留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于决策树算法的鄂东地区冰雹识别技术

袁凯 李武阶 庞晶

袁凯, 李武阶, 庞晶. 基于决策树算法的鄂东地区冰雹识别技术. 应用气象学报, 2023, 34(2): 234-245. DOI:  10.11898/1001-7313.20230209..
引用本文: 袁凯, 李武阶, 庞晶. 基于决策树算法的鄂东地区冰雹识别技术. 应用气象学报, 2023, 34(2): 234-245. DOI:  10.11898/1001-7313.20230209.
Yuan Kai, Li Wujie, Pang Jing. Hail identification technology in Eastern Hubei based on decision tree algorithm. J Appl Meteor Sci, 2023, 34(2): 234-245. DOI:  10.11898/1001-7313.20230209.
Citation: Yuan Kai, Li Wujie, Pang Jing. Hail identification technology in Eastern Hubei based on decision tree algorithm. J Appl Meteor Sci, 2023, 34(2): 234-245. DOI:  10.11898/1001-7313.20230209.

基于决策树算法的鄂东地区冰雹识别技术

DOI: 10.11898/1001-7313.20230209
资助项目: 武汉市气象局临近预报创新团队项目(2022年度)
详细信息
    通信作者:

    李武阶, 邮箱: 1669625159@qq.com

Hail Identification Technology in Eastern Hubei Based on Decision Tree Algorithm

  • 摘要: 冰雹是对流性天气常见的灾害之一,雷达是识别冰雹强有利的工具,为克服现有方法主观性强、特征量阈值不明确以及虚警率高的不足,探究机器学习算法用于冰雹识别的可行性,基于决策树算法利用2015年1月1日—2021年12月31日鄂东地区冰雹灾情资料、武汉多普勒天气雷达以及探空资料,将湿球温度高度引入冰雹识别因子中,并根据命中率、虚警率和临界成功指数定量评估其识别能力。结果表明:仅包含回波强度的决策树(强度决策树)和包含回波强度和湿球温度高度的决策树(强度-高度决策树)均能有效识别冰雹,强度-高度决策树较强度决策树的命中率和临界成功指数均小幅提高,且虚警率明显降低;强度决策树识别冰雹的关键因子为组合反射率因子,底层多为0.5°和1.5°仰角反射率因子,强度-高度决策树的关键因子为0.5°仰角反射率因子,底层多为风暴的整体强度属性;个例分析显示强度-高度决策树减少了湿球0℃层高度较高时的虚警次数,展现出良好的应用前景。
  • 图  1  鄂东地区判别降雹的强度决策树

    (a)Rc≤56.7 dBZ, (b)Rc>56.7 dBZ

    Fig. 1  Decision tree of intensity for hail in eastern Hubei

    (a)Rc≤56.7 dBZ, (b)Rc>56.7 dBZ

    图  2  鄂东地区判别降雹的强度-高度决策树

    (a)R0.5≤51.8 dBZ, (b)R0.5>51.8 dBZ

    Fig. 2  Decision tree of intensity-height for hail in eastern Hubei

    (a)R0.5≤51.8 dBZ, (b)R0.5>51.8 dBZ

    图  3  2020年3月26日16:18武汉雷达组合反射率因子(相邻距离圈相距50 km, 下同)(a) 和过降雹点的反射率因子剖面(b)

    Fig. 3  Combined reflectivity(the distance between adjacent rings is 50 km, hereinafter)(a) and reflectivity factor profile(b) of hail point of Wuhan radar at 1618 BT 26 Mar 2020

    图  4  2020年3月26日16:18决策树的识别结果

    (红色圆圈表示以降雹点为中心,半径为5 km的区域)

    Fig. 4  Identification of decision tree at 1618 BT 26 Mar 2020

    (red circle area denotes the hail location with a radius of 5 km)

    图  5  2021年5月14日16:12武汉雷达组合反射率因子(a)和过降雹点的反射率因子剖面(b)

    Fig. 5  Combined reflectivity(a) and reflectivity factor profile(b) of hail point of Wuhan radar at 1612 BT 14 May 2021

    图  6  2021年5月14日16:12决策树识别结果

    (红色圆圈表示以降雹点为中心半径为5 km的区域)

