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Identification Method of Hail Weather Based on Fuzzy-logical Principle
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摘要: 该文确定了冰雹天气的5个雷达识别指标和不同季节识别指标对应的隶属函数，采用等权重系数法建立了基于模糊逻辑原理的冰雹天气识别算法。应用雷达回波拼图数据、冰雹灾害报告和常规探空资料，对2008—2012年华北地区103个冰雹样本进行了识别效果检验，给出了识别评分结果、识别提前量和冰雹位置等。结果表明：华北区域性冰雹的识别命中率、虚警率和临界成功指数分别为73.9%，36.4%和51.9%，其中石家庄地区的零散冰雹能够被完全识别，最大直径超过30 mm冰雹对应风暴单体综合识别判据在0.85以上；在空间分布上，被识别到可能出现冰雹的风暴单体区域和实况有冰雹的测站空间分布基本一致，冰雹出现位置一般位于强风暴单体的周边区域；相对单要素识别，综合识别算法识别准确率有所提高，识别范围得到改善，自动化程度也较高；冰雹被识别到的最早时间普遍早于冰雹出现时间，平均提前量为30 min。Abstract:
Based on previous researches and hail warning indexes in Hebei Province, five main radar identification indices for hail detection are given: Storm maximum reflectivity, storm maximum vertical integrated liquid water content, echo top, vertical integrated liquid (VIL) density and storm center height. The corresponding membership functions of each identification index in different seasons are also calculated. Identification method of hail on fuzzy-logical principles is established adopting the equal weight coefficient method.Based on radar mosaic data, disaster report of hail and route sounding data, 103 hail cases from 2008 to 2012 in North China are statistically analyzed and tested. The hitting rate of hail, the leading time and position of hail are given.The hitting rate, the false alarm rate and the critical success index of regional hail in North China are 73.9%, 36.4% and 51.9%, respectively, and all the scattered hail in Shijiazhuang can be identified. When the radar identification index is greater, the corresponding probability and the maximum diameter of hail is also bigger. The identification index is above 0.85 when the maximum diameter is more than 30 mm. On the spatial distribution, the area of identified storm and hail station is consistent. The hail station is nearby and around the corresponding storm monomer. The omission of hail occurs mostly in Zhangjiakou and Chengde, which may be caused by radar band range and regional characteristics. In contrast of single factor identification, the accuracy rate of comprehensive recognition is improved, and it also has a high degree of automation. The first time when the recognition criterion continuous is greater than the threshold value is always ahead of the epoch of hail, and the mean leading time is 30 minutes. By the recognition of hail in Shijiazhuang, the hitting rate, the false alarm rate and the critical success index of radar own recognition software are 100%, 78.2% and 21.8%, respectively, while the result of identification method on fuzzy-logical principles reaches 100%, 44.4% and 55.6%. Obviously, all hails are correctly identified, while the false alarm rate is significantly reduced, and the critical success index is increased.In summary, the automatic identification method based on fuzzy-logical principles is efficient and feasible, with more automatic algorithm. It can reduce the forecaster workload and has important practical guiding significance for short-term forecasting, nowcast warning and system development.
