同化GPS可降水量资料在安徽地区暴雨预报中的应用
|
摘要
利用2014年5月10日08时和5月24日08时的中国GPS(Global Positioning System)可降水量资料、地面气象观测资料及探空资料,基于WRF(Weather Research Forecast)模式及其三维变分同化系统对安徽地区2014年5月10日区域性暴雨过程和5月24日局地性暴雨过程进行了同化研究。结果表明:同化GPS水汽资料能较好地改善WRF模式的湿度场,对风场的调整较小;同化探空资料和地面常规气象观测资料可以同时调整湿度场与风场,但对湿度场的改善效果明显不如同化GPS水汽资料。仅同化GPS可降水资料,可以改进暴雨落区和强度的WRF模式模拟预报效果;而同化探空资料和地面常规气象观测资料对暴雨落区与强度预报的改进效果低于仅同化GPS水汽资料;同时同化地面常规气象观测资料、探空资料和GPS可降水资料对暴雨强度与落区的预报效果改进明显,与实况更接近,暴雨预报的TS评分更高,空报率更低。
GPS/PWV(Precipitable Water Vapor) data,sounding and surface data are assimilated into the WRF model together with a 3DVar assimilation system to investigate their effects on a regional rainstorm on May 10 and a local one on May 24,2015,respectively,over Anhui province.The results show that GPS/PWV data assimilated to WRF can improve the relative humidities in an initial field,but have little influence on wind.However,sounding and surface data assimilation can improve both relative humilities and wind,with a slightly worse result than that of GPS/PWV for the humidities.The forecast capacities of rainstorm locations and intensities may be enhanced with the assimilated GPS/PWV data only,superior to those with sounding and surface data.If all these data were assimilated to the WRF,the rainstorm locations and intensities can be significantly improved and closer to the observations,leading to higher TS scores and lower false-alarm rates.
GPS/PWV(Precipitable Water Vapor) data,sounding and surface data are assimilated into the WRF model together with a 3DVar assimilation system to investigate their effects on a regional rainstorm on May 10 and a local one on May 24,2015,respectively,over Anhui province.The results show that GPS/PWV data assimilated to WRF can improve the relative humidities in an initial field,but have little influence on wind.However,sounding and surface data assimilation can improve both relative humilities and wind,with a slightly worse result than that of GPS/PWV for the humidities.The forecast capacities of rainstorm locations and intensities may be enhanced with the assimilated GPS/PWV data only,superior to those with sounding and surface data.If all these data were assimilated to the WRF,the rainstorm locations and intensities can be significantly improved and closer to the observations,leading to higher TS scores and lower false-alarm rates.
引文
[1]Bevis M,Businger S,Herring T A,et al.GPS meteorology:Remote sensing of atmospheric water vapor using the global positioning system[J].Journal of Geophysical Research:Atmospheres,1992,97(D14):15787-15801.
[2]Ohtanil R.Detection of water vapor variations driven by thermally-induced local circulations using the Japanese Continuous GPS Array[J].Geophysical Research Letters,2001,28(1):151-154.
[3]Guo Y R,Kuo Y H,Dudhia J,et al.Four-dimensional variational data assimilation of heterogeneous mesoscale observations for a strong convective case[J].Monthly Weather Review,2000,128(3):619-643.
[4]Cucurull L,Vandenberghe F,Barker D.Three-dimensional variational data assimilation of ground-based GPS ZTD and meteorological observations during the 14 December2001 storm event over the western mediterranean sea[J].Monthly Weather Review,2004,132(3):749-763.
[5]王勇,刘严萍,柳林涛,等.区域GPS网对流层延迟直接推算可降水量研究[J].热带气象学报,2007,23(5):510-514.
[6]王勇,柳林涛,郝晓光,等.准实时地基GPS可降水量的解算方案与可靠性研究[J].热带气象学报,2007,23(2):177-181.
[7]张斌,薛根元,张双成.基于IGS超级跟踪站近实时解算浙江地基GPS大气可降水量[J].热带气象学报,2009,25(2):222-226.
[8]李国翠,孙云,李国平,等.“20110809”石家庄西部大暴雨分析[J].气象与环境学报,2013,29(5):1-5.
[9]程航,程相坤,朱晶,等.GPS遥感大气可降水量在大连地区3次降水过程中的应用[J].气象与环境学报,2014,30(5):38-48.
