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Introducing a rainfall compound distribution model based on weather patterns sub-sampling

Abstract : Cet article présente un modèle probabiliste pour la distribution des pluies journalières maximales, basé sur la composition de lois exponentielles de sous-échantillons par type de temps météorologique. Une classification en huit types de temps a été retenue sur la France, à partir de la forme des champs pluvieux et des géopotentiels. Elle est cohérente avec la climatologie et produit pour une saison donnée des échantillons homogènes par type de temps. La combinaison des différentes lois par type de temps, pondérées par la fréquence des types de temps, permet de mieux représenter le comportement asymptotique de la distribution des pluies. Ce modèle multi-exponentiel a été comparé aux lois de valeurs extrêmes sur les valeurs supérieurs à un seuil (loi exponentielle et loi Pareto généralisée), sur un jeu de 478 séries pluviométriques journalières sur la période 1953-2000. Il donne des résultats tout à fait intéressants, en terme de robustesse et de justesse. / This paper presents a probabilistic model for daily rainfall, using sub-sampling based on meteorological circulation. We classified eight typical but contrasted synoptic situations (weather patterns) for France and surrounding areas, using a bottom-up approach, i.e. from the shape of the rain field to the synoptic situations described by geopotential fields. These weather patterns (WP) provide a discriminating variable that is consistent with French climatology, and allows seasonal rainfall records to be split into more homogeneous sub-samples, in term of meteorological genesis. First results show how the combination of seasonal and WP sub-sampling strongly influences the identification of the asymptotic behaviour of rainfall probabilistic models. Furthermore, with this level of stratification, an asymptotic exponential behaviour of each sub-sample appears as a reasonable hypothesis. This first part is illustrated with two daily rainfall records from SE of France. The distribution of the multi-exponential weather patterns (MEWP) is then defined as the composition, for a given season, of all WP sub-sample marginal distributions, weighted by the relative frequency of occurrence of each WP. This model is finally compared to Exponential and Generalized Pareto distributions, showing good features in terms of robustness and accuracy. These final statistical results are computed from a wide dataset of 478 rainfall chronicles spread on the southern half of France. All these data cover the 19532005 period.
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Submitted on : Thursday, June 24, 2010 - 11:36:23 AM
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F. Garavaglia, J. Gailhard, E. Paquet, M. Lang, R. Garcon, et al.. Introducing a rainfall compound distribution model based on weather patterns sub-sampling. Hydrology and Earth System Sciences Discussions, European Geosciences Union, 2010, 14, p. 951 - p. 964. ⟨10.5194/hess-14-951-2010⟩. ⟨hal-00494939⟩

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