    Fig. 6  Identification of decision tree at 1612 BT 14 May 2021

    (red circle area denotes the hail location with a radius of 5 km)

    图  7  2021年9月28日19:48武汉雷达组合反射率因子(a)和过汉川降雹点反射率因子剖面(b)

    Fig. 7  Combined reflectivity(a) and reflectivity factor profile(b) of Hanchuan hail point of Wuhan radar at 1948 BT 28 Sep 2021

    图  8  2021年9月28日19:48决策树识别结果

    (红色圆圈表示以降雹点为中心半径为5 km的区域)

    Fig. 8  Identification of decision tree at 1948 BT 28 Sep 2021

    (red circle area denotes the hail location with a radius of 5 km)

    表  1  非冰雹样本选取标准及对应的样本量

    Table  1  Selection standard and corresponding quantity of non-hail samples

    组合反射率因子/dBZ 样本量
    40.0~44.9 63
    45.0~49.9 158
    50.0~54.9 188
    55.0~59.9 158
    ≥60.0 63
    下载: 导出CSV

    表  2  不同决策树算法的评分

    Table  2  Scores of different decision trees

    决策树算法 命中率 虚警率 临界成功指数
    强度决策树 0.88 0.12 0.80
    强度-高度决策树 0.93 0.07 0.86
    下载: 导出CSV
  • [1] 郑永光, 周康辉, 盛杰, 等.强对流天气监测预报预警技术进展.应用气象学报, 2015, 26(6):641-657. doi:  10.11898/1001-7313.20150601

    Zheng Y G, Zhou K H, Sheng J, et al. Advance in techniques of monitoring, forecasting and warning of severe convective weather. J Appl Meteor Sci, 2015, 26(6): 641-657. doi:  10.11898/1001-7313.20150601
    [2] 徐双柱, 韦慧红. 关于强对流天气的几点思考. 暴雨灾害, 2016, 35(3): 197-202. doi:  10.3969/j.issn.1004-9045.2016.03.001

    Xu S Z, Wei H H. Some thoughts on the weather forecast of severe convective storms. Torrential Rain Disaste, 2016, 35(3): 197-202. doi:  10.3969/j.issn.1004-9045.2016.03.001
    [3] 施望芝, 金琪, 郭施, 等. 湖北一次冰雹天气过程的落区诊断分析和预报. 热带气象学报, 2004, 20(2): 212-217. doi:  10.3969/j.issn.1004-4965.2004.02.014

    Shi W Z, Jin Q, Guo S, et al. An analysis and forecast for the area of an hail weather in Hubei Province. J Trop Meteor, 2004, 20(2): 212-217. doi:  10.3969/j.issn.1004-4965.2004.02.014
    [4] 俞小鼎, 王迎春, 陈明轩, 等. 新一代天气雷达与强对流天气预警. 高原气象, 2005, 24(3): 456-464. doi:  10.3321/j.issn:1000-0534.2005.03.025

    Yu X D, Wang Y C, Chen M X, et al. Severe convective weather warnings and its improvement with the introduction of the NEXRAD. Plateau Meteor, 2005, 24(3): 456-464. doi:  10.3321/j.issn:1000-0534.2005.03.025
    [5] 刁秀广, 朱君鉴, 黄秀韶, 等. VIL和VIL密度在冰雹云判据中的应用. 高原气象, 2008, 27(5): 1131-1139. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX200805023.htm

    Diao X G, Zhu J J, Huang X S, et al. Application of VIL and VIL density in warning criteria for hailstorm. Plateau Meteor, 2008, 27(5): 1131-1139. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX200805023.htm
    [6] 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. Dopper radar features of severe hailstorm in Guangdong Province. J Appl Meteor Sci, 2015, 26(1): 57-65. doi:  10.11898/1001-7313.20150106
    [7] 王萍, 潘跃. 基于显著性特征的大冰雹识别模型. 物理学报, 2013, 62(6): 515-524. https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201306077.htm

    Wang P, Pan Y. Severe hail identification model based on saliency characteristics. Acta Physica Sinica, 2013, 62(6): 515-524. https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201306077.htm
    [8] 王莎, 沙勇, 宋金妹, 等. 冀东地区冰雹云多普勒雷达参数特征分析. 气象, 2019, 45(5): 713-722.

