Assessment of ECMWF’s Precipitation Forecasting Performance for China from 2017 to 2022  

作　　者：PAN Liu-jie ZHANG Hong-fang LIANG Mian LIU Jia-huimin DAI Chang-ming 潘留杰;张宏芳;梁绵;刘嘉慧敏;戴昌明(Shaanxi Meteorological Observatory,Xi’an 710014 China;Key Laboratory of Eco-Environment and Meteorology for the Qinling Mountains and Loess Plateau,Xi’an 710014 China;Shaanxi Meteorological Service Centre,Xi’an 710014 China)

机构地区：[1]Shaanxi Meteorological Observatory,Xi’an 710014 China [2]Key Laboratory of Eco-Environment and Meteorology for the Qinling Mountains and Loess Plateau,Xi’an 710014 China [3]Shaanxi Meteorological Service Centre,Xi’an 710014 China

出　　处：《Journal of Tropical Meteorology》2024年第3期257-274,共18页热带气象学报（英文版）

基　　金：Special Innovation and Development Program of China Meteorological Administration(CXFZ2022J023);Projects in Key Areas of Social Development in Shaanxi Province(2024SF-YBXM-556);Shaanxi Province Basic Research Pro-gram of Natural Science(2023-JC-QN-0285)。

摘　　要：This study used the China Meteorological Administration(CMA)three-source fusion gridded precipitation analysis data as a reference to evaluate the precipitation forecast performance of the European Centre for Medium-Range Weather Forecasts(ECMWF)model for China from 2017 to 2022.The main conclusions are as follows.The precipitation forecast capability of the ECMWF model for China has gradually improved from 2017 to 2022.Various scores such as bias,equitable threat score(ETS),and Fractions Skill Score(FSS)showed improvements for different categories of precipitation.The bias of light rain forecasts overall adjusted towards smaller values,and the increase in forecast scores was greater in the warm season than in the cold season.The ETS for torrential rain more intense categories significantly increased,although there were large fluctuations in bias across different months.The model exhibited higher precipitation bias in most areas of North China,indicating overprediction,while it showed lower bias in South China,indicating underprediction.The ETSs indicate that the model performed better in forecasting precipitation in the northeastern part of China without the influence of climatic background conditions.Comparison of the differences between the first period and the second period of the forecast shows that the precipitation amplitude in the ECMWF forecast shifted from slight underestimation to overestimation compared to that of CMPAS05,reducing the likelihood of missing extreme precipitation events.The improvement in ETS is mainly due to the reduction in bias and false alarm rates and,more importantly,an increase in the hit rate.From 2017 to 2022,the area coverage error of model precipitation forecast relative to observations showed a decreasing trend at different scales,while the FSS showed an increasing trend,with the highest FSS observed in 2021.The ETS followed a parabolic trend with increasing neighborhood radius,with the better ETS neighborhood radius generally being larger for moderate rain and heavy rain co

关 键 词：ECMWF forecasting verification neighborhood verification FSS 

分 类 号：P456.7[天文地球—大气科学及气象学]