26 citations · 85 across the 25 of their papers we have counts for
12 papers · 1 filter
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…
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…
A data-driven global ocean forecasting model with sub-daily and eddy-resolving resolution
Yuan Niu, Qiusheng Huang, Xiaohui Zhong +7
High-fidelity ocean forecasting at high spatial and temporal resolution is essential for capturing fine-scale dynamical features, with profound implications for hazard prediction,…
Enhanced predictions of the Madden-Julian oscillation using the FuXi-S2S machine learning model: Insights into physical mechanisms
Can Cao, Xiaohui Zhong, Lei Chen +2
The Madden-Julian Oscillation (MJO) is the dominant mode of tropical atmospheric variability on intraseasonal timescales, and reliable MJO predictions are essential for protecting…
Generative artificial intelligence improves projections of climate extremes
Ruian Tie, Xiaohui Zhong, Zhengyu Shi +4
Climate change is amplifying extreme events, posing escalating risks to biodiversity, human health, and food security. GCMs are essential for projecting future climate, yet their c…
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…
Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields
Chensen Lin, Ruian Tie, Shihong Yi +2
High-resolution wind information is essential for wind energy planning and power forecasting, particularly in regions with complex terrain. However, most AI-based weather forecasti…
FuXi-RTM: A Physics-Guided Prediction Framework with Radiative Transfer Modeling
Qiusheng Huang, Xiaohui Zhong, Xu Fan +2
Similar to conventional video generation, current deep learning-based weather prediction frameworks often lack explicit physical constraints, leading to unphysical outputs that lim…
FuXi-2.0: Advancing machine learning weather forecasting model for practical applications
Xiaohui Zhong, Lei Chen, Xu Fan +3
Machine learning (ML) models have become increasingly valuable in weather forecasting, providing forecasts that not only lower computational costs but often match or exceed the acc…
FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models
Lei Chen, Xiaohui Zhong, Hao Li +8
Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning based weather forecasting models outperf…
Machine Learning Parameterization of the Multi-scale Kain-Fritsch (MSKF) Convection Scheme
Xiaohui Zhong, Xing Yu, Hao Li
Warm-sector heavy rainfall often occurs along the coast of South China, and it is usually localized and long-lasting, making it challenging to predict. High-resolution numerical we…