Recent development of post-processing methods in short-term hydrometeorological ensemble forecasting
Contributed by Wentao Li and Qingyun Duan Due to various uncertainties in model inputs and outputs, initial and boundary conditions, model structures and parameters, raw forecasts from meteorological or hydrological models suffer from systematic bias and under/overdispersion errors and they need to be corrected before being used in applications. Various statistical post-processing methods have been developed to correct these errors and achieve “sharp” forecasts subject to “reliability”. As in the book “Statistical methods in the atmospheric sciences” by Wilks, statistical…