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• W151965937 abstract "We present a technique for using data assimilation to estimate uncertain model parameters and discuss its application within the context of coastal morphodynamic modelling. A key difficulty in the construction of a data assimilation algorithm is specification of the background error covariances. For parameter estimation, it is particularly important that the cross-covariances between the parameters and the state are given a good a priori specification. We have combined the methods of three dimensional variational data assimilation (3D Var) and the Kalman filter to produce a new hybrid data assimilation scheme that captures the flow dependent nature of the state-parameter cross covariances without explicitly propagating the full system covariance matrix. Here, an idealised two parameter 1D non-linear test model with pseudo-observations is used to demonstrate the method. The results are postive with the scheme able to recover the model parameters to a high level of accuracy. We believe that there is potential for successful application of the methodology to larger, more complex models." @default.
• W151965937 created "2016-06-24" @default.
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• W151965937 date "2009-01-01" @default.
• W151965937 modified "2023-09-26" @default.
• W151965937 title "Data assimilation for morphodynamic model parameter estimation: a hybrid approach" @default.
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