CHARACTERISTICS OF CLOUD MICROPHYSICS AT POSITIONS WITH FLASH INITIATIONS AND CHANNELS IN CONVECTION AND STRATIFORM AREAS OF TWO SQUALL LINES
-
摘要: 利用广州S波段双偏振雷达观测数据和低频电场探测阵列三维闪电定位数据, 分析了2017年5月4日和5月8日华南地区两次飑线过程中闪电起始和通道位置处的雷达偏振参量和降水粒子特征。两次飑线中约80%的闪电起始和通道(统称闪电放电)定位于对流区。对流区闪电放电位置处的雷达反射率(ZH)要比层云区平均大4~5 dBZ, 其它偏振参量的平均值较为接近。闪电放电位置处的ZH中值随高度增加而减小, 但差分反射率(ZDR)、差分传播相移率(KDP)和共极化相关系数(CC)在-10 ℃层以上随高度变化不大; -10 ℃层以下, 对流区闪电放电位置对应ZDR和KDP随高度下降明显增大。闪电起始位置的平均ZH比闪电通道位置处的平均ZH大1~2 dBZ, 但前者在对流区内对应ZH分布峰值区间为25~30 dBZ, 弱于后者的30~35 dBZ; 同时, 它们的对比关系在-20 ℃层上下不同。对流区内闪电放电位置处的主导性粒子是霰和冰晶, 它们的区域占比接近。在层云区内, 闪电放电位置主要是干雪和冰晶, 干雪区域的占比显著大于冰晶。Abstract: Using data from polarimetric radars and three-dimensional lightning detection, we investigate the characteristics of polarimetric variables and hydrometeors at positions with flash initiations and channels in two squall lines that occurred over Southern China on 4 and 8 May 2017, respectively. It is found that the located lightning pulse discharge events associated with flash initiations and channels (they are collectively called lightning discharges (LDs)) in the convection areas both account for approximately 80%of their respective samples. The average horizontal reflectivity (ZH) at the radar grid boxes with LDs in the convection areas is about 4~5 dBZ larger than that in the stratiform area, while the average values of other polarimetric variables (differential reflectivity (ZDR), specific differential phase (KDP) and co-polar correlation coefficient (CC)) associated with the LDs are similar in the convection and stratiform areas. The median ZHat the positions with LDs decreases with increasing altitude. Meanwhile, median ZDR, KDP, and CC associated with the LDs keep relatively constant above the -10 ℃layer. Below the -10 ℃layer, median ZDRand KDPat positions with LDs distinctly increase with decreasing altitude. Moreover, the average ZH associated with flash initiations is approximately 1~2 dBZ bigger than that corresponding to the flash channels, whereas, in the convection areas, the peak interval of ZH associated with flash initiations is 25~30 dBZ, smaller than that corresponding to flash channels (30~35 dBZ). Furthermore, the peak interval of ZH corresponding to flash initiations and channels is different above and below the -20 ℃ level. The dominant hydrometeors at positions with LDs are graupel and ice crystals in the convection areas, and their areas are analogous; meanwhile, the corresponding dominant hydrometeors are dry snow and ice crystals in the stratiform area, and the area of dry snow is distinctly bigger than that of ice crystals.
-
图 1 研究区域以及观测系统分布阴影部分是选取的研究区域
“●”：清远气象站；黑色“■”：雷达位置；“▲”LFEDA子站；“”：LFEDA站网中心；黑色圆圈表示距离雷达位置100 km；红色圆圈表示距离LFEDA站网中心70 km；黑色虚线圆表示距离雷达位置25 km，该范围内雷达难以在垂直方向完整地观测雷暴。
图 2 LFEDA于18:19:37.56(北京时间)在飑线B中探测的一次闪电
a. LPDE高度随时间的变化；b. LPDE平面分布；c. LPDE在南-北方向上的垂直剖面；d. 雷达回波叠加LPDE的垂直剖面，沿南-北方向过闪电起始位置。其中，紫色方块表示闪电通道起始位置，黑色点划线为环境温度高度。
图 3 ST95和P09的对流云、层云识别结果对比
a1、a2以及a3是飑线A在05:48时的组合反射率、沿a1中黑色虚线的反射率垂直剖面以及基于ST95方法的粒子识别结果剖面。b1、b2和b3同a1、a2和a3，但所示个例为飑线B在17:12的观测。a1和b1的灰色等值线以及图a2、a3和b2、b3的灰色竖线代表P09算法识别的对流区，相对应的黑色线表示ST95算法识别的对流区，蓝色圆点是剖面两边半宽2 km范围内的LPDE，紫色方块表示闪电通道起始位置，黑色点划线为环境温度高度。
