江苏省雷灾特征分析及灾情灰色关联评估
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摘要
雷电是雷雨云之间或云地之间产生的瞬间放电现象。雷电产生的强大的雷电流、高电压及强电磁辐射产生的巨大破坏力会导致各类无法挽回的损失。因此在对各类雷电灾害特征进行全面统计分析的基础上建立合适的评估模型,对雷电评估与防护工作的开展有一定的实际参考与指导意义。根据1998—2009年雷电灾害汇编资料及近30年江苏省雷暴日资料,利用数理统计方法分析了江苏省近12年来的人员伤亡、经济损失等雷电灾害特征及行业雷灾特征,在此基础上结合灰色理论建立与之相适应的灰色评估模型。经统计分析发现,近12年间江苏省雷灾程度较重的城市集中在扬州、泰州、无锡、盐城等市;雷灾经济损失涉及的行业主要分布在石油化工、制造业、居民用户及广电通信等行业。依据灰色研究理论,在对原始数据作归一化处理的基础上再对各因素进行比较,同时在对分级指标进行函数转换的前提下统一灾情等级的划分标准。依据灰色关联分析方法,以灰色关联系数求解公式为基础,最终确立江苏省雷电灾害的灰色关联度,并根据该灾害关联度从大到小的排序即关联度序,得到江苏省各市灾情轻重的比较结果:近年来江苏省无锡、苏州、扬州市为重灾及中灾区,其余各市均为小灾区。
Lightning is a transient discharge phenomenon between thunderstorm clouds or clouds and land. It could cause great destructive power due to strong lightning current and high voltage and strong radiation then lead to various irreparable losses. Therefore establishing a suitable evaluation model on the basis of comprehensive statistical analysis of the characteristics of all kinds of lightning disasters have a great practical reference and guiding significance for effective development of lightning assessment and protection. The characteristics of lightning disaster on casualties and economic losses and industry in Jiangsu province in recent 12 years were analyzed by mathematical statistics according to the compilation data of lightning disaster from 1998—2009 and the data of thunderstorm days in recent 30 years in Jiangsu province. On this basis a grey evaluation model was established in accordance with the grey theory.It was found that the city with heavy disaster degree concentrated in Yangzhou, Taizhou, Wuxi, Yancheng city in Jiangsu province in recent 12 years and lightning disaster economic loss is mainly involved in the industry of petroleum chemical, manufacturing, residential users and communication. The factors were compared on the basis of normalization of the original data according to the theory of grey research and unified dividing standard of disaster degrees on the premise of function conversion to the classification index. The gray correlation degree of lightning disaster in Jiangsu province was established on the basis of the solving formula of grey correlation coefficient according to the grey relational analysis method, the comparative relationship between the seriousness of the disasters in each city were obtained finally. The results showed that Wuxi, Suzhou and Yangzhou were the worst and moderate-hit areas, and the rest cities in Jiangsu were light-hit areas in recent years.
Lightning is a transient discharge phenomenon between thunderstorm clouds or clouds and land. It could cause great destructive power due to strong lightning current and high voltage and strong radiation then lead to various irreparable losses. Therefore establishing a suitable evaluation model on the basis of comprehensive statistical analysis of the characteristics of all kinds of lightning disasters have a great practical reference and guiding significance for effective development of lightning assessment and protection. The characteristics of lightning disaster on casualties and economic losses and industry in Jiangsu province in recent 12 years were analyzed by mathematical statistics according to the compilation data of lightning disaster from 1998—2009 and the data of thunderstorm days in recent 30 years in Jiangsu province. On this basis a grey evaluation model was established in accordance with the grey theory.It was found that the city with heavy disaster degree concentrated in Yangzhou, Taizhou, Wuxi, Yancheng city in Jiangsu province in recent 12 years and lightning disaster economic loss is mainly involved in the industry of petroleum chemical, manufacturing, residential users and communication. The factors were compared on the basis of normalization of the original data according to the theory of grey research and unified dividing standard of disaster degrees on the premise of function conversion to the classification index. The gray correlation degree of lightning disaster in Jiangsu province was established on the basis of the solving formula of grey correlation coefficient according to the grey relational analysis method, the comparative relationship between the seriousness of the disasters in each city were obtained finally. The results showed that Wuxi, Suzhou and Yangzhou were the worst and moderate-hit areas, and the rest cities in Jiangsu were light-hit areas in recent years.
