中文摘要：

针对B08RDP（The Beijing 2008 Olympics Research and Development Project）5套区域集合预报资料,系统分析了各套集合预报温度场的预报质量。在此基础上运用集合预报的综合偏差订正方法对温度场进行偏差订正,并对其效果进行了分析讨论。结果显示：5套B08RDP区域集合预报中,美国国家环境预报中心（NCEP）区域集合预报温度场的整体预报质量最高,平均预报误差最小,离散度也最为合理,预报可信度和可辨识度均较优;而中国气象科学研究院（CAMS）的温度预报误差过大,预报质量最差。整体上看,除NCEP之外的4套集合预报的温度场均存在集合离散度偏小的问题;综合偏差订正能有效减小各集合预报温度场的集合平均均方根误差,改善集合离散度的质量,显示出综合偏差订正方案对集合预报温度场偏差订正的良好能力。

英文摘要：

Five sets of regional ensemble forecasts with lead times of 36 h over two months from 24 June 2008 to 24 August 2008 from the Beijing 2008 Olympics Research and Development Project （B08RDP） are evaluated and analyzed.This is firstly done by means of standard probabilistic verification scores,including root-mean-square er- ror （ RMSE ）, ensemble spread, talagrand diagrams, reliability, and ROC（Relative Operating Characteristic） curves.Then,to improve the forecast quality, a combined decaying averaging bias correction scheme（BC） is ap- plied to the ensemble forecasts of B08RDP to reduce the bias in the ensemble mean and to adjust the improper spread of ensembles with sufficient performance evaluation.The BC scheme is designed based on the original Kal- man filter.It contains the first moment bias correction ,mainly for correcting the bias in the ensemble mean to im- prove the reliability of the ensemble forecasts, and the second moment bias correction mainly for adjusting the en- semble spread to make the ensemble forecasts fully representative of the uncertainties in the observations.Lastly, the BC scheme＇s capacity is evaluated and discussed by means of the verification scores mentioned above.Tem- peratures at 850 hPa are corrected and verified in this study, wherein ECMWF reanalysis data are used as the ref- erence for the verification. The results show that, among the five sets of regional ensemble forecasts in B08RDP,the regional ensemble forecasts from NCEP possess the best forecast quality, with minimal bias, the most appropriate spread, and the best performance in terms of reliability, resolution and talagrand distributions. Meanwhile, the regional ensemble forecast from CAMS demonstrates the worst forecast quality, due to its largest forecast bias.On the whole, a rela- tively small spread is a common problem for several of the ensemble forecasts, except those from NCEP.In gener- al,the combined bias correction scheme is proven to be efficient in reducing the RMSE of the ensemble mean, an