A collection of articles on Large Weather Models (LWMs), to make it easier to find and learn. 👏 Contributions to this hub are welcome!
- 2025/06/12: Environment and Climate Change Canada (ECCC) has released an experimental version of its Global Deterministic Prediction System (GDPS), where the physical weather model is spectrally nudged toward forecasts from GEML, an AI-based emulator (Global Environmental eMuLator) [link].
- 2025/02/25: Artificial Intelligence Forecasting System (AIFS), ECMWF’s AI forecasts, become operational [link].
- 2025/02/01: OneForecast, a global-regional nested weather forecasting framework based on graph neural networks [link].
- 2024/12/20: AIFS–CRPS, an extension of ECMWF's Artificial Intelligence Forecast System (AIFS), focuses on optimizing probabilistic forecasts using the Continuous Ranked Probability Score (CRPS) [link].
- 2024/12/20: GraphDOP, a new data-driven, end-to-end forecast system developed by ECMWF that is trained and initialised exclusively from Earth System observations, with no physics-based reanalysis inputs or feedbacks [link].
- 2024/12/17: ArchesWeatherGen, a compact and accessible probabilistic weather model from built on INRIA's ArchesWeather deterministic predictions, is tailored for academic research with minimal computational resources [link].
- 2024/12/11: ECMWF releases its Artificial Intelligence Forecast System (AIFS) model and weights freely available on the web [link]
- 2024/12/04: Google DeepMind releases GenCast, an ensemble AI forecast model [link]
- 2024/09/20: IBM and Nasa Prithvi-WxC Foundation model [link]
- 2024/08/15: MetMamba, a DLWP model built on a state-of-the-art state-space model, Mamba, offers notable performance gains [link];
- 2024/07/30: FuXi-S2S published in Nature Communications [link];
- 2024/06/20: WEATHER-5K: A Large-scale Global Station Weather Dataset Towards Comprehensive Time-series Forecasting Benchmark [link];
- 2024/05/24: ORCA: A Global Ocean Emulator for Multi-year to Decadal Predictions [link];
- 2024/05/22: Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [link];
- 2024/05/20: Aurora: A Foundation Model of the Atmosphere [link];
- 2024/05/09: FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting [link];
- 2024/05/06: CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer [link];
- 2024/04/15: ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs [link];
- 2024/04/12: FuXi-DA: A Generalized Deep Learning Data Assimilation Framework for Assimilating Satellite Observations [link];
- 2024/03/29: SEEDS: Generative emulation of weather forecast ensembles with diffusion models [link];
- 2024/03/13: KARINA: An Efficient Deep Learning Model for Global Weather Forecast [link];
- 2024/02/06: CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling [link];
- 2024/02/04: XiHe, the first data-driven 1/12° resolution global ocean eddy-resolving forecasting model [link];
- 2024/02/02: ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [link];
Expand to see more LWMs news
- 2024/01/28: FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09∘ horizontal resolution [link];
- 2023/12/27: GenCast, a ML-based generative model for ensemble weather forecasting [link];
- 2023/12/16: Four-Dimensional Variational (4DVar) assimilation, and develop an AI-based cyclic weather forecasting system, FengWu-4DVar [link];
- 2023/12/15: FuXi-S2S: An accurate machine learning model for global subseasonal forecasts [link];
- 2023/12/11: A unified and flexible framework that can equip any type of spatio-temporal models is proposed based on residual diffusion DiffCast [link];
- 2023/11/13: GCMs are physics-based simulators which combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. [link];
- 2023/12/13: FuXi is open source [link];
- 2023/11/14: GraphCast published in Science [link];
- 2023/10/25: IBM and Nasa Prithvi-100M Model [link];
- 2023/09/14: Pangu-Weather published in Nature [link];
- 2023/08/25: ClimaX published in ICML 2023 [link];
| LWM name | From | Date(1st) | Publication | Links | Model Licence | Weights Licence |
|---|---|---|---|---|---|---|
