Supercooled water clouds (SWCs) are prevalent in the atmosphere and crucial for global and local radiation balance, aviation safety, and weather modification techniques like artificial precipitation. Therefore, there is an imperative need for the continuous and precise monitoring of SWCs at high temporal and spatial resolution, encompassing observations under all sky conditions. This study aims to enhance the identification of SWCs by leveraging thermal infrared (TIR) channels of the Himawari-8 geostationary satellite, regardless of solar illumination or sun glint effects, which can be problematic for reflectivity bands. Principal component analysis (PCA) is utilized to perform a sensitivity analysis on a dataset comprising TIR bands from the Himawari-8 satellite and labels derived from the Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) cloud profile products, to assess the efficacy of the data in differentiating between supercooled water and ice clouds (ICs). Subsequently, machine learning techniques are employed to develop an all-day SWC identification model. The model is assessed using a time-independent dataset, yielding an overall accuracy rate for cloud phase (CPH) identification of over 90%, as well as high performance for detecting SWCs. The model demonstrates consistent performance across various surfaces, times of day, and seasons. Notably, it outperforms traditional algorithms that rely on reflectivity bands by accurately identifying SWCs even in sun glint regions, thus improving the reliability of CPH detection for applications in meteorology, climate research, and aviation safety.
