利用多普勒雷达资料识别低空风切变和辐合线方法研究

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王楠, 刘黎平, 徐宝祥, 张建云. 利用多普勒雷达资料识别低空风切变和辐合线方法研究[J]. 应用气象学报, 2007, 18(3): 314-320. 复制到剪切板

Wang Nan, Liu Liping, Xu Baoxiang, Zhang Jianyun. Recognizing Low-altitude Wind Shear and Convergence Line with Doppler Radar[J]. Journal of Applied Meteorological Science, 2007, 18(3): 314-320 复制到剪切板

利用多普勒雷达资料识别低空风切变和辐合线方法研究

王楠^1,2,3, 刘黎平², 徐宝祥², 张建云⁴

1. 北京大学物理学院大气科学系, 北京 100871;
2. 中国气象科学研究院灾害天气国家重点实验室, 北京 100081;
3. 陕西省气象台, 西安 710015;
4. 北京敏视达雷达有限公司, 北京 100085

2006-03-10 收到, 2007-01-12 收到修改稿.

资助项目: 国家重点基础研究发展规划项目“我国南方致洪暴雨监测与预测的理论和方法研究”(2004CB418305) 和北京敏视达雷达有限公司课题“低层风场反演系统和边界层辐合线/风切变识别系统研制”共同资助

摘要: 该文研究了利用多普勒雷达径向速度资料识别低空风切变和辐合线的方法, 讨论了不同的计算“窗口”大小对资料预处理效果和梯度计算的影响, 并对几次强对流天气进行识别、分析。结果表明:预处理采取先中值滤波后滑动平均, 选择合适的“窗口”能在有效去除库间脉动的同时保持中尺度信息; 经过资料预处理后, 从径向速度计算的切变结果与径向速度中反映的中尺度结构比较一致, 能够从这些资料中自动提取辐合和切变的中尺度信息; 强降水回波与风切变高值区位置、变化趋势一致; 垂直切变能够提供径向风场的高低层配置信息; 利用径向速度资料可以实现对风切变和辐合线的自动识别, 为灾害性天气预警、预报提供重要的客观依据。

关键词: 多普勒天气雷达径向速度辐合线切变线

Recognizing Low-altitude Wind Shear and Convergence Line with Doppler Radar

Wang Nan^1,2,3, Liu Liping², Xu Baoxiang², Zhang Jianyun⁴

1. Department of Atmospheric Sciences, Peking University, Beijing 100871;
2. National Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081;
3. Shaanxi Meteorological Observatory, Xi'an 710015;
4. Beijing Metstar Radar Co Ltd, Beijing 100085

Abstract: An algorithm for detecting low-altitude wind shear and convergence line with Doppler radial velocities is developed. The algorithm includes two parts:data pretreatment and shear calculating. Pretreatment is in order to smooth some noise in records, make them consecutive and keep the med-scale information meanwhile. Median filtering firstly and then moving average is performed for pretreatment in the algorithm. According to meteorology, convergence is defined radial grads of radial velocity and shear is defined azimuthal grads. Least square method is adopted to calculate shear. Besides of that, the algorithm gives vertical shear also. The number of treating data represents the space related to treatment. Therefore, the effect of number on results of pretreatment and shear calculating is separately analyzed. Both in median filter and moving average, the smoothing effect is more obviously while more data are used. But there are still some differences between two methods. Although median filter do better in smoothing noise and keep med-scale trend of data, the sequent is that data become discontinuous. With median filtering means, the best result is btained that the meso-scale movement of data is slippery, consecutive and preserving. Just as pretreatment, different number will lead different outcome of shear calculating. The value of shear will become large while the amount of data calculated is small. And the shear line or convergence line will become narrow. Furthermore, a gate value of result show must be set to help forecaster recognize the line easily, which is detected by the algorithm. In the end, only when the amounts of both pretreatment and shear calculating are all suit with scale of the line which will be detected, the production will be perfect. After theoretical discussion, the algorithm is tested on some heavy rain practically. The first one is caused by a squall line, the second is about of a strong convective line, last is a precipitation of mixing cloud. In all of these cases, the algorithm woks very well. First, both magnitude and position of wind shear or convergence line which are detected are all consistent with the actual. Secondly, high shear value of shear or convergence line is corresponding to reflectivity of heavy rain. Thirdly, Different gate is necessary for different rain. Fourthly, vertical shear exhibits the variety of velocity fields in different elevation. As a conclusion, it is feasible to detect low-altitude wind shear or convective line using Doppler radial velocities. The production will provide an important and objective evidence for weather forecasting and alarming.

