Retrieval of Cloud Macro-Physical Properties Using theFY-4A Advanced Geostationary Radiation Imager (AGRI) and the Geostationary Interferometric Infrared Sounder (GIIRS)

Abstract

This study presents a novel approach for conducting all-day retrieval of cloud macro-physical properties (single-layer cloud phase, cloud top height, and cloud base height for optical thickness less than 10) using the Advanced Geostationary Radiation Imager (AGRI) and the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the geostationary meteorological satellite Fengyun-4A based on machine learning methods. Model accuracy was compared after integrating ECMWF Reanalysis v5 (ERA-5) data, atmospheric temperature and moisture profiles, and GIIRS clear-column radiance. Results demonstrate that integrating GIIRS clear-column radiances can enhance the precision of cloud phase classification and the retrieval of cloud macro-physical properties. This effectively replaces the role of atmospheric temperature and humidity profiles, which are typically required for thermal infrared remote sensing retrieval. Moreover, the issue of delayed acquisition of ERA-5 atmospheric temperature and humidity profiles is mitigated, enabling near real-time and all-day retrieval of cloud macro-physical properties.