山东夏季强降水的影响系统和物理量特征
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摘要
应用2009—2013年6—9月山东全省加密自动站资料、地面和探空观测资料,选出了98次区域性强降水过程。统计分析了产生强降水的天气系统特征,把500 hPa天气系统分为6种类型,850~700 hPa天气系统分为5种类型,地面影响系统分为7种类型。统计分析了强降水过程中及前期24个代表大气热力、水汽和动力特征的物理量,给出了最小值、最大值、平均值和各阈值所占百分率。850 hPa和700 hPa偏南风达到急流(≥12 m·s~(-1))强度的分别占56.1%和62.2%。对流有效位能(CAPE)≥300 J·kg~(-1)占72.6%。K指数≥30℃占86.7%。沙氏指数SI≤0占75.5%。925 hPaθse≥68℃占82.2%,850 hPaθ_(se)≥66℃占74.8%。GPS/MET水汽监测大气可降水量≥55mm占81.8%。850 hPa和700 hPa的水汽通量平均值分别为8.0和5.9 g·(cm·hPa·s)-1,水汽通量散度平均值分别为-4.6×10~(-9)和-2.7×10~(-9)g·(hPa·cm~2·s)~(-1)。925 hPa、850 hPa和700 hPa的涡度平均值分别为12.6×10~(-6)、12.3×10~(-6)和9×10~(-6)s~(-1),散度平均值分别为-5.5×10~(-6)、-3.1×10~(-6)、-3.4×10~(-6)s~(-1)。850 hPa、700 hPa和500 hPa的垂直速度平均值分别为-4.5×10~(-4)、-7.4×10~(-4)和-11.1×10~(-4)hPa·s~(-1)。
Based on the data of Intensified Automatic surface Weather observation System( IAWS),ground observation data and sounding data of Shandong Province from June to September from 2009 to2013,98 regional heavy rainfall cases are selected,and the synoptic system characteristics of heavy rainfall are statistically analyzed. The synoptic systems at 500 hPa are divided into 6 types,the synoptic systems from 850 hPa to 700 hPa are divided into 5 types,and the surface mesoscale systems are divided into 7 types. The 24 physical quantities representing atmospheric thermal,water vapor and dynamic characteristics during the heavy rainfall and the earlier period are statistically analyzed,and the minimum values,maximum values, mean values, and percentages of threshold values are calculated. Thepercentages of southerly wind at 850 hPa and 700 hPa that reached jet stream( ≥ 12 m · s~(-1)) are respectively 56. 1% and 62. 2%. CAPE( Convective Available Potential Energy) equal to or higher than300 J·kg-1 takes up 72. 6%,K index equal to or higher than 30 ℃ takes up 86. 7 %,SI( Showalter Index) equal to or lower than 0 takes up 75. 5%,θseequal to or higher than 68 ℃ at 925 hPa takes up82. 2%,and θseequal to or higher than 66 ℃ at 850 hPa takes up 74. 8%. The precipitable water equal to or more than 55 mm from GPS/MET water vapour monitoring system takes up 81. 8%. At 850 hPa and 700 hPa,the mean values of water vapour flux are respectively 8. 0 g·( cm·hPa·s)~(-1) and 5. 9 g·( cm·hPa·s)~(-1),and the mean values of water vapour flux divergence are respectively~(-4). 6×10~(-9) g·( hPa·cm~2·s)~(-1) and-2. 7×10~(-9) g·( hPa·cm~2·s)~(-1). At 925 hPa,850 hPa and 700 hPa,the mean values of vorticity are respectively 12. 6×10~(-6) s~(-1),12. 3×10~(-6) s~(-1) and 9×10~(-6) s~(-1),and the mean values of divergence are respectively-5. 5×10~(-6) s~(-1),-3. 1×10~(-6) s~(-1) and-3. 4×10~(-6) s~(-1). At 850 hPa,700 hPa and 500 hPa,the mean vertical velocities are respectively~(-4). 5 × 10~(-4) hPa · s~(-1),-7. 4×10~(-4) hPa · s~(-1) and -11. 1×10~(-4) hPa·s~(-1).
