Vol.34, NO.3, 2023
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Article Navigation >  Journal of Applied Meteorological Science  > 2023  >  34(3): 309-323
Tian Ye, Pang Wenjing, Chen Zefang, et al. Application of the 2σ lightning jump algorithm based on DBSCAN cluster. J Appl Meteor Sci, 2023, 34(3): 309-323. DOI:  10.11898/1001-7313.20230305.
Citation: Tian Ye, Pang Wenjing, Chen Zefang, et al. Application of the 2σ lightning jump algorithm based on DBSCAN cluster. J Appl Meteor Sci, 2023, 34(3): 309-323. DOI:  10.11898/1001-7313.20230305.
shu

Application of the 2σ Lightning Jump Algorithm Based on DBSCAN Cluster

DOI: 10.11898/1001-7313.20230305
  • 1.

    Beijing Meteorological Observation Center, Beijing 100089

  • 2.

    Meteorological Observation Center of China Meteorological Administration, Beijing 100081

  • 3.

    Beijing Meteorological Observatory, Beijing 100097

  • Received Date: 2022-11-28
  • Rev Recd Date: 2023-03-06
  • Publish Date: 2023-05-31
    Abstract
  • A DBSCAN (density-based spatial clustering of applications with noise) cluster of lightning data is proposed as the substitute for radar products to solve the problem of beam blockage in radar observation and the delay of radar products in service operations. Two lightning data, BJTLS (Beijing Total Lightning System) and upgraded National Lightning Positioning Network (DDW1), are used and the 2σ lightning jump algorithm is applied to perform severe weather nowcasting on 4 June and 12 June in 2022. The nowcasting effects of the strong convective cell identification method and the DBSCAN clustering method are further compared and analyzed. Based on a determined search radius (R) for neighboring lightning data and a determined minimum number of location results (number of minimum points) in R, the DBSCAN's clustering effect on lightning location data corresponds well with the strong convective radar echo. The ideal parameter combinations for BJTLS, R is 0.05, number of minimum points is 5; and for DDW1 data R is 0.22 and number of minimum points is 3. The results show that both methods and two kinds of data could effectively be used in severe weather nowcast. For BJTLS data, the effects of two methods are equivalent. The probability of detection, false alarm rate, critical success index and lead time of two methods are 100% and 100%, 11.9% and 13.3%, 88.1% and 86.7%, 38.9 min and 42.8 min, respectively. The 2σ lightning jump algorithm can be applied for nowcasting with lightning data, reducing the dependence on radar products. For DDW1 lightning data, compared with the identification method, the start time of the clustering method delays, leading to missing alarms. Since the flash rate of the DDW1 lightning data is low, there will be more missed cases if the flash rate threshold is set to trigger the lightning jump. But without the threshold, there will be more false alarms in operation. Therefore, BJTLS data is more suitable than DDW1 data for applying the 2σ lightning jump algorithm in the service operation. The detection efficiency of BJTLS in Beijing is high and it is necessary to further improve the detection efficiency of DDW1. In conclusion, the DBSCAN clustering method provides a new idea for the service operation of the 2σ lightning jump algorithm.
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  • Fig. 1  Distribution map of S-band radar, BJTLS and DDW1

    (the circle denotes the detection range of the radar)

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    Fig. 2  Identified strong convective cells on 12 Jun 2022

    (· denotes an intracloud(IC) flash, × denotes a negative cloud-to-ground(CG) flash, + denotes a positive CG flash, the blue irregular circle denotes the range of an identified cell, similarly hereinafter)
    (a)total flashes of BJTLS and the identified strong convective cells at 1920 BT, (b)DBSCAN clustering of total flashes of BJTLS at 1920 BT, (c)total flashes of DDW1 and the identified strong convective cells at 1950 BT, (d)DBSCAN clustering of total flashes of DDW1 at 1950 BT

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    Fig. 3  Identified strong convective cells and total flashes of BJTLS in 3 minutes before and after the corresponding time during the development of the supercell on 4 Jun 2022

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    Fig. 4  DBSCAN clustering of BJTLS total flashes on 4 Jun 2022

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    Fig. 5  Comparison in nowcasting effect of the 2σ lightning jump algorithm between identification method and DBSCAN method using total flash data of BJTLS and DDW1 for the supercell on 4 Jun 2022

    (a)identification method and BJTLS data, (b)DBSCAN method and BJTLS data, (c)identification method and DDW1 data, (d)DBSCAN method and DDW1 data

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    Fig. 6  Identified strong convective cells and total flashes of BJTLS on 12 Jun 2022

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    Fig. 7  DBSCAN clustering of total flashes of BJTLS on 12 Jun 2022

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    Fig. 8  Identified strong convective cells and total flashes of DDW1 on 12 Jun 2022

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    Fig. 9  DBSCAN clustering of DDW1 total flashes on 12 Jun 2022

