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🌍 A Collection of Awesome Large Weather Models (LWMs) | AI for Earth (AI4Earth) | AI for Science (AI4Science)
https://github.com/jaychempan/Awesome-LWMs
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🌍 A Collection of Awesome Large Weather Models (LWMs) | AI for Earth (AI4Earth) | AI for Science (AI4Science)
Host: GitHub
URL: https://github.com/jaychempan/Awesome-LWMs
Owner: jaychempan
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Created: 2023-12-07T16:25:55.000Z (9 months ago)
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Topics: ai-for-earth, ai-for-science, large-weather-models, meteorology, nowcasting, ocean-modelling, oceanography, weather, weather-forecast
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- Readme: README.md
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ultimate-awesome - Awesome-LWMs - 🌍 A Collection of Awesome Large Weather Models (LWMs) | AI for Earth (AI4Earth) | AI for Science (AI4Science). (Other Lists / PowerShell Lists)
README

        # 🌍 Awesome Large Weather Models (LWMs) | AI for Earth (AI4Earth) | AI for Science (AI4Science)

## 🧭 Guideline

A collection of articles on **Large Weather Models (LWMs)**, to make it easier to find and learn. 👏 Contributions to this hub are welcome!

- [🌍 Awesome Large Weather Models (LWMs) | AI for Earth (AI4Earth) | AI for Science (AI4Science)](#-awesome-large-weather-models-lwms--ai-for-earth-ai4earth--ai-for-science-ai4science)

  - [🧭 Guideline](#-guideline)

  - [🆕 LWMs News](#-lwms-news)

  - [🗂️ LWMs Lists](#️-lwms-lists)

  - [🗃️ Dataset Lists](#️-dataset-lists)

  - [📖 Papers](#-papers)

    - [WeatherBench](#weatherbench)

    - [MetNet](#metnet)

    - [FourCastNet](#fourcastnet)

    - [Pangu-Weather](#pangu-weather)

    - [GraphCast](#graphcast)

    - [ClimaX](#climax)

    - [FengWu](#fengwu)

    - [FuXi](#fuxi)

    - [AI-GOMS](#ai-goms)

    - [XiHe](#xihe)

    - [FNO](#fno)

    - [Nowcast](#nowcast)

    - [Physics-AI](#physics-ai)

    - [Datasets](#datasets)

    - [More](#more)

  - [🚀 Code](#-code)

## 🆕 LWMs News

- 2024/06/20: WEATHER-5K: A Large-scale Global Station Weather Dataset Towards Comprehensive Time-series Forecasting Benchmark [[link]](https://arxiv.org/abs/2406.14399);

- 2024/05/24: ORCA: A Global Ocean Emulator for Multi-year to Decadal Predictions [[link]](https://arxiv.org/abs/2405.15412);

- 2024/05/22: Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [[link]](https://arxiv.org/abs/2405.13796);

- 2024/05/20: Aurora: A Foundation Model of the Atmosphere [[link]](https://arxiv.org/abs/2405.13063);

- 2024/05/09: FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting [[link]](https://arxiv.org/abs/2405.05925);

- 2024/05/06: CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer [[link]](https://arxiv.org/abs/2405.03376);

- 2024/04/15: ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs [[link]](https://arxiv.org/abs/2404.10024);

- 2024/04/12: FuXi-DA: A Generalized Deep Learning Data Assimilation Framework for Assimilating Satellite Observations [[link]](https://arxiv.org/abs/2404.08522);

- 2024/03/29: SEEDS: Generative emulation of weather forecast ensembles with diffusion models [[link]](https://www.science.org/doi/full/10.1126/sciadv.adk4489);

- 2024/03/13: KARINA: An Efficient Deep Learning Model for Global Weather Forecast [[link]](https://arxiv.org/abs/2403.10555);

- 2024/02/06: CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling [[link]](https://arxiv.org/abs/2402.04290);

