摘要
This paper proposes a simple and powerful optimal integration(OPI)method for improving hourly quantitative precipitation forecasts(QPFs,0-24 h)of a single-model by integrating the benefits of different biascorrected methods using the high-resolution CMA-GD model from the Guangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration(CMA).Three techniques are used to generate multi-method calibrated members for OPI:deep neural network(DNN),frequency-matching(FM),and optimal threat score(OTS).The results are as follows:(1)The QPF using DNN follows the basic physical patterns of CMA-GD.Despite providing superior improvements for clear-rainy and weak precipitation,DNN cannot improve the predictions for severe precipitation,while OTS can significantly strengthen these predictions.As a result,DNN and OTS are the optimal members to be incorporated into OPI.(2)Our new approach achieves state-of-the-art performances on a single model for all magnitudes of precipitation.Compared with the CMA-GD,OPI improves the TS by 2.5%,5.4%,7.8%,8.3%,and 6.1%for QPFs from clear-rainy to rainstorms in the verification dataset.Moreover,OPI shows good stability in the test dataset.(3)It is also noted that the rainstorm pattern of OPI relies heavily on the original model and that OPI cannot correct for deviations in the location of severe precipitation.Therefore,improvements in predicting severe precipitation using this method should be further realized by improving the numerical model's forecasting capability.
作者
LIU Jin-qing
DAI Guang-feng
OU Xiao-feng
刘金卿;戴光丰;欧小锋(Hunan Meteorological Observatory,Changsha 410118 China;Guangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction,CMA,Guangzhou 510640 China;Heavy Rain and Drought Flood Disasters in Plateau and Basin Key Laboratory of Sichuan,Chengdu 610072 China;Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction,Changsha 410118 China)
基金
Open Project Fund of Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction,CMA(J202009)
Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province(SZKT202005)
Innovation and Development Project of China Meteorological Administration(CXFZ2021J020)。
