FY-3A卫星微波资料的集合变分混合同化试验
|
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。
Hybrid variational-ensemble data assimilation method was used to directly assimilate both Microwave Temperature Sounder (MWTS) data and Microwave Humidity Sounder (MWHS) data of FY-3A satellite in the case of Beijing "7.21" extreme precipitation event in 2012. This method was also 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. The former one 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. No matter for the thermodynamic variable or the dynamical variable,the root mean square error of the initial field obtained those two methods by the hybrid variational-ensemble data assimilationmethod is smaller than that obtained by 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 greatly different in both magnitude and distribution. Three-dimensional variational assimilation method has larger adjustment region higher intensity in the initial 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 "flowdependent". Hybrid variational-ensemble data assimilation method makes the distribution of the initial field more reasonable,and it can improve the simulation of precipitation.
Hybrid variational-ensemble data assimilation method was used to directly assimilate both Microwave Temperature Sounder (MWTS) data and Microwave Humidity Sounder (MWHS) data of FY-3A satellite in the case of Beijing "7.21" extreme precipitation event in 2012. This method was also 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. The former one 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. No matter for the thermodynamic variable or the dynamical variable,the root mean square error of the initial field obtained those two methods by the hybrid variational-ensemble data assimilationmethod is smaller than that obtained by 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 greatly different in both magnitude and distribution. Three-dimensional variational assimilation method has larger adjustment region higher intensity in the initial 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 "flowdependent". Hybrid variational-ensemble data assimilation method makes the distribution of the initial field more reasonable,and it can improve the simulation of precipitation.
引文
[1]Velden C S,Smith W L.Monitoring tropical cyclone evolution with NOAA satellite microwave observations.J.Climate Appl.Meteor.,1983,22(5):714-724.
[2]朱国富,薛纪善,张华,等.GRAPES变分同化系统中卫星辐射率资料的直接同化.科学通报,2008,53(20):2424-2427.ZHU Guofu,XUE Jishan,ZHANG Hua,et al.Direct assimilation of satellite radiance data in GRAPES variational assimilation system.Chinese Science Bulletin,2008,53(22):3465-3469.
[3]张华,丑纪范,邱崇践.西北太平洋威马逊台风结构的卫星观测同化分析.科学通报,2004,49(5):493-498.ZHANG Hua,CHOU Jifan,QIU Chongjian.Assimilation analysis of Rammasun typhoon structure over Northwest Pacific using satellite data.Chinese Science Bulletin,2004,49(4):389-395.
[4]QIN Zhengkun,ZOU Xiaolei,WENG Fuzhong.Evaluating added benefits of assimilating GOES imager radiance data in GSI for coastal QPFs.Mon.Wea.Rev.,2013,141(1):75-92.
[5]吴新荣,韩桂军,李冬,等.集合滤波和三维变分混合数据同化方法研究.热带海洋学报,2011,30(6):24-30.WU Xinrong,HAN Guijun,LI Dong,et al.A hybrid ensemble filter and 3D variational analysis scheme.Journal of Tropical Oceanography(in Chinese),2011,30(6):24-30.
[6]马旭林,陆续,于月明,等.数值天气预报中集合-变分混合资料同化及其研究进展.热带气象学报,2014,30(6):1188-1195.MA Xulin,LU Xu,YU Yueming,et al.Progress on hybrid ensemble-variational data assimilation in numerical weather prediction.Journal of Tropical Meteorology(in Chinese),2014,30(6):1188-1195.
[7]WANG Xuguang,Barker D M,Snyder C,et al.A hybrid ETKF-3DVAR data assimilation scheme for the WRF model.Part I:observing system simulation experiment.Mon.Wea.Rev.,2008,136(12):5116-5131.
[8]WANG Xuguang,Barker D M,Snyder C,et al.A hybrid ETKF-3DVAR data assimilation scheme for the WRF model.Part II:real observation experiments.Mon.Wea.Rev.,2008,136(12):5132-5147.
[9]熊春晖,张立凤,关吉平,等.集合-变分数据同化方法的发展与应用.地球科学进展,2013,28(6):648-656.XIONG Chunhui,ZHANG Lifeng,GUAN Jiping,et al.Development and application of ensemble-variational data assimilation methods.Advances in Earth Science(in Chinese),2013,28(6):648-656.
