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Lidar and Ka-band Millimeter-Wave Cloud Radar (MMCR) are powerful equipment to detect the height distribution of cloud boundaries, which can monitor the whole life cycle of cloud layers. In this paper, we employ lidar and MMCR to jointly detect cloud boundaries under different conditions (e.g., single-layer clouds, multilayer clouds, and precipitating clouds). By enhancing the echo signal of lidar @1064nm and combining its SNR, he cloud signal can be accurately extracted from the aerosol signals and background noise. The interference signal is eliminated from the power spectrum of the MMCR by using SNRmin and the spectral point continuous threshold, and the quality control of the meteorological signal (echo reflectivity factor) obtained by the inversion is carried out, which improves the detection accuracy of the cloud signal. Based on the advantages and disadvantages of the two devices in detecting cloud boundaries under different conditions, cloud boundary statistical rules are established to analyze the characteristics of cloud boundary changes in Xi'an in 2021. The seasonal variation characteristics of clouds show that the frequency distribution of cloud boundaries in vertical height in spring and summer has a similar variation trend. The normalized cloud amount is the lowest in spring (0.65) and the highest in summer 2.46). The frequency distribution of high-level clouds (at 11~12 km) is the highest in autumn, and the clouds in winter are mainly distributed below 8 km. Furthermore, the cloud boundary frequency distribution results for the whole year of 2021 show that the cloud bottom boundary below 1.5 km is more than 10%, the frequency within the height range of 3.06 km~3.6 km is approximately 3.24%, and the frequency above 8 km is less than 2%. The cloud top boundary frequency distribution has the characteristics of a bimodal distribution. The first narrow peak lies at approximately 1.5~3.1 km, and the second peak appears at 7.5~10.5 km. [ABSTRACT FROM AUTHOR]

To further exploit atmospheres cloud-water resources (CWR), it is necessary to correctly evaluate the amount of CWR in an area. CWR are hydrometeors that have not participated in precipitation formation at the surface and are suspended in the atmosphere to be exploited to maximize possible precipitation in the atmosphere (Zhou, Y., et al. (2020)). CWR includes three items: the existing hydrometeors at a time, the influx of atmospheric hydrometeors along the boundaries of the study area, and the mass of hydrometeors converted from water vapor through condensation or desublimation, defined as condensate. Condensate is the most important part of CWR. At present, there is a lack of effective observation methods for atmospheric column condensates, so the direct observation data of CWR are insufficient. The method for detecting atmospheric column condensate in the atmosphere is proposed and presented. The formation of condensate is closely related to atmospheric meteorological parameters (e.g., temperature and vertical airflow velocity). For stratiform clouds, the amount of atmospheric column condensate can be calculated by the saturated water vapor density and the ascending velocity at the cloud base and top. Active and passive remote sensing technology are applied to detect the mass of atmospheric column condensate. Combining millimetre-wave radar (MWR), lidar and microwave radiometers can well observe the vertical velocity and temperature at the cloud boundary. The detailed detection scheme and data calculation method are presented, and the presented method can realize the deduction of atmospheric column condensate. A case of cloud layer change before precipitation was monitored, and atmospheric column condensate was deduced and obtained. This is the first application, to our knowledge, to operate observations of atmospheric column condensates, which is significant for research on the hydrologic cycle and the assessment of cloud water resources. [ABSTRACT FROM AUTHOR]