| FY-3A卫星微波资料的集合变分混合同化试验 |
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| 引用本文： | 谢宾鹏,张立凤,张明阳,杨雨轩,汤鹏宇.FY-3A卫星微波资料的集合变分混合同化试验[J].气象科学,2018,38(5):606-615. |
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| 作者姓名： | 谢宾鹏 张立凤 张明阳 杨雨轩 汤鹏宇 |
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| 作者单位： | 国防科技大学 气象海洋学院, 南京 211101;解放军94783部队, 浙江 湖州 313100,国防科技大学 气象海洋学院, 南京 211101,国防科技大学 气象海洋学院, 南京 211101,国防科技大学 气象海洋学院, 南京 211101,解放军93886部队, 乌鲁木齐 830000 |
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| 基金项目： | 国家自然科学基金资助项目（41375063） |
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| 摘 要： | 以2012年"北京7.21暴雨"为例,实现了集合变分混合同化方法对FY-3A的微波温度仪和微波湿度仪资料的直接同化,并与三维变分方法进行了比较。结果表明:虽然两种同化方法同化FY-3A微波资料都能改进降水模拟效果,但是与实况相比,集合变分混合同化方法改进效果更为明显,其能有效减少虚假强降水的模拟,改进强降水中心位置的模拟,SAL评分定量检验也同样表明,集合变分混合同化方法对暴雨的模拟效果要优于三维变分同化方法;无论是热力学变量还是动力学变量,集合变分同化得到的初始场均方根误差均显著小于三维变分同化的结果;两种方法同化FY-3A微波资料均能改变初始场中的各种物理量信息,但不同方法得到的同化增量大小和分布却有明显的差异:三维变分同化方法对初始场的调整区域和强度都要大于混合同化方法,且其同化增量表现出均匀和各向同性的分布特点;而利用集合信息的混合同化方法得到的同化增量分布表现为非均匀性和各向异性,具有"流依赖性"的特征,这使得初始场的分布更合理,有利于改善降水的模拟效果。
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| 关 键 词： | 集合变分混合同化 三维变分同化 风云3A卫星 微波资料 |
| 收稿时间： | 2017/5/22 0:00:00 |
| 修稿时间： | 2017/7/7 0:00:00 |
Assimilation experiments of FY-3A satellite microwave data based on hybrid variational-ensemble assimilation method |
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| XIE Binpeng,ZHANG Lifeng,ZHANG Mingyang,YANG Yuxuan and TANG Pengyu.Assimilation experiments of FY-3A satellite microwave data based on hybrid variational-ensemble assimilation method[J].Scientia Meteorologica Sinica,2018,38(5):606-615. |
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| Authors: | XIE Binpeng ZHANG Lifeng ZHANG Mingyang YANG Yuxuan and TANG Pengyu |
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| Institution: | College of Meteorology and OceanographyNational University of Defense Technology, Nanjing 211101, China;Unit 94783 of PLA, Zhejiang Huzhou 313100, China,College of Meteorology and OceanographyNational University of Defense Technology, Nanjing 211101, China,College of Meteorology and OceanographyNational University of Defense Technology, Nanjing 211101, China,College of Meteorology and OceanographyNational University of Defense Technology, Nanjing 211101, China and Unit 93886 of PLA, Urumchi 830000 |
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| Abstract: | Using Hybrid variational-ensemble data assimilation method directly assimilate both Microwave Temperature Sounder (MWTS) data and Microwave Humidity Sounder (MWHS) data of FY-3A satellite in the case of the historical Beijing "7.21" extreme precipitation event in 2012, and compared to the three-dimensional variational assimilation method. Results show that both assimilation methods can improve the simulated precipitation results,but the Hybrid variational-ensemble data assimilation method is better than the three-dimensional variational assimilation method,which can effectively reduce the false heavy rainfall, and improve the simulation of the location of heavy rainfall center. The result of the SAL quantitative verification method for precipitation forecast shows the same conclusion. Comparing the root mean square error between the initial field and the reanalysis data obtained those two methods, the results of the the Hybrid variational-ensemble data assimilation method is smaller than the three-dimensional variational assimilation method,which means the analysis field of the Hybrid variational-ensemble data assimilation method is closer to real situation than that of the three-dimensional variational assimilation method. Both assimilation methods can change the physical information in the initial field, but the increments produced by those two methods are different in both magnitude and distribution. Three-dimensional variational assimilation method affects larger region in the simulation area for wind field,temperature field and relative humidity field than that of the Hybrid variational-ensemble data assimilation method. Increments obtained by the three-dimensional variational assimilation method are nearly homogeneous and isotropic,while increments obtained by the Hybrid variational-ensemble data assimilation method are inhomogeneous,anisotropic and "flow-dependent". Hybrid variational-ensemble data assimilation method makes the distribution of the initial field more reasonable, and it can improve the simulation of precipitation. |
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| Keywords: | Variational-ensemble data assimilation method Three-dimensional variational assimilation method FY-3A satellite Microwave data |
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