陕西雷暴大风时空分布和气象条件分析

doi:10.11755/j.issn.1006-7639(2024)-04-0576

摘要/Abstract

摘要：

分析陕西不同区域雷暴大风形成环境差异，有助于更好地掌握此类过程的热力、动力和环流特征，为该类天气的预报预警提供参考。基于2017—2022年地面观测资料、闪电资料和欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts，ECMWF）发布的第五代全球气候再分析资料（ERA5），分析陕西雷暴大风时空分布特征，并分区域对比分析暖型雷暴大风的环境参数和环流特征。结果表明：陕北和关中东部为雷暴大风高发区，暖型雷暴大风明显多于冷型；夏季远多于其他季节，6—8月暖型雷暴大风陕北明显多于关中和陕南。雷暴大风高发时段为15：00—21：00（北京时，下同），且14：00—18：00暖型雷暴大风发生频率陕北明显高于关中和陕南。不同区域暖型雷暴大风发生前热力、动力条件存在一定差异，陕北过程前能量和水汽条件相对较弱，动力条件相对较强；陕南能量和水汽条件相对更强，动力条件相对较弱。频率高于15%的环流型为陕北西风型和反气旋配合西风型、关中西风型和反气旋配合西风型、陕南气旋配合西风型和反气旋配合西风型。陕北西风型和反气旋配合西风型，陕北位于冷涡低槽底部或低槽底部与副热带高压之间，850 hPa和500 hPa温差较大，为对流天气发生提供了一定的不稳定条件，过程平均发生位置附近有切变存在，有利于对流天气触发；关中西风型，低层偏南气流较强，温度露点差较小；陕南气旋配合西风型，T-ln P图表现为近V型且能量条件较好；关中和陕南反气旋配合西风型，T-ln P图表现为近V型且水汽条件较好。

关键词: 雷暴大风, 环境参数, 环流特征, 陕西

Abstract:

The analysis of the difference of forming environment of thunderstorm gales in different regions of Shaanxi Province is helpful to better understand thermal, dynamic and circulation characteristics of this kind of processes, and provide reference for the forecast and early warning of this kind of weather. Based on the surface observation data, lightning data and ERA5 reanalysis data of European Centre for Medium-Range Weather Forecasts from 2017 to 2022, the temporal and spatial distribution characteristics of thunderstorm gales in Shaanxi are analyzed, and the environmental parameters and circulation characteristics of warm type thunderstorm gales are compared and analyzed in different regions. The results show that northern Shaanxi and eastern Guanzhong are high-occurrence areas of thunderstorm gales, and warm type process is significantly more than cold type one. In summer this kind of weather process is much more than that in other seasons, and from June to August, the frequency of warm type processes in northern Shaanxi is significantly higher than that in Guanzhong and southern Shaanxi. The high occurrence period of thunderstorm gales is from 15:00 BT to 21:00 BT, and from 14:00 BT to 18:00 BT, the frequency of warm type processes in northern Shaanxi is significantly higher than that in Guanzhong and southern Shaanxi. There are some differences in thermal and dynamic conditions before the occurrence of warm type thunderstorm gales in different regions. Before the occurrence of warm type processes in northern Shaanxi, the energy and water vapor conditions are relatively weak, while the dynamic condition is relatively strong. But in southern Shaanxi, the energy and water vapor conditions are relatively strong, and the dynamic condition is relatively weak. The circulation types with frequency higher than 15% are northern Shaanxi west wind type and anticyclone with west wind type, Guanzhong west wind type and anticyclone with west wind type, southern Shaanxi cyclone with west wind type and anticyclone with west wind type. For the northern Shaanxi west wind type and anticyclone with west wind type, northern Shaanxi is located at the bottom of cold vortex or low trough, or between the bottom of low trough and the subtropical high, and the temperature difference between 850 and 500 hPa is larger, which provides certain unstable conditions for the occurrence of convective weathers. There is shear near the average occurrence position of warm type thunderstorm gales, which is conducive to the triggering of convective weather. For the west wind type in Guanzhong, the southerly airflow in the lower layer is stronger, and the dew point depression is smaller. For the cyclone with west wind type in southern Shaanxi, the T-ln P diagram shows a near-V shape and the energy condition is better. For the anticyclone with west wind type in Guanzhong and southern Shaanxi, the T-ln P diagram shows a near-V shape and the water vapor conditions are better.