    Wang S, Sha Y, Song J M, et al. Characteristic analysis of hail cloud Doppler radar parameters in the eastern Hebei Province. Meteor Mon, 2019, 45(5): 713-722.
    [9] 吴剑坤, 陈明轩, 秦睿, 等. 变分回波跟踪算法及其在对流临近预报中的应用研究. 气象学报, 2019, 77(6): 999-1014. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201906003.htm

    Wu J K, Chen M X, Qin R, et al. The veriatiobal echo tracking method and its application in convective storm nowcasting. Acta Meteor Sinica, 2019, 77(6): 999-1014. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201906003.htm
    [10] 王洪, 吴乃庚, 万齐林, 等. 变分回波跟踪算法及其在对流临近预报中的应用研究. 气象学报, 2018, 76(1): 92-103. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201906003.htm

    Wang H, Wu N G, Wan Q L, et al. Analysis of S-band polarimetric radar observations of a hail-producing supercell. Acta Meteor Sinica, 2018, 76(1): 92-103. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201906003.htm
    [11] 冯晋勤, 张深寿, 吴陈锋, 等. 双偏振雷达产品在福建强对流天气过程中的应用分析. 气象, 2018, 44(12): 1565-1574. doi:  10.7519/j.issn.10000526.2018.12.006

    Feng J Q, Zhang S S, Wu C F, et al. Appliation of dual polarization weather radar products to severe convective weather in Fujian. Meteor Mon, 2018, 44(12): 1565-1574. doi:  10.7519/j.issn.10000526.2018.12.006
    [12] 刁秀广, 李芳, 万夫敬. 两次强冰雹超级单体风暴双偏振特征对比. 应用气象学报, 2022, 33(4): 414-428. doi:  10.11898/1001-7313.20220403

    Diao X G, Li F, Wan F J. Comparative analysis on dual polarization features of two severe hail supercells. J Appl Meteor Sci, 2022, 33(4): 414-428. doi:  10.11898/1001-7313.20220403
    [13] 徐舒扬, 吴翀, 刘黎平. 双偏振雷达水凝物相态识别算法的参数改进. 应用气象学报, 2020, 31(3): 350-360. doi:  10.11898/1001-7313.20200309

    Xu S Y, Wu C, Liu L P. Parameter improvements of hydrometeor classification algorithm for the dual-polarimetric radar. J Appl Meteor Sci, 2020, 31(3): 350-360. doi:  10.11898/1001-7313.20200309
    [14] 俞小鼎, 郑永光. 中国当代强对流天气研究与业务进展. 气象学报, 2020, 78(3): 391-418. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202003006.htm

    Yu X D, Zheng Y G. Advances in severe convective weather research and operational service in China. Acta Meteor Sinica, 2020, 78(3): 391-418. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202003006.htm
    [15] 王婷波, 周康辉, 郑永光. 我国中东部雷暴活动特征分析. 气象, 2020, 46(2): 189-199.

    Wang T B, Zhou K H, Zheng Y G. Statistic analysis of thunderstorm characteristics in central and eastern China. Meteor Mon, 2020, 46(2): 189-199.
    [16] 周康辉, 郑永光, 韩雷, 等. 机器学习在强对流监测预报中的应用进展. 气象, 2021, 47(3): 274-289. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202103002.htm

    Zhou K H, Zheng Y G, Han L, et al. Advances in application of machine learning to severe weather monitoring and forecasting. Meteor Mon, 2021, 47(3): 274-289. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202103002.htm
    [17] 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 WRS-88D hail detection algorithm over Guizhou Region. J Appl Meteor Sci, 2011, 22(1): 96-106. http://qikan.camscma.cn/article/id/20110110
    [18] 周康辉. 基于卷积神经网络的强对流天气预报方法研究. 北京: 中国科学院大学, 2021.

