Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the eva...Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.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primarily supported by the National Key R&D Program of China[grant number 2021YFC3000904]the Jiangsu Provincial Key Technology R&D Program[grant number BE2022851]National Natural Science Foundation of China[grant number 42405035]。
文摘Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.
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。
