Taking into account correlated observation errors by progressive assimilation of multiscale information

Vincent Chabot 1, 2 Maëlle Nodet 2 Arthur Vidard 2
2 AIRSEA - Mathematics and computing applied to oceanic and atmospheric flows
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, UJF - Université Joseph Fourier - Grenoble 1, INPG - Institut National Polytechnique de Grenoble
Abstract : The description of correlated observation error statistics is a challenge in data assimilation. Currently, the observation errors are assumed uncorrelated (the covariance matrix is diagonal) which is a severe approximation that leads to suboptimal results. It is possible to use multi-scale transformations to retain the diagonal matrix approximation while accounting for some correlation. However this approach can lead to some convergence problems due to scale interactions. We propose an online scale selection algorithm that improves the convergence properties in such case.
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Poster
American Geophysical Union Fall Meeting, Dec 2016, San Francisco, United States. 2016
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Vincent Chabot, Maëlle Nodet, Arthur Vidard. Taking into account correlated observation errors by progressive assimilation of multiscale information. American Geophysical Union Fall Meeting, Dec 2016, San Francisco, United States. 2016. 〈hal-01402906〉

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