| 北半球中纬度地区地面气温的超级集合预报 |
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| 引用本文： | 智协飞,林春泽,白永清,祁海霞.北半球中纬度地区地面气温的超级集合预报[J].气象科学,2009,29(5):569-574. |
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| 作者姓名： | 智协飞 林春泽 白永清 祁海霞 |
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| 作者单位： | 1. 南京信息工程大学,气象灾害省部共建教育部重点实验室,南京,210044
2. 南京信息工程大学,气象灾害省部共建教育部重点实验室,南京,210044;中国气象局,武汉暴雨研究所,武汉,430074 |
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| 基金项目： | 2007年度公益性行业(气象)科研专项"面向TIGGE的集合预报关键应用技术研究"(GYHY |
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| 摘 要： | 基于TIGGE资料中的ECMWF、JMA、NCEP和UKMO四个中心2007年6月1日-8月31日北半球中纬度地区地面气温24～168 h集合预报资料,分别利用固定训练期超级集合(SUP, Superensemble)和滑动训练期超级集合(R-SUP, Running Training Period Superensemble )对2007年8月8-31日预报期24 d进行超级集合预报试验.采用均方根误差对预报结果进行检验评估,比较了两种超级集合方法与最好的单个中心模式预报、多模式集合平均的预报效果.结果表明,SUP预报有效降低了预报误差,24～144 h的预报效果优于多模式集合平均(EMN, Ensemble Mean)和最好的单个中心预报,168 h的预报效果略差于EMN.R-SUP预报进一步改善了预报效果.对于24～168 h的预报,R-SUP预报效果都要优于EMN.尤其对于168 h的预报,R-SUP改进了预报效果,优于EMN.
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| 关 键 词： | 超级集合 多模式集合平均 滑动训练期 |
| 收稿时间： | 3/9/2009 12:00:00 AM |
Superensemble forecasts of the surface temperature in Northern Hemisphere middle latitudes |
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| Zhi Xiefei,Lin Chunze,Bai Yongqing and Qi Haixia.Superensemble forecasts of the surface temperature in Northern Hemisphere middle latitudes[J].Scientia Meteorologica Sinica,2009,29(5):569-574. |
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| Authors: | Zhi Xiefei Lin Chunze Bai Yongqing and Qi Haixia |
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| Institution: | Zhi Xiefei1 Lin Chunze1,2 Bai Yongqing1 Qi Haixia1(1 Key Laboratory of Meteorological Disaster of Ministry of Education,Nanjing University ofInformation Science & Technology,Nanjing 210044,China)(2 Wuhan Institute of Heavy Rain,China Meteorological Administration,Wuhan 430074,China) |
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| Abstract: | Based on the ensemble forecasts of ECMWF, JMA, NCEP and UKMO in the TIGGE datasets in Northern Hemisphere middle latitudes during the period from 1 June until 31 August 2007, the multimodel superensemble forecasts of the surface temperature for the forecast period from 8 to 31 August 2007 have been conducted by using fixed training period and running training period, respectively. The root mean square error is utilized to evaluate the forecast errors of two kinds of superensemble forecasts, the best single model and the ensemble mean. Results show that SUP(the multimodel superensemble forecast with fixed training period) reduces the forecast error considerably. The forecast skill of the multimodel superensemble is higher than that of EMN(the ensemble mean) and the best single model among ECMWF, JMA, NCEP and UKMO models for the 24 h～144 h surface temperature forecast. However, for the 168h forecast the forecast skill of the superensemble is lower than that of EMN. The R-SUP(multimodel superensemble with running training period) further improves the forecast skill. It has higher forecast skill than EMN for the 24 h～168 h forecast. For the 168 h forecast, in particular, R-SUP improves the forecast skill and has higher forecast skill than EMN. |
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| Keywords: | Superensemble Multimodel ensemble mean Running training period |
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