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A posteriori error covariances in variational data assimilation

Shutyaev, V.P. and Le Dimet, F.X. and Gejadze, I.Yu., Russian Foundation for Basic Research (Funder), MOISE Project (Funder), Scottish Funding Council via GRPE (Funder) (2009) A posteriori error covariances in variational data assimilation. Russian Journal of Numerical Analysis and Mathematical Modelling, 24 (2). pp. 161-169. ISSN 0927-6467

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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 control problem based on tangent linear model constraints.