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Assimilation de Données

Assimilation de Données

          
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About the Book

Cette thèse porte sur les méthodes d'assimilation de données, qui consistent à combiner des informations provenant d'un modèle dynamique avec des observations. Nous présentons des méthodes d'assimilation de données: l'interpolation statistique, les méthodes variationnelles et les méthodes séquentielles. Nous nous intéressons particulièrement au filtre de Kalman d'ensemble qui est de plus en plus utilisé dans les applications océanographiques. Nous démontrons que, quand le nombre d'éléments tend vers l'infini, dans le cas où la fonction du modèle dynamique est continue et localement lipschitzienne avec un accroissement au plus polynomial à l'infini, les éléments du filtre de Kalman d'ensemble convergent vers les éléments indépendants et identiquement distribués selon une loi qui diffère de la loi a posteriori du filtre bayésien optimal dans le cas général. Dans le cas du modèle linéaire gaussien, cette loi asymptotique n'est autre que la loi a posteriori du filtre de Kalman. Nous présentons aussi des résultats de simulations du filtre de Kalman d'ensemble et du filtre particulaire sur le modèle de Lorenz afin de comparer la performance des deux filtres.


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Product Details
  • ISBN-13: 9786131536823
  • Publisher: Omniscriptum
  • Publisher Imprint: Omniscriptum
  • Height: 229 mm
  • No of Pages: 180
  • Series Title: Omn.Univ.Europ.
  • Weight: 272 gr
  • ISBN-10: 6131536821
  • Publisher Date: 28 Feb 2018
  • Binding: Paperback
  • Language: French
  • Returnable: N
  • Spine Width: 10 mm
  • Width: 152 mm


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