图 4 闪电初始以及通道位置处雷达偏振参量中值随高度的分布
上排是飑线A，下排是飑线B。红色(蓝色)线条表示对流(层云)区中的偏振参量；实线(虚线)表示闪电起始(通道)位置处的偏振参量。图中标注了环境温度线，来自于清远探空。
图 5 闪电起始以及通道位置处ZH的分布特征
字母a代表飑线A，字母b代表飑线B; a1和b1对应所有高度层的统计；a2和b2对应-20 ℃层以上的统计；a3和b3对应-20 ℃层以下的统计；五角星表示峰值频率。
图 6 飑线A(a1和a2)、B(b1和b2)对流区闪电起始(a1和b1)和通道位置(a2和b2)及其对应降水粒子在垂直方向的分布
红色线分别对应闪电起始和通道位置的高度分布，灰色阴影指出了闪电起始和通道位置最密集的高度。填色为降水粒子类型，粒子类型及其缩写的意义详见表 1。
表 1 降水粒子识别中不同粒子对应T的取值范围
降水粒子 温度T/℃ 干雪(DS) 1 -52 湿雪(WS) 5 -3 冰晶(CR) -3 -75 霰(GR) 20 -60 大滴(BD) 30 -10 雨(RA) 30 -5 大雨(HR) 30 -5 雨夹雹(RH) 30 -15 
下载: 导出CSV
表 2 飑线A、B中闪电起始和通道对应LPDE在对流区和层云区的各雷达偏振参量统计特征
雷达偏振参里 A B 对流云闪电起始(通道) 层云闪电起始(通道) 对流云闪电起始(通道) 层云闪电起始(通道) ZH/dBZ 中值 29.4(28.2) 25.7(23.4) 28.0(27.5) 23.5(21.5) 平均值 29.3(27.7) 25.0(23.3) 28.0(27.3) 23.8(22.4) ZDR/dB 中值 0.13(0.17) 0.33(0.29) 0.14(0.16) 0.23(0.26) 平均值 0.21(0.25) 0.38(0.34) 0.16(0.19) 0.24(0.27) KDp/(°/km) 中值 0.07(0.08) 0.07(0.08) 0.07(0.07) 0.07(0.08) 平均值 0.11(0.11) 0.08(0.10) 0.08(0.09) 0.07(0.09) CC 中值 0.99(0.99) 0.99(0.99) 0.99(0.99) 0.99(0.99) 平均值 0.98(0.98) 0.98(0.98) 0.99(0.99) 0.99(0.99)
下载: 导出CSV
表 3 飑线A、B对流区闪电起始和通道位置及其对应降水粒子类型分布
降水粒子类型 A B 闪电起始 闪电通道 闪电起始 闪电通道 高度/km 3.5~13 3~16 4~14.5 3~16 峰值高度(km)/环境温度(C) 9/-23~-24 7.5/-14~-15 9/-24~-25 9.5/-27~-28 CR 44% 52% 47% 49% GR 47% 39% 51% 48% RA 7% 7% 1% 2%
下载: 导出CSV
表 4 飑线A、B层云区闪电起始和通道位置及其对应降水粒子类型分布
降水粒子类型 A B 闪电起始 闪电通道 闪电起始 闪电通道 高度/km 4~12 3~14.5 4~11.5 3~15 峰值高度(km)/环境温度(C) 5.5/-3~-4 6.5/-8~-9 7.5/-15~-16 8.5/-20~-21 DS 63% 52% 67% 52% CR 19% 33% 26% 39% RA 17% 14% 6% 7%
下载: 导出CSV
-
[1] MECIKALSKI R M, CAREY L D. Radar reflectivity and altitude distributions of lightning flashes as a function of three main storm types[J]. J Geophys Res, 2018, 123: 12 814-12 828. [2] KARUNARATHNA N, MARSHALL T C, KARUNARATHNE S, et al. Initiation locations of lightning flashes relative to radar reflectivity in four small Florida thunderstorms[J]. J Geophy Res, 2017, 122: 6 565-6 591. [3] BRUNING E C, RUST W D, SCHUUR T J, et al. Electrical and polarimetric radar observations of a multicell storm in TELEX[J]. Mon Wea Rev, 2007, 135(7): 2 525-2 544. [4] CAREY L D, MURPHY M J, MCCORMICK T L, et al. Lightning location relative to storm structure in a leading-line, trailing-stratiform mesoscale convective system[J]. J Geophy Res, 2005, 110: D03105. [5] LUND N R, MACGORMAN D R, SCHUUR T J, et al. Relationships between lightning location and polarimetric radar signatures in a small Mesoscale Convective System[J]. Mon Wea Rev, 2009, 137(2): 4 151-4 170. [6] ZHENG D, MACGORMAN D R. Characteristics of flash initiations in a supercell cluster with tornadoes[J]. Atmos Res, 2016, 167: 249-264. [7] LIU Z, ZHENG D, GUO F, et al. Lightning activity and its associations with cloud structures in a rainstorm dominated by warm precipitation[J]. Atmos Res, 2020, 246(12): 105120. [8] RIBAUD J F, BOUSQUET O, COQUILLAT S, et al. Evaluation and application of hydrometeor classification algorithm outputs inferred from multi-frequency dual-polarimetric radar observations collected during HyMeX[J]. Quart J R Meteor Soc, 2016, 142(1): 95-107. [9] 甘明骏, 郭凤霞, 黎奇, 等. 广东一次飑线过程中一个雷暴单体成熟阶段的电荷结构演变特征的数值模拟[J]. 热带气象学报, 2020, 36(4): 562-576. [10] 张义军, 刘欣生, 肖庆复. 