基于多元线性回归的滇西南短时强降水预报模型研究
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摘要
利用云南省普洱市2015—2017年多普勒天气雷达资料、探空资料和气象观测站5 min雨量观测资料,分析了普洱地区研究期间41次短时强降水的环境场和雷达回波演变特征。结果表明:中尺度辐合线、中气旋、逆风区是强降水触发和维持的重要成因。短时强降水发生前,整层大气水汽充沛,静力不稳定层结,大气可降水量(PW)≥35 mm、SI≤-0. 23、K> 35,可作为环境场对流潜势的判定因子;短时强降水发生时,雷达回波最强反射率因子≥40 d Bz,35 d Bz回波顶高> 5 km,径向速度的辐合切变量> 5 m·s~(-1)。通过多元线性回归分析,选取4个相关性显著的影响因子,建立普洱市短时强降水预报模型。所选预报因子包括:35 d Bz回波顶高、30 d Bz垂直剖面中心高度、30 d Bz以上雷达回波面积和SI。预报模型的回报检验表明,普洱短时强降水平均雨强相对均方根误差为17. 0%,局地降水持续时间相对均方根误差为33. 9%,局地过程降水相对均方根误差为25. 6%,回报效果较好。4次短时强降水预报检验中,平均雨强的预报误差每5 min小于1. 2 mm,局地强降水持续时间的预报误差小于10 min,局地过程降水的预报误差小于4 mm,模型均预报出局地连续性降水超过50 mm。预报模型有较好的预报能力,可应用于普洱短时强降水的临近预报预警。
Using the data from a Doppler weather radar,the sounding data,and 5-min precipitation from meteorological stations in Puer,the evolution characteristics of environmental fields and radar echoes during 41 flash heavy rainfall events from 2015 to 2017 were analyzed. The results showed that mesoscale convergence lines,mesocyclones and adverse wind zones are important causes for the triggering and maintenance of flash heavy rainfall events. Before flash heavy rainfall occurs,the entire atmosphere has abundant water vapor and statically unstable stratification,with precipitable water (PW) ≥ 35 mm,a Showalter index (SI) ≤-0. 23,and a K index > 35,which can be used as determining factors of the convection potential in the environmental fields. When flashing heavy rainfall occurs,the strongest reflectance factor of radar echoes reaches 40 d Bz at least,the echo tops of 35 dBz (RHI35) is larger than 5 km,and the radial velocity of convergence shear variable is larger than 5 m·s~(-1). A forecast model for flash heavy rainfall in Puer is established using four influencing factors with significant correlation selected through the multiple linear regression analysis,including RHI35,vertical section center height of 30 dBz,radar echo area more than 30 d Bz,and the SI. The validation of this model shows that the relative root mean square (RMS) error of average rainfall intensity is 17. 0%,and those of precipitation duration and the amount in local processes are 33. 9% and 25. 6%,respectively. The forecast error of average rainfall intensity in four flash heavy rainfall events is less than 1. 2 mm/5 min,and that for precipitation duration and amount is less than 10 min and 4 mm,respectively. The consistent precipitation exceeds 50 mm for the four events,which is also captured in the model. Overall,the forecast model has good performance and can be used for the short-time forecast of flash heavy rainfall events in Puer area.
Using the data from a Doppler weather radar,the sounding data,and 5-min precipitation from meteorological stations in Puer,the evolution characteristics of environmental fields and radar echoes during 41 flash heavy rainfall events from 2015 to 2017 were analyzed. The results showed that mesoscale convergence lines,mesocyclones and adverse wind zones are important causes for the triggering and maintenance of flash heavy rainfall events. Before flash heavy rainfall occurs,the entire atmosphere has abundant water vapor and statically unstable stratification,with precipitable water (PW) ≥ 35 mm,a Showalter index (SI) ≤-0. 23,and a K index > 35,which can be used as determining factors of the convection potential in the environmental fields. When flashing heavy rainfall occurs,the strongest reflectance factor of radar echoes reaches 40 d Bz at least,the echo tops of 35 dBz (RHI35) is larger than 5 km,and the radial velocity of convergence shear variable is larger than 5 m·s~(-1). A forecast model for flash heavy rainfall in Puer is established using four influencing factors with significant correlation selected through the multiple linear regression analysis,including RHI35,vertical section center height of 30 dBz,radar echo area more than 30 d Bz,and the SI. The validation of this model shows that the relative root mean square (RMS) error of average rainfall intensity is 17. 0%,and those of precipitation duration and the amount in local processes are 33. 9% and 25. 6%,respectively. The forecast error of average rainfall intensity in four flash heavy rainfall events is less than 1. 2 mm/5 min,and that for precipitation duration and amount is less than 10 min and 4 mm,respectively. The consistent precipitation exceeds 50 mm for the four events,which is also captured in the model. Overall,the forecast model has good performance and can be used for the short-time forecast of flash heavy rainfall events in Puer area.
引文
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[16]李燕,赛翰,刘静,等.辽宁省短时强降水气候特征分析[J].气象与环境学报,2017,33(4):56-63.
[17]徐璐璐,李慧琳,孙连强,等.2010年8月丹东连续两次副热带高压暴雨多普勒雷达回波对比分析[J].气象与环境学报,2012,28(4):49-54.
