“大禹”辐射-云-降水分析系统

“大禹”辐射云降水分析系统（DaYu Radiation-Cloud-Precipitation Analysis Systems, DaYu-RCPAS）是由遥感大数据与人工智能团队研发的辐射-云-降水特性综合分析平台，专注于辐射模拟计算，云和降水的实时监测、精准反演及短临预报，为天气气候研究和业务应用提供高精度数据支持。

该系统集成了团队“大禹”辐射传输模式（DaYu Radiation Transfer Model, DaYu-RTM）、“大禹”云分析系统（DaYu CLoud Analysis System, DaYu-CLAS）、“大禹”降水分析系统（DaYu PRecipitation Analysis System, DaYu‐PRAS）和“大禹”短临预报系统（DaYu-Nowcast）。其中DaYu-RTM主要用于辐射模拟计算（W. Li et al., 2023）；DaYu-CLAS主要包括用于云物理特性反演的Cloud-ResUNet（Tong et al., 2023; Zhao et al., 2024），Cloud-SmaAtUNet（J. Li et al., 2023），DaYu-RTM与深度学习的混合算法（W. Li et al., 2022, 2024），CloudDiff（Xiao et al., 2025a）以及Overlap‐CloudDiff（J. Li et al., 2026）模型，以及用于云物理特性短临预测的Cloud-FNO模型（Zhang et al., 2026）；DaYu- PRAS主要包括用于降水监测和短临预测的TPWDiff‐CB（Xiao et al., 2025b）和RePPIC‐Net （Yang et al., 2026）模型；DaYu-Nowcast主要包括适用于静止卫星成像仪的亮温云图预报大模型（Wei et al., 2024）。

目前，DaYu-RCPAS提供了中国新一代静止气象卫星风云四号AGRI成像仪多通道亮温、全天时云物理特性等实时监测和预报产品，已成功应用于东亚区域云辐射相互作用研究及极端天气诊断。其中，DaYu-CLAS中的静止卫星云物理特性全天时反演算法已在中国气象局“天擎”气象大数据云平台实现业务化运行，并在人工影响天气中心 “天工” 平台实时显示，面向全国省、市、县及自治区人影业务中心实时发布，在2024年西南林火和上海进博会期间、2025年“春雨” 行动和宁夏六盘山增雨试验中为无人机、飞机作业方案设计和跟踪指挥提供了重要的数据支撑。未来，团队将持续优化算法并拓展卫星数据应用范围。

主要参考文献（按年份排序）:

1. “大禹”辐射传输模式（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. “大禹”云分析系统（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. “大禹”降水分析系统（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. “大禹”短临预报系统（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.