雷达强度数据中的阵风锋特征统计和自动识别

Statistical Characteristics and Automatic Detection of the Gust Front in Radar Reflectivity Data

利用江苏南京2009-2012年天气雷达数据结合地面自动站风场资料分析江苏沿江地区阵风锋变化特征、阵风锋弧长与移速关系,及其在雷达反射率因子图像中呈现的总体、局部特征,详细分析了三种窄带回波回波带反射率因子分布特征。通过设计反映回波带平坦性的计算方法实现定量分析窄带回波分布异同功能。根据回波带径向波形特征判断径向波形的波宽、波峰个数、波峰阈值和波形双边梯度等特性,实现阵风锋径向波段识别。在对反射率因子图像预处理基础上,结合回波平坦性测试方法和阵风锋径向波形识别算法达到自动识别阵风锋回波的目的。识别效果表明:回波带平均值>5 d BZ的独立阵风锋回波识别准确率达87%以上,回波带平均值>10 d BZ的混合型阵风锋回波识别准确率达89%以上。对弱阵风锋识别成功率仍较低。

The variation features,the relationship betw een the moving speed and the arc length,and the overall and partial characteristics in the radar reflectivity data images of the gust front along the Yangtze River in Jiangsu are analyzed using Nanjing CINRAD data combined with the AWS wind data from 2009 to 2012. The radar reflectivity data distribution features of the three kinds of NBE are also analyzed in detail. An echo flatness calculation method is designed to analyze the similarities of the three NBEs in quantity. It is also found that the radial bands belong to the gust front echo can be identified by the wave width,the number of the wave peak,the peak threshold,and the bilateral gradient waveform from the radial wave characteristics in this article. The automatic detection of the gust front echo is achieved by combining the echo flatness testing method with the gust front radial waveform detection algorithm based on the radar reflectivity data preprocessing method. Finally,the effect results of the detection algorithm show that the detection accuracy rate is about 87% or more in the independent gust front whose average reflectivity data > 5 d BZ,and the detection accuracy rate is about 89% or more in the blended gust front whose average reflectivity data > 10 d BZ. The identification rate of the weak gust front is still low.