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不同闪电跃增算法在北京地区应用效果对比

田野 姚雯 尹佳莉 郄秀书 曹海维 李晋 袁善锋 王东方

田野, 姚雯, 尹佳莉, 等. 不同闪电跃增算法在北京地区应用效果对比. 应用气象学报, 2021, 32(2): 217-232. DOI:  10.11898/1001-7313.20210207..
引用本文: 田野, 姚雯, 尹佳莉, 等. 不同闪电跃增算法在北京地区应用效果对比. 应用气象学报, 2021, 32(2): 217-232. DOI:  10.11898/1001-7313.20210207.
Tian Ye, Yao Wen, Yin Jiali, et al. Comparison of the performance of different lightning jump algorithms in Beijing. J Appl Meteor Sci, 2021, 32(2): 217-232. DOI:  10.11898/1001-7313.20210207.
Citation: Tian Ye, Yao Wen, Yin Jiali, et al. Comparison of the performance of different lightning jump algorithms in Beijing. J Appl Meteor Sci, 2021, 32(2): 217-232. DOI:  10.11898/1001-7313.20210207.

不同闪电跃增算法在北京地区应用效果对比

DOI: 10.11898/1001-7313.20210207
资助项目: 

灾害天气国家重点实验室开放课题 2019LASW-B07

详细信息
    通信作者:

    姚雯, yaowen@cma.gov.cn

Comparison of the Performance of Different Lightning Jump Algorithms in Beijing

  • 摘要: 基于S波段多普勒天气雷达基数据、北京闪电定位网全闪定位数据和北京地区降雹的人工观测结果,对比分析Gatlin和σ两种闪电跃增算法在不同配置下对北京地区2015—2018年共177次冰雹天气过程的预警效果。结果表明:不同倍数的σ算法预警结果差别很大,2σ(要求当前闪电频数变化率超过之前平均闪电频数变化率两倍标准差)在σ算法中的预警效果最佳;不同N(总闪频数变化率的数量)配置下的Gatlin算法的预警结果差别不大,其中当N=6时的预警效果最佳。2σ算法的命中率、虚警率和临界成功指数分别为80.2%,41.6%和51.1%,N=6的Gatlin算法的相应结果分别为82.5%,62.0%和35.2%。另外,详细分析了一次多单体雷暴过程和一次飑线过程中两种算法的应用情况,结果也表明Gatlin算法比2σ算法的命中率略高,但虚警率偏高很多,临界成功指数偏低。综合Gatlin算法和σ算法对冰雹预报结果评估情况,发现2σ闪电跃增算法更适于对北京冰雹天气的预警,对提升闪电数据在北京地区冰雹预报业务的可用度有一定参考价值。
  • 图  1  北京闪电定位网(BLNET)站点分布

    Fig. 1  Distribution map of BLNET stations

    图  2  不同σ阈值的预警效果

    Fig. 2  Comparison of early warning effects of different σ thresholds

    图  3  Gatlin算法中总闪频数变化率D数量N不同取值的预警效果

    Fig. 3  Comparison of the early warning effects of Gatlin algorithm with different N of D

    图  4  2017年8月8日北京多单体对流系统的雷达组合反射率因子演变

    (黑色六角形为降雹点)

    Fig. 4  Radar composite reflectivity of a multi-cell convective system across Beijing on 8 Aug 2017

    (the black six-pointed star indicates the hailfall position)

    图  5  2017年8月8日北京多单体对流系统识别结果

    (红色圈为产生降雹的单体,黑色六角形为降雹点)

    Fig. 5  Identification results of strong convection cells on 8 Aug 2017

    (the red polygon marks the hail-producing convection cell, the black six-pointed star indicates the hailfall position)

    图  6  叠加前后3 min内总闪定位的2017年8月8日北京强对流单体识别结果

    (红色圆点代表云闪, 红色×表示负地闪, 红色+表示正地闪;黑色六角形为降雹点,19:48图中插图为分裂单体与总闪定位结果叠加的放大图)

