Efficient Reduced Kalman Filtering and Application to Altimetric Data Assimilation in Tropical Pacific

Ibrahim Hoteit 1 Dinh-Tuan Pham 1 Jacques Blum 1
1 IDOPT - System identification and optimization in physics and environment
Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : UMR5527
Abstract : Several studies have demonstrated the effectiveness of the Singular Evolutive Extended Kalman (SEEK) filter and its interpolated variant called SEIK in their capacity to assimilate altimetric data into ocean models. However, these filters remain expensive for real operational assimilation. The purpose of this paper is to develop degraded forms of the SEIK filter which are less costly and yet perform reasonably well. Our approach essentiall- y consists in simplifying the evolution of the correction basis of the SEIK filter, which is the most expensive part of this filter. To deal with model unstabilities, we also introduce two adaptive tuning schemes based on the correction basis evolution and the use of a variable forgetting factor. The filters have been implemented in a realistic setting of the OPA model over the tropical pacific zone and their performance studied through twin experiments in which the observations are taken to be synthetic altimeter data sampled on the sea surface. The SEIK filter is used as a reference for comparison. Our new filters perform nearly as well as the SEIK, but can be 2 to 10 times faster.
Type de document :
Rapport
[Research Report] RR-3937, INRIA. 2000
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https://hal.inria.fr/inria-00072715
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Soumis le : mercredi 24 mai 2006 - 10:41:23
Dernière modification le : jeudi 11 janvier 2018 - 06:20:05
Document(s) archivé(s) le : dimanche 4 avril 2010 - 23:19:25

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Ibrahim Hoteit, Dinh-Tuan Pham, Jacques Blum. Efficient Reduced Kalman Filtering and Application to Altimetric Data Assimilation in Tropical Pacific. [Research Report] RR-3937, INRIA. 2000. 〈inria-00072715〉

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