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Identification of Optimal Topography by Variational Data Assimilation

Eugene Kazantsev 1 
1 MOISE - Modelling, Observations, Identification for Environmental Sciences
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : The use of data assimilation technique to identify optimal topography is discussed in frames of time-dependent motion governed by non-linear barotropic ocean model. Assimilation of artificially generated data allows to measure the influence of various error sources and to classify the impact of noise that is present in observational data and model parameters. The choice of assimilation window is discussed. Assimilating noisy data with longer windows provides higher accuracy of identified topography. The topography identified once by data assimilation can be successfully used for other model runs that start from other initial conditions and are situated in other parts of the model's attractor.
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Submitted on : Monday, December 1, 2008 - 11:01:50 AM
Last modification on : Thursday, January 20, 2022 - 5:28:01 PM
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Eugene Kazantsev. Identification of Optimal Topography by Variational Data Assimilation. Journal of Physical Mathematics, 2009, 1, pp.58-80. ⟨10.4303/jpm/S090702⟩. ⟨inria-00340394⟩



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