Yang Youlin, Ji Xiaoling, Zhang Suzhao, et al. Automatic identification of precipitation cloud based on radar reflectivity area spectrum. J Appl Meteor Sci, 2018, 29(6): 690-700. DOI:  10.11898/1001-7313.20180605.
Citation: Yang Youlin, Ji Xiaoling, Zhang Suzhao, et al. Automatic identification of precipitation cloud based on radar reflectivity area spectrum. J Appl Meteor Sci, 2018, 29(6): 690-700. DOI:  10.11898/1001-7313.20180605.

Automatic Identification of Precipitation Cloud Based on Radar Reflectivity Area Spectrum

DOI: 10.11898/1001-7313.20180605
  • Received Date: 2018-04-16
  • Rev Recd Date: 2018-07-16
  • Publish Date: 2018-11-30
  • Based on the principle of spectral analysis, the concept and algorithm of radar echo intensity area spectrum are proposed. Stratiform cloud, embedded convective cloud and convective cloud with different nature are investigated. Their parameter characteristics of total area, spectral shape, spectral peak value, spectral mid-value, spectral width and strong echo area(where the echo intensity exceeds 40 dBZ), basic precipitation echo area(where the echo intensity exceeds 20 dBZ) of the echo intensity area spectrum are analyzed using radar intensity data of Yinchuan Doppler weather radar. According to characteristic parameters of precipitation cloud area spectrum with different properties of radar echo intensity, a technical method is established to identify precipitation cloud types based on radar echo intensity area spectrum. The percentage of strong echo area in the total area of echo and the percentage of basic precipitation echo area in the total area of echo are used as main factors to distinguish precipitation cloud types, and the discriminant index of precipitation clouds of different types are given, such as stratiform cloud, embedded convective cloud and convective cloud and so on, based on characteristic parameters of radar echo intensity area spectrum. Meanwhile, an automatic recognition models of precipitation cloud type based on radar echo are established, and the automatic classification of precipitation cloud types based on radar echo intensity area spectrum is realized. The model is used to judge the precipitation type of 6 strong precipitation cases from 2016 to 2017. All of 6 strong precipitation processes are accurately identified, including 2 times as convective precipitation and 4 times as mixed cloud precipitation. Discriminant results are satisfied. It is better to reflect the type of precipitation cloud and verifies the feasibility of the identification method. And it is also a great significance for further intelligent analysis of precipitation properties, automatic monitoring of heavy precipitation and refined quantitative precipitation estimation.
  • Fig. 1  Echo intensity area spectrum of Yinchuan radar volume scanning CR product at 1409 BT 14 Jun 2004

    Fig. 2  Radar echo intensity area spectrum during laminar cloud precipitation

    (a)sample mean echo intensity area spectrum, (b)echo intensity area spectrum of process on 6 Sep 2014

    Fig. 3  Radar echo intensity area spectrum of embedded convective precipitation cloud

    (a)sample mean echo intensity area spectrum, (b)echo intensity area spectrum of process on 6 Aug 2014

    Fig. 4  Radar echo intensity area spectrum of convective precipitation cloud

    (a)sample mean echo intensity area spectrum, (b)echo intensity area spectrum of process on 1 Jul 2014

    Table  1  Radar echo intensity area spectrum characteristics of layered cloud precipitation

    过程日期 回波总面积(栅格数) 谱中值/dBZ 谱峰值/dBZ T/dBZ 面积谱宽度/dBZ P20/% P40/%
    2014-04-18 199657 13.5 14.0 0.5 41 12.56 0.00
    2014-04-19 28462 12.5 12.0 -0.5 43 20.80 0.05
    2014-08-07 85991 9.5 10.0 0.5 31 2.36 0.00
    2014-08-11 168577 15.5 15.0 -0.5 42 31.80 0.01
    2014-08-20 119059 12.5 12.0 -0.5 46 12.14 0.03
    2014-08-30 84821 14.5 14.0 -0.5 47 28.63 0.11
    2014-09-06 209815 14.5 15.0 0.5 47 22.66 0.14
    2014-09-07 86242 11.5 12.0 0.5 33 5.34 0.00
    2014-09-08 130821 14.5 16.0 1.5 42 23.27 0.01
    2014-09-10 178529 13.5 14.0 0.5 46 17.92 0.04
    2014-09-11 196738 16.5 17.0 0.5 49 33.68 0.20
    2014-09-13 132455 13.5 13.0 -0.5 52 16.70 0.06
    2014-09-14 675906 16.5 16.0 -0.5 45 29.24 0.01
    2014-09-21 406476 14.5 15.0 0.5 48 23.74 0.05
    2014-09-27 265382 12.5 15.0 2.5 55 16.43 0.24
    2014-09-28 176407 12.2 13.0 0.8 36 8.29 0.00
    DownLoad: Download CSV