- 2024/02/04: XiHe, the first data-driven 1/12° resolution global ocean eddy-resolving forecasting model [[link]](https://arxiv.org/abs/2402.02995);

- 2024/02/02: ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [[link]](https://arxiv.org/abs/2402.01295);

- 2024/01/28: FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09∘ horizontal resolution [[link]](https://arxiv.org/abs/2402.00059);

- 2023/12/27: GenCast, a ML-based generative model for ensemble weather forecasting [[link]](https://arxiv.org/abs/2312.15796);

- 2023/12/16: Four-Dimensional Variational (4DVar) assimilation, and develop an AI-based cyclic weather forecasting system, FengWu-4DVar [[link]](https://arxiv.org/abs/2312.12455);

- 2023/12/15: FuXi-S2S: An accurate machine learning model for global subseasonal forecasts [[link]](https://arxiv.org/abs/2312.09926);

- 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]](https://arxiv.org/abs/2312.06734);

- 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]](https://arxiv.org/abs/2311.07222);

- 2023/12/13: FuXi is open source [[link]](https://github.com/tpys/ai-models-fuxi);

- 2023/11/14: GraphCast published in Science [[link]](https://www.science.org/doi/abs/10.1126/science.adi2336);

- 2023/09/14: Pangu-Weather published in Nature [[link]](https://www.nature.com/articles/s41586-023-06185-3);

- 2023/08/25: ClimaX published in ICML 2023 [[link]](https://openreview.net/forum?id=TowCaiz7Ui);

## 🗂️ LWMs Lists

| LWM name     | From            | Date(1st) | Publication | Links                                     |

| ------------- | --------------- | ---------------- | -------------- | ------------------------------------------------------------ |

| MetNet   | Google        | 2020.03        | -              | [[paper]](https://arxiv.org/abs/2003.12140) [[github]](https://github.com/openclimatefix/metnet) |

| FourCastNet   | NVIDIA        | 2022.02          | PASC 23              | [[paper]](https://arxiv.org/abs/2202.11214) [[github]](https://github.com/NVlabs/FourCastNet) |

| MetNet-2   | Google        | 2022.09        | Nature Communications | [[paper]](https://www.nature.com/articles/s41467-022-32483-x) [[github]](https://github.com/openclimatefix/metnet)  |

| Pangu-Weather | Huaiwei         | 2022.11          | Nature         | [[paper]](https://www.nature.com/articles/s41586-023-06185-3) [[github]](https://github.com/198808xc/Pangu-Weather) |

| GraphCast     | DeepMind        | 2022.12          | Science        | [[paper]](https://www.science.org/doi/10.1126/science.adi2336) [[github]](https://github.com/google-deepmind/graphcast) |

| ClimaX        | Microsoft       | 2023.01          | ICML 2023      | [[paper]](https://arxiv.org/abs/2301.10343) [[github]](https://github.com/microsoft/ClimaX) |

| Fengwu        | Shanghai AI Lab | 2023.04          | -              | [[paper]](https://arxiv.org/abs/2304.02948) [[github]](https://github.com/OpenEarthLab/FengWu) |

| MetNet-3  | Google        | 2023.06        | - | [[paper]](https://arxiv.org/abs/2306.06079)  | 

| FuXi          | Fudan           | 2023.06          | npj 2023              | [[paper]](https://arxiv.org/abs/2306.12873) [[github]](https://github.com/tpys/ai-models-fuxi) |

| NowcastNet   | Tsinghua        | 2023.07         | Nature      | [[paper]](https://www.nature.com/articles/s41586-023-06184-4) |

| AI-GOMS       | Tsinghua        | 2023.08          | -              | [[paper]](https://arxiv.org/abs/2308.03152)                  |

| FuXi-Extreme    | Fudan           | 2023.10        | -              | [[paper]](https://arxiv.org/abs/2310.19822) |

| NeuralGCM    | DeepMind           | 2023.11        | -              | [[paper]](https://arxiv.org/abs/2311.07222) |

