A Comparison of Three Kinds of Multimodel Ensemble Forecast Techniques Based on the TIGGE Data  

作　　者：智协飞 祁海霞 白永清 林春泽 

机构地区：[1]Key Laboratory of Meteorological Disaster of Ministry of Education,Nanjing University of Information Science & Technology [2]Wuhan Institute of Heavy Rain,China Meteorological Administration

出　　处：《Acta meteorologica Sinica》2012年第1期41-51,共11页

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)

摘　　要：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.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.

关 键 词：multimodel superensemble bias-removed ensemble mean multiple linear regression NEURALNETWORK running training period TIGGE 

分 类 号：P456[天文地球—大气科学及气象学]