中国南北方雷暴及人工触发闪电电特性对比分析[J]. 高原气象, 1997, 16(2): 113-121. [11] 张义军, 吕伟涛, 陈绍东, 等. 广东野外雷电综合观测试验十年进展[J]. 气象学报, 2016, 74(5): 655-671. [12] 张敏锋, 刘欣生, 张义军, 等. 广东地区雷电活动的气候分布特征[J]. 热带气象学报, 2000, 16(1): 46-53. [13] SHI D, ZHENG D, ZHANG Y, et al. Low-frequency E-field Detection Array(LFEDA)-Construction and preliminary results[J]. Science China Earth Sciences, 2017, 60: 1896-1908. [14] ZHENG D, SHI D, ZHANG Y, et al. Initial leader properties during the preliminary breakdown processes of lightning flashes and their associations with initiation positions[J]. J Geophys Res, 2019, 124: 8 025-8 042. [15] 冯璐, 夏丰, 万齐林, 等. 广东两次飑线过程的微物理特征分析研究[J]. 热带气象学报, 2019, 35(6): 812-821. [16] 熊英, 彭霁霁. 华南前汛期冷空气影响下两次飑线大风过程的对比分析[J]. 广东气象, 2019, 41(1): 19-23. [17] FAN X P, ZHANG Y J, ZHENG D, et al. A new method of three-dimensional location for low-frequency electric field detection array[J]. J Geophys Res, 2018, 123: 8 792-8 812. [18] PARK H, RYZHKOV A V, ZRNIC D S, et al. The hydrometeor classification algorithm for the polarimetric WSR-88D: Description and application to an MCS[J]. Wea Forecasting, 2009, 24(3): 730-748. [19] BRINGI V N, CHANDRASEKAR V. Polarimetric Doppler Weather Radar: Principles and Applications[M]. Cambridge University Press, 2001: 636. [20] WU C, LIU L P, WEI M, et al. Statistics-based optimization of the polarimetric radar Hydrometeor Classification Algorithm and its application for a squall line in South China[J]. Adv Atmos Sci, 2018, 35: 296-316. [21] KEENAN T D. Hydrometeor classification with a C-band polarimetric radar[J]. Aust Meteor Mag, 2003, 52: 23-31. [22] MARZANO F, SCARANARI D, MONTOPOLI M, et al. Supervised classification and estimation of hydrometeors from C-band dualpolarized radars: A Bayesian approach[J]. IEEE T Geosci Remote, 2007, 46: 85-98. [23] DOLAN B, RUTLEDGE S A. A theory-based hydrometeor identification algorithm for X-band polarimetric radars[J]. J Atmos Oceanic Technol, 2009, 26: 2 071-2 088. [24] ZHAO C H, ZHANG Y J, ZHENG D, et al. An improved hydrometeor identification method for X-band dual-polarization radar and its application for one summer Hailstorm over Northern China[J]. Atmos Res, 2020, 245: 105075. [25] KUMJIAN M R. Principles and applications of dual-polarization weather radar. Part Ⅰ: Description of the polarimetric radar variables[J]. J Operational Meteor, 2013, 1: 226-242. [26] STEINER M, HOUZE JR R A, YUTER S E. Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data[J]. J Appl Meteor, 1995, 34: 1 978-2 007. [27] CHEN X, ZHAO K, XUE M. Spatial and temporal characteristics of warm season convection over Pearl River Delta region, China, based on 3 years of operational radar data[J]. J Geophys Res, 2014, 119: 12 447-12 465. [28] RIBAUD J F, MACHADO L A T, BISCARO T. X-band dual-polarization radar-based hydrometeor classification for Brazilian tropical precipitation systems[J]. Atmos Meas Tech, 2019, 12: 811-837. [29] TAKAHASHI T. Riming electrification as a charge generation mechanism in thunderstorms[J]. J Atmos Sci, 1978, 35(8): 1 536-1 548. -
点击查看大图
图(7) / 表(4)
计量
- 文章访问数: 223
- HTML全文浏览量: 44
- PDF下载量: 31
- 被引次数: 0
粤公网安备 4401069904700003号