郑州CINRAD风暴追踪产品误差统计分析
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摘要
利用2009—2016年5—9月郑州新一代天气雷达资料,对5615个单体回波按不同预报时效、不同生命史和不同类型强对流的风暴追踪信息产品进行统计,较系统地分析了单体回波的距离误差和方向误差,探讨了实况与预报的偏差方向。结果表明:30和60 min距离误差分别为9. 3和16. 9 km,方向误差变化不大,随着预报时效的延长,距离误差增大;生命史≤1h、1～2 h和超过2 h的30 min预报平均距离误差分别为9. 8、8. 8和7.7 km,方向误差分别为25. 2°、25. 7°和22.8°;生命史在1～2 h和超过2 h的60 min预报平均距离误差分别为17. 3和15. 9 km,方向误差为24. 4°和22. 7°,随着单体生命史的延长,方向和距离误差呈减小趋势;西北气流型、西南气流型两种类型单体回波和整体误差一致,两种类型回波单体30和60 min方向和距离误差基本相当;不同类型、不同预报时效统计均为实况偏于预报路径右侧的次数多于偏左的次数,30和60 min偏右比例分别为57. 5%和55.6%。
Based on the new generation weather radar data of Zhengzhou from May to September during2009-2016, this paper checked the storm tracking information products of 5615 cell echoes according to different forecast time, different life cycles and different types of severe convection, processes, and also systematically analyzed the distance errors and direction errors of storm tracking information products of cell echoes and discussed the direction deviation between real condition and forecast products. The results show that the distance errors of 30 and 60 min products are 9. 3 and 16. 9 km respectively, and the direction error is not so big. The error of distance increases with the extension of lead time of forecasts; The average distance error of the 30 min forecast products of cell echoes with life cycles less than 1 h, 1-2 h and more than 2 h are 9. 8, 8. 8 and 7. 7 km, and the azimuth errors are 25. 5°, 25. 7° and 22. 8° respectively. The average distance error of the 60 min forecast products with life cycles of 1-2 h and more than 2 h are 17. 3 and15. 9 km, and the direction errors are 24. 4° and 22. 7°, respectively. With the extension of life cycles of the cells, the errors of direction and distance are decreasing, the errors of northwest airflow type and the southwest airflow type are entirety the same, and the direction and distance errors of the two types of echoes are almost the same in 30 and 60 min. The errors of the southwest airflow type are slightly smaller than those of the northwest airflow type. The number of different types and forecast efficiency are that the observation data biased to the right of the forecast path are more than that of the left side, and the proportion of 30 and 60 min biased to right are 57. 5 % and 55. 6 %, respectively.
Based on the new generation weather radar data of Zhengzhou from May to September during2009-2016, this paper checked the storm tracking information products of 5615 cell echoes according to different forecast time, different life cycles and different types of severe convection, processes, and also systematically analyzed the distance errors and direction errors of storm tracking information products of cell echoes and discussed the direction deviation between real condition and forecast products. The results show that the distance errors of 30 and 60 min products are 9. 3 and 16. 9 km respectively, and the direction error is not so big. The error of distance increases with the extension of lead time of forecasts; The average distance error of the 30 min forecast products of cell echoes with life cycles less than 1 h, 1-2 h and more than 2 h are 9. 8, 8. 8 and 7. 7 km, and the azimuth errors are 25. 5°, 25. 7° and 22. 8° respectively. The average distance error of the 60 min forecast products with life cycles of 1-2 h and more than 2 h are 17. 3 and15. 9 km, and the direction errors are 24. 4° and 22. 7°, respectively. With the extension of life cycles of the cells, the errors of direction and distance are decreasing, the errors of northwest airflow type and the southwest airflow type are entirety the same, and the direction and distance errors of the two types of echoes are almost the same in 30 and 60 min. The errors of the southwest airflow type are slightly smaller than those of the northwest airflow type. The number of different types and forecast efficiency are that the observation data biased to the right of the forecast path are more than that of the left side, and the proportion of 30 and 60 min biased to right are 57. 5 % and 55. 6 %, respectively.
引文
陈明轩,俞小鼎,谭晓光,等,2004.对流天气临近预报技术的发展与研究进展[J].应用气象学报,15(6):754-766.
方翀,郑媛媛,2007.新一代天气雷达中气旋产品特征值统计和个例分析[J].气象,33(11):16-20.
冯晋勤,汤达章,俞小鼎,等,2010.新一代天气雷达中气旋识别产品的统计分析[J].气象,36(8):47-52.
韩雷,王洪庆,谭晓光,等,2007.基于雷达数据的风暴体识别、追踪及预警的研究进展[J].气象,33(1):3-10.
胡胜,罗兵,黄晓梅,等,2010.临近预报系统(SWIFT)中风暴产品的设计及应用[J].气象,36(1):54-58.
吕晓娜,牛淑贞,袁春风,等,2013. SWAN中定量降水估测和预报产品的检验与误差分析[J].暴雨灾害,32(2):142-150.
牛淑贞,鲍向东,乔春贵,等,2008.强对流风暴新一代雷达产品特征分析[J].气象,34(7):92-100.
牛淑贞,张一平,席世平,等,2012.基于加密探测资料解析2009年6月3日商丘强飑线形成机制[J].暴雨灾害,31(3):255-263.
王福侠,俞小鼎,闫雪瑾,2014.一次超级单体分裂过程的雷达回波特征分析[J].气象学报,72(1):152-167.
吴芳芳,俞小鼎,张志刚,等,2012.对流风暴内中气旋特征与强烈天气[J].气象,38(11):1330-1338.
