What question did this study set out to answer?

The study aims to develop high-resolution global cloud products from geostationary satellite data.

March 13, 2026Open Access

All-day global cloud physical properties products with 0.07° resolution retrieved from geostationary satellite imagers covering the period from 2000 to 2022

Key Points

The study aims to develop high-resolution global cloud products from geostationary satellite data.
Utilized thermal infrared brightness temperature data from the GridSat-B1 project.
Applied Cloud-SmaAtUNet algorithm in DaYu CLoud Analysis System.
Generated cloud products with 3-hour temporal resolution and 0.07° spatial resolution.
Annual cloud phase identification accuracy is stable at 85% ± 0.7%.
Root Mean Square Error for cloud top height, optical thickness, and effective radius are 1.50 ± 0.03 km, 10.71 ± 0.15, and 6.75 ± 0.10 µm respectively.
Correlation coefficients with other cloud products demonstrate reasonable consistency.

Abstract

Abstract. Clouds play a crucial role in the Earth's energy budget and the hydrological cycle. However, differences in the spatiotemporal resolution of satellite sensors and in retrieval algorithms lead to substantial heterogeneity among retrieved cloud products. Based on global geostationary satellite thermal infrared brightness temperature data from the Gridded Satellite (GridSat-B1) project, this study applied the single-layer Cloud retrieval model – Small Attention-UNet (Cloud-SmaAtUNet) algorithm in the DaYu CLoud Analysis System (DaYu-CLAS) to retrieve a global cloud product with a 3 h temporal resolution, 0.07° spatial resolution, and 23 year temporal span (2000–2022). This product is referred to as the DaYu Global Cloud physical properties Products (DaYu-GCP). The DaYu-GCP includes CLoud Phase (CLP), Cloud Top Height (CTH), Cloud Optical Thickness (COT), and Cloud Effective Radius (CER), covering all regions between 70° S–70° N and 180° W–180° E. Evaluation based on the Moderate-resolution Imaging Spectroradiometer (MODIS) official cloud products shows that the annual CLP identification accuracy of DaYu-GCP remains stable at 85 % ± 0.7 %, while the annual RMSE for CTH, COT, and CER stabilize at 1.50 ± 0.03 km, 10.71 ± 0.15, and 6.75 ± 0.10 µm, respectively. The multi-year variations in accuracy are within 2 %, with no evident interannual differences, and the spatiotemporal distributions are continuous. In addition, evaluation based on observations from the Cloud Profiling Radar and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) indicates that the DaYu-GCP products show reasonable day–night consistency for optically thin cloud. Furthermore, the DaYu-GCP products are compared with other global cloud products. Taking the Northern Hemisphere as an example, the interannual variations of Cloud Cover Frequency (CCF), CTH, COT, and CER retrieved from DaYu-GCP show correlation coefficients of 0.760, 0.486, 0.764, and 0.514 with the ISCCP product, respectively, and 0.444, 0.778, 0.171, and 0.412 with the CLARA-A3 product. The DaYu-GCP dataset, which is stored in the Network Common Data Format (NetCDF), is freely available on the Science Data Bank at https://doi.org/10.57760/sciencedb.26292 (Zhao et al., 2026). The corresponding code can be found at https://github.com/lingxiao-zhao/DaYu-GCP (last access: 25 June 2025).

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