A collection of articles on Large Weather Models (LWMs), to make it easier to find and learn. π Contributions to this hub are welcome!
- 2024/05/06: CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer [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 1/12Β° resolution global ocean eddy-resolving forecasting model [link];
- 2024/02/02: ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [link];
- 2024/01/28: FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09β 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];
- 2023/12/11: 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]οΌ
- 2023/09/14: Pangu-Weather published in Nature [link]οΌ
- 2023/08/25: ClimaX published in ICML 2023 [link]οΌ
| LWM name | From | Date(1st) | Publication | Links |
|---|---|---|---|---|
| MetNet | 2020.03 | - | [paper] [github] | |
| FourCastNet | NVIDIA | 2022.02 | - | [paper] [github] |
| MetNet-2 | 2022.09 | Nature Communications | [paper] [github] | |
| Pangu-Weather | Huaiwei | 2022.11 | Nature | [paper] [github] |
| GraphCast | DeepMind | 2022.12 | Science | [paper] [github] |
| ClimaX | Microsoft | 2023.01 | ICML 2023 | [paper] [github] |
| Fengwu | Shanghai AI Lab | 2023.04 | - | [paper] [github] |
| MetNet-3 | 2023.06 | - | [paper] | |
| Fuxi | Fudan | 2023.06 | - | [paper] [github] |
| NowcastNet | Tsinghua | 2023.07 | Nature | [paper] |
| AI-GOMS | Tsinghua | 2023.08 | - | [paper] |
| Fuxi-Extreme | Fudan | 2023.10 | - | [paper] |
| NeuralGCM | DeepMind | 2023.11 | - | [paper] |
| FengWu-4DVar | Tsinghua | 2023.12 | ICML 2024 | [paper] |
| GenCast | DeepMind | 2023.12 | - | [paper] |
| DiffCast | HITsz | 2023.12 | CVPR 2024 | [paper] |
| FengWu-GHR | Shanghai AI Lab | 2024.01 | - | [paper] |
| ExtremeCast | Shanghai AI Lab | 2024.02 | - | [paper] [github] |
| XiHe | NUDT | 2024.02 | - | [paper] [github] |
| CasCast | Shanghai AI Lab | 2024.02 | ICML 2024 | [paper] |
| KARINA | KARINA | 2024.03 | - | [paper] |
| SEEDS | 2024.03 | Science Advances | [paper] | |
| CRA5 | Shanghai AI Lab | 2024.05 | - | [paper] [github] |
- WeatherBench: A benchmark dataset for data-driven weather forecasting [paper]
- WeatherBench 2: A benchmark for the next generation of data-driven global weather models [paper]
- MetNet: A Neural Weather Model for Precipitation Forecasting (MetNet) [paper]
- Deep learning for twelve hour precipitation forecasts (MetNet-2) [paper]
- Deep Learning for Day Forecasts from Sparse Observations (MetNet-3) [paper]
- FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators (FourCastNet) [paper]
- Accurate medium-range global weather forecasting with 3D neural networks (Pangu-Weather) [paper]
- Learning skillful medium-range global weather forecasting (GraphCast) [paper]
- ClimaX: A foundation model for weather and climate (ClimaX) [paper]
- FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead (FengWu) [paper]
- FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation [paper]
- FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [paper]
- ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [paper]
- FuXi: A cascade machine learning forecasting system for 15-day global weather forecast (FuXi) [paper]
- FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model (FuXi-Extreme) [paper]
- AI-GOMS: Large AI-Driven Global Ocean Modeling System (AI-GOMS) [paper]
- XiHe: A Data-Driven Model for Global Ocean Eddy-Resolving Forecasting [paper]
- Fourier Neural Operator with Learned Deformations for PDEs on General Geometries [paper]
- SFNO: Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere [paper]
- Earthformer: Exploring Space-Time Transformers for Earth System Forecasting [paper]
- PreDiff: Precipitation Nowcasting with Latent Diffusion Models [paper]
- DGMR: Skilful precipitation nowcasting using deep generative models of radar [paper]
- Skilful nowcasting of extreme precipitation with NowcastNet (NowcastNet) [paper]
- DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcasting [paper]
- CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling [paper]
- Can deep learning beat numerical weather prediction? [paper]
- AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning [paper]
- Anthropogenic fingerprints in daily precipitation revealed by deep learning [paper]
- Neural General Circulation Models [paper]
- GenCast: Diffusion-based ensemble forecasting for medium-range weather [paper]
- KARINA: An Efficient Deep Learning Model for Global Weather Forecast [paper]
- SEEDS: Generative emulation of weather forecast ensembles with diffusion models [paper];
- CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer [paper];
- IPCC
- IMO | WMO
- ECMWF