A reduced-order strategy for 4D-Var data assimilation

Abstract : This paper presents a reduced-order approach for four-dimensional variational data assimilation, based on a prior EO F analysis of a model trajectory. This method implies two main advantages: a natural model-based definition of a mul tivariate background error covariance matrix $\textbf{B}_r$, and an important decrease of the computational burden o f the method, due to the drastic reduction of the dimension of the control space. % An illustration of the feasibility and the effectiveness of this method is given in the academic framework of twin experiments for a model of the equatorial Pacific ocean. It is shown that the multivariate aspect of $\textbf{B}_r$ brings additional information which substantially improves the identification procedure. Moreover the computational cost can be decreased by one order of magnitude with regard to the full-space 4D-Var method.
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Contributeur : Céline Acary-Robert <>
Soumis le : mardi 18 septembre 2007 - 14:51:32
Dernière modification le : mardi 27 novembre 2018 - 16:38:02
Document(s) archivé(s) le : vendredi 9 avril 2010 - 02:23:32


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Céline Robert, S. Durbiano, Eric Blayo, Jacques Verron, Jacques Blum, et al.. A reduced-order strategy for 4D-Var data assimilation. Journal of Marine Systems, Elsevier, 2005, 57, pp.70-82. 〈10.1016/j.jmarsys.2005.04.003〉. 〈hal-00172943〉



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