    Table  2  Radar echo intensity area spectrum characteristics of embedded convective precipitation cloud

    过程日期 回波总面积(栅格数) 谱中值/dBZ 谱峰值/dBZ T/dBZ 面积谱宽度/dBZ P20/% P40/%
    2006-07-14 186049 17.5 17.0 -0.5 72 39.30 0.79
    2012-06-27 599747 22.5 18.0 -4.5 49 67.22 0.44
    2012-07-29 634179 17.5 17.0 -0.5 60 55.25 1.00
    2014-04-16 216877 16.5 15.0 -1.5 45 32.95 0.07
    2014-06-03 244169 20.5 18.0 -2.5 57 58.93 1.78
    2014-08-05 613856 18.5 17.0 -1.5 48 47.32 0.08
    2014-08-06 356512 19.5 18.0 -1.5 50 50.66 0.20
    2014-08-21 474473 17.5 17.0 -0.5 59 41.58 0.58
    2014-08-10 340469 19.5 16.0 -3.0 59 33.67 0.61
    2014-09-22 453561 17.5 17.0 -0.5 50 45.99 0.52
    DownLoad: Download CSV

    Table  3  Radar echo intensity area spectrum characteristics of convective precipitation cloud

    过程日期 回波总面积(栅格数) 谱中值/dBZ 谱峰值/dBZ T/dBZ 面积谱宽度/dBZ P20/% P40/%
    2004-06-14 137816 15.5 15 -0.5 72 32.98 1.66
    2011-08-23 257622 19.5 20 0.5 66 55.75 2.74
    2014-04-15 52091 15.5 16 0.5 55 31.05 0.97
    2014-05-08 178329 18.5 18 -0.5 64 44.42 1.71
    2014-06-19 23823 15.5 16 0.5 60 30.64 1.09
    2014-06-20 196820 18.5 17 -1.5 60 45.68 1.53
    2014-07-01 95463 18.5 19 0.5 59 43.78 1.36
    2014-07-28 176821 19.5 20 0.5 64 53.33 3.08
    2014-08-09 101141 18.5 18 -0.5 62 48.34 3.31
    2014-08-12 40514 19.8 20 0.5 60 55.31 3.49
    2014-08-15 195714 18.5 18 -0.5 66 46.03 2.42
    2014-08-16 209400 20.5 21 0.5 66 53.03 2.30
    2014-08-26 35390 18.5 17 -1.5 63 43.42 1.93
    2014-08-27 141886 19.5 21 -1.5 62 47.49 1.65
    2014-09-01 169070 14.5 16 1.5 63 26.42 1.10
    DownLoad: Download CSV

    Table  4  Precipitation cloud automatic classification index based on radar echo intensity area spectrum

    P20 P40 T/dBZ 云类型
    [0,35%) [0,0.25%) T>-1.0 层状云降水
    [0,35%) [0,0.25%) T<-1.0 积层混合云
    [0,35%) [0.25%,1%] T≤-0.5 积层混合云
    [0,35%) [0.25%,1%] T>-0.5 对流云
    [0,35%) [1.0%,100%] 对流云
    [35%,100%] [0,1%] 积层混合云
    [35%,100%] (1.0%,100%] T≤-1.5 积层混合云
    [35%,100%] (1.0%,100%] T>-1.5 对流云
    DownLoad: Download CSV

    Table  5  Radar echo intensity area spectrum parameters and precipitation cloud classification results during 6 major precipitation periods

