (NAO), deep convections in the Labrador Sea (LSW) and Greenland-Iceland-Norwegian Sea (GSW), as well as the North Atlantic gyre circulation system (GRS). The low-frequency NAO accurately- resolved from the atmosphere and sea-surface temperature (SST) observations drives a 5-10 year lag LSW variation, whereas direct sub-surface constraints provided by the 20th/21st-century ocean temperature/temperature and salinity (XBT/Argo) observations reconstruct the LSW's phase beyond 80%. While monitoring GSW which is influenced by sea-ice activities requires both the atmospheric and oceanic observation constraints to work together coherently, GRS as a wind-driven circulation system can be monitored by either an atmospheric or an oceanic observing system beyond 60%. The LSW's variation represents the decadal variability of AMOC whereas GRS and GSW have significant impacts on AMOC in interannual and intra-decadal scales. Combined with the constraint of the atmospheric wind and temperature, assimilating the XBT/Argo observations retrieves 98% of the phase of the a priori defined AMOC, but while the 21st-century Argo reconstructs 90% of the strength of the AMOC, without salinity and deep ocean observations the 20th-century XBT does only 52%. As a function of observing system, the monitoring accuracy for each aspect of the North Atlantic climate has an impact on the prediction of AMOC in different time scale.

102

INDEX

A BROUSSEAU, PIERRE ...... 9 BRUN, ERIC ...... 34 ABIDA, RACHID ...... 8 BUEHNER, MARK ...... 10, 11 ADANI, MARIO ...... 22 BUTTERY, HELEN...... 50 ALBERS,, STEVEN C...... 94 ALBERTELLA, ALBERTA ...... 42 C ALLEN, STEWART...... 1 ALVES, O...... 1, 2, 97 CAMPBEL, WILLIAM F...... 10 AMRAOUI, LAAZIZ EL ...... 34 CAMPOS, EDMO ...... 77 ANDERSON, JEFFREY L...... 54, 100 CARON, JEAN-FRANÇOIS ...... 11 ANDREU-BURILLO, ISABEL...... 2 CARRIER, MATTHEW J...... 76 AONASHI, KAZUMASA ...... 2 CARRIERES, TOM...... 11 ARANGO, HERNAN ...... 60, 100 CARTON, JAMES ...... 66 ARAVÉQUIA, JOSÉ ANTONIO...... 3, 36 CAYA, ALAIN...... 11 ARELLANO, AVELINO F...... 3 CHABOT, VINCENT...... 20 ARSENAULT, KRISTI...... 17 CHAMBERLAIN, MATTHEW...... 79 AULIGNE, THOMAS...... 4 CHAPNIK, BERNARD ...... 20 AUROUX, DIDIER...... 4 CHARETTE, CECILIEN ...... 10 AVALLONE, LINNEA ...... 34 CHARLTON-PEREZ, CRISTINA ...... 50 AWAJI , TOSHIYUKI ...... 39, 40, 63 CHATTOPADHYAY, MOHAR...... 11, 49 CHEN, FENG...... 95 B CHERNIAWSKY, JOSEF Y...... 92 CHERUKURU, NAGUR ...... 12 BAKALIAN, FAEZ ...... 70 CHUA, BOON...... 95 BAKER, NANCY ...... 72, 95 CHUNXIANG, SHI ...... 13 BALLARD, SUSAN P ...... 50 CIRANO, MAURO ...... 77 BALMASEDA, MAGDALENA A...... 1, 92 CLARISSE, LIEVEN...... 13 BALSAMO, GIANPAOLO...... 25, 54 CLARK, PETER ...... 50 BANNISTER, ROSS ...... 30 CLAYTON, ADAM...... 5 BARCIELA, ROSA...... 48 CLERBAUX, CATHY...... 13 BARKE, DALE ...... 5 COCQUEREZ, PHILIPPE ...... 34 BARRETT, DAMIAN ...... 71 COHEUR, PIERRE-FRANÇOIS...... 13 BASTOS, HUGO...... 77 COHN, STEPHEN A...... 34 BAUER, PETER...... 31 COHN, STEPHEN E...... 14 BEAL, DAVID...... 24 COLE, HAL...... 34 BEEKMANN, MATTHIAS ...... 14 COLLINS, NANCY ...... 100 BEGGS, HELEN ...... 2, 5 COMAN, ADRIANA...... 14 BEKKI, SLIMANE ...... 15 COMPO, GILBERT P...... 83 BÉLANGER, JEAN-MARC...... 70 CORNUELLE, B. D...... 78 BENEDETTI, ANGELA...... 5 COSGROVE, BRIAN...... 23 BERRE, LOÏK ...... 6, 9, 20, 59, 68 COSME, EMMANUEL...... 7, 15, 90, 91 BERTINO, LAURENT ...... 6, 72, 73 COSTA, SAULO B...... 36 BHATTACHARYA, KAUSTUBHA ...... 7 COT, CHARLES...... 15 BISHOP, CRAIG H...... 7, 10 COUNILLON, FRANCOIS...... 6, 72 BLAYO, ERIC ...... 7 COX, OWEN ...... 50 BLUM, JACQUES...... 4 CROW, WADE T...... 16 BOCQUET, MARC...... 8, 81 CUCURULL, LIDIA...... 17, 19 BONATTI, JOSÉ PAULO ...... 3 CUGNET, DAVID...... 15 BONAVITA, MASSIMO...... 85 CUMMINGS, J.A...... 101 BONAZZI, ALESSANDRO ...... 22 CUMMINS, PATRICK F...... 92 BOUCHARD, AURÉLIE...... 34 BOUTTIER, FRANÇOIS ...... 9 D BOWLER, NEILL ...... 5 BOYNARD, ANNE ...... 13, 14 DABAS, ALAIN ...... 82 BRANDO, VITTORIO...... 12 DAHLGREN, P...... 51 BRANKART, JEAN-MICHEL...... 15, 24, 90, 91 DAI, AIGUO ...... 95 BRASSEUR, PIERRE ...... 15, 24, 90, 91 DE JEU, RICHARD ...... 32 BRASSINGTON, GARY...... 2, 9 DE KLOE, JOS...... 82 BRIGGS, PETER ...... 86 DE LANNOY, GABRIËLLE J.M...... 17 BRIN, GENIA ...... 70 DE MEY, PIERRE...... 18 BROQUET, GREGOIRE ...... 60, 100 DE ROSNAY, PATRICIA ...... 25

103

DE SOUZA, SOLANGE SOLANGE SILVA...... 3 FRANKLIN, D.S...... 101 DEE, DICK...... 18 FREDERIKSEN, JORGEN S...... 64 DEKKER, ARNOLD ...... 12 FREEMAN, JUSTIN ...... 2, 9 DELLE MONACHE, LUCA ...... 19 FREITAS, SAULO R...... 37 DELWORTH, T...... 102 FRENOT, YVES...... 34 DENG, SHIOW-MING ...... 50 FUJII, YOSHIYUKI ...... 80 DERBER, JOHN ...... 17, 19, 49 FUJII, YOSUKE...... 43, 88 DERKOVA, MARIA...... 86 FUKUMORI, ICHIRO...... 47 DESHLER, TERRY ...... 34 DESPORTES, CHARLES...... 82 G DESROZIERS, GÉRALD ...... 9, 6, 20, 68 DEUSHI, MAKOTO ...... 20 GAFFARD, CATHERINE ...... 50 DI PIETRO, PIERLUIGI...... 78 GAILIS, RALPH...... 34 D'ISIDORO, MASSIMO...... 81 GARCÍA TRIANA, IVAN D...... 30 DIXON, MARK ...... 50 GAUSSIAT, NICOLAS ...... 50 DOBRICIC, SRDJAN ...... 21, 22, 78 GEER, ALAN...... 31 DOERENBECHER, ALEXIS...... 34 GEJADZE, I...... 21 DONG, JIARUI ...... 23 GELARO, RONALD ...... 31 DONG, PEIMING...... 23 GENTEMANN, CHELLE ...... 5 DORON, MAEVA ...... 24 GENTHON, CHRISTOPHE ...... 34 DRAPER, CLARA...... 24, 48 GEORGE, MAYA...... 13 DRINKWATER, MARK ...... 25 GHIL, MICHAEL ...... 47 DRUSCH, MATTHIAS...... 25 GONCALVES, LUIS G. G...... 36 DUFOUR, GAELLE...... 14 GORIN, VADIM...... 87 DUNLAP, EWA ...... 82 GOTTWALD, GEORG...... 57 DURAND, MICHAEL...... 45 GOUWELEEUW, BEN ...... 32 DYCE, PETER...... 32 GREENSLADE, DIANA ...... 1, 32 GREYBUSH, STEVEN ...... 33 E GUEDJ, STÉPHANIE...... 34 GUERSCHMAN, JUAN PABLO ...... 32 EBITA, AYATAKA ...... 46 GUIDARD, VINCENT ...... 33, 34 EDIANG, ANIEKAN ARCHIBONG...... 26 GUNATILAKA, AJITH...... 34 EDIANG, OKUKU ARCHIBONG ...... 26 GURNEY, ROBERT ...... 71 EDWARDS, CHRIS...... 60, 100 GUSTAFSSON, N...... 51 EDWARDS, DAVID P...... 3 EDWARDS, KAREN ...... 48 H EITO, HISAKI ...... 2 EK, MIKE...... 23 HAASE, JENNIFER...... 34 EL SERAFY, GHADA ...... 30, 65 HABEN, S.A...... 63 ELBERN, HENDRIK ...... 26, 37 HADJI-LAZARO, JULIETTE...... 13 ENGEL, CHERMELLE...... 27 HAINES, KEITH...... 48 ENOMOTO, TAKESHI ...... 27, 28, 60 HAMADA, HISASHI ...... 41 ENTEKHABI, PROF. DARA ...... 55, 62 HAN, GUIJUN...... 35 EREMENKO, MAXIME ...... 14 HAN, WEI ...... 35 ERESMAA, REIMA...... 73 HAN, XUJUN ...... 50 EVENSEN, GEIR...... 72 HANEA, REMUS ...... 36 EYRE, JOHN...... 28, 30 HATTORI, MIKI ...... 27 HAUCHECORNE, ALAIN ...... 15 F HAVERD, VANESSA ...... 86 HE, BIN...... 10 FACCANI, C...... 59 HE, ZHONGJIE...... 35 FEDEROVA, IRINA...... 29 HEEMINK, ARNOLD...... 30, 36, 65 FENG, MING ...... 79 HERDIES, DIRCEU LUIZ ...... 3, 36 FILLION, LUC ...... 11 HERTZOG, ALBERT...... 34 FISHER, MICHAEL ...... 29 HIEMSTRA, CHRISTOPHER A...... 30 FLAMANT, PIERRE...... 82 HIGUCHI, TOMOYUKI ...... 60 FLETCHER, STEVEN J...... 30 HILLER, WOLFGANG ...... 61 FORD, DAVID ...... 48 HIROSE, NAOKI...... 37, 80 FOREMAN, MICHAEL G. G...... 91 HIYOSHI, YOSHIHISA ...... 39 FORET, GILLES ...... 14 HOAR, TIM ...... 100 FOURRIE, NADIA ...... 33, 59 HOCK, TERRY ...... 34 FOWLER, ALISON ...... 30 HODYSS, DANIEL ...... 7, 10 FOX, JACK ...... 34 HOELZEMANN, JUDITH J...... 37 FRANCIS, PETE ...... 44 HOFMAN, RADEK ...... 38

