Supercooled Water Cloud Identification by Himawari-8, Its Validation by CALIPSO and Application to the Northeast China Cold Vortex

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.