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Adjoint methods in data assimilation for estimating model error

Griffith, A. K. and Nichols, N. (2000) Adjoint methods in data assimilation for estimating model error. Flow, Turbulence and Combustion, 65 (3/4). pp. 469-488. ISSN 1386-6184

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To link to this article DOI: 10.1023/A:1011454109203

Abstract/Summary

Data assimilation aims to incorporate measured observations into a dynamical system model in order to produce accurate estimates of all the current (and future) state variables of the system. The optimal estimates minimize a variational principle and can be found using adjoint methods. The model equations are treated as strong constraints on the problem. In reality, the model does not represent the system behaviour exactly and errors arise due to lack of resolution and inaccuracies in physical parameters, boundary conditions and forcing terms. A technique for estimating systematic and time-correlated errors as part of the variational assimilation procedure is described here. The modified method determines a correction term that compensates for model error and leads to improved predictions of the system states. The technique is illustrated in two test cases. Applications to the 1-D nonlinear shallow water equations demonstrate the effectiveness of the new procedure.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical and Physical Sciences > Department of Meteorology
Faculty of Science > School of Mathematical and Physical Sciences > Department of Mathematics and Statistics
ID Code:27471
Uncontrolled Keywords:data assimilation, adjoint methods, model error, bias estimation, nonlinear shallow water equations
Publisher:Springer

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