On analysis error covariances in variational data assimilation

I.Y. Gejadze, F. Le-Dimet, V. Shutyaev

Research output: Contribution to journalArticle

51 Citations (Scopus)
16 Downloads (Pure)

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 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.
Original languageEnglish
Pages (from-to)1847-1874
Number of pages28
JournalSIAM Journal on Scientific Computing
Volume30
Issue number4
Early online date2 May 2008
DOIs
Publication statusPublished - Jun 2008

Keywords

  • data assimilation
  • optimal control
  • analysis error
  • hessian
  • covariance operator

Fingerprint Dive into the research topics of 'On analysis error covariances in variational data assimilation'. Together they form a unique fingerprint.

Cite this