Key words: thunderstorm gales, environmental parameters, circulation characteristics, Shaanxi

中图分类号:

P457.9

井宇, 陈闯, 赵强, 刘菊菊. 陕西雷暴大风时空分布和气象条件分析[J]. 干旱气象, 2024, 42(4): 576-587.

JING Yu, CHEN Chuang, ZHAO Qiang, LIU Juju. Spatial-temporal distribution and meteorological conditions of thunderstorm gales in Shaanxi[J]. Journal of Arid Meteorology, 2024, 42(4): 576-587.

图/表 11

图1 陕西各区域环流分型中心点注:基于自然资源部国家地理信息公共服务平台(天地图)网站标准地图[审图号为GS(2024)0650号]制作，底图边界无修改

Fig.1 Central points of circulation classification in each region of Shaanxi Province

图2 计算环流分型的格点（陕北）

Fig.2 The grids used for circulation type classification（northern Shaanxi）

图3 2017—2022年陕西雷暴大风发生频次空间分布（灰色阴影为地形，单位：m）注:基于自然资源部国家地理信息公共服务平台(天地图)网站标准地图[审图号为GS(2024)0650号]制作，底图边界无修改

Fig.3 Spatial distribution of thunderstorm gales frequency in Shaanxi Province from 2017 to 2022 （The grey shaded is terrain， Unit： m）

图4 2017—2022年陕西雷暴大风发生频率逐月分布（a）全部、冷型和暖型，（b）不同区域暖型

Fig.4 Monthly frequency of thunderstorm gales in Shaanxi Province during 2017-2022 (a) all，cold and warm type, (b) warm type in different regions

图5 2017—2022年陕西雷暴大风发生频率日变化（a）全部、冷型和暖型，（b）不同区域暖型

Fig.5 Diurnal variation of frequency of thunderstorm gales in Shaanxi Province during 2017-2022 (a) all, cold and warm type, (b) warm type in different regions

图6 陕西各区域暖型雷暴大风发生前6小时至发生时MLCAPE（a）、MLLCL（b）和DCAPE（c）箱线图（折线为各参数中位数随时间变化，下同）

Fig.6 Boxplots of MLCAPE (a), MLLCL (b) and DCAPE (c) from 6 hours before the occurrence of warm type thunderstorm gales to the occurrence time in each region of Shaanxi Province (The broken line is the median of each parameter changing with time. the same as bellow)

图7 陕西各区域暖型雷暴大风发生前6小时至发生时QML（a）、PWV（b）、(T-Td)700-400（c）和K指数（d）箱线图

Fig.7 Boxplots of QML (a), PWV (b), (T-Td)700-400 (c), and K index (d) from 6 hours before the occurrence of warm type thunderstorm gales to the occurrence time in each region of Shaanxi Province

图8 陕西各区域暖型雷暴大风发生前6小时至发生时SHR6（a）和U850-300（b）箱线图

Fig.8 Boxplots of SHR6 (a) and U850-300 (b) from 6 hours before the occurrence of warm type thunderstorm gales to the occurrence time in each region of Shaanxi Province

图9 陕西各区域暖型雷暴大风频率大于15%环流型500 hPa高度场（等值线，单位：dagpm）和风场（风矢，单位：m·s-1）合成（实心圆和三角分别为暖型雷暴大风发生及平均发生位置，空心圆为风速小于2 m·s-1）注:基于自然资源部国家地理信息公共服务平台(天地图)网站标准地图[审图号为CS(2024)0650号]制作，底图边界无修改

