###
气象:2021,47(8):901-918
←前一篇   |   后一篇→
本文二维码信息
码上扫一扫!
强对流天气资料同化和临近预报技术研究
崔春光,杜牧云,肖艳姣,赖安伟,李红莉,王志斌,王珏,孙玉婷,王俊超,冷亮,王斌,张文,彭菊香,马鹤翟,康兆萍
(中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室,武汉 430205)
Study on the Technique of Data Assimilation and Nowcasting of Severe Convective Weather
CUI Chunguang,DU Muyun,XIAO Yanjiao,LAI Anwei,LI Hongli,WANG Zhibin,WANG Jue,SUN Yuting,WANG Junchao,LENG Liang,WANG Bin,ZHANG Wen,PENG Juxiang,MA Hedi,KANG Zhaoping
(Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205)
摘要
图/表
参考文献
相似文献
本文已被:浏览 171次   下载 934
投稿时间:2020-08-03    修订日期:2021-03-02
中文摘要: 强对流天气的精准预报依然具有极大难度和挑战性。为了提高强天气监测预报服务能力,“灾害性天气资料同化与临近预报系统开发”研究共开展了以下工作:研发了新的中气旋和龙卷涡旋特征识别算法,并在十几个龙卷风实例中成功地识别出龙卷涡旋特征;从多普勒天气雷达体扫数据中提取了诸多参数(超过20个),开展分类强对流天气(下击暴流、龙卷、冰雹和短时强降水)自动识别预警技术研究。快速更新循环预报系统可以有效地提高模式初值的质量,非常适合于短时天气预报应用。为进一步提高强雷暴预报的精度,提出了一种新的基于雷达反演水汽的“伪水汽”同化方法,以更好地初始化对流尺度的数值天气模式。旨在克服目前中尺度数值模式在对流尺度定量降水短时预报方面的不足,弥补基于“外推”的临近预报技术在2 h以上定量降水预报能力快速下降的缺陷而研发的融合技术具有提高短时临近降水预报能力的潜力。
Abstract:It is still extremely difficult and challenging for accurate prediction of convective weather systems. In order to improve the service ability in severe weather monitoring and prediction, the following studies have been carried out recently. The feature recognition algorithms for new mesocyclone and tornado vortex are developed and proved to be successful in identifying tornado vortex characteristics in more than a dozen tornado cases. Extracted from Doppler radar volume scan data, more than twenty parameters are used in the study on the automatic recognition and warning technology of classified severe convective weather (downburst, tornado, hail and short-time intense precipitation). Rapidly updating cycle forecast system can effectively improve the quality of model initial values, which is very suitable for short-time forecast application. For the sake of improving severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize numerical weather prediction (NWP) at convection-resolving scales. The blending technology, which is expected to overcome the deficiency of the short-time quantitative precipitation forecast (QPF) by a mesoscale NWP model at convective scales and the rapidly descending skill of rainfall forecast based on radar extrapolation method beyond the first few hours, is under development, and it would have potential in enhancing the ability of rainfall forecast within the nowcasting period.
文章编号:     中图分类号:    文献标志码:
基金项目:国家重点研发计划(2016YFE0109400)和国家自然科学基金项目(41620104009)共同资助
作者单位
崔春光 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
杜牧云 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
肖艳姣 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
赖安伟 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
李红莉 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
王志斌 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
王珏 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
孙玉婷 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
王俊超 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
冷亮 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
王斌 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
张文 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
彭菊香 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
马鹤翟 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
康兆萍 中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室武汉 430205 
Author NameAffiliation
CUI Chunguang Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
DU Muyun Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
XIAO Yanjiao Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
LAI Anwei Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
LI Hongli Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
WANG Zhibin Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
WANG Jue Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
SUN Yuting Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
WANG Junchao Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
LENG Liang Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
WANG Bin Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
ZHANG Wen Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
PENG Juxiang Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
MA Hedi Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
KANG Zhaoping Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205 
引用文本:
崔春光,杜牧云,肖艳姣,赖安伟,李红莉,王志斌,王珏,孙玉婷,王俊超,冷亮,王斌,张文,彭菊香,马鹤翟,康兆萍,2021.强对流天气资料同化和临近预报技术研究[J].气象,47(8):901-918.
CUI Chunguang,DU Muyun,XIAO Yanjiao,LAI Anwei,LI Hongli,WANG Zhibin,WANG Jue,SUN Yuting,WANG Junchao,LENG Liang,WANG Bin,ZHANG Wen,PENG Juxiang,MA Hedi,KANG Zhaoping,2021.Study on the Technique of Data Assimilation and Nowcasting of Severe Convective Weather[J].Meteor Mon,47(8):901-918.