https://github.com/jaychempan/Awesome-LWMs
🌍 A Collection of Awesome Large Weather Models (LWMs) | AI for Earth (AI4Earth) | AI for Science (AI4Science)
https://github.com/jaychempan/Awesome-LWMs
List: Awesome-LWMs
ai-for-earth ai-for-science atmosphere-model foundation-model large-weather-models meteorology nowcasting ocean-modelling oceanography weather weather-forecast
Last synced: about 2 months ago
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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
- License: mit
- Created: 2023-12-07T16:25:55.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-16T13:36:53.000Z (7 months ago)
- Last Synced: 2024-10-29T10:00:10.461Z (7 months ago)
- Topics: ai-for-earth, ai-for-science, atmosphere-model, foundation-model, large-weather-models, meteorology, nowcasting, ocean-modelling, oceanography, weather, weather-forecast
- Homepage:
- Size: 168 KB
- Stars: 156
- Watchers: 9
- Forks: 17
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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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 / Julia 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
- 2025/02/25: Artificial Intelligence Forecasting System (AIFS), ECMWF’s AI forecasts, become operational [[link]](https://www.ecmwf.int/en/about/media-centre/news/2025/ecmwfs-ai-forecasts-become-operational).
- 2025/02/01: OneForecast, a global-regional nested weather forecasting framework based on graph neural networks [[link]](https://arxiv.org/abs/2502.00338).
- 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]](https://arxiv.org/abs/2412.15832).
- 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]](https://arxiv.org/abs/2412.15687).
- 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]](https://arxiv.org/abs/2412.12971).
- 2024/12/11: ECMWF releases its Artificial Intelligence Forecast System (AIFS) model and weights freely available on the web [[link]](https://www.ecmwf.int/en/about/media-centre/aifs-blog/2024/first-aifs-model-weights-are-now-open)
- 2024/12/04: Google DeepMind releases GenCast, an ensemble AI forecast model [[link]](https://deepmind.google/discover/blog/gencast-predicts-weather-and-the-risks-of-extreme-conditions-with-sota-accuracy/)
- 2024/09/20: IBM and Nasa Prithvi-WxC Foundation model [[link]](https://arxiv.org/abs/2409.13598)
- 2024/08/15: MetMamba, a DLWP model built on a state-of-the-art state-space model, Mamba, offers notable performance gains [[link]](https://www.arxiv.org/pdf/2408.06400);
- 2024/07/30: FuXi-S2S published in Nature Communications [[link]](https://www.nature.com/articles/s41467-024-50714-1);
- 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);
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]](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);
- 2023/10/25: IBM and Nasa Prithvi-100M Model [[link]](https://arxiv.org/abs/2310.18660);
- 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 | Model Licence |Weights Licence |
| ------------- | --------------- | ---------------- | -------------- | ------------------------------------------------------------ | ------------- | ------- |
| MetNet | Google | 2020.03 | - | [[arXiv paper]](https://arxiv.org/abs/2003.12140) [[github]](https://github.com/openclimatefix/metnet) | [[MIT]](https://github.com/openclimatefix/metnet/blob/main/LICENSE) | N/A |
| FourCastNet | NVIDIA | 2022.02 | PASC 23 | [[arXiv paper]](https://arxiv.org/abs/2202.11214) [[github]](https://github.com/NVlabs/FourCastNet) | [[BSD-3]](https://github.com/NVlabs/FourCastNet/blob/master/LICENSE) | [[BSD-3]](https://github.com/NVlabs/FourCastNet/blob/master/LICENSE) |
