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-R...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 compared with light rain and torrential rain events.展开更多
针对ECMWF(European Centre for Medium-range Weather Forecasts)集合预报,融合降水产品在海河流域的偏差特征,进行基于频率匹配法的降水偏差订正,并对订正前后降水评分结果进行了系统检验。结果表明:经过2016年5—8月逐日试验分析表明...针对ECMWF(European Centre for Medium-range Weather Forecasts)集合预报,融合降水产品在海河流域的偏差特征,进行基于频率匹配法的降水偏差订正,并对订正前后降水评分结果进行了系统检验。结果表明:经过2016年5—8月逐日试验分析表明,改进后的ECMWF集合预报融合产品显著改善了原产品降水量和雨区范围偏大的特征,订正后降水预报的平均强度与实况更接近,且预报时效越长、降水量级越大、预报偏差越大改进效果越明显;改进后ECMWF的集合预报融合产品降水预报的TS评分均有一定程度的提高,降水预报的Bias评分更接近1,特别是对于小雨和暴雨、大暴雨量级的改进尤其明显,消除了大片降水虚报区;降水预报的空报率明显减小,但漏报率有所增加。展开更多
为做好ECMWF(European Centre for Medium-Range Weather Forecasting)模式本地化释用,提高四川省降水预报准确率,对四川省2020—2021年7—9月模式各量级降水预报系统性偏差规律分析发现,该模式预报的雨日较实况偏多,尤其是攀西地区和...为做好ECMWF(European Centre for Medium-Range Weather Forecasting)模式本地化释用,提高四川省降水预报准确率,对四川省2020—2021年7—9月模式各量级降水预报系统性偏差规律分析发现,该模式预报的雨日较实况偏多,尤其是攀西地区和川西高原;预报的大雨日数盆地西南部及攀西地区多于实况,而盆地南部少于实况。然后,基于分位数映射法对模式预报的24 h累积降水开展大量级降水订正试验与检验。基于分位数映射法订正后,暴雨及以上量级TS(Threat Score)提高7%~15%,且各量级降水TS均高于多模式集成客观预报产品2%~4%,大雨及以上、暴雨及以上量级命中率提高10%~20%,订正后雨带位置特别是暴雨落区与实况更接近。展开更多
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)。
文摘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 compared with light rain and torrential rain events.
文摘针对ECMWF(European Centre for Medium-range Weather Forecasts)集合预报,融合降水产品在海河流域的偏差特征,进行基于频率匹配法的降水偏差订正,并对订正前后降水评分结果进行了系统检验。结果表明:经过2016年5—8月逐日试验分析表明,改进后的ECMWF集合预报融合产品显著改善了原产品降水量和雨区范围偏大的特征,订正后降水预报的平均强度与实况更接近,且预报时效越长、降水量级越大、预报偏差越大改进效果越明显;改进后ECMWF的集合预报融合产品降水预报的TS评分均有一定程度的提高,降水预报的Bias评分更接近1,特别是对于小雨和暴雨、大暴雨量级的改进尤其明显,消除了大片降水虚报区;降水预报的空报率明显减小,但漏报率有所增加。
文摘为做好ECMWF(European Centre for Medium-Range Weather Forecasting)模式本地化释用,提高四川省降水预报准确率,对四川省2020—2021年7—9月模式各量级降水预报系统性偏差规律分析发现,该模式预报的雨日较实况偏多,尤其是攀西地区和川西高原;预报的大雨日数盆地西南部及攀西地区多于实况,而盆地南部少于实况。然后,基于分位数映射法对模式预报的24 h累积降水开展大量级降水订正试验与检验。基于分位数映射法订正后,暴雨及以上量级TS(Threat Score)提高7%~15%,且各量级降水TS均高于多模式集成客观预报产品2%~4%,大雨及以上、暴雨及以上量级命中率提高10%~20%,订正后雨带位置特别是暴雨落区与实况更接近。
