Data assimilation for geophysical fluids
Annales de la Faculté des sciences de Toulouse : Mathématiques, Serie 6, Volume 26 (2017) no. 4, pp. 767-793.

Data assimilation is the domain at the interface between observations and models, which makes it possible to identify the global structure of a geophysical system from a set of discrete space-time data. After recalling state-of-the-art data assimilation methods, the variational 4D-VAR algorithm and the dual variational 4D-PSAS algorithm, and sequential Kalman filters, we will present the Back and Forth Nudging (BFN) algorithm, and the Diffusive Back and Forth Nudging (DBFN) algorithm, which is a natural extension of the BFN to some particular diffusive models.

L’assimilation de données est l’ensemble des techniques qui permettent de combiner un modèle et des observations. Le but est ici d’identifier l’état d’un système géophysique à partir de données discrètes en temps et en espace. Après un rappel de l’état de l’art en assimilation de données (méthode variationnelle 4D-VAR et approche duale 4D-PSAS, filtres séquentiels de type Kalman), nous présentons l’algorithme du nudging direct et rétrograde, ainsi que son extension naturelle (le nudging direct et rétrograde diffusif) à certains modèles géophysiques contenant un terme de diffusion.

Published online:
DOI: 10.5802/afst.1552

Didier Auroux 1

1 Université Côte d’Azur, Inria, CNRS, LJAD, France
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights
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Didier Auroux. Data assimilation for geophysical fluids. Annales de la Faculté des sciences de Toulouse : Mathématiques, Serie 6, Volume 26 (2017) no. 4, pp. 767-793. doi : 10.5802/afst.1552. https://afst.centre-mersenne.org/articles/10.5802/afst.1552/

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