ANALYSIS OF THREE-DIMENSIONAL WIND FIELD OF CONVECTIVE STORM DURING HEAVY PRECIPITATION
-
摘要: 使用S波段天气雷达和X波段相控阵天气雷达资料，通过三维变分方法反演风场，分析了2020年6月6日发生在广东佛山的强降水对流风暴三维风场特征：（1）SA雷达因低仰角体扫受遮挡和水平分辨率不够精细，无法反演出对流风暴内部低层水平风场真实特征；SA雷达仰角间隔大和扫描同时性较差致使其反演风场比较平滑。（2）相控阵雷达仰角密集、快速协同的扫描模式能够获取精细的反演风场，反映对流单体内部精细的三维风场特征。相控阵雷达反演风场显示对流风暴低层前侧和南侧强南风流入风暴中心并呈气旋式旋转，对流风暴中层附近具有清晰的辐合线。（3）对流风暴成熟阶段上升和下沉气流交错并存，强烈上升气流自风暴前侧与南侧扭转上升至风暴顶端向南北两侧辐散，其中风暴北侧自高层到低层下沉气流控制为主，风暴南侧下沉气流扭转式下沉至强降水中心和风暴后侧，风暴后侧下沉气流与南侧入流在新、旧单体之间形成小涡旋。Abstract: Based on S-band weather radar and X-band phased-array radar data, we retrieved and studied the three-dimensional wind field of a convective storm during the heavy precipitation on June 6, 2020 in Foshan, Guangdong Province. The results are summarized as follows: (1) The S-band radar data was unable to retrieve the characteristics of the low-level horizontal wind field inside the convective storm because its 0.5 ° volume coverage was partly blocked and its spatial resolution was lower compared with that of the X-band phased-array radar. The retrieved wind field was relatively smooth due to large elevation interval and a lack of simultaneous scan. (2) With more elevation and more simultaneous scan, the X-band phased-array radar data produced high spatial resolution wind field that described the fine structure of the convective storm. Three-dimensional variation wind field retrieved by the X-band phasedarray radar displayed strong southerlies cyclonic flow into the center from the low level and south part of the convective storm. Meanwhile, a convergence line located at the middle level of storm. (3) Updrafts and downdrafts co-existed at the mature stage of the convective storm. Strong updrafts stretched to the top of the storm, and then diverged to the south part and north part of the storm. Downdrafts dominated the north part of the convective storm, while at the south part they reached the precipitation center and rear-flank. A small vortex emerged from the rear-flank downdrafts and southerly inflows between the old and new cells.
-
图 1 a. 广州多普勒雷达(GZ SA)和肇庆多普勒雷达(ZQ SA) 0.5 °仰角遮挡（填色）示意图和三维风场反演区域（红框）示意图；b. 三水潮湾（SS CW）和三水独树岗（SS DSG）相控阵雷达体扫区域（黑色圆圈）、三维风场反演区域（红框）、广东清远探空站（59280）和三水风廓线仪（SS WP）示意图
图 2 a. 2020年6月6日15—18时佛山北部自动气象站累积雨量和极大风分布图；b. 15:25—18:00 G6946自动气象站逐5分钟降水量时序图（柱状）和平均风力风向。
图 4 2020年6月6日广州天气雷达15:30、15:48的1.5 °仰角强度图(a、c)和径向速度图(b、d)；三水潮湾相控阵雷达15:30、15:48的1.4 °仰角强度图(e、g)和径向速度图(f、h)
黑圈：旧单体；白圈：新单体。
图 5 2020年6月6日15:36 SA雷达（a、c、e、g）和15:40相控阵雷达（b、d、f、h）反射率因子和水平风场（水平高度分别为0.5 km(a)、0.48 km(b)、1.0 km(c)、0.96 km(d)、3.0 km(e)、3.0 km(f)、5.0 km(g)、5.04 km(h)）
