## 2 Materials and Methods
### Data Processing of Cloud Radar Doppler Spectra  
The TIPEX-III cloud radar produces vertically resolved measurements of Doppler spectra, which consist of 256 spectral bins and represent the distribution of returned radar power as a function of hydrometer radial velocity in the sampling volume. Cloud radar Doppler spectra have been commonly used for cloud property retrieval and microphysical studies because of their wealth of information on drop size distribution, turbulence, vertical air motion, and so on. However, Doppler spectra simultaneously contain radar noise and meteorological signals, and they suffer from velocity aliasing under special conditions. To minimize these effects before further application, data processing procedures were performed, as presented here. First, a 3-point boxcar averaging window was used to smooth all Doppler spectra to improve spectral clarity and decrease the fluctuation of radar noise. Noise level calculation was a crucial step to accurately separate the radar noise and cloud signal. An objective method presented by [PERSON] and [PERSON] has been commonly used for millimeter-wave cloud radar studies [18; 26; 30; 31]. However, a recent study argues that this approach can overestimate the radar noise power and so is not appropriate for solid-state cloud radars. In contrast, a segmental approach reported by [PERSON] et al. can achieve better accuracy and stability [29; 32]. So, in this study, a simple 8-segment technology  
Figure 1: Statistical probability distribution of radar-measured reflectivity at different heights, collected over the Tibetan Plateau (TP) from June to August 2014 and July to September 2015. The spectrogram illustrates the radar’s actual detection capability for weak and strong meteorological targets, and the marked curves represent the theoretical sensitivities of three operational modes. In the figure, CI, BL, and PR represent cirrus mode, boundary mode, and precipitation mode, respectively. was utilized to calculate radar noise level. Spectra were divided into eight segments, with the lowest integrated power segment regarded as the range that was dominated by radar noise, without any meteorological signal. The mean power of this segment was calculated as the noise level for the whole Doppler spectrum. Subsequently, all continuous spectral bins above the noise level were picked and further judged by a Signal-to-Noise Ratio (SNR) threshold and a bin-number threshold, since cloud signal typically has a higher power and larger spectral width than the noise level. Only the consecutive signals with the first three powers were reserved, and several spectral parameters, such as SNRs, left endpoints, right endpoints, and peaks, were also recorded. Due to more coherent integration numbers being used, radar BL and CI modes obtained relatively small measurable velocity ranges from \(-4.635\) to \(4.635\) m\(\cdot\)s\({}^{-1}\) and from \(-9.27\) to \(9.27\) m\(\cdot\)s\({}^{-1}\), respectively. In the presence of large droplets or strong airflows in convective systems, the measured Doppler spectra may be folded, which leads not only to discontinuity of the cloud signal, but also to spectral moments (such as MV and \(\sigma_{v}\)) that generate significant errors. Here, a previously presented algorithm was used for velocity dealiasing of Doppler spectra [29]. This algorithm can iteratively judge and correct the folded Doppler spectra from cloud top to base and can automatically determine the folding type, a property that is caused by either rapid hydrometer sinking or a strong updraft in the interior of the convective system. After the ambiguous Doppler spectra were corrected, the corresponding spectral parameters were also readjusted. BL and CI Doppler spectra, collected from TIPEX-III in the summers of 2014 and 2015, were used for assessing the dealiasing effects, by comparison with the corresponding spectra of the PR mode. Results show a success rate of 99.8% when this algorithm was used in only a one-time fold.