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Hybridisation of data assimilation methods for applications in oceanography

Monika Krysta 1 Eric Blayo 1 Emmanuel Cosme 2 Céline Robert 1 Jacques Verron 2 Arthur Vidard 1
1 MOISE - Modelling, Observations, Identification for Environmental Sciences
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : A data assimilation method based on variational approach is presented. The novelty of the hybrid method consists in a coupling of the cost function of the variational approach with an optimal linear smoother issued from the singular evolutive extended Kalman filter (SEEK). The background error covariance matrix of the usual variational framework remains unchanged. In the hybrid method, however, at each transition between the assimilation windows, it is replaced with the one provided by the smoother. The latter is updated whenever new background states are produced. It can be shown that the background states issued from an appropriately constructed variational framework and some particular optimal linear smoother are mathematically equivalent. Hence the matrix injection into the cost function is done in a consistent manner. The hybrid method has been implemented in a shallow water model which mimics a double-gyre circulation in the North Atlantic. Realistic OSSEs have been performed. Comparisons illustrate superiority of the 4D-Var-smoother hybrid over an ordinary 4D-Var on the one hand and on the other over the 4D-Var-filter hybrid.
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Submitted on : Thursday, December 4, 2008 - 10:31:38 PM
Last modification on : Friday, April 22, 2022 - 9:02:01 AM


  • HAL Id : inria-00344502, version 1



Monika Krysta, Eric Blayo, Emmanuel Cosme, Céline Robert, Jacques Verron, et al.. Hybridisation of data assimilation methods for applications in oceanography. 2008 Ocean Sciences Meeting, American Geophysical Union, Mar 2008, Orlando, United States. ⟨inria-00344502⟩



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