Automatic Recognition Algorithm of Convergence Region Based on Relative Storm Radial Velocity Field

Zhu Li; Kang Lan

doi:10.11898/1001-7313.20210109

Vol.32, NO.1, 2021

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Zhu Li, Kang Lan. Automatic recognition algorithm of convergence region based on relative storm radial velocity field. J Appl Meteor Sci, 2021，32(1)：102-114. DOI: 10.11898/1001-7313.20210109.

Citation:

Zhu Li, Kang Lan. Automatic recognition algorithm of convergence region based on relative storm radial velocity field. J Appl Meteor Sci, 2021，32(1)：102-114. DOI: 10.11898/1001-7313.20210109.

Zhu Li, Kang Lan. Automatic recognition algorithm of convergence region based on relative storm radial velocity field. J Appl Meteor Sci, 2021，32(1)：102-114. DOI: 10.11898/1001-7313.20210109.

Citation:

Zhu Li, Kang Lan. Automatic recognition algorithm of convergence region based on relative storm radial velocity field. J Appl Meteor Sci, 2021，32(1)：102-114. DOI: 10.11898/1001-7313.20210109.

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Automatic Recognition Algorithm of Convergence Region Based on Relative Storm Radial Velocity Field

DOI: 10.11898/1001-7313.20210109

Zhu Li^{1, 2},
Kang Lan^{1, 3
,
,}

1.
Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072
2.
Nanchong Meteorological Bureau of Sichuan, Nanchong 637006
3.
Sichuan Meteorological Observatory, Chengdu 610071

Received Date: 2020-08-10
Rev Recd Date: 2020-11-24
Publish Date: 2021-01-31

Abstract

Abstract

An algorithm for automatically identifying the mid-altitude radial convergence from the storm-relative radial velocity field is proposed. The algorithm first identifies the positive-negative velocity segments in each radial direction on the single-elevation radial velocity field, before pairing them to form a radial convergence segment. A two-dimensional radial convergence block is obtained through horizontal correlation analysis, and then three-dimensional radial convergence body of the storm is obtained through vertical correlation analysis. Thus, the parameters such as strength, thickness and center height are calculated.The algorithm is verified using two squall line radar data with a typical "positive-negative velocity zone pairs" radial convergence characteristics, and the results show that the radial convergence feature identified in the relative storm radial velocity field is more complete than the original radial velocity field. The flow field of the meso-small-scale weather system is mainly composed of rotation and translation combined with ascending motion. When the translational motion speed is greater than the rotational speed, the shear (rotation, convergence, or divergence) of the system in the basic radial velocity field may be affected, while using the relative storm radial velocity can overcome this to identify the mid-level convergence better. A batch experiment of 10 thunderstorms and strong convective weather indicates the recognition accuracy of this algorithm is 82.4%, including a typical MARC features.Statistical analysis of the correlation between characteristic parameters and strength of squall line winds shows that the average radial convergence strength, maximum radial convergence strength, thickness have good positive linear correlations with wind speed. The correlation coefficient between convergence intensity and wind speed is the largest, reaching 0.79. According to the radial convergence characteristic parameter value, the intensity of the ground gale can be roughly judged, which provides a certain reference for the monitoring and early warning of convective gale and disaster assessment. The radial convergence feature identified by the algorithm can alert squall line gale about 30 minutes in advance. Therefore, the application of this algorithm will effectively improve the advancement of the warning signal release time.
- storm-relative radial velocity;
- mid-altitude radial convergence;
- storm-relative mid-altitude radial convergence;
- thunderstorm gale

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References(25)

Figures and Tables

图 1 2016年8月6日23:34乐山雷达3.4°仰角反射率因子(a)、原始径向速度(b)以及处理后的相对风暴径向速度图(c)及对应沿黑色直线的反射率因子剖面图(d)、径向速度剖面图(e)和相对风暴径向速度剖面图(f)

(剖面由雷达站沿径向指向远离雷达侧)

Fig. 1 The reflectivity factor(a), raw radial velocity(b), processed relative storm radial velocity graph(c), corresponding reflectivity factor profile(d), radial velocity profile(e), and relative storm radial velocity profile(f) along the black lines based on Leshan Radar at 3.4° elevation angle at 2334 BT 6 Aug 2016

(the profile is directed away from the radar side by the radar station in the radial direction)

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Fig. 2 Simulated maximum reflectance factor (the shaded) and mid-level environmental wind field (the barb, unit:m·s^-1) of the squall line at 0100 BT 7 Aug 2016(a), vertical section along the red line in Fig. 2a (where the shaded is the reflectivity factor, the arrow is the wind vector(the vertical component of the wind speed vector represents 2.0 times of vertical velocity), the contour is relative humidity(unit:%)) (b)

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图 3 径向辐合对示意图

(方格内数值为等距离抽取图 1c中剖面线所在径向上有效范围内的径向速度整数值，单位：m·s^-1，开始和结束的径向距离分别为51 km和61 km)

Fig. 3 Schematic diagram of radial convergence pair

(the values are the integral values of radial velocity(unit:m·s^-1) in the radial direction of the section line in Fig. 1c, and the radial distances at the beginning and the end are 51 km and 61 km, respectively)

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Fig. 4 Identification algorithm of positive-negative velocity convergence pair flowchart

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图 5 2013年8月18日00:49宜宾雷达2.4°仰角反射率因子(a)、原始径向速度(b)以及2018年8月7日20:15南充雷达4.3°仰角反射率因子(c)、原始径向速度(d)

(实线和虚线分别表示原始和相对风暴径向速度场上识别出的径向辐合块，标注A，B，C，E，F，G分别代表算法识别出的径向辐合块)

Fig. 5 The reflectivity factor(a) and raw radial velocity(b) based on Yibin radar station at 2.4° elevation angle at 0049 BT 18 Aug 2013 and the reflectivity factor(c) and original radial velocity(d) based on Nanchong radar station at 4.3° elevation angle at 2015 BT 7 Aug 7 2018

(the solid line and the dashed line respectively represent the radial convergent blocks identified in the RRV and RSRV, and the labels A, B, C, E, F and G represent the radial convergent blocks identified by the algorithm)

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Fig. 6 Scattered point distribution and fitting curve of the ground maximum wind speed and relative storm radial convergence characteristic parameters

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Table 1 Examples of squall line to test the recognition algorithm and recognition result

时间	是否典型MARC特征	测试样本量	算法成功识别样本量	雷达站名
2009-07-26T01:00—02:40	是	5	4	广元
2010-06-20T19:30—21:30	是	4	2	南充
2012-08-18T19:00—20:50	是	8	7	宜宾
2013-08-18T00:00—02:00	是	7	7	宜宾
2015-07-27T20:00—21:00	是	4	2	宜宾
2016-06-04T13:00—15:00	非	6	5	广元
2016-08-06T22:00—23:50	非	5	5	乐山
2016-08-14T22:40—23:50	非	8	6	宜宾
2017-07-16T00:00—01:30	是	4	4	成都
2018-08-07T19:00—20:40	非	6	5	南充

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