云顶高度

DaYu Radiation-Cloud-Precipitation Analysis Systems

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 Remote Sensing Big Data and Artificial Intelligence Research Team. 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 Nowcast system (DaYu-Nowcast) 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-Nowcast primarily consists of a large foundation model for forecasting brightness temperature cloud imagery from geostationary satellite imagers (Wei et al., 2024).

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, which 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 on the "Tianqing" meteorological big data cloud platform of China Meteorological Administration and also displayed in real time on the "Tiangong" platform at the Weather Modification Center, providing timely dissemination to operational weather modification centers at provincial, municipal, county, and autonomous region levels across China. 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)

• Wenwen Li, Feng Zhang, Yi-Ning Shi, Hironobu Iwabuchi, Mingwei Zhu, Jiangnan Li, Wei Han, Husi Letu, and Hiroshi Ishimoto. (2020). Efficient radiative transfer model for thermal infrared brightness temperature simulation in cloudy atmospheres. Optics Express, 28(18), 25730-25749.
• Wenwen Li, Feng Zhang, Cancan Lu, Jiaqi Jin, Yi-Ning Shi, Yue Cai, Shuai Hu, and Wei Han. (2023). Integrated efficient radiative transfer model named Dayu for simulating the imager measurements in cloudy atmospheres. Optics Express, 31(10), 15256-15288.
• Yue Cai, Feng Zhang, Han Lin, Jiangnan Li, Hua Zhang, Wenwen Li, Shuai Hu. (2023). Optimized alternate mapping correlated k-distribution method for atmospheric longwave radiative transfer. Journal of Advances in Modeling Earth Systems, 15(5), e2022MS003419.
• Yue Cai, Feng Zhang, Jiangnan Li, Kun Wu, Quan Yang. (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)

• Wenwen Li, Feng Zhang, Han Lin, Xiaoran Chen, Jun Li, Wei Han. (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.
• Zhijun Zhao, Feng Zhang, Qiong Wu, Zhengqiang Li, Xuan Tong, Jingwei Li. (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.
• Xuan Tong, Jingwei Li, Feng Zhang, Wenwen Li, BaoXiang Pan, Jun Li, Husi Letu. (2023). The Deep-Learning-Based fast efficient nighttime retrieval of thermodynamic phase from Himawari-8 AHI measurements. Geophysical Research Letters, 50(11), e2022GL100901.
• Jingwei Li, Feng Zhang, Wenwen Li, Xuan Tong, Baoxiang Pan, Jun Li. (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.
• Bin Guo, Feng Zhang, Wenwen Li, Zhijun Zhao. (2024). Cloud classification by machine learning for geostationary radiation imager. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-14.
• Zhijun Zhao, Feng Zhang, Wenwen Li, Jingwei Li. (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.
• Wenwen Li, Feng Zhang, Bin Guo, Haoyang Fu, Husi Letu. (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.
• Bin Guo, Feng Zhang, Zhijun Zhao, Jinyu Guo, Wenwen Li. (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.
• Zhixin Yang, Zhijun Zhao, Tingting Zhou, Haoyang Fu, Jingwei Li. (2025). All-day retrieval of cloud physical properties from Meteosat second generation satellite. IEEE Transactions on Geoscience and Remote Sensing, 63, 1-12.
• Cuiping Liu, Feng Zhang, Huiling Ouyang, Wenwen Li, Zhijun Zhao. (2025). Diurnal variation of cloud physical properties for tropical cyclones over North Atlantic in 2019-2023. Geophysical Research Letters, 52(13), e2025GL115566.
• Cuiping Liu, Feng Zhang, Huiling Ouyang, Wenwen Li, Zhijun Zhao. (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.
• Haixia Xiao, Feng Zhang, Lingxiao Wang, Baoxiang Pan, Yannian Zhu, Minghuai Wang, Wenwen Li, Bin Guo, and Jun Li. (2025a). High-resolution ensemble retrieval of cloud properties for all-day based on geostationary satellite. npj Climate and Atmospheric Science, 8(1), 386.
• Jingwei Li, Baoxiang Pan, Feng Zhang, Bin Guo, Wenwen Li, Geng-Ming Jiang, Xin Wu, and Quan Wang. (2026). Probabilistic Retrieval of All-Day Overlapping Cloud Microphysical Properties. Advances in Atmospheric Sciences, 1-14.
• Feng Zhang, Xin Hong, Zhijun Zhao, Zeyu Gan, Pengxiang Ouyang, Haixia Xiao, Renhe Zhang, Xujun Wei, Miao Cai, Feng Lu. (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)

• Haixia Xiao, Feng Zhang, Renhe Zhang, Feng Lu, Miao Cai, Lingxiao Wang. (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.
• Chunlei Yang, Haoran Li, Runzhe Zhu, Yan Wang, Feng Zhang, Mingjian Gu, Geng-Ming Jiang, Renhe Zhang, and Xu Tang. (2026). Snow or rain? hybrid AI deciphers surface precipitation phase from satellite observations. Nature Communications, 17, 2813.

4. DaYu Nowcast System (DaYu-Nowcast)

• Xujun Wei, Feng Zhang, Renhe Zhang, Wenwen Li, Cuiping Liu, Bin Guo, Jingwei Li, Haoyang Fu, Xu Tang. (2024). DaYu: Data-Driven Model for Geostationary Satellite Observed Cloud Images Forecasting. arXiv preprint arXiv:2411.10144.
• Feng Zhang, Xin Hong, Zhijun Zhao, Zeyu Gan, Pengxiang Ouyang, Haixia Xiao, Renhe Zhang, Xujun Wei, Miao Cai, Feng Lu. (2026). Short-term forecasting of cloud physical properties based on Fourier neural operator method. Geophysical Research Letters, 53(8), e2025GL119553.