Weather types prediction at medium-range from ensemble forecasts

Gabriel Jouan (1, 2) , Anne Cuzol (3) , Valérie Monbet (4, 2) , Goulven Monnier (2)

1 L@b SCALIAN
2 IRMAR - Institut de Recherche Mathématique de Rennes
3 LMBA - Laboratoire de Mathématiques de Bretagne Atlantique
4 SIMSMART - SIMulation pARTiculaire de Modèles Stochastiques

Medium-range weather forecasts can be of high economic value in many fields: agriculture, renewable energy production, maintenance operations planning. Such forecasts can be based on ensembles derived from weather models, and the postprocessing of such ensembles is an active research problem in the statistical weather community. In this work, we try to face the problem of long forecasting horizons, and focus on the multivariate case where different meteorological variables interact. The prediction problem is simplified and defined as the prediction of a weather type, which is a categorical variable defined by the interaction of the meteorological variables. We use machine learning techniques to predict this weather type from the multivariate ensemble forecasts. The algorithms are applied to a 5 to 10 days weather forecasting in the north-west of France, based on wind and precipitation data from the ECMWF ensemble system.

Domaines: Statistiques [math.ST]

Gabriel Jouan, Anne Cuzol, Valérie Monbet, Goulven Monnier. Weather types prediction at medium-range from ensemble forecasts. 9th International workshop on Climate Informatics, Oct 2019, Paris, France. ⟨hal-02425230v2⟩