引文
[1]马明,吕伟涛,张义军,等.我国雷电灾害及相关因素分析[J].地球科学进展,2008,23(8):856-865.
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[4]许小峰.雷电灾害与监测预报[J].气象,2004,12(30):277-284.
[5]郭虎,熊亚军,扈海波,等.北京市雷电灾害灾情综合评估模式[J].灾害学,2008,23(1):14-17.
[6]林溪猛,卢辉林,黄月清,等.福建古雷石化基地雷电活动特征及对策分析[J].气象与环境科学,2016,39(4):109-114.
[7]邸悦伦,袁铁.全球热带副热带地区春冬季闪电活动对ENSO的响应特征[J].干旱气象,2015,33(3):395-404.
[8]马明,吕伟涛,张义军,等.1997-2000年我国雷电灾情特征[J].应用气象学报,2009,19(4):393-400.
[9]史培军.三论灾害研究的理论与实践[J].自然灾害学报,2002,11(3):1-9.
[10]方标,张逸,严小冬.雷达资料在黔东北雷电预警中的应用研究[J].气象与环境科学,2017,40(3):133-137.
[11]杨晓军,刘维成,宋强,等.甘肃中部地区短时强降水与闪电关系初步分析[J].干旱气象,2015,33(5):802-807.
[12]袁湘玲,王振会,肖稳安,等.雷电灾害潜在与现实易损性分析及区划研究—以黑龙江省为例[J].灾害学,2011,26(1):20-25.
[13]尹娜,肖稳安.区域雷灾易损性分析、评估及易损度区划[J].热带气象学报,2005,21(4):441-449.
[14]程萌,王秀丽.基于闪电定位数据的鲁西南地区雷电灾害易损度区划[J].气象与环境科学,2017,40(4):126-131.
[15]姚蓉,许霖,唐佳,等.湖南省雷暴时空特征及变化趋势[J].气象与环境科学,2017,40( 3):65-70.
[16]朱义青,赵海军,庄重,等.山东中部一次强对流天气的中尺度结构和闪电特征分析[J].干旱气象,2015,33(5):830-837.
[17]孙京,柴健,冷亮,等.湖北地区一次雷暴云电荷结构和放电特征的数值模拟研究[J].沙漠与绿洲气象,2017,11(6):52-60.
[18]李家启,申双和,秦健,等.重庆市雷电灾害易损性风险综合评估与区划[J].西南大学学报,2011,33(1):96-102.
[19]冯民学,焦雪,韦海容,等.江苏省雷电分布特征分析[J].气象科学,2009,29(2):246-251.
[20]刘岩,李征,程向阳,等.安徽省雷电灾害风险区划[J].南京信息工程大学学报(自然科学版),2014,6(2):163-168.
[21]刘晓东,李松如,冯旭宇,等.内蒙古雷灾特征及灾情的灰色关联评估[J].安全与环境学报,2012,26(5):251-255.
[22]葛全胜,邹铭,郑景云,等.中国自然灾害风险综合评估初步研究[M].北京:科学出版社,2008.
[23]刘思峰,党耀国,方志耕.灰色系统理论及其应用[M].3版.北京:科学出版社,2004.
[24]邓聚龙.灰理论基础[M].武汉:华中科技大学出版社,2002:1-10.
[25]胡召音.灰色理论及其应用研究[J].武汉理工大学学报,2003,27(3):405-411.
[26]马亚龙,王精业,徐享忠,等.基于理想方案的灰色关联综合评价研究[J].系统工程与电子技术,2002,24(7):51-53.
[27]王曙钊,刘兴堂,段锁力.利用灰色关联度理论对仿真模型的评估研究[J].空军工程大学学报(自然科学版),2007,8(1):73-76.
