改进的超级集成预报方法在长江三角洲地区O_3预报中的应用  

Application of Improved Super Ensemble Forecast Method for O_3 and Its Performance Evaluation over the Yangtze River Delta Region

作　　者：姚雪峰[1,2] 葛宝珠 王自发[1,3,4] 范凡[1,5] 汤莉莉 郝建奇 张祥志[6] 晏平仲[1] 张稳定 吴剑斌[7] YAO Xuefeng;GE Baozhu;WANG Zifa;FAN Fan;TANG Lili;HAO Jianqi;ZHANG Xiangzhi;YAN Pingzhong;ZHANG Wending;WU Jianbin(State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029;96631 Army,People＇s Liberation Army of China,Beijing 102208;Center for Exeellence in Urban Atomsprherie Environment,Institute of Urban Environment,Chinese Academy of Sciences,Xiamen 361021;University of Chinese Academy of Sciences,Beijing 100049;Nanjing University of Information Science and Technology,Nanjing 210044;Jiangsu Environmental Monitoring Center,Nanjing 210036;Shanghai Meteorological Service,Shanghai 200030)

机构地区：[1]中国科学院大气物理研究所大气边界层物理与大气化学国家重点试验室 [2]解放军96631部队 [3]中国科学院城市环境研究所城市环境科学卓越中心 [4]中国科学院大学 [5]南京信息工程大学 [6]江苏省环境监测中心 [7]上海市气象局

出　　处：《大气科学》2018年第6期1273-1285,共13页Chinese Journal of Atmospheric Sciences

基　　金：国家自然科学基金项目41305113;41575123;41620104008;41611540340;91744206;国家科技支撑计划项目2014BAC22B04;中国科学院先导项目XDA19040204;中国科学院重点部署项目ZDRW-CN-2018-1-03~~

摘　　要：针对当前单模式系统臭氧(O_3)预报的不确定性问题,提出了一种基于活动区间的多模式超级集成的、高效的预报方法。本研究基于长江三角洲(长三角)地区多模式空气质量预报系统,将改进后的超级集成预报方法(AR-SUP)运用到2015年长三角地区的O_3预报中,并与滑动训练期的超级集成预报(R-SUP)、多模式集成平均预报(EMN)、消除偏差的集成平均预报(BREM)对比,结果表明AR-SUP对预报效果的改善最明显,其在暖季和冷季的均方根误差(RMSE)较最优单模式平均下降了20%和23%。将AR-SUP运用到48h和72h预报中发现,当预报时效增加时该方法依旧保持较高的预报技巧。多项统计数据均证明AR-SUP在研究时段内所有站点均能显著减小O_3预报误差、提高整体相关性和一致性,有效提高当前短期(三天)预报准确率。Aiming at existing problems in current O3 single model forecast, an efficient superensemble forecast based on running active range（AR-SUP） is proposed and applied to the EMS-YRD（multi-model ensemble air quality forecast system for the Yangtze River Delta） O3 forecast during the study period in 2015. The performance of the newly proposed method is compared with those of R-SUP（Running Training Period Superensemble）, EMN（Ensemble Mean）, and BREM（Bias-Removed Ensemble Mean）.The results show that compared with the other three ensemble methods, the AR-SUP exhibits significant improvement in daily O3 forecast with the RMSE reduced by 20% and 23% from that of the best single model in cool and warm seasons respectively. Further application of the AR-SUP in O3 ensemble forecast also shows high forecasting skills when the predicting time is extended to 48 h and 72 h. A number of statistical measures（i.e., reduced errors, increased correlation coefficients, and index of agreement） show that the forecasting skill has been improved at all the locations within the study region during all seasons, which indicates this method can be used to help improve the accuracy and reliability of short-term forecasts.

关 键 词：臭氧 多模式系统 超级集成预报 活动区间 

分 类 号：P456.8[天文地球—大气科学及气象学]