    Zhou K H. Convective Weather Forecasting with Convolutional Neural Networks. Beijing: University of Chinese Academy of Sciences, 2021.
    [19] 刘新伟, 蒋盈沙, 黄武斌, 等. 基于雷达产品和随机森林算法的冰雹天气分类识别及预报. 高原气象, 2021, 40(4): 898-908. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX202104016.htm

    Liu X W, Jiang Y S, Huang W B, et al. Classified identification and nowcast of hail weather based on radar products and random forest algorithm. Plateau Meteor, 2021, 40(4): 898-908. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX202104016.htm
    [20] 王萍, 高毅, 李聪. 50 km以内雷暴系统的分类识别方法研究. 气象, 2016, 42(2): 230-237. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201602011.htm

    Wang P, Gao Y, Li C. Method study of classification and recognition of thunderstorm system less than 50 km. Meteor Mon, 2016, 42(2): 230-237. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201602011.htm
    [21] 方德贤, 李红斌, 董新宁, 等. 风暴分类识别技术在人工防雹中的应用. 气象, 2016, 42(9): 1124-1134. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201609010.htm

    Fang D X, Li H B, Dong X N, et al. Application of storm auto-classification technology in artificial hail prevention. Meteor Mon, 2016, 42(9): 1124-1134. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201609010.htm
    [22] 郑建琴, 路明月, 王曙东, 等. 基于决策树的天津地区冰雹天气雷达因子分析. 气象科技, 2017, 45(2): 349-354. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ201702020.htm

    Zheng J Q, Lu M Y, Wang S D, et al. Analysis of hail weather based on decision-making tree using radar data in Tianjin. Meteor Sci Technol, 2017, 45(2): 349-354. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ201702020.htm
    [23] 濮文耀, 李红斌, 宋煜, 等. 0℃层高度的变化对冰雹融化影响的分析与应用. 气象, 2015, 41(8): 980-985. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201508007.htm

    Pu W Y, Li H B, Song Y, et al. Analysis and application of the effect of 0℃ layer height on melting hail. Meteor Mon, 2015, 41(8): 980-985. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201508007.htm
    [24] 俞小鼎. 关于冰雹的融化层高度. 气象, 2014, 40(6): 649-654. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201406001.htm

    Yu X D. A note on the melting level of hail. Meteor Mon, 2014, 40(6): 649-654. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201406001.htm
    [25] Steiner M, Smith J. Use of three-dimensional reflectivity structure for automated detection and removal of non-precipitating echoes in radar data. J Atmos Ocean Technol, 2014, 40(6): 649-654.
    [26] 刘伯骏, 张亚萍, 黎中菊, 等. 一种基于地面实况的降雹风暴体客观标识方法. 应用气象学报, 2021, 32(1): 78-90. doi:  10.11898/1001-7313.20210107

    Liu B J, Zhang Y P, Li Z J, et al. An objective hailstorm labeling algorithm based on ground observation. J Appl Meteor Sci, 2021, 32(1): 78-90. doi:  10.11898/1001-7313.20210107
    [27] 王研峰, 黄武斌, 王聚杰, 等. 一次甘肃天水强冰雹的雷达回波特征及成因分析. 高原气象, 2019, 38(2): 368-376. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201902013.htm

    Wang Y F, Huang W B, Wang J J, et al. Analysis on the characteristic of radar echo and the causes of a strong hail in Tianshui City of Gansu Province. Plateau Meteor, 2019, 38(2): 368-376. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201902013.htm
    [28] 郭欣, 郭学良, 陈宝君, 等. 一次大冰雹形成机制的数值模拟. 应用气象学报, 2019, 30(6): 651-664. doi:  10.11898/1001-7313.20190602

    Guo X, Guo X L, Chen B J, et al. Numerical simulation on the formation of large-size hailstone. J Appl Meteor Sci, 2019, 30(6): 651-664. doi:  10.11898/1001-7313.20190602
    [29] 张曦, 黄兴友, 刘新安, 等. 北京大兴国际机场相控阵雷达强对流天气监测. 应用气象学报, 2022, 33(2): 192-204. doi:  10.11898/1001-7313.20220206

    Zhang X, Huang X Y, Liu X A, et al. The hazardous convective storm monitoring of phased-array antenna radar at Daxing International Airport of Beijing. J Appl Meteor Sci, 2022, 33(2): 192-204. doi:  10.11898/1001-7313.20220206
    [30] Knox J A, Nevius D S, Knox P N. Two simple and accurate approximations for wet-bulb temperature in moist conditions with forecasting applications. Bull Amer Meteor Soc, 2017, 98(9): 1897-1906.
    [31] 修媛媛, 韩雷, 冯海磊. 基于机器学习方法的强对流天气识别研究. 电子设计工程, 2016, 24(9): 4-7. https://www.cnki.com.cn/Article/CJFDTOTAL-GWDZ201609002.htm

