北半球中纬度地区地面气温的超级集合预报  

Superensemble forecasts of the surface temperature in Northern Hemisphere middle latitudes

作　　者：智协飞[1] 林春泽[1,2] 白永清[1] 祁海霞[1] 

机构地区：[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]中国气象局武汉暴雨研究所,武汉430074

出　　处：《气象科学》2009年第5期569-574,共6页Journal of the Meteorological Sciences

基　　金：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(QX)2007-6-1)

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24～168h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24d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，24～144h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，168h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24～168h的预报，R－SUP预报效果都要优于EMN。尤其对于168h的预报，R－SUP改进了预报效果，优于EMN。Based on the ensemble forecasts of ECMWF, JMA, NCEP and UKMO in the TIGGE datasets in Northem Hemisphere middle latitudes during the period from 1 June until 31 August 2007, the muhimodel 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.

关 键 词：超级集合 多模式集合平均 滑动训练期 

分 类 号：P457.3[天文地球—大气科学及气象学]