Key words: Doppler radar radial velocity shear line convergence line

[1]	朱炳海, 王鹏飞, 束家鑫. 气象学词典. 上海: 上海辞书出版社, 1985: 96-97.
[2]	张家国, 吴翠红, 王珏, 等. 一次冷锋大暴雨过程的多普勒雷达观测分析. 应用气象学报, 2006, 17, (2): 226–231.
[3]	何彩芬, 姚秀萍, 胡春蕾, 等. 一次台风前部龙卷的多普勒天气雷达分析. 应用气象学报, 2006, 17, (3): 370–375.
[4]	姚建群, 戴建华, 姚祖庆. 一次强飑线的成因及维持和加强机制分析. 应用气象学报, 2005, 16, (6): 746–753.
[5]	陶祖钰, 曾立新. 中尺度系统的模拟Doppler图像. 应用气象学报, 1993, 4, (1): 8–15.
[6]	陈明轩, 俞小鼎, 谭晓光, 等. 对流天气临近预报技术的发展与研究进展. 应用气象学报, 2004, 15, (6): 754–766.
[7]	Wilson J W, Roberts R D, Kessinger C, et al. Microburst wind structure and evaluation of Doppler radar for airport wind shear detection. J Appl Meteor, 1984, 23: 898–915. DOI:10.1175/1520-0450(1984)023<0898:MWSAEO>2.0.CO;2
[8]	Uyeda H, Zrnić D S, Automatic detection of gust fronts. J Atmos Oceanic Technol, 1986, 3: 36–50. DOI:10.1175/1520-0426(1986)003<0036:ADOGF>2.0.CO;2
[9]	Hermes L G, Witt A, Smith S D, et al. The gust-front detection and wind-shift algorithms for the terminal Doppler weather radar system. J Atmos Oceanic Technol, 1993, 10: 693–708. DOI:10.1175/1520-0426(1993)010<0693:TGFDAW>2.0.CO;2
[10]	Zrnić D S, Burgess D W, Hennington L D, Automatic detection of mesocyclonic shear with Doppler radar. J Atmos Oceanic Technol, 1985, 2: 425–438. DOI:10.1175/1520-0426(1985)002<0425:ADOMSW>2.0.CO;2
[11]	Campbell S D, Olson S H, Recognizing low-altitude wind shear hazards from Doppler weather radar:An artificial intelligence approach. J Atmos Oceanic Technol, 1987, 4: 5–18. DOI:10.1175/1520-0426(1987)004<0005:RLAWSH>2.0.CO;2
[12]	胡明宝, 谈曙青, 汤达章, 等. 单部多卜勒雷达探测低空风切变方法. 南京气象学院学报, 2000, 23, (1): 113–118.
[13]	中国人民解放军总参谋部气象局. 多普勒天气雷达资料分析与应用. 北京: 解放军出版社, 2000: 150-159.
[14]	陆汉城, 杨国祥. 中尺度天气原理和预报. 北京: 气象出版社, 2004: 106-114.
[15]	张玉玲. 中尺度大气动力学引论. 北京: 气象出版社, 1999: 147-176.


图 1. 资料预处理效果对比（a）中值滤波效果对比，（b）滑动平均效果对比，（c）中值滤波后滑动效果对比 Fig 1. Contrast of data pretreatment (a) contrast of media filtering, (b) contrast of moving average, (c) contrast of media filtering first and then moving average