    Xiu Y Y, Han L, Feng H L. The identification of strong convective weather based on machine learning methods. Electronic Design Engineering, 2016, 24(9): 4-7. https://www.cnki.com.cn/Article/CJFDTOTAL-GWDZ201609002.htm
    [32] 陈训来, 刘军, 郑群峰, 等. 基于卷积门控循环单元神经网络的临近预报方法研究. 高原气象, 2020, 40(2): 411-423. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX202102018.htm

    Chen X L, Liu J, Zheng Q F, et al. A study on radar echo nowcasting based on convolutional gated recurrent unit neural network. Plateau Meteor, 2020, 40(2): 411-423. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX202102018.htm
    [33] 章国材. 强对流天气分析与预报. 北京: 气象出版社, 2011.

    Zhang G C. Analysis and Forecast for Severe Convective Weather. Beijing: China Meteorological Press, 2011.
    [34] 李颖, 陈怀亮. 机器学习技术在现代农业气象中的应用. 应用气象学报, 2020, 31(3): 257-266. doi:  10.11898/1001-7313.20200301

    Li Y, Chen H L. Review of machine learning approaches for modern agrometeoroly. J Appl Meteor Sci, 2020, 31(3): 257-266. doi:  10.11898/1001-7313.20200301
    [35] 史达伟, 张静, 曹庆, 等. 基于决策树算法的海州湾地区海雾预测. 气象科学, 2022, 42(1): 136-142. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKX202201015.htm

    Shi D W, Zhang J, Cao Q, et al. Research on sea fog diagnosis in Haizhou Bay based on decision tree algorithm. J Meteor Sci, 2022, 42(1): 136-142. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKX202201015.htm
    [36] 俞小鼎, 周小刚, 王秀明, 等. 雷暴与强对流临近天气预报技术进展. 气象学报, 2012, 70(3): 311-337. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201203001.htm

    Yu X D, Zhou X G, Wang X M, et al. The advances in the nowcasting techniques on thunderstorms and severe convection. Acta Meteor Sinica, 2012, 70(3): 311-337. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201203001.htm
    [37] 俞小鼎, 王秀明, 李万莉, 等. 雷暴与强对流临近预报. 北京: 气象出版社, 2020.

    Yu X D, Wang X M, Li W L, et al. Thunderstorm and Strong Convection Nowcasting. Beijing: China Meteorological Press, 2020.
    [38] 李博勇, 胡志群, 郑佳锋, 等. 利用贝叶斯方法改进华南地区冰雹识别效果. 热带气象学报, 2021, 37(1): 112-125. https://www.cnki.com.cn/Article/CJFDTOTAL-RDQX202101011.htm

    Li B Y, Hu Z Q, Zheng J F, et al. Using Bayesian method to improve hail identification in South China. J Trop Meteor, 2021, 37(1): 112-125. https://www.cnki.com.cn/Article/CJFDTOTAL-RDQX202101011.htm
    [39] 刘海知, 徐辉, 包红军, 等. 机器学习分类算法在降雨型滑坡预报中的应用. 应用气象学报, 2022, 33(3): 282-292. doi:  10.11898/1001-7313.20220303

    Liu H Z, Xu H, Bao H J, et al. Application of machine learning classification algorithm to precipitation landslides forecasting. J Appl Meteor Sci, 2022, 33(3): 282-292. doi:  10.11898/1001-7313.20220303
    [40] 刘娜, 熊安元, 张强, 等. 强对流天气人工智能应用训练基础数据集构造. 应用气象学报, 2021, 32(5): 530-541. doi:  10.11898/1001-7313.20210502

    Liu N, Xiong A Y, Zhang Q, et al. Development of basic dataset of severe convective weather for artificial intelligence training. J Appl Meteor Sci, 2021, 32(5): 530-541. doi:  10.11898/1001-7313.20210502
  • 加载中
图(8) / 表(2)
计量
  • 摘要浏览量:  796
  • HTML全文浏览量:  99
  • PDF下载量:  122
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-09-19
  • 修回日期:  2022-12-17
  • 刊出日期:  2023-03-31

目录

    /

    返回文章
    返回