| FengWu-4DVar    | Tsinghua           | 2023.12        | ICML 2024         | [[paper]](https://arxiv.org/abs/2312.12455) |

| FengWu-Adas     | Shanghai AI Lab  | 2023.12        | -              | [[paper]](https://arxiv.org/abs/2312.12462) | 

| FuXi-S2S     | Fudan  | 2023.12        | -              | [[paper]](https://arxiv.org/abs/2312.09926) | 

| GenCast    | DeepMind           | 2023.12        | -              | [[paper]](https://arxiv.org/abs/2312.15796) |

| DiffCast    | HITsz    | 2023.12        | CVPR 2024              | [[paper]](https://arxiv.org/abs/2312.06734) | 

| FengWu-GHR    | Shanghai AI Lab           | 2024.01        | -              | [[paper]](https://arxiv.org/abs/2402.00059) |

| ExtremeCast   | Shanghai AI Lab           | 2024.02        | -              | [[paper]](https://arxiv.org/abs/2402.01295) [[github]](https://github.com/black-yt/ExtremeCast)| 

| XiHe    | NUDT           | 2024.02        | -              | [[paper]](https://arxiv.org/abs/2402.02995) [[github]](https://github.com/Ocean-Intelligent-Forecasting/XiHe-GlobalOceanForecasting)| 

| CasCast    | Shanghai AI Lab           | 2024.02        | ICML 2024              | [[paper]](https://arxiv.org/abs/2402.04290) [[github]](https://github.com/OpenEarthLab/CasCast)| 

| KARINA    | KIST           | 2024.03        | -             | [[paper]](https://arxiv.org/abs/2403.10555) | 

| SEEDS    | Google           | 2024.03        | Science Advances              | [[paper]](https://www.science.org/doi/full/10.1126/sciadv.adk4489) | 

| FuXi-DA          | Fudan           | 2024.04          | -              | [[paper]](https://arxiv.org/abs/2404.08522)  |

| ClimODE  | Aalto University | 2024.04        | ICLR 2024 (Oral)           | [[paper]](https://arxiv.org/abs/2404.10024) [[github]](https://github.com/Aalto-QuML/ClimODE)  |

| FuXi-ENS     | Fudan  | 2024.05        | -              | [[paper]](https://arxiv.org/abs/2405.05925) | 

| Aurora     | Microsoft  | 2024.05        | -              | [[paper]](https://arxiv.org/abs/2405.13063)| 

| WeatherGFT     | Shanghai AI Lab  | 2024.05        | -              | [[paper]](https://arxiv.org/abs/2405.13796) [[github]](https://github.com/black-yt/WeatherGFT)| 

| ORCA     | Shanghai AI Lab  | 2024.05        | -              | [[paper]](https://arxiv.org/abs/2405.15412) [[github]](https://github.com/OpenEarthLab/ORCA)| 

## 🗃️ Dataset Lists

| Dataset name     | From            | Date(1st) | Publication | Links                                     |

| ------------- | --------------- | ---------------- | -------------- | ------------------------------------------------------------ |

| WeatherBench     | Google        | 2020.02        | JAMES 2020 | [[paper]](https://arxiv.org/abs/2002.00469) [[github]](https://github.com/pangeo-data/WeatherBench) |

| ERA5             | ECMWF         | 2020.05        | -              | [[paper]](https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.3803?_hsenc=p2ANqtz-_Rot9IrSLOikF_COUhGRbbp9PzsUsmjSNJP6g-f4x5EegqaFipfLPkl9hMvTpyZVGACYUneXK1UVgfCc-V_Lx98XGPtw) [[link]](https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset&keywords=((%20%22Product%20type:%20Reanalysis%22%20)%20AND%20(%20%22Variable%20domain:%20Atmosphere%20(surface)%22%20)%20AND%20(%20%22Spatial%20coverage:%20Global%22%20)%20AND%20(%20%22Temporal%20coverage:%20Past%22%20)%20AND%20(%20%22Provider:%20Copernicus%20C3S%22%20))) |