| MetNet | 2020.03 | - | [arXiv paper] [github] | [MIT] | N/A | |
| FourCastNet | NVIDIA | 2022.02 | PASC 23 | [arXiv paper] [github] | [BSD-3] | [BSD-3] |
| MetNet-2 | 2022.09 | Nature Communications 2022 | [Nature paper] [github] | [MIT] | N/A | |
| Pangu-Weather | Huaiwei | 2022.11 | Nature 2023 | [Nature paper] [github] | Not Specified | [CC-BY-NC-SA 4.0] |
| GraphCast | DeepMind | 2022.12 | Science 2023 | [Science paper] [github] | [Apache 2.0] | [CC-BY-NC-SA 4.0] |
| ClimaX | Microsoft | 2023.01 | ICML 2023 | [arXiv paper] [github] | [MIT] | Not specificied ([MIT]?) |
| Fengwu | Shanghai AI Lab | 2023.04 | - | [arXiv paper] [github] | Not Specified | Not Specified |
| MetNet-3 | 2023.06 | - | [arXiv paper] | - | - | |
| FuXi | Fudan | 2023.06 | npj 2023 | [arXiv paper] [github] | [Apache 2.0] | [CC-BY-NC-SA 4.0] |
| NowcastNet | Tsinghua | 2023.07 | Nature 2023 | [Nature paper] | - | - |
| AI-GOMS | Tsinghua | 2023.08 | - | [arXiv paper] | - | - |
| Prithvi-100M | IBM / Nasa | 2023.08 | [arXiv paper] [Hugging Face] | [Apache 2.0] | [Apache 2.0] | |
| FuXi-Extreme | Fudan | 2023.10 | - | [arXiv paper] | - | - |
| NeuralGCM | DeepMind | 2023.11 | - | [arXiv paper] | - | - |
| FengWu-4DVar | Tsinghua | 2023.12 | ICML 2024 | [arXiv paper] | - | - |
| FengWu-Adas | Shanghai AI Lab | 2023.12 | - | [arXiv paper] | - | - |
| FuXi-S2S | Fudan | 2023.12 | Nature Communications 2024 | [arXiv paper] [NC paper] | - | - |
| GenCast | Google DeepMind | 2023.12 | Nature | [paper] [github] | [Apache 2.0] | [CC-BY-NC-SA 4.0] |
| DiffCast | HITsz | 2023.12 | CVPR 2024 | [arXiv paper] | - | - |
| FengWu-GHR | Shanghai AI Lab | 2024.01 | - | [arXiv paper] | - | - |
| ExtremeCast | Shanghai AI Lab | 2024.02 | - | [arXiv paper] [github] | Not Specified | Not Specified |
| XiHe | NUDT | 2024.02 | - | [arXiv paper] [github] | Not Specified | Not Specified |
| CasCast | Shanghai AI Lab | 2024.02 | ICML 2024 | [arXiv paper] [github] | Not Specified | Not Specified |
| KARINA | KIST | 2024.03 | - | [arXiv paper] | - | - |
| SEEDS | 2024.03 | Science Advances | [Science Advances paper] | - | - | |
| FuXi-DA | Fudan | 2024.04 | - | [arXiv paper] | - | - |
| ClimODE | Aalto University | 2024.04 | ICLR 2024 (Oral) | [arXiv paper] [github] | [MIT] | Not specified or non applicable |
| FuXi-ENS | Fudan | 2024.05 | - | [arXiv paper] | - | - |
| Aurora | Microsoft | 2024.05 | - | [arXiv paper] [github] | [MIT] | [CC-BY-NC-SA 4.0] |
| WeatherGFT | Shanghai AI Lab | 2024.05 | NeurIPS 2024 | [arXiv paper] [github] | Not Specified | Not Specified |
| ORCA | Shanghai AI Lab | 2024.05 | - | [arXiv paper] [github] | Not Specified | Not Specified |
| MetMamba | Beijing PRESKY Technology | 2024.08 | - | [paper] | Not Specified | Not Specified |
| Prithvi-WxC | IBM / Nasa | 2024.09 | - | [arXiv paper] [Hugging Face] | [CDLA Permissive 2.0] | [CDLA Permissive 2.0] |
| AIFS | ECMWF | 2024.12 | - | [arXiv paper] [Hugging Face] | [CC BY 4.0] | [CC BY 4.0] |
| ArchesWeatherGen | INRIA | 2024.12 | - | [arXiv paper][github] | [CC-BY-NC-SA 4.0] | [CC-BY-NC-SA 4.0] |
| GraphDOP | ECMWF | 2024.12 | - | [arXiv paper] | - | - |
| AIFS-CRPS | ECMWF | 2024.12 | - | [arXiv paper] | - | - |
| OneForecast | Tsinghua | 2025.02 | - | [arXiv paper] [github] | [MIT] | [MIT]? |
| Appa | University of Liège | 2025.04 | - | [arXiv paper] | - | - |
| GEML | Environment and Climate Change Canada | 2025.06 | - | [Documentation] [Hugging Face] |
[Apache 2.0] | Open Government Licence |
| Dataset name | From | Date(1st) | Publication | Links |
|---|---|---|---|---|
| WeatherBench | 2020.02 | JAMES 2020 | [paper] [github] | |
| ERA5 | ECMWF | 2020.05 | - | [paper] [link] |
| SEVIR | MIT | 2020.06 | NeurIPS 2020 | [paper] [github] [link] |
| WeatherBench2 | 2023.08 | - | [paper] [github] | |
| CRA5 | Shanghai AI Lab | 2024.05 | - | [paper] [github] |
| WEATHER-5K | Beijing PRESKY Technology | 2024.08 | - | [paper] |
| Extreme Weather Bench | Brightband | 2025.01 | - | [blog][github] |
- WeatherBench: A benchmark dataset for data-driven weather forecasting [pdf]