    Fig. 6  Identified strong convection cells and the located total flashes in 3 min before and after the corresponding time

    (the red dot indicates the intracloud flash, the red × indicates the negative cloud-to-ground flash and the red + indicates the positive cloud-to-ground flash, the black six-pointed star indicates the hailfall position, the illustration is an enlarged view of superposition of the split cell and the total flashes in the figure of 1948 BT)

    图  7  多单体对流系统降雹单体内的闪电频数变化和两种算法总闪频数变化率(柱状)和闪电跃增阈值(曲线)

    Fig. 7  The lightning flash rate of the hail-producing cell of the multi-cell system and total flash rates(the columns) and jump thresholds(the pink curves) derived by 2σ algorithm and Gatlin algorithm

    图  8  2015年8月7日北京飑线过程的雷达组合反射率因子

    (黑色六角形为降雹点,数字代表降雹顺序)

    Fig. 8  Radar composite reflectivity of a squall line across Beijing on 7 Aug 2015

    (the black six-pointed star indicates the hailfall position, the number in each subgraph indicates the sequence of the hailfall events)

    图  9  飑线过程强对流单体识别结果

    (黑色六角形为降雹点,数字代表降雹顺序)

    Fig. 9  Identification results of every strong convection cells during the squall line process

    (the black six-pointed star indicates the hailfall position, the number indicates the sequence of the hailfall events)

    图  10  飑线降雹单体内的闪电频数变化和两种算法总闪频数变化率(柱状)和闪电跃增阈值(曲线)

    Fig. 10  The lightning flash rate of the haill-producing cell of the squall line system and total flash rates(the columns) and jump thresholds(the pink curves) derived by 2σ algorithm and Gatlin algorithm

    表  1  2015年8月7日北京飑线过程的2σ算法闪电跃增信息

    Table  1  The lightning jump information of 2σ algorithm for a squall line process in Beijing on 7 Aug 2015

    跃增时刻 降雹时刻 与首次跃增时刻相比得到的预警提前时间/min
    16:42,16:48 17:40—17:41 58
    17:10,17:16,17:26,17:28,17:40 18:00—18:06 50
    18:28 18:50—18:53 22
    18:28,18:30,18:52 19:15—19:20 47
    18:28,18:30,18:52 19:16—19:17 48
    19:58—20:01 漏报
    下载: 导出CSV

    表  2  2015年8月7日北京界飑线过程的Gatlin算法闪电跃增信息

    Table  2  The lightning jump information of Gatlin algorithm for a squall line process in Beijing on 7 Aug 2015

    跃增时刻 降雹时刻 与首次跃增时刻相比得到的预警提前时间/min
    16:42,16:48,16:54 17:40—17:41 58
    17:00,17:06,17:10,17:16,17:26,17:28,17:34,17:40,17:42 18:00—18:06 60
    18:28,18:30,18:32,18:52 18:50—18:53 22
    18:56,19:16 19:15—19:20 19
    18:56,19:16 19:16—19:17 20
    19:28,19:36 19:58—20:01 30
    下载: 导出CSV
  • [1] 王婷波,郑栋,周康辉,等.暴雨和雹暴个例中闪电特征对比.应用气象学报,2017,28(5):568-578. doi:  10.11898/1001-7313.20170505