| MetNet-2 | Google | 2022.09 | Nature Communications 2022 | [[Nature paper]](https://www.nature.com/articles/s41467-022-32483-x) [[github]](https://github.com/openclimatefix/metnet) | [[MIT]](https://github.com/openclimatefix/metnet/blob/main/LICENSE) | N/A |
| Pangu-Weather | Huaiwei | 2022.11 | Nature 2023 | [[Nature paper]](https://www.nature.com/articles/s41586-023-06185-3) [[github]](https://github.com/198808xc/Pangu-Weather) | Not Specified | [[CC-BY-NC-SA 4.0]](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
| GraphCast | DeepMind | 2022.12 | Science 2023 | [[Science paper]](https://www.science.org/doi/10.1126/science.adi2336) [[github]](https://github.com/google-deepmind/graphcast) | [[Apache 2.0]](https://github.com/google-deepmind/graphcast/blob/main/LICENSE) | [[CC-BY-NC-SA 4.0]](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
| ClimaX | Microsoft | 2023.01 | ICML 2023 | [[arXiv paper]](https://arxiv.org/abs/2301.10343) [[github]](https://github.com/microsoft/ClimaX) | [[MIT]](https://github.com/microsoft/ClimaX/blob/main/LICENSE) | Not specificied ([[MIT]](https://github.com/microsoft/ClimaX/blob/main/LICENSE)?) |
| Fengwu | Shanghai AI Lab | 2023.04 | - | [[arXiv paper]](https://arxiv.org/abs/2304.02948) [[github]](https://github.com/OpenEarthLab/FengWu) | Not Specified | Not Specified |
| MetNet-3 | Google | 2023.06 | - | [[arXiv paper]](https://arxiv.org/abs/2306.06079) | - | - |
| FuXi | Fudan | 2023.06 | npj 2023 | [[arXiv paper]](https://arxiv.org/abs/2306.12873) [[github]](https://github.com/tpys/ai-models-fuxi) | [[Apache 2.0]](https://github.com/tpys/ai-models-fuxi/blob/main/LICENSE) | [[CC-BY-NC-SA 4.0]](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
| NowcastNet | Tsinghua | 2023.07 | Nature 2023 | [[Nature paper]](https://www.nature.com/articles/s41586-023-06184-4) | - | - |
| AI-GOMS | Tsinghua | 2023.08 | - | [[arXiv paper]](https://arxiv.org/abs/2308.03152) | - | - |
| Prithvi-100M | IBM / Nasa | 2023.08 | | [[arXiv paper]](https://arxiv.org/abs/2310.18660) [[Hugging Face]](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M/tree/main) | [[Apache 2.0]](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) | [[Apache 2.0]](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) |
| FuXi-Extreme | Fudan | 2023.10 | - | [[arXiv paper]](https://arxiv.org/abs/2310.19822) | - | - |
| NeuralGCM | DeepMind | 2023.11 | - | [[arXiv paper]](https://arxiv.org/abs/2311.07222) | - | - |
| FengWu-4DVar | Tsinghua | 2023.12 | ICML 2024 | [[arXiv paper]](https://arxiv.org/abs/2312.12455) | - | - |
| FengWu-Adas | Shanghai AI Lab | 2023.12 | - | [[arXiv paper]](https://arxiv.org/abs/2312.12462) | - | - |
| FuXi-S2S | Fudan | 2023.12 | Nature Communications 2024| [[arXiv paper]](https://arxiv.org/abs/2312.09926) [[NC paper]](https://www.nature.com/articles/s41467-024-50714-1) | - | - |
| GenCast | Google DeepMind | 2023.12 | Nature | [[paper]](https://www.nature.com/articles/s41586-024-08252-9) [[github]](https://github.com/google-deepmind/graphcast) | [[Apache 2.0]](https://github.com/google-deepmind/graphcast/blob/main/LICENSE) | [[CC-BY-NC-SA 4.0]](https://creativecommons.org/licenses/by-nc-sa/4.0/)
| DiffCast | HITsz | 2023.12 | CVPR 2024 | [[arXiv paper]](https://arxiv.org/abs/2312.06734) | - | - |
| FengWu-GHR | Shanghai AI Lab | 2024.01 | - | [[arXiv paper]](https://arxiv.org/abs/2402.00059) | - | - |
| ExtremeCast | Shanghai AI Lab | 2024.02 | - | [[arXiv paper]](https://arxiv.org/abs/2402.01295) [[github]](https://github.com/black-yt/ExtremeCast) | Not Specified | Not Specified |
| XiHe | NUDT | 2024.02 | - | [[arXiv paper]](https://arxiv.org/abs/2402.02995) [[github]](https://github.com/Ocean-Intelligent-Forecasting/XiHe-GlobalOceanForecasting) | Not Specified | Not Specified |
