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Etude et développement d'algorithmes d'assimilation de données variationnelle adaptés aux modèles couplés océan-atmosphère

Abstract : In the context of operational meteorology and oceanography, forecast skills heavily rely on the model used and its initial state. This initial state is produced by a proper combination of model dynamics and available observations via data assimilation techniques. Historically, numerical weather prediction is made separately for the ocean and the atmosphere in an uncoupled way. However, in recent years, fully coupled ocean-atmosphere models are increasingly used in operational centres. Yet the use of separated data assimilation schemes in each medium is not satisfactory for coupled problems. Indeed, the result of such assimilation process is generally inconsistent across the interface, thus leading to unacceptable artefacts. Hence, there is a strong need for adapting existing data assimilation techniques to the coupled framework. This PhD thesis is related to this context and is part of the FP7 ERA-Clim2 project, which aim to produce an earth system global reanalysis.We first introduce data assimilation and model coupling concepts, followed by some existing algorithms of coupled data assimilation. Since these methods are not satisfactory in terms of coupling strengh or numerical cost, we suggest, in a second part, some alternatives. These are based on optimal control theory and differ by the choice of the cost function to minimize, controled variable and coupling algorithm used. A theoretical study of these algorithms exhibits a necessary and sufficient convergence criterion in a linear case. To conclude about this second part, the different methods are compared in terms of analysis quality and numerical cost using a 1D linear model. In a third part, a 1D non-linear model with subgrid parametrizations was developed and implemented in OOPS (Object-Oriented Prediction System), a software overlay allowing the implementation of a set of data assimilation algorithms. We then assess the robustness of the different algorithms in a more realistic case, and concluded about their performances against existing methods. By implementing our methods in OOPS, we hope it should be easier to use them with operational forecast models. Finally, we expose some propects for improving these algorithms.
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Submitted on : Wednesday, October 17, 2018 - 9:59:20 AM
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Rémi Pellerej. Etude et développement d'algorithmes d'assimilation de données variationnelle adaptés aux modèles couplés océan-atmosphère. Théorie de l'information et codage [math.IT]. Université Grenoble Alpes, 2018. Français. ⟨NNT : 2018GREAM017⟩. ⟨tel-01806281v2⟩

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