    Wang T B, Zheng D, Zhou K H, et al. Contrastive analysis of lightning characteristics between rainstorm case and hailstorm case. J Appl Meteor Sci, 2017, 28(5): 568-578. doi:  10.11898/1001-7313.20170505
    [2] Goodman S J, Christian H J, Rust W D. A comparison of the optical pulse characteristics of intracloud and cloud-to-ground lightning as observed above clouds. J Appl Meteorol, 1988, 27(12): 1369-1381. doi:  10.1175/1520-0450(1988)027<1369:ACOTOP>2.0.CO;2
    [3] Lang T J, Rutledge S A.One Severe Storm with Two Distinct Electrical Regimes During its Lifetime: Implications for Nowcasting Severe Weather with Lightning Data//Proceedings of 1st Conference of Meteorological Applications of Lightning Data, San Diego, CA, 2005.
    [4] McKinney C M, Carey L D, Patrick G R. Total lightning observations of supercells over north central Texas. Electronic J Severe Storms Meteor, 2009, 4(2): 1-25. http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=52225677&site=ehost-live
    [5] Darden C B, Nadler D J, Carcione B C, et al. Utilizing total lightning information to diagnose convective trends. Bull Amer Meteor Soc, 2010, 91(2): 167-175. doi:  10.1175/2009BAMS2808.1
    [6] Rudlosky S D, Fuelberg H E. Documenting storm severity in the mid-Atlantic region using lightning and radar information. Mon Wea Rev, 2013, 141(9): 3186-3202. doi:  10.1175/MWR-D-12-00287.1
    [7] 朱君鉴, 刁秀广, 黄秀韶. 一次冰雹风暴的CINRAD/SA产品分析. 应用气象学报, 2004, 15(5): 579-589. http://qikan.camscma.cn/article/id/20040571

    Zhu J J, Diao X G, Huang X S. Study of CINRAD/SA products for a hail storm. J Appl Meteor Sci, 2004, 15(5): 579-589. http://qikan.camscma.cn/article/id/20040571
    [8] 王瑾, 刘黎平. WSR-88D冰雹探测算法在贵州地区的评估检验. 应用气象学报, 2011, 22(1): 96-106. http://qikan.camscma.cn/article/id/20110110

    Wang J, Liu L P. The evaluation of WSR-88D hail detection algorithm over Guizhou region. J Appl Meteor Sci, 2011, 22(1): 96-106. http://qikan.camscma.cn/article/id/20110110
    [9] 张秉祥, 李国翠, 刘黎平, 等. 基于模糊逻辑的冰雹天气雷达识别算法. 应用气象学报, 2014, 25(4): 415-426. http://qikan.camscma.cn/article/id/20140404

    Zhang B X, Li G C, Liu L P, et al. Identification method of hail weather based on fuzzy-logical principle. J Appl Meteor Sci, 2014, 25(4): 415-426. http://qikan.camscma.cn/article/id/20140404
    [10] 石宝灵, 王红艳, 刘黎平. 云南多普勒天气雷达网探测冰雹的覆盖能力. 应用气象学报, 2018, 29(3): 270-281. doi:  10.11898/1001-7313.20180302

    Shi B L, Wang H Y, Liu L P. Coverage capacity of hail detection for Yunnan doppler weather radar network. J Appl Meteor Sci, 2018, 29(3): 270-281. doi:  10.11898/1001-7313.20180302
    [11] Kane R J. Correlating lightning to severe local storms in the northeastern United States. Wea Forecasting, 1991, 6(1): 3-12. doi:  10.1175/1520-0434(1991)006<0003:CLTSLS>2.0.CO;2
    [12] Williams E R, Boldi B, Matlin A, et al. The behavior of total lightning activity in severe Florida thunderstorms. Atmos Res, 1999, 51(3/4): 245-265. http://www.sciencedirect.com/science/article/pii/S0169809599000113
    [13] Metzger E, Nuss W A. The relationship between total cloud lightning behavior and radar-derived thunderstorm structure. Wea Forecasting, 2010, 28(1): 237-253. http://adsabs.harvard.edu/abs/2013WtFor..28..237M
    [14] Goodman S J, Blakeslee T R, Christian H, et al. The North Alabama lightning mapping array: Recent severe storm observations and future prospects. Atmos Res, 2005, 76(1/2/3/4): 423-437. http://www.sciencedirect.com/science/article/pii/S0169809505000645
    [15] Schultz C J, Petersen W A, Carey L D. Preliminary development and evaluation of lightning jump algorithms for the real-time detection of severe weather. J Appl Meteorol Climatol, 2009, 48(12): 2543-2563. doi:  10.1175/2009JAMC2237.1
    [16] Wapler K. The life-cycle of hailstorms: Lightning, radar reflectivity and rotation characteristics. Atmos Res, 2017, 193: 60-72. doi:  10.1016/j.atmosres.2017.04.009
    [17] 陈哲彰. 冰雹与雷暴大风的云对地闪电特征. 气象学报, 1995, 53(3): 367-374. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB503.012.htm

