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arXiv · 0709.2825

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

Céline Robert·S. Durbiano·Eric Blayo·Jacques Verron·Jacques Blum·François-Xavier Le Dimet
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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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BibTeXRIS

Céline Robert, S. Durbiano, Eric Blayo, Jacques Verron, Jacques Blum, François-Xavier Le Dimet. 2007-09-18. A reduced-order strategy for 4D-Var data assimilation. https://doi.org/10.1016/j.jmarsys.2005.04.003

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