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Selection of dynamical model using analog data assimilation

Abstract : Data assimilation is a relevant framework to merge a dynamical model with noisy observations. When various models are in competition, the question is to find the model that best matches the observations. This matching can be measured by using the model evidence, defined by the likelihood of the observations given the model. This study explores the performance of model attribution based on model evidence computed using data-driven data assimilation, where dynamical models are emulated using machine learning methods.
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Submitted on : Friday, September 4, 2020 - 9:29:23 AM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM
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  • HAL Id : hal-02927329, version 1


Juan Ruiz, Pierre Ailliot, Thi Tuyet Trang Chau, Valérie Monbet, Pierre Tandeo. Selection of dynamical model using analog data assimilation. Climate Informatics 2020, Sep 2020, Oxford, United Kingdom. ⟨hal-02927329⟩



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