雷达径向速度资料同化中不同坐标转换方案的对比试验
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摘要
利用江苏省气象局与美国强风暴实验室联合开发的高精度数值分析及预报系统(Precision Weather Analysis and Forecast System,PWAFS)对雷达资料同化中径向速度资料的两种坐标转换方案进行对比分析。Grid方案将雷达径向速度资料通过最小二乘法从极坐标映射到模式三维网格;Tilt方案将雷达径向速度资料通过双线性插值在水平方向插值至标量水平网格,但在垂直方向不进行插值,保留在雷达仰角对应的高度上。两种方案对反射率资料的处理均是插值到模式三维网格点。Grid方案在近雷达处进行平滑,在远雷达处进行插值,会导致低层数据平滑,Tilt方案减少了雷达径向风观测垂直插值引发的误差,更多的保留了雷达观测的特性。本研究分别通过龙卷、大风及梅雨锋暴雨个例对这两种方案的同化结果进行对比分析。龙卷个例中Grid方案得到了部分虚假的较大的同化风场,Tilt方案结果清楚展示了龙卷发生位置的回波及流场的精细结构。大风个例中两种方案得到的最大风速值差3 m·s-1,Tilt方案的结果更接近观测最大风速值,且得到的大风速区分布更符合观测。梅雨锋暴雨个例中Grid方案对东北及西南两个区域的大风速区均未能很好的反映,Tilt方案得到的水平风速大值区范围明显优于Grid方案。在靠近雷达中心的低层,观测资料密集,Tilt方案能够更好的反应实际大气状态。但是因为缺乏其他观测资料进行验证,两种方案的效果还需要利用数值预报或其他方法进行对比。
Two interpolation schemes of radial velocity data in radar data assimilation are compared and analyzed through the high-precision numerical analysis and forecasting system jointly developed by Jiangsu Meteorological Bureau and Center for Analysis and Prediction of Storms.In Grid-scheme,the radar radial velocity data is interpolated from the polar coordinates to the three-dimensional model Grid by least squares method.In Tilt-scheme,the radar radial velocity data is only interpolated to the horizontal Grid through the bilinear interpolation in the horizontal direction but is not interpolated in the vertical direction,retained in the radial coordinates of the radar.In both schemes,radar reflectivity data is interpolated into the three-dimensional model Grid.The Grid-scheme results in smoothing of low-level data,and the Tilt-scheme retains more characteristics of radar observations.In this paper,the assimilation results of the two schemes are compared and analyzed through the cases of tornado,gust and heavy rain in Meiyu front.In the tornado case,the Grid-scheme obtained a part of larger assimilation wind field,while the Tilt-scheme result clearly showed the echo of tornado and the fine structure of the vortex.In the gust case,the maximum wind speed difference obtained by the two schemes is 3 m·s-1.The Tilt-scheme result was closer to the maximum wind speed observation and the distribution of the assimilated high wind speed region is more in line with the observation.In the Meiyu case,the Grid-scheme failed to reflect the high wind speed regions in the northeast and southwest regions.The horizontal high wind speed region obtained by the Tiltscheme was obviously better than the Grid-scheme.At the lower altitude near the radar,the observations are dense,and the Tilt-scheme is better able to reflect the real atmospheric state.However,because of the lack of other observations for verification,the effects of the two schemes need to be compared by using numerical prediction or other methods.
Two interpolation schemes of radial velocity data in radar data assimilation are compared and analyzed through the high-precision numerical analysis and forecasting system jointly developed by Jiangsu Meteorological Bureau and Center for Analysis and Prediction of Storms.In Grid-scheme,the radar radial velocity data is interpolated from the polar coordinates to the three-dimensional model Grid by least squares method.In Tilt-scheme,the radar radial velocity data is only interpolated to the horizontal Grid through the bilinear interpolation in the horizontal direction but is not interpolated in the vertical direction,retained in the radial coordinates of the radar.In both schemes,radar reflectivity data is interpolated into the three-dimensional model Grid.The Grid-scheme results in smoothing of low-level data,and the Tilt-scheme retains more characteristics of radar observations.In this paper,the assimilation results of the two schemes are compared and analyzed through the cases of tornado,gust and heavy rain in Meiyu front.In the tornado case,the Grid-scheme obtained a part of larger assimilation wind field,while the Tilt-scheme result clearly showed the echo of tornado and the fine structure of the vortex.In the gust case,the maximum wind speed difference obtained by the two schemes is 3 m·s-1.The Tilt-scheme result was closer to the maximum wind speed observation and the distribution of the assimilated high wind speed region is more in line with the observation.In the Meiyu case,the Grid-scheme failed to reflect the high wind speed regions in the northeast and southwest regions.The horizontal high wind speed region obtained by the Tiltscheme was obviously better than the Grid-scheme.At the lower altitude near the radar,the observations are dense,and the Tilt-scheme is better able to reflect the real atmospheric state.However,because of the lack of other observations for verification,the effects of the two schemes need to be compared by using numerical prediction or other methods.