104

HOGAN, TIM ...... 72 KUMAR, NATARAJAN VENKAT ...... 61, 78 HONDA, YUKI...... 73 KUSHWAHA, H.S...... 41 HOTEIT, IBRAHIM ...... 78, 79, 100 HOUSER, PAUL R...... 17 L HOUTEKAMER, P.L...... 10 HUANG, XIANG-YU...... 39 LAFORE, J-P...... 59 HUANG, XINMEI ...... 9 LAKSHMIVARAHAN, S...... 47 HUBER, DORIT...... 82 LANE, TODD ...... 27 HUI, QIAN...... 13 LANGLAND, ROLF...... 31, 72, 95 HUNT, BRIAN ...... 66 LAU, WILLIAM ...... 70 HURLBURT, H.E...... 101 LAWLESS, A.S...... 63 HURTMANS, DANIEL...... 13 LE , TAN ...... 49 LE DIMET, FRANÇOIS-XAVIER...... 21, 91 I LE HENAFF, MATTHIEU ...... 18 LE MARSHALL, JOHN ...... 11, 49, 70, 71, 75 IDE, KAYO ...... 33, 46, 58, 66 LE RILLE, OLIVIER...... 82 IGARASHI, HIROMICHI...... 39, 40 LE, TAN ...... 11, 75 IKEDA, MOTOYOSHI ...... 65 LEA, DANIEL ...... 48 INDUMATI, S...... 41 LEAN, HUMPHREY...... 50 INGLEBY, N BRUCE ...... 62 LEE, JIN ...... 48 INOUE, JUN ...... 28 LEMUS-DESCHAMPS, LILIA ...... 49 ISAKSEN, LARS...... 39, 82 LENARTZ, FABIAN...... 89 ISHIKAWA, YOICHI ...... 39, 40, 63 LEWIS, JOHN M...... 47 ISHIZAKI, SHIRO...... 88 LI, DONG ...... 35 ITO, KOSUKE ...... 40, 63 LI, GENE...... 58 IWASAKI, TOSHIKI ...... 41 LI, HONG ...... 58 LI, WEI ...... 35, 50 J LI, XIN...... 50 LI, ZHIHONG ...... 50 JACKSON, BETHANNA...... 55 LIMA, JOSÉ ANTONIO...... 77 JACKSON, DAVID R ...... 62 LINDSKOG, M...... 51 JACOBS, GREGG A...... 76 LISTON, GLEN E...... 30, 80 JANA, R, ...... 41 LIU, CHENGSI...... 51 JANJIC, TIJANA...... 42 LIU, HONGYA ...... 95 JÄRVINEN, HEIKKI ...... 73 LIU, SHUN ...... 19 JIA, BINGHAO ...... 42, 95 LIU, SHUOSONG ...... 23 JUNG, JAMES ...... 49 LIU, YAN...... 35, 52 JUNG, JIM...... 70 LIU, YIMIN ...... 83 LONGO, KARLA M...... 37 K LOPEZ, PHILIPPE...... 31 LORD, STEPHEN...... 49 KALNAJS, LARS...... 34 LORENC, ANDREW...... 5, 62 KALNAY, EUGENIA ...... 33, 43, 58, 66, 96 LOZZA, HOMERO F...... 52 KAMACHI, MASAFUMI ...... 43, 88 LU, HUIJUAN ...... 53 KARBOU, FATIMA ...... 34, 59 LUCHIN, VLADIMIR...... 65 KATSUMATA, MASAKI...... 60 LUNDQUIST, JULIE...... 19 KAWABATA, TAKUYA ...... 44 LUO, YONG...... 63 KAWABATA, TAKUYA ...... 44 KAY, JUN KYUNG...... 101 M KEPERT, JEFFREY D...... 44, 45 KERR, YANN ...... 71 MA, JIRUI ...... 35 KIKUCHI , TAKASHI...... 64 MADSEN, HENRIK...... 53 KIM, EDWARD...... 45, 71 MAHFOUF, JEAN-FRANÇOIS ...... 24, 54 KIM, HYUN MEE ...... 101 MARCUCCI, FRANCESCA...... 85 KING, EDWARD ...... 86 MARGULIS, STEVEN ...... 45 KLEIST, DARYL T...... 46 MARSEILLE, GERT-JAN...... 82 KLIMOVA, EKATERINA G...... 46 MARTIN, MATTHEW...... 48, 92 KOBAYASHI, SHINYA ...... 46 MARTINS, RENATO ...... 77 KOCH, STEVEN E...... 94 MASINA, SIMONA ...... 78 KONDRASHOV, DMITRI ...... 47 MASSART, SEBASTIEN ...... 54 KOREN, VICTOR ...... 23 MASUDA, SHUHEI...... 39, 40 KRYSTA, MONIKA ...... 7 MATEAR, RICHARD...... 79 KUBOTA, PAULO...... 3 MATSUO, TOMOKO ...... 54 KUMABE, RYOUJI ...... 46 MATTOS, JOÃO G...... 36

105

MAXWELL, DEBORAH ...... 55 PANNEKOUCKE, OLIVIER ...... 6, 54 MCGREGOR, JAMES...... 55 PANTELEEV, GLEB ...... 64, 65, 97 MCLAUGHLIN, DENNIS...... 55, 62, 93 PARK, YOUNG-YOUN...... 101 MCNALLY, ANTHONY ...... 34 PARRISH, DAVID...... 19 MCWILLIAMS, JIM ...... 58 PARSONS, DAVID...... 34 MECHOSO C. ROBERTO, ...... 34 PAUWELS, VALENTIJN R.N...... 17 MÉNARD, RICHARD ...... 55, 56 PAYAN, CHRISTOPHE...... 82 MERCER, JENNIFER ...... 34 PECHA, PETR...... 38 METZGER, E.J...... 101 PELC, JOANNA S...... 65 MIAOLING, LIANG ...... 13 PELLEGRINI, ANDREA...... 34 MICHEL, YANN...... 56 PENG, GE...... 47 MILLER, ROBERT N...... 69 PENNY, STEVE...... 66 MILLER, STEVE D...... 30 PETERS-LIDARD, CHRISTA...... 23 MIROUZE, ISABELLE ...... 56 PETTENUZZO, DANIELE ...... 22 MITCHELL, HERSCHEL...... 10 PEUCH, VINCENT-HENRI ...... 34 MITCHELL, LEWIS ...... 57 PHAM, DINH-TUAN ...... 100 MIYAZAKI, KAZUYUKI...... 41, 57 PIACENTINI, ANDREA ...... 54 MIYOSHI, TAKEMASA.....27, 28, 33, 58, 60, 74 PICARD, GHISLAIN...... 34 MOGENSEN, KRISTIAN...... 92 PICCOLO, CHIARA...... 66 MOLL, P...... 59 PINARDI, NADIA...... 21, 22 MONTEIRO, IGOR ...... 77 PIOVESAN, RAFAEL...... 77 MONTMERLE, THIBAUT...... 59 PIPUNIC, ROBERT ...... 66 MONTROTY, RÉMI ...... 6 PIRES, CARLOS ALBERTO ...... 81 MOON, SEONOK...... 101 PITALIADDA, DINESH ...... 34 MOORE, ANDREW...... 60, 100 POKROVSKY, OLEG M...... 67 MORCRETTE, JEAN-JACQUES ...... 5 POLAVARAPU, SAROJA...... 62 MORELANDE MARK...... 34 POLI, PAUL...... 82 MORIYA, MASAMI...... 46 POLKINGHORNE, ROSANNE ...... 91 MOTEKI, QOOSAKU ...... 60 POMMIER, MATTHIEU ...... 13 MUNOZ-SABATER, JOAQUIN ...... 25 POSSELT, DEREK J...... 71 POTAPOVA, TATIANA...... 29 N POWELL, BRIAN ...... 60, 100 POWERS, JORDAN...... 34 NAKANO, HIDEYUKI...... 43 PROSHUTINSKY, ANDREY ...... 64 NAKANO, SHIN’YA...... 60 PRZYBYSZ-JARNUT, JUSTYNA...... 36 NECHAEV, DMITRI...... 64, 65, 97 PUECH, DOMINIQUE ...... 34 NEEF, LISA...... 79 PUGH, TIM ...... 9 NERGER, LARS ...... 61 PURANIK, V.D...... 41 NETT, HERBERT...... 82 NEZLIN, YULIA...... 62 R NG, GENE-HUA CRYSTAL...... 62 NGAN, KEITH ...... 62 RABIER, FLORENCE ...... 33, 34, 59 NGODOCK, HANS E ...... 76 RAUPACH, MICHAEL ...... 86 NICHOLS, N.K...... 63 RAYNAUD, LAURE...... 6, 68 NIE, SUPING...... 63 REALE, ORESTE...... 70 NILSSON, JENNY A.U...... 22 REDELSPERGER, J-L...... 59 NISHINA, KEI...... 63 REICH, SEBASTIAN...... 57 NURET, M...... 59 REICHLE, ROLF. H...... 16, 17 REITEBUCH, OLIVER ...... 82 O RENSHAW, RICHARD...... 62 RENZULLO, LUIGI J...... 68, 88 O’KANE, TERENCE J...... 64 RESTREPO, PEDRO ...... 23 OKE, PETER R...... 1, 2, 9, 64, 97 RESZKA, MATT ...... 62 OKI, TAIKAN...... 80 RICHMAN, JAMES G...... 69 ONOGI, KAZUTOSHI...... 46 RIDLER, MARC...... 69 OTA, YUKINARI...... 46 RIISHOJGAARD, LARS PETER ...... 49, 70 OWE, MANFRED ...... 32 RISTIC, BRANKO ...... 34 OZA, R.B...... 41 RITCHIE, HAROLD ...... 70 RIXEN, MICHEL ...... 89 P ROBSON, BARBARA...... 12 ROCHON, YVES ...... 62 PAGET, MATT ...... 86 ROMANOVSKAYA, MARIA...... 29 PAIVA, AFONSO ...... 77 ROMBERG, KEITH ...... 34 PANGAUD, THOMAS...... 33 ROSATI, A...... 102

