用1951—2008年58 a 1月10日—2月2日1 000 hPa高度场逐日NCEP/NCAR再分析格点资料,求得逐日蒙古高压的强度P和中心位置λc、c指数,用其对"0801南方雪灾"期间蒙古高压的中期演变过程进行统计分析。结果表明,2008年1月10日—...用1951—2008年58 a 1月10日—2月2日1 000 hPa高度场逐日NCEP/NCAR再分析格点资料,求得逐日蒙古高压的强度P和中心位置λc、c指数,用其对"0801南方雪灾"期间蒙古高压的中期演变过程进行统计分析。结果表明,2008年1月10日—2月2日蒙古高压强度P和中心所在纬度φc出现了连续4次振荡,它们与我国南方降温、降水振荡过程准同步。由P、(λc,c)给出的综合动态图上蒙古高压4次活动过程也很明显地与降温、降水中期过程一一对应。分析表明2008年1月10日—2月2日蒙古高压的这一中期演变特征,是1951年以来仅有的一次。因此,可以认为,在充沛水汽供应条件下蒙古高压强而连续的爆发是导致"0801南方雪灾"产生的直接环流成因。展开更多
总结了目前最具代表性的3个全球集合预报系统(global ensemble forecast system,GEFS)——美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)、欧洲中期天气预报中心(European Centre for Medium-Range Weathe...总结了目前最具代表性的3个全球集合预报系统(global ensemble forecast system,GEFS)——美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)、欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)和加拿大气象中心(Canadian Meteoro-logical Centre,CMC)建成至今的发展概况。由于计算资源的不断扩展,各中心集合预报系统的模式分辨率、集合成员数也随之增加。同时各中心都在不断地致力于发展和完善初始和模式扰动方法,来更好地估计与初值和模式有关的不确定性,促进预报技巧的提高。其中初始扰动方法从最初的奇异向量法(ECMWF)、增殖向量法(NCEP)和观测扰动法(CMC)更新为现在的集合资料同化—奇异向量法(ECMWF)、重新尺度化集合转换法(NCEP)和集合卡尔曼滤波(CMC)。在估计模式不确定性方面,ECMWF和CMC都修订了各自的随机参数化方案和多参数化方案,NCEP最近也在模式中加入了随机全倾向扰动。为提高全球高影响天气预报的准确率,TIGGE计划(the THORPEX interactive grand global ensemble)的提出增进了国际间对多模式、多中心集合预报的合作研究,北美集合预报系统(North American ensemble forecast system,NAEFS)为建立全球多模式集合预报系统提供了业务框架,这都将有助于未来全球交互式业务预报系统的构建。展开更多
Two important questions are addressed in this paper using the Global Ensemble Forecast System （GEFS） from the National Centers for Environmental Prediction （NCEP）： （1） How many ensemble members are needed to be...Two important questions are addressed in this paper using the Global Ensemble Forecast System （GEFS） from the National Centers for Environmental Prediction （NCEP）： （1） How many ensemble members are needed to better represent forecast uncertainties with limited computational resources？ （2） What is tile relative impact on forecast skill of increasing model resolution and ensemble size？ Two-month experiments at T126L28 resolution were used to test the impact of varying the ensemble size from 5 to 80 members at the 500- hPa geopotential height. Results indicate that increasing the ensemble size leads to significant improvements in the performance for all forecast ranges when measured by probabilistic metrics, but these improvements are not significant beyond 20 members for long forecast ranges when measured by deterministic metrics. An ensemble of 20 to 30 members is the most effective configuration of ensemble sizes by quantifying the tradeoff between ensemble performance and the cost of computational resources. Two representative configurations of the GEFS the T126L28 model with 70 members and the T190L28 model with 20 members, which have equivalent computing costs--were compared. Results confirm that, for the NCEP GEFS, increasing the model resolution is more （less） beneficial than increasing the ensemble size for a short （long） forecast range.展开更多
Based on The Observing System Research and Predictability Experiment （THORPEX） Interactive Grand Global Ensemble （TIGGE） dataset, using various verification methods, the performances of four typical ensemble predi...Based on The Observing System Research and Predictability Experiment （THORPEX） Interactive Grand Global Ensemble （TIGGE） dataset, using various verification methods, the performances of four typical ensemble prediction systems （EPSs） from the China Meteorological Administration （CMA）, the European Centre for Medium-Range Weather Forecasts （ECMWF）, the US National Centers for Environmental Pre- diction （NCEP）, and the Japan Meteorological Agency （JMA） are compared preliminarily. The verification focuses on the 500-hPa geopotential height forecast fields in the mid- and higb-latitude Eurasian region during July 2007 and January 2008. The results show that for the forecast of 500-hPa geopotential height, in both summer and winter, the ECMWF EPS exhibits the highest forecast skill, followed by that of NCEP, then by JMA, and the CMA EPS gets in the last. The better system behaviors benefit from the better com- bination of the following： data assimilation system, numerical models, initial perturbations, and stochastic model perturbations. For the medium-range forecast, the ensemble forecasting can effectively filter out the forecast errors associated with the initial uncertainty, and the reliability and resolution （the two basic attri- butions of the forecast system） of these EPSs are better in winter than in summer. Specifically, the CMA EPS has certain advantage on the reliability of ensemble probabilistic forecasts. The forecasts are easy to be underestimated by the JMA EPS. The deficiency of ensemble spread, which is the universal problem of El＇S, also turns up in this study. Although the systems of ECMWF, NCEP, and JMA have more ensemble mem- bers, this problem cannot be ignored. This preliminary comparison helps to further recognize the prediction capability of the four EPSs over the Eurasian region, provides important references for wide applications of the TIGGE dataset, and supplies useful information for improving the CMA EPS.展开更多
