In this paper we present the current capabilities for numerical weather prediction of precipitation over China using a suite of ten multimodels and our superensemble based forecasts. Our suite of models includes the o...In this paper we present the current capabilities for numerical weather prediction of precipitation over China using a suite of ten multimodels and our superensemble based forecasts. Our suite of models includes the operational suite selected by NCARs TIGGE archives for the THORPEX Program. These are： ECMWF, UKMO, JMA, NCEP, CMA, CMC, BOM, MF, KMA and the CPTEC models. The superensemble strategy includes a training and a forecasts phase, for these the periods chosen for this study include the months February through September for the years 2007 and 2008. This paper addresses precipitation forecasts for the medium range i.e. Days 1 to 3 and extending out to Day 10 of forecasts using this suite of global models. For training and forecasts validations we have made use of an advanced TRMM satellite based rainfall product. We make use of standard metrics for forecast validations that include the RMS errors, spatial correlations and the equitable threat scores. The results of skill forecasts of precipitation clearly demonstrate that it is possible to obtain higher skills for precipitation forecasts for Days 1 through 3 of forecasts from the use of the multimodel superensemble as compared to the best model of this suite. Between Days 4 to 10 it is possible to have very high skills from the multimodel superensemble for the RMS error of precipitation. Those skills are shown for a global belt and especially over China. Phenomenologically this product was also found very useful for precipitation forecasts for the Onset of the South China Sea monsoon, the life cycle of the mei-yu rains and post typhoon landfall heavy rains and flood events. The higher skills of the multimodel superensemble make it a very useful product for such real time events.展开更多
Based on the past related research work,a new analogy-dynamical monthly predic- tion model is established with the operational dynamic extended-range forecast model T63L16 (hereafter T63) as a dynamic kernel.The month...Based on the past related research work,a new analogy-dynamical monthly predic- tion model is established with the operational dynamic extended-range forecast model T63L16 (hereafter T63) as a dynamic kernel.The monthly mean circulation predicition with T63 is considered as a control experiment,and the prediction with the analogy-dynamical model as a contrast one.It is found that the anal- ogy-dynamical model has more precise forecast skill than the T63 model through monthly mean numerical prediction experiment.展开更多
利用TIGGE（THORPEX Interactive Grand Global Ensemble）资料中的CMC、ECMWF、NCEP和UKMO 4个中心全球集合预报模式对2007年10月3日—2008年2月29日逐日累积降水进行多模式集成预报试验。通过集合平均、多模式消除偏差集合平均、加权...利用TIGGE（THORPEX Interactive Grand Global Ensemble）资料中的CMC、ECMWF、NCEP和UKMO 4个中心全球集合预报模式对2007年10月3日—2008年2月29日逐日累积降水进行多模式集成预报试验。通过集合平均、多模式消除偏差集合平均、加权消除偏差集成3种方法进行试验对比,重点分析各中心模式及多模式集成的240~360h（10~15d）延伸期预报的检验效果。结果表明,多模式集成对逐日累积降水240~360h延伸期预报优于单个中心模式,将逐日降水的预报时效提高了72~168h。3种集成方法对比发现,多模式消除偏差集合平均方法预报效果最好,该方法将晴雨量级的降水预报时效在中短期和延伸期至少提高了1d和5d。展开更多
利用2007年6月8日—8月31日东亚地区TIGGE集合预报资料中欧洲中期天气预报中心（European Centre for Medium-range Weather Forecasts,ECMWF）和英国气象局（United Kingdom Met Office,UKMO）两个中心的地面2 m气温资料进行集合成员...利用2007年6月8日—8月31日东亚地区TIGGE集合预报资料中欧洲中期天气预报中心（European Centre for Medium-range Weather Forecasts,ECMWF）和英国气象局（United Kingdom Met Office,UKMO）两个中心的地面2 m气温资料进行集合成员优选研究。结果表明,对于24-96 h预报,集合成员优选方法能够较好地选出预报技巧较高和预报技巧较低的集合成员。个例分析表明,在极端天气出现的地区,优选集合平均的预报优势较为明显。对比ECMWF和UKMO的集合成员优选结果发现,ECMWF的预报效果优于UKMO的预报效果。展开更多
Based on the dynamic framework of Lorenz 96 model,the ensemble prediction system(EPS)containing stochastic forcing has been developed.In this system,effects of stochastic forcing on the model climate state and ensembl...Based on the dynamic framework of Lorenz 96 model,the ensemble prediction system(EPS)containing stochastic forcing has been developed.In this system,effects of stochastic forcing on the model climate state and ensemble mean prediction have been studied.The results show that the climate mean and standard deviation provided by a new computing paradigm by means of introduction of the proper stochastic forcing into numerical model integration process are closer to that of the true value than that made by the non-stochastic forcing.In other words,numerical model integration process with stochastic forcing has positive effect on the model climate state,and the effect is found to be positive mainly in the long lead time.Meanwhile,with respect