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[19]王宪彬,张建国,辛艳辉.2010年8月19日阜新暴雨过程的多普勒雷达回波特征[J].气象与环境学报,2012,28(5):53-57.
[20]Riley G T,Landin M G,Bosart L F.The diurnal variability of precipitation across the central Rockies and adjacent Great Plains[J].M onthly Weather Review,1987,115(6):1161-1172.
[21]丁一汇.高等天气学[M].北京:气象出版社,2005:443-443.
[22]柴东红,景华,孟凯,等.地形暴雨的多普勒天气雷达观测分析[J].气象科学,2010,30(3):366-372.
[23]雷蕾,孙继松,魏东.利用探空资料判别北京地区夏季强对流的天气类别[J].气象,2011,37(2):136-141.
[24]俞小鼎,周小刚,王秀明.雷暴与强对流临近天气预报技术进展[J].气象学报,2012,70(3):311-337.
[25]邢雯.东亚季风区夏季降水预测:统计模型的建立与可预报性分析[D].青岛:中国海洋大学,2015.
[26]范可,林美静,高煜中.用年际增量方法预测华北汛期降水[J].中国科学D辑:地球科学,2008,38(11):1452-1459.
[27]范可,田宝强.东北地区冬半年大雪-暴雪日数气候预测[J].科学通报,2013,58(8):699-706.
[28]范可.西北太平洋台风生成频次的新预测因子和新预测模型[J].中国科学D辑:地球科学,2007,37(9):1260-1266.
[29]刘颖,范可,张颖.基于CFS模式的中国站点夏季降水统计降尺度预测[J].大气科学,2013,37(6):1287-1296.
[2]Brooks H E,Stensrud D J.Climatology of heavy rain events in the united states from hourly precipitation observations[J].M onthly Weather Review,2000,128(4):1194-1201.
[3]Iwashima T,Yamamoto R.A statistical analysis of the extreme events:Long-term trend of heavy daily precipitation[J].Journal of the M eteorological Society of Japan,1993(71):637-640.
[4]Zhai P M,Zhang X B,Wan H,et al.Trends in total precipitation and frequency of daily precipitation extremes over China[J].Journal of Climate,2005,18(7):1096-1108.
[5]DoswellⅢC A,Brooks H E,Maddox R A.Flash flood forecasting:An ingredients-based methodology[J].Weather and Forecasting,1996,11(4):560-581.
[6]DoswellⅢC A.Severe convective storms-an overview[J].Meteorological Monographs,2001,28(50):1-26.
[7]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.
[8]孙晓巍.辽宁短时强降水分布特征及预报技术研究[D].兰州:兰州大学硕士论文,2014.
[9]郝莹,姚也青,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.
[10]应冬梅,许爱华,黄祖辉.江西冰雹、大风与短时强降水的多普勒雷达产品的对比分析[J].气象,2007,33(3):48-53.
[11]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[J].高原气象,2005,24(3):456-464.
[12]廖玉芳,俞小鼎,吴林林,等.强雹暴的雷达三体散射统计与个例分析[J].高原气象,2007,26(4):812-820.
[13]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.
[14]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.
[15]李崇,吉曹翔,夏传栋,等.沈阳地区强对流天气潜势预报环境参数特征分析[J].气象与环境学报,2016,32(6):43-51.
[16]李燕,赛翰,刘静,等.辽宁省短时强降水气候特征分析[J].气象与环境学报,2017,33(4):56-63.
[17]徐璐璐,李慧琳,孙连强,等.2010年8月丹东连续两次副热带高压暴雨多普勒雷达回波对比分析[J].气象与环境学报,2012,28(4):49-54.
[18]李大为,梁红,贾冬婵,等.“20110714”沈阳短时强降水多普勒雷达回波特征[J].气象与环境学报,2013,29(2):75-80.
[19]王宪彬,张建国,辛艳辉.2010年8月19日阜新暴雨过程的多普勒雷达回波特征[J].气象与环境学报,2012,28(5):53-57.
[20]Riley G T,Landin M G,Bosart L F.The diurnal variability of precipitation across the central Rockies and adjacent Great Plains[J].M onthly Weather Review,1987,115(6):1161-1172.
[21]丁一汇.高等天气学[M].北京:气象出版社,2005:443-443.
[22]柴东红,景华,孟凯,等.地形暴雨的多普勒天气雷达观测分析[J].气象科学,2010,30(3):366-372.
[23]雷蕾,孙继松,魏东.利用探空资料判别北京地区夏季强对流的天气类别[J].气象,2011,37(2):136-141.
[24]俞小鼎,周小刚,王秀明.雷暴与强对流临近天气预报技术进展[J].气象学报,2012,70(3):311-337.
[25]邢雯.东亚季风区夏季降水预测:统计模型的建立与可预报性分析[D].青岛:中国海洋大学,2015.
[26]范可,林美静,高煜中.用年际增量方法预测华北汛期降水[J].中国科学D辑:地球科学,2008,38(11):1452-1459.
[27]范可,田宝强.东北地区冬半年大雪-暴雪日数气候预测[J].科学通报,2013,58(8):699-706.
[28]范可.西北太平洋台风生成频次的新预测因子和新预测模型[J].中国科学D辑:地球科学,2007,37(9):1260-1266.
[29]刘颖,范可,张颖.基于CFS模式的中国站点夏季降水统计降尺度预测[J].大气科学,2013,37(6):1287-1296.