On Analysis Error Covariances in Variational Data Assimilation

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 eq...

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Bibliographic Details
Published in:SIAM journal on scientific computing 2008-01, Vol.30 (4), p.1847-1874
Main Authors: Gejadze, I. Yu, Dimet, F.-X. Le, Shutyaev, V.
Format: Article
Language:English
Subjects:
Algorithms
Applied mathematics
Data assimilation
Hilbert space
Mathematics
Optimization and Control
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Summary: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 used to show that in a nonlinear case the analysis error covariance operator can be approximated by the inverse Hessian of an auxiliary data assimilation problem which involves the tangent linear model constraints. The inverse Hessian is constructed by the quasi-Newton BFGS algorithm when solving the auxiliary data assimilation problem. A fully nonlinear ensemble procedure is developed to verify the accuracy of the proposed algorithm. Numerical examples are presented.
ISSN:1064-8275
1095-7197
DOI:10.1137/07068744X