Some Variants to the Singular Evolutive Extended Kalman (SEEK) Filter for Data Assimilation

Thursday, May 2, 2002 - 3:30pm - 4:00pm
Keller 3-180
Dinh-Tuan Pham (Centre National de la Recherche Scientifique (CNRS))
Joint work with Ibrahim Hoteit.

In this talk we introduce some variants to the Singular Evolutive Extended Kalman (SEEK) filter which has been proposed for Data Assimilation. We shall begin with the Singular Evolutive Interpolated Kalman (SEIK) filter, in which the model and observation operator is not linearized but interpolated. This filter also makes use of the Monte-Carlo drawing and thus possesses some similarities to the Ensemble Kalman filter (EnKF). Then we introduce the semi-evolutive filter in which only a small part of the correction basis evolves while the rest remain fixed. This helps reducing drastically the computation cost with only some degradation on performance. Finally, we introduce the concept of local correction basis. The use of such basis combined with the usual global basis to form a the idea of semi-evolutivity leads us to the so called semi-evolutive partially local Kalman filter, which has better performance than the SEIK filter with a lower cost. Some simulations are presented, concerning twin experiments of altimetric data assimilation to the OPA model for the Pacific ocean, illustrating our methods.