A: 对流风暴前侧；B: 对流风暴南侧；C: 强降水中心；D: 对流风暴后侧；虚线: 辐合线。
图 7 2020年6月6日15:40相控阵雷达垂直速度(红色上升气流、蓝色下沉气流)叠加水平风场（水平高度分别为0.6 km(a)、3.0km(b)、7.2km(c)、10.8 km(d)）
C: 强降水中心，箭头: 风暴移动方向，黑色虚线：自北向南为强降水中心北侧、强降水中心、反演中心、强降水中心南侧。
-
[1] 广东省气象局《广东省天气预报技术手册》编写组. 广东省天气预报技术手册[M]. 北京: 气象出版社, 2006: 86-150. [2] 吴乃庚, 温之平, 邓文剑, 等. 华南前汛期暖区暴雨研究新进展[J]. 气象科学, 2020, 40(5) : 605-616. [3] 伍红雨, 李芷卉, 李文媛, 等. 基于区域自动气象站的广东极端强降水特征分析[J]. 气象, 2020, 46(6): 801-812. [4] 俞小鼎, 姚秀萍, 熊廷南, 等. 多普勒天气雷达原理与业务应用[M]. 北京: 气象出版社, 2006: 43-45、90-129. [5] 郝莹, 姚叶青, 郑媛媛, 等. 短时强降水的多尺度分析及临近预警[J]. 气象, 2012, 38(8): 903-912. [6] 伍志方, 叶爱芬, 胡胜, 等. 中小尺度天气系统的多普勒统计特征[J]. 热带气象学报, 2004, 20(4): 391-400. [7] 吴芳芳, 俞小鼎, 张志刚, 等. 对流风暴内中气旋特征与强烈天气[J]. 气象, 2012, 38(11): 1 330-1 338. [8] 冯晋勤, 汤达章, 王新强, 等. 新一代天气雷达超级单体风暴中气旋特征分析[J]. 大气科学学报, 2010, 33(6): 738-744. [9] 陶祖钰. 从单Doppler速度场反演风矢量场的VAP方法[J]. 气象学报, 1992, 50(1): 81-90. [10] 王峰云, 王燕雄, 陶祖钰. 单多普勒天气雷达的中尺度风场探测技术研究[J]. 热带气象学报, 2003, 19(3): 291-298. [11] 周海光, 张沛源. 笛卡尔坐标系的双多普勒天气雷达三维风场反演技术[J]. 气象学报, 2002, 60(5): 585-593. [12] 周文昊, 陆春松, 高文华, 等. 华南一次暖区暴雨的演变及云微物理机制模拟研究[J]. 热带气象学报, 2020, 36(6): 805-820. [13] 周海光. "6.12"华南局地暴雨中β和γ结构的双多普勒雷达反演试验[J]. 热带气象学报, 2007, 23(2): 117-125. [14] 马舒庆, 陈洪滨, 王国荣, 等. 阵列天气雷达设计与初步实现[J]. 应用气象学报, 2019, 30(1): 1-12. [15] SHAPIRO A, POTVIN C, GAO J. Use of a vertical vorticity equation in variational dual-Doppler wind analysis[J]. J Atmos Ocean Tech, 2009, 26: 2 089-2 106. [16] POTVIN C, SHAPIRO A, XUE M. Impact of a vertical vorticity constraint in variational dual-Doppler wind analysis: Test with real and simulated supercell data[J]. J Atmos Ocean Tech, 2012, 29(1): 32-49. [17] HELMUS J J, COLLIS S M. The Python ARM Radar Toolkit (Py-ART), a Library for working with weather radar data in the Python programming language[J]. Journal of Open Research Software, 2016, 4: e25. [18] FELDMANN M, JAMES C N, BOSCACCI M, et al. R2D2: A region-based recursive doppler dealiasing algorithm for operational weather radar[J]. J Atmos Ocean Tech, 2020, 37: 2 341-2 356. [19] 钱维宏, 艾阳, 陈绿文, 等. 一次广东典型龙舟水暴雨过程的扰动形势分析[J]. 热带气象学报, 2020, 36(4): 433-443. [20] THOMPSON R L, EDWARDS R, HART J A. An assessment of supercell and tornado forecast parameters with RUC-2 model close proximity soundings[C]. Preprints 21st Conference on Severe Local Storms. San Antonio, American Meteorological Society, 2000: 595-598. [21] 费海燕, 周小刚, 王秀明. 多普勒雷达中气旋判据及算法的发展与应用[J]. 气象科技进展, 2016, 6(5): 24-29. [22] DOSWELL C A Ⅲ. The Operational Meteorology of Convective Weather. Ⅱ: Storm Scale Analysis[M]. NOAA Tech. Memo. ERL ESG-15, 59-67. -
下载:
点击查看大图
计量
- 文章访问数: 593
- HTML全文浏览量: 249
- PDF下载量: 144
- 被引次数: 0
粤公网安备 4401069904700003号