ECMWF集合预报统计量产品在重庆降水预报中的检验与分析
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摘要
利用ECMWF集合预报降水资料和重庆市自动站降水资料,运用晴雨、TS评分、预报偏差等检验方法对重庆地区2014—2016年ECMWF集合预报降水产品在短期时效的预报性能进行检验分析。结果表明:最小值的晴雨预报准确率最高。对于TS评分检验,小雨量级可优先参考最小值、10%分位数和融合产品,中雨量级参考平均数和概率匹配平均,大雨和暴雨量级分别参考75%分位数和90%分位数。对于预报偏差检验,小雨量级可优先参考最小值、Mode,中雨量级参考融合产品、中位数,大雨量级参考控制预报、融合产品,暴雨量级参考90%分位数。对于百分位值预报产品和概率预报产品,小雨量级可参考5%～10%分位数和80%～90%概率预报产品,中雨量级可参考45%～55%分位数和40%概率预报产品,大雨量级可参考70%～80%分位数和20%概率预报产品,暴雨量级可参考90%～95%分位数和10%概率预报产品。
Based on ECMWF ensemble forecast precipitation data and Chongqing precipitation observational data, and with clear-rain prediction(PC), Threat Score(TS) and Bias Score(BS)methods, the precipitation forecasting performance in short-time prediction of the ECMWF ensemble precipitation products from 2014 to 2016 in Chongqing area were analyzed. The results showed that the highest PC accuracy appeared in the minimum product. For the TS value, the minimum product,the 10% quantile product and the statistic fusing product could be referred to with priority for light rain, while the mean product and probability matching product for moderate rain, and 75% quantile product for heavy rain, 90% quantile product for rainstorm. For the BS value, the minimum product,and the Mode product could be used as prior reference for light rain, the statistic fusing product and the medium product for moderate rain, while the control forecast product and the statistic fusing product for heavy rain, and the 90% quantile product for rainstorm. For quantile products and probability forecast products, the 5%-10% quantile product, the 80%-90% probability forecast product could all be the reference for light rain, while the 45%-55% quantile products and the 40%probability forecast product for moderate rain, the 70%-80% quantile products and the 20%probability forecast product for heavy rain, and the 90%-95% quantile products and the 10%probability forecast product for rainstorm.
Based on ECMWF ensemble forecast precipitation data and Chongqing precipitation observational data, and with clear-rain prediction(PC), Threat Score(TS) and Bias Score(BS)methods, the precipitation forecasting performance in short-time prediction of the ECMWF ensemble precipitation products from 2014 to 2016 in Chongqing area were analyzed. The results showed that the highest PC accuracy appeared in the minimum product. For the TS value, the minimum product,the 10% quantile product and the statistic fusing product could be referred to with priority for light rain, while the mean product and probability matching product for moderate rain, and 75% quantile product for heavy rain, 90% quantile product for rainstorm. For the BS value, the minimum product,and the Mode product could be used as prior reference for light rain, the statistic fusing product and the medium product for moderate rain, while the control forecast product and the statistic fusing product for heavy rain, and the 90% quantile product for rainstorm. For quantile products and probability forecast products, the 5%-10% quantile product, the 80%-90% probability forecast product could all be the reference for light rain, while the 45%-55% quantile products and the 40%probability forecast product for moderate rain, the 70%-80% quantile products and the 20%probability forecast product for heavy rain, and the 90%-95% quantile products and the 10%probability forecast product for rainstorm.
引文
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[13]朱占云,潘娅英,骆月珍,等.浙江省水库流域面雨量的多模式预报效果分析与检验[J].气象与环境科学,2017,40(3):93-100.
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[15]Hersbach H,Mureau R,Opsteegh J D,et al.A short-range ensemble prediction system for the European area[J].Mon Wea Rev,2000,128(10):3501-3519.
[16]Jones M S,Colle B A.Evaluation of a mesoscale short-range ensemble forecast system over the Northeast United States[J].Wea Forecasting,2007,22(1):36-55.
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[18]李俊,王明欢,公颖,等.AREM短期集合预报系统及其降水预报检验[J].暴雨灾害,2010,29(1):30-37.
