Adjoint Methods in Data Assimilation for Estimating Model Error |
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Authors: | AK Griffith NK Nichols |
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Institution: | (1) Department of Mathematics, The University of Reading, Box 220 Whiteknights, Reading, RG6 6AX, U.K. |
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Abstract: | 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.
This revised version was published online in July 2006 with corrections to the Cover Date. |
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Keywords: | data assimilation adjoint methods model error bias estimation nonlinear shallow water equations |
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