# FuXi-2.0: Advancing machine learning weather forecasting model for practical applications

arXiv: https://arxiv.org/abs/2409.07188 · HTML: https://mlsanity.com/paper/2409.07188/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 87 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [FuXi: A cascade machine learning forecasting system for 15-day global weather forecast](https://mlsanity.com/paper/2306.12873/) (2023)
- [WeatherBench 2: A benchmark for the next generation of data-driven global weather models](https://mlsanity.com/paper/2308.15560/) (2023)
- [Rotary Position Embedding for Vision Transformer](https://mlsanity.com/paper/2403.13298/) (2024)
- [Depth-Aware Video Frame Interpolation](https://mlsanity.com/paper/1904.00830/) (2019)
- [Video Frame Interpolation via Adaptive Separable Convolution](https://mlsanity.com/paper/1708.01692/) (2017)
- [FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead](https://mlsanity.com/paper/2304.02948/) (2023)
- [Video Frame Interpolation via Adaptive Convolution](https://mlsanity.com/paper/1703.07514/) (2017)
- [Video Frame Interpolation Transformer](https://mlsanity.com/paper/2111.13817/) (2021)
- [FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model](https://mlsanity.com/paper/2310.19822/) (2023)
- [Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model](https://mlsanity.com/paper/2406.08632/) (2024)
- [Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction](https://mlsanity.com/paper/2405.15412/) (2024)
- [Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models](https://mlsanity.com/paper/2401.16669/) (2024)

## Built on by (most cited first, 11 of 11)

- [Innovator-VL: A Multimodal Large Language Model for Scientific Discovery](https://mlsanity.com/paper/2601.19325/) (2026)
- [Utilizing Strategic Pre-training to Reduce Overfitting: Baguan -- A Pre-trained Weather Forecasting Model](https://mlsanity.com/paper/2505.13873/) (2025)
- [FuXi-Air: Urban Air Quality Forecasting Based on Emission-Meteorology-Pollutant multimodal Machine Learning](https://mlsanity.com/paper/2506.07616/) (2025)
- [Compressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model](https://mlsanity.com/paper/2410.09109/) (2024)
- [Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models](https://mlsanity.com/paper/2504.00128/) (2025)
- [DEF: Diffusion-augmented Ensemble Forecasting](https://mlsanity.com/paper/2506.07324/) (2025)
- [KunPeng: A Global Ocean Environmental Model](https://mlsanity.com/paper/2504.04766/) (2025)
- [FuXi-Nowcast: Environment-conditioned deep learning for severe convection nowcasting](https://mlsanity.com/paper/2512.08974/) (2025)
- [Integrating Weather Foundation Model and Satellite to Enable Fine-Grained Solar Irradiance Forecasting](https://mlsanity.com/paper/2603.14845/) (2026)
- [Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography](https://mlsanity.com/paper/2610.01835/) (2026)
- [SoftSEEPS improves ML-based precipitation forecasting](https://mlsanity.com/paper/2610.09752/) (2026)

## Abstract (arXiv)

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 accuracy of traditional numerical weather prediction (NWP) models. Despite their potential, ML models typically suffer from limitations such as coarse temporal resolution, typically 6 hours, and a limited set of meteorological variables, limiting their practical applicability. To overcome these challenges, we introduce FuXi-2.0, an advanced ML model that delivers 1-hourly global weather forecasts and includes a comprehensive set of essential meteorological variables, thereby expanding its utility across various sectors like wind and solar energy, aviation, and marine shipping. Our study conducts comparative analyses between ML-based 1-hourly forecasts and those from the high-resolution forecast (HRES) of the European Centre for Medium-Range Weather Forecasts (ECMWF) for various practical scenarios. The results demonstrate that FuXi-2.0 consistently outperforms ECMWF HRES in forecasting key meteorological variables relevant to these sectors. In particular, FuXi-2.0 shows superior performance in wind power forecasting compared to ECMWF HRES, further validating its efficacy as a reliable tool for scenarios demanding precise weather forecasts. Additionally, FuXi-2.0 also integrates both atmospheric and oceanic components, representing a significant step forward in the development of coupled atmospheric-ocean models. Further comparative analyses reveal that FuXi-2.0 provides more accurate forecasts of tropical cyclone intensity than its predecessor, FuXi-1.0, suggesting that there are benefits of an atmosphere-ocean coupled model over atmosphere-only models.