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 U.S.Navy Operational Global Atmospheric Pr...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 U.S.Navy 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.The 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 forecast.The RMSE decreases in the south of central Bohai Sea and the middle of the Yellow Sea were the most obvious.In addition,the BRE forecast improved evidently the forecast skill of the gale process which occurred during July 13-14 and August 7 in 2009.The forecast accuracy of the wind speed and the gale location was also improved.展开更多
基于贝叶斯模式平均方法(Bayesian Model Averaging),发展了一个NINO3.4指数的多模式客观权重集合预报方法(简称OBJ)。该方法基于训练期内单个模式的预报结果,用线性回归订正单个预报的偏差,依据模式的预报效果估计单个模式的权重。利用...基于贝叶斯模式平均方法(Bayesian Model Averaging),发展了一个NINO3.4指数的多模式客观权重集合预报方法(简称OBJ)。该方法基于训练期内单个模式的预报结果,用线性回归订正单个预报的偏差,依据模式的预报效果估计单个模式的权重。利用2002年2月—2015年10月美国哥伦比亚大学国际气候与社会研究所(IRI)提供的7个单一模式对NINO3.4指数的预报结果进行OBJ试验,并采用均方根误差对多模式集合平均预报(简称ENS)和OBJ的预报结果进行检验和评估。结果表明,ENS的预报效果优于7个单一模式的预报效果,而OBJ预报效果优于ENS预报效果,其NINO3.4指数的均方根误差比ENS方法降低了4%。将单一模式预报结果按时间划分为训练期和预报期,利用独立样本估计OBJ的参数并进行预报试验,这些试验也表明,OBJ能进一步提高预报精度。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
基金Supported by Chinese Meteorological Administration's Special Funds(Meteorology) for Scientific Research on Public Causes( GYHY200906007)Gale Forecast Item of the Shengli Oil Field Observatory (2008001)~~
文摘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 U.S.Navy 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.The 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 forecast.The RMSE decreases in the south of central Bohai Sea and the middle of the Yellow Sea were the most obvious.In addition,the BRE forecast improved evidently the forecast skill of the gale process which occurred during July 13-14 and August 7 in 2009.The forecast accuracy of the wind speed and the gale location was also improved.
文摘基于贝叶斯模式平均方法(Bayesian Model Averaging),发展了一个NINO3.4指数的多模式客观权重集合预报方法(简称OBJ)。该方法基于训练期内单个模式的预报结果,用线性回归订正单个预报的偏差,依据模式的预报效果估计单个模式的权重。利用2002年2月—2015年10月美国哥伦比亚大学国际气候与社会研究所(IRI)提供的7个单一模式对NINO3.4指数的预报结果进行OBJ试验,并采用均方根误差对多模式集合平均预报(简称ENS)和OBJ的预报结果进行检验和评估。结果表明,ENS的预报效果优于7个单一模式的预报效果,而OBJ预报效果优于ENS预报效果,其NINO3.4指数的均方根误差比ENS方法降低了4%。将单一模式预报结果按时间划分为训练期和预报期,利用独立样本估计OBJ的参数并进行预报试验,这些试验也表明,OBJ能进一步提高预报精度。
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.
