Assimilation of Image Sequences in Numerical Models

Olivier Titaud 1 Arthur Vidard 1 Innocent Souopgui 1 François-Xavier Le Dimet 1
1 MOISE - Modelling, Observations, Identification for Environmental Sciences
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Understanding and forecasting the evolution of geophysical fluids is a major scientific and societal challenge. Forecasting algorithms should take into account all the available information on the considered dynamic system. The variational data assimilation (VDA) technique combines all these informations in an optimality system (O.S.) in a consistent way to reconstruct the model inputs. VDA is currently used by the major meteorological centres. During the last two decades about 30 satellites were launched to improve the knowledge of the atmosphere and of the oceans. They continuously provide a huge amount of data that are still underused by numerical forecast systems. In particular, the dynamic evolution of certain meteorological or oceanic features (such as eddies, fronts, etc.) that the human vision may easily detect is not optimally taken into account in realistic applications of VDA. Image Assimilation in VDA framework can be performed using 'pseudo-observation' techniques: they provide apparent velocity fields, which are assimilated as classical observations. These measurements are obtained by certain external procedures, which are decoupled with the considered dynamic system. In this paper, we suggest a more consistent approach, which directly incorporates image sequences into the O.S.
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Journal articles
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Submitted on : Friday, June 5, 2009 - 10:16:36 AM
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Olivier Titaud, Arthur Vidard, Innocent Souopgui, François-Xavier Le Dimet. Assimilation of Image Sequences in Numerical Models. Tellus A, Co-Action Publishing, 2010, 62 (1), pp.30-47. ⟨10.1111/j.1600-0870.2009.00416.x⟩. ⟨inria-00391876⟩

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