A precise measurement for the aerosol particle size distribution and fog droplet size distribution simultaneously in the open atmosphere is proposed. The extinction coefficient and small-angle forward scattering measurement are integrated into the detection for particle size distribution in the open atmosphere, and can achieve the fine detection of the particles in the atmosphere with radius between 0.1 to 30 μm. The key technology including optimal scattering angle in small-angle forward scattering measurement and optimal wavelengths selection are discussed and solved in detail. The fourteen different particle size distributions including aerosol size distributions and fog droplet size distributions are used for the determination of optimal forward scattering angle and wavelengths. The optimal forward scattering angle is calculated to be 1.1°. Seven wavelengths for extinction coefficients and five wavelengths for forward scattering coefficients are chosen for the retrieval of particle size distribution in the measurement. The regularization inversion of optical parameters for the retrieval of particle size distribution is described. The aerosol particle size distributions measured by particle spectrometer and actual fog particle size distributions are used for the method test and the reconstructions of particle size distributions. The inversion results show that the method can achieve the precise measurements of aerosol particle size distribution and fog droplet size distribution. The error influence on the inversion results of distributions is discussed. Based on the sensitivity analysis of inversion results, the feasibility of measurement in the real atmosphere is analyzed and discussed, and the scheme of detection system is provided.

Lidar at 1064 nm and Ka-band millimetre-wave cloud radar (MMCR) are powerful tools for detecting the height distribution of cloud boundaries and can monitor the entire life cycle of cloud layers. In this study, lidar and MMCR are employed to jointly detect cloud boundaries under different conditions. By enhancing the echo signal of lidar at 1064 nm and combining its signal-to-noise ratio (SNR), the cloud signal can be accurately extracted from the aerosol signals and background noise. The interference signal is eliminated from Doppler spectra of the MMCR by using the noise ratio of the smallest measurable cloud signal (SNR_min⁡) and the spectral point continuous threshold (N_ts). Moreover, the quality control of the reflectivity factor of MMCR obtained by the inversion is conducted, which improves the detection accuracy of the cloud signal. We analysed three typical cases studies; case one presents two interesting phenomena: (a) at 19:00–20:00 CST (China standard time), the ice crystal particles at the cloud top boundary are too small to be detected by MMCR, but they are well detected by lidar. (b) At 19:00–00:00 CST, the cirrus cloud changes to altostratus where the cloud particles eventually grow into large sizes, producing precipitation. Further, MMCR has more advantages than lidar in detection of the cloud top boundary within this period. Considering the advantages of the two devices, the change characteristics of the cloud boundary in Xi'an from December 2020 to November 2021 were analysed, with MMCR detection data as the main data and lidar data as the assistant data. The seasonal variation characteristics of clouds show that, in most cases, high clouds often occur in summer and autumn, and the low clouds are usually in winter. The normalized cloud cover shows that the maximum and minimum cloud cover occur in summer and winter, respectively. Furthermore, the cloud boundary frequency distribution results for the whole of the observation period show that the cloud bottom boundary below 1.5 km is more than 1 %, the frequency within the height range of 3.06–3.6 km is approximately 0.38 %, and the frequency above 8 km is less than 0.2 %. The cloud top boundary frequency distribution exhibits the characteristics of a bimodal distribution. The first narrow peak lies at approximately 1.0–3.1 km, and the second peak appears at 6.4–9.8 km.

The use of Raman and high-spectral lidars enables measurements of a stratospheric aerosol extinction profile independent of backscatter, and a multi-wavelength (MW) lidar can obtain additional information that can aid in retrieving the microphysical characteristics of the sampled aerosol. The inversion method for retrieving aerosol particle size distributions and microphysical particle parameters from MW lidar data was studied. An inversion algorithm for retrieving aerosol particle size distributions based on the regularization method was established. Based on the inversion of regularization, the inversion method was optimized by choosing the base function closest to the aerosol distribution. The logarithmic normal distribution function was selected over the triangle function as the base function for the inversion. The averaging procedure was carried out for three main types of aerosol. The 1% averaging result near the minimum of the discrepancy gave the best estimate of the particle parameters. The accuracy and stabilization of the optimized algorithm for microphysical parameters were tested by scores of aerosol size distributions. The systematic effects and random errors impacting the inversion were also considered, and the algorithm was tested by the data, showing 10% systematic error and 15% random error. At the same time, the reliability of the proposed algorithm was also verified by using the aerosol particle size distribution data of the aircraft. The inversion results showed that the algorithm was reliable in retrieving the aerosol particle size distributions at vertical heights using lidar data.

The vibration-rotational Raman lidar system built in Xi’an, China (34.233°N, 108.911°E) was used to simultaneously detect atmospheric temperature, water vapor, and aerosols under different weather conditions. Temperature measurement examples showed good agreement with radiosonde data in terms of the lapse rates and heights of the inversion layer under the lower stratosphere. The statistical temperature error due to the signal-to-noise ratio is less than 1 K up to a height of 15 km, and is estimated to be less than 3 K below a height of 22 km. High-quality temperature data were collected from 70 nighttime observations from October 2013 to May 2014, and were used to analyze the temperature inversion characteristics at Xi’an, which is a typical city in the northwest of China. The tropopause height over the Xi’an area was almost 17–18 km, and the inversion layer often formed above the cloud layer. In the winter at night, inversions within the boundary layer can easily form with a high occurrence of $\sim 60\%$ based on 47 nights from 01 November 2013 to 21 January 2014. Continuous observation of atmospheric temperature, water vapor (relative humidity), and aerosols was carried out during one night, and the relevant changes were analyzed in the boundary layer via the joint observation of atmospheric visibility, PM2.5 and PM10 from a ground visibility meter and from a monitoring site, which revealed that the temperature inversion layer has a great influence on the formation of fog and haze during the winter night and early morning.

To further exploit atmospheric cloud water resources (CWRs), it is necessary to correctly evaluate the number of CWRs in an area. The CWRs are hydrometeors that have not participated in precipitation formation at the surface and are suspended in the atmosphere to be exploited and maximise possible precipitation in the atmosphere (Zhou et al., 2020). Three items are included in CWRs: the existing hydrometeors at a certain time, the influx of atmospheric hydrometeors along the boundaries of the study area, and the mass of hydrometeors converted from water vapour through condensation or desublimation, defined as condensate. Condensate constitutes the most important part of CWRs. At present, there is a lack of effective observation methods for atmospheric column condensate evaluation, and direct observation data of CWRs are thus insufficient. A detection method for atmospheric column condensate is proposed and presented. The formation of condensate is closely related to atmospheric meteorological parameters (e.g. temperature and vertical airflow velocity). The amount of atmospheric column condensate can be calculated by the saturated water vapour density and the ascending velocity at the cloud base and top. Active and passive remote sensing technologies are applied to detect the mass of atmospheric column condensate. Combining millimetre cloud radar, lidar and microwave radiometers can suitably observe the vertical velocity and temperature at the cloud boundary. The saturated vapour density can be derived from the temperature, and then, water vapour flux and the maximum possible condensate can be deduced. A detailed detection scheme and data calculation method are presented, and the presented method can realise the determination of atmospheric column condensate. A case of cloud layer change before precipitation is considered, and atmospheric column condensate is deduced and obtained. This is the first application, to our knowledge, of observations for atmospheric column condensate evaluation, which is significant for research on the hydrologic cycle and the assessment of CWRs.