Based on the data of Intensified Automatic surface Weather observation System( IAWS),ground observation data and sounding data of Shandong Province from June to September from 2009 to2013,98 regional heavy rainfall cases are selected,and the synoptic system characteristics of heavy rainfall are statistically analyzed. The synoptic systems at 500 hPa are divided into 6 types,the synoptic systems from 850 hPa to 700 hPa are divided into 5 types,and the surface mesoscale systems are divided into 7 types. The 24 physical quantities representing atmospheric thermal,water vapor and dynamic characteristics during the heavy rainfall and the earlier period are statistically analyzed,and the minimum values,maximum values, mean values, and percentages of threshold values are calculated. Thepercentages of southerly wind at 850 hPa and 700 hPa that reached jet stream( ≥ 12 m · s~(-1)) are respectively 56. 1% and 62. 2%. CAPE( Convective Available Potential Energy) equal to or higher than300 J·kg-1 takes up 72. 6%,K index equal to or higher than 30 ℃ takes up 86. 7 %,SI( Showalter Index) equal to or lower than 0 takes up 75. 5%,θseequal to or higher than 68 ℃ at 925 hPa takes up82. 2%,and θseequal to or higher than 66 ℃ at 850 hPa takes up 74. 8%. The precipitable water equal to or more than 55 mm from GPS/MET water vapour monitoring system takes up 81. 8%. At 850 hPa and 700 hPa,the mean values of water vapour flux are respectively 8. 0 g·( cm·hPa·s)~(-1) and 5. 9 g·( cm·hPa·s)~(-1),and the mean values of water vapour flux divergence are respectively~(-4). 6×10~(-9) g·( hPa·cm~2·s)~(-1) and-2. 7×10~(-9) g·( hPa·cm~2·s)~(-1). At 925 hPa,850 hPa and 700 hPa,the mean values of vorticity are respectively 12. 6×10~(-6) s~(-1),12. 3×10~(-6) s~(-1) and 9×10~(-6) s~(-1),and the mean values of divergence are respectively-5. 5×10~(-6) s~(-1),-3. 1×10~(-6) s~(-1) and-3. 4×10~(-6) s~(-1). At 850 hPa,700 hPa and 500 hPa,the mean vertical velocities are respectively~(-4). 5 × 10~(-4) hPa · s~(-1),-7. 4×10~(-4) hPa · s~(-1) and -11. 1×10~(-4) hPa·s~(-1).
引文
[1]陈炯,郑永光,张小玲,等.中国暖季短时强降水分布和日变化特征及其与中尺度对流系统日变化关系分析[J].气象学报,2013,71(3):367-382.
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[13]张小玲,谌芸,张涛.对流天气预报中的环境场条件分析[J].气象学报,2012,70(4):642-654.
[14]郝莹,姚叶青,郑媛媛,等.短时强降水的多尺度分析及临近预警[J].气象,2012,38(8):903-912.
[15]杨晓霞,胡顺起,姜鹏,等.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,2014,33(4):1057-1068.
[16]段鹤,夏文梅,苏晓力,等.短时强降水特征统计及临近预警[J].气象,2014,40(10):1194-1206.
[17]雷蕾,孙继松,王国荣,等.基于中尺度数值模式快速循环系统的强对流天气分类概率预报试验[J].气象学报,2012,70(4):752-765.
[18]仇娟娟,何立富.苏沪浙地区短时强降水与冰雹天气分布及物理量特征对比分析[J].气象,2013,39(5):577-584.
[19]沈澄,孙燕,魏晓奕,等.基于物理量参数的江苏短时强降水预报模型的研究[J].气象,2016,42(5):557-566.
[20]杨晓霞,周庆亮,郑永光,等.2009年5月9—10日华北南部强降水天气分析[J].气象,2010,36(6):43-49.
[21]孙兴池,王西磊,周雪松.纬向切变线暴雨落区的精细化分析[J].气象,2012,38(7):779-785.
[22]杨成芳,阎丽凤,周雪松.利用加密探测资料分析冷式切变线类大暴雨的动力结构[J].气象,2012,38(7):819-827.
[23]孙兴池,吴炜,周雪松,等.经向切变线暴雨落区分析[J].气象,2013,39(7):832-841.
[24]杨晓霞,吴炜,姜鹏,等.山东省三次暖切变线极强降水的对比分析[J].气象,2013,39(12):1550-1560.
[25]杨晓霞,蒋义芳,胡顺起,等.2009年“8·17”鲁南低涡暖式切变线极强降水分析[J].暴雨灾害,2013,32(3):224-234.
[26]阎丽凤,孙兴池,周雪松.影响山东的黄淮气旋暴雨落区分析[J].气象,2014,40(12):1439-1445.
[27]杨晓霞,王金东,姜鹏,等.山东半岛南部一次沿海强降雨成因分析[J].气象科技,2015,43(3):512-521.
[28]孙兴池,郭俊建,王业宏,等.低涡和副热带高压共同影响下的暴雨落区分析[J].气象,2015,41(4):401-408.
