Review on Statistical Post-processing of Ensemble Forecasts

Abstract

Statistical post-processing of the ensemble outputs of meteorological and hydrological forecasts is essential to address the errors and uncertainty introduced in the forecasting. Post-processing (PP) generates well-calibrated predictions by analyzing the statistical relationship among the historical predictions and their corresponding observations. In the scientific communities of statistical, meteorological, climatological, hydrological, and engineering many recent developments are now thriving. These techniques range from simple to complex from straightforward bias corrections to quite complex procedures for modifying the distribution that take correlations between the prognostic factors considered. The foremost activities working out in the area of statistical post-processing from statistical advancements to functioning applications, focusing on different methods available and their comparison is summarized in the paper. The review shows good performance of the logistic regression in several studies for the PP of numerical weather forecasts.