东亚地区冬季地面气温延伸期概率预报研究

摘要

Based on the TIGGE datasets including the European Centre for Medium-Range Weather Forecasts(ECMWF),the U.S.National Centers for Environmental Prediction (NCEP),the United Kingdom Met Office (UKMO) and its multi-center ensemble systems,Bayesian Model Averaging (BMA) probabilistic forecasts of winter surface air temperature over East Asia are established.Anomaly correlation coefficient (ACC) and root mean square (RMSE) are used for the evaluation of the BMA deterministic forecasts.Furthermore,Brier score (BS),Ranked probability score(RPS),BSS and RPSS are applied to evaluate the performance of BMA probabilistic forecasts.The results show that the BMA forecast distributions are considerably better calibrated than the raw ensemble forecasts,and BMA forecasts of ECMWF,NCEP and UKMO EPSs provide better deterministic forecasts than the individual model forecasts.The BMA models for multi-center EPSs outperform those for single-center EPS for lead times of 240-360 h,and the optimal length of the training period is about 35 days.In addition,BMA provides a more reasonable probability distribution,which depicts the quantitative uncertainty of the forecasts.The uncertainty on the land(higher latitude) is larger than that on the sea(lower latitude).%利用TIGGE资料中的ECMWF、NCEP、UKMO三个中心集合预报系统以及由此构成的多中心集合预报系统所提供的地面2m气温10～ 15 d延伸期集合预报产品,建立贝叶斯模式平均(Bayesian Model Averaging,BMA)概率预报模型,对东亚地区冬季地面气温进行延伸期概率预报研究.采用距平相关系数、均方根误差、布莱尔评分、等级概率评分等指标分别对BMA确定性结果与概率预报进行评估.结果表明,BMA方法明显地改进了原始集合预报结果,预报技巧优于原始集合预报,且多中心BMA预报优于单中心BMA预报,最佳滑动训练期取35 d.BMA预报为气温的延伸期概率预报提供了更合理的概率分布,定量描述了预报的不确定性.