Annual seminar on Recent developments in data assimilation for atmosphere and ocean

The one week seminar in 2003 was on Recent developments in data assimilation for atmosphere and ocean. The seminar was held from 8 to 12 September.

Description

Three dimensional variational assimilation (3D-Var) methods have been widely adopted for operational use, and there is widening operational adoption of four-dimensional variational assimilation (4D-Var) methods.

The purpose of the seminar was to give a pedagogical overview of recent developments in atmospheric and ocean data assimilation, and to outline the likely lines of development in the next five to ten years, including implementation of more complete physical representations in 4D-Var, longer assimilation windows, reduced rank Kalman filters, ensemble Kalman filters, and diagnostics.

Recent developments in data assimilation of relevance to ensemble forecasting were presented. In addition there were discussions on modelling and assimilating data on variations in atmospheric composition due to trace gases and aerosol.

Presentations

Overview  

Observations, assimilation and the improvement of global weather prediction

A Simmons (ECMWF)

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Variational data assimilation: theory and overview

F Rabier (Meteo-France)

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Background error covariance modelling

M Fisher (ECMWF)

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Detection and correction of model bias during data assimilation

D Dee (ECMWF, on leave from NASA-GMAO)

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Satellite data assimilation overview

J-N Thepaut (ECMWF)

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Validation and diagnostics  

Overview of observing system experiments

R Dumelow (UKMO)

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Objective validation and evaluation of data assimilation

O Talagrand (LMD)

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Influence matrix diagnostic to monitor the assimilation system

C Cardinali (ECMWF)

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Modelling of innovation statistics Including physical processes

E Andersson (ECMWF)

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Land data assimilation

P Houser (NASA-OH)

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Physical processes in adjoint models: potential pitfalls and benefits

M Janiskova (ECMWF)

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Flow dependent Jb in a global grid-point 3D-Var Ensemble data assimilation

J Derber (NOAA)

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Practical ensemble data assimilation

P Houtekamer (CMC)

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Relative merits of 4D-Var and Ensemble methods

A Lorenc (Met Office)

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The ensemble Kalman filter: theoretical formulation and practical implementation

G Evensen (Nansen)

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Nonlinear ensemble data assimilation for the ocean

P J van Leeuwen (University of Utrecht)

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Other new developments  

Constructing a background error covariance model for variational ocean data assimilation

A Weaver (CERFACS)

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Ocean Data Assimilation for Seasonal Forecasts

M Balmaseda (ECMWF)

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Stratosphere and chemistry

H Eskes (KNMI)

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Model error in variational data assimilation

Y Tremolet (ECMWF)

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The reduced rank Kalman filter based on balanced truncation

B Farrell (Harvard)

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Adaptive observations, the Hessian metric and singular vectors

M Leutbecher (ECMWF)

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Realism of sensitivity patterns

L Isaksen (ECMWF)

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Applications  

Current limited area applications

N Gustafsson (SMHI)

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The AROME mesoscale project   

F Bouttier (Meteo-France)

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The GEMS perspective

A Hollingsworth (ECMWF)

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Proceedings

Modelling the temporal evolution of innovation statistics

E Andersson

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Ocean data assimilation for seasonal forecasts

M A Balmeseda

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The AROME mesoscale project

F Bouttier

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Influence matrix diagnostic of a data assimilation system

C Cardinali

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Detection and correction of model bias during data assimilation

D Dee

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Flow dependent Jb in grid-point 3D-Var

J C Derber

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Overview of observing system experiments

R Dumelow

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Stratospheric ozone: satellite observations, data assimilation and forecasts

H Eskes

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The Ensemble Kalman Filter: theoretical formulation and practical implementation

G Evensen

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Approximating optimal state estimation

B Farrell

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Background error covariance modelling

M Fisher

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Current limited area applications

N Gustafsson

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GEMS - global earth-system monitoring using space and in-situ data

A Hollingsworth

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Practical ensemble data assimilation

P L Houtekamer

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Realism of sensitivity patterns

L Isaksen

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Physical processes in adjoint models: potential pitfalls and benefits

M Janiskova

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Adaptive observations, the Hessian metric and singular vectors

M Leutbecher

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Relative merits of 4D-Var and Ensemble Kalman Filter

A C Lorenc

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Variational data assimilation: theory and overview

F Rabier

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Observations, assimilation and the improvement of global weather prediction - some results from operational forecasting and ERA-40

A Simmons

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Objective validation and evaluation of data assimilation

O Talagrand

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Satellite data assimilation in numerical weather prediction: an overview

J-N Thepaut

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Model error in variational data assimilation

Y Tremolet

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Constructing a background-error correlation model using generalized diffusion operators

A T Weaver

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Nonlinear ensemble data assimilation for the ocean

P J van Leeuwen

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Local information


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