aurora
Implementation of the Aurora model for Earth system forecasting
Science Score: 49.0%
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Repository
Implementation of the Aurora model for Earth system forecasting
Basic Info
- Host: GitHub
- Owner: microsoft
- License: other
- Language: Python
- Default Branch: main
- Homepage: https://microsoft.github.io/aurora
- Size: 19.3 MB
Statistics
- Stars: 724
- Watchers: 18
- Forks: 110
- Open Issues: 43
- Releases: 14
Topics
Metadata Files
README.md
Aurora: A Foundation Model for the Earth System
Implementation of the Aurora model for Earth system forecasting.
Please see the documentation for detailed instructions and more examples. You can also directly go to a full-fledged example that runs the model on ERA5.
Cite us as follows:
@article{bodnar2025aurora,
title = {A Foundation Model for the Earth System},
author = {Cristian Bodnar and Wessel P. Bruinsma and Ana Lucic and Megan Stanley and Anna Allen and Johannes Brandstetter and Patrick Garvan and Maik Riechert and Jonathan A. Weyn and Haiyu Dong and Jayesh K. Gupta and Kit Thambiratnam and Alexander T. Archibald and Chun-Chieh Wu and Elizabeth Heider and Max Welling and Richard E. Turner and Paris Perdikaris},
journal = {Nature},
year = {2025},
month = {May},
day = {21},
issn = {1476-4687},
doi = {10.1038/s41586-025-09005-y},
url = {https://doi.org/10.1038/s41586-025-09005-y},
}
Contents:
- What is Aurora?
- Getting Started
- Contributing
- License
- Security
- Responsible AI Transparency Documentation
- Trademarks
- FAQ
Please email AIWeatherClimate@microsoft.com if you are interested in using Aurora for commercial applications. For research-related questions or technical support with the code here, please open an issue or reach out to the authors of the paper.
What is Aurora?
Aurora is a machine learning model that can predict atmospheric variables, such as temperature. It is a foundation model, which means that it was first generally trained on a lot of data, and then can be adapted to specialised atmospheric forecasting tasks with relatively little data. We provide four such specialised versions: one for medium-resolution weather prediction, one for high-resolution weather prediction, one for air pollution prediction, and one for ocean wave prediction.
Getting Started
Install with pip:
bash
pip install microsoft-aurora
Or with conda / mamba:
bash
mamba install microsoft-aurora -c conda-forge
Run the pretrained small model on random data:
```python from datetime import datetime
import torch
from aurora import AuroraSmallPretrained, Batch, Metadata
model = AuroraSmallPretrained() model.load_checkpoint()
batch = Batch( surfvars={k: torch.randn(1, 2, 17, 32) for k in ("2t", "10u", "10v", "msl")}, staticvars={k: torch.randn(17, 32) for k in ("lsm", "z", "slt")}, atmosvars={k: torch.randn(1, 2, 4, 17, 32) for k in ("z", "u", "v", "t", "q")}, metadata=Metadata( lat=torch.linspace(90, -90, 17), lon=torch.linspace(0, 360, 32 + 1)[:-1], time=(datetime(2020, 6, 1, 12, 0),), atmoslevels=(100, 250, 500, 850), ), )
prediction = model.forward(batch)
print(prediction.surf_vars["2t"]) ```
Note that this will incur a 500 MB download.
Please read the documentation for more detailed instructions and for which models are available.
Contributing
See CONTRIBUTING.md.
License
See LICENSE.txt.
Security
See SECURITY.md.
Responsible AI Transparency Documentation
An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Creating a system that is fit for its intended purpose requires an understanding of how the technology works, its capabilities and limitations, and how to achieve the best performance. Microsoft has a broad effort to put our AI principles into practice. To find out more, see Responsible AI principles from Microsoft.
Use of this code
Our goal in publishing this code is (1) to facilitate reproducibility of our paper and (2) to support and accelerate further research into foundation model for atmospheric forecasting. This code has not been developed nor tested for non-academic purposes and hence should only be used as such completely at your own risk.
Limitations
Although Aurora was trained to accurately predict future weather, air pollution, and ocean waves, Aurora is based on neural networks, which means that there are no strict guarantees that predictions will always be accurate. Altering the inputs, providing a sample that was not in the training set, or even providing a sample that was in the training set but is simply unlucky may result in arbitrarily poor predictions. In addition, even though Aurora was trained on a wide variety of data sets, it is possible that Aurora inherits biases present in any one of those data sets. A forecasting system like Aurora is only one piece of the puzzle in a weather prediction pipeline, and its outputs are not meant to be directly used by people or businesses to plan their operations. A series of additional verification tests are needed before it can become operationally useful.