[10]沈菲菲,闵锦忠,许冬梅,等.WRF-Hybrid背景误差协方差调整在台风同化及预报中的应用研究.气象科学,2015,35(2):150-159.SHEN Feifei,MIN Jinzhong,XU Dongmei,et al.WRF-Hybrid background error covariance adjustment in typhoon assimilation and forecasting.Journal of the Meteorological Sciences(in Chinese),2015,35(2):150-159.
[11]ZHANG Man,Zupanski M,Kim M J,et al.Assimilating AMSU-A radiances in the TC core area with NOAA operational HWRF(2011)and a hybrid data assimilation system:Danielle(2010).Mon.Wea.Rev.,2013,141(11):3889-3907.
[12]WANG Xuguang.Application of the WRF hybrid ETKF-3DVARdata assimilation system for hurricane track forecasts.Wea.Forecasting,2011,26(6):868-884.
[13]张晓慧,张立凤,熊春晖,等.基于集合变分混合同化方法的双台风数值模拟.热带气象学报,2015,31(4):505-516.ZHANG Xiaohui,ZHANG Lifeng,XIONG Chunhui,et al.Analysis and numerical study of a Hybrid ensemble-3DVAR data assimilation scheme for binary typhoons.Journal of Tropical Meteorology(in Chinese),2015,31(4):505-516.
[14]DONG Chaohua,YANG Jun,YANG Zhongdong,et al.An overview of a new Chinese weather satellite FY-3A.Bull.Amer.Meteor.Soc.,2009,90(10):1531-1544.
[15]YANG Jun,ZHANG Peng,LU Naimeng,et al.Improvements on global meteorological observations from the current Fengyun 3satellites and beyond.Int.J.Digit.Earth,2012,5(3):251-265.
[16]LU Qifeng,Bell W,Bauer P,et al.Monitoring and assimilation of FY-3A data at ECMWF.
[17]杨引明,杜明斌,张洁.FY-3A微波资料在“莫拉克”台风预报中的同化试验.热带气象学报,2012,28(1):23-30.YANG Yinming,DU Mingbin,ZHANG Jie.Experiments of assimilating FY-3A microwave data in forecast of Typhoon Morakot Journal of Tropical Meteorology(in Chinese),2012,28(1):23-30.
[18]张旭煜,朱克云,张杰,等.FY-3A卫星MWHS资料同化及四川盆地暴雨数值模拟分析.成都信息工程学院学报,2013,28(3):258-266.ZHANG Xuyu,ZHU Keyun,ZHANG Jie,et al.Use o assimilating FY-3A MWHS data of the heavy rain over Sichuan Province.Journal of Chengdu University of Information Technology(in Chinese),2013,28(3):258-266.
[19]董佩明,黄江平,刘桂青,等.FY-3A微波探测资料的直接同化应用及云雨条件下的亮温模拟.热带气象学报,2014,30(2):302-310.DONG Peiming,HUANG Jiangping,LIU Guiqing,et al Assimilation of FY-3A Microwave Observations and Simulation o Brightness Temperature under cloudy and rainy condition.Journa of Tropical Meteorology(in Chinese),2014,30(2):302-310.
[20]Talagrand O,Courtier P.Variational assimilation of meteorologica observations with the adjoint vorticity equation.I:theory.Quart.JRoy.Meteor.Soc.,1987,113(478):1311-1328.
[21]Etherton B J,Bishop C H.Resilience of hybrid ensemble/3DVARanalysis schemes to model error and ensemble covariance error Mon.Wea.Rev.,2004,132(5):1065-1080.
[22]WANG Xuguang,Snyder C,Hamill T M.On the theoretica equivalence of differently proposed ensemble-3DVAR hybrid analysis schemes.Mon.Wea.Rev.,2007,135(1):222-227.
[23]俞小鼎.2012年7月21日北京特大暴雨成因分析.气象,2012,38(11):1313-1329.YU Xiaoding.Investigation of Beijing extreme flooding event on 21July 2012.Meteorological Monthly(in Chinese),2012,38(11):1313-1329.
[24]Wernli H,Paulat M,Hagen M,et al.SAL-a novel quality measure for the verification of quantitative precipitation forecasts.Mon.Wea.Rev.,2008,136(11):4470-4487.
