26 citations · 84 across the 28 of their papers we have counts for
30 papers
WeatherBridge: Forecast-Aware Multi-Source Input Adaptation for Pretrained Weather Forecasting Models
Mutian Xi, Xinyu Su, Xiaohui Zhong +1
Data-driven weather forecasting models are commonly trained on ERA5, yet operational deployment must use timely initial states from physics-based numerical weather prediction (NWP)…
MambaRain: Multi-Scale Mamba-Attention Framework for 0-3 Hour Precipitation Nowcasting
Chunlei Shi, Cui Wu, Xiang Xu +10
Accurate precipitation nowcasting over extended horizons (0-3 hours) is essential for disaster mitigation and operational decision-making, yet remains a critical challenge in the f…
VMU-Diff: A Coarse-to-fine Multi-source Data Fusion Framework for Precipitation Nowcasting
Chunlei Shi, Hao Li, Yufeng Zhu +6
Precipitation nowcasting is a vital spatio-temporal prediction task for meteorological applications but faces challenges due to the chaotic property of precipitation systems. Exist…
SeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies
Hao Li, Lu Zhang, Liu Chong +3
Instance normalization (IN) is widely used in non-stationary multivariate time series forecasting to reduce distribution shifts and highlight common patterns across samples. Howeve…
Composable multi-satellite precipitation estimation for evolving observing systems
Yunfan Yang, Haofei Sun, Xiuyu Sun +7
Rapid and spatially continuous precipitation monitoring is critical for flood, landslide, and other hydrometeorological hazard warnings, particularly in regions where rain-gauge an…
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…
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…
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…
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…
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…
FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution
Qiusheng Huang, Yuan Niu, Xiaohui Zhong +5
Accurate, high-resolution ocean forecasting is crucial for maritime operations and environmental monitoring. While traditional numerical models are capable of producing sub-daily,…
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…
A machine learning model for skillful climate system prediction
Chenguang Zhou, Lei Chen, Xiaohui Zhong +8
Climate system models (CSMs), through integrating cross-sphere interactions among the atmosphere, ocean, land, and cryosphere, have emerged as pivotal tools for deciphering climate…
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…
Skillful Nowcasting of Convective Clouds With a Cascade Diffusion Model
Haoming Chen, Xiaohui Zhong, Qiang Zhai +6
Accurate nowcasting of convective clouds from satellite imagery is essential for mitigating the impacts of meteorological disasters, especially in developing countries and remote r…
Compressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model
Qian Liu, Bing Gong, Xiaoran Zhuang +3
The rapid advancement of artificial intelligence (AI) in weather research has been driven by the ability to learn from large, high-dimensional datasets. However, this progress also…
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…