C波段双线偏振天气雷达零度层亮带识别和订正
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摘要
利用2013年8月北京C波段双偏振多普勒天气雷达体扫数据、探空资料和地面雨量站资料,获得反射率因子垂直廓线(vertical profile of reflectivity,VPR)识别零度层亮带,比较平均反射率因子垂直廓线(mean VPR,MVPR)、显著反射率因子垂直廓线(apparent VPR,AVPR)和显著相关系数垂直廓线(apparent vertical profile of correlation coefficient,AVPCC)3种零度层亮带订正方法的效果,并利用地面雨量站资料进行定量降水估计(quantity precipitation estimation,QPE)验证订正效果。结果表明:采用MVPR和0℃层高度能有效识别零度层亮带,零度层亮带厚度为0.8~1.5 km;经3种方法订正后,零度层亮带影响区得到了不同程度的抑制,其中MVPR法订正效果最差,基本未能减弱零度层亮带的影响,AVPR法和AVPCC法的订正效果较好,明显减弱了零度层亮带影响区的回波强度,订正后回波更均匀。利用地面雨量站数据进行QPE验证表明:经零度层亮带订正后雷达估测的降水与地面雨量站实测降水更接近,也表明AVPR法和AVPCC法效果更好。
The bright band is a layer of enhanced reflectivity due to melting of aggregated snow and ice crystals.The occurrence of a bright band causes significant overestimation in radar-based quantitative precipitation estimation(QPE). The bright band signature can be normally identified from vertical profiles of reflectivity(VPRs) of stratiform precipitation echoes and the freezing level height which is derived from radiosonde data. The VPRs correction is desirable to mitigate the bright band contamination and reduce the overestimation of the radar-based QPE. However, a well-defined bright band bottom, which is critical for the correction of bright band, is sometimes not found in VPRs. Fortunately, polarimetric variables, especially the correlation coefficient, can provide a much better depiction of vertical bright band structure than reflectivity.The volume scanning data of a C-band dual polarization radar from Beijing Meteorological Bureau, radiosonde data and measured rainfall data from ground rain gauge stations are used to test the methodology of the bright band identification and correction. Three bright band correction schemes including mean vertical profile of reflectivity(MVPR), apparent vertical profile of reflectivity(AVPR) and apparent vertical profile of correlation coefficient(AVPCC), which are derived from stratiform precipitation echoes, are applied to the reflectivity field in the given tilt, and radar-based QPEs are derived from the corrected reflectivity field based on traditional Z-R relations. Results indicate that the bright band top, peak and bottom can be easily identified from the volume scanning MVPR and the freezing level height, and most of bright band depths are between 0. 8 km and 1. 5 km. The AVPR and AVPCC schemes are shown to be more effective in mitigating the bright band contamination and reducing the overestimation of radar-derived QPE associated with the bright band than the MVPR correction. Corrected reflectivity fields are physically continuous in distribution, and the corrected radar-derived QPEs are close to the measured value of ground rain gauge stations.
The bright band is a layer of enhanced reflectivity due to melting of aggregated snow and ice crystals.The occurrence of a bright band causes significant overestimation in radar-based quantitative precipitation estimation(QPE). The bright band signature can be normally identified from vertical profiles of reflectivity(VPRs) of stratiform precipitation echoes and the freezing level height which is derived from radiosonde data. The VPRs correction is desirable to mitigate the bright band contamination and reduce the overestimation of the radar-based QPE. However, a well-defined bright band bottom, which is critical for the correction of bright band, is sometimes not found in VPRs. Fortunately, polarimetric variables, especially the correlation coefficient, can provide a much better depiction of vertical bright band structure than reflectivity.The volume scanning data of a C-band dual polarization radar from Beijing Meteorological Bureau, radiosonde data and measured rainfall data from ground rain gauge stations are used to test the methodology of the bright band identification and correction. Three bright band correction schemes including mean vertical profile of reflectivity(MVPR), apparent vertical profile of reflectivity(AVPR) and apparent vertical profile of correlation coefficient(AVPCC), which are derived from stratiform precipitation echoes, are applied to the reflectivity field in the given tilt, and radar-based QPEs are derived from the corrected reflectivity field based on traditional Z-R relations. Results indicate that the bright band top, peak and bottom can be easily identified from the volume scanning MVPR and the freezing level height, and most of bright band depths are between 0. 8 km and 1. 5 km. The AVPR and AVPCC schemes are shown to be more effective in mitigating the bright band contamination and reducing the overestimation of radar-derived QPE associated with the bright band than the MVPR correction. Corrected reflectivity fields are physically continuous in distribution, and the corrected radar-derived QPEs are close to the measured value of ground rain gauge stations.