106

ROSMOND, TOM...... 95 RÜDIGER, CHRISTOPH...... 71 TAKAHASHI, KIYOTOSHI...... 46 RUGGIERO, GIOVANNI ...... 77 TAKAYAMA, KATSUMI...... 80 RUMMEL, REINER ...... 42 TALAGRAND, OLIVIER...... 81 RUSTON, BENJAMIN...... 72 TAN, DAVID ...... 82 TANAJURA, CLEMENTE ...... 77 S TANAKA, TAICHU Y...... 74 TANG, CHARLES ...... 82 SAITO, KAZUO...... 44 TANG, XIAO ...... 83 SAKOV, PAVEL...... 72, 73 TANGBORN, ANDREW...... 34 SALMOND, DEBORAH ...... 31 TESTUT, CHARLES-EMMANUEL ...... 90 SALONEN, KIRSTI ...... 73 THEPAUT, JEAN-NOËL ...... 34, 83 SANDERY, PAUL ...... 2 THOMPSON, KEITH R...... 70, 83 SAPUCCI, LUIZ F...... 36 THOPPIL, P.G...... 101 SARAVANAN , SUBBARAYAN...... 61, 78 TIAN, XIANGJUN ...... 42, 95 SASAKI, YUJI ...... 39, 40 TINGWELL, CHRIS...... 11, 75, 84 SATHIYAMURTHI, SUBBARAYAN ...... 61, 78 TITAUD, OLIVIER ...... 91 SAWADA, KEN...... 73 TODLING, RICARDO...... 31, 84, 85 SCANNELL, CLAIRE...... 13 TOLSTYKH, MIKHAIL A...... 74 SCHRÖTER, JENS ...... 42, 61 TONANI, MARINA...... 22 SEECAMP, ROLF ...... 49 TONG, MINGJING...... 19 SEKIYAMA, TSUYOSHI THOMAS ...... 20, 74 TORNFELDT SORENSEN, JACOB ...... 53 SEKO, HIROMU...... 44 TORRISI, LUCIO ...... 85 SEO, DONGJUN ...... 23 TOURE, ALLY ...... 45 SHEN, XUESHUN ...... 35 TOWN, MICHAEL...... 34 SHI, CHUNXIANG...... 42, 95 TOYODA, TAKAHIRO ...... 39, 40 SHIBATA, KIYOTAKA...... 20 TRÉMOLET, YANNICK...... 85, 86 SHIMIZU, ATSUSHI ...... 74 TREVISAN, ANNA ...... 81 SHIROOKA, RYUICHI ...... 60 TROJAKOVA, ALENA...... 86 SHLYAEVA, ANNA V...... 74 TRUDINGER, CATHY ...... 86 SHOJI, YOSHINORI...... 44 TSEITKIN, FAINA...... 97 SHRIVASTAVA, R ...... 41 TSUCHIYA, TAKASHI ...... 87 SHRIVER, J.F...... 101 TSUJINO, HIROYUKI...... 43 SHULMAN, IGOR ...... 75 TSYRULNIKOV, MIKHAIL ...... 87 SHUTYAEV, V...... 21 TUBBS, ROBERT ...... 44, 50 SIMONIN, DAVID ...... 50 SIMS, HOLLY...... 75 U SKACHKO, SERGEY ...... 42 SKVORTSOV, ALEX ...... 34 UBELMANN, CLÉMENT...... 90 SMITH, SCOTT R...... 75, 76 UCHIYAMA, YUSUKE ...... 58 SMEDSTAD, OLE MARTIN...... 101 UENO, GENTA...... 60, 87 SMITH, SCOTT R...... 75, 76 UNDÉN, P...... 51 SNYDER, CHRIS ...... 77 USUI, NORIHISA...... 43, 88 SOARES, IVAN...... 77 SON, JOOHYUNG ...... 101 V SONG, H...... 78 SOUOPGUI, INNOCENT ...... 91 VAN ANDEL JOSEPH ...... 34 STABENO, PHYLLIS...... 65 VAN DIJK, ALBERT I.J.M...... 32, 88 STEINLE, PETER ...5, 11, 24, 34, 48, 49, 75, 80 VAN LEEUWEN, PETER JAN ...... 89 STENGEL, M...... 51 VANDELST, PAUL...... 19 STOFFELEN, AD ...... 82 VANDENBULCKE, LUC ...... 89 STORTO, ANDREA ...... 78 VENEZIANI, MILENA...... 60 STRAUME, ANNE-GRETE...... 82 VERHOEST, NIKO E.C...... 17 STRUNK, ACHIM...... 26 VERLAAN, MARTIN...... 90 SUBRAMANIAN, ANEESH ...... 78, 79 VERRON, JACQUES ...... 7, 15, 90, 91, 93 SUGIURA, NOZOMI...... 39, 40 VIAL, FRANÇOIS...... 34 SULAIMAN, ASRI ...... 49 VIDARD, ARTHUR...... 91, 92 SUN, CHAOJIAO ...... 79 VUKIĆEVIĆ, TOMISLAVA ...... 71, 91 SUN, XUDONG ...... 80 SUSSKIND, JOEL...... 70 W SUZUKI, KAZUYOSHI ...... 80 SWADLEY, STEVE...... 72 WAKAMATSU, TSUYOSHI ...... 91 WALKER, JEFFREY...... 24, 66, 71 T WALLCRAFT, A.J...... 101

107

WANG, BIN ...... 51 WANG, LIANGXU ...... 50 WANG, XIDONG ...... 35 WANG, ZIFA ...... 83 WARNE, JANE ...... 32 WARREN, GRAHAM ...... 9 WATANABE, TATSURO ...... 80 WEAVER, ANTHONY ...... 56, 92 WEDD, R...... 2 WESTERN, ANDREW ...... 66 WHITAKER, JEFFREY S...... 83, 92 WIRTH, ACHIM ...... 93 WOJCIK, RAFAL ...... 93 WOODGATE, REBECCA ...... 64 WU, XINRONG...... 35 WU, YONGSHENG ...... 82

X

XIANGJUN, TIAN...... 13 XIAO, QINGNONG...... 51 XIAO, YI...... 11, 75 XIE, JIPING ...... 93 XIE, YUANFU...... 50, 94 XIE, ZHENGHUI ...... 42, 94 XU, LIANG...... 95 XU, QIN ...... 53 XUE, JISHAN ...... 23, 35, 52, 95, 99 XUE, MING...... 95

Y

YAMANE, SHOZO ...... 27, 28, 60 YAN, BANGHUA...... 19 YAN, CHANGXIANG ...... 96 YAN, YAN...... 55 YANG, SHU-CHIH...... 96 YAREMCHUK, MAX ...... 65, 76, 97 YASUNARI, TETSUZO...... 80 YI, XIAO...... 49 YIN, YONGHONG ...... 1, 2, 97 YONEYAMA, KUNIO ...... 60 YOO, HEE-DONG ...... 101 YOSHIZAKI, MASANORI ...... 60

Z

ZAVALA-GARAY, JAVIER...... 60 ZHANG, JINLUN...... 64 ZHANG, RONG-HUA...... 98 ZHANG, SHAOQING ...... 102 ZHANG, XUEFENG ...... 35 ZHENG, FEI ...... 97 ZHENG, XIAOGU...... 98 ZHENGHUI, XIE ...... 13 ZHU, JIANG ...... 63, 83, 93, 96, 97 ZHUANG, ZHAORONG ...... 35, 99 ZUPANSKI, DUSANKA ...... 99 ZUPANSKI, MILIJA ...... 99

