基于TIGGE资料的地面气温和降水的多模式集成预报

Multimodel ensemble forecasts of surface air temperature and precipitation using TIGGE datasets

摘要:

利用TIGGE资料集下中国气象局(CMA)、欧洲中期天气预报中心(ECMWF)、日本气象厅(JMA)、美国国家环境预报中心(NCEP)和英国气象局(UKMO)5个中心集合预报结果,对多模式集成预报方法进行讨论。结果表明,多模式集成方法的预报效果优于单个中心的预报,但对于不同预报要素多模式集成方法的适用性存在差异。滑动训练期超级集合(R-SUP)对北半球地面气温的改进效果最优,但此方法对降水场的改进效果并不理想。在北半球中低纬24 h累积降水的回报试验中,消除偏差(BREM)的结果优于单个中心的预报,且此方法预报结果稳定。进一步利用滑动训练期消除偏差(R-BREM)集合平均对2008年1月中国南方极端雨雪冰冻过程进行多模式集成预报试验,结果表明,在固定误差范围内,R-BREM将中国南方大部分地区的地面气温预报时效由最优数值预报中心的96 h延长至192 h,且除个别时效外,小雨、中雨的TS评分得到明显提高。

Abstract:

Based on the ensemble forecasting data of China Meteorological Administration(CMA),European Centre for Medium Range Weather Forecasts(ECMWF),Japan Meteorological Agency(JMA),U.S.National Centers for Environmental Prediction(NCEP),and United Kingdom Met Office(UKMO) in the TIGGE datasets,the multimodel ensemble forecasting techniques have been investigated.Results show that the multimodel ensemble forecasts are superior to that of the individual model.However,the improvement of multimodel ensemble forecast skill is different for different meteorological elements.The multimodel superensemble with running training period(R-SUP) has the best performance for surface air temperature in the Northern Hemisphere.For precipitation forecast in the Northern Hemisphere,the bias-removed ensemble mean(BREM) is more skillful and stable than every single model.In addition,the multimodel ensemble forecasting experiments of extreme weather event with freezing rain and snow over southern China during early 2008 have been conducted by using BREM with running training period(R-BREM).Taking root-mean-square errors(RMSEs) of ECMWF 96 h forecasts as the criterion,the forecast lead time of surface air temperature over the southeastern coast of China may be prolonged from 96 h to 192 h by using the R-BREM technique.And the threat scores(TS) of the light and moderate rain forecasts have been significantly improved except for some particular lead time.