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Key words:
- hail;
- identification method;
- radar mosaic data;
- fuzzy-logical principle
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图 2 不同时段冰雹天气雷达识别指标对应的隶属函数
Fig. 2 Membership functions of each identification index in different months for hail
图 3 2008年6月23日13:00—18:00识别风暴单体与实况有冰雹的测站分布
Fig. 3 The identified thunderstorms and stations of hail from 1300 BT to 1800 BT on 23 June 2008
图 4 雷达CR产品、被识别风暴单体和实况有冰雹的测站
(a)2008年6月23日16:00，(b)2008年5月17日12:30，(c)2011年7月26日20:48
Fig. 4 The radar CR, identified thunderstorm and stations of hail
(a)1600 BT 23 June 2008, (b)1230 BT 17 May 2008, (c)2048 BT 26 July 2011
图 5 冰雹最大直径与RM, MVIL, TE, P散点图
Fig. 5 Scatter diagram between maximum diameter of hail and RM, MVIL, TE, P
图 6 雷达CR产品和被识别冰雹位置 (▲表示被识别冰雹)
(a)2008年6月23日16:00, (b)2008年5月17日12:30, (c)2011年7月26日20:48
Fig. 6 The radar CR and identified hail (▲ denotos identified hail)
(a)1600 BT 23 June 2008, (b)1230 BT 17 May 2008, (c)2048 BT 26 July 2011
表 1 2008—2012年5—9月河北邢台探空站0℃层和-20℃层平均高度
Table 1 Average height of 0℃ and-20℃ at Xingtai Sounding Station from May to September during 2008-2012
月份 H0℃/km H-20℃/km 5 3.9 6.8 6 4.2 7.3 7 4.8 8.1 8 4.7 8.0 9 4.0 7.0 
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表 2 5—9月雷达识别指标阈值
Table 2 Standard values of identified indices from May to September
雷达识别指标 5月与9月 6—8月 阈值下限 阈值上限 阈值下限 阈值上限 RM/dBZ 45 55 50 60 MVIL/(kg·m-2) 10 20 35 55 TE/km 6 8 10 12 DVIL/(g·m-3) 3.2 4.0 3.2 4.0 HM H0℃ H-20℃ H0℃ H-20℃
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表 3 华北区域性冰雹天气过程
Table 3 The regional hail weather processes in North China
出现时间 冰雹站数 命中率/% 虚警率/% 临界成功指数/% 冰雹影响区域 2008-05-11T18:00—21:00 8 50.0 33.3 40.0 石家庄、邢台、保定、张家口、承德 2008-06-23T13:00—18:00 12 91.7 31.3 64.7 北京、石家庄、廊坊、承德、保定 2009-07-23T14:00—19:00 7 85.7 33.3 60.0 北京、石家庄、廊坊、张家口、承德 2009-08-27T16:00—18:00 8 100 38.5 61.5 石家庄、邢台 2008-05-03T09:00—16:00 6 100 60.0 40.0 北京、天津、邯郸、保定、沧州 2008-06-25T14:00—20:00 9 88.9 38.5 57.1 天津、邯郸、张家口、承德 2010-06-17T11:00—21:00 10 40.0 33.3 33.3 天津、廊坊、沧州、唐山、承德 2011-06-11T10:00—18:00 17 58.8 16.7 52.6 北京、保定、廊坊、沧州、张家口、承德 2011-06-23T13:00—18:00 7 85.7 25.0 66.7 北京、保定、张家口、承德 2012-09-27T11:00—18:00 8 62.5 44.4 41.7 保定、衡水、张家口、秦皇岛
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表 4 零散性冰雹和对应雷达识别指标
Table 4 The scattered hail and identified indices of thunderstorm
冰雹出现时间 站名 冰雹直
径/mm风暴单体 识别提前
量/minP RM/dBZ MVIL/(kg·m-2) DVIL/(g·m-3) TE/km HM/km 2008-05-17T12:04 灵寿 2 0.60 58.8 21 3.4 7 2.7 16 2008-05-17T12:31 正定 6 0.80 63.8 37 4.4 9 3.5 43 2008-05-17T14:01 新河 6 0.80 60.4 30 3.8 10 4.1 60 2011-07-26T20:52 石家庄 15 0.88 59.0 56 5.6 12 5.8 52 2012-05-25T15:22 新乐 5 0.80 58.4 33 3.4 10 3.7 16 2012-06-01T16:28 涉县 5 0.70 55.0 28 4.0 12 7.8 4 2012-06-01T18:05 平山 6 0.40 60.8 32 5.2 7 3.1 60 2012-06-21T16:03 栾城 6 0.62 56.6 44 3.3 14 6.2 15 2012-06-21T16:06 曲阳 4 0.50 61.2 35 3.1 12 4.1 36 2012-06-21T17:43 怀安 6 0.49 59.4 30 4.3 10 5.2 7 2012-07-03T20:06 赞皇 8 0.53 55.8 36 3.6 12 5.4 30
