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。
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.
出处
《计算机应用》
CSCD
北大核心
2018年第3期655-660,共6页
journal of Computer Applications
基金
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)~~
