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Applications of bias-removed ensemble mean in the Gale Forecasts over the Yellow Sea and the Bohai Sea

Meteorological and Environmental Research 2010年第11期1卷 4-8页

作者：朱桦智协飞俞永庆南京信息工程大学气象灾害省部共建教育部重点实验室江苏南京210044 安徽省气象局公共气象服务中心安徽合肥230061 胜利油田气象台山东东营257000

Based on the daily sea surface wind field prediction data of Japan Meteorological Agency(JMA) forecast model,National Centers for Environmental Prediction(NCEP GFS) model and *** Operational Global Atmospheric Prediction System(NOGAPS) model at 12:00 UTC from June 28 to August 10 in 2009,the bias-removed ensemble mean(BRE) was used to do the forecast test on the sea surface wind fields,and the root-mean-square error(RMSE) was used to test and evaluate the forecast *** results showed that the BRE considerably reduced the RMSEs of 24 and 48 h sea surface wind field forecasts,and the forecast skill was superior to that of the single model *** RMSE decreases in the south of central Bohai Sea and the middle of the Yellow Sea were the most *** addition,the BRE forecast improved evidently the forecast skill of the gale process which occurred during July 13-14 and August 7 in *** forecast accuracy of the wind speed and the gale location was also improved.

关键词： bias-removed ensemble mean Gale over the Yellow Sea and the Bohai Sea Forecast skill China

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

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Acta meteorologica Sinica 2012年第1期26卷 41-51页

作者：智协飞祁海霞白永清林春泽 Key Laboratory of Meteorological Disaster of Ministry of Education Nanjing University of Information Science & Technology Wuhan Institute of Heavy Rain China Meteorological Administration

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

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