8 citations · 11 across the 11 of their papers we have counts for
17 papers
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…
PA-Net: Precipitation-Adaptive Mixture-of-Experts for Long-Tail Rainfall Nowcasting
Xinyu Xiao, Sen Lei, Eryun Liu +5
Precipitation nowcasting is vital for flood warning, agricultural management, and emergency response, yet two bottlenecks persist: the prohibitive cost of modeling million-scale sp…
A Synergistic Approach: Dynamics-AI Ensemble in Tropical Cyclone Forecasting
Yonghui Li, Wansuo Duan, Hao Li +3
This study addresses a critical challenge in AI-based weather forecasting by developing an AI-driven optimized ensemble forecast system using Orthogonal Conditional Nonlinear Optim…
AviaSafe: A Physics-Informed Data-Driven Model for Aviation Safety-Critical Cloud Forecasts
Zijian Zhu, Qiusheng Huang, Anboyu Guo +2
Current AI weather forecasting models predict conventional atmospheric variables but cannot distinguish between cloud microphysical species critical for aviation safety. We introdu…
Revealing the Potential of Learnable Perturbation Ensemble Forecast Model for Tropical Cyclone Prediction
Jun Liu, Tao Zhou, Jiarui Li +5
Tropical cyclones (TCs) are highly destructive and inherently uncertain weather systems. Ensemble forecasting helps quantify these uncertainties, yet traditional systems are constr…