Reduced-space inverse Hessian for analysis error covariances in variational data assimilation

Abstract : The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find the initial condition function (analysis). The equation for the analysis error is derived through the errors of the input data (background and observation errors). This equation is considered in a reduced control space to show that the analysis error covariance operator can be approximated by the inverse Hessian of an auxiliary data assimilation problem based on the tangent linear model constraints. The reduced-space Hessian is constructed in the explicit form, which allows an efficient computation of the analysis error covariance operator.
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Russian Journal of Numerical Analysis and Mathematical Modelling, De Gruyter, 2010, 25 (2), pp.169-185. 〈10.1515/rjnamm.2010.011〉
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Soumis le : lundi 11 février 2013 - 17:11:28
Dernière modification le : mercredi 11 avril 2018 - 01:57:45

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Victor P. Shutyaev, François-Xavier Le Dimet, Igor Yu. Gejadze. Reduced-space inverse Hessian for analysis error covariances in variational data assimilation. Russian Journal of Numerical Analysis and Mathematical Modelling, De Gruyter, 2010, 25 (2), pp.169-185. 〈10.1515/rjnamm.2010.011〉. 〈hal-00787295〉

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