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A posteriori error covariances in variational data assimilation

Victor P. Shutyaev 1 François-Xavier Le Dimet 2 Igor Yu Gejadze 3
2 MOISE - Modelling, Observations, Identification for Environmental Sciences
Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
Abstract : The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find some unknown parameters of the model. The equation for the error of the optimal solution is derived through the statistical errors of the input data (background, observation, and model errors). A numerical algorithm is developed to construct an a posteriori covariance operator of the analysis error using the Hessian of an auxiliary optimal control problem based on the tangent linear model constraints.
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https://hal.inria.fr/inria-00391869
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Submitted on : Friday, June 5, 2009 - 10:16:19 AM
Last modification on : Tuesday, October 19, 2021 - 11:12:57 PM

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Victor P. Shutyaev, François-Xavier Le Dimet, Igor Yu Gejadze. A posteriori error covariances in variational data assimilation. Russian Journal of Numerical Analysis and Mathematical Modelling, De Gruyter, 2009, 24 (2), pp.161-169. ⟨10.1515/RJNAMM.2009.011⟩. ⟨inria-00391869⟩

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