108 The 5th WMO International Symposium on Data A ssimilation ORAL PROGRAM

Mon 05-Oct Tue 06-Oct Wed 07-Oct Thu 08-Oct Fri 09-Oct

Developments in 4D-Var Developments in Ensemble Data Re cent advances in variational The TOPAZ ice-ocean data Opening TRÉMOLET, WHITAKER, WEAVER, 9:00 AM Assimil ation assimil ation for global ocean 9:00 AM SAKOV, Pavel assimil ation system addresses etc. Yannick Jeffrey Anthony applications An Investigation of Model error in a Change-of-variable in an Ensemble On the Assimilation of Argo Float and Non-linear extensions of the SEEK Quasi-G eostrophic, Weak-Constraint Kalman Filter Surface Drifter Trajectories into the filter for DA and parameter estimation DOBRICIC, 9:40 AM FISHER, Michael 4d-Var Analysis System KEPERT, Jeffrey Mediterranean Forecasting System 9:20 AM DORON, Maeva into coupled physical-biogeochemical Srdjan models of the ocean

Recent developments in land data On the existence of an optimal Estimation of observation error The Regional Ocean Modeling Development of a 4-dimensional assimilation for numerical weather subspac e dimension for 4DVar correla tion and the treatment in System (ROMS) 4D-Var Assimilation variati onal coupled DA system for MAHFOUF, Jean- TALAGRAND, MIYOSHI, BROQUET, ISHIKAWA, 10:00 AM prediction ensemble Kalman filter Systems applied to the California 9:40 AM enhanced analysis and prediction of François Olivier Takemas a Gregoir e Current System Yoi chi seasonal to interannual variations

Comparison of observation impacts in Ro bust Characterization of Model Model and observation bias 4D-Var and EnKF Intercomparisons two forecast systems using adjoint POSSELT, Physics Uncertainty and Implications correct ion in altimeter ocean data KALNAY, 10:20 AM GELARO, Ron LEA, Daniel 10:00 AM methods Derek for Ensemble-Based Prediction assimilation in FOAM Eugen ia

Morning tea & Morning tea & Morning tea & Morning tea & 10:40 AM 10:40 AM Morning tea Posters Posters Posters Posters Recent Progress In Hybrid 4D- 12:00 PM lunch lunch lunch lunch 11:10 AM BARKER, Dale Variati onal/Ensemble Data Assimilation Challenges for Land Data Changes to the Global Observing Advanced nonlinear DA Chemical Data Assimilation with Consistent operational ensemble McLAUGHL IN, ELBERN, 1:30 PM Assimil ation EYRE, John System – evolution or design? SNYDER, Chris Multisc ale Emission Inversion 11:30 AM BERRE, Loïk variati onal assimilation Dennis Hendrik Enhancing Adaptive Filtering Application of singular vector analysis The Principle of Energetic As similating Retrievals of Chemical Intercomparison of Variational and Approac hes for Land Data to the Kuroshio large meander Consist ency Constituents in CAM-Chem and WRF- Ensembl e Kalman Filter DA KAMACHI, ARELLANO, 2:10 PM CROW, Wade Assimilation Systems COHN, Stephen Chem Using an Ensemble 11:50 AM BUEHNER, Mark Approaches in the Context of Global Masafum i Avelino Adjustment Kalman Filter Approach Deterministic NWP

Using SMOS observations in Data assimilation of remote sensing Particle filtering: beating the curse of Infrared remote sensing of Ensemble Data Assimilation at ECMWF’s Land Surface Analysis inform ation from satellite and radar VAN LEEUWEN, dimensionality CLERBAUX, atmosph eric composition and air ECMWF 2:30 PM DRUSCH, Matthias 12:10 PM ISAKSEN, Lars System data Peter Jan Cathy quality: towards operational applications Snow Radiance Assimilation: Case DERBER, John Direct Assimilation of Image Representation of climate signals in Cloud-Resolving Ensemble Data Studies using the Cold Land Sequenc es in Numerical Models reanaly sis. ZUPANSKI, Assimil ation 2:5 0 PM KIM, Edward Processes Experiment-1 VIDARD, Arthur 12:30 PM Milija DEE, Dick Radar Data Assimilation in Assimilation of GPS radio occultation 3:10 PM XUE, Jishan GRAPES CUCURULL, Lidia observa tions at NOAA/NCEP Afternoon tea 11:10 AM end

Regional ocean applications of the 3:30 PM Afternoon tea Afternoon tea OKE, Peter EnKF/En OI Afternoon tea

Fine-scale versus large-scale Sate llite Data Assimilation Ass imilation of Optical Remote JRA-55: Japanese 55-year reanalysis BOU TTIER, atmosph eric data assimilation LEMARSHALL, CHERUKURU, Sensing Data into Coastal Aquatic KOBAYASHI, project - status and plan 4:00 PM François John Nagur Biogeochemical Models Shinya

The 2009 WRFDA overview Spatial satellite observation-error Fore casting Mesoscale Variability of Precursory signals of significant statistics for AMSU-A data: estimation the Nor th Atlantic Using a Physically weather events found in ensemble TSYRULNIKOV, THOMPSON, ENOMOTO, 4:40 PM HUANG, Xiang-Yu and implications for data assimilation Motivated Scheme for Assimilating reanalysis ALERA Mikhail Keith Altimeter and Argo Observations Takeshi

Upgrade of the Operational All-sky assimilation of microwave Comparisons of Brewer-Dobson IWASAKI, 5:00 PM SAWADA, Ken Mesosca le 4D-Var at the Japan GEER, Alan observa tions sensitive to water end Circula tions diagnosed from Meteorological Agency vapour, cloud and rain To shiki Reanalysis Ensemble and Variational Radar The development of hyperspectral A Comparison of Variational and DA for Convective Storm and RIISHOJGAARD, infrare d water vapor radiance THÉPAUT, Jean - Ensembl e-Based DA Systems for 5:20 PM XUE, Ming Hurricane Predictions Lars Pet er assimilation techniques in the NCEP Noël Reanalysis of Sparse Observations Global Forecast System

5:40 PM end end end

109

Poster Topics : Location

AREA MONDAY WEDNESDAY Put up Monday – take down Tuesday Put up Wednesday morning – take down afternoon Friday noon. To the left of the Registration Desk To the left of the Registration Desk A Developments in Variational Data Assim Hydrological and Land Surface Data Assim Developments in Ensemble Data Assim B Forward and to the left of the Registration Desk Forward and to the left of the Registration Desk Chemical Data Assimilation Observing System Design Continue past Area B Continue past Area B C Mesoscale Data Assimilation Data Assim in Remotely Sensed Obs Area to the right of the Auditorium Area to the right of the Auditorium D Developments in Advanced Data Assim Coupled Data Assim Reanalysis Area far right near 2nd Refreshment area. Area far right near 2nd Refreshment area. E NWP Data Assimilation Oceanic Data Assimilation F Continue past Area E Continue past Area E Intercomparisons & Hybrid Data Assim Oceanic Data Assimilation

Poster Topics

Chemical Data Assimilation

Mesoscale Data Assimilation

Numerical Weather Prediction

Developments in Variational Data Assimilation

Developments in Ensemble Data Assimilation

Developments in Advanced Data Assimilation

Intercomparisons & Hybrid Data Assimilation

Hydrological and Land Surface Data Assimilation

Data Assimilation of remotely sensed observations

Observing System Design

Oceanic Data Assimilation

Coupled Data Assimilation

Reanalysis

110

LAYOUT OF CONFERENCE AREA

111 5th WMO Symposium On Data Assimilation

SUMMARY PROGRAM

MONDAY, 5TH OCTOBER 2009

Monday, 5th October 2009 - 0900-1740 09:00 Opening Addresses

10:00 Jean-François Mahfouf Recent Developments In Land Data Assimilation For Numerical Weather Prediction

10:40 Morning Tea & Posters 12:00 Lunch 13:30 Dennis McLaughlin Challenges for Land Data Assimilation

14:10 Wade Crowe Enhancing Adaptive Filtering Approaches For Land Data Assimilation Systems

14:30 Matthias Drusch Using SMOS observations in ECMWF’s Land Surface Analysis System

14:50 Edward Kim Snow Radiance Assimilation: Case Studies using the Cold Land Processes Experiment-1

15:10 Jishan Xue Radar Data Assimilation in GRAPES

15:30 Afternoon Tea 16:00 François Bouttier Fine-scale versus large-scale atmospheric data assimilation

16:40 Xiang Yu Huang The 2009 WRFDA overview

17:00 Ken Sawada Upgrade of the Operational Mesoscale 4D-Var at the Japan Meteorological Agency

17:20 Ming Xue Ensemble and Variational Radar Data Assimilation for Convective Storm and Hurricane Predictions

17:40 End

POSTERS

Monday Presentations: Chemical Data Assimilation Mesoscale Data Assimilation Numerical Weather Prediction

112

TUESDAY, 6TH OCTOBER 2009

Tuesday, 6th October 2009 - 0900-1740 09:00 Yannick Trémolet Developments In 4d-Var

09:40 Michael Fisher An Investigation Of Model Error In A Quasi-Geostrophic, Weak-Constraint 4d-Var Analysis System

10:00 Olivier Talagrand On The Existence Of An Optimal Subspace Dimension For 4dvar

10:20 Ron Gelaro Comparison Of Observation Impacts In Two Forecast Systems Using Adjoint Methods

10:40 Morning Tea & Posters 12:00 Lunch 13:30 John Eyre Changes To The Global Observing System – Evolution Or Design?

