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An advanced algorithm for recognizing wind shear using airborne Doppler weather radar

机译:机载多普勒天气雷达识别风切变的高级算法

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Wind shear must be rapidly recognized during flight because it represents a safety threat. The conventional recognition algorithms rely mainly on the processing of radar signals. Consequently, their results are not two-dimensional and are difficult to visualize, preventing pilots from rapidly and accurately recognizing and predicting the position of shear lines. This study proposes an algorithm for recognizing regions of horizontal wind shear at different altitudes from airborne weather radar. A simulation analysis is used to analyze the radial velocity and spectral width data gathered by ground-based Doppler radar. The algorithm employs the Perona-Malik partial differential equation model to pre-treat Doppler radar base data to reduce noise, maintain fidelity, and remove isolated points. A region-growing algorithm, using radar spectral width and average radial velocity data, is used to rapidly identify regions of horizontal wind shear at different altitudes. By analyzing precipitation events in Fuyang, Chengdu, and Nanjing in China, this study verifies the feasibility of the proposed algorithm. As the simulation results show, the proposed algorithm has better accuracy, better speed, and better shear line continuity than conventional recognition approaches. The improved recognition speed could help pilots recognize and predict wind shear to guarantee flight safety.
机译:飞行过程中必须迅速识别风切变,因为它对安全构成威胁。常规的识别算法主要依靠雷达信号的处理。因此,它们的结果不是二维的并且难以可视化,从而阻止飞行员快速,准确地识别和预测剪切线的位置。这项研究提出了一种从机载气象雷达识别不同高度水平风切变区域的算法。仿真分析用于分析地面多普勒雷达采集的径向速度和谱宽数据。该算法采用Perona-Malik偏微分方程模型对多普勒雷达基础数据进行预处理,以降低噪声,保持保真度并消除孤立点。使用雷达频谱宽度和平均径向速度数据的区域增长算法可快速识别不同高度的水平风切变区域。通过分析中国富阳,成都和南京的降水事件,本研究验证了该算法的可行性。仿真结果表明,与常规识别方法相比,该算法具有更高的精度,更好的速度和更好的剪切线连续性。提高的识别速度可以帮助飞行员识别和预测风切变,以确保飞行安全。

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