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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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Contributor : Céline Acary-Robert <>
Submitted on : Tuesday, September 18, 2007 - 2:51:32 PM
Last modification on : Wednesday, March 10, 2021 - 1:50:03 PM
Long-term archiving on: : Friday, April 9, 2010 - 2:23:32 AM


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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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