引文
Abhilash S,Sahai A K,Mohankumar K,et al,2012.Assimilation of Doppler Weather Radar Radial Velocity and reflectivity observations in WRF-3DVAR system for short-range forecasting of convective storms[J].Pure and Applied Geophysics,169(11):2047-2070.
Brewster K,Hu M,Xue M,et al,2005.Efficient assimilation of radar data at high resolution for short-range numerical weather prediction[C]//World Weather Research Program Symposium on Nowcasting and Very Short-Range Forecasting,WSN05,Tolouse,France,WMO,Symposium CD,Paper.2005,3.
Crook N A,Sun J,2002.Assimilating radar,surface,and profiler data for the Sydney 2000 forecast demonstration project[J].Journal of Atmospheric and Oceanic Technology,19(6):888-898.
Dong J,Xue M,2013.Assimilation of radial velocity and reflectivity data from coastal WSR-88D radars using ensemble Kalman filter for the analysis and forecast of landfalling hurricane Ike(2008)[J].Quarterly Journal of Royal Meteorological Society,139(671):467-487.
Doviak R J,Zrnic'D S,1993.Doppler Radar andweather observations[M].2nd ed.Academic Press,87-105
Ellis M D,Smith S D,1990.Efficientdealiasing of Doppler velocities using local environment constraints[J].Journal of Atmospheric and Oceanic Technology,7(1):118-128.
Gao J,Smith T M,Stensrud D J,et al,2013.Areal-time weather-adaptive3DVAR analysis system for severe weather detections and warnings[J].Weather and Forecasting,28(3):727-745.
Gao J,Xue M,Brewster K,et al,2004.Athree-dimensional variational data analysis method with recursive filter for Doppler Radars[J].Journal of Atmospheric and Oceanic Technology,21(3):457-469.
Ge G,Gao J,Xue M,2013.Impacts of assimilating measurements of different state variables with a simulated supercell storm and threedimensional variational method[J].Monthly Weather Review,141(8):2759-2777.
Hu M,Xue M,Brewster K,2006a.3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth tornadic thunderstorms.Part I:Cloud analysis and its impact[J].Monthly Weather Review,134(2),675-698.
Hu M,Xue M,Gao J,et al,2006b.3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth,Texas,tornadic thunderstorms.Part II:Impact of radial velocity analysis via 3DVAR[J].Monthly Weather Review,134(2):699-721.
Lakshmanan V,Smith T,Stumpf G,et al,2007.The warning decision support system-integrated information[J].Weather and Forecasting,22(3):596-612.
Lemon L R,Doswell C A,1979.Severethunderstorm evolution and mesocyclone structure as related to Tornado genesis[J].Monthly Weather Review,107(9):1184-1197.
Lhermitt R M,Miller L J,1970.Doppler Radar methodology for observation of convective storms[C]//Bulletin of the American Meteorological Society.45 Beacon S T,Boston,MA 02108-3693:Amer Meteorological SOC.
Li W,Xie Y,Deng S M,et al,2008.Application of the multi-Grid method to the two-dimensional Doppler Radar radial velocity data assimilation[J].Journal of Atmospheric and Oceanic Technology,27(27):319-332.
Li Y Z,Wang X G,Xue M,2012.Assimilation of radar radial velocity data with the WRF hybrid ensemble-3DVAR system for the prediction of hurricane Ike(2008)[J].Monthly Weather Review,140(11):3507-3524
Miller L J,Strauch R G,1974.A dual Doppler radar method for the determination of wind velocities within precipitating weather systems[J].Remote Sensing of Environment.3(4):219-235.
Miller L J,Sun J,2003.Initialization and forecasting of thunderstorms:Specification of radar measurement errors[J].Clinical Chemistry,21(4):588-590.
Schenkman A,Xue M,Shapiro A,et al,2011.The analysis and prediction of the 8-9 May 2007 Oklahoma tornadic mesoscale convective system by assimilating WSR-88D and CASA radar data using3DVAR[J].Monthly Weather Review,139(1):224-246.
Sun J,Crook N A,2001.Real-time low-level wind and temperature analysis using single WSR-88D data[J].Weather and Forecasting,16(1):117-132.