to ensemble forecast effect yielded by white noise stochastic forcing,most results are better than those provided by no-stochastic forcing,and improvements pertaining to white noise stochastic forcing vary non-monotonically with the increase of the size of white noise.Moreover,the effects made by the identical white noise stochastic forcing also are different in various non-linear systems.With respect to EPS effect yielded by red noise stochastic forcing,most results are better than those provided by no-stochastic forcing,but only a part of ensemble forecast effect influenced by red noise is superior to that influenced by white noise.Finally,improvements pertaining to red noise stochastic forcing vary non-symmetrically and non-monotonically with the distribution of coefficientΦ.Besides,the selection of correlation coefficientΦis also dependent on non-linear models.展开更多
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 Su...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。展开更多
Based on the ensemble mean outputs of the ensemble forecasts from the ECMWF （European Centre for Medium-Range Weather Forecasts）, JMA （Japan Meteorological Agency）, NCEP （National Centers for Environmental Predic...Based on the ensemble mean outputs of the ensemble forecasts from the ECMWF （European Centre for Medium-Range Weather Forecasts）, JMA （Japan Meteorological Agency）, NCEP （National Centers for Environmental Prediction）, and UKMO （United Kingdom Met Office） in THORPEX （The Observing System Research and Predictability Experiment） Interactive Grand Global Ensemble （TIGGE） datasets, for the Northern Hemisphere （10~ 87.5~N, 0~ 360~） from i June 2007 to 31 August 2007, this study carried out multimodel ensemble forecasts of surface temperature and 500-hPa geopotential height, temperature and winds up to 168 h by using the bias-removed ensemble mean （BREM）, the multiple linear regression based superensemble （LRSUP）, and the neural network based superensemble （NNSUP） techniques for the forecast period from 8 to 31 August 2007. A running training period is used for BREM and LRSUP ensemble forecast techniques. It is found that BREM and LRSUP, at each grid point, have different optimal lengths of the training period. In general, the optimal training period for BREM is less than 30 days in most areas, while for LRSUP it is about 45 days.展开更多
文摘In this paper we present the current capabilities for numerical weather prediction of precipitation over China using a suite of ten multimodels and our superensemble based forecasts. Our suite of models includes the operational suite selected by NCARs TIGGE archives for the THORPEX Program. These are： ECMWF, UKMO, JMA, NCEP, CMA, CMC, BOM, MF, KMA and the CPTEC models. The superensemble strategy includes a training and a forecasts phase, for these the periods chosen for this study include the months February through September for the years 2007 and 2008. This paper addresses precipitation forecasts for the medium range i.e. Days 1 to 3 and extending out to Day 10 of forecasts using this suite of global models. For training and forecasts validations we have made use of an advanced TRMM satellite based rainfall product. We make use of standard metrics for forecast validations that include the RMS errors, spatial correlations and the equitable threat scores. The results of skill forecasts of precipitation clearly demonstrate that it is possible to obtain higher skills for precipitation forecasts for Days 1 through 3 of forecasts from the use of the multimodel superensemble as compared to the best model of this suite. Between Days 4 to 10 it is possible to have very high skills from the multimodel superensemble for the RMS error of precipitation. Those skills are shown for a global belt and especially over China. Phenomenologically this product was also found very useful for precipitation forecasts for the Onset of the South China Sea monsoon, the life cycle of the mei-yu rains and post typhoon landfall heavy rains and flood events. The higher skills of the multimodel superensemble make it a very useful product for such real time events.
文摘Based on the past related research work,a new analogy-dynamical monthly predic- tion model is established with the operational dynamic extended-range forecast model T63L16 (hereafter T63) as a dynamic kernel.The monthly mean circulation predicition with T63 is considered as a control experiment,and the prediction with the analogy-dynamical model as a contrast one.It is found that the anal- ogy-dynamical model has more precise forecast skill than the T63 model through monthly mean numerical prediction experiment.