[25]Wernli H,Hofmann C,Zimmer M.Spatial forecast verification methods intercomparison project:application of the SALtechnique.Wea.Forecasting,2009,24(6):1472-1484.
[26]公颖.SAL定量降水预报检验方法的解释与应用.暴雨灾害,2010,29(2):153-159.GONG Ying.The explanation and application of SAL quantitative verification for precipitation forecasts.Torrential Rain and Disasters(in Chinese),2010,29(2):153-159.
[27]张明阳,张立凤,张斌,等.集合变分混合同化背景误差协方差流依赖性分析.气象科学,2015,35(6):728-736.ZHANG Mingyang,ZHANG Lifeng,ZHANG Bin,et al.Flowdependent characteristics of background error covariance in hybrid variational-ensemble data assimilation.Journal of the Meteorological Sciences(in Chinese),2015,35(6):728-736.
[2]朱国富,薛纪善,张华,等.GRAPES变分同化系统中卫星辐射率资料的直接同化.科学通报,2008,53(20):2424-2427.ZHU Guofu,XUE Jishan,ZHANG Hua,et al.Direct assimilation of satellite radiance data in GRAPES variational assimilation system.Chinese Science Bulletin,2008,53(22):3465-3469.
[3]张华,丑纪范,邱崇践.西北太平洋威马逊台风结构的卫星观测同化分析.科学通报,2004,49(5):493-498.ZHANG Hua,CHOU Jifan,QIU Chongjian.Assimilation analysis of Rammasun typhoon structure over Northwest Pacific using satellite data.Chinese Science Bulletin,2004,49(4):389-395.
[4]QIN Zhengkun,ZOU Xiaolei,WENG Fuzhong.Evaluating added benefits of assimilating GOES imager radiance data in GSI for coastal QPFs.Mon.Wea.Rev.,2013,141(1):75-92.
[5]吴新荣,韩桂军,李冬,等.集合滤波和三维变分混合数据同化方法研究.热带海洋学报,2011,30(6):24-30.WU Xinrong,HAN Guijun,LI Dong,et al.A hybrid ensemble filter and 3D variational analysis scheme.Journal of Tropical Oceanography(in Chinese),2011,30(6):24-30.
[6]马旭林,陆续,于月明,等.数值天气预报中集合-变分混合资料同化及其研究进展.热带气象学报,2014,30(6):1188-1195.MA Xulin,LU Xu,YU Yueming,et al.Progress on hybrid ensemble-variational data assimilation in numerical weather prediction.Journal of Tropical Meteorology(in Chinese),2014,30(6):1188-1195.
[7]WANG Xuguang,Barker D M,Snyder C,et al.A hybrid ETKF-3DVAR data assimilation scheme for the WRF model.Part I:observing system simulation experiment.Mon.Wea.Rev.,2008,136(12):5116-5131.
[8]WANG Xuguang,Barker D M,Snyder C,et al.A hybrid ETKF-3DVAR data assimilation scheme for the WRF model.Part II:real observation experiments.Mon.Wea.Rev.,2008,136(12):5132-5147.
[9]熊春晖,张立凤,关吉平,等.集合-变分数据同化方法的发展与应用.地球科学进展,2013,28(6):648-656.XIONG Chunhui,ZHANG Lifeng,GUAN Jiping,et al.Development and application of ensemble-variational data assimilation methods.Advances in Earth Science(in Chinese),2013,28(6):648-656.
[10]沈菲菲,闵锦忠,许冬梅,等.WRF-Hybrid背景误差协方差调整在台风同化及预报中的应用研究.气象科学,2015,35(2):150-159.SHEN Feifei,MIN Jinzhong,XU Dongmei,et al.WRF-Hybrid background error covariance adjustment in typhoon assimilation and forecasting.Journal of the Meteorological Sciences(in Chinese),2015,35(2):150-159.
[11]ZHANG Man,Zupanski M,Kim M J,et al.Assimilating AMSU-A radiances in the TC core area with NOAA operational HWRF(2011)and a hybrid data assimilation system:Danielle(2010).Mon.Wea.Rev.,2013,141(11):3889-3907.
[12]WANG Xuguang.Application of the WRF hybrid ETKF-3DVARdata assimilation system for hurricane track forecasts.Wea.Forecasting,2011,26(6):868-884.
