A Calibrated Combination of Probabilistic Precipitation Forecasts to Achieve a Seamless Transition from Nowcasting to Very Short-Range Forecasting

Abstract

Abstract In this paper, a new model for the combination of two or more probabilistic forecasts is presented. The proposed combination model is based on a logit transformation of the underlying initial forecasts involving interaction terms. The combination aims at approximating the ideal calibration of the forecasts, which is shown to be calibrated, and to maximize the sharpness. The proposed combination model is applied to two precipitation forecasts, Ensemble-MOS and RadVOR, which were developed by Deutscher Wetterdienst. The proposed combination model shows significant improvements in various forecast scores for all considered lead times compared to both initial forecasts. In particular, the proposed combination model is calibrated, even if both initial forecasts are not calibrated. It is demonstrated that the method enables a seamless transition between both initial forecasts across several lead times to be created. Moreover, the method has been designed in such a way that it allows for fast updates in nearly real time.