| SEVIR            | MIT           | 2020.06        | NeurIPS 2020   | [[paper]](https://proceedings.neurips.cc/paper_files/paper/2020/file/fa78a16157fed00d7a80515818432169-Paper.pdf) [[github]](https://github.com/MIT-AI-Accelerator/neurips-2020-sevir) [[link]](https://sevir.mit.edu/) |

| WeatherBench2    | Google        | 2023.08        | -              | [[paper]](https://arxiv.org/abs/2308.15560) [[github]](https://github.com/google-research/weatherbench2) |

| CRA5             | Shanghai AI Lab        | 2024.05        | -              | [[paper]](https://arxiv.org/abs/2405.03376) [[github]](https://github.com/taohan10200/CRA5)| 

| WEATHER-5K       | Shanghai AI Lab        | 2024.06        | -              | [[paper]](https://arxiv.org/abs/2406.14399) [[github]](https://github.com/taohan10200/WEATHER-5K) |

## 📖 Papers

### WeatherBench

- WeatherBench: A benchmark dataset for data-driven weather forecasting [[pdf]](https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020MS002203)

- WeatherBench 2: A benchmark for the next generation of data-driven global weather models [[pdf]](https://arxiv.org/pdf/2308.15560)

### MetNet

- MetNet: A Neural Weather Model for Precipitation Forecasting (MetNet) [[pdf]](https://arxiv.org/pdf/2003.12140)

- Deep learning for twelve hour precipitation forecasts (MetNet-2) [[pdf]](https://www.nature.com/articles/s41467-022-32483-x.pdf)

- Deep Learning for Day Forecasts from Sparse Observations (MetNet-3) [[pdf]](https://arxiv.org/pdf/2306.06079)

### FourCastNet

- FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators (FourCastNet) [[pdf]](https://arxiv.org/pdf/2202.11214)

### Pangu-Weather

- Accurate medium-range global weather forecasting with 3D neural networks (Pangu-Weather) [[pdf]](https://www.nature.com/articles/s41586-023-06185-3.pdf)

### GraphCast

- Learning skillful medium-range global weather forecasting (GraphCast) [[pdf]](https://arxiv.org/pdf/2212.12794)

### ClimaX

- ClimaX: A foundation model for weather and climate (ClimaX) [[pdf]](https://arxiv.org/pdf/2301.10343)

### FengWu

- FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead (FengWu) [[pdf]](https://arxiv.org/pdf/2304.02948)

- FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation [[pdf]](https://arxiv.org/pdf/2312.12455)

- Towards an end-to-end artificial intelligence driven global weather forecasting system [[pdf]](https://arxiv.org/pdf/2312.12462)

- FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [[pdf]](https://arxiv.org/pdf/2402.00059)

- ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [[pdf]](https://arxiv.org/pdf/2402.01295)

### FuXi

- FuXi: A cascade machine learning forecasting system for 15-day global weather forecast (FuXi) [[pdf]](https://arxiv.org/pdf/2306.12873)

- FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model (FuXi-Extreme) [[pdf]](https://arxiv.org/pdf/2310.19822)

- FuXi-S2S: An accurate machine learning model for global subseasonal forecasts [[pdf]](https://arxiv.org/pdf/2312.09926)

- Fuxi-DA: A Generalized Deep Learning Data Assimilation Framework for Assimilating Satellite Observations [[pdf]](https://arxiv.org/pdf/2404.08522)

- FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting [[pdf]](https://arxiv.org/pdf/2405.05925)

### AI-GOMS

- AI-GOMS: Large AI-Driven Global Ocean Modeling System (AI-GOMS) [[pdf]](https://arxiv.org/pdf/2308.03152)

### XiHe

- XiHe: A Data-Driven Model for Global Ocean Eddy-Resolving Forecasting [[pdf]](https://arxiv.org/pdf/2402.02995)

