计算机应用 ›› 2018, Vol. 38 ›› Issue (3): 655-660.DOI: 10.11772/j.issn.1001-9081.2017081942

• 人工智能 • 上一篇    下一篇

基于模糊C均值的低空风切变预警算法

熊兴隆1, 杨立香1, 马愈昭1, 庄子波2   

  1. 1. 中国民航大学 天津市智能信号与图像处理重点实验室, 天津 300300;
    2. 中国民航大学 民航气象研究所, 天津 300300
  • 收稿日期:2017-08-09 修回日期:2017-10-20 出版日期:2018-03-10 发布日期:2018-03-07
  • 通讯作者: 熊兴隆
  • 作者简介:熊兴隆(1962-),男,陕西西安人,教授,硕士,主要研究方向:信号与信息处理、激光雷达气象探测;杨立香(1990-),女,山东聊城人,硕士研究生,主要研究方向:天气雷达气象探测、图像处理;马愈昭(1978-),女,吉林吉林人,副教授,博士,主要研究方向:光纤光学、航空气象探测、电磁计算;庄子波(1980-),男,山东潍坊人,讲师,硕士,主要研究方向:航空气象探测、数据处理。
  • 基金资助:
    国家自然科学基金资助项目(U1533113,U1433202);中央高校基本科研业务费资助项目(3122016B001)。

Alerting algorithm of low-level wind shear based on fuzzy C-means

XIONG Xinglong1, YANG Lixiang1, MA Yuzhao1, ZHUANG Zibo2   

  1. 1. Tianjin Key Laboratory for Intelligent Signal and Image Processing, Civil Aviation University of China, Tianjin 300300, China;
    2. Civil Aviation Meteorological Institute, Civil Aviation University of China, Tianjin 300300, China
  • Received:2017-08-09 Revised:2017-10-20 Online:2018-03-10 Published:2018-03-07
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (U1533113, U1433202), the Fundamental Research Funds for the Central Universities (3122016B001).

摘要: 针对新一代多普勒天气雷达CINRAD在径向或切向检测风切变时容易丢失小切变的问题,提出了一种基于模糊C均值(FCM)的低空风切变预警算法用于阵风锋和龙卷风引起的风切变识别中。该算法的核心思想是运用8邻域系统,根据风速梯度识别不同程度切变,从而实现高切变及低切变预警。首先,采用全变分(TV)模型对雷达速度基数据进行去噪,同时保持速度基数据的细节特征;其次,采用每个速度基数据及其8邻域系统分别对应的速度值依次与4个方向模板卷积,获取4个方位速度梯度值;然后,采用FCM算法将梯度值分为高低两类,实现不同强度的风切变预警。采用武汉暴雨研究所提供的实测基数据进行测试和验证,能较为准确地识别出小切变。实验结果表明,该算法检测出来的风切变在定位精度和边缘识别两个方面均优于基于径向或切向的风切变识别算法,这对判断风切变的位置和强度以及分析不同天气引起的风切变具有重要指导意义。

关键词: CINRAD, 低空风切变, 模糊C均值, 全变分模型, 小切变

Abstract: To solve the problem that the China new-generation Doppler weather radar named CINRAD is easy to lose small shear in radial or tangential direction, a new alerting algorithm of low-level wind shear based on Fuzzy C-Means (FCM) was proposed for wind shear identification of front and tornado. In order to achieve high shear and low shear warning, the core idea of this algorithm was to use 8-neighborhood system, according to the wind speed divergence characteristics to identify varying degrees of shear. Firstly, the Total Variation (TV) model was used in radar velocity base data denoising while maintaining the details of the data. Secondly, the 8-neighborhood system was convoluted in turn with 4-direction template to obtain the omni directional velocity gradient. Then, in order to achieve different intensity of wind shear altering, the FCM algorithm was used to classify the gradient values into two categories. Using the measured data provided with the Wuhan Rainstorm Research Institute to test and verify, the small shear was identified. The results show that the algorithm to detect wind shear is superior to the wind shear recognition algorithm based on radial or tangential direction in terms of both position accuracy and edge recognition, which has important guiding significance to judgment of position and intensity and analysis of wind shear caused by different weather.

Key words: CINRAD, low-level wind shear, Fuzzy C-Means (FCM), Total Variation (TV) model, small shear

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