违反了 PRIMARY KEY 约束 'PK_t_counter'。不能在对象 'dbo.t_counter' 中插入重复键。 语句已终止。 基于欧洲中心细网格预报资料和观测资料的浙江省春夏降水性质分类指标-Precipitation Classification Index Based on ECMWF Fine Grid Forecast Data and Observation Data for Spring and Summer in Zhejiang Province
doi:  10.3878/j.issn.1006-9585.2017.17066
基于欧洲中心细网格预报资料和观测资料的浙江省春夏降水性质分类指标

Precipitation Classification Index Based on ECMWF Fine Grid Forecast Data and Observation Data for Spring and Summer in Zhejiang Province
摘要点击 288  全文点击 252  投稿时间:2017-04-13  
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基金:  浙江省气象科技计划项目2016ZD01-03
中文关键词:  雷雨  阵雨  决策树  降水分类指标  欧洲中心细网格预报资料和观测资料
英文关键词:  Precipitation thunderstorm  Non-thunderstorm precipitation  Decision tree  Precipitation classification index  ECMWF fine grid forecast data and observation data
              
作者中文名作者英文名单位
王霁吟WANG Jiyin浙江省气象台, 杭州 310021
陈懿妮CHEN Yini浙江省气象台, 杭州 310021
孙长SUN Zhang浙江省气象台, 杭州 310021
王丽颖WANG Liying浙江省气象台, 杭州 310021
俞佩YU Pei浙江省气象台, 杭州 310021
引用:王霁吟,陈懿妮,孙长,王丽颖,俞佩.2018.基于欧洲中心细网格预报资料和观测资料的浙江省春夏降水性质分类指标[J].气候与环境研究,23(5):543-550,doi:10.3878/j.issn.1006-9585.2017.17066.
Citation:WANG Jiyin,CHEN Yini,SUN Zhang,WANG Liying,YU Pei.2018.Precipitation Classification Index Based on ECMWF Fine Grid Forecast Data and Observation Data for Spring and Summer in Zhejiang Province[J].Climatic and Environmental Research(in Chinese),23(5):543-550,doi:10.3878/j.issn.1006-9585.2017.17066.
中文摘要:
      利用浙江省2004~2013年3~8月Micaps(气象信息综合分析处理系统)地面填图数据和T–logP数据研究杭州、衢州和台州三市的阵雨和雷雨个例,同时选取能表征雷雨并能区分阵雨与雷雨的气象预报因子:对流有效位能、850 hPa与500 hPa的温差、K指数、地面2 m温度,用临近探空的分析方法和决策树的分类方法初步建立了一个适用于浙江省春夏季降水性质分类指标。利用欧洲中心(ECMWF)细网格预报资料,对历史样本和2016年春、夏季分别作了检验。结果表明:除去有降水预报误差个例后,指标TS(Threat Score)评分超过0.53,雷雨阵雨综合命中率达到71%,空报率阵雨(10%)小于雷雨(43%),在不同地区和季节稍有区别;同时对浙江省2016年春夏两次典型大范围阵雨雷雨过程进行预报,效果很好。此方法不仅可以依据预报数据在短期内做出精细化降水性质分类预报,在中长期预报上也有表现力。
Abstract:
      The authors used the plot data and T-logP data of Micaps (Meteorological Information Comprehensive Analysis and Processioning System) to analyze thunderstorm precipitation and non-thunderstorm precipitation in Hangzhou, Quzhou, and Taizhou. The prediction factors in this study that can tell the differences between the environments of thunderstorm precipitation and non-thunderstorm precipitation are convective available potential energy, temperature differences between 850 hPa and 500 hPa, K index,and 2-m height temperature. An index has been formulated from analysis of parameters derived from proximity soundings and decision trees. The quality of this index is examined with precipitation activities for the period from 2004 to 2013 and 2016 using ECMWF fine grid forecast data and observation data. The results show that the threat score (TS) is more than 0.53, the hit rate is 71% and the false rate of thunderstorm forecast (43%) is higher than that of non-thunderstorm precipitation (10%) for most areas and seasons. This method basically can also predict the areas of different types of precipitation from examining two thunderstorm processes which occurred in the spring and summer of 2016, respectively. In summary, the advantage of this method is reflected in the fact that it not only can make fine precipitation classification forecast but also can create expressive prediction for medium and long-term forecast.
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