文摘利用TIGGE（THORPEX Interactive Grand Global Ensemble）资料中的CMC、ECMWF、NCEP和UKMO 4个中心全球集合预报模式对2007年10月3日—2008年2月29日逐日累积降水进行多模式集成预报试验。通过集合平均、多模式消除偏差集合平均、加权消除偏差集成3种方法进行试验对比,重点分析各中心模式及多模式集成的240~360h（10~15d）延伸期预报的检验效果。结果表明,多模式集成对逐日累积降水240~360h延伸期预报优于单个中心模式,将逐日降水的预报时效提高了72~168h。3种集成方法对比发现,多模式消除偏差集合平均方法预报效果最好,该方法将晴雨量级的降水预报时效在中短期和延伸期至少提高了1d和5d。
文摘利用2007年6月8日—8月31日东亚地区TIGGE集合预报资料中欧洲中期天气预报中心（European Centre for Medium-range Weather Forecasts,ECMWF）和英国气象局（United Kingdom Met Office,UKMO）两个中心的地面2 m气温资料进行集合成员优选研究。结果表明,对于24-96 h预报,集合成员优选方法能够较好地选出预报技巧较高和预报技巧较低的集合成员。个例分析表明,在极端天气出现的地区,优选集合平均的预报优势较为明显。对比ECMWF和UKMO的集合成员优选结果发现,ECMWF的预报效果优于UKMO的预报效果。
基金Supported by National Natural Science Foundation of China(41205073,41275099)General Program of Nanjing Joint Center of Atmospheric Research(NJCAR2016MS02)
文摘Based on the dynamic framework of Lorenz 96 model,the ensemble prediction system(EPS)containing stochastic forcing has been developed.In this system,effects of stochastic forcing on the model climate state and ensemble mean prediction have been studied.The results show that the climate mean and standard deviation provided by a new computing paradigm by means of introduction of the proper stochastic forcing into numerical model integration process are closer to that of the true value than that made by the non-stochastic forcing.In other words,numerical model integration process with stochastic forcing has positive effect on the model climate state,and the effect is found to be positive mainly in the long lead time.Meanwhile,with respect to ensemble forecast effect yielded by white noise stochastic forcing,most results are better than those provided by no-stochastic forcing,and improvements pertaining to white noise stochastic forcing vary non-monotonically with the increase of the size of white noise.Moreover,the effects made by the identical white noise stochastic forcing also are different in various non-linear systems.With respect to EPS effect yielded by red noise stochastic forcing,most results are better than those provided by no-stochastic forcing,but only a part of ensemble forecast effect influenced by red noise is superior to that influenced by white noise.Finally,improvements pertaining to red noise stochastic forcing vary non-symmetrically and non-monotonically with the distribution of coefficientΦ.Besides,the selection of correlation coefficientΦis also dependent on non-linear models.
文摘基于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。
基金Supported by the China Meteorological Administration Special Public Welfare Research Fund (GYHY(QX)2007-6-1)National Key Basic Research and Development (973) Program of China (2012CB955204)
文摘Based on the ensemble mean outputs of the ensemble forecasts from the ECMWF （European Centre for Medium-Range Weather Forecasts）, JMA （Japan Meteorological Agency）, NCEP （National Centers for Environmental Prediction）, and UKMO （United Kingdom Met Office） in THORPEX （The Observing System Research and Predictability Experiment） Interactive Grand Global Ensemble （TIGGE） datasets, for the Northern Hemisphere （10~ 87.5~N, 0~ 360~） from i June 2007 to 31 August 2007, this study carried out multimodel ensemble forecasts of surface temperature and 500-hPa geopotential height, temperature and winds up to 168 h by using the bias-removed ensemble mean （BREM）, the multiple linear regression based superensemble （LRSUP）, and the neural network based superensemble （NNSUP） techniques for the forecast period from 8 to 31 August 2007. A running training period is used for BREM and LRSUP ensemble forecast techniques. It is found that BREM and LRSUP, at each grid point, have different optimal lengths of the training period. In general, the optimal training period for BREM is less than 30 days in most areas, while for LRSUP it is about 45 days.
