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Communication Dans Un Congrès Année : 2017

Analog assimilation for high-dimensional geophysical dynamics

Résumé

Analog assimilation has recently been introduced as a data-driven alternative to model-driven data assimilation. The key idea is to build an exemplar-based dynamical model from a representative dataset of exemplars of the considered state-space. Here, we specifically address and discuss analog assimilation for high-dimensionsal state-space, more specifically spatio-temporal fields. We introduce a novel model, which combines a patch-based representation to a multi-scale and PCA-based decomposition. This model amounts to decomposing the global high-dimensional assimilation problem into a series of local low-dimensional assimilation problems. We demonstrate its relevance through an application to the reconstruction of spatio-temporal fields from irregularly-sampled observations. As case-study, we consider the spatio-temporal interpolation of satellite-derived sea surface geophysical fields. We further discuss large-scale implementation issues associated with the analog assimilation.
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Dates et versions

hal-01586349 , version 1 (12-09-2017)

Identifiants

  • HAL Id : hal-01586349 , version 1

Citer

Ronan Fablet, Redouane Lguensat, Phi Huynh Viet, Pierre Ailliot, Bertrand Chapron, et al.. Analog assimilation for high-dimensional geophysical dynamics. DSE 2017 : Workshop on Data Science and Environment, Jul 2017, Brest, France. ⟨hal-01586349⟩
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