14:10 Masafumi Kamachi Application Of Singular Vector Analysis To The Kuroshio Large Meander

14:30 John Derber Data Assimilation Of Remote Sensing Information From Satellite And Radar Data

15:10 Lidia Cucurull Assimilation Of GPS Radio Occultation Observations At NOAA/NCEP

15:30 Afternoon Tea 16:00 John Le Marshall Satellite Data Assimilation

16:40 Mikhail Tsyrulnikov Spatial Satellite Observation-Error Statistics For Amsu-A Data: Estimation And Implications For Data Assimilation

17:00 Alan Geer All-Sky Assimilation Of Microwave Observations Sensitive To Water Vapour, Cloud And Rain

17:20 Lars-Peter Riishojgaard The Development Of Hyperspectral Infrared Water Vapor Radiance Assimilation Techniques In The Ncep Global Forecast System

17:40 End

POSTERS

Tuesday Presentations: Developments in Variational Data Assimilation Developments in Ensemble Data Assimilation Developments in Advanced Data Assimilation Intercomparisons & Hybrid Data Assimilation

113

WEDNESDAY, 7TH OCTOBER 2009

Wednesday, 7th October 2009 - 0900-1700 09:00 Jeffrey Whitaker Developments In Ensemble Data Assimilation

09:40 Jeffrey Kepert Change-Of-Variable In An Ensemble Kalman Filter

10:00 Takemasa Miyoshi Estimation Of Observation Error Correlation And The Treatment In Ensemble Kalman Filter

10:20 Derek Posselt Robust Characterization Of Model Physics Uncertainty And Implications For Ensemble-Based Prediction

10:40 Morning Tea & Posters 12:00 Lunch 13:30 Chris Snyder Non-Gaussian And Nonlinear Data Assimilation

14:10 Stephen E Cohn The Principle Of Energetic Consistency

14:30 Peter Jan Van Leeuwen Particle Filtering: Beating The Curse Of Dimensionality

14:50 Arthur Vidard Direct Assimilation Of Image Sequences In Numerical Models

15:10 Afternoon Tea 15:30 Peter Oke Regional Ocean Applications Of The EnKF/ENOI

16:00 Nagur Cherukuru Assimilation Of Optical Remote Sensing Data Into Coastal Aquatic Biogeochemical Models

16:40 Keith Thompson Forecasting Mesoscale Variability Of The North Atlantic Using A Physically Motivated Scheme For Assimilating Altimeter And Argo Observations

17:00 End

POSTERS

Wednesday Presentations: Hydrological and Land Surface Data Assimilation Data Assimilation of remotely sensed observations Observing System Design

114

THURSDAY, 8TH OCTOBER 2009

Thursday, 8th October 2009 - 0900-1740 09:00 Anthony Weaver Recent Advances In Variational Assimilation For Global Ocean Applications

09:40 Srdjan Dobricic On the Assimilation of Argo Float and Surface Drifter Trajectories into the Mediterranean Forecasting System

10:00 Gregoire Broquet The Regional Ocean Modeling System (ROMS) 4D-Var Assimilation Systems Applied To The California Current System

10:20 Daniel Lea Model And Observation Bias Correction In Altimeter Ocean Data Assimilation In FOAM

10:40 Morning Tea & Posters 12:00 Lunch 13:30 Hendrik Elbern Chemical Data Assimilation With Multiscale Emission Inversion

14:10 Avelino Arellano Assimilating Retrievals Of Chemical Constituents In CAM-Chem And WRF- Chem Using An Ensemble Adjustment Kalman Filter Approach

14:30 Cathy Clerbaux Infrared Remote Sensing Of Atmospheric Composition And Air Quality: Towards Operational Applications

14:50 Dick Dee Representation Of Climate Signals In Reanalysis

15:30 Afternoon Tea 16:00 Shinya Kobayashi JRA-55: Japanese 55-Year Reanalysis Project - Status And Plan

16:40 Takeshi Enomoto Precursory Signals Of Significant Weather Events Found In Ensemble Reanalysis ALERA

17:00 Toshiki Iwasaki Comparisons Of Brewer-Dobson Circulations Diagnosed From Reanalysis

17:20 Jean-Noël Thépaut A Comparison Of Variational And Ensemble-Based Data Assimilation Systems For Reanalysis Of Sparse Observations

17:40 End

POSTERS

Thursday Presentations: Oceanic Data Assimilation Coupled Data Assimilation Reanalysis

115

FRIDAY, 9TH OCTOBER 2009

Friday, 9th October 2009 - 0900-1240 09:00 Pavel Sakov The TOPAZ ice-ocean data assimilation system

09:20 Maeva Doron Non-Linear Extensions Of The SEEK Filter For Data Assimilation And Parameter Estimation Into Coupled Physical-Biogeochemical Models Of The Ocean

09:40 Yoichi Ishikawa Development Of A 4-Dimensional Variational Coupled Data Assimilation System For Enhanced Analysis And Prediction Of Seasonal To Interannual Variations

10:00 Eugenia Kalnay 4D-Var And EnKF Intercomparisons

10:40 Morning Tea 11:10 Dale Barker Recent Progress In Hybrid 4d-Variational/Ensemble Data Assimilation

11:30 Loïk Berre Consistent Operational Ensemble Variational Assimilation

11:50 Mark Beuhner Intercomparison Of Variational And Ensemble Kalman Filter Data Assimilation Approaches In The Context Of Global Deterministic NWP

12:10 Lars Isaksen Ensemble Data Assimilation At ECMWF

12:30 Milija Zupanski Cloud-Resolving Ensemble Data Assimilation

12:50 End

116 POSTERS MONDAY

POSTERS: SECTION D mCHEM2: Coman: First Results Of The Assimilation Of Ozone Tropospheric Columns Provided By The Developments in Advanced Data Assimilation IASI Instrument To Assess Air Quality With A Chemical Transport Model - CHIMERE At A mADV1: Bocquet: Choosing The Geometry Of Continental Scale Control Space For An Optimal Assimilation Of Observations mCHEM3: Deschamps: Ozone And UV Index Forecast mADV2: Gandhi: Applying A Robust Extended Kalman Filter To Climate Transition Tracking mCHEM4: Deushi: Ensemble Kalman Filter Assimilation Of Atmospheric Chemical Constituents mADV3: Hofman: Data Assimilation In Early Phase Data With A MRI Chemistry-Climate Model: OSS Of Radiation Accident Using Particle Filter Experiments mADV4: Hoteit: Particle Kalman Filtering: A mCHEM5: Gunatilaka: Chemical Source Nonlinear Framework For Ensemble Kalman Filters Backtracking In Turbulent Boundary Layer (TBL) mADV5: Madsen: A Combined Filtering And Error mCHEM6: Hoelzemann: A Chemical Data Prediction Procedure For Data Assimilation In Assimilation System For South America Using The Hydrological And Hydrodynamic Forecasting CCATT-BRAMS Atmospheric Model To Access The Systems Impact Of Fire Emissions mADV6: Menard: Data Assimilation Experiments mCHEM7: Menard: Convergence And Stability Of With L1-Norm And Related Laplace Distributed Estimated Error Variances Derived From Assimilation Errors Residuals In Observation Space mADV7: Mitchell: A Modified Kalman Filter For mCHEM8: Miyazaki: Performance Of A Local Variance Constraint Ensemble Transform Kalman Filter Data Assimilation System For The Analysis Of The Atmospheric mADV8: Nakano: Merging Particle Filter For High- Circulation And The Distribution Of Long-Lived Dimensional Nonlinear Problems Tracers mADV9: Pecha: Simulation Of Random 3-D mCHEM9: Sébastien: How Important Is To Use Trajectories Of The Toxic Plume Spreading Over The Diagnosed Background Error Covariances For The Terrain Atmospheric Ozone Analysis? mADV10: Poulson: Dual Ensemble Kalman/Particle mCHEM10: Sekiyama: Aerosol Data Assimilation Filters For State-Parameter Estimation Of Large- With An Ensemble Kalman Filter Using CALIPSO Dimensional Nonlinear Models And Ground-Based Lidar Observations mADV11: Talagrand: Modelling Non-Gaussianity Of mCHEM11: Tang: Ensemble Data Assimilation For Background And Observational Errors By The Ozone Forecast: Uncertainty Identification And Maximum Entropy Method Constraint mADV12: Paper Withdrawn POSTERS: SECTION A mADV13: Vandenbulcke: Multi-Model Data Assimilation Aka Super-Ensembles Developments in Ensemble Data Assimilation mADV14: Wojcik: Feature Based Ensemble Estimation For Rainfall Applications mENS1: Paper Withdrawn

mENS2: Klimova: Technique Of Adaptive POSTERS: SECTION B Observations Planning Based On Ensemble Kalman Filter

Chemical Data Assimilation mENS3: Nerger: SEIK - The Unknown Ensemble Kalman Filter mCHEM1: Benedetti: Aerosol Analysis And Forecast In The ECMWF Integrated Forecast System

