A spatial postprocessing method of precipitation forecast based on ECMWF ensemble predication system and application effect evaluation

Modeling postprocessing methods can improve the accuracy of quantitative precipitation forecasts. At present, postprocessing methods for precipitation based on statistical analysis are mainly used to correct the precipitation rates or to estimate the precipitation probability. It usually ignores the spatial displacement errors of the precipitation area forecast, thus resulting in low forecast scores. In this study, a new spatial postprocessing method based on rain cluster matching is developed to correct the spatial errors of the precipitation area forecast, in order to improve the forecasting accuracy. With the identification and separation of rain clusters, this method applies the Bayesian multi-objective tracking approach and compares the model forecasting and observed rain clusters at the current time window, so as to obtain the displacement and intensity errors between the model forecasting results and the observations. Finally, these discrepancies are used to correct the model output in the coming time window. With the method proposed in this study, the precipitation forecast based on ECMWF ensemble predication system for summer precipitation processes during 2018—2019 in North China are corrected and tested. Using the CMPAS hourly precipitation analysis dataset as observations, the test results show that, after correction, the mean TS score of the precipitation forecasts at coming time window increases from 0.333 to 0.369, with the correlation coefficient increasing from 0.260 to 0.327, and the mean absolute error decreasing from 2.788 mm to 2.541 mm. We suggest that the method proposed in this study can effectively improve the accuracy of precipitation forecasts.