臭氧卫星观测资料同化系统构建及其试验研究  

Ozone satellite data assimilation system development and its experimental research

作　　者：董亚宁 鲍艳松[1,2] 闵锦忠[1] 陆其峰[4] 赵立龙[1] 官元红[1] 程韵初 DONG Yaning;BAO Yansong;MIN Jinzhong;LU Qifeng;ZHAO Lilong;GUAN Yuanhong;CHEN Yunchu(Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster(CIC-FEMD)/Key Laboratory of Meteorological Disaster,Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Key Laboratory for Aerosol-Cloud-Precipitation of ;School of Atmospheric Physics,Nanjing University of Information Science&Technology,Nanjing 210044,China;Weather Modification Office of Shanxi Province,Taiyuan 030032,China;National Satellite Meteorological Center,Beijing 100081,China)

机构地区：[1]南京信息工程大学气象灾害预报预警与评估协同创新中心/气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/中国气象局气溶胶与云降水重点开放实验室,江苏南京210044 [2]南京信息工程大学大气物理学院,江苏南京210044 [3]山西省人工降雨防雹办公室,山西太原030032 [4]国家卫星气象中心,北京100081

出　　处：《大气科学学报》2018年第2期282-288,共7页Transactions of Atmospheric Sciences

基　　金：国家重点研发计划项目(2016YFA0600703);国家自然科学基金国际(地区)合作与交流项目(61661136005);江苏省高等学校大学生创新创业训练计划项目(20171030091x)

摘　　要：为检验臭氧卫星资料同化对臭氧分析场和预报场的影响,基于集合平方根滤波(ENSRF)理论,结合通用地球系统模式(CESM),构建了CESM-ENSRF同化预报系统。系统构建过程考虑了卡尔曼滤波同化中的关键问题:利用全场随机扰动对初始场加扰,结合一般协方差膨胀和松弛协方差膨胀方法实现协方差膨胀,使用五阶距离相关函数进行协方差局地化。将构建的系统用于微波临边探测器(MLS)臭氧廓线数据的同化,分析臭氧卫星资料同化对模式预报的影响。结果表明:构建的CESM-ENSRF同化系统有效实现了臭氧资料同化,臭氧卫星资料同化对臭氧分析场和预报场精度有较大改进。The ozone,as an important greenhouse gas,has an important influence on global climate change.In order to test the effects of ozone satellite data assimilation on the analysis field and prediction field of ozone,in this study the CESM-ENSRF assimilation system was constructed based on the ensemble square root filter(ENSRF)theory and community earth system model(CESM).Several key problems concerning the data assimilation methodology of the Kalman filter have been considered,namely utilizing random perturbations to achieve the perturbation involving the initial field,combining the regular variance inflation and relax inflation to complete the variance inflation,and using the five order distance correlation function to conduct the variance localization.In order to analyze the effects of the assimilation of the ozone satellite data on the model prediction,the system was then used to assimilate the ozone profile data of the microwave limb sounding(MLS).The results show that the CESM-ENSRF assimilation system is able to effectively assimilate the ozone data,and that the ozone satellite data assimilation achieves a great improvement in the analysis field and prediction field of atmospheric ozone.

关 键 词：臭氧 卫星资料同化 集合卡尔曼滤波 CESM模式 

分 类 号：P412[天文地球—大气科学及气象学]