Accounting for correlated observation errors in image assimilation

Maëlle Nodet 1, 2 Vincent Chabot 2 Arthur Vidard 2
2 MOISE - Modelling, Observations, Identification for Environmental Sciences
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Satellites images can provide a lot of information on the earth system evolution. Although those sequences are frequently used, the importance of spatial error correlation is rarely taken into account in practice. This results in discarding a huge part of the information content of satellite image sequences. In this talk, we investigate a method based on wavelet or curvelet transforms to represent (at an affordable cost) some of the observation error correlation in a data assimilation context. We address the topic of monitoring the initial state of a system through the variational assimilation of images corrupted by a spatially correlated noise. The feasibility and the reliability of the approach is demonstrated in an academic context with a 2D Shallow-Water model.
Type de document :
Communication dans un congrès
Réunion des Sciences de la Terre RST2014, Oct 2014, Pau, France. 2014
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https://hal.inria.fr/hal-01096782
Contributeur : Maëlle Nodet <>
Soumis le : jeudi 18 décembre 2014 - 11:29:52
Dernière modification le : mercredi 11 avril 2018 - 01:57:50

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  • HAL Id : hal-01096782, version 1

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Maëlle Nodet, Vincent Chabot, Arthur Vidard. Accounting for correlated observation errors in image assimilation. Réunion des Sciences de la Terre RST2014, Oct 2014, Pau, France. 2014. 〈hal-01096782〉

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