Fig.9 Synthetic analysis of 500 hPa height field (contours, Unit: dagpm) and wind field (wind vectors, Unit: m·s-1) for warm type thunderstorm gales with circulation type frequency greater than 15% in each region of Shaanxi Province (The solid circles and triangles are the occurrence and average location of warm type thunderstorm gale, respectively, the hollow circles indicate the wind speed is less than 2 m·s-1)

图10 陕西各区域暖型雷暴大风500 hPa频率大于15%的环流型风场（风矢，单位：m·s-1）、垂直速度（填色，单位：Pa·s-1）和相对湿度（等值线，单位：%）合成场沿过程平均发生位置的垂直剖面（灰色阴影为地形，五角星为过程平均发生位置，空心圆为风速小于2 m·s-1）

Fig.10 The vertical profiles of the composite fields of wind field (wind vectors, Unit: m·s-1), vertical velocity (the color shaded, Unit: Pa·s-1) and relative humidity (isolines, Unit: %) along the average location of warm type thunderstorm gales with 500 hPa circulation type frequency greater than 15% in each region of Shaanxi Province Grey shaded is for topography, the five-pointed star is the average occurrence position of the processes, the hollow circle indicates the wind speed is less than 2 m·s-1

图11 陕西各区域暖型雷暴大风500 hPa频率大于15%的环流型合成T-ln P图（红色曲线为环境温度，绿色曲线为环境露点，黑色曲线为气块温度，黑色圆点为抬升凝结高度）

Fig.11 Composite T-ln P diagram of warm type thunderstorm gales with 500 hPa circulation type frequency greater than 15% in each region of Shaanxi Provinse (The red curve is environmental temperature, the green curve is environmental dew point temperature, the black curve is parcel temperature, the black dot is lifting condensation level)

