Non-Parametric Methods for Post-Processing of Ensemble Forecasts.

Summary In numerical weather prediction, ensemble forecasting models have become an essential tool to quantify forecast uncertainty and provide probabilistic forecasts. Unfortunately, these models are not perfect and a simultaneous correction of their bias and dispersion is necessary. Contrary to most of the usual techniques, these non-parametric methods allow to take into account the non-linear dynamics of the atmosphere, to add covariates (other meteorological variables, temporal variables, geographical variables...) easily and to select themselves the most useful predictors in the regression. Moreover, we do not make any assumption on the distribution of the variable to be treated. This new approach outperforms existing methods for variables such as temperature and wind speed.For variables known to be difficult to calibrate, such as sexti-hourly precipitation, hybrid versions of our techniques have been created. We show that these hybrid versions (as well as our original versions) are better than the existing methods. The last part of this thesis concerns the evaluation of ensemble forecasts for extreme events. We have shown some properties of the Continuous Ranked Probability Score (CRPS) for extreme values. We have also defined a new measure combining CRPS and extreme value theory, and we examine its coherence on a simulation as well as in an operational framework.The results of this work are intended to be inserted within the forecasting and verification chain at Météo-France.