[10]黄振,李万彪,梁军.GPS遥感大气可降水量在降水天气过程分析中的应用[J].气象与环境学报,2013,29(4):31-36.
[11]袁招洪,丁金才,陈永林.中尺度数值预报模式预报水汽与GPS观测的比较研究[J].大气科学,2004,28(3):433-440.
[12]张朝林,季崇萍,Kuo Y H,等.地形对“00.7”北京特大暴雨过程影响的数值研究[J].自然科学进展,2005,15(5):572-578.
[13]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[J].气象科学,2014,34(1):77-86.
[14]Schenkman A D,Xue M,Shapiro A,et al.The analysis and prediction of the 8-9 May 2007 Oklahoma Tornadic mesoscale convective system by assimilating WSR-88D and CASA radar data using 3DVAR[J].Monthly Weather Review,2011,139(1):224-246.
[15]万文龙,张杰,朱克云,等.GPS-PW资料在川西暴雨中的应用研究[J].安徽农业科学,2010,38(13):6759-6764.
[16]徐广阔,孙建华,雷霆,等.多普勒天气雷达资料同化对暴雨模拟的影响[J].应用气象学报,2009,20(1):36-46.
[17]何志新,江杨,邱学兴,等.多部多普勒天气雷达资料同化对暴雨个例模拟的影响分析[J].暴雨灾害,2015,34(2):168-176.
[2]Ohtanil R.Detection of water vapor variations driven by thermally-induced local circulations using the Japanese Continuous GPS Array[J].Geophysical Research Letters,2001,28(1):151-154.
[3]Guo Y R,Kuo Y H,Dudhia J,et al.Four-dimensional variational data assimilation of heterogeneous mesoscale observations for a strong convective case[J].Monthly Weather Review,2000,128(3):619-643.
[4]Cucurull L,Vandenberghe F,Barker D.Three-dimensional variational data assimilation of ground-based GPS ZTD and meteorological observations during the 14 December2001 storm event over the western mediterranean sea[J].Monthly Weather Review,2004,132(3):749-763.
[5]王勇,刘严萍,柳林涛,等.区域GPS网对流层延迟直接推算可降水量研究[J].热带气象学报,2007,23(5):510-514.
[6]王勇,柳林涛,郝晓光,等.准实时地基GPS可降水量的解算方案与可靠性研究[J].热带气象学报,2007,23(2):177-181.
[7]张斌,薛根元,张双成.基于IGS超级跟踪站近实时解算浙江地基GPS大气可降水量[J].热带气象学报,2009,25(2):222-226.
[8]李国翠,孙云,李国平,等.“20110809”石家庄西部大暴雨分析[J].气象与环境学报,2013,29(5):1-5.
[9]程航,程相坤,朱晶,等.GPS遥感大气可降水量在大连地区3次降水过程中的应用[J].气象与环境学报,2014,30(5):38-48.
[10]黄振,李万彪,梁军.GPS遥感大气可降水量在降水天气过程分析中的应用[J].气象与环境学报,2013,29(4):31-36.
[11]袁招洪,丁金才,陈永林.中尺度数值预报模式预报水汽与GPS观测的比较研究[J].大气科学,2004,28(3):433-440.
[12]张朝林,季崇萍,Kuo Y H,等.地形对“00.7”北京特大暴雨过程影响的数值研究[J].自然科学进展,2005,15(5):572-578.
[13]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[J].气象科学,2014,34(1):77-86.
[14]Schenkman A D,Xue M,Shapiro A,et al.The analysis and prediction of the 8-9 May 2007 Oklahoma Tornadic mesoscale convective system by assimilating WSR-88D and CASA radar data using 3DVAR[J].Monthly Weather Review,2011,139(1):224-246.
[15]万文龙,张杰,朱克云,等.GPS-PW资料在川西暴雨中的应用研究[J].安徽农业科学,2010,38(13):6759-6764.
[16]徐广阔,孙建华,雷霆,等.多普勒天气雷达资料同化对暴雨模拟的影响[J].应用气象学报,2009,20(1):36-46.
[17]何志新,江杨,邱学兴,等.多部多普勒天气雷达资料同化对暴雨个例模拟的影响分析[J].暴雨灾害,2015,34(2):168-176.