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    Fig. 10  Comparison in nowcasting effect of the 2σ lightning jump algorithm between identification method and DBSCAN method using total flash data of BJTLS and DDW1 for disastrous weather cell on 12-13 Jun 2022

    (a)identification method and BJTLS data, (b)DBSCAN method and BJTLS data, (c)identification method and DDW1 data, (d)DBSCAN method and DDW1 data

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    Table  1  Records of disastrous weather in Beijing on 4 Jun and 12 Jun in 2022

    日期 起始时刻 灾害类型 代表符号 发生位置 强度或最大直径 产生单体
    4日 18:18 大风 W1 怀柔杏树台 17.2 m·s-1 18:20单体1
    19:00 冰雹 H2 怀柔城区 20 mm 19:02单体1
    19:02 大风 W3 怀柔桥梓镇 18.1 m·s-1 19:02单体1
    19:10 大风 W4 顺义兴农天力 24.2 m·s-1 19:08单体1
    19:17 冰雹 H5 顺义木林 10 mm 19:20单体1
    19:18 大风 W6 顺义牛栏山 17.4 m·s-1 19:20单体1
    19:19 大风 W7 奥林匹克水上公园 24.9 m·s-1 19:20单体1
    19:21 冰雹 H8 顺义北小营 10 mm 19:20单体1
    19:29 强降水 R9 顺义牛栏山 20 mm·h-1 19:26单体1
    19:49 冰雹 H10 顺义杨镇 10 mm 19:50单体1
    20:05 大风 W11 通州小邓各庄村 25.2 m·s-1 20:02单体1
    20:06 大风 W12 通州北刘各庄村 22.6 m·s-1 20:08单体1
    20:08 大风 W13 通州副中心办公楼 14.1 m·s-1 20:08单体1
    20:15 冰雹, 大风 H14, W15 通州潞城镇, 中农富通 40 mm, 24.4 m·s-1 20:14单体1
    20:17 大风 W16 通州大豆各庄小学 20.2 m·s-1 20:14单体1
    20:20 大风 W17 通州西定环卫所 19.7 m·s-1 20:20单体1
    20:22 大风 W18 通州马坊村 17.4 m·s-1 20:20单体1
    20:23 大风 W19 通州漷县村 18.6 m·s-1 20:20单体1
    20:28 大风 W20 通州101农场 29.9 m·s-1 20:26单体1
    20:31 大风 W21 通州永乐店二村 20.3 m·s-1 20:32单体1
    20:34 大风 W22 通州曹庄村 26.7 m·s-1 20:32单体1
    20:35 大风 W23 通州老槐庄 19.3 m·s-1 20:38单体1
    20:40 强降水 R24 通州101农场 30 mm·h-1 20:38单体1
    12日 19:53 强降水, 大风 R10, W11 顺义北小营 30 mm·h-1, 17.2 m·s-1 19:56单体4
    19:55 冰雹 H12 密云不老屯 8 mm 19:56单体4
    20:25 冰雹 H13 怀柔北房 8 mm 20:26单体3
    20:36 强降水 R14 顺义北小营 50 mm·h-1 20:38单体3
    20:38 冰雹 H15, H16 顺义李遂, 龙屯湾 10 mm, 25 mm 20:38单体3
    20:58 冰雹 H17 朝阳常营 5 mm 21:02单体3
    21:04 冰雹，大风 H18, W19 通州永顺, 通州气象局 30 mm, 17.2 m·s-1 21:02单体3
    21:10 强降水 R20 朝阳楼梓庄 20 mm·h-1 21:08单体5
    21:37 冰雹 H21 朝阳气象局 8 mm 21:44单体2
    21:38 冰雹 H22 北京市观象台 20 mm 21:44单体2
    21:45 冰雹 H23 丰台木樨园 30 mm 21:44单体2
    22:48 冰雹 H25 朝阳小红门 60 mm 21:50单体6
    22:55 冰雹 H26 大兴瀛海 5 mm 21:50单体6
    23:02 强降水 R27 大兴瀛海 20 mm·h-1 23:02单体2
    23:21 冰雹 H28 大兴气象局 10 mm 23:20单体2
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    Table  2  Comparison in disastrous weather nowcasting of identification method and DBSCAN method using total flash data of BJTLS and DDW1

    统计量 BJTLS DDW1
    强对流单体识别法 DBSCAN聚类法 强对流单体识别法 DBSCAN聚类法
    预警命中次数 52 52 48 49
    预警误报次数 7 8 3 8
    预警漏报次数 0 0 4 3
    命中率/% 100 100 92.3 94.2
    误报率/% 11.9 13.3 5.8 14.0
    临界成功指数/% 88.1 86.7 87.3 81.7
    预警提前时间/min 38.9 42.8 35.8 28.9
    DownLoad: Download CSV
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    • Received : 2022-11-28
    • Accepted : 2023-03-06
    • Published : 2023-05-31
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