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利用多普勒雷达资料识别低空风切变和辐合线方法研究
Recognizing Low-altitude Wind Shear and Convergence Line with Doppler Radar
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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。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.
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Key words:
- Doppler radar;
- radial velocity;
- shear line;
- convergence line
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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
图 2 2004年6月21日17：40济南多普勒天气雷达0.53°仰角速度图计算结果
（a）~（c）用原始径向速度计算径向切变（显示阈值：2.5m·s-1·km-1）n=10（a）, n=20（b）, n=40（c）, （d）~（f）用预处理后数据计算的径向切变（显示阈值：1.5 m·s-1·km-1）n=10（d）, n=20（e）, n=40（f）
Fig. 2 Radial shear of elevation 0.53° in Jinan at 17:40 on June 21, 2004
(a)-(c) radial shear calculated with original data (show gate:2.5 m·s-1·km-1) n=10(a), n=20(b), n=40(c), (d)-(f) radial shear calculated with pretreated data (show gate:1.5 m·s-1·km-1) n=10(d), n=20(e), n=40(f)
图 3 2005年3月22日01：32广州飑线过程多普勒大气雷达回波及切变识別结果
(a) 0.53°仰角反射率因子，(b) 2.5°仰角反射率因子，(c)0.53°仰角径向速度, (d) 2.5°仰角径向速度, (e)0.53°仰角径向切变（显示阈值：1.2 m·s-1·km-1，（f) 0.53°仰角切向切变（显示阈值：1.5 m·s-1·km-1), (g) 0.53°仰角组合切变（显示阈值：1.7 m·s-1·km-1), (h) 0.53°和1.49°仰角间垂直切变，（i) 2.5°和3.5°仰角间垂直切变
Fig. 3 Doppler radar echo and detecting result of a squall line in Guangzhou at 01:32 on March 22, 2005
(a) reflectivity in elevation 0.53°, (b) reflectivity in elevation 2.5°, (c) velocity in elevation 0.53°, (d) velocity in elevation 2.5°, (e) radial shear in elevation 0.53° (show gate: 1.2 m·s-1·km-1), (f) azimuthal shear in elevation 0.53° (show gate:l.5 m·s-1·km-1), (g) combined shear in elevation 0.53° (show gate: 1.7 m·s-1·km-1), (h) vertical shear between elevation 0.53° and 1.49°, (i) vertical shear between elevation 2.5° and 3.5°
图 4 2004年6月21日17:34济南带状强回波降水多普勒天气雷达回波及切变识别结果
(a)0.53°仰角反射率因子，(b)0.53°仰角径向速度，(c) 0.53°仰角和0.49°仰角间垂直切变, (d) 0.53°仰角径向切变（显示阈值：1.2 m·s-1·km-1), (e)0.53°仰角切向切变（显示阈值：1.7 m·s-1·km-1)，(f)0.53°仰角组合切变（显示阈值：1.7 m·s-1·km-1）
Fig. 4 Doppler radar echo and detecting result of a strong convective line at 17:34 on June 21, 2004
(a) reflectivity in elevation 0.53°, (b) velocity in elevation 0.53°, (c) vertical shear between elevation 0.53° and 1.49°, (d) radial shear in elevation 0.53° (show gate:1.2 m·s-1·km-1), (e) azimuthal shear in elevation 0.53° (show gate:l.7 m·s-1·km-1), (f) combined shear in elevation 0.53° (show gate:1.7 m·s-1·km-1)
图 5 2002年7月22日11:49荆州混合云暴雨过程多普勒天气雷达回波及切变识别结果
(a) 0.6°仰角反射率因子，（b）0.6°仰角径向速度，（c）0.6°和1.4°仰角间垂直切变，（d) 0.6°仰角径向切变（显示阈值：0.5 m·s-1·km-1), (e) 0.6°仰角切向切变（显示阈值：0.8 m·s-1·km-1）, (f）0.6°仰角组合切变（显示阈值：0.8 m·s-1·km-1）
Fig. 5 Doppler radar echo and detecting result of a mixing cloud in Jinzhou at 1l:49 on July 22, 2002
(a) reflectivity in elevation 0.6°, (b) velocity in elevation 0.6°, (c) vertical shear between elevation 0.6° and 1.4°, (d) radial shear in elevation 0.6° (show gate:0.5 m·s-1·km-1), (e) azimuthal shear in elevation 0.6° (show gate:0.8 m·s-1·km-1), (f) combined shear in elevation 0.6* (show gate:0.8 m·s-1·km-1)
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