1,- 1College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, China
- 2Department of Atmospheric and Oceanic Sciences & Institutes of Atmospheric Sciences, Fudan University, Shanghai, China
- 3Engineering Research Center of Optical Instrument and System, the Ministry of Education, University of Shanghai for Science and Technology, Shanghai, China
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.
How to cite: Guo, B., Zhang, F., Zhao, Z., Guo, J., and Li, W.: Retrieval of Cloud Macro-Physical Properties Using the FY-4A Advanced Geostationary Radiation Imager (AGRI)and the Geostationary Interferometric Infrared Sounder(GIIRS), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3451, https://doi.org/10.5194/egusphere-egu26-3451, 2026.
