Assimilation de mesures satellitaires dans des modèles numériques par méthodes de contrôle optimal

Abstract : Satellite data assimilation methods are investigated. There are mainly two kinds of technics: the sequential methods, derived from the Kalman filter and the variational methods, based on the adjoint equations of the optimal control theory. Variational methods are more recent. This work attempts to assess their potentialities in remote sensing through two examples. The first study, carried out with the advection-diffusion equation as a numerical model, demonstrates the feasibility of the variational methods. It is possible to minimize a cost function measuring the distance between the model trajectory and observations distributed in time and space. The second application is performed with actual observations of the Seasat satellite borne-wind scatterometer. Its aim is the mapping of the wind fields at the sea surface. Within a twelve hours time period, data are assimilated in a non linear numerical model. This model discretizes the vorticity equation on a rectangular domain covering approximatively the North Atlantic Ocean. A comparison between the wind fields obtained with the variational assimilation and those produced by a sequential assimilation highlights the adjoint equations ability to propagate information backward in time. The resulting analyzes are better, particulary at the beginning of the assimilation time period, and the analyzed fieds are consistent with the model dynamics.
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Submitted on : Monday, March 3, 2014 - 8:54:14 AM
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  • HAL Id : pastel-00954478, version 1

Citation

François van den Berghe. Assimilation de mesures satellitaires dans des modèles numériques par méthodes de contrôle optimal. Traitement du signal et de l'image [eess.SP]. École Nationale Supérieure des Mines de Paris, 1992. Français. ⟨pastel-00954478⟩

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