GPS/PWV资料在梅雨锋暴雨个例中的同化试验
|
摘要
基于WRF(Weather Research and Forecasting Model,天气预报模式)及其三维变分同化系统3DVAR,利用江苏省GPS/PWV(PWV:Precipitable Water Vapor,GPS反演得到可降水量)资料,并将其与探空资料比对订正,针对2011年6月18日梅雨锋暴雨进行3 h循环同化模拟。在降水参数化方案敏感性试验与单点同化试验基础上,设计多组试验对6 h降水量进行TS(Threat Score)评估。结果表明:(1)同化订正GPS/PWV资料对降水预报能力显著提高,特别是大雨、暴雨量级以上的预报能力;(2)降水量的RMSE(Root Mean Squared Error,均方根误差)相比控制试验均减小,CC(Correlation Coefficient,相关系数)均增大,最显著试验RMSE从19.1 mm下降到12.6 mm,CC从0.45上升到0.74;(3)NMC方法统计的背景误差协方差条件下中雨至暴雨量级TS评分均有一定程度提高,默认的背景误差协方差在大雨以上量级TS评分大幅提高。
Based on WRF( Weather Research and Forecasting Model) and its three- demensional variational data assimilation system 3DVAR,three hours cycling assimilation experiments of Meiyu front rainstorm occured on June 18,2011 are carried out with GPS / PWV( PWV: Precipitable Water Vapor)data which are corrected by comparing with sounding data. Based on the sensitivity researches of parameterization schemes for precipitation forecast and single point assimilation tests,several experiments are designed to assess 6 hour accumulated rainfall by TS scores( Threat Score). The results show that:( 1) the capacity of precipitation forecast is significantly increased with the assimilation of the corrected GPS / PWV data,especially in heavy rain and torrential rain.( 2) RMSE( Root Mean Squared Error) are reduced and CC( correlation coefficient) are increased by comparing with control tests. The RMSE drops from19. 1 mm to 12. 6 mm and the CC increases from 0. 45 to 0. 74 in the most significant tests.( 3) The TS scores are improved in the magnitudes from middle rain to torrential rain under the background error co-variance derived from NMC method,while TS scores are improved significantly in the magnitudes beyond heavy rain under the default background error covariance.
Based on WRF( Weather Research and Forecasting Model) and its three- demensional variational data assimilation system 3DVAR,three hours cycling assimilation experiments of Meiyu front rainstorm occured on June 18,2011 are carried out with GPS / PWV( PWV: Precipitable Water Vapor)data which are corrected by comparing with sounding data. Based on the sensitivity researches of parameterization schemes for precipitation forecast and single point assimilation tests,several experiments are designed to assess 6 hour accumulated rainfall by TS scores( Threat Score). The results show that:( 1) the capacity of precipitation forecast is significantly increased with the assimilation of the corrected GPS / PWV data,especially in heavy rain and torrential rain.( 2) RMSE( Root Mean Squared Error) are reduced and CC( correlation coefficient) are increased by comparing with control tests. The RMSE drops from19. 1 mm to 12. 6 mm and the CC increases from 0. 45 to 0. 74 in the most significant tests.( 3) The TS scores are improved in the magnitudes from middle rain to torrential rain under the background error co-variance derived from NMC method,while TS scores are improved significantly in the magnitudes beyond heavy rain under the default background error covariance.
引文
[1]KUO Y H,ZOU X,GUO Y R.Variational assimilation of precipitable water using a nonhydrostatic mesoscale adjoint model.Part I:Moisture retrieval and sensitivity experiments.Mon.Wea.Rev.,1996,124(1):122-147.
[2]张朝林,陈敏,KUO Ying Hwa,等.“00.7”北京特大暴雨模拟中气象资料同化作用的评估.气象学报,2005,63(6):922-932.ZHANG Chaolin,CHEN Min,KUO Ying Hwa,et al.Numerical assessing experiments on the individual components impact of the meteorological observation network on the"00.7"torrential rain in Beijing.Acta Meteorologica Sinica(in Chinese),2005,63(6):922-932.
