Progressive assimilation of multiscale observations

Vincent Chabot 1, 2 Arthur Vidard 1 Maëlle Nodet 1
1 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. In this paper we propose an online scale selection algorithm that improves the convergence properties in such case.
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Communication dans un congrès
ICCS 2016 - International Conference on Computational Science, Nov 2016, Paris, France. 〈http://iccs2016.conferences-events.org〉
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Vincent Chabot, Arthur Vidard, Maëlle Nodet. Progressive assimilation of multiscale observations. ICCS 2016 - International Conference on Computational Science, Nov 2016, Paris, France. 〈http://iccs2016.conferences-events.org〉. 〈hal-01411753〉

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