In addition to the above, the models published here are streamlined versions of the models originally developed internally. Whereas we tried to be as thorough as possible, it is possible that the models published here deviate from the original model in subtle, unintended ways. This may affect predictive performance.
Data
The models included in the code have been trained on a variety of publicly available data. A description of all data, including download links, can be found in Supplementary C of the paper. The checkpoints include data from ERA5, CMIP6 (CMCC-CM2-VHR4 and ECMWF-IFS-HR), HRES forecasts, GFS T0, GFS forecasts, HRES T0, HRES analysis, HRES-WAM analysis, CAMS reanalysis, and CAMS analysis.
Evaluations
All versions of Aurora were extensively evaluated by evaluating predictions on data not seen during training. These evaluations not only compare measures of accuracy, such as the root mean square error and anomaly correlation coefficient, but also look at the behaviour in extreme situations, like extreme heat and cold, and rare events, like Storm Ciarán in 2023. These evaluations are the main topic of the paper.
Note: The documentation included in this file is for informational purposes only and is not intended to supersede the applicable license terms.
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
FAQ
How do I setup the repo for local development?
First, install the repository in editable mode and setup pre-commit:
bash
make install
To run the tests and print coverage, run
bash
make test
You can then explore the coverage in the browser by opening htmlcov/index.html.
To locally build the documentation, run
bash
make docs
To locally view the documentation, open docs/_build/index.html in your browser.
Owner
- Name: Microsoft
- Login: microsoft
- Kind: organization
- Email: opensource@microsoft.com
- Location: Redmond, WA
- Website: https://opensource.microsoft.com
- Twitter: OpenAtMicrosoft
- Repositories: 7,257
- Profile: https://github.com/microsoft
Open source projects and samples from Microsoft
GitHub Events
Total
- Create event: 29
- Release event: 5
- Issues event: 65
- Watch event: 354
- Delete event: 14
- Member event: 2
- Issue comment event: 171
- Push event: 191
- Pull request review comment event: 18
- Pull request review event: 47
- Pull request event: 48
- Fork event: 67
Last Year
- Create event: 29
- Release event: 5
- Issues event: 65
- Watch event: 354
- Delete event: 14
- Member event: 2
- Issue comment event: 171
- Push event: 191
- Pull request review comment event: 18
- Pull request review event: 47
- Pull request event: 48
- Fork event: 67
Committers
Last synced: 6 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Wessel Bruinsma | w****a@g****m | 76 |
| megstanley | m****4@g****m | 34 |
| Ana Lucic | a****c@m****m | 2 |
| luciano-drozda | d****o@g****m | 1 |
| leonardledy | 1****y | 1 |
| isaaccorley | 2****y | 1 |
| Sebastian Ehlert | 2****k | 1 |
| Scott Chamberlin | s****a@l****m | 1 |
| Lukas Kuhn | l****u@g****m | 1 |
| Hrrsmjd | 6****d | 1 |
| Emmanuel Ferdman | e****n@g****m | 1 |
| AnUnpronounceableName | b****n@k****i | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 79
- Total pull requests: 112
- Average time to close issues: 10 days
- Average time to close pull requests: 3 days
- Total issue authors: 54
- Total pull request authors: 16
- Average comments per issue: 1.22
- Average comments per pull request: 0.45
- Merged pull requests: 91
- Bot issues: 3
- Bot pull requests: 1
Past Year
- Issues: 70
- Pull requests: 83
- Average time to close issues: 11 days
- Average time to close pull requests: 3 days
- Issue authors: 50
- Pull request authors: 9
- Average comments per issue: 1.3
- Average comments per pull request: 0.45
- Merged pull requests: 67
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
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- microsoft-github-policy-service[bot] (3)
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- danicamacho (1)
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Pull Request Authors
- wesselb (77)
- megstanley (6)
- a-lucic (4)
- scottcha (4)
- leonardledy (2)
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- Total packages: 2
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Total downloads:
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Total dependent packages: 0
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Total dependent repositories: 0
(may contain duplicates) - Total versions: 14
- Total maintainers: 3
spack.io: py-microsoft-aurora
Implementation of the Aurora model.
- Homepage: https://github.com/microsoft/aurora
- License: []
-
Latest release: 1.7.0
published 7 months ago
Rankings
Maintainers (1)
pypi.org: microsoft-aurora
Implementation of the Aurora model
- Documentation: https://microsoft-aurora.readthedocs.io/
- License: MIT License Implementation of the Aurora model. Copyright (c) Microsoft Corporation. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-
Latest release: 1.7.0
published 8 months ago
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Maintainers (2)
Dependencies
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