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Localisation des méthodes d'assimilation de donnée d'ensemble

Abstract : Data assimilation is the mathematical discipline which gathers all the methods designed to improve the knowledge of the state of a dynamical system using both observations and modelling results of this system. In the geosciences, data assimilation it mainly applied to numerical weather prediction. It has been used in operational centres for several decades, and it has significantly contributed to the increase in quality of the forecasts.Ensemble methods are powerful tools to reduce the dimension of the data assimilation systems. Currently, the two most widespread classes of ensemble data assimilation methods are the ensemble Kalman filter (EnKF) and the particle filter (PF). The success of the EnKF in high-dimensional geophysical systems is largely due to the use of localisation. Localisation is based on the assumption that correlations between state variables in a dynamical system decrease at a fast rate with the distance. In this thesis, we have studied and improved localisation methods for ensemble data assimilation.The first part is dedicated to the implementation of localisation in the PF. The recent developments in local particle filtering are reviewed, and a generic and theoretical classification of local PF algorithms is introduced, with an emphasis on the advantages and drawbacks of each category. Alongside the classification, practical solutions to the difficulties of local particle filtering are suggested. The local PF algorithms are tested and compared using twin experiments with low- to medium-order systems. Finally, we consider the case study of the prediction of the tropospheric ozone using concentration measurements. Several data assimilation algorithms, including local PF algorithms, are applied to this problem and their performances are compared.The second part is dedicated to the implementation of covariance localisation in the EnKF. We show how covariance localisation can be efficiently implemented in the deterministic EnKF using an augmented ensemble. The proposed algorithm is tested using twin experiments with a medium-order model and satellite-like observations. Finally, the consistency of the deterministic EnKF with covariance localisation is studied in details. A new implementation is proposed and compared to the original one using twin experiments with low-order models
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Submitted on : Monday, February 22, 2021 - 4:46:11 PM
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  • HAL Id : tel-03149002, version 1



Alban Farchi. Localisation des méthodes d'assimilation de donnée d'ensemble. Ingénierie de l'environnement. Université Paris-Est, 2019. Français. ⟨NNT : 2019PESC1034⟩. ⟨tel-03149002⟩



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