This study presents two methods for evaluating the reflectivity calibration of W-band cloud radars. Both methods use natural rain as a reference target. The first approach is based on a self-consistency method of polarimetric radar variables, which is widely used in the precipitation radar community. As previous studies pointed out, the method cannot be directly applied to higher frequencies where non-Rayleigh scattering effects and attenuation have a nonnegligible influence on radar variables. The method presented here solves this problem by using polarimetric Doppler spectra to separate backscattering and propagational effects. New fits between the separated radar variables allow one to estimate the absolute radar calibration using a minimization technique. The main advantage of the self-consistency method is its lower dependence on the spatial variability in radar drop size distribution (DSD). The estimated uncertainty of the method is ±0.7 dB. The method was applied to three intense precipitation events, and the retrieved reflectivity offsets were within the estimated uncertainty range. The second method is an improvement on the conventional disdrometer-based approach, where reflectivity from the lowest range gate is compared to simulated reflectivity using surface disdrometer observations. The improved method corrects, first, for the time lag between surface DSD observations and the radar measurements at a certain range. In addition, the effect of evaporation of raindrops on their way towards the surface is mitigated. The disdrometer-based method was applied to 12 rain events observed by vertically pointed W-band radar and showed repeatable estimates of the reflectivity offsets at rain rates below 4 mm h⁻¹ within ±0.9 dB. The proposed approaches can analogously be extended to Ka-band radars. Although very different in terms of complexity, both methods extend existing radar calibration evaluation approaches, which are inevitably needed for the growing cloud radar networks in order to provide high-quality radar observation to the atmospheric community.

中文翻译：

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基于雨天观测的W波段雷达反射率校准评估

本研究提出了两种评估W波段云雷达反射率校准的方法。两种方法都以自然降雨为参考目标。第一种方法基于极化雷达变量的自洽方法，该方法在降水雷达社区中被广泛使用。如先前的研究指出，该方法不能直接应用于较高的频率，在这些频率中非瑞利散射效应和衰减对雷达变量的影响不可忽略。此处介绍的方法通过使用偏振多普勒光谱分离反向散射和传播效应来解决此问题。分离的雷达变量之间的新拟合允许使用最小化技术估算绝对雷达校准。自洽方法的主要优点是它对雷达液滴尺寸分布（DSD）的空间变异性的依赖性较低。该方法的估计不确定度为±0.7 分贝 该方法被应用于三个强降水事件，并且所获得的反射率偏移在估计的不确定性范围内。第二种方法是对传统的基于测速仪的方法的改进，在传统的基于测速仪的方法中，使用表面测速仪的观测结果将最低量程门的反射率与模拟反射率进行了比较。改进的方法首先校正在特定范围内的表面DSD观测与雷达测量之间的时间差。另外，减轻了雨滴在其朝向地面的途中蒸发的影响。基于disdrometer-方法在降雨率应用于由垂直尖W波段雷达观察到12个降雨事件并显示出的反射率偏移的可重复的估计低于4毫米高^-1内±0.9 分贝 所提出的方法可以类似地扩展到Ka波段雷达。尽管在复杂性方面有很大不同，但是这两种方法都扩展了现有的雷达校准评估方法，而不断增长的云雷达网络不可避免地需要这种方法，以便向大气界提供高质量的雷达观测。