摘要
基于欧洲中期天气预报中心、日本气象厅、美国国家环境预报中心3个中心的气温模式预报资料,采用多模式简单集合平均(EMN)、滑动训练期消除偏差集合平均(Running Training Period Bias-removed Ensemble Mean,R-BREM)、滑动训练期超级集合预报(Running Training Period Superensemble Forecast,R-SUP)3种多模式集成方法,通过均方根误差(Root Mean Square Error,RMSE)和距平相关系数(Anomaly Correlation Coefficient,ACC)两种检验评估方法,比较了气温的单模式预报和多模式集成预报结果,建立了针对江苏省泰州市的地面气温多模式集成预报系统。结果表明:对于该市08:00和20:00起报的气温预报,R-BREM均是相对最优的多模式集成方法,且基于该方法的多模式集成预报结果明显优于单模式预报结果,其RMSE相对于最优单模式减小了0.5℃左右,ACC增大了约0.16,改进效果显著。同时,将R-BREM方法投入到泰州市的日常气温业务预报中,有效提高了业务预报准确率。
Based on the model prediction outputs from the European Centre for Medium-Range Weather Forecasts（ECMWF）,Japan Meteorological Agency（JMA）and National Centers for EnvironmentalPrediction（NCEP）,the multimodel ensemble forecasts of surface temperature are carried out by methods of the Multimodel Ensemble Mean（EMN）,the Running Training Period Bias-removed Ensemble Mean（R-BREM）and the Running Training Period Superensemble Forecast（R-SUP）,for Taizhou,Jiangsu Province.Their forecasts are compared with single model forecasts by means of Root Mean Square Error（RMSE）and Anomaly Correlation Coefficient（ACC）.Thus,the multimodel ensemble forecast system is established.For the surface temperature forecast at 08：00and 20：00,it is found from the comparative analysis of forecast results that the forecast skill of R-BREM is apparently superior to those of individual models,EMN,and R-SUP.Its average RMSE is reduced by about 0.5 ℃ with respect to the optimal single mode,and ACC increased by about 0.16.Additionally,the R-BREM is applied into the daily operational forecast of Taizhou for the temperature.The forecast accuracy rate is improved efficiently due to the R-BREM multimodel ensemble forecast system.
出处
《气象科技》
北大核心
2016年第4期605-611,共7页
Meteorological Science and Technology
基金
泰州市气象局自立项目(201301)
江苏高校优势学科建设工程资助项目(PAPD)资助
关键词
地面气温
多模式集成
预报系统
surface air temperature
multi-model ensemble
forecast system