引文
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[3]Qi Y,Zhang J,Zhang P,et al.VPR correction of bright band effects in radar QPEs using polarimetric radar observations.J Geophys Res Atmos,2013,118:3627-3633.
[4]Qi Y,Zhang J,Cao Q,et al.Correction of radar QPE errors for nonuniform VPRs in mesoscale convective systems using TRMM observations.J Hydrometeor,2013,14:1672-1682.
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[6]金龙,阮征,葛润生,等.CFMCW雷达对江淮降水云零度层亮带探测研究.应用气象学报,2016,27(3):312-322.
[7]张乐坚,程明虎,陶岚.CINRAD-SA/SB零度层亮带识别方法.应用气象学报,2010,21(2):171-179.
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[9]程周杰,刘宪勋,朱亚平.双偏振雷达对一次水凝物相态演变过程的分析.应用气象学报,2009,20(5):594-601.
[10]何彩芬,黄旋旋,卢晶晶.基于多普勒天气雷达产品的降雪及冻雨综合分析.应用气象学报,2009,20(6):767-771.
[11]陈明轩,高峰.利用一种自动识别箅法移除天气雷达反射率因子中的亮带.应用气象学报,2006,17(2):207-214.
[12]Sánchez-Diezma R,Zawadzki I,Sempere-Torres D.Identification of the bright band through the analysis of volumetric radar data.J Geophys Res,2000,105:2225-2236.
[13]肖艳姣,刘黎平,李中华,等.雷达反射率因子数据中的亮带自动识别和抑制.高原气象,2010,29(1):197-205.
[14]庄薇,刘黎平,胡志群.青藏高原零度层亮带的识别订正方法及在雷达估测降水中的应用.气象,2013,39(8):1004-1013.
[15]孙赫敏,张沛源,蒋志,等.雷达定量估测降水的亮带自动消除改进方法.大气科学学报,2015,38(4):492-501.
[16]王致君,蔡启铭,徐宝祥.713雷达的双线偏振改装.高原气象,1988,7(2):177-185.
[17]苏德斌,孟庆春,沈永海,等.双线偏振天气雷达天线性能要求及其检测方法.高原气象,2012,31(3):847-861.
[18]孟庆春,沈永海,苏德斌.双线偏振雷达双通道一致性及测试方法研究.高原气象,2014,33(5):1440-1447.
[19]曹俊武,刘黎平,陈晓辉,等.3836 C波段双线偏振多普勒雷达及其在一次降水过程中的应用研究.应用气象学报,2006,17(2):192-200.
[20]Giangrande S E,Krause J M,Ryzhkov A V.Automatic designation of the melting layer with a polarimetric prototype of the WSR-88D radar.J Appl Meteor Climatol,2007,47:1354-1364.
[21]Boodoo S,Hudak D,Donaldson N,et al.Application of dualpolarization radar melting-layer detection algorithm.J Appl Meteor Climatol,2010,49:1779-1793.
[22]曹杨,苏德斌,周筠珺,等.C波段双线偏振多普勒雷达差分相位质量分析.高原气象,2016,35(2):548-559.
[23]曹杨,苏德斌,周筠珺,等.基于双线偏振多普勒雷达识别地物回波.成都信息工程学院学报,2014,29(5):509-516.
[24]Qi Y,Zhang J,Zhang P.A real-time automated convective and stratiform precipitation segregation algorithm in native radar coordinates.Q J R Meteorol Soc,2013,139:2233-2240.
[25]吴翠红,万玉发,吴涛,等.雷达回波垂直廓线及其生成方法.应用气象学报,2006,17(2):232-239.
[26]肖艳娇,刘黎平,李中华.任意基线雷达反射率因子垂直剖面生成算法.应用气象学报,2008,19(4):428-434.
[27]Chandrasekar V,Gorgucci E,Scarchilli G.Optimization of multiparameter radar estimates of rainfall.Appl Meteor,1993,32:1288-1293.
[28]Ruzanski E,Chandrasekar V.Nowcasting rainfall fields derived from specific differential phase.J Appl Meteor Climatol,2012,51(11):1950-1959.
[2]Zhang J,Qi Y,Kingsmill D,et al.Radar-based quantitative precipitation estimation for the cool season in complex terrain:Case studies from the NOAA hydrometeorology testbed.J Hydrometeor,2012,13:1836-1854.
