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对流涡度矢量和湿涡度矢量在暴雨诊断分析中的应用研究
引用本文:赵宇,崔晓鹏.对流涡度矢量和湿涡度矢量在暴雨诊断分析中的应用研究[J].气象学报,2009,67(4):540-548.
作者姓名:赵宇  崔晓鹏
作者单位:赵宇(南京信息工程大学,南京,210044中国气象局武汉暴雨研究所,武汉,430074);崔晓鹏(中国科学院大气物理研究所云降水物理与强风暴实验室,北京,100029) 
基金项目:中国气象局武汉暴雨研究所暴雨研究开放基金项目,国家自然科学基金项目,山东省气象局项目 
摘    要:利用对流涡度矢量(CVV, (ζa×θe)/ρ)和湿涡度矢量(MVV, (ζa×θv)/ρ)对华北地区一次大范围的大到暴雨天气过程进行了诊断研究.结果表明,CVV和MVV垂直分量与云和降水密切相关,暴雨区区域平均和垂直积分的CVV和MVV垂直分量与云中水凝物混合比的相关系数分别为0.92和0.95,与降水率的相关系数分别为0.71和0.47.云中水凝物分为液态水凝物和固态水凝物,虽然同态水凝物的含量相对较少,但它在云的变化中起着更重要的作用.滞后相关分析表明,降水率的峰值到来之前4-5 h云中水凝物含晕最少,降水率的峰值到来之后1-2 h,云中水凝物含量最多.云中水凝物与降水率的滞后相关系数主要是由液态水凝物与降水率的滞后相关系数决定的,只有约1/4的滞后相关系数是由固态水凝物与降水率的滞后相关系数贡献的.CVV和MVV的垂直分量与云中水凝物具有非常好的相关性(同时及滞后),与降水率的相关也较好,可以代表云和对流系统的发展.CVV和MVV垂直分量的局地变化超前降水率3 h左右,可以作为降水发生的先兆,对降水的预报有潜在的意义.CVV垂直分量的局地变化与降水率的相关系数大于MVV垂直分量的局地变化与降水率的相关系数,因此在实际预报中可以利用CVV垂直分量的局地变化来估计未来降水的变化.

关 键 词:对流涡度矢量  湿涡度矢量  暴雨  滞后相关
收稿时间:2007/11/11 0:00:00
修稿时间:2008/5/23 0:00:00

Application of convective and moist vorticity vectors in the analysis of a heavy rainfall event
ZHAO Yu and CUI Xiaopeng.Application of convective and moist vorticity vectors in the analysis of a heavy rainfall event[J].Acta Meteorologica Sinica,2009,67(4):540-548.
Authors:ZHAO Yu and CUI Xiaopeng
Institution:Nanjing University of Information Science & Technology, Nanjing 210044, China; Institute of Heavy Rain, CMA Wuhan, Wuhan 430074, China and Laboratory of Cloud Precipitation Physics and Severe Storms (LACS), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Abstract:Two new vorticity vectors (convective vorticity vector (CVV), defined as ) and moist vorticity vector (MVV), defined as )) introduced by Gao et al. (2004, 2005, 2007) are used to the study of a heavy rainfall event in North China. The result shows that vertical components of the CVV and the MVV are closely associated with clouds and precipitation. Analysis of domain-averaged and mass-integrated quantities shows that the linear correlation coefficients between the vertical component of the CVV (MVV) and the sum of mixing ratio of cloud hydrometeors is 0.92 (0.95), and that between the vertical component of the CVV (MVV) and precipitation rate is 0.71 (0.47). Cloud hydrometeors can be further broken into ice hydrometeors and water hydrometeors. Although ice hydrometeors are little, they contribute more to the evolution of clouds. Analysis of lag correlation suggests that minimum cloud hydrometeors leads the maximum precipitation rate by 4-5 hour, and maximum cloud hydrometeors lags maximum precipitation rate by 1-2 hour. The lag correlation coefficient between cloud hydrometeors and precipitation rate is mainly determined by the weighted lag correlation coefficient between water hydrometeors and surface rain rate, only one fourth of the lag correlation coefficient is contributed to the weighted lag correlation coefficients between ice hydrometeors and surface rain rate. Vertical components of CVV and MVV are well correlated with cloud hydrometeors (in phase or lag correlation) and precipitation rate, and they may represent the development of clouds and convective system. The local change of Cz and Mz lead surface rain rate by 3 hours, which may be a precursor for rainfall and has potential significance for rainfall prediction. The linear correlation coefficient between the local change of Cz and surface rain rate is larger than that between the local change of Mz and surface rain rate. Therefore, the local change of Cz can be used to estimate the change of surface rain rate in operational forecast.
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