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An Adaptively Reduced Order Extended Kalman Filter for Data Assimilation in the Tropical Pacific

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 : The reduced-order extended Kalman (ROEK) filter has been introduced by Cane et al.[3] as a means to reduce the cost of the extended Kalman (EK) filter. It essentially consists in projecting the dynamic of the model onto a low dimensional subspace obtained via an empirical orthogonal functions (EOF) analysis. However, the choice of the dimension of the reduced state space (or the number of EOFs to be retained) remains a delicate question. Indeed, Cane et al.[3] have been surprised by the fact that increasing the number of EOFs does not improve, and even sometimes worsen, the performance of the ROEK filter. We suspect that it is due to the optimal character of the EOF analysis which is optimal in a time-mean sense only. In this respect, we develop a simple efficient adaptive scheme to tune, according to the model mode, the dimension of the reduced state space, which would be variable in time. In a first application, twin experiments are conducted in a realistic setting of the OPA model in the tropical pacific. The observations are assumed to be synthetic altimeter data sampled according to the Topex/Poseidon mission features. Our adaptive scheme is shown to improve the performance of the ROEK filter especially during the model unstable periods.
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Submitted on : Tuesday, May 23, 2006 - 8:19:13 PM
Last modification on : Friday, February 4, 2022 - 3:22:45 AM
Long-term archiving on: : Sunday, April 4, 2010 - 11:02:17 PM


  • HAL Id : inria-00072289, version 1



Ibrahim Hoteit, Dinh-Tuan Pham. An Adaptively Reduced Order Extended Kalman Filter for Data Assimilation in the Tropical Pacific. [Research Report] RR-4298, INRIA. 2001. ⟨inria-00072289⟩



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