[29]杨晓霞.副热带高压边缘连续两次强降水形成机制分析[J].海洋气象学报,2017,37(3):62-72.
[30]张永婧,高帆,于丽娟,等.济南市区短时强降水特征分析与天气分型[J].海洋气象学报,2017,37(3):109-116.
[2]陶诗言.有关暴雨分析预报的一些问题[J].大气科学,1977,1(1):64-72.
[3]陶诗言,丁一汇,周晓平.暴雨和强对流天气的研究[J].大气科学,1979,3(3):227-238.
[4]鲍名.近50年我国持续性暴雨的统计分析及其大尺度环流背景[J].大气科学,2007,31(5):779-792.
[5]丁一汇.暴雨和中尺度气象学问题[J].气象学报,1994,52(3):274-284.
[6]丁一汇.陶诗言先生在中国暴雨发生条件和机制研究中的贡献[J].大气科学,2014,38(4):616-626.
[7]程麟生,Kuo K-H,彭新东,等.中国暴雨中尺度系统发生与发展的诊断分析和数值模拟(Ⅰ)诊断分析[J].应用气象学报,1993,4(3):257-268.
[8]程麟生,Kuo Y-H,彭新东.中国暴雨中尺度系统发生与发展的诊断分析和数值模拟(Ⅱ)数值模拟[J].应用气象学报,1993,4(3):269-277.
[9]赵洋洋,张庆红,杜宇,等.北京“7.21”特大暴雨环流形势极端性客观分析[J].气象学报,2013,71(5):817-824.
[10]杨波,孙继松,毛旭,等.北京地区短时强降水过程的多尺度环流特征[J].气象学报,2016,74(6):919-934.
[11]李耀东,刘健文,高守亭.动力和能量参数在强对流天气预报中的应用研究[J].气象学报,2004,62(4):401-409.
[12]樊李苗,俞小鼎.中国短时强对流天气的若干环境参数特征分析[J].高原气象,2013,32(1):156-165.
[13]张小玲,谌芸,张涛.对流天气预报中的环境场条件分析[J].气象学报,2012,70(4):642-654.
[14]郝莹,姚叶青,郑媛媛,等.短时强降水的多尺度分析及临近预警[J].气象,2012,38(8):903-912.
[15]杨晓霞,胡顺起,姜鹏,等.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,2014,33(4):1057-1068.
[16]段鹤,夏文梅,苏晓力,等.短时强降水特征统计及临近预警[J].气象,2014,40(10):1194-1206.
[17]雷蕾,孙继松,王国荣,等.基于中尺度数值模式快速循环系统的强对流天气分类概率预报试验[J].气象学报,2012,70(4):752-765.
[18]仇娟娟,何立富.苏沪浙地区短时强降水与冰雹天气分布及物理量特征对比分析[J].气象,2013,39(5):577-584.
[19]沈澄,孙燕,魏晓奕,等.基于物理量参数的江苏短时强降水预报模型的研究[J].气象,2016,42(5):557-566.
[20]杨晓霞,周庆亮,郑永光,等.2009年5月9—10日华北南部强降水天气分析[J].气象,2010,36(6):43-49.
[21]孙兴池,王西磊,周雪松.纬向切变线暴雨落区的精细化分析[J].气象,2012,38(7):779-785.
[22]杨成芳,阎丽凤,周雪松.利用加密探测资料分析冷式切变线类大暴雨的动力结构[J].气象,2012,38(7):819-827.
[23]孙兴池,吴炜,周雪松,等.经向切变线暴雨落区分析[J].气象,2013,39(7):832-841.
[24]杨晓霞,吴炜,姜鹏,等.山东省三次暖切变线极强降水的对比分析[J].气象,2013,39(12):1550-1560.
[25]杨晓霞,蒋义芳,胡顺起,等.2009年“8·17”鲁南低涡暖式切变线极强降水分析[J].暴雨灾害,2013,32(3):224-234.
[26]阎丽凤,孙兴池,周雪松.影响山东的黄淮气旋暴雨落区分析[J].气象,2014,40(12):1439-1445.
[27]杨晓霞,王金东,姜鹏,等.山东半岛南部一次沿海强降雨成因分析[J].气象科技,2015,43(3):512-521.
[28]孙兴池,郭俊建,王业宏,等.低涡和副热带高压共同影响下的暴雨落区分析[J].气象,2015,41(4):401-408.
[29]杨晓霞.副热带高压边缘连续两次强降水形成机制分析[J].海洋气象学报,2017,37(3):62-72.
[30]张永婧,高帆,于丽娟,等.济南市区短时强降水特征分析与天气分型[J].海洋气象学报,2017,37(3):109-116.