117 POSTERS MONDAY

mENS4: Ng: Causes Of Enkf Divergence With mINTC7: Raynaud: Ensemble Background-Error Atmospheric Models Variances : Objective Filtering And Impact Studies mENS5: O'Kane: Effects Of Gain Specification And mINTC8: Ritchie: Towards Joint Data Assimilation Covariance Estimation Using The Square Root, For A Coupled Atmosphere-Ocean System Statistical Dynamical And Ensemble Kalman Filters mINTC9: Xiao: An Ensemble-Based Four mENS6: Sakov: Asynchronous Data Assimilation Dimensional Variational (EN4D-VAR) Data With The ENKF Assimilation Scheme And Its Experiments mENS7: Sakov: On Two Common Localisation mINTC10: Xie: A Sequential Hybrid 4DVAR System Methods In ENKF Implemented Using A Multigrid Technique mENS8: Shlyaeva: Local Ensemble Transform mINTC11: Zheng: An Estimation Of Forecast Error Kalman Filter For Semi-Lagrangian Barotropic Model Covariance Matrix Using Multivariate Inflation For Of Atmosphere Kalman Filtering Data Assimilation mENS9: Song: An Adaptive Approach To Mitigate Background Covariance Limitations In The Ensemble POSTERS: SECTION C Kalman Filter mENS10: Subramanian: Implementation Of The Mesoscale Data Assimilation Nonlinear Filtering Problem And Balanced Dynamics mMESO1: Auligne: Development Of A Cloud mENS11: Verlaan: Iterative Kalman Filtering Analysis System mENS12: Verron: Efficient Parameterization Of The mMESO2: Delle Monache: Ensemble-Based Data Observation Error Covariance Matrix For Square Assimilation For Wind Energy Predictions At Fine Root Or Ensemble Kalman Filters: Application To Scales Ocean Altimetry mMESO3: Dixon: From A Convective-Scale mENS13: Zhuang: Design Of The GRAPES Ensemble System Towards A New Covariance Model Ensemble Kalman Filter Data Assimilation System And Its Tentative Experiment mMESO4: Engel: A Scale-Based Distortion Metric For Mesoscale Weather Verification

POSTERS: SECTION F mMESO5: Paper withdrawn

mMESO6: Ito: Estimates Of Air-Sea Fluxes In A Intercomparisons and Hybrid Data Assimilation Tropical Cyclone Using An Adjoint Method mINTC1: Bishop: Data Assimilation Using Modulated mMESO7: Kawabata: Cloud Resolving 4DVAR Ensembles Experiment Of A Local Heavy Rainfall Event Using GPS Slant Delay Data mINTC2: Blayo: Hybridization Of The 4D-VAR With A SEEK* Smoother In View Of Oceanic Applications mMESO8: Kelly: Use Of SEVIRI Radiances In The Met Office Mesoscale Models mINTC3: Brousseau: Use Of Ensemble Assimilation To Represent Flow-Dependence In The AROME mMESO9: Kepert: An Observation Operator For The Data Assimilation System Variational Assimilation Of Vortex Position And Intensity mINTC4: Desroziers: A Posteriori Diagnostics In An Ensemble Variational Assimilation mMESO10: Li: Development Of Data Assimilation For 1.5km Nwp Nowcasting System mINTC5: Hanea: The Choice Of The “Best” Data Assimilation Algorithm For Subsurface mMESO11: Migliorini: Short-Range Ensemble Characterization Predictions At Convective Scale: The Impact Of Surface Precipitation Radar Data mINTC6: Liu: An Ensemble-Based Four Dimensional Variational Data Assimilation Scheme mMESO12: Montmerle: Use Of Heterogeneous Background Error Covariance Matrices At Mesoscale

118 POSTERS MONDAY

mMESO13: Snyder: Mesoscale, Ensemble Data mNWP12: Ruston: Four-Dimensional Observation Assimilation For WRF With The Data Assimilation Impact On The US Navy’s Atmospheric Analyses Research Testbed And Forecasts: Part 2: Channel Selection And Real- Time Monitoring mMESO14: Trojakova: Blendvar - A New Analysis Scheme For Limited Area Model ALADIN/CE mNWP13: Sims: Implementation And Impact Of Scatterometer And AMV Data Assimilation With The mMESO15: Yang: Running In Place Method With ACCESS Code Local Ensemble Transform Kalman Filter For Typhoon Assimilation And Prediction mNWP14: Sun: Use Of Latitude Dependent Covariance For Australian Regional Model Data mMES016: Lindskog: Assimilation of SEVIRI satellite Assimilation radiances in HIRLAM 4D-Var mNWP15: Tingwell: Regional And Australian Data Assimilation And Numerical Weather Prediction In POSTERS: SECTION E ACCESS

mNWP16: Torrisi: Ensemble Data Assimilation With Numerical Weather Prediction The CNMCA Regional Forecasting System mNWP1: Ngan: A New Moist Control Variable For mNWP17: Zheng: A Multivariate Empirical The Met Office's Variational System Orthogonal Function Based Scheme For Balanced Initial Ensemble Generation Of An Ensemble Kalman mNWP2: Aravéquia: Preliminary Results Of Filtering Assimilation AIRS Radiances With Local Ensemble Transform Kalman Filter For The CPTEC/INPE POSTERS: SECTION A Global Model mNWP3: Caron: The Balance Characteristics Of Developments in Variational Data Assimilation Short-Term Forecast Errors Estimated From An Ensemble Kalman Filter mVAR1: Auroux: The "Back And Forth Nudging" Algorithm For Oceanographic Data Assimilation mNWP4: Greybush: Enkf Localization Techniques And Balance mVAR2: Dobricic: An Application Of Sequential Variational Algorithm mNWP5: Han: Recent Developments In Data Assimilation Of Chinese New GFS mVAR3: Fowler: Positional Error In The Boundary Layer Capping Inversion mNWP6: Kim: Sensitivity Of Ensemble Forecasts To Ensemble Size In Ensemble Transform Kalman Filter mVAR4: Kaustubha: Variational Assimilation With A Three Level Atmospheric Model mNWP7: Kleist: Calculating Analysis Sensitivity For The NCEP Global Data Assimilation System mVAR5: Lakshmivarahan: Finding Sources Of Bias Error In Forecast Models: A Framework mNWP8: Liu: Use And Impact Of COSMIC/GPS Radio Occultation Data In GRAPES Global Data mVAR6: Le Dimet: Error Covariance Via Hessian In Assimilation System Variational Data Assimilation mNWP9: Lu: Trade-Offs Between Measurement mVAR7: Li: A Multi-Scale Three-Dimensional Accuracy And Resolutions In Configuring Phased- Variational Data Assimilation Scheme For Very High Array Radar Velocity Scans For Ensemble-Based Resolution Models Storm-Scale Data Assimilation mVAR8: Michel: Inhomogeneous Background Error mNWP10: Piccolo: Ensemble-Derived Background- Modeling and Estimation Over Antarctica Error Covariances: Evaluation In The Operational Met Office NWP System mVAR9: Mirouze: Representation Of Correlation mNWP11: Riishojgaard: AIRS Impact On Tropical Functions Using A One-Dimensional Implicit Diffusion Cyclone Representation In A Global Data Equation, With Application To Variational Ocean Data Assimilation And Forecasting System Assimilation

119 POSTERS MONDAY

mVAR10: Ngodock: Cycling The Representer Method With Nonlinear Models mVAR11: Nichols: Conditioning And Preconditioning Of The 4-D Variational Data Assimilation Problem mVAR12: Pelc: Model-Reduced 4D-Var Data Assimilation In Ecological Modeling mVAR13: Todling: The GMAO 4dvar System: Preliminary Results mVAR14: Ueno: Covariance Regularization In Inverse Space mVAR15: Yaremchuk: A Method Of Successive Corrections Of The Control Subspace In The Reduced-Order 4DVAR mVAR16: Zhang: An Incremental Analysis Updating Implementation Of 4DVAR

120 POSTERS WEDNESDAY

POSTERS: SECTION D wHYLS8: Jia: A Soil Moisture Assimilation Scheme Based On The Ensemble Kalman Filter Using Coupled Data Assimilation Microwave Brightness Temperature wCPLD1: Andreu-Burillo: Can Ocean Data wHYLS9: Li: A Common Software For Nonlinear And Assimilation Improve Tropical Cyclone Forecasts? Non-Gaussian Land Data Assimilation wCPLD2: Caya: Three-Dimensional Variational Data wHYLS10: Lozza: Simulations Of Remotely-Sensed Assimilation In The Gulf Of St. Lawrence Coupled Surface Soil Moisture Assimilations For Future Earth Ice-Ocean Model Observation Missions wCPLD3: Kondrashov: State And Parameter wHYLS11: Maxwell: Improving The Prediction Of Estimation For A Coupled Ocean--Atmosphere Model Inflows To Lake Taupo wCPLD4: Lea: Assimilation Of Chlorophyll Data Into wHYLS12: Meng: Variational Assimilation And FOAM-Hadocc, A Coupled Ocean Physical And Sensitivity Analysis Of Land Surface Temperature Biological Model From The Common Land Model (Colm) wCPLD5: Smith: Predicting Sources And Sinks Of wHYLS13: Natarajan: Comparative Study For The Bio-Optical Tracers With A 4DVAR Ocean Environmental Water Quality Assessment In Assimilation System Tiruchirappalli India wCPLD6: Verron: Linking Altimetry And Ocean wHYLS14: Nie: Simultaneous Estimation Of Land Color: A Data Assimilation Approach Using Lyapunov Surface Model States And Parameters Using A Exponents Constraint Ensemble Kalman Filter wCPLD7: Yin: An Ensemble Ocean Data wHYLS15: Pipunic: A Comparison Of Land Surface Assimilation For Seasonal Prediction Model Data Assimilation Approaches To Improve Heat Flux Estimates For Numerical Weather Prediction POSTERS: SECTION A wHYLS16: Pokrovsky: Assimilation Of Land Surface Site And Remotely Sensing Data In The Atmosphere- Hydrological and Land Surface Data Assimilation Land Energy Exchange Model wHYLS1: Crow: Land Data Assimilation Activities In wHYLS17: Renzullo: Assimilating Geostationary Preparation Of The NASA Soil Moisture Active Satellite MTSAT-1R Thermal Data To Constrain Passive (SMAP) Mission Regional Estimates Of Surface Water And Energy Parameters wHYLS2: Draper: A Comparison Of Soil Moisture Analyses From The EKF Assimilation Of Near- wHYLS18: Ridler: Data Assimilation In A Soil- Surface Soil Moisture And Screen-Level Temperature Vegetation-Atmosphere Transfer Model Using A And Humidity Filtering Framework wHYLS3: Drusch: Future Satellite Data Products wHYLS19: Rudiger: Moisturemap: A Soil Moisture Suitable For Land Surface Analyses Monitoring, Predicting And Reporting System For Sustainable Land And Water Management wHYLS4: Fedorova: Antarctic Lacustrine Environment As A Result Of Climate Change And wHYLS20: Shi: A CLSMDAS Using FY2C Human Impact Precipitation And AMSR-E wHYLS5: Fletcher: Assimilation Of MODIS Snow wHYLS21: Subbarayan: ANN Based Drought Cover Data And AMSR-E Snow Water Equivalent Forecasting For Chittar River Basin India - A Case Data Into Snowmodel Study wHYLS6: Gouweleeuw: AMSR-E Passive Microwave wHYLS22: Suzuki: Snow Data Assimilation For Soil Moisture And Dynamic Open Water Fraction Water Budget In Siberian Lena River Basin wHYLS7: Jana: Impact Assessment Of Data wHYLS23: Trudinger: Model-Data Fusion For State Assimilation On Fine Scale Air Dispersion For A And Parameter Estimation In Continental-Scale Complex Terrain Hydrological Modelling