[3]范水勇,郭永润,陈敏,等.高分辨率WRF三维变分同化在北京地区降水预报中的应用.高原气象,2008,27(6):1181-1188.FAN Shuiyong,GUO Yongrun,CHEN Min,et al.Application of WRF 3D VAR to a high resolution model over Beijing area.Plateau Meteorology(in Chinese),2008,27(6):1181-1188.
[4]张兵,辜旭赞.GPSPW资料在强降水个例中的同化试验[C].S1灾害天气研究与预报,2012.ZHANG Bing,GU Xuzan.Assimilation experiment of GPSPW data in the case of intense rainfall[C].S1 Research and Forecast of Disaster Weather(in Chinese),2012.
[5]曾明剑,张备,周嘉陵,等.GPS/PWV资料同化在强降水过程中的定量作用评估.气象科学,2014,34(1):77-86.ZENG Mingjian,ZHANG Bei,ZHOU Jialing,et al.Quantitative evaluation for GPS/PWV data assimilation in heavy precipitation events.Journal of the Meteorological Science(in Chinese),2014,34(1):77-86.
[6]李成才,毛节泰,李建国,等.全球定位系统遥感水汽总量.科学通报,1999,44(3):333-336.LI Chengcai,MAO Jietai,LI Jianguo,et al.Remote sensing precipitable water vapor with Global Positioning System(GPS).Chinese Science Bulletin(in Chinese),1999,44(3):333-336.
[7]何平,徐宝祥,周秀骥,等.地基GPS反演大气水汽总量的初步试验.应用气象学报,2002,13(2):169-183.HE Ping,XU Baoxiang,ZHOU Xiuji,et al.The preliminary experiment on deriving integrated atmospheric water vapor from ground-based GPS.Journal of Applied Meteorological Science(in Chinese),2002,13(2):169-183.
[8]王啸华,王易,王伟丽,等.2011年江苏两场大暴雨的观测对比分析.天气预报技术总结专刊,2012,4(2):49-55.WANG Xiaohua,WANG Yi,WANG Weili,et al.An observation comparative analysis of two severe torrential rains in Jiangsu province of 2011.Weather Forecast Review(in Chinese),2012,4(2):49-55.
[9]黄海波,陈春艳,朱雯娜.WRF模式不同云微物理参数化方案及水平分辨率对降水预报效果的影响.气象科技,2011,39(5):529-536.HUANG Haibo,CHEN Chunyan,ZHU Wenna.Impacts of different cloud microphysical processes and horizontal resolutions of WRF model on precipitation forecast effect.Meteorological Science and Technology(in Chinese),2011,39(5):529-536.
[10]陈静,薛纪善,颜宏.物理过程参数化方案对中尺度暴雨数值模拟影响的研究.气象学报,2003,61(2):203-218.CHEN Jing,XUE Jishan,YAN Hong.The impact of physics parameterization schemes on mesoscale heavy rainfall simulation.Acta Meteorologica Sinica(in Chinese),2003,61(2):203-218.
[11]张大林.各种非绝热物理过程在中尺度模式中的作用.大气科学,1998,22(4):548-561.ZHANG Dalin.Roles of various diabatic physical processes in mesoscale models.Scientia Atmospherica Sinica(in Chinese),1998,22(4):548-561.
[12]林文实,李江南,樊琦,等.云微物理参数化对华北降雪影响的数值模拟.高原气象,2007,26(1):107-115.LIN Wenshi,LI Jiangnan,FAN Qi,et al.Numerical simulation of impact of the microphysical parameterization on snowfall over North China.Plateau Meteorology(in Chinese),2007,26(1):107-115.
[13]Krain J S.The Kain-Fritsch convective parameterization:An update.J.Appl.Meteor.,2004,43:170-181.
[14]LIN Y L,Farley R D,Orville H D.Bulk parameterization of the snow field in a cloud model.J.Appl.Meteor.,1983,22(6):1065-1092.
[15]Parrish D F,Derber J C.The National Meteorological Center's spectral statistical-interpolation analysis system.Mon.Wea.Rev.,1992,120(8):1747-1763.
[2]张朝林,陈敏,KUO Ying Hwa,等.“00.7”北京特大暴雨模拟中气象资料同化作用的评估.气象学报,2005,63(6):922-932.ZHANG Chaolin,CHEN Min,KUO Ying Hwa,et al.Numerical assessing experiments on the individual components impact of the meteorological observation network on the"00.7"torrential rain in Beijing.Acta Meteorologica Sinica(in Chinese),2005,63(6):922-932.
