MODEL ERROR ESTIMATION IN ENSEMBLE DATA ASSIMILATION FRAMEWORK
MODEL ERROR ESTIMATION IN ENSEMBLE DATA ASSIMILATION FRAMEWORK Dusanka Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University Fort Collins, CO 80523 -1375 Acknowledgements: M. Zupanski, MLEF Dusanka Zupanski, CIRA/CSU Zupanski@CIRA. colostate. edu Do. D Center for Geosciences/Atmospheric Research at Colorado State University, Nov. 17 -18, 2003
Ens. DA framework 4 DVAR framework Forecast error covariance Observations First guess Data assimilation (Init. Cond. and Model Error adjust. ) Init. Cond. and Model Error opt. estimates Forecast error covariance Observations First guess Data assimilation (Init. Cond. and Model Error adjust. ) Analysis error Covariance (in ensemble subspace) Init. Cond. and Model Error opt. estimates Ens. forecasting In Ens. DA framework model error does not depend on assumptions regarding forecast error covariance; data assimilation problem is solved in ensemble subspace
State augmentation approach (a model bias example) Control variable for the analysis cycle k: Solve En. KF equations (or MLEF) equations in terms of control variable z and forecast model F : Dusanka Zupanski, CIRA/CSU Zupanski@CIRA. colostate. edu
Ens. DA experiments with Korteweg-de Vries-Burgers (Kd. VB) model - one-dimensional model - includes non-linear advection, diffusion and dispersion From Zupanski and Zupanski 2003 (submitted to MWR) Dusanka Zupanski, CIRA/CSU Zupanski@CIRA. colostate. edu
Ens. DA experiments with Kd. VB model Analysis error covariance matrix From Zupanski and Zupanski 2003 (submitted to MWR) Dusanka Zupanski, CIRA/CSU Zupanski@CIRA. colostate. edu
Dusanka Zupanski, CIRA/CSU Zupanski@CIRA. colostate. edu
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