Evaluation and Statistical Post-Processing of Two Precipitation Reforecast Products During Summer in the Mainland of China

Raw weather forecasts from numerical prediction models usually suffer from systematic bias, which can be removed by statistical post-processing methods to achieve accurate and reliable ensemble forecasts. The post-processing of precipitation forecasts often requires reforecasts with long historical archives to ensure sufficient sample size. In this work, we provide an evaluation of two widely used short-range reforecast products during summer in the mainland of China, including reforecasts from the Integrated Forecasting System of the European Center for Medium-Range Weather Forecasts (ECMWF), and reforecasts from Global Ensemble Forecast System version 12 (GEFSv12) of the National Centers for Environmental Prediction. The results suggest that ECMWF reforecasts outperform GEFSv12 reforecasts in accuracy and discrimination in most regions of China, especially for heavy rain. On the other hand, the post-processed GEFSv12 reforecasts are better than post-processed ECMWF reforecasts in reliability for light rain in several dry regions. Moreover, we combine ECMWF and GEFSv12 reforecasts by Bayesian model averaging (BMA). The results show that BMA is able to combine the advantages of two reforecasts. Post-processed forecasts from BMA perform as well as ECMWF reforecasts in accuracy and discrimination skill for heavy rain in wet regions of southern China. BMA results are also as reliable as post-processed GEFSv12 reforecasts in dry regions of northern China. The evaluation results in this study could serve as a useful guide for further applications of those reforecast products in China.