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4D-Var/SEEK : A Consistent Hybrid Variational-Smoothing Data Assimilation Method

Fabrice Veersé 1 Dinh-Tuan Pham 1 Jacques Verron 1
1 IDOPT - System identification and optimization in physics and environment
Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : UMR5527
Abstract : A mixed variational-smoothing data assimilation method is derived under the perfect-model hypothesis. An incremental 4D-Var analysis scheme is supplemented with a low-rank approximation of its equivalent Kalman smoother, under the perfect-model assumption. A consistent method results, where the analysis and forecast error covariances provided by the smoother part describe the errors performed respectively during the incremental 4D-Var analysis and the model prediction phases. This is because the whole method is built around a low-rank approximation of the forecast error covariance matrix, and different hypotheses for the computation of the analysis and that of its error covariances are avoided. The method provides both flow-depen- dent analysis and forecast error covariances. In addition, most current developments for pre-operational or operational variational data assimilation systems are either already embedded within the method or may be straightforwar- dly included to it.
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https://hal.inria.fr/inria-00072752
Contributor : Rapport de Recherche Inria <>
Submitted on : Wednesday, May 24, 2006 - 10:48:31 AM
Last modification on : Wednesday, November 4, 2020 - 2:45:19 PM
Long-term archiving on: : Sunday, April 4, 2010 - 11:20:55 PM

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Fabrice Veersé, Dinh-Tuan Pham, Jacques Verron. 4D-Var/SEEK : A Consistent Hybrid Variational-Smoothing Data Assimilation Method. [Research Report] RR-3902, INRIA. 2000. ⟨inria-00072752⟩

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