HESS Hydrology and Earth System Sciences HESS Hydrol. Earth Syst. Sci. 1607-7938 Copernicus Publications Göttingen, Germany 10.5194/hess-14-951-2010 Introducing a rainfall compound distribution model based on weather patterns sub-sampling Garavaglia F. 1 Gailhard J. 1 Paquet E. 1 Lang M. 2 Garçon R. 1 Bernardara P. 3 EDF – DTG, 21 Avenue de l'Europe, BP 41, 38040 Grenoble Cedex 9, France HHLY, CEMAGREF, 3bis quai Chauveau, CP220, 69366 Lyon Cedex 09, France EDF – R&D LNHE, 6 quai Watier, 78401 Chatou, France 16 06 2010 14 6 951 964 Copyright: © 2010 F. Garavaglia et al. 2010 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://hess.copernicus.org/articles/14/951/2010/hess-14-951-2010.html The full text article is available as a PDF file from https://hess.copernicus.org/articles/14/951/2010/hess-14-951-2010.pdf

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. <br><br> 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. <br><br> 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 1953–2005 period.

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