Fudan University DaYu Radiation-Cloud-Precipitation Analysis System (DaYu-RCPAS)

The DaYu Radiation-Cloud-Precipitation Analysis System (DaYu-RCPAS) is a comprehensive analysis platform for radiation, cloud, and precipitation properties, which was independently developed by the Department of Atmospheric and Oceanic Sciences, Fudan University. It focuses on radiation simulation calculations, real-time monitoring, accurate retrieval, and short-term nowcasting of clouds and precipitation, providing high-precision data support for weather and climate research and operational applications.

This system primarily consists of the DaYu Radiation Transfer Model (DaYu‐RTM), the DaYu CLoud Analysis System (DaYu‐CLAS), the DaYu PRecipitation Analysis System (DaYu‐PRAS), and DaYu Forecasting Model for Cloud Images. Among these, the DaYu‐RTM is used for radiation simulation and calculation (W. Li et al., 2023). The DaYu‐CLAS integrates the Cloud‐ResUNet (Tong et al., 2023; Zhao et al., 2024), Cloud‐SmaAtUNet (J. Li et al., 2023), a hybrid algorithm that combines DaYu‐RTM simulation with deep learning (W. Li et al., 2022, 2024), CloudDiff (Xiao et al., 2025a), Overlap‐CloudDiff (J. Li et al., 2026) and Cloud-FNO (Zhang et al., 2026) models for cloud retrieval and forecasting. The DaYu‐PRAS integrates the TPWDiff‐CB (Xiao, Zhang, Zhang, et al., 2025) and RePPIC‐Net (Yang et al., 2026) models for precipitation monitoring and nowcasting. DaYu Forecasting Model for Cloud Images is mainly used for short-term forecasting of brightness temperature cloud images.

Currently, DaYu-RCPAS provides real-time and forecast products such as multi-channel brightness temperatures, all-day cloud physical properties, and radiation parameters derived from the Advanced Geosynchronous Radiation Imager (AGRI) aboard Fengyun-4 geostationary meteorological satellite. Leveraging Fudan University's disciplinary strengths in atmospheric science and remote sensing, the system has been applied to the research on cloud-radiation interactions in the East Asian region and the diagnosis of extreme weather events. The all-day cloud physical property retrieval algorithm for geostationary satellites in DaYu-CALS has been operationally deployed at the Weather Modification Center of the China Meteorological Administration and successfully launched on the "Tiangong" command platform of the center. It has provided critical data support for designing unmanned aerial vehicle operation plans and tracking command during events such as the Southwest China forest fires and the Shanghai International Import Expo in 2024, as well as the "Spring Rain" campaign and the rainfall enhancement experiment in Liupan Mountains in 2025. In the future, the research team will continue to optimize the algorithms and expand the application scope of satellite data.

References:

1. DaYu Radiation Transfer Model (DaYu-RTM)

• Li, W., Zhang, F., Shi, Y. N., Iwabuchi, H., Zhu, M., Li, J., ... & Ishimoto, H. (2020). Efficient radiative transfer model for thermal infrared brightness temperature simulation in cloudy atmospheres. Optics Express, 28(18), 25730-25749.
• Li, W., Zhang, F., Lu, C., Jin, J., Shi, Y. N., Cai, Y., ... & Han, W. (2023). Integrated efficient radiative transfer model named Dayu for simulating the imager measurements in cloudy atmospheres. Optics Express, 31(10), 15256-15288.
• Cai, Y., Zhang, F., Lin, H., Li, J., Zhang, H., Li, W., & Hu, S. (2023). Optimized alternate mapping correlated k‐distribution method for atmospheric longwave radiative transfer. Journal of Advances in Modeling Earth Systems, 15(5), e2022MS003419.
• Cai, Y., Zhang, F., Li, J., Wu, K., & Yang, Q. (2025). An accurate shortwave gaseous transmittance scheme using modified alternate mapping correlated K‐distribution method. Journal of Geophysical Research: Atmospheres, 130(10), e2024JD041921.

