The new Chinese Ka-band solid-state transmitter cloud radar (CR) uses four operational modes with different pulse widths and coherent integration and non-coherent integration numbers to meet long-term cloud measurement requirements. The CR and an instrument-equipped aircraft were used to observe clouds and precipitation on the east side of Taihang Mountain in Hebei Province in 2018. To resolve the data quality problems caused by attenuation in the precipitation area; we focused on developing an algorithm for attenuation correction based on rain drop size distribution (DSD) retrieved from the merged Doppler spectral density data of the four operational modes following data quality control (QC). After dealiasing Doppler velocity and removal of range sidelobe artifacts; we merged the four types of Doppler spectral density data. Vertical air speed and DSD are retrieved from the merged Doppler spectral density data. Finally, we conducted attenuation correction of Doppler spectral density data and recalculated Doppler moments such as reflectivity; radial velocity; and spectral width. We evaluated the consistencies of reflectivity spectra from the four operational modes and DSD retrieval performance using airborne in situ observation. We drew three conclusions: First, the four operational modes observed similar reflectivity and velocity for clouds and low-velocity solid hydrometeors; however; three times of coherent integration underestimated Doppler reflectivity spectra for velocities greater than 2 m s⁻¹. Reflectivity spectra were also underestimated for low signal-to-noise ratios in the low-sensitivity operational mode. Second, QC successfully dealiased Doppler velocity and removed range sidelobe artifacts; and merging of the reflectivity spectra mitigated the effects of coherent integration and pulse compression on radar data. Lastly, the CR observed similar DSD and liquid water content vertical profiles to airborne in situ observations. Comparing CR and aircraft data yielded uncertainty due to differences in observation space and temporal and spatial resolutions of the data.

In this study, we investigated the macro- and microstructures of layered precipitation clouds in spring in Jilin Province, China. The premise of the campaign was to observe cloud particles in the melting layer (ML). The weather was developed under the influence of the Mongolia cyclone, which brought a large range of precipitation to the northeast. Combining the Droplet Measurement Technology (DMT) and Particle Measuring Systems (PMS) data, small particles accounted for the majority of all particles at each level above and below the ML. In our observations, both ice crystals (50–300 μm) and snowflakes (>300 μm) had two peaks between −5 and −2 °C. The high concentration of ice crystals at a temperature of −2.65 °C (4865 m) attained a maximum value of 287 L⁻¹ and snowflakes with 47 L⁻¹, which was similar to the previous studies. The Hallett–Mossop ice multiplication process operated most effectively at the temperature of −5 °C in this study. Even at the cloud dissipation stage, new droplets were still generated between −5 and −6 °C, providing abundant liquid water content (LWC) for the upper cloud. Although irregulars were observed, needles and spheres dominated in the observed cloud region of low LWC (<0.1 g m⁻³) at temperatures of −6 to −3 °C. These cloud conditions fit into the Hallett–Mossop criteria.

Accurate identification of supercooled water cloud (SWC) is critical for precipitation enhancement, reducing the risk of aircraft icing, and advancing our understanding of Earth’s radiative energy budget. However, SWC detection is frequently missed in the current official satellite products. To fill this observational gap and to investigate the potential use of the Himawari-8 satellite observation in the Northeast China Cold Vortex (NCCV) research, this study introduces an efficient algorithm to detect SWC from the Advanced Himawari Imager (AHI), which first uses a random forest (RF) model to classify the cloud into nine cloud types—high ice cloud, high water cloud, high mixed cloud, middle ice cloud, middle water cloud, middle mixed cloud, low water cloud, low ice cloud and low mixed cloud. Then, with the level 2 products, i.e., cloud optical thickness (COT), cloud effective radius (CER) and cloud-top temperature (CTT), the SWCs will further be identified from the water clouds and the mixed clouds. Validated by the dataset from AHI and labels derived from the Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) vertical feature mask (VFM), our algorithm can correctly detect 89.3% of SWC pixels in the dataset, which satisfies the requirements for operational cold-cloud seeding, enhances precipitation efficiency, and facilitates the integration of satellite remote sensing into weather modification practices. Based on AHI data in late spring 2021 during the NCCV weather event, SWCs exhibit significant spatial heterogeneity and distinct diurnal vertical evolution. These clouds occur most frequently in the southwest quadrant and evolve progressively from mid- and low-level layers to the high-level layer.