Adjoint to the Hessian derivative and error covariances in variational data assimilation

V. P. Shutyaev, I. Y. Gejadze

Research output: Contribution to journalArticle

4 Citations (Scopus)

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. The optimal solution error is considered through the errors of input data (background and observation errors). The optimal solution error covariance operator is approximated by the inverse Hessian of the auxiliary (linearized) data assimilation problem, which involves the tangent linear model constraints. We show that the derivative of the inverse Hessian with respect to the exact solution may be treated as the measure of nonlinearity for analysis error covariances in variational data assimilation problems.
LanguageEnglish
Pages179-188
Number of pages10
JournalRussian Journal of Numerical Analysis and Mathematical Modelling
Volume26
Issue number2
DOIs
Publication statusPublished - Apr 2011

Fingerprint

Data Assimilation
Derivatives
Derivative
Optimal Solution
Covariance Operator
Error Analysis
Tangent line
Error analysis
Optimal Control Problem
Linear Model
Initial conditions
Exact Solution
Nonlinearity

Keywords

  • theoretical aspects
  • adjoint
  • hessian derivative
  • error covariances
  • variational data
  • assimilation

Cite this

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Adjoint to the Hessian derivative and error covariances in variational data assimilation. / Shutyaev, V. P.; Gejadze, I. Y.

In: Russian Journal of Numerical Analysis and Mathematical Modelling, Vol. 26, No. 2, 04.2011, p. 179-188.

Research output: Contribution to journalArticle

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