[3]范水勇,郭永润,陈敏,等.高分辨率WRF三维变分同化在北京地区降水预报中的应用.高原气象,2008,27(6):1181-1188.FAN Shuiyong,GUO Yongrun,CHEN Min,et al.Application of WRF 3D VAR to a high resolution model over Beijing area.Plateau Meteorology(in Chinese),2008,27(6):1181-1188.
[4]张兵,辜旭赞.GPSPW资料在强降水个例中的同化试验[C].S1灾害天气研究与预报,2012.ZHANG Bing,GU Xuzan.Assimilation experiment of GPSPW data in the case of intense rainfall[C].S1 Research and Forecast of Disaster Weather(in Chinese),2012.
[5]曾明剑,张备,周嘉陵,等.GPS/PWV资料同化在强降水过程中的定量作用评估.气象科学,2014,34(1):77-86.ZENG Mingjian,ZHANG Bei,ZHOU Jialing,et al.Quantitative evaluation for GPS/PWV data assimilation in heavy precipitation events.Journal of the Meteorological Science(in Chinese),2014,34(1):77-86.
[6]李成才,毛节泰,李建国,等.全球定位系统遥感水汽总量.科学通报,1999,44(3):333-336.LI Chengcai,MAO Jietai,LI Jianguo,et al.Remote sensing precipitable water vapor with Global Positioning System(GPS).Chinese Science Bulletin(in Chinese),1999,44(3):333-336.
[7]何平,徐宝祥,周秀骥,等.地基GPS反演大气水汽总量的初步试验.应用气象学报,2002,13(2):169-183.HE Ping,XU Baoxiang,ZHOU Xiuji,et al.The preliminary experiment on deriving integrated atmospheric water vapor from ground-based GPS.Journal of Applied Meteorological Science(in Chinese),2002,13(2):169-183.
[8]王啸华,王易,王伟丽,等.2011年江苏两场大暴雨的观测对比分析.天气预报技术总结专刊,2012,4(2):49-55.WANG Xiaohua,WANG Yi,WANG Weili,et al.An observation comparative analysis of two severe torrential rains in Jiangsu province of 2011.Weather Forecast Review(in Chinese),2012,4(2):49-55.
[9]黄海波,陈春艳,朱雯娜.WRF模式不同云微物理参数化方案及水平分辨率对降水预报效果的影响.气象科技,2011,39(5):529-536.HUANG Haibo,CHEN Chunyan,ZHU Wenna.Impacts of different cloud microphysical processes and horizontal resolutions of WRF model on precipitation forecast effect.Meteorological Science and Technology(in Chinese),2011,39(5):529-536.
[10]陈静,薛纪善,颜宏.物理过程参数化方案对中尺度暴雨数值模拟影响的研究.气象学报,2003,61(2):203-218.CHEN Jing,XUE Jishan,YAN Hong.The impact of physics parameterization schemes on mesoscale heavy rainfall simulation.Acta Meteorologica Sinica(in Chinese),2003,61(2):203-218.
[11]张大林.各种非绝热物理过程在中尺度模式中的作用.大气科学,1998,22(4):548-561.ZHANG Dalin.Roles of various diabatic physical processes in mesoscale models.Scientia Atmospherica Sinica(in Chinese),1998,22(4):548-561.
[12]林文实,李江南,樊琦,等.云微物理参数化对华北降雪影响的数值模拟.高原气象,2007,26(1):107-115.LIN Wenshi,LI Jiangnan,FAN Qi,et al.Numerical simulation of impact of the microphysical parameterization on snowfall over North China.Plateau Meteorology(in Chinese),2007,26(1):107-115.
[13]Krain J S.The Kain-Fritsch convective parameterization:An update.J.Appl.Meteor.,2004,43:170-181.
[14]LIN Y L,Farley R D,Orville H D.Bulk parameterization of the snow field in a cloud model.J.Appl.Meteor.,1983,22(6):1065-1092.
[15]Parrish D F,Derber J C.The National Meteorological Center's spectral statistical-interpolation analysis system.Mon.Wea.Rev.,1992,120(8):1747-1763.