    Chen Z Z. The characteristics of lightning from clouds to ground accompanying with hailstones, thunderstorms and gusts. Acta Meteor Sinica, 1995, 53(3): 367-374. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB503.012.htm
    [18] 周筠珺, 张义军, 郄秀书, 等. 陇东地区冰雹云系发展演变与其地闪的关系. 高原气象, 1999, 18(2): 236-244. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX902.012.htm

    Zhou Y J, Zhang Y J, Qie X S, et al. The relationship between the variation of hail cloud system and its cloud to ground lightning in the east part of Gansu province. Plateau Meteor, 1999, 18(2): 236-244. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX902.012.htm
    [19] 蔡晓云, 宛霞, 郭虎. 北京地区闪电定位资料的应用分析. 气象科技, 2001, 29(4): 33-35. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ200104007.htm

    Cai X Y, Wan X, Guo H. Application analysis of lightning location data in Beijing area. Meteor Sci Technol, 2001, 29(4): 33-35. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ200104007.htm
    [20] 冯桂力, 边道相, 刘洪鹏, 等. 冰雹云形成发展与闪电演变特征分析. 气象, 2001, 27(3): 33-37. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200103008.htm

    Feng G L, Bian D X, Liu H P, et al. The evolution of hail cloud system and characterof its cloud to ground lightning. Meteor Mon, 2001, 27(3): 33-37. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200103008.htm
    [21] 冯桂力, 郄秀书, 袁铁, 等. 一次冷涡天气系统中雹暴过程的地闪特征分析. 气象学报, 2006, 64(2): 85-94. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200602008.htm

    Feng G L, Qie X S, Yuan T, et al. A case study of cloud-to-ground lightning activities in hailstorms under cold eddy synoptic situation. Acta Meteor Sinica, 2006, 64(2): 85-94. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200602008.htm
    [22] 冯桂力, 郄秀书, 吴书君. 山东地区冰雹云的闪电活动特征. 大气科学, 2008, 32(2): 289-299. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200802007.htm

    Feng G L, Qie X S, Wu S J. Cloud-to-ground lightning characteristics of hail clouds in Shandong province. Chin J Atmos Sci, 2008, 32(2): 289-299. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200802007.htm
    [23] Liu D, Feng G, Wu S. The characteristics of cloud-to-ground lightning activity in hailstorms over northern China. Atmos Res, 2009, 91(2-4): 459-465. http://www.sciencedirect.com/science/article/pii/S0169809508002196
    [24] Gatlin P N.Severe Weather Precursors in the Lightning Activity of Tennessee Valley Thunderstorms.Department of Atmospheric Sciences, University of Alabama, 2007.
    [25] Schultz C J, Petersen W A, Carey L D. Lightning and severe weather: A comparison between total and cloud-to-ground lightning trends. Wea Forecasting, 2011, 26(5): 744-755. http://adsabs.harvard.edu/abs/2011WtFor..26..744S
    [26] Yao W, Zhang Y, Meng Q, et al. A comparison of the characteristics of total and cloud-to-ground lightning activities in hailstorms. Acta Meteor Sinica, 2013, 27(2): 282-293. http://www.cnki.com.cn/Article/CJFDTotal-QXXW201302013.htm
    [27] Chronis T, Carey L D, Schultz C J, et al. Exploring lightning jump characteristics. Wea Forecasting, 2015, 30(1): 23-37. http://adsabs.harvard.edu/abs/2015WtFor..30...23C
    [28] Tian Y, Qie X S, Sun Y, et al. Total lightning signatures of thunderstorms and lightning jumps in hailfall nowcasting in the Beijing area. Atmos Res, 2019, 230: 104646. http://www.sciencedirect.com/science/article/pii/S0169809519309020
    [29] 张文娟, 孟青, 吕伟涛, 等. 时间差闪电监测网的误差分析和布局优化. 应用气象学报, 2009, 20(4): 402-410. http://qikan.camscma.cn/article/id/20090403