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[1] 闵晶晶, 刘还珠, 曹晓钟, 等.天津"6.25"大冰雹过程的中尺度特征及成因.应用气象学报, 2011, 22(5):525-536. doi: 10.11898/1001-7313.20110502 [2] 郜彦娜, 何立富.2011年7月12—20日华北冷涡阶段性特征.应用气象学报, 2011, 24(6):704-713. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX201306007.htm [3] 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: 10.11898/1001-7313.20140206 [4] Winston H A, Ruthi L J.Evaluation of RADAP Ⅱ severe storm detection algorithms.Bull Amer Meteor Soc, 1986, 61(2):142-150. https://www.researchgate.net/publication/234261921_Evaluation_of_RADAP_II_Severe-Storm-Detection_Algorithms [5] Amburn S A, Wolf P L.VIL density as a hail indicator.Wea Forecasting, 1997, 12:473-478. doi: 10.1175/1520-0434(1997)012<0473:VDAAHI>2.0.CO;2 [6] Roy P D.On the relationship of severe weather to radar tops.Mon Wea Rev, 1978, 106:1332-1339. doi: 10.1175/1520-0493(1978)106<1332:OTROSW>2.0.CO;2 [7] 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. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX200503025.htm [8] 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. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX200805023.htm [9] 肖艳姣, 李中华, 张端禹, 等."07.7"鄂东南强对流天气的多普勒雷达资料分析.暴雨灾害, 2008, 27(3):213-218. http://www.cnki.com.cn/Article/CJFDTOTAL-HBQX200803003.htm [10] 肖艳姣, 马中元, 李中华.改进的雷达回波顶高、垂直积分液态水含量及其密度算法.暴雨灾害, 2009, 28(3):210-214. http://www.cnki.com.cn/Article/CJFDTOTAL-HBQX200903003.htm [11] 孙莹, 寿绍文, 沈新勇, 等.灾害天气的识别和自动预警.广西气象, 2006, 27(4):20-23. http://cpfd.cnki.com.cn/Article/CPFDTOTAL-ZGQX200610013286.htm [12] 李金辉, 樊鹏.冰雹云提前识别及预警的研究.南京气象学院学报, 2007, 30(1):114-119. http://cpfd.cnki.com.cn/Article/CPFDTOTAL-SXQX200600001089.htm [13] 段勇, 贺敬安, 谈曙青.冰雹云雷达回波自动识别系统.成都信息工程学院学报, 2001, 16(2):110-116. http://www.cnki.com.cn/Article/CJFDTOTAL-CDQX200102005.htm [14] 冯晋勤, 黄爱玉, 张治洋.基于新一代天气雷达产品闽西南强对流天气临近预报方法研究.气象, 2012, 38(2):197-203. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201202011.htm [15] 肖艳娇, 刘黎平.新一代天气雷达组网资料的三维格点化及拼图方法研究.气象学报, 2006, 64(5):647-656. doi: 10.11676/qxxb2006.063 [16] 王红艳, 刘黎平, 王改利, 等.多普勒天气雷达三维数字组网系统开发及应用.应用气象学报, 2009, 20(2):214-223. doi: 10.11898/1001-7313.20090211 [17] 王红艳, 刘黎平, 肖艳娇, 等.新一代天气雷达三维数字组网软件系统设计与实现.气象, 2009, 35(6):13-18. doi: 10.7519/j.issn.1000-0526.2009.06.002 [18] 杨吉, 刘黎平, 李国平, 等.基于雷达回波拼图资料的风暴单体和中尺度对流系统识别?跟踪及预报技术.气象学报, 2012, 70(6):1347-1355. doi: 10.11676/qxxb2012.113 [19] Kessinger C, Ellis S, Vanande l J, et al.The APC lutter Miti-gation Scheme for the WSR-88D 2003∥Preprints, 31st Con-ference on Radar Meteorology.Amer Meteor Soc, 2003:526-529. [20] 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):203-212. doi: 10.11898/1001-7313.20090210 [21] Smith P L, Myers C G, Orville H D.Radar reflectivity factor calculations innumerical cloud models using bulk parameter-ization of precipitation.J Appl Meteor, 1975, 14(9):1156-1165. [22] 李云川, 王福侠, 裴宇杰, 等.用CINRAD-SA雷达产品识别冰雹?大风和强降水.气象, 2006, 32(10):64-69. doi: 10.7519/j.issn.1000-0526.2006.10.010 [23] 王福侠, 张守保, 裴宇杰, 等.可能降雹多普勒雷达产品特征指标分析.气象科技, 2008, 36(2):228-232. http://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ200802022.htm [24] 俞小鼎, 姚秀萍, 熊廷南, 等.多普勒天气雷达原理与业务应用.北京:气象出版社, 2006 http://www.cnki.com.cn/Article/CJFDTOTAL-SYQY201603027.htm [25] 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. doi: 10.11898/1001-7313.20110110 -
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