[3]Qi Y,Zhang J,Zhang P,et al.VPR correction of bright band effects in radar QPEs using polarimetric radar observations.J Geophys Res Atmos,2013,118:3627-3633.
[4]Qi Y,Zhang J,Cao Q,et al.Correction of radar QPE errors for nonuniform VPRs in mesoscale convective systems using TRMM observations.J Hydrometeor,2013,14:1672-1682.
[5]Hall W,Rico-Ramirez M A,Kr(a|¨)mer S.Classification and correc-tion of the bright band using an operational C-band polarimetric radar.J Hydrology,2015,15:248-258.
[6]金龙,阮征,葛润生,等.CFMCW雷达对江淮降水云零度层亮带探测研究.应用气象学报,2016,27(3):312-322.
[7]张乐坚,程明虎,陶岚.CINRAD-SA/SB零度层亮带识别方法.应用气象学报,2010,21(2):171-179.
[8]Zhang J,Langston C,Howard K.Bright band identification based on vertical profile of reflectivity from the WSR-88D.J Atmos Oceanic Technol,2008,25:1859-1872.
[9]程周杰,刘宪勋,朱亚平.双偏振雷达对一次水凝物相态演变过程的分析.应用气象学报,2009,20(5):594-601.
[10]何彩芬,黄旋旋,卢晶晶.基于多普勒天气雷达产品的降雪及冻雨综合分析.应用气象学报,2009,20(6):767-771.
[11]陈明轩,高峰.利用一种自动识别箅法移除天气雷达反射率因子中的亮带.应用气象学报,2006,17(2):207-214.
[12]Sánchez-Diezma R,Zawadzki I,Sempere-Torres D.Identification of the bright band through the analysis of volumetric radar data.J Geophys Res,2000,105:2225-2236.
[13]肖艳姣,刘黎平,李中华,等.雷达反射率因子数据中的亮带自动识别和抑制.高原气象,2010,29(1):197-205.
[14]庄薇,刘黎平,胡志群.青藏高原零度层亮带的识别订正方法及在雷达估测降水中的应用.气象,2013,39(8):1004-1013.
[15]孙赫敏,张沛源,蒋志,等.雷达定量估测降水的亮带自动消除改进方法.大气科学学报,2015,38(4):492-501.
[16]王致君,蔡启铭,徐宝祥.713雷达的双线偏振改装.高原气象,1988,7(2):177-185.
[17]苏德斌,孟庆春,沈永海,等.双线偏振天气雷达天线性能要求及其检测方法.高原气象,2012,31(3):847-861.
[18]孟庆春,沈永海,苏德斌.双线偏振雷达双通道一致性及测试方法研究.高原气象,2014,33(5):1440-1447.
[19]曹俊武,刘黎平,陈晓辉,等.3836 C波段双线偏振多普勒雷达及其在一次降水过程中的应用研究.应用气象学报,2006,17(2):192-200.
[20]Giangrande S E,Krause J M,Ryzhkov A V.Automatic designation of the melting layer with a polarimetric prototype of the WSR-88D radar.J Appl Meteor Climatol,2007,47:1354-1364.
[21]Boodoo S,Hudak D,Donaldson N,et al.Application of dualpolarization radar melting-layer detection algorithm.J Appl Meteor Climatol,2010,49:1779-1793.
[22]曹杨,苏德斌,周筠珺,等.C波段双线偏振多普勒雷达差分相位质量分析.高原气象,2016,35(2):548-559.
[23]曹杨,苏德斌,周筠珺,等.基于双线偏振多普勒雷达识别地物回波.成都信息工程学院学报,2014,29(5):509-516.
[24]Qi Y,Zhang J,Zhang P.A real-time automated convective and stratiform precipitation segregation algorithm in native radar coordinates.Q J R Meteorol Soc,2013,139:2233-2240.
[25]吴翠红,万玉发,吴涛,等.雷达回波垂直廓线及其生成方法.应用气象学报,2006,17(2):232-239.
[26]肖艳娇,刘黎平,李中华.任意基线雷达反射率因子垂直剖面生成算法.应用气象学报,2008,19(4):428-434.
[27]Chandrasekar V,Gorgucci E,Scarchilli G.Optimization of multiparameter radar estimates of rainfall.Appl Meteor,1993,32:1288-1293.
[28]Ruzanski E,Chandrasekar V.Nowcasting rainfall fields derived from specific differential phase.J Appl Meteor Climatol,2012,51(11):1950-1959.