Abstract. This study presents a method to identify and distinguish insects, clouds, and precipitation in 35 GHz (Ka-band) vertically pointing polarimetric radar Doppler velocity power spectra and then produce masks indicating the occurrence of hydrometeors (i.e., clouds or precipitation) and insects at each range gate. The polarimetric radar used in this study transmits a linear polarized wave and receives signals in collinear (CoPol) and cross-linear (XPol) polarized channels. The insect-hydrometeor discrimination method uses CoPol and XPol spectral information in two separate algorithms with their spectral results merged and then filtered into single value products at each range gate. The first algorithm discriminates between insects and clouds in the CoPol Doppler velocity power spectra based on the spectra texture, or spectra roughness, which varies due to the scattering characteristics of insects versus cloud particles. The second algorithm distinguishes insects from raindrops and ice particles by exploiting the larger Doppler velocity spectra linear depolarization ratio (LDR) produced by asymmetric insects. Since XPol power return is always less than CoPol power return for the same target (i.e., insect or hydrometeor), fewer insects and hydrometeors are detected in the LDR algorithm than the CoPol algorithm, which drives this need for a CoPol based algorithm. After performing both CoPol and LDR detection algorithms, regions of insect and hydrometeor scattering from both algorithms are combined in the Doppler velocity spectra domain and then filtered to produce a binary hydrometeor mask indicating the occurrence of cloud, raindrops, or ice particles at each range gate. Comparison with a collocated ceilometer indicates that hydrometeor mask column bottoms are within +/-100 meters of simultaneous ceilometer cloud base heights. Forty-seven (47) summer-time days were processed with the insect-hydrometeor discrimination method using U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Ka-band zenith pointing radar observations in northern Oklahoma (USA). All datasets and images are available on public repositories.

中文翻译：

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使用垂直指向的极化雷达多普勒速度谱识别昆虫，云和降水

摘要。这项研究提出了一种方法来识别和区分35 GHz（Ka波段）垂直指向的极化雷达多普勒速度功率谱中的昆虫，云和降水，然后产生指示水汽（即云或降水）和昆虫发生的掩模。每个范围门。本研究中使用的极化雷达发射线性极化波，并在共线（CoPol）和交叉线（XPol）极化通道中接收信号。昆虫水气鉴别方法在两个单独的算法中使用CoPol和XPol光谱信息，将其光谱结果合并，然后在每个距离门处过滤为单值乘积。第一种算法根据光谱纹理或光谱粗糙度来区分CoPol多普勒速度功率谱中的昆虫和云，由于昆虫对云颗粒的散射特性而有所不同。第二种算法通过利用由不对称昆虫产生的更大的多普勒速度谱线性去极化比（LDR）来将昆虫与雨滴和冰粒区分开。由于对于相同的目标（即昆虫或水流星），XPol功率返回总是小于CoPol功率返回，因此在LDR算法中检测到的昆虫和水流星少于CoPol算法，这推动了对基于CoPol的算法的需求。在执行CoPol和LDR检测算法之后，两种算法中的昆虫和水凝物散射区域在多普勒速度谱域中合并，然后进行过滤以生成二元水凝物掩模，指示每个测距闸处出现云，雨滴或冰粒。与并置的云高仪进行的比较表明，水流计面罩柱底距云高仪同时的云底高度在+/- 100米以内。在美国俄克拉荷马州北部，使用美国能源部（DOE）大气辐射测量（ARM）程序Ka波段天顶指向雷达观测资料，通过昆虫-水星鉴别方法处理了四十七（47）个夏季日。所有数据集和图像都可以在公共存储库中获得。能源部（DOE）大气辐射测量（ARM）计划在美国俄克拉荷马州北部的Ka波段天顶指向雷达观测。所有数据集和图像都可以在公共存储库中获得。能源部（DOE）大气辐射测量（ARM）计划在美国俄克拉荷马州北部的Ka波段天顶指向雷达观测。所有数据集和图像都可以在公共存储库中获得。