26 citations · 64 across the 18 of their papers we have counts for
19 papers
TimeBlocks: Foundational and Continual Time-Series Blockbase -- Extended Version
David Campos, Bin Yang, Tung Kieu +3
The ongoing digitization has led to a proliferation of time-series data streams that monitor a variety of processes, from which valuable insights may be obtained. Further, the emer…
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…
Searth Transformer: A Transformer Architecture Incorporating Earth's Geospheric Physical Priors for Global Mid-Range Weather Forecasting
Tianye Li, Qi Liu, Hao Li +15
Accurate global medium-range weather forecasting is fundamental to Earth system science. Most existing Transformer-based forecasting models adopt vision-centric architectures that…
A unified multimodal understanding and generation model for cross-disciplinary scientific research
Xiaomeng Yang, Zhiyu Tan, Xiaohui Zhong +5
Scientific discovery increasingly relies on integrating heterogeneous, high-dimensional data across disciplines nowadays. While AI models have achieved notable success across vario…
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…
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,…
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…
E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models
Zhiyu Tan, WenXu Qian, Hesen Chen +3
Diffusion models have established themselves as the de facto primary paradigm in visual generative modeling, revolutionizing the field through remarkable success across various div…
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-ENS: A machine learning model for medium-range ensemble weather forecasting
Xiaohui Zhong, Lei Chen, Hao Li +7
Ensemble forecasting is crucial for improving weather predictions, especially for forecasts of extreme events. Constructing an ensemble prediction system (EPS) based on conventiona…
Fuxi-DA: A Generalized Deep Learning Data Assimilation Framework for Assimilating Satellite Observations
Xiaoze Xu, Xiuyu Sun, Wei Han +3
Data assimilation (DA), as an indispensable component within contemporary Numerical Weather Prediction (NWP) systems, plays a crucial role in generating the analysis that significa…
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…
FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model
Xiaohui Zhong, Lei Chen, Jun Liu +3
Significant advancements in the development of machine learning (ML) models for weather forecasting have produced remarkable results. State-of-the-art ML-based weather forecast mod…