2. DaYu CLoud Analysis System (DaYu‐CLAS)

• Li, W., Zhang, F., Lin, H., Chen, X., Li, J., & Han, W. (2022). Cloud detection and classification algorithms for Himawari-8 imager measurements based on deep learning. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-17.
• Zhao, Z., Zhang, F., Wu, Q., Li, Z., Tong, X., Li, J., & Han, W. (2023). Cloud identification and properties retrieval of the Fengyun-4A satellite using a ResUnet model. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-18.
• Tong, X., Li, J., Zhang, F., Li, W., Pan, B., Li, J., & Letu, H. (2023). The Deep-Learning-Based fast efficient nighttime retrieval of thermodynamic phase from Himawari-8 AHI measurements. Geophysical Research Letters, 50(11), e2022GL100901.
• Li, J., Zhang, F., Li, W., Tong, X., Pan, B., Li, J., ... & Mustafa, F. (2023). Transfer-learning-based approach to retrieve the cloud properties using diverse remote sensing datasets. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-10.
• Guo, B., Zhang, F., Li, W., & Zhao, Z. (2024). Cloud classification by machine learning for geostationary radiation imager. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-14.
• Zhao, Z., Zhang, F., Li, W., & Li, J. (2024). Image-based retrieval of all-day cloud physical parameters for FY4A/AGRI and its application over the Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 129(18), e2024JD041032.
• Li, W., Zhang, F., Guo, B., Fu, H., & Letu, H. (2024). Physics-driven machine learning algorithm facilitates multilayer cloud property retrievals from geostationary passive imager measurements. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-18.
• Guo, B., Zhang, F., Zhao, Z., Guo, J., & Li, W. (2024). Retrieval of cloud macro-physical properties using the FY-4A advanced geostationary radiation imager (AGRI) and the geostationary interferometric infrared sounder (GIIRS). Geophysical Research Letters, 51(24), e2024GL109772.
• Yang, Z., Zhao, Z., Zhou, T., Fu, H., & Li, J. (2025). All-day retrieval of cloud physical properties from Meteosat second generation satellite. IEEE Transactions on Geoscience and Remote Sensing, 63, 1-12.
• Liu, C., Zhang, F., Ouyang, H., Li, W., & Zhao, Z. (2025). Diurnal variation of cloud physical properties for tropical cyclones over North Atlantic in 2019–2023. Geophysical Research Letters, 52(13), e2025GL115566.
• Zhao, Z., Zhang, F., Li, W., Yang, B., He, Q., Fu, H., & Cai, M. (2025). Identification and tracking of deep convection systems over the Tibetan Plateau and its surrounding areas in summer using all-day cloud physical properties. Geophysical Research Letters, 52, e2025GL118433.
• Xiao, H., Zhang, F., Wang, L., Pan, B., Zhu, Y., Wang, M., ... & Li, J. (2025a). High-resolution ensemble retrieval of cloud properties for all-day based on geostationary satellite. npj Climate and Atmospheric Science, 8(1), 386.
• Li, J., Pan, B., Zhang, F., Guo, B., Li, W., Jiang, G. M., ... & Wang, Q. (2026). Probabilistic Retrieval of All-Day Overlapping Cloud Microphysical Properties. Advances in Atmospheric Sciences, 1-14.
• Zhang, F., Hong, X., Zhao, Z., Gan, Z., Ouyang, P., Xiao, H., ... & Lu, F. (2026). Short‐term forecasting of cloud physical properties based on Fourier neural operator method. Geophysical Research Letters, 53(8), e2025GL119553.

3. DaYu PRecipitation Analysis System (DaYu‐PRAS)

• Xiao, H., Zhang, F., Zhang, R., Lu, F., Cai, M., & Wang, L. (2025b). Retrieval of total precipitable water under all‐weather conditions from Himawari‐8/AHI observations using the generative diffusion model. Geophysical Research Letters, 52(15), e2025GL117075.
• Yang, C., Li, H., Zhu, R., Wang, Y., Zhang, F., Gu, M., ... & Tang, X. (2026). Snow or rain? hybrid AI deciphers surface precipitation phase from satellite observations. Nature Communications, 17, 2813.

4. DaYu Forecasting Model for Cloud Images

• Wei, X., Zhang, F., Zhang, R., Li, W., Liu, C., Guo, B., ... & Tang, X. (2024). DaYu: Data-Driven Model for Geostationary Satellite Observed Cloud Images Forecasting. arXiv preprint arXiv:2411.10144.