Sun J,Crook N A,1997.Dynamical and microphysical retrieval from Doppler Radar observations using a cloud model and its adjoint.Part I:Model development and simulated data experiments[J].Journal of Atmospheric Science,54:1642-1661.
Tong M,Xue M,2008.Simultaneous estimation of microphysical parameters and atmospheric state with radar data and ensemble Kalman filter.Part I:Sensitivity analysis and parameter identifiability[J].Monthly Weather Review,136(5):1630-1648.
Xue M,Wang D,Gao J,et al,2003.The Advanced Regional Prediction System(ARPS),storm scale numerical weather prediction and data assimilation[J].Meteorology and Atmospheric Physics,82(1):139-170.
Xue M,Droegemeier K K,Wong V,2000.The Advanced Regional Prediction System(ARPS)-A multi-scale non-hydrostatic atmospheric simulation and prediction model.Part I:Model dynamics and verification[J].Meteorology and Atmospheric Physics,75(3/4):161-193.
Xue M,Droegemeier K K,Wong V,et al,2001.The Advanced Regional Prediction System(ARPS)-A multi-scale nonhydrostatic atmospheric simulation and prediction tool.Part II:Model physics and applications[J].Meteorology and atmospheric physics,76(3/4):143-165.
Ziegler C L,1978.A dual Doppler variational objective analysis as applied to studies of convective storms[M].Norman:University of Oklahoma,52-67.
陈力强,杨森,肖庆农,2009.多普勒雷达资料在冷涡强对流天气中的同化应用试验[J].气象,35(12):12-20.
陈明轩,王迎春,高峰,等,2011.基于雷达资料4DVar的低层热动力反演系统及其在北京奥运期间的初步应用分析[J].气象学报,69(1):64-78.
罗义,梁旭东,陈明轩,2014.单多普勒雷达径向风同化的改进[J].气象科学,34(6):620-628.
马昊,梁旭东,罗义,等,2016.GRAPES 3Dvar中雷达径向风同化改进观测算子的应用[J].气象(1):34-43.
盛春岩,浦一芬,高守亭,2006.多普勒天气雷达资料对中尺度模式短时预报的影响[J].大气科学,30(1):93-107.
孙娟珍,陈明轩,范水勇,2016.雷达资料同化方法:回顾与前瞻[J].气象科技进展,6(3):17-27.
覃月凤,顾建峰,吴钲,等,2015.雷达资料同化频次对一次西南涡暴雨的影响试验[J].高原气象,34(4):963-972.DOI:10.7522/j.issn.1000-0534.2014.00050.
王晓峰,王平,张蕾,等,2017.多源观测在快速更新同化系统中的敏感性试验[J].高原气象,36(1):148-161.DOI:10.7522/j.issn.1000-0534.2016.00018.
王永明,高山红,2016.黄海海雾数值模拟中多普勒雷达径向风数据同化试验[J].中国海洋大学学报(自然科学版),46(8):1-12.
文秋实,王东海,2017.基于GSI的华南地区对流尺度快速循环同化预报试验[J].气象,43(6):653-664.
肖艳姣,2018.基于多普勒天气雷达体扫资料的MARC特征自动识别算法[J].高原气象,37(1):264-274.DOI:10.7522/j.issn.1000-0534.2016.00143.
徐琪,慕熙昱,刘韻蕊,等,2015.南京空域一次高空致灾冰粒过程的可预报性分析[J].高原气象,34(1):258-268.DOI:10.7522/j.issn.1000-0534.2013.00105.
薛谌彬,陈娴,吴俞,等,2017.雷达资料同化在局地强对流预报中的应用[J].大气科学,41(4):673-690.
杨毅,邱崇践,龚建东,等,2007.同化多普勒雷达风资料的两种方法比较[J].高原气象,26(3):547-555.
杨银,朱克云,张杰,等,2015.复杂地形下多普勒雷达资料同化的研究[J].高原气象,34(5):1495-1501.DOI:10.7522/j.issn.1000-0534.2014.00059.
张桂莲,常欣,黄晓璐,等,2018.东北冷涡背景下超级单体风暴环境条件与雷达回波特征[J].高原气象,37(5):1364-1374.DOI:10.7522/j.issn.1000-0534.2018.00068.
张林,倪允琪,2006.雷达径向风资料的四维变分同化试验[J].大气科学,30(3):433-440.
张沛源,周海光,胡绍萍,2002.双多普勒天气雷达风场探测的可靠性研究[J].应用气象学报,13(4):485-496.
张小玲,杨波,朱文剑,等,2016.2016年6月23日江苏阜宁EF4级龙卷天气分析[J].气象,42(11):1304-1314.