- WeatherBench 2: A benchmark for the next generation of data-driven global weather models [pdf]
- MetNet: A Neural Weather Model for Precipitation Forecasting (MetNet) [pdf]
- Deep learning for twelve hour precipitation forecasts (MetNet-2) [pdf]
- Deep Learning for Day Forecasts from Sparse Observations (MetNet-3) [pdf]
- FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators (FourCastNet) [pdf]
- Accurate medium-range global weather forecasting with 3D neural networks (Pangu-Weather) [pdf]
- Learning skillful medium-range global weather forecasting (GraphCast) [pdf]
- ClimaX: A foundation model for weather and climate (ClimaX) [pdf]
- FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead (FengWu) [pdf]
- FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation [pdf]
- Towards an end-to-end artificial intelligence driven global weather forecasting system [pdf]
- FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [pdf]
- ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [pdf]
- FuXi: A cascade machine learning forecasting system for 15-day global weather forecast (FuXi) [pdf]
- FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model (FuXi-Extreme) [pdf]
- FuXi-S2S: An accurate machine learning model for global subseasonal forecasts [pdf]
- Fuxi-DA: A Generalized Deep Learning Data Assimilation Framework for Assimilating Satellite Observations [pdf]
- FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting [pdf]
- AI-GOMS: Large AI-Driven Global Ocean Modeling System (AI-GOMS) [pdf]
- XiHe: A Data-Driven Model for Global Ocean Eddy-Resolving Forecasting [pdf]
- Fourier Neural Operator with Learned Deformations for PDEs on General Geometries [pdf]
- SFNO: Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere [pdf]
- Earthformer: Exploring Space-Time Transformers for Earth System Forecasting [pdf]
- PreDiff: Precipitation Nowcasting with Latent Diffusion Models [pdf]
- DGMR: Skilful precipitation nowcasting using deep generative models of radar [odf]
- Skilful nowcasting of extreme precipitation with NowcastNet (NowcastNet) [pdf]
- DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcasting [pdf]
- CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling [pdf]
- Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [pdf]
- NeuralGCM: Neural General Circulation Models for Weather and Climate [pdf]
- ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs [pdf]
- Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [pdf]
- WeatherBench: A benchmark dataset for data-driven weather forecasting [pdf]
- The ERA5 global reanalysis [pdf]
- SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology [pdf]
- WeatherBench 2: A benchmark for the next generation of data-driven global weather models [pdf]
- CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer [pdf]
- WEATHER-5K: A Large-scale Global Station Weather Dataset Towards Comprehensive Time-series Forecasting Benchmark [pdf]
- Can deep learning beat numerical weather prediction? [pdf]
- AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning [pdf]
- Anthropogenic fingerprints in daily precipitation revealed by deep learning [pdf]
- GenCast: Diffusion-based ensemble forecasting for medium-range weather [pdf]
- KARINA: An Efficient Deep Learning Model for Global Weather Forecast [pdf]
- SEEDS: Generative emulation of weather forecast ensembles with diffusion models [pdf]
- Aurora: A Foundation Model of the Atmosphere [pdf]
- ORCA: A Global Ocean Emulator for Multi-year to Decadal Predictions [pdf]
- GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations [pdf]
- ECMWF AI Models: AI-based weather forecasting models.
- Skyrim: AI weather models united.
- NVIDIA Earth2Mip: Earth-2 Model Intercomparison Project (MIP) is a python framework that enables climate researchers and scientists to inter-compare AI models for weather and climate.
- AI Models for All: Run AI NWP forecasts hassle-free, serverless in the cloud!
- OpenEarthLab: OpenEarthLab, aiming at developing cutting-edge Spatiaotemporal Generation algorithms and promoting the development of Earth Science.