文摘用1951—2008年58 a 1月10日—2月2日1 000 hPa高度场逐日NCEP/NCAR再分析格点资料,求得逐日蒙古高压的强度P和中心位置λc、c指数,用其对"0801南方雪灾"期间蒙古高压的中期演变过程进行统计分析。结果表明,2008年1月10日—2月2日蒙古高压强度P和中心所在纬度φc出现了连续4次振荡,它们与我国南方降温、降水振荡过程准同步。由P、(λc,c)给出的综合动态图上蒙古高压4次活动过程也很明显地与降温、降水中期过程一一对应。分析表明2008年1月10日—2月2日蒙古高压的这一中期演变特征,是1951年以来仅有的一次。因此,可以认为,在充沛水汽供应条件下蒙古高压强而连续的爆发是导致"0801南方雪灾"产生的直接环流成因。
文摘总结了目前最具代表性的3个全球集合预报系统(global ensemble forecast system,GEFS)——美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)、欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)和加拿大气象中心(Canadian Meteoro-logical Centre,CMC)建成至今的发展概况。由于计算资源的不断扩展,各中心集合预报系统的模式分辨率、集合成员数也随之增加。同时各中心都在不断地致力于发展和完善初始和模式扰动方法,来更好地估计与初值和模式有关的不确定性,促进预报技巧的提高。其中初始扰动方法从最初的奇异向量法(ECMWF)、增殖向量法(NCEP)和观测扰动法(CMC)更新为现在的集合资料同化—奇异向量法(ECMWF)、重新尺度化集合转换法(NCEP)和集合卡尔曼滤波(CMC)。在估计模式不确定性方面,ECMWF和CMC都修订了各自的随机参数化方案和多参数化方案,NCEP最近也在模式中加入了随机全倾向扰动。为提高全球高影响天气预报的准确率,TIGGE计划(the THORPEX interactive grand global ensemble)的提出增进了国际间对多模式、多中心集合预报的合作研究,北美集合预报系统(North American ensemble forecast system,NAEFS)为建立全球多模式集合预报系统提供了业务框架,这都将有助于未来全球交互式业务预报系统的构建。
文摘Two important questions are addressed in this paper using the Global Ensemble Forecast System （GEFS） from the National Centers for Environmental Prediction （NCEP）： （1） How many ensemble members are needed to better represent forecast uncertainties with limited computational resources？ （2） What is tile relative impact on forecast skill of increasing model resolution and ensemble size？ Two-month experiments at T126L28 resolution were used to test the impact of varying the ensemble size from 5 to 80 members at the 500- hPa geopotential height. Results indicate that increasing the ensemble size leads to significant improvements in the performance for all forecast ranges when measured by probabilistic metrics, but these improvements are not significant beyond 20 members for long forecast ranges when measured by deterministic metrics. An ensemble of 20 to 30 members is the most effective configuration of ensemble sizes by quantifying the tradeoff between ensemble performance and the cost of computational resources. Two representative configurations of the GEFS the T126L28 model with 70 members and the T190L28 model with 20 members, which have equivalent computing costs--were compared. Results confirm that, for the NCEP GEFS, increasing the model resolution is more （less） beneficial than increasing the ensemble size for a short （long） forecast range.
基金Supported by the China Meteorological Administration Public Welfare Research Fund (GYHY200706001 and GYHY200906007)the Priority Academic Development Project of Jiangsu Higher Education Institutions
文摘Based on The Observing System Research and Predictability Experiment （THORPEX） Interactive Grand Global Ensemble （TIGGE） dataset, using various verification methods, the performances of four typical ensemble prediction systems （EPSs） from the China Meteorological Administration （CMA）, the European Centre for Medium-Range Weather Forecasts （ECMWF）, the US National Centers for Environmental Pre- diction （NCEP）, and the Japan Meteorological Agency （JMA） are compared preliminarily. The verification focuses on the 500-hPa geopotential height forecast fields in the mid- and higb-latitude Eurasian region during July 2007 and January 2008. The results show that for the forecast of 500-hPa geopotential height, in both summer and winter, the ECMWF EPS exhibits the highest forecast skill, followed by that of NCEP, then by JMA, and the CMA EPS gets in the last. The better system behaviors benefit from the better com- bination of the following： data assimilation system, numerical models, initial perturbations, and stochastic model perturbations. For the medium-range forecast, the ensemble forecasting can effectively filter out the forecast errors associated with the initial uncertainty, and the reliability and resolution （the two basic attri- butions of the forecast system） of these EPSs are better in winter than in summer. Specifically, the CMA EPS has certain advantage on the reliability of ensemble probabilistic forecasts. The forecasts are easy to be underestimated by the JMA EPS. The deficiency of ensemble spread, which is the universal problem of El＇S, also turns up in this study. Although the systems of ECMWF, NCEP, and JMA have more ensemble mem- bers, this problem cannot be ignored. This preliminary comparison helps to further recognize the prediction capability of the four EPSs over the Eurasian region, provides important references for wide applications of the TIGGE dataset, and supplies useful information for improving the CMA EPS.