### FNO

- Fourier Neural Operator with Learned Deformations for PDEs on General Geometries [[pdf]](https://arxiv.org/pdf/2207.05209)

- SFNO: Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere [[pdf]](https://arxiv.org/pdf/2306.03838)

### Nowcast

- Earthformer: Exploring Space-Time Transformers for Earth System Forecasting [[pdf]](https://proceedings.neurips.cc/paper_files/paper/2022/file/a2affd71d15e8fedffe18d0219f4837a-Paper-Conference.pdf)

- PreDiff: Precipitation Nowcasting with Latent Diffusion Models [[pdf]](https://arxiv.org/pdf/2307.10422)

- DGMR: Skilful precipitation nowcasting using deep generative models of radar [[odf]](https://www.nature.com/articles/s41586-021-03854-z.pdf)

- Skilful nowcasting of extreme precipitation with NowcastNet (NowcastNet) [[pdf]](https://www.nature.com/articles/s41586-023-06184-4.pdf)

- DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcasting [[pdf]](https://arxiv.org/pdf/2312.06734)

- CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling [[pdf]](https://arxiv.org/pdf/2402.04290)

- Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [[pdf]](https://arxiv.org/pdf/2405.13796)

### Physics-AI

- Neural General Circulation Models for Weather and Climate [[pdf]](https://arxiv.org/pdf/2311.07222)

- ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs [[pdf]](https://arxiv.org/pdf/2404.10024)

- Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [[pdf]](https://arxiv.org/pdf/2405.13796)

### Datasets

- WeatherBench: A benchmark dataset for data-driven weather forecasting [[pdf]](https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020MS002203)

- The ERA5 global reanalysis [[pdf]](https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/qj.3803)

- SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology [[pdf]](https://proceedings.neurips.cc/paper_files/paper/2020/file/fa78a16157fed00d7a80515818432169-Paper.pdf)

- WeatherBench 2: A benchmark for the next generation of data-driven global weather models [[pdf]](https://arxiv.org/pdf/2308.15560)

- CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer [[pdf]](https://arxiv.org/pdf/2405.03376)

- WEATHER-5K: A Large-scale Global Station Weather Dataset Towards Comprehensive Time-series Forecasting Benchmark [[pdf]](https://arxiv.org/pdf/2406.14399)

### More

- Can deep learning beat numerical weather prediction? [[pdf]](https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2020.0097?download=true)

- AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning [[pdf]](https://arxiv.org/pdf/2308.13280)

- Anthropogenic fingerprints in daily precipitation revealed by deep learning [[pdf]](https://www.nature.com/articles/s41586-023-06474-x.pdf)

- GenCast: Diffusion-based ensemble forecasting for medium-range weather [[pdf]](https://arxiv.org/pdf/2312.15796)

- KARINA: An Efficient Deep Learning Model for Global Weather Forecast [[pdf]](https://arxiv.org/pdf/2403.10555)

- SEEDS: Generative emulation of weather forecast ensembles with diffusion models [[pdf]](https://www.science.org/doi/pdf/10.1126/sciadv.adk4489)

- Aurora: A Foundation Model of the Atmosphere [[pdf]](https://arxiv.org/pdf/2405.13063)

- ORCA: A Global Ocean Emulator for Multi-year to Decadal Predictions [[pdf]](https://arxiv.org/pdf/2405.15412)

## 🚀 Code

- [ECMWF AI Models](https://github.com/ecmwf-lab/ai-models): AI-based weather forecasting models.

- [Skyrim](https://github.com/secondlaw-ai/skyrim): AI weather models united.

- [NVIDIA Earth2Mip](https://github.com/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](https://github.com/darothen/ai-models-for-all): Run AI NWP forecasts hassle-free, serverless in the cloud!

- [OpenEarthLab](https://github.com/OpenEarthLab): OpenEarthLab, aiming at developing cutting-edge Spatiaotemporal Generation algorithms and promoting the development of Earth Science.