一种改进的全自动多普勒天气雷达速度退模糊算法

An improved automated velocity dealiasing algorithm for Doppler weather radar

  • 摘要: 为了改善强风切变区、远距离孤立回波区的速度退模糊效果和减轻速度噪点影响,在肖艳姣等(2012)的速度退模糊算法基础上提出一种改进算法,主要包括:(1)为了优化强风切变区的速度退模糊效果,在原算法第一、二步骤之间增加了水平邻域法退模糊,并采用先径向后切向方式进行逐根径向退模糊处理,(2)针对远距离孤立回波区,在水平邻域法退模糊之后增加了垂直邻域法退模糊,(3)在退模糊过程的最后增加了噪点滤波处理,以减轻噪点对小尺度辐合/辐散和涡旋特征识别的影响。使用4个强天气个例对比分析了原算法和改进算法的速度退模糊效果,使用S波段多普勒天气雷达观测的龙卷、飑线、孤立强风暴、台风等事件的3519个体扫数据对改进算法进行了测试和评估,结果发现,改进算法能正确处理原算法难以处理的强风切变区、远距离孤立回波区的速度模糊问题,显著减少了速度噪点。速度退模糊正确率近似100%,不恰当的速度退模糊或模糊速度残留现象偶尔出现在有杂波干扰或远距离小块孤立回波的情况下。由此可见改进的速度退模糊算法显著优于原算法,能极好地处理各种速度模糊和噪点问题。

     

    Abstract: To improve velocity dealiasing performance in strong wind shear zones and distant isolated echo regions as well as to mitigate the impact of velocity noise, an improved velocity dealiasing algorithm has been developed based on the method proposed by Xiao et al. (2012).The key enhancements are as follows. (1) To improve the performance under intense horizontal wind shear, steps utilizing horizontal neighborhood dealiasing and a radial-then-tangential sequence are inserted between the first and second rounds of full-azimuth radial dealiasing (which employs a tangential-then-radial sequence in the original algorithm). (2) For distant isolated echo regions, vertical neighborhood dealiasing is added following horizontal neighborhood dealiasing. (3) A noise filtering process is implemented after all dealiasing steps to mitigate the impact of noise on the identification of small-scale convergence, divergence, and vortex features. The velocity dealiasing performances of both the original and the improved algorithms are analyzed based on four severe weather cases. The improved algorithm is further tested and evaluated using 3519 volume scans of tornadoes, squall lines, isolated severe storms, and typhoons observed by S-band Doppler weather radars. The results indicate that the improved algorithm effectively resolves velocity aliasing in strong wind shear zones and distant isolated echo regions — both challenging scenarios for the original algorithm — and significantly mitigates velocity noise artifacts. The dealiasing accuracy nearly approaches 100%, with residual aliasing or improper corrections occurring only under clutter-contaminated conditions or in small, isolated echoes at long ranges. The enhanced algorithm demonstrates superior performance compared to the original method, robustly handling various velocity aliasing and noise interference.

     

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