Computation of the analysis error covariance in variational data assimilation problems with nonlinear dynamics

DOI10.1016/j.jcp.2011.03.039zbMath1408.65035OpenAlexW1995518538MaRDI QIDQ655076

G. J. M. Copeland, Victor P. Shutyaev, François-Xavier Le Dimet, Igor Yu. Gejadze

Publication date: 28 December 2011

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2011.03.039

zbMATH Keywords

optimal controlMonte Carlononlinear dynamicsdata assimilationensemble methodsanalysis error covarianceinverse Hessianlarge-scale flow models

Mathematics Subject Classification ID

KdV equations (Korteweg-de Vries equations) (35Q53) Numerical methods for inverse problems for initial value and initial-boundary value problems involving PDEs (65M32) Numerical solution to inverse problems in abstract spaces (65J22)

Related Items (13)

Stability analysis of functionals in variational data assimilation with respect to uncertainties of input data for a sea thermodynamics model ⋮ Sensitivity of functionals with respect to observations in variational data assimilation ⋮ On model error in variational data assimilation ⋮ A Posteriori Error Estimates for the Solution of Variational Inverse Problems ⋮ An optimization framework to improve 4D-Var data assimilation system performance ⋮ On Gauss-verifiability of optimal solutions in variational data assimilation problems with nonlinear dynamics ⋮ Sensitivity with respect to observations in variational data assimilation ⋮ A 4D-Var method with flow-dependent background covariances for the shallow-water equations ⋮ On the use of derivatives in the polynomial chaos based global sensitivity and uncertainty analysis applied to the distributed parameter models ⋮ Hessian-based covariance approximations in variational data assimilation ⋮ Sensitivity of response functions in variational data assimilation for joint parameter and initial state estimation ⋮ A Multilevel Approach for Computing the Limited-Memory Hessian and its Inverse in Variational Data Assimilation ⋮ Optimal sensor placement for joint parameter and state estimation problems in large-scale dynamical systems with applications to thermo-mechanics

Uses Software

• TAPENADE

Cites Work

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• On the limited memory BFGS method for large scale optimization
• On optimal solution error covariances in variational data assimilation problems
• Regularized estimation of large covariance matrices
• On Newton methods in data assimilation
• Approximate iterative methods for variational data assimilation
• On Analysis Error Covariances in Variational Data Assimilation

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