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On model error in variational data assimilation

  • Victor Shutyaev EMAIL logo , Arthur Vidard , François-Xavier Le Dimet und Igor Gejadze
Veröffentlicht/Copyright: 28. März 2016

Abstract

The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find the initial condition. The optimal solution (analysis) error arises due to the errors in the input data (background and observation errors). Under the Gaussian assumption the optimal solution error covariance can be constructed using the Hessian of the auxiliary data assimilation problem. The aim of this paper is to study the evolution of model errors via data assimilation. The optimal solution error covariances are derived in the case of imperfect model and for the weak constraint formulation, when the model euations determine the cost functional.

MSC: 65K10

Funding

This work was carried out within the SAMOVAR project (CNRS-RAS), Russian Science Foundation project 14-11-00609 (studies in Section 3), and the project 15-01-01583 of the Russian Foundation for Basic Research.

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Received: 2015-12-25
Accepted: 2016-1-14
Published Online: 2016-3-28
Published in Print: 2016-4-1

© 2016 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 9.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/rnam-2016-0011/pdf
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