基于TIGGE数据的西太平洋副热带高压多模式集成预报及检验

基于TIGGE(THORPEX Interactive Grand Global Ensemble)资料中的中国气象局（CMA）、日本气象厅（JMA）、欧洲中期天气预报中心（ECMWF）、美国国家环境预报中心（NCEP）和英国气象局（UKMO）等5个中心的500 hPa位势高度场数据，评估了各中心对西太副高控制预报和集合预报的效果，并采用了多模式集成平均（EMN）、消除偏差集成平均（BREM）和滑动训练期超级集合（R_SUP）3种方法对各中心数据进行集成.评估方法包括Talagrand分布、相关系数、均方根误差、Brier技巧评分等.结果表明：各中心预报效果有明显差异，各模式对500 hPa位势高度场控制预报中，UKMO预报效果最好，而各模式对500 hPa位势高度场集合预报中，则是ECMWF预报效果最好.从均方根误差改进率来看，基于控制预报的BREM和R_SUP集成方法明
Abstract：The skill of a set of control and ensemble forecasts of Western Pacific Subtropical High was evaluated based on the 500 hPa geopotential height information from the THORPEX Interactive Grand Global Ensemble (TIGGE) datasets, which consist of model outputs from CMA, JMA, ECMWF, UKMO and NCEP. Three methods were adopted, i.e., Ensemble Mean (EMN), Bias-Removed Ensemble Mean (BREM) and running Training Period Superensemble (R_SUP), to integrate the data from different sources, and the metrics for performance evaluation include Talagrand distribution, correlation coefficient, Root Mean Square Error (RMSE), and Brier Skill Score (BSS). A comparison of the outputs of these models shows significant variation in forecast performance. The results indicate that the UKMO model has the best forecast skill for the 500 hPa geopotential height among all control forecasts, while the ECMWF model ranks on the top of all ensemble forecasts. From the improvement of RMSE, both BREM and R_SUP can significantly reduce the RMSE of the integrated forecast results compared to the original control forecasts in TIGGE, but EMN does not show similar improvement. However, none of the three integration methods shows discernable improvement of ensemble forecast of the 500 hPa geopotential height, with all having less skills than ECMWF single model ensemble forecast.