Mixture probabilistic model for precipitation ensemble forecasting

Statistical post-processing approaches are widely employed to construct improved probabilistic meteorological forecasts from numerical weather prediction. However, generating calibrated and sharp probabilistic forecasts is challenging. In this article, a post-processing approach, Mixture Probabilistic Model (MPM), is proposed to calibrate probabilistic ensemble forecasts subject to sharpness. In particular, the proposed MPM is applied to precipitation forecasting. First, the Censored and Shifted Gamma (CSG0) distribution is considered as the probability density function (PDF) for precipitation. Then, the predictive PDF of MPM is mixed by the individual PDFs which are fitted from raw ensemble members. Finally, to estimate optimal weight parameters for the mixture of individual PDFs, the Dirichlet distribution is utilized and the skills of the mixture model and individuals are both taken into consideration. The proposed MPM was tested using Innsbruck ensemble precipitation data and 6 h accumulated precipitation ensemble forecast data in east China from August to November 2017. Compared with raw forecasts and three state-of-the-art post-processing approaches, MPM showed improved performance for all verification scores. The quantitative and qualitative analyses of results in both cases indicate the potential and effectiveness of MPM for precipitation ensemble forecasting.