    Zhang W J, Meng Q, Lü W T, et al. Error analyses and network optimization for time-of-arrival lightning locating system. J Appl Meteor Sci, 2009, 20(4): 402-410. http://qikan.camscma.cn/article/id/20090403
    [30] Wang Y, Qie X S, Wang D F, et al. Beijing lightning network (BLNET) and the observation on preliminary breakdown processes. Atmos Res, 2016, 171: 121-132. http://www.sciencedirect.com/science/article/pii/S0169809515004020
    [31] Srivastava A, Tian Y, Qie X S, et al. Performance assessment of Beijing Lightning Network (BLNET) and comparison with other lightning location networks across Beijing. Atmos Res, 2017, 197: 76-83. http://www.sciencedirect.com/science/article/pii/S016980951730217X
    [32] Dixon M, Wiener G. TITAN: Thunderstorm identification, tracking, analysis, and nowcasting-A radar-based methodology. J Atmos Ocean Technol, 1993, 10(10): 785-797. http://ci.nii.ac.jp/naid/80007371603
    [33] Schultz C J, Carey L D, Schultz E V, et al. Kinematic and microphysical significance of lightning jumps versus nonjump increases in total flash rate. Wea Forecasting, 2017, 32(1): 275-288. http://europepmc.org/abstract/MED/29158622
    [34] Gatlin P N, Goodman S J. A total lightning trending algorithm to identify severe thunderstorms. J Atmos Ocean Technol, 2010, 27(1): 3-22. http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010JAtOT..27....3G&db_key=PHY&link_type=ABSTRACT
    [35] 蓝渝, 郑永光, 毛冬艳, 等. 华北区域冰雹天气分型及云系特征. 应用气象学报, 2014, 25(5): 538-549. http://qikan.camscma.cn/article/id/20140503

    Lan Y, Zheng Y G, Mao D Y, et al. Classification and satellite nephogram features of hail weather in North China. J Appl Meteor Sci, 2014, 25(5): 538-549. http://qikan.camscma.cn/article/id/20140503
    [36] Xu S, Zheng D, Wang Y, et al. Characteristics of the two active stages of lightning activity in two hailstorms. J Meteor Res, 2016, 30(2): 265-281. http://www.cqvip.com/QK/88418X/20162/668824630.html
    [37] 胡胜, 罗聪, 张羽, 等. 广东大冰雹风暴单体的多普勒天气雷达特征. 应用气象学报, 2015, 26(1): 57-65. doi:  10.11898/1001-7313.20150106

    Hu S, Luo C, Zhang Y, et al. Doppler radar features of severe hailstorms in Guangdong Province. J Appl Meteor Sci, 2015, 26(1): 57-65. doi:  10.11898/1001-7313.20150106
    [38] 刘泽, 郭凤霞, 郑栋, 等. 一次暖云强降水主导的对流单体闪电活动特征. 应用气象学报, 2020, 31(2): 185-196. doi:  10.11898/1001-7313.20200206

    Liu Z, Guo F X, Zheng D, et al. Lightning activities in a convection cell dominated by heavy warm cloud precipitation. J Appl Meteor Sci, 2020, 31(2): 185-196. doi:  10.11898/1001-7313.20200206
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  • 收稿日期:  2020-09-29
  • 修回日期:  2020-12-23
  • 刊出日期:  2021-03-31

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