参考文献 51

[1]	柴东红, 杨晓亮, 吴紫煜, 等, 2017. 京津冀地区雷暴大风天气的统计分析[J]. 暴雨灾害, 36(3): 193-199.
[2]	陈涛, 代刊, 张芳华, 2013. 一次华北飑线天气过程中环境条件与对流发展机制研究[J]. 气象, 39(8): 945-954.
[3]	陈晓欣, 俞小鼎, 王秀明, 2022. 中国大范围雷暴大风事件(Derechos)研究:时空分布, 环境背景和对流系统形态特征[J]. 气象学报, 80(1): 67-81.
[4]	陈云辉, 许爱华, 许彬, 等, 2019. 江西一次极端雷暴大风过程的中尺度特征与成因分析[J]. 暴雨灾害, 38(2): 126-134.
[5]	董春卿, 武永利, 郭媛媛, 等, 2021. 山西强对流天气分类指标与判据的应用[J]. 干旱气象, 39(2): 345-355.
[6]	方翀, 王西贵, 盛杰, 等, 2017. 华北地区雷暴大风的时空分布及物理量统计特征分析[J]. 高原气象, 36(5): 1 368-1 385.
[7]	费海燕, 王秀明, 周小刚, 等, 2016. 中国强雷暴大风的气候特征和环境参数分析[J]. 气象, 42(12): 1 513-1 521.
[8]	付炜, 叶成志, 王东海, 等, 2018. 一次南岭山脉前汛期强对流天气过程诊断分析[J]. 暴雨灾害, 37(6): 511-521.
[9]	高晓梅, 俞小鼎, 王令军, 等, 2018. 鲁中地区分类强对流天气环境参量特征分析[J]. 气象学报, 76(2): 196-212.
[10]	贾丽伟, 李维京, 陈德亮, 等, 2006. 东北地区月平均大气环流型与哈尔滨气候关系的初步研究[J]. 气象学报, 64(2): 236-245.
[11]	李强, 王秀明, 张亚萍, 等, 2019. 一次副高影响下的局地强风暴触发及维持机制探析[J]. 气象, 45(2): 203-215.
[12]	刘彬, 邹灵宇, 李晓鹏, 等, 2022. 云南雷暴大风天气的环境条件特征分析[J]. 气象, 48(11): 1 402-1 417.
[13]	刘娜, 熊安元, 张强, 等, 2021. 强对流天气人工智能应用训练基础数据集构建[J]. 应用气象学报, 32(5): 530-541.
[14]	马鸿青, 张江涛, 李彦, 等, 2019. 河北保定“7.9”致灾雷暴大风环境场与风暴特征[J]. 干旱气象, 37(4): 613-621.
[15]	马淑萍, 王秀明, 俞小鼎, 2019. 极端雷暴大风的环境参量特征[J]. 应用气象学报, 30(3): 292-301.
[16]	蒙伟光, 张艳霞, 吴亚丽, 等, 2019. 季风槽环境中暴雨中尺度对流系统的分析与数值预报试验[J]. 气象学报, 77(6): 980-998.
[17]	慕建利, 李泽椿, 李耀辉, 2009. 高原东侧特大暴雨过程中秦岭山脉的作用[J]. 高原气象, 28(6): 1 282-1 290.
[18]	孙建华, 郑淋淋, 赵思雄, 2014. 水汽含量对飑线组织结构和强度影响的数值试验[J]. 大气科学, 38(4): 742-755.
[19]	谭丹, 黄玉霞, 沙宏娥, 2022. 甘肃省强对流天气特征分析[J]. 自然灾害学报, 31(2): 222-232.
[20]	王东方, 郄秀书, 袁善锋, 等, 2020. 北京地区的闪电时空分布特征及不同强度雷暴的贡献[J]. 大气科学, 44(2): 225-238.
[21]	王黉, 李英, 宋丽莉, 等, 2020. 川藏地区雷暴大风活动特征和环境因子对比[J]. 应用气象学报, 31(4): 435-446.
[22]	王华, 孙继松, 2008. 下垫面物理过程在一次北京地区强冰雹天气中的作用[J]. 气象, 34(3): 16-21.
[23]	王艳春, 陈宏, 尉英华, 等, 2023. 天津雷暴大风天气的环流场及雷达回波特征分析[J]. 沙漠与绿洲气象, 17(6): 119-126.
[24]	王毅, 张晓美, 盛杰, 等, 2020. 基于Logistic模型的干、湿环境下江淮夏季雷暴大风潜势预报研究[J]. 气象科学, 40(2): 241-248.
[25]	韦惠红, 许冠宇, 刘希文, 等, 2022. 湖北省不同类型雷暴大风的时空分布及环境参数特征[J]. 暴雨灾害, 41(1): 66-75.
[26]	魏东, 孙继松, 雷蕾, 等, 2011. 三种探空资料在各类强对流天气中的应用对比分析[J]. 气象, 37(4): 412-422.
[27]	辛蕊, 段克勤, 2019. 2017年夏季秦岭降水的数值模拟及其空间分布[J]. 地理学报, 74(11): 2 329-2 341.
[28]	许霖, 姚蓉, 王晓雷, 等, 2017. 湖南省雷暴大风的时空分布和变化特征[J]. 高原气象, 36(4): 993-1 000. DOI
[29]	杨晓霞, 胡顺起, 姜鹏, 等, 2014. 雷暴大风落区的天气学模型和物理量参数研究[J]. 高原气象, 33(4):1057-1 068.
[30]	杨晓霞, 尤莉, 夏凡, 等, 2019. 山东内陆和半岛雷暴大风的环境物理量参数特征[J]. 沙漠与绿洲气象, 13(6): 47-56.
[31]	杨新林, 孙建华, 鲁蓉, 等, 2017. 华南雷暴大风天气的环境条件分布特征[J]. 气象, 43(7): 769-780.
[32]	余蓉, 张小玲, 李国平, 等, 2012. 1971—2000年我国东部地区雷暴、冰雹、雷暴大风发生频率的变化[J]. 气象, 38(10): 1 207-1 216.
[33]	袁慧敏, 2019. 利用探空资料确定呼和浩特地区3类强对流天气预警阈值[J]. 气象科技, 47(3): 476-485.