杨引明,1999. WSR-88D多普勒天气雷达冰雹探测算法及评价[J].气象,25(5):39-43.
俞小鼎,姚秀萍,熊廷南,等,2006a.多普勒天气雷达原理与业务应用[M].北京:气象出版社.
俞小鼎,郑媛媛,张爱民,等,2006c.安徽一次强烈龙卷的多普勒天气雷达分析[J].高原气象,25(5):914-924.
俞小鼎,郑媛媛,廖玉芳,等,2008.一次伴随强烈龙卷的强降水超级单体风暴研究[J].大气科学,32(3):508-522.
俞小鼎,张爱民,郑媛媛,等,2006b.一次系列下击暴流事件的多普勒天气雷达分析[J].应用气象学报,17(4):385-393.
俞小鼎,周小刚,王秀明,2012.雷暴与强对流临近天气预报技术进展[J].气象学报,70(3):311-337.
张一平,俞小鼎,孙景兰,等,2014.一次槽后型大暴雨伴冰雹的形成机制和雷达观测分析[J].高原气象,33(4):1093-1104.
张一平,俞小鼎,吴蓁,等,2012.区域暴雨过程中两次龙卷风事件分析[J].气象学报,70(5):961-973.
郑媛媛,俞小鼎,方翀,等,2004.2003年7月8日安徽系列龙卷的新一代天气雷达分析[J].气象,30(1):38-40,45.
郑媛媛,张备,王啸华,等,2015.台风龙卷的环境背景和雷达回波结构分析[J].气象,41(8):942-952.
钟敏,吴翠红,王珊珊,等,2012. CINRAD/SA雷达两种识别跟踪产品的评估分析[J].气象,38(6):722-727.
朱君鉴,刁秀广,黄秀韶,2004.一次冰雹风暴的CINRAD/SA产品分析[J].应用气象学报,15(5):579-589.
Johnson J T,MacKeen P L,Witt A,et al, 1998. The storm cell identification and tracking algorithm:an enhanced WSR-88D algorithm[J]. Wea Forecasting, 13(2):263-276.
方翀,郑媛媛,2007.新一代天气雷达中气旋产品特征值统计和个例分析[J].气象,33(11):16-20.
冯晋勤,汤达章,俞小鼎,等,2010.新一代天气雷达中气旋识别产品的统计分析[J].气象,36(8):47-52.
韩雷,王洪庆,谭晓光,等,2007.基于雷达数据的风暴体识别、追踪及预警的研究进展[J].气象,33(1):3-10.
胡胜,罗兵,黄晓梅,等,2010.临近预报系统(SWIFT)中风暴产品的设计及应用[J].气象,36(1):54-58.
吕晓娜,牛淑贞,袁春风,等,2013. SWAN中定量降水估测和预报产品的检验与误差分析[J].暴雨灾害,32(2):142-150.
牛淑贞,鲍向东,乔春贵,等,2008.强对流风暴新一代雷达产品特征分析[J].气象,34(7):92-100.
牛淑贞,张一平,席世平,等,2012.基于加密探测资料解析2009年6月3日商丘强飑线形成机制[J].暴雨灾害,31(3):255-263.
王福侠,俞小鼎,闫雪瑾,2014.一次超级单体分裂过程的雷达回波特征分析[J].气象学报,72(1):152-167.
吴芳芳,俞小鼎,张志刚,等,2012.对流风暴内中气旋特征与强烈天气[J].气象,38(11):1330-1338.
杨引明,1999. WSR-88D多普勒天气雷达冰雹探测算法及评价[J].气象,25(5):39-43.
俞小鼎,姚秀萍,熊廷南,等,2006a.多普勒天气雷达原理与业务应用[M].北京:气象出版社.
俞小鼎,郑媛媛,张爱民,等,2006c.安徽一次强烈龙卷的多普勒天气雷达分析[J].高原气象,25(5):914-924.
俞小鼎,郑媛媛,廖玉芳,等,2008.一次伴随强烈龙卷的强降水超级单体风暴研究[J].大气科学,32(3):508-522.
俞小鼎,张爱民,郑媛媛,等,2006b.一次系列下击暴流事件的多普勒天气雷达分析[J].应用气象学报,17(4):385-393.
俞小鼎,周小刚,王秀明,2012.雷暴与强对流临近天气预报技术进展[J].气象学报,70(3):311-337.
张一平,俞小鼎,孙景兰,等,2014.一次槽后型大暴雨伴冰雹的形成机制和雷达观测分析[J].高原气象,33(4):1093-1104.
张一平,俞小鼎,吴蓁,等,2012.区域暴雨过程中两次龙卷风事件分析[J].气象学报,70(5):961-973.
郑媛媛,俞小鼎,方翀,等,2004.2003年7月8日安徽系列龙卷的新一代天气雷达分析[J].气象,30(1):38-40,45.
郑媛媛,张备,王啸华,等,2015.台风龙卷的环境背景和雷达回波结构分析[J].气象,41(8):942-952.
钟敏,吴翠红,王珊珊,等,2012. CINRAD/SA雷达两种识别跟踪产品的评估分析[J].气象,38(6):722-727.
朱君鉴,刁秀广,黄秀韶,2004.一次冰雹风暴的CINRAD/SA产品分析[J].应用气象学报,15(5):579-589.
Johnson J T,MacKeen P L,Witt A,et al, 1998. The storm cell identification and tracking algorithm:an enhanced WSR-88D algorithm[J]. Wea Forecasting, 13(2):263-276.