8:30 - Welcome |
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Session A Theory of data assimilation |
8:45 - Talagrand O. (Invited) |
On the basics of data assimilation |
9:35 - Lorenc A. (Invited) |
Practical modelling of time-evolved covariances |
10:25 Break (20 min) |
10:45 Buehner M. |
Use of Ensemble Statistics in Variational Data Assimilation |
Session B Ensemble Kalman filtering |
11:05 Mitchell H. (Invited) |
An ensemble Kalman filter for operational atmospheric data assimilation |
12:00 13:20 Lunch in the Dinning Room |
13:20 Bishop C. |
Amelioration of bias in the Ensemble Transform Kalman Filter |
13:40 Anderson J. |
A Self-Calibrating, Error-Tolerant Ensemble Filtering System |
14:00 McLaughlin D. |
Optimality of the Ensemble Kalman Filter for Land Surface Data Assimilation |
Session C Observation-space data assimilation methods and diagnostics |
14:20 Courtier P. (Invited) |
(T.B.A.) |
15:10 Break (20 min) |
15:30 Dee D. (Invited) |
Statistical diagnostics in observation space |
16:20 Tranchant B. |
The Mercator Assimilation System (SAM): A modular assimilation system for operational oceanography
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16:40 Xu L. |
Formulation and Initial Testing of NAVDAS-AR |
17:00 Cardinali C. |
Influence matrix diagnostic for monitoring assimilation system performance |
17:20 End of oral presentations for the day |
18:00 19:30 Buffet Dinner in the Dinning Room |
19:30 21:00 Poster session (Reading Room) |
Session D Operational data assimilation systems |
8:30 - Hollingsworth A. (Invited) |
Reflections on Roger Daley’s work at ECMWF |
9:20 - Barker E. (Invited) |
The Navy Atmospheric Variational Data Assimilation System |
10:10 Gauthier P. |
Preliminary results with the pre-operational 4D variational assimilation system of the Meteorological Service of Canada
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10:30 Break (20 min) |
10:50 Purser J. |
A scheme for the characterization and synthesis of anisotropic background error covariances suitable for adaptive variational assimilation
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11:10 Weaver A. |
Multivariate, flow-dependent covariance modeling for variational ocean data assimilation |
11:30 Li Z. |
A three-dimensional variational data assimilation system for coastal oceans |
11:50 Barker D. |
A 3DVAR system for the mesoscale data assimilation research and operational communities |
12:10 13:30 Buffet lunch in the Dinning Room |
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Session E Balance |
13:30 Errico R, and S. Polavarapu (Invited) |
The continuing importance of considering dynamical balances |
14:20 Neef L. |
Four-dimensional data assimilation and balanced dynamics: Illustrations with a simple model |
14:40 Machenhauer B. |
Data-assimilation problems in connection with the determination of systematic initial model tendency errors from six-hourly reanalyzes
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15:00 Aonashi K. |
Variational assimilation of TMI rain type and precipitation retrievals into global numerical weather prediction
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15:20 Clayton A. |
Gravity wave control in the UK Met Office’s 4D-Var system |
15:40 Break (20 min) |
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Session F- Sensitivity analysis |
16:00 Baker N. (Invited) |
Sensitivity Analysis in the Context of Data Assimilation |
16:50 Reynolds C. |
Singular vectors with an analysis variance metric |
17:10 Mahfouf J.-F. |
Linearized diabatic processes for variational data assimilation |
Session G- Land-surface assimilation |
17:30 Balsamo G. |
Simplified 2D-VAR analysis of soil moisture from screen-level observations for the initialization of NWP models
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17:50 Sun C. |
Uncertainty analysis of passive microwave snow water equivalent observations and its assimilation into a land surface model
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18:10 - End of oral presentations for the day |
19:00 21:00 Banquet in the Dinning Room |
Session H New developments in data assimilation |
8:30 Menard R. (Invited) |
Evolution of error variances for chemical data assimilation |
9:20 Khattatov B. |
Utilization of satellite data in tropospheric chemistry and transport modeling: Assimilation, bias estimation and surface fluxes inversion. |
9:40 Polavarapu S. |
Development of a middle atmosphere data assimilation system |
10:00 Houben H. |
Planetary meteorology by onboard sequential assimilation |
10:20 Break (20min) |
10:40 Matsuo T. |
Optimal interpolation analysis of high-altitude ionospheric electrodynamic variables using EOF bases and maximum likelihood method for error covariance estimation.
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11:00 Karspeck A. |
Low-dimensional adjoint approach to initializing El Nino forecasts |
11:20 - Adjourn |
Carrassi A. |
Data-Assimilation and the stability of the observation-analysis-forecast cycle solution
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Deng X. |
Application of the mother-daughter approach to the coastal mountains in BC
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Heimbach P. |
The ECCO ocean state estimation system: a dynamical consistent WOCE-data versus MITgcm-model synthesis based on the adjoint technique
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Pétron G. |
Inverse Modeling of CO Sources Using MOPITT Data
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Kerry S. R. |
Data Assimilation by Field Alignment
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Ricci S. |
I n c o r p o r ating flow- d e p e n d e n t Te m p e r ature - Salinity constraints in the background error covariance of va r i ational ocean data assimilat i o n
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Robichaud A. |
An objective analysis scheme of surface ozone at CMC
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Syndergaard S. |
An observation operator for the assimilation of satellite occultation data
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Tangborn A. |
Experimental Studies in Assimilation of Wind Speed Observations
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Wakamatsu T. |
Estimating North Pacific ocean circulation with the representer data assimilation technique
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Wan J. |
Estimation of Initial Condition and Parameter in Physical ”On-Off” Processes by Adjoint Variational Data Assimilation
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Xu Q, |
New Statistical Analyses of Innovation Vectors
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Xiaobao Y. |
An Ocean Data Assimilation System for the the China Sea
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Zhou G. |
Estimating background error covariances from model outputs for oceanic data assimilation
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