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Etude de la météorologie de la planète Mars par assimilation de données satellite et modélisation

Abstract : The study of the atmosphere of Mars is based on the understanding of its thermal structure and the cycle of its aerosols (airborne dust and water ice). To do so, one preferred method is the use of a global climate model, compared to satellite observations.In this thesis, I employ the Laboratoire de Météorologie Dynamique (LMD) global climate model, first including a microphysical scheme of water ice clouds, resulting in a better reproduction of satellite observations of the Martian water cycle.I continue this study with the setup of a data assimilation scheme for the LMD model. Data assimilation is a technique that estimates the atmospheric state using an optimal combination of a model and observations. The goal is to assimilate observations of vertical profiles of temperature and aerosols from the instrument Mars Climate Sounder with an ensemble Kalman filter technique. The assimilation of temperature only shows the necessity to estimate dust using the ensemble covariances in order to decrease the bias between model and observations. The joint assimilation of vertical profiles of temperature, dust, and water ice shows that the assimilation performance is limited due to model biases: an incorrect phasing of the thermal tide, unexplained observed dust diurnal variations, a wrong prediction of water ice clouds location, and a model cold bias for temperatures below 100 Pa. However, dust estimation makes possible the predictability of the atmosphere, up to around ten days in the most favorable cases.
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Thomas Navarro. Etude de la météorologie de la planète Mars par assimilation de données satellite et modélisation. Planétologie et astrophysique de la terre [astro-ph.EP]. Université Pierre et Marie Curie - Paris VI, 2016. Français. ⟨NNT : 2016PA066189⟩. ⟨tel-01418163⟩

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