(1)中国气象局雷电防护管理办公室.1998—2009年全国雷电灾害典型事例汇编[G].北京:中国气象局,2009.
[2]RakovV A,Uman M A.Lightning:Physics and effects[M].Cambridge.UK:Cambridge university press,2003.
[3]张鸿发,程国栋,董万胜,等.青藏铁路沿线区域闪电分布和闪电气候[J].干旱气象,2005,23(1):1-9.
[4]许小峰.雷电灾害与监测预报[J].气象,2004,12(30):277-284.
[5]郭虎,熊亚军,扈海波,等.北京市雷电灾害灾情综合评估模式[J].灾害学,2008,23(1):14-17.
[6]林溪猛,卢辉林,黄月清,等.福建古雷石化基地雷电活动特征及对策分析[J].气象与环境科学,2016,39(4):109-114.
[7]邸悦伦,袁铁.全球热带副热带地区春冬季闪电活动对ENSO的响应特征[J].干旱气象,2015,33(3):395-404.
[8]马明,吕伟涛,张义军,等.1997-2000年我国雷电灾情特征[J].应用气象学报,2009,19(4):393-400.
[9]史培军.三论灾害研究的理论与实践[J].自然灾害学报,2002,11(3):1-9.
[10]方标,张逸,严小冬.雷达资料在黔东北雷电预警中的应用研究[J].气象与环境科学,2017,40(3):133-137.
[11]杨晓军,刘维成,宋强,等.甘肃中部地区短时强降水与闪电关系初步分析[J].干旱气象,2015,33(5):802-807.
[12]袁湘玲,王振会,肖稳安,等.雷电灾害潜在与现实易损性分析及区划研究—以黑龙江省为例[J].灾害学,2011,26(1):20-25.
[13]尹娜,肖稳安.区域雷灾易损性分析、评估及易损度区划[J].热带气象学报,2005,21(4):441-449.
[14]程萌,王秀丽.基于闪电定位数据的鲁西南地区雷电灾害易损度区划[J].气象与环境科学,2017,40(4):126-131.
[15]姚蓉,许霖,唐佳,等.湖南省雷暴时空特征及变化趋势[J].气象与环境科学,2017,40( 3):65-70.
[16]朱义青,赵海军,庄重,等.山东中部一次强对流天气的中尺度结构和闪电特征分析[J].干旱气象,2015,33(5):830-837.
[17]孙京,柴健,冷亮,等.湖北地区一次雷暴云电荷结构和放电特征的数值模拟研究[J].沙漠与绿洲气象,2017,11(6):52-60.
[18]李家启,申双和,秦健,等.重庆市雷电灾害易损性风险综合评估与区划[J].西南大学学报,2011,33(1):96-102.
[19]冯民学,焦雪,韦海容,等.江苏省雷电分布特征分析[J].气象科学,2009,29(2):246-251.
[20]刘岩,李征,程向阳,等.安徽省雷电灾害风险区划[J].南京信息工程大学学报(自然科学版),2014,6(2):163-168.
[21]刘晓东,李松如,冯旭宇,等.内蒙古雷灾特征及灾情的灰色关联评估[J].安全与环境学报,2012,26(5):251-255.
[22]葛全胜,邹铭,郑景云,等.中国自然灾害风险综合评估初步研究[M].北京:科学出版社,2008.
[23]刘思峰,党耀国,方志耕.灰色系统理论及其应用[M].3版.北京:科学出版社,2004.
[24]邓聚龙.灰理论基础[M].武汉:华中科技大学出版社,2002:1-10.
[25]胡召音.灰色理论及其应用研究[J].武汉理工大学学报,2003,27(3):405-411.
[26]马亚龙,王精业,徐享忠,等.基于理想方案的灰色关联综合评价研究[J].系统工程与电子技术,2002,24(7):51-53.
[27]王曙钊,刘兴堂,段锁力.利用灰色关联度理论对仿真模型的评估研究[J].空军工程大学学报(自然科学版),2007,8(1):73-76.
(1)中国气象局雷电防护管理办公室.1998—2009年全国雷电灾害典型事例汇编[G].北京:中国气象局,2009.
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