AGCPP: All-day Global Cloud Physical Properties dataset with 0.07° resolution retrieved from geostationary satellite imagers covering the period from 2000 to 2022

The use of remote sensing to accurately measure cloud properties and their spatial and temporal variability has become an important area of atmospheric science research. However, the heterogeneity of data formats across national agencies and the calibrate and navigate associated with the use of data from different agencies have prevented the climate research community from using the full continuum of global cloud physical properties products. In this paper, All-day Global Cloud Physical Properties (AGCPP) is proposed, which provides cloud physical properties covering nearly the entire globe, from latitude -70° to 70° and longitude -180° to 180°. The main attributes of this dataset include cloud phase, cloud top height, cloud optical thickness, and cloud effective radius, with a time range from 1 January 2000 to 31 December 2022. AGCPP combines the observational advantages of geostationary satellites and polar-orbiting satellites. It uses the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 cloud product (MOD06/MYD06) to train the cloud-based attention-UNet (CloudAtUNet) model, and then evaluates AGCPP using MOD06/MYD06 and the Cloud–Aerosol Lidar with Orthogonal Polarisation (CALIOP) 1 km cloud layer product. The evaluation results indicate that AGCPP demonstrates excellent continuity and consistency in both temporal and spatial accuracy, as well as high consistency in diurnal accuracy. Due to the long time series and all-day global nature of the dataset, it is expected that the dataset AGCPP will significantly increase the potential for climate change research, particularly with respect to potential feedback effects between clouds, surface albedo, and radiation. AGCPP is stored in the Network Common Data Format (netCDF), a standard that allows various tools and libraries to process the data quickly and easily. The AGCPP dataset is freely available on the Science Data Bank at https://doi.org/10.57760/sciencedb.26292 (Zhao et al., 2025), and the corresponding code can be found at https://github.com/lingxiao-zhao/AGCPP (last access: 25 June 2025).