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Automatic Identification of Precipitation Cloud Based on Radar Reflectivity Area Spectrum
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摘要: 基于谱分析原理提出了雷达回波强度面积谱的概念及算法，利用宁夏银川多普勒天气雷达回波资料，分析了不同性质降水云的雷达回波强度面积谱，并根据不同性质降水云雷达回波强度面积谱特征，提出了基于雷达回波强度面积谱识别降水云类型的方法，利用强回波面积（回波强度不小于40 dBZ的回波面积）占总回波面积百分比和基本降水回波面积（回波强度不小于20 dBZ的回波面积）占总回波面积百分比作为降水云类型判别的主要因子，提炼出基于雷达回波强度面积谱特征参数的层状云、积层混合云、对流云等不同类型降水云的判别指标，建立了基于雷达回波的降水云类型自动判识模型。利用该模型对2016-2017年6次强降水过程进行了降水云类型判别试验，模型准确判别出6次强降水过程中2次为对流云降水、4次为混合云降水，判别结果较好地反映了降水云类型，验证了判识方法的可行性。Abstract: 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.
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图 1 2004年6月14日14:09(北京时)银川雷达体扫产品回波强度面积谱
Fig. 1 Echo intensity area spectrum of Yinchuan radar volume scanning CR product at 1409 BT 14 Jun 2004
图 2 层状云降水过程雷达回波强度面积谱
(a)样本平均回波强度面积谱，(b)2014年9月6日典型过程回波强度面积谱
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
图 3 混合云降水过程雷达回波强度面积谱
(a)样本平均回波面积谱，(b)2014年8月6日典型过程回波面积谱
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
图 4 对流云降水过程雷达回波强度面积谱
(a)样本平均回波面积谱，(b)2014年7月1日典型过程回波面积谱
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
表 1 层状云降水过程雷达回波强度面积谱特征
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 
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表 2 混合层降水过程雷达回波强度面积谱特征
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
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表 3 对流云降水过程雷达回波强度面积谱特征
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
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表 4 基于雷达回波强度面积谱特征的降水云自动分类指标
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
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表 5 6次天气过程主要降水时段雷达回波强度面积谱参数及其降水云分类结果
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
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[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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