    参数 过程1 过程2 过程3 过程4 过程5 过程6
    P20/% 26.7 52.12 54.14 45.3 35.5 45.4
    P40/% 2.0 1.52 0.46 1.96 0.16 1.32
    T/dBZ -0.5 -3.5 -3.5 -1.5 -2.0 -2.5
    云类型判别结果 对流云 积层混合云 积层混合云 对流云 积层混合云 积层混合云
    降水持续时间/h 4 24 24 6 39 38
    最大小时雨强/(mm·h-1) 65.4 56.5 16.1 82.5 26.7 62.0
    最大累积雨量/mm 85.3 76.8 24.4 241.7 118.6 81.2
    DownLoad: Download CSV
  • [1]
    朱乾根, 林锦瑞, 寿绍文.天气学原理和方法.北京:气象出版社, 2003.
    [2]
    俞小鼎, 姚秀萍, 熊延南, 等.多普勒天气雷达原理与业务应用.北京:气象出版社, 2005.
    [3]
    张培昌, 杜秉玉, 戴铁丕.雷达气象学.北京:气象出版社, 2001.
    [4]
    Collier C G, Lovejoy S, Austin G L.Analysis of Bright Bands from 3-D Radar Data//19th Conference on Radar Meteorology.Amer Meteor Soc, 1980: 44-47.
    [5]
    Herve A, Jean G.Identifyion of Vertical profiles of radar reflectivity for hydrological application using an inverse method.Part Ⅰ:Formulation.Meteor, 1995, 34(1):225-239. http://adsabs.harvard.edu/abs/1995JApMe..34..225A
    [6]
    Herve A, Jean G.Identification of vertical profiles of radar reflectivity for hydrological applications using an inverse method.Part Ⅱ:Formulation.J Applied Meteor, 1994, 34(12):240-259. http://adsabs.harvard.edu/abs/1995JApMe..34..240A
    [7]
    陈明轩, 高峰.利用一种自动识别算法移除天气雷达反射率因子中的亮带动.应用气象学报, 2006, 17(2):207-214. doi:  10.3969/j.issn.1001-7313.2006.02.011
    [8]
    张乐坚, 储凌, 叶芳, 等.使用雷达回波三维信息自动识别降水类型的方法.大气科学学报, 2012, 35(1):95-102. doi:  10.3969/j.issn.1674-7097.2012.01.011
    [9]
    汤达章, 周咏梅.雷达回波跟踪的两种方法及精度比较.应用气象学报, 1994, 5(3):304-311. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19940354&flag=1
    [10]
    周德平, 杨洋, 王吉宏, 等.冰雹云雷达识别方法及防雹作业经验.气象科技, 2007, 35(2):258-263. doi:  10.3969/j.issn.1671-6345.2007.02.021
    [11]
    冷亮, 黄兴友, 杨洪平, 等.多普勒雷达晴空回波识别与应用.气象科技, 2012, 40(4):534-541. doi:  10.3969/j.issn.1671-6345.2012.04.004
    [12]
    郑佳锋, 张杰, 朱克云, 等.阵风锋自动识别与预警.应用气象学报, 2013, 24(1):117-125. doi:  10.3969/j.issn.1001-7313.2013.01.012
    [13]
    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. doi:  10.3969/j.issn.1001-7313.2014.04.004
    [14]
    刘晓璐, 刘建西, 张世林, 等.基于探空资料因子组合分析方法的冰雹预报.应用气象学报, 2014, 25(2):168-175. doi:  10.3969/j.issn.1001-7313.2014.02.006
    [15]
    徐永胜, 刘耀宗, 韩嗣荧.夏、秋积云的雷达回波和降水特征及其估算.应用气象学报, 1992, 3(3):334-339. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19920355&flag=1
    [16]
    刘黎平, 吴林林, 杨引明.基于模糊逻辑的分步式超折射地物回波识别方法的建立和效果分析.气象学报, 2007, 65(2):252-260. doi:  10.3321/j.issn:0577-6619.2007.02.011
    [17]
    江源, 刘黎平, 庄薇.多普勒天气雷达地物回波特征及其识别方法改进.应用气象学报, 2009, 20(2):203-213. doi:  10.3969/j.issn.1001-7313.2009.02.010
    [18]
    庄薇, 刘黎平, 余燕群, 等.雷达地物回波模糊逻辑识别法的改进及效果检验.气象学报, 2012, 70(3):576-584. http://d.old.wanfangdata.com.cn/Periodical/qxxb201203018
    [19]
    李丰, 刘黎平, 王红艳, 等.C波段多普勒天气雷达地物回波识别方法.应用气象学报, 2014, 25(2):158-167. doi:  10.3969/j.issn.1001-7313.2014.02.005
    [20]
    李爱贞, 刘厚凤.气象学与气候学基础.北京:气象出版社, 2004.
    [21]
    陈宝君, 李子华, 刘吉成, 等.三类降水云雨滴谱分布模式.气象学报, 1998, 56(4):506-512. doi:  10.3321/j.issn:0577-6619.1998.04.013
    [22]
    金祺, 袁野, 纪雷, 等.安徽滁州夏季一次飑线过程的雨滴谱特征.应用气象学报, 2015, 26(6):725-734. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20150609&flag=1
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    • Received : 2018-04-16
    • Accepted : 2018-07-16
    • Published : 2018-11-30

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