121 POSTERS WEDNESDAY

wHYLS24: Tymofeiev: Application Of Weather wOCNC13: Miyoshi: Ensemble Data Assimilation For Forecast Model (WRF) In Ukraine Idealized California Current System With ROMS- LETKF wHYLS25: Van Dijk: The Role Of Data Assimilation In Large-Scale Hydrological Modelling To Support wOCNC14: Ngodock: Variational Data Assimilation Water Resources Assessment In Australia Using The Navy Coastal Ocean Model wHYLS26: Xie: A Dual-Pass Variational Data wOCNC15: Nishina: Effectiveness Of Drifter Data Assimilation Framework For Estimating Soil Moisture Assimilation In Improving Hindcast Of Meso-Scale Profiles From AMSR-E Microwave Brightness Variability In Kuroshio Extension Region Temperature wOCNC16: Panteleev: Mean Ocean Dynamical Topography And Local Volume Balance In The POSTERS: SECTION E AND F Bering Sea

wOCNC17: Penny: Data Assimilation Of The Global Oceanic Data Assimilation Ocean Using The Local Ensemble Transform Kalman Filter (LETKF) And The Modular Ocean Model wOCNC1: Allen: Refinement Of Simulations Of (MOM2) Deep-Water Tsunami Propagation Through The Use Of Observations wOCNC18: Smedstad: The 1/12 Degree Global HYCOM Nowcast/Forecast System wOCNC2: Brassington: Operational Ocean Data Assimilation For The Bluelink Ocean Forecasting wOCNC19: Soares: Data Assimilation In A Regional System Modeling Off The Brazilian East Coast: Preliminary Results Obtained With The Princeton Ocean Model wOCNC3: Broquet: Adjustment Of Ocean Model Initial Conditions And Atmospheric Forcing From wOCNC20: Sun: Future Changes In The Leeuwin Ocean Data Assimilation In The California Current Current Transport Inferred From Statistical And System Dynamical Downscaling wOCNC4: Cosme: Implementation Of A Reduced wOCNC21: Takayama: Impact Of The In-Situ CTD Rank Smoother For High Resolution Oceanography Data For The Assimilated Estimates In The Japan Sea wOCNC5: Dobricic: Data Assimilation In Open Ocean And Shelf Areas Of The Mediterranean Sea wOCNC22 Tang: Assimilation Of Sea Surface Temperature And Sea Ice Data In The BIO Ocean wOCNC6: Gaytan: Neural Networks And Ensamble Forecasting System Kalman Filter Application For Salinity And Temperature Forecasting wOCNC23: Paper Withdrawn wOCNC7: Hirose: Inverse Estimation Of Empirical wOCNC24: Usui: Improving Strategies With Parameter In A Circulation Model For The East Asian Constraints Regarding Non-Gaussian Statistics In Marginal Seas MOVE/MRI.COM wOCNC8: Hoteit: A Mitgcm/DART Ocean Analysis wOCNC25: Wakamatsu: Observability Of A Large And Prediction System With Application To Control Vector In A 4D-Var Ocean Data Assimilation wOCNC9: Ishikawa: Impact Of 4d-Var Assimilation wOCNC26: Wakamatsu: On The Influence Of Products Random Wind Stress Errors On The Four- Dimensional, Midlatitude Ocean Inverse Problem wOCNC10: Janjic: Observational Error Covariance Specification In Ensemble Based Kalman Filter wOCNC27: Wedd: Modelling Equatorial Pacific Algorithms Salinity Fields With PEODAS wOCNC11: Li: A Three-Dimensional Variational Data wOCNC28: Wirth: Estimation Of Friction Parameters Assimilation Scheme In Support Of Coastal Ocean And Laws In Oceanic Gravity Currents Observing Systems wOCNC12: Mello: Study Of Data Assimilation In The Princeton Ocean Model

122 POSTERS WEDNESDAY wOCNC29: Xie: Comparisons Of Some Ensemble POSTERS: SECTION D Optimal Interpolation Schemes For Assimilating Argo Profiles Into A Hybrid Coordinate Ocean Model Reanalysis wOCNC30: Yan: Data Assimilation In Indian And Western Pacific Ocean wREAN1: Garcia: Data Assimilation In Morphodynamical Models wOCNC31: Richman: Model Representation Error Estimation for Ocean Data Assimilation wREAN2: Han: Development Of A Regional Ocean Reanalysis System In The China Seas

POSTERS: SECTION B wREAN3: Paper Withdrawn

wREAN4: Alves: A Comparison Of The Observing System Design Representation Of The Main Modes Of Ocean Climate Variability By Two State-Of-The-Art Ocean wOSD1: Bocquet: Targeting Of Observations For Re-Analyses Radionuclides Accidental Release Monitoring wREAN5: Panteleev: Hindcast Of The Circulation In wOSD2: DeMey: Assessment Of Coastal Ocean The Chukchi And East Siberian Seas Observational Networks By Ensemble-Based Representer Spectral Analysis wREAN6: Storto: Global Oceanographic Variational Data Assimilation Of In-Situ Observations And wOSD3: Greenslade: Network Design And Space-Borne Altimeter Data For Reanalysis Assessment For A Tsunami Observing System Applications wOSD4: Inoue: Reduced Arctic Sea Ice Hinders wREAN7: Zheng: Application Of ENKF To ENSO Accurate Climate Monitoring - Impact Of Depleted Ensemble Prediction With An Intermediate Coupled Arctic Drifting Buoy Network – Model wOSD5: Moll: Data Assimilation Experiments For AMMA, Using Radiosonde Observations And POSTERS: SECTION C Satellite Observations Over Land wOSD6: Moteki: Propagation Of The Impact Signal Data Assimilation of Remotely Sensed Of The Additionally-Assimilated Observations Over Observations The Indian Ocean Through Tropical Waves wRSOB1: Aonashi: Neighboring Ensemble wOSD7: Nezlin: Some New Applications Of Variational Assimilation Method To Incorporate Observing System Simulation Experiments Microwave Radiometer Data Into A Cloud-Resolving Model wOSD8: Panteleev: Optimization Of Mooring Observations In Northern Bering Sea wRSOB2: Beggs: Real-Time Skin Sea Surface Temperature Analyses For Quality Control Of Data wOSD9: Pokrovsky: Implementation Of Singular Assimilated Into NWP Models Vectors To Determine Adaptive Observations Design Provided The Efficient Representation Of The wRSOB3: Cot: Comparison Between Sequential Ensemble Prediction Systems Assimilation And Krigeage For Satellite Data Interpolation wOSD10: Todling: An Approach To Assess Observation Impact Based On Observation-Minus- wRSOB4: Chattopadhyay: Impact Of Using 4D-VAR Forecast Residuals Assimilation Of SSM/I Data Over Australian Region wOSD11: Xu: Four-Dimensional Observation Impact wRSOB5: To be an oral presentation On The US Navy’s Atmospheric Analyses And Forecasts: System Development And Test wRSOB6: De Lannoy: Fine Scale Snow Analyses Improvement Through Coarse Scale Snow Water wOSD12: Zhang: The Adequacy Of Existing Equivalent Assimilation Observing Systems To Monitor AMOC And The North Atlantic Climate wRSOB7: Dong: Assimilation Of MODIS Snow Cover Data Into The LIS SAC-HT/SNOW17 Model Over The Continental United States (CONUS)

123 POSTERS WEDNESDAY

wRSOB8: Dong: The Error Characteristics Of Simulated Microwave Satellite Observation In Cloudy And Rainy Area wRSOB9: Guidard: Impact Of Advanced Sounder Radiances In The French Numerical Weather Prediction Models wRSOB10: Guidard: The Concordiasi Field Campaign Over Antarctica wRSOB11 Herdies: The Moisture Budget Over Amazon Region During The Mini-BARCA Campaign wRSOB12: Lee: Assimilation Of AMSR-E In The ACCESS Limited Area NWP Model wRSOB13: Li: Application Of The Multi-Grid Method To The 2-Dimensional Doppler Radar Radial Velocity Data Assimilation wRSOB14: Matsuo: Ensemble Kalman Filtering For Assimilation Of Upper Atmospheric Observations wRSOB15: Salonen: Impact Assessment Of Doppler Radar Radial Wind Observations wRSOB16: Tan: Preparing The ECMWF Forecast System For ADM-Aeolus Doppler Wind Lidar Data wRSOB17: Yudin: Resolution-Dependent Data Analysis In Remote Sensing, Numerical And Chemical Weather Applications

