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Efficient Bayesian data assimilation via inverse regression

Abstract : We propose a Bayesian approach to data assimilation problems, involving two steps. We first approximate the forward physical model with a parametric invertible model, and we then use its properties to leverage the availaibility of a priori information. This approach is particularly suitable when a large number of inversions has to be performed. We illustrate the proposed methodology on a multilayer snowpack model.
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Contributor : Benoit Kugler <>
Submitted on : Monday, May 3, 2021 - 11:11:35 AM
Last modification on : Friday, May 28, 2021 - 9:28:15 AM


Efficient Bayesian data assimi...
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  • HAL Id : hal-03215200, version 1


Benoit Kugler, Florence Forbes, Sylvain Douté, Michel Gay. Efficient Bayesian data assimilation via inverse regression. SFdS 2020 - 52èmes Journées de Statistiques de la Société Française de Statistique, Jun 2021, Nice, France. pp.1-6. ⟨hal-03215200⟩



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