# FuXi Weather: A data-to-forecast machine learning system for global weather

arXiv: https://arxiv.org/abs/2408.05472 · HTML: https://mlsanity.com/paper/2408.05472/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 76 references that are in the map.
- First posted on arXiv 10 Aug 2024.

## Builds on (most cited first, 15 of 15)

- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [FuXi: A cascade machine learning forecasting system for 15-day global weather forecast](https://mlsanity.com/paper/2306.12873/) (2023)
- [Neural General Circulation Models for Weather and Climate](https://mlsanity.com/paper/2311.07222/) (2023)
- [FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead](https://mlsanity.com/paper/2304.02948/) (2023)
- [DAPPLE: A Pipelined Data Parallel Approach for Training Large Models](https://mlsanity.com/paper/2007.01045/) (2020)
- [Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review](https://mlsanity.com/paper/2303.10462/) (2023)
- [Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model](https://mlsanity.com/paper/2001.01520/) (2020)
- [Machine Learning for the Geosciences: Challenges and Opportunities](https://mlsanity.com/paper/1711.04708/) (2017)
- [Learning Variational Data Assimilation Models and Solvers](https://mlsanity.com/paper/2007.12941/) (2020)
- [FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model](https://mlsanity.com/paper/2310.19822/) (2023)
- [Data driven weather forecasts trained and initialised directly from observations](https://mlsanity.com/paper/2407.15586/) (2024)
- [Fuxi-DA: A Generalized Deep Learning Data Assimilation Framework for Assimilating Satellite Observations](https://mlsanity.com/paper/2404.08522/) (2024)
- [A Fortran-Keras Deep Learning Bridge for Scientific Computing](https://mlsanity.com/paper/2004.10652/) (2020)
- [HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array](https://mlsanity.com/paper/1901.02067/) (2019)
- [FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting](https://mlsanity.com/paper/2405.05925/) (2024)

## Built on by (most cited first, 21 of 21)

- [OMG-HD: A High-Resolution AI Weather Model for End-to-End Forecasts from Observations](https://mlsanity.com/paper/2412.18239/) (2024)
- [PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models](https://mlsanity.com/paper/2606.10642/) (2026)
- [EPT-2 Technical Report](https://mlsanity.com/paper/2507.09703/) (2025)
- [Benchmarking AI-based data assimilation to advance data-driven global weather forecasting](https://mlsanity.com/paper/2408.11438/) (2024)
- [TianQuan-S2S: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State](https://mlsanity.com/paper/2504.09940/) (2025)
- [Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction](https://mlsanity.com/paper/2508.18486/) (2025)
- [Skillful high-resolution weather forecasting independent of physical models](https://mlsanity.com/paper/2605.28153/) (2026)
- [Bridging short- and medium-range weather forecasting with machine learning](https://mlsanity.com/paper/2608.26822/) (2026)
- [XiChen: A global weather observation-to-forecast machine learning system via four-dimensional variational gradient-guided flexible assimilation](https://mlsanity.com/paper/2507.09202/) (2025)
- [Nowcast3D: Reliable precipitation nowcasting via gray-box learning](https://mlsanity.com/paper/2511.04659/) (2025)
- [Probabilistic Transformers for Joint Modeling of Global Weather Dynamics and Decision-Centric Variables](https://mlsanity.com/paper/2601.03753/) (2026)
- [Accurate and Efficient Hybrid-Ensemble Atmospheric Data Assimilation in Latent Space with Uncertainty Quantification](https://mlsanity.com/paper/2603.04395/) (2026)
- [Physics-Guided Multimodal Transformers are the Necessary Foundation for the Next Generation of Meteorological Science](https://mlsanity.com/paper/2504.14174/) (2025)
- [EmissionNet: Air Quality Pollution Forecasting for Agriculture](https://mlsanity.com/paper/2507.05416/) (2025)
- [Data-driven solar forecasting enables near-optimal economic decisions](https://mlsanity.com/paper/2509.06925/) (2025)
- [DPSformer: A long-tail-aware model for improving heavy rainfall prediction](https://mlsanity.com/paper/2509.25208/) (2025)
- [Green-NAS: A Global-Scale Multi-Objective Neural Architecture Search for Robust and Efficient Edge-Native Weather Forecasting](https://mlsanity.com/paper/2602.00240/) (2026)
- [The Rise of AI in Weather and Climate Information and its Impact on Global Inequality](https://mlsanity.com/paper/2603.05710/) (2026)
- [3DTCR: Vortex-following generative reconstruction improves tropical cyclone intensity prediction from global AI weather forecasts](https://mlsanity.com/paper/2603.13049/) (2026)
- [FuXiWeather2: Learning accurate atmospheric state estimation for operational global weather forecasting](https://mlsanity.com/paper/2603.15358/) (2026)
- [Timestep-Conditioned Transformers for Global Weather Forecasting](https://mlsanity.com/paper/2608.06241/) (2026)

## Abstract (arXiv)

Weather forecasting traditionally relies on numerical weather prediction (NWP) systems that integrates global observational systems, data assimilation (DA), and forecasting models. Despite steady improvements in forecast accuracy over recent decades, further advances are increasingly constrained by high computational costs, the underutilization of vast observational datasets, and the challenges of obtaining finer resolution. These limitations, alongside the uneven distribution of observational networks, result in global disparities in forecast accuracy, leaving some regions vulnerable to extreme weather. Recent advances in machine learning present a promising alternative, providing more efficient and accurate forecasts using the same initial conditions as NWP. However, current machine learning models still depend on the initial conditions generated by NWP systems, which require extensive computational resources and expertise. Here we introduce FuXi Weather, a machine learning weather forecasting system that assimilates data from multiple satellites. Operating on a 6-hourly DA and forecast cycle, FuXi Weather generates reliable and accurate 10-day global weather forecasts at a spatial resolution of 0.25^circ. FuXi Weather is the first system to achieve all-grid, all-surface, all-channel, and all-sky DA and forecasting, extending skillful forecast lead times beyond those of the European Centre for Medium-range Weather Forecasts (ECMWF) high-resolution forecasts (HRES) while using significantly fewer observations. FuXi Weather consistently outperforms ECMWF HRES in observation-sparse regions, such as central Africa, demonstrating its potential to improve forecasts where observational infrastructure is limited.