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江苏雷暴日发生规律及其大气环流预报模型的建立
引用本文:张旭晖,高苹,许祥,陈广昌.江苏雷暴日发生规律及其大气环流预报模型的建立[J].气象科技,2006,34(5):532-537.
作者姓名:张旭晖  高苹  许祥  陈广昌
作者单位:1. 江苏省气象科学研究所,南京,210008
2. 江苏省溧水县气象局,南京,211200
3. 江苏省防雷中心,南京,210008
基金项目:上海市区域气象中心集资基金
摘    要:根据江苏省1961~2003年逐日气象资料,通过数理统计方法分析得出:江苏各地全年有96%~98%雷暴日出现在3~9月,平均年雷暴日数为27~36天。平均年雷暴日太湖周边地区最多,其次是里下河水网密集区及洪泽湖周边地区,徐州市最少。年雷暴日的年际间波动大,最多年份的雷暴日是最少年份的2.5倍。根据500 hPa大气环流特征量能表征天气形势和控制天气条件的这一特性,利用最优化因子技术对环流因子进行普查、对比分析,挑选一批与年雷暴日相关极其显著、稳定性强、因子间相互独立、可靠的大气环流特征量作为预报因子,建立了江苏省年雷暴日预报的大气环流模型,模拟及预报效果佳,可投入实际业务应用。

关 键 词:雷暴日  发生规律  大气环流特征量  预报模型
收稿时间:2005-04-29
修稿时间:2005-06-29

Statistical Characteristics and Atmospheric Circulation Patterns of Thunder Days in Jiangsu Province
Zhang Xuhui,Gao Ping,Xu Xiang,Chen Guangchang.Statistical Characteristics and Atmospheric Circulation Patterns of Thunder Days in Jiangsu Province[J].Meteorological Science and Technology,2006,34(5):532-537.
Authors:Zhang Xuhui  Gao Ping  Xu Xiang  Chen Guangchang
Institution:1. Jiangsu Provincial Institute of Meteorology, Nanjing 210008; 2. Lishui Meteorological Bureau, Jiangsu Province, Nanjing 211200; 3 .Center for Thunder and Lightning Defense, Nanjing 210008
Abstract:Based on the meteorological data from 1961 to 2003 in Jiangsu Province,the statistical analysis indicates: 96% to 98% thunder days occurs in March and September,and the number of thunder days is 27 to 36 days in a normal year.The area around the Taihu Lake sees thunder days most frequently,whereas Xuzhou the least,and the Lixiahe and Hongzehu Lake region is in the middle.The number of thunder days can change greatly year by year with the maximum value being 2.5 times as great as the minimum one.For the characteristics of atmospheric circulation factors on 500 hPa can indicate weather trend and control weather condition,a number of factors are selected as prediction factors,which are highly correlative,stable and independent,and the prediction model of atmospheric circulation patterns for thunder days in Jiangsu Province is developed.These patterns can be used in practical forecasting and get good results.
Keywords:thunder day  occurrence regularity  characteristic feature  atmospheric circulation pattern  prediction model
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