[13]张晓慧,张立凤,熊春晖,等.基于集合变分混合同化方法的双台风数值模拟.热带气象学报,2015,31(4):505-516.ZHANG Xiaohui,ZHANG Lifeng,XIONG Chunhui,et al.Analysis and numerical study of a Hybrid ensemble-3DVAR data assimilation scheme for binary typhoons.Journal of Tropical Meteorology(in Chinese),2015,31(4):505-516.
[14]DONG Chaohua,YANG Jun,YANG Zhongdong,et al.An overview of a new Chinese weather satellite FY-3A.Bull.Amer.Meteor.Soc.,2009,90(10):1531-1544.
[15]YANG Jun,ZHANG Peng,LU Naimeng,et al.Improvements on global meteorological observations from the current Fengyun 3satellites and beyond.Int.J.Digit.Earth,2012,5(3):251-265.
[16]LU Qifeng,Bell W,Bauer P,et al.Monitoring and assimilation of FY-3A data at ECMWF.
[17]杨引明,杜明斌,张洁.FY-3A微波资料在“莫拉克”台风预报中的同化试验.热带气象学报,2012,28(1):23-30.YANG Yinming,DU Mingbin,ZHANG Jie.Experiments of assimilating FY-3A microwave data in forecast of Typhoon Morakot Journal of Tropical Meteorology(in Chinese),2012,28(1):23-30.
[18]张旭煜,朱克云,张杰,等.FY-3A卫星MWHS资料同化及四川盆地暴雨数值模拟分析.成都信息工程学院学报,2013,28(3):258-266.ZHANG Xuyu,ZHU Keyun,ZHANG Jie,et al.Use o assimilating FY-3A MWHS data of the heavy rain over Sichuan Province.Journal of Chengdu University of Information Technology(in Chinese),2013,28(3):258-266.
[19]董佩明,黄江平,刘桂青,等.FY-3A微波探测资料的直接同化应用及云雨条件下的亮温模拟.热带气象学报,2014,30(2):302-310.DONG Peiming,HUANG Jiangping,LIU Guiqing,et al Assimilation of FY-3A Microwave Observations and Simulation o Brightness Temperature under cloudy and rainy condition.Journa of Tropical Meteorology(in Chinese),2014,30(2):302-310.
[20]Talagrand O,Courtier P.Variational assimilation of meteorologica observations with the adjoint vorticity equation.I:theory.Quart.JRoy.Meteor.Soc.,1987,113(478):1311-1328.
[21]Etherton B J,Bishop C H.Resilience of hybrid ensemble/3DVARanalysis schemes to model error and ensemble covariance error Mon.Wea.Rev.,2004,132(5):1065-1080.
[22]WANG Xuguang,Snyder C,Hamill T M.On the theoretica equivalence of differently proposed ensemble-3DVAR hybrid analysis schemes.Mon.Wea.Rev.,2007,135(1):222-227.
[23]俞小鼎.2012年7月21日北京特大暴雨成因分析.气象,2012,38(11):1313-1329.YU Xiaoding.Investigation of Beijing extreme flooding event on 21July 2012.Meteorological Monthly(in Chinese),2012,38(11):1313-1329.
[24]Wernli H,Paulat M,Hagen M,et al.SAL-a novel quality measure for the verification of quantitative precipitation forecasts.Mon.Wea.Rev.,2008,136(11):4470-4487.
[25]Wernli H,Hofmann C,Zimmer M.Spatial forecast verification methods intercomparison project:application of the SALtechnique.Wea.Forecasting,2009,24(6):1472-1484.
[26]公颖.SAL定量降水预报检验方法的解释与应用.暴雨灾害,2010,29(2):153-159.GONG Ying.The explanation and application of SAL quantitative verification for precipitation forecasts.Torrential Rain and Disasters(in Chinese),2010,29(2):153-159.
[27]张明阳,张立凤,张斌,等.集合变分混合同化背景误差协方差流依赖性分析.气象科学,2015,35(6):728-736.ZHANG Mingyang,ZHANG Lifeng,ZHANG Bin,et al.Flowdependent characteristics of background error covariance in hybrid variational-ensemble data assimilation.Journal of the Meteorological Sciences(in Chinese),2015,35(6):728-736.