18 papers
HybridOM: Hybrid Physics-Based and Data-Driven Global Ocean Modeling with Efficient Spatial Downscaling
Ruiqi Shu, Xiaohui Zhong, Qiusheng Huang +4
Global ocean modeling is vital for climate science but struggles to balance computational efficiency with accuracy. Traditional numerical solvers are accurate but computationally e…
FuXi-Nowcast: Environment-conditioned deep learning for severe convection nowcasting
Lei Chen, Zijian Zhu, Xiaoran Zhuang +4
Severe convection produces localized hazards that often require warnings before radar echoes fully reveal storm development. Convective initiation and the maintenance of intense co…
QuantWeather: Quantile-Aware Probabilistic Forecasting for Subseasonal Precipitation
Lei Chen, Xinyu Su, Xiaohui Zhong +1
Subseasonal precipitation forecasting is inherently uncertain due to chaotic atmospheric dynamics, making reliable uncertainty estimation essential for real-world applications. Exi…
FuXi-TC: A generative framework integrating deep learning and physics-based models for improved tropical cyclone forecasts
Shan Guo, Lei Chen, Yangyang Zhao +6
Tropical cyclones (TCs) are among the most devastating natural hazards, yet their intensity remains notoriously difficult to predict. NWP models are constrained by both computation…
Data-driven ensemble prediction of the global ocean
Qiusheng Huang, Xiaohui Zhong, Anboyu Guo +3
Data-driven models have advanced deterministic ocean forecasting, but extending machine learning to probabilistic global ocean prediction remains an open challenge. Here we introdu…
FuXiWeather2: Learning accurate atmospheric state estimation for operational global weather forecasting
Xiaoze Xu, Xiuyu Sun, Songling Zhu +5
Numerical weather prediction has long been constrained by the computational bottlenecks inherent in data assimilation and numerical modeling. While machine learning has accelerated…