段旭,段玮,邢冬. 2017. 昆明准静止锋客观判识方法研究[J]. 气象学报, 75(5):811-822, doi:10.11676/qxxb2017.055
昆明准静止锋客观判识方法研究
A study of objective determination method for the Kunming quasi-stationary frontline
投稿时间:2016-11-08  修订日期:2017-05-16
DOI:10.11676/qxxb2017.055
中文关键词:  昆明准静止锋(KQSF)  客观判识  锋面位置  云贵高原
英文关键词:Kunming Quasi-Stationary Front (KQSF)  Objective judgment method  Frontline  Yunnan-Guizhou Plateau
基金项目:国家自然科学基金项目(41365006、41665004、41205067)。
作者单位E-mail
段旭 云南省气象科学研究所, 昆明, 650034  
段玮 云南省气象科学研究所, 昆明, 650034 duanwain@hotmail.com 
邢冬 云南省气象科学研究所, 昆明, 650034  
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中文摘要:
      利用云贵高原1961-2010年逐日地面观测资料和ERA-Interim 0.125°×0.125°再分析资料,根据昆明准静止锋(KQSF)的天气学特性,综合考虑热力场、动力场和锋面系统空间尺度等要素,开展昆明准静止锋锋线的客观判识研究。经过典型天气过程分析和气候资料统计的验证,客观判识方法是合理的。在用客观判识方法计算50年全部昆明准静止锋样本的基础上,统计分析了其时、空分布特征。这一客观判识方法为统一、客观、定量地描述昆明准静止锋的特征及其在气象业务中精细化和数值化的应用提供了途径。
英文摘要:
      According to synoptic characteristics of Kunming Quasi-Stationary Front (KQSF), an objective method to determine its frontal position is proposed, implemented and verified based on daily ground observations over the Yunnan-Guizhou Plateau and the surrounding area for the period of 1961-2010 and ERA-Interim reanalysis data (0.125°×0.125°).The objective determination method incorporates multiple frontal synoptic characteristics, including vertical and horizontal distributions of thermal fields, wind shears in the direction of latitude and the scale of KQSF over the Yunnan-Guizhou Plateau. Verification of typical weather processes and statistical analysis of relevant climate data indicate that the objective method proposed in the present study is reasonable. Identification of the frontline and its position is basically consistent with subjective results of experienced forecasters. Based on the method, all cases of KQSF are identified for the period of 1961-2010.Some climatic characteristics of KQSF such as its average direction and position, its long-term and annual variations and others spatiotemporal variability characteristics are updated or first revealed by the new statistics work. The objective judgment method can avoid inconsistency in KQSF analyses from different forecasters, and ensure the consistency of long sequence sample identification. This method provides the possibility for quantitative study and numerical prediction of KQSF.
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