124

5th WMO Symposium On Data Assimilation Alphabetic list of Poster Authors

Jana, Rina 5 A Janjic, Tijana 6 Jia, Binghao 5 Allen, Stewart 6 Alves, Oscar 7 K Andreu-Burillo, Isabel 5 Aonashi, Kazumasa 7 Kaustubha Bhattacharya 3 Aravéquia, José 3 Kawabata, Takuya 2 Auligne, Thomas 2 Kelly, Graeme 2 Auroux, Didier 3 Kepert, Jeffrey 2 Kim, Hyun Mee 3 B Kleist, Daryl 3 Klimova, Ekaterina 1 Beggs, Helen 7 Kondrashov, Dmitri 5 Benedetti, Angela 1 Bishop, Craig 2 L Blayo, Eric 2 Bocquet, Marc 1, 7 Lakshmivarahan, S. 3 Brassington, Gary 6 Le Dimet, F.-X. 3 Broquet, Gregoire 6 Lea, Daniel 5 Brousseau, Pierre 2 Lee, Jin 8 Li, Wei 8 C Li, Xin 5 Li, Zhihong 2 Caron, Jean-Francois 3 Li, Zhijin 3, 6 Caya, Alain 5 Lindskog, M 3 Chattopadhyay, Mohar 7 Liu, Chengsi 2 Coman, Adriana 1 Liu, Yan 3 Cosme, Emmanuel 6 Lozza, Homero Fernando 5 Cot, Charles 7 Lu, Huijuan 3 Crow, Wade 5 M D Madsen, Henrik 1 De Lannoy, Gabriëlle 7 Matsuo, Tomoko 8 De Mey, Pierre 7 Maxwell, Deborah 5 Delle Monache, Luca 2 Mello, Raquel 6 Deschamps, Lilia 1 Menard, Richard 1 Desroziers, Gerald 2 Meng, Chunlei 5 Deushi, Makoto 1 Michel, Yann 3 Dixon, Mark 2 Migliorini, Stefano 2 Dobricic, Srdjan 3, 6 Mirouze, Isabelle 4 Dong, Jiarui 8 Mitchell, Lewis 1 Dong, Peiming 8 Miyazaki, Kazuyuki 1 Draper, Clara 5 Miyoshi, Takemasa 6 Drusch, Matthias 5 Moll, Patrick 7 Montmerle, Thibaut 3 E Moteki, Qoosaku 7

Engel, Chermelle 2 N

F Nakano, Shin'ya 1 Natarajan , Venkat Kumar 5 Fedorova, Irina 5 Nerger, Lars 1 Fletcher, Steven 5 Nezlin, Yulia 7 Fowler, Alison 3 Ng, Gene-Hua Crystal 2 Ngan, Keith 3 G Ngodock, Hans 4, 6 Nichols, Nancy 4 Gandhi, Mital 1 Nie, Suping 5 Garcia Triana, Ivan D. 7 Nishina, Kei 6 Gaytan Aguilar, Sandra 6 Gouweleeuw, Ben 5 O Greenslade, Diana 7 Greybush, Steven 3 O'Kane, Terry 2 Guidard, Vincent 8 Gunatilaka, Ajith 1 P

H Panteleev, Gleb 6, 7 Pecha, Petr 1 Han, Guijun 7 Pelc, Joanna 4 Han, Wei 3 Penny, Steve 6 Hanea, Remus Gabriel 2 Piccolo, Chiara 3 Herdies, Dirceu 8 Pipunic, Robert 5 Hirose, Naoki 6 Pokrovsky, Oleg 5, 7 Hoelzemann, Judith Johanna 1 Poulson, Jack 1 Hofman, Radek 1 Hoteit, Ibrahim 1, 6

I

Inoue, Jun 7 Ishikawa, Yoichi 6 Ito, Kosuke 2

J

125

R

Raynaud, Laure 2 Renzullo, Luigi 5 Ridler, Marc 5 Riishojgaard, Lars Peter 3 Ritchie, Harold 2 Richman, James 7 Rudiger, Christoph 5 Ruston, Benjamin 3

S

Sakov, Pavel 2 Salonen, Kirsti 8 Sébastien, Massart 1 Sekiyama, Thomas 1 Shi, Chunxiang 5 Shlyaeva, Anna 2 Sims, Holly 3 Smedstad, Ole Martin 6 Smith, Scott 5 Snyder, Chris 3 Soares, Ivan 6 Song, Hajoon 2 Storto, Andrea 7 Subbarayan, Sararavanan 5 Subramanian, Aneesh 2 Sun, Chaojiao 6 Sun, Xudong 3 Suzuki, Kazuyoshi 5

T

Takayama, Katsumi 6 Talagrand, Olivier 1 Tan, David 8 Tang, Charles 6 Tang, Xiao 1 Tingwell, Chris 3 Todling, Ricardo 4, 7 Torrisi, Lucio 3 Trojakova, Alena 3 Trudinger, Cathy 5 Tymofeiev, Vladyslav 6

U

Ueno, Genta 4 Usui, Norihisa 6

V

Van Dijk, Albert 6 Vandenbulcke, Luc 1 Verlaan, Martin 2 Verron, Jacques 2, 5

W

Wakamatsu, Tsuyoshi 6 Wedd, Robin 6 Wirth, Achim 6 Wojcik, Rafal 1

X

Xiao, Qingnong 2 Xie, Jiping 7 Xie, Yuanfu 2 Xie, Zhenghui 6 Xu, Liang 7

Y

Yan, Changxiang 7 Yang, Shu-Chih 3 Yaremchuk, Max 4 Yin, Yonghong 5 Yudin, Valery 8

Z

Zhang, Banglin 4 Zhang, Shaoqing 7 Zheng, Fei 3, 7 Zheng, Xiaogu 2 Zhuang, Zhao Rong 2

126 World Weather Research Programme (WWRP) Report Series

Sixth WMO Inte rnational Worksho p on Trop ical Cyclones (IWTC-VI), Sa n Jose, Costa Rica , 21 -30 Nove mber 200 6 (WMO TD No. 1383) (WWRP 2007 - 1).

Third WMO Int ernational Verification Workshop Emphasizing Training Aspects, ECMWF, Reading, UK, 29 Ja nuary - 2 February 2007) (WMO TD No. 1391) (WWRP 2007 - 2).

WMO International Training Workshop on Tropical Cyclone Disaster Reduction (Guangzhou, China, 26 - 31 March 2007) (WMO TD No. 1392) (WWRP 2007 - 3).

Report of the WMO/CAS Working Group on Tropical Meteorology Research (Guangzhou, China, 22-24 March 2007) (WMO TD No. 1393) (WWRP 2007 - 4).

Report of th e First Ses sion of th e Jo int Scientific C ommittee (JS C) for th e Wor ld Weather Research Pro gramme (WWRP), (Geneva, Switzerland, 23-25 April 2007) (WMO TD No. 1412) (WWRP 2007 – 5).

Report of the CAS Working Group on Tropical Meteorology Research (Shenzhen, China, 12-16 December 2005) (WMO TD No. 1414) (WWRP 2007 – 6).

Preprints of Abstracts of Papers for the Fourth WMO International Workshop on Monsoons (IWM-IV) (Beijing, China, 20- 25 October 2008) (WMO TD No. 1446) (WWRP 2008 – 1).

Proceedings of the Fo urth WMO International Workshop on Monsoons (IWM-IV) (Beijing, China, 20-25 October 2008) (WMO TD No. 1447) (WWRP 2008 – 2).

WMO Train ing Worksh op on Oper ational Monsoon Research a nd For ecast Issu es – Lect ure N otes, Beij ing, C hina, 24-25 October 2008 (WMO TD No. 1453) (WWRP 2008 – 3).

Expert Meeting to Evaluate Skill of Tropical Cyclone Seasonal Forecasts (Boulder, Colorado, USA, 24-25 April 2008) (WMO TD No. 1455) (WWRP 2008 – 4).

Recommendations for th e V erification and Intercom parison of QPFS a nd PQPFS fro m Oper ational NWP Mod els – Revision 2 - October 2008 (WMO TD No. 1485) (WWRP 2009 - 1).

Strategic Plan for the Implementation of WMO’s World Weather Research Programme (WWRP): 2009-2017 (WMO TD No. 1505) (WWRP 2009 – 2).

4th WMO International Verification Methods Workshop, Helsinki, Finland, 8-10 June 2009 (WMO TD No. 15 40) (WWRP 2010 - 1).

1st WMO I nternational C onference on I ndian Oce an Tro pical Cyclones a nd Climate Change, Mu scat, Su ltanate of Oman, 8-11 March 2009 (WMO TD No. 1541) (WWRP 2010 - 2).

Training Workshop on Tropical Cyclone Forecasting WMO Typhoon Landfall Forecast Demonstration Project, Shanghai, China, 24-28 May 2010 (WMO TD No. 1547 ) (WWRP 2010 - 3) (CD only).

2nd WMO International Workshop on Tropical Cyclone Landfall Processes (IWTCLP-II), Shanghai, China, 19-23 October 2009 (WMO TD No. 1548) (WWRP 2010 - 4).

127