Classification of Cloud Phases in Cold-Air Outbreak Events Based on Polarization Lidar and Cloud Radar Observations

Cold air outbreak (CAO) clouds significantly impact regional weather, precipitation and aviation safety. This impact is especially pronounced in confluence zones, where cold continental air masses interact with warm, moisture-laden air currents emanating from oceans or lakes. In this paper, an objective classification algorithm of cloud phase was suggested using K-means clustering algorithm based on PollyXT lidar method and a gradient based cloud identification algorithm, in order to determine depolarization ratio threshold for supercooled water, mixed-phase and ice clouds. The analyses of cloud phase characteristics and vertical structure were conducted for cold air outbreak events over the Shandong Peninsula, a region frequently affected by sea-effect snow disasters, using the winter period of 2024-2025 continuous observation data from the Eastern CHina cold-air OutbrEak Snowfall (ECHOES) campaign. The results show: the supercooled water clouds were predominantly located above the ice clouds in the mid-to-upper troposphere, implying heterogeneous ice nucleation processes; the analysis of diurnal variation reveals that the probability of occurrence of supercooled water clouds remains constant during the whole day because of the stable atmospheric conditions; synergy of lidar and radar observations clearly demonstrates their complementary detection abilities: radar outperforming lidar in lower troposphere and daytime conditions; the high coverage of the supercooled water in the lower troposphere poses a significant safety threat for aircraft icing. These results provide observational basis for improving the cloud microphysics parametric schemes in numerical weather prediction and aviation safety.