[34]	郑淋淋, 孙建华, 2016. 风切变对中尺度对流系统强度和组织结构影响的数值试验[J]. 大气科学, 40 (2): 324-340.
[35]	郑永光, 陶祖钰, 俞小鼎, 2017. 强对流天气预报的一些基本问题[J]. 气象, 43(6): 641-652.
[36]	郑永光, 王颖, 寿绍文, 2010. 我国副热带地区夏季深对流活动气候分布特征[J]. 北京大学学报(自然科学版), 46(5): 793-804.
[37]	郑媛媛, 姚晨, 郝莹, 等, 2011. 不同类型大尺度环流背景下强对流天气的短时临近预报预警研究[J]. 气象, 37(7): 795-801.
[38]	钟利华, 曾鹏, 李勇, 等, 2011. 广西雷暴大风环流特征和物理量诊断分析[J]. 气象, 37(1): 59-65.
[39]	钟利华, 曾鹏, 史彩霞, 等, 2017. 西江流域面雨量与区域大气环流型关系[J]. 应用气象学报, 28(4): 470-480.
[40]	周康辉, 郑永光, 王婷波, 等, 2017. 基于模糊逻辑的雷暴大风和非雷暴大风区分方法[J]. 气象, 43(7): 781-791.
[41]	ALLEN J T, KAROLY D J, MILLS G A, 2011. A severe thunderstorm climatology for Australia and associated thunderstorm environments[J]. Australian Meteorological and Oceanographic Journal, 61(3): 143-158.
[42]	BROWN A, DOWDY A, 2021. Severe convection-related winds in Australia and their associated environments[J]. Journal of Southern Hemisphere Earth Systems Science, 71(1): 30-52.
[43]	EMANUEL K A, 1994. Atmospheric convection[M]. New York: Oxford University Press: 165-188.
[44]	EVANS J S, DOSWELL C A, 2001. Examination of derecho environments using proximity soundings[J]. Weather and Forecasting, 16(3): 329-342.
[45]	KUCHERA E L, PARKER M D, 2006. Severe convective wind environments[J]. Weather and Forecasting, 21(4): 595-612.
[46]	MIRÓ J R, PEPIN N, PEÑA J C, et al, 2020. Daily atmospheric circulation patterns for Catalonia (northeast Iberian Peninsula) using a modified version of Jenkinson and Collison method[J]. Atmospheric Research, 231: 104674. DOI: 10.1016/j.atmosres.2019.104674.
[47]	PACEY G P, SCHULTZ D M, GARCIA-CARRERAS L, 2021. Severe convective windstorms in Europe: Climatology, preconvective environments, and convective mode[J]. Weather and Forecasting, 36(1): 237-252.
[48]	PÚČIK T, GROENEMEIJER P, RÝVA D, et al, 2015. Proximity soundings of severe and nonsevere thunderstorms in central Europe[J]. Monthly Weather Review, 143(12): 4 805-4 821.
[49]	TASZAREK M, BROOKS H E, CZERNECKI B, 2017. Sounding-derived parameters associated with convective hazards in Europe[J]. Monthly Weather Review, 145(4): 1 511-1 528.
[50]	TASZAREK M, ALLEN J T, PÚČIK T, et al, 2020. Severe convective storms across Europe and the United States.Part II: ERA5 environments associated with lightning, large hail, severe wind, and tornadoes[J]. Journal of Climate, 33(23): 10 263-10 286.
[51]	YANG X, SUN J, ZHENG Y, 2017. A 5-yr climatology of severe convective wind events over China[J]. Weather and Forecasting, 32(4): 1 289-1 299.