张新忠,陈军明,赵平,2015.多普勒天气雷达资料同化对江淮暴雨模拟的影响[J].应用气象学报26(5):555-566.
郑永光,朱文剑,姚聃,等,2016.风速等级标准与2016年6月23日阜宁龙卷强度估计[J].气象,42(11):1289-1303.
周海光,王玉彬,2002.多部多普勒雷达同步探测三维风场反演系统[J].气象,28(9):7-11.
周荣卫,何晓凤,2018.新疆哈密复杂地形风场的数值模拟及特征分析[J].高原气象,37(5):1413-1427.DOI:10.7522/j.issn.1000-0534.2018.00021.
Brewster K,Hu M,Xue M,et al,2005.Efficient assimilation of radar data at high resolution for short-range numerical weather prediction[C]//World Weather Research Program Symposium on Nowcasting and Very Short-Range Forecasting,WSN05,Tolouse,France,WMO,Symposium CD,Paper.2005,3.
Crook N A,Sun J,2002.Assimilating radar,surface,and profiler data for the Sydney 2000 forecast demonstration project[J].Journal of Atmospheric and Oceanic Technology,19(6):888-898.
Dong J,Xue M,2013.Assimilation of radial velocity and reflectivity data from coastal WSR-88D radars using ensemble Kalman filter for the analysis and forecast of landfalling hurricane Ike(2008)[J].Quarterly Journal of Royal Meteorological Society,139(671):467-487.
Doviak R J,Zrnic'D S,1993.Doppler Radar andweather observations[M].2nd ed.Academic Press,87-105
Ellis M D,Smith S D,1990.Efficientdealiasing of Doppler velocities using local environment constraints[J].Journal of Atmospheric and Oceanic Technology,7(1):118-128.
Gao J,Smith T M,Stensrud D J,et al,2013.Areal-time weather-adaptive3DVAR analysis system for severe weather detections and warnings[J].Weather and Forecasting,28(3):727-745.
Gao J,Xue M,Brewster K,et al,2004.Athree-dimensional variational data analysis method with recursive filter for Doppler Radars[J].Journal of Atmospheric and Oceanic Technology,21(3):457-469.
Ge G,Gao J,Xue M,2013.Impacts of assimilating measurements of different state variables with a simulated supercell storm and threedimensional variational method[J].Monthly Weather Review,141(8):2759-2777.
Hu M,Xue M,Brewster K,2006a.3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth tornadic thunderstorms.Part I:Cloud analysis and its impact[J].Monthly Weather Review,134(2),675-698.
Hu M,Xue M,Gao J,et al,2006b.3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth,Texas,tornadic thunderstorms.Part II:Impact of radial velocity analysis via 3DVAR[J].Monthly Weather Review,134(2):699-721.
Lakshmanan V,Smith T,Stumpf G,et al,2007.The warning decision support system-integrated information[J].Weather and Forecasting,22(3):596-612.
Lemon L R,Doswell C A,1979.Severethunderstorm evolution and mesocyclone structure as related to Tornado genesis[J].Monthly Weather Review,107(9):1184-1197.
Lhermitt R M,Miller L J,1970.Doppler Radar methodology for observation of convective storms[C]//Bulletin of the American Meteorological Society.45 Beacon S T,Boston,MA 02108-3693:Amer Meteorological SOC.
Li W,Xie Y,Deng S M,et al,2008.Application of the multi-Grid method to the two-dimensional Doppler Radar radial velocity data assimilation[J].Journal of Atmospheric and Oceanic Technology,27(27):319-332.
Li Y Z,Wang X G,Xue M,2012.Assimilation of radar radial velocity data with the WRF hybrid ensemble-3DVAR system for the prediction of hurricane Ike(2008)[J].Monthly Weather Review,140(11):3507-3524
Miller L J,Strauch R G,1974.A dual Doppler radar method for the determination of wind velocities within precipitating weather systems[J].Remote Sensing of Environment.3(4):219-235.
Miller L J,Sun J,2003.Initialization and forecasting of thunderstorms:Specification of radar measurement errors[J].Clinical Chemistry,21(4):588-590.
Schenkman A,Xue M,Shapiro A,et al,2011.The analysis and prediction of the 8-9 May 2007 Oklahoma tornadic mesoscale convective system by assimilating WSR-88D and CASA radar data using3DVAR[J].Monthly Weather Review,139(1):224-246.
Sun J,Crook N A,2001.Real-time low-level wind and temperature analysis using single WSR-88D data[J].Weather and Forecasting,16(1):117-132.
