CACM-Net++: FY-4B AGRI Day and Night Cloud Mask Algorithm Modeling

Cloud detection is critical for satellite-based atmospheric observations and subsequent applications. This study presents a deep learning-based cloud mask model, Convolutional Attention Cloud Mask Network Plus Plus (CACM-Net++), developed for both daytime and nighttime cloud detection using multispectral observations from the Advanced Geostationary Radiation Imager (AGRI) onboard the FY-4B geostationary satellite. The model incorporates physically meaningful input features and is independently trained across different satellite zenith angles to better capture radiative characteristics and enhance retrieval accuracy in complex scenes. Cloud labels derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations are used for both training and evaluation. CACM-Net++ achieves over 90% detection accuracy under varying illumination conditions, with particularly strong performance over land and ocean at moderate satellite zenith angles. Compared to the operational cloud mask product from the National Satellite Meteorological Center (NSMC), CACM-Net++ demonstrates significant improvements, especially in detecting clouds over nighttime land areas and at high satellite zenith angles. These gains are primarily attributed to the model’s deep learning architecture and its tailored spatiotemporal partitioning strategy. Independent validation against CALIPSO confirms the model’s robust generalization across surface types. Furthermore, annual comparisons with ERA5 reanalysis data reveal that CACM-Net++ produces more consistent cloud distributions, particularly over nighttime land regions such as Australia. Despite its strong overall performance, the model remains challenged during twilight periods due to the scarcity of reliable reference data. Future work will focus on addressing this limitation by developing specialized algorithms for transitional lighting conditions, advancing the model’s all-sky detection capabilities.