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

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dc.creatorRobert, Céline
dc.creatorDurbiano, S.
dc.creatorBlayo, Eric
dc.creatorVerron, Jacques
dc.creatorBlum, Jacques
dc.creatorDimet, François-Xavier Le
dc.date2007-09-18
dc.date.accessioned2026-07-07T08:30:28Z
dc.date.available2026-07-07T08:30:28Z
dc.descriptionThis 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.
dc.identifierhttps://arxiv.org/abs/0709.2825
dc.identifierhttp://arxiv.org/abs/0709.2825
dc.identifierJournal of Marine Systems 57 (2005) 70-82
dc.identifierdoi:10.1016/j.jmarsys.2005.04.003
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/138251
dc.subjectGeophysics
dc.subjectAnalysis of PDEs
dc.subjectOptimization and Control
dc.titleA reduced-order strategy for 4D-Var data assimilation
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