| CasCast | Shanghai AI Lab | 2024.02 | ICML 2024 | [[arXiv paper]](https://arxiv.org/abs/2402.04290) [[github]](https://github.com/OpenEarthLab/CasCast) | Not Specified | Not Specified |
| KARINA | KIST | 2024.03 | - | [[arXiv paper]](https://arxiv.org/abs/2403.10555) | - | - |
| SEEDS | Google | 2024.03 | Science Advances | [[Science Advances paper]](https://www.science.org/doi/full/10.1126/sciadv.adk4489) | - | - |
| FuXi-DA | Fudan | 2024.04 | - | [[arXiv paper]](https://arxiv.org/abs/2404.08522) | - | - |
| ClimODE | Aalto University | 2024.04 | ICLR 2024 (Oral) | [[arXiv paper]](https://arxiv.org/abs/2404.10024) [[github]](https://github.com/Aalto-QuML/ClimODE) | [[MIT]](https://github.com/Aalto-QuML/ClimODE/blob/main/LICENSE) | Not specified or non applicable |
| FuXi-ENS | Fudan | 2024.05 | - | [[arXiv paper]](https://arxiv.org/abs/2405.05925) | - | - |
| Aurora | Microsoft | 2024.05 | - | [[arXiv paper]](https://arxiv.org/abs/2405.13063) [[github]](https://github.com/microsoft/aurora/tree/main) | [[MIT]](https://github.com/microsoft/aurora/blob/main/LICENSE.txt) | [[CC-BY-NC-SA 4.0]](https://huggingface.co/microsoft/aurora/blob/main/README.md) |
| WeatherGFT | Shanghai AI Lab | 2024.05 | NeurIPS 2024 | [[arXiv paper]](https://arxiv.org/abs/2405.13796) [[github]](https://github.com/black-yt/WeatherGFT) | Not Specified | Not Specified |
| ORCA | Shanghai AI Lab | 2024.05 | - | [[arXiv paper]](https://arxiv.org/abs/2405.15412) [[github]](https://github.com/OpenEarthLab/ORCA) | - | - |
| MetMamba | Beijing PRESKY Technology | 2024.08 | - | [[paper]](https://www.arxiv.org/pdf/2408.06400) | Not Specified | Not Specified |
| Prithvi-WxC | IBM / Nasa | 2024.09 | - | [[arXiv paper]](https://arxiv.org/abs/2409.13598) [[Hugging Face]](https://huggingface.co/Prithvi-WxC) | [[CDLA Permissive 2.0]](https://spdx.org/licenses/CDLA-Permissive-2.0) | [[CDLA Permissive 2.0]](https://spdx.org/licenses/CDLA-Permissive-2.0) |
| AIFS | ECMWF | 2024.12 | - | [[arXiv paper]](https://arxiv.org/abs/2406.01465) [[Hugging Face]](https://huggingface.co/ecmwf/aifs-single) | [[CC BY 4.0]](https://creativecommons.org/licenses/by/4.0/) | [[CC BY 4.0]](https://creativecommons.org/licenses/by/4.0/)
| ArchesWeatherGen | INRIA | 2024.12 | - | [[arXiv paper]](https://arxiv.org/pdf/2412.12971)[[github]](https://github.com/gcouairon/ArchesWeather) | [[CC-BY-NC-SA 4.0]](https://creativecommons.org/licenses/by-nc-sa/4.0/) | [[CC-BY-NC-SA 4.0]](https://creativecommons.org/licenses/by-nc-sa/4.0/)
| GraphDOP | ECMWF | 2024.12 | - | [[arXiv paper]](https://arxiv.org/abs/2412.15687) | - | -
| AIFS-CRPS | ECMWF | 2024.12 | - | [[arXiv paper]](https://arxiv.org/abs/2412.15832) | - | -
| OneForecast | Tsinghua | 2025.02 | - | [[arXiv paper]](https://arxiv.org/abs/2502.00338) [[github]](https://github.com/YuanGao-YG/OneForecast) | [[MIT]](https://github.com/YuanGao-YG/OneForecast/blob/main/LICENSE) | [[MIT]](https://github.com/YuanGao-YG/OneForecast/blob/main/LICENSE)?
## 🗃️ 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 | Beijing PRESKY Technology | 2024.08 | - | [[paper]](https://www.arxiv.org/pdf/2408.06400) |
| Extreme Weather Bench | Brightband | 2025.01 | - | [[blog]](https://www.brightband.com/blog/extreme-weather-bench/)[[github]](https://github.com/brightbandtech/ExtremeWeatherBench) |
## 📖 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
- NeuralGCM: 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)
- GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations [[pdf]](https://arxiv.org/abs/2412.15687)
## 🚀 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.
## 🌟 Star History
## 🎉 Contributors
