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 Br, 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 Br 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.
@article{arxiv.0709.2825,
title = {A reduced-order strategy for 4D-Var data assimilation},
author = {Céline Robert and S. Durbiano and Eric Blayo and Jacques Verron and Jacques Blum and François-Xavier Le Dimet},
journal= {arXiv preprint arXiv:0709.2825},
year = {2007}
}