[19]邓国,龚建东,邓莲堂,等.国家级区域集合预报系统研发和性能检验[J].应用气象学报,2010,21(5):513-523.
[20]陈静,薛纪善,颜宏.一种新型的中尺度暴雨集合预报初值扰动方法研究[J].大气科学,2005,29(5):717-726.
[21]杜钧,邓国.单一值预报向概率预报转变的价值:谈谈概率预报的检验和应用[J].气象,2010,36(12):10-18.
[22]冯慧敏,智协飞,崔慧慧,等.基于多模式集成技术的地面气温精细化预报[J].气象与环境科学,2016,39(4):73-79.
[2]廖洞贤.大气数值模式的设计[M].北京:气象出版社,1999:10-30.
[3]陈阳权,杜安妮,王清平,等.WRF模式对乌鲁木齐机场一次冻雾天气的数值预报对比试验分析[J].沙漠与绿洲气象,2018,12(2):63-70.
[4]万瑜.ECMWF细网格数值预报产品在乌鲁木齐东南大风预报中的释用[J].沙漠与绿洲气象,2014,8(1):32-38.
[5]邓国,陈怀亮,周玉淑.集合预报技术在暴雨灾害风险分析中的应用[J].自然灾害学报,2006,15(1):115-122.
[6]Stensrud D J,Bao J W,Warner T T.Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems[J].Mon Wea Rev,2000,128(7):2077-2107.
[7]Buizza R,Doblas R F,Jung T,et al.Stochastic parameterization and model uncertainty[M].England:European Centre for Medium-Range Weather Forecasts,2009:589.
[8]Buizza R,Miller M,Palmer T N.Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System[J].Quart J Roy Meteor Soc,1999,125(560):2887-2908.
[9]杜钧.集合预报的现状和前景[J].应用气象学报,2002,13(1):17-27.
[10]杜钧,陈静.单一值预报向概率预报转变的基础:谈谈集合预报及其带来的变革[J].气象,2010,36(11):1-11.
[11]陈博宇,代刊,郭云谦.2013年汛期ECMWF集合统计量产品的降水预报检验与分析[J].暴雨灾害,2015,34(1):64-73.
[12]董全,金荣花,代刊,等.ECMWF集合预报和确定性预报对淮河流域暴雨预报的对比分析[J].气象,2016,42(9):1146-1153.
[13]朱占云,潘娅英,骆月珍,等.浙江省水库流域面雨量的多模式预报效果分析与检验[J].气象与环境科学,2017,40(3):93-100.
[14]Du J,Mullen S L,Sanders F.Short-range ensemble forecasting(SREF)of quantitative precipitation[J].Mon Wea Rev,1997,125(10):2427-2459.
[15]Hersbach H,Mureau R,Opsteegh J D,et al.A short-range ensemble prediction system for the European area[J].Mon Wea Rev,2000,128(10):3501-3519.
[16]Jones M S,Colle B A.Evaluation of a mesoscale short-range ensemble forecast system over the Northeast United States[J].Wea Forecasting,2007,22(1):36-55.
[17]Peel S,Wilson L J.A diagnostic verification of the precipitation forecasts produced by the Canadian ensemble prediction system[J].Wea Forecasting,2008,23(4):596-616.
[18]李俊,王明欢,公颖,等.AREM短期集合预报系统及其降水预报检验[J].暴雨灾害,2010,29(1):30-37.
[19]邓国,龚建东,邓莲堂,等.国家级区域集合预报系统研发和性能检验[J].应用气象学报,2010,21(5):513-523.
[20]陈静,薛纪善,颜宏.一种新型的中尺度暴雨集合预报初值扰动方法研究[J].大气科学,2005,29(5):717-726.
[21]杜钧,邓国.单一值预报向概率预报转变的价值:谈谈概率预报的检验和应用[J].气象,2010,36(12):10-18.
[22]冯慧敏,智协飞,崔慧慧,等.基于多模式集成技术的地面气温精细化预报[J].气象与环境科学,2016,39(4):73-79.
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