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* Corresponding author 1 IDOPT - System identification and optimization in physics and environment Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : UMR5527 2 LAMA - Laboratoire de Mathématiques 3 LMC - IMAG - Laboratoire de Modélisation et Calcul 4 LEGI - Laboratoire des écoulements géophysiques et industriels 5 JAD - Laboratoire Jean Alexandre Dieudonné

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.

Keywords : Tropical Pacific Ocean reduced order data assimilation method

Autor: Céline Robert - S. Durbiano - Eric Blayo - Jacques Verron - Jacques Blum - François-Xavier Le Dimet -

Fuente: https://hal.archives-ouvertes.fr/


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