Sun J,Crook N A,1997.Dynamical and microphysical retrieval from Doppler Radar observations using a cloud model and its adjoint.Part I:Model development and simulated data experiments[J].Journal of Atmospheric Science,54:1642-1661.
Tong M,Xue M,2008.Simultaneous estimation of microphysical parameters and atmospheric state with radar data and ensemble Kalman filter.Part I:Sensitivity analysis and parameter identifiability[J].Monthly Weather Review,136(5):1630-1648.
Xue M,Wang D,Gao J,et al,2003.The Advanced Regional Prediction System(ARPS),storm scale numerical weather prediction and data assimilation[J].Meteorology and Atmospheric Physics,82(1):139-170.
Xue M,Droegemeier K K,Wong V,2000.The Advanced Regional Prediction System(ARPS)-A multi-scale non-hydrostatic atmospheric simulation and prediction model.Part I:Model dynamics and verification[J].Meteorology and Atmospheric Physics,75(3/4):161-193.
Xue M,Droegemeier K K,Wong V,et al,2001.The Advanced Regional Prediction System(ARPS)-A multi-scale nonhydrostatic atmospheric simulation and prediction tool.Part II:Model physics and applications[J].Meteorology and atmospheric physics,76(3/4):143-165.
Ziegler C L,1978.A dual Doppler variational objective analysis as applied to studies of convective storms[M].Norman:University of Oklahoma,52-67.
陈力强,杨森,肖庆农,2009.多普勒雷达资料在冷涡强对流天气中的同化应用试验[J].气象,35(12):12-20.
陈明轩,王迎春,高峰,等,2011.基于雷达资料4DVar的低层热动力反演系统及其在北京奥运期间的初步应用分析[J].气象学报,69(1):64-78.
罗义,梁旭东,陈明轩,2014.单多普勒雷达径向风同化的改进[J].气象科学,34(6):620-628.
马昊,梁旭东,罗义,等,2016.GRAPES 3Dvar中雷达径向风同化改进观测算子的应用[J].气象(1):34-43.
盛春岩,浦一芬,高守亭,2006.多普勒天气雷达资料对中尺度模式短时预报的影响[J].大气科学,30(1):93-107.
孙娟珍,陈明轩,范水勇,2016.雷达资料同化方法:回顾与前瞻[J].气象科技进展,6(3):17-27.
覃月凤,顾建峰,吴钲,等,2015.雷达资料同化频次对一次西南涡暴雨的影响试验[J].高原气象,34(4):963-972.DOI:10.7522/j.issn.1000-0534.2014.00050.
王晓峰,王平,张蕾,等,2017.多源观测在快速更新同化系统中的敏感性试验[J].高原气象,36(1):148-161.DOI:10.7522/j.issn.1000-0534.2016.00018.
王永明,高山红,2016.黄海海雾数值模拟中多普勒雷达径向风数据同化试验[J].中国海洋大学学报(自然科学版),46(8):1-12.
文秋实,王东海,2017.基于GSI的华南地区对流尺度快速循环同化预报试验[J].气象,43(6):653-664.
肖艳姣,2018.基于多普勒天气雷达体扫资料的MARC特征自动识别算法[J].高原气象,37(1):264-274.DOI:10.7522/j.issn.1000-0534.2016.00143.
徐琪,慕熙昱,刘韻蕊,等,2015.南京空域一次高空致灾冰粒过程的可预报性分析[J].高原气象,34(1):258-268.DOI:10.7522/j.issn.1000-0534.2013.00105.
薛谌彬,陈娴,吴俞,等,2017.雷达资料同化在局地强对流预报中的应用[J].大气科学,41(4):673-690.
杨毅,邱崇践,龚建东,等,2007.同化多普勒雷达风资料的两种方法比较[J].高原气象,26(3):547-555.
杨银,朱克云,张杰,等,2015.复杂地形下多普勒雷达资料同化的研究[J].高原气象,34(5):1495-1501.DOI:10.7522/j.issn.1000-0534.2014.00059.
张桂莲,常欣,黄晓璐,等,2018.东北冷涡背景下超级单体风暴环境条件与雷达回波特征[J].高原气象,37(5):1364-1374.DOI:10.7522/j.issn.1000-0534.2018.00068.
张林,倪允琪,2006.雷达径向风资料的四维变分同化试验[J].大气科学,30(3):433-440.
张沛源,周海光,胡绍萍,2002.双多普勒天气雷达风场探测的可靠性研究[J].应用气象学报,13(4):485-496.
张小玲,杨波,朱文剑,等,2016.2016年6月23日江苏阜宁EF4级龙卷天气分析[J].气象,42(11):1304-1314.
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