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Evolutivity of the Reduced State Space and Data Assimilation Schemes Based on The Kalman Filter

Ibrahim Hoteit 1 Dinh-Tuan Pham 1
1 IDOPT - System identification and optimization in physics and environment
Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : UMR5527
Abstract : Brute-force implementation of the extended Kalman (EK) filter in realistic ocean models is not possible because of its prohibitive cost. Different degraded forms of the EK filter, which basically reduce the dimension of the system through some kind of projection onto a low dimensional subspace, have been proposed [1,6,10,11]. The goal of this paper is to study the usefulness of the evolution in time of these reduced order spaces. This is based on the comparison, both from the theoretical and practical points of view of the singular evolutive extended Kalman (SEEK) filter introduced by Pham et al.[25] and the reduced-order extended Kalman (ROEK) filter introduced by Cane et al.[1] To reduce the cost of the ROEK filter, we further approximate the nonlinear dynamics of the system by a first order autoregressive stochastic model. Finally, using a twin experiment approach, the above filters are compared in assimilation experiments with altimetric data in a realistic setting of the OPA model in the tropical Pacific ocean.
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https://hal.inria.fr/inria-00072304
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Submitted on : Tuesday, May 23, 2006 - 8:21:28 PM
Last modification on : Wednesday, November 4, 2020 - 2:45:19 PM
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Ibrahim Hoteit, Dinh-Tuan Pham. Evolutivity of the Reduced State Space and Data Assimilation Schemes Based on The Kalman Filter. [Research Report] RR-4283, INRIA. 2001. ⟨inria-00072304⟩

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