Microsoft Research’s Aurora team has published a peer-reviewed study of an artificial-intelligence weather model in Nature. The paper, published 21 May 2025 (volume 641, pages 1180–1187), describes Aurora, a 1.3-billion-parameter foundation model trained on more than one million hours of geophysical data. The model is designed to support several Earth-system forecasts rather than a single weather task, and the paper reports stronger results than operational systems in selected tests at substantially lower computational cost.
What Microsoft published
The study is titled “A Foundation Model for the Earth System.” It presents Aurora as a general atmospheric model that can be pretrained once and then fine-tuned for different prediction jobs. Microsoft’s researchers evaluated it on medium-resolution and high-resolution weather forecasting, air-pollution prediction, ocean-wave forecasting and tropical-cyclone tracking.
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This is a peer-reviewed research publication, not a claim that every local forecast will automatically improve. The reported comparisons concern defined benchmark datasets, forecast ranges and operational systems. Real-world performance can vary with location, observations, model configuration and how a forecast is delivered to users.
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How Aurora works
A foundation model for multiple tasks
Traditional numerical forecasting systems solve a particular physical simulation workflow and often require specialized configurations for different products. Aurora instead learns patterns from a broad collection of geophysical data. After pretraining, the model can be fine-tuned for a specific output, allowing one underlying system to support several Earth-system applications.
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Scale and training data
- Model size: 1.3 billion parameters.
- Training material: more than one million hours of diverse geophysical data.
- Target uses: weather at medium and high resolution, air quality, ocean waves and tropical-cyclone tracks.
The breadth of the training set is important because atmospheric conditions, pollution, waves and tropical systems are connected through the same Earth system. A reusable model can transfer learned representations between tasks, although each specialized forecast still requires appropriate fine-tuning and evaluation.
What the Nature paper found
The paper reports that Aurora outperformed operational forecasts in the study’s evaluations for four categories: air quality, ocean waves, tropical-cyclone tracks and high-resolution weather. It also achieved those results with orders-of-magnitude lower computational cost than the traditional forecasting techniques used in the cited comparisons.
Microsoft’s research presentation gives a more specific comparison, describing Aurora as approximately 5,000 times faster than traditional weather-forecasting techniques. That ratio is a Microsoft-reported result for the cited comparison; it should not be interpreted as a universal speed advantage for every hardware setup, forecast resolution or operational pipeline.
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Storm Ciarán case study
The paper includes Storm Ciarán, a high-impact storm that crossed Northwest Europe in late 2023. Microsoft says Aurora correctly predicted the storm’s path and maximum wind speed in that case. A single case study illustrates what the model can capture, but it is not a substitute for statistics across many storms and regions.
Why computational cost matters
Forecast skill is only one practical measure. Operational weather centers also need to generate forecasts repeatedly, assimilate new observations, run ensembles and distribute results under tight time and energy constraints. A model that produces useful predictions much faster could make more frequent updates, larger uncertainty ensembles or higher-resolution products feasible on a given computing budget.
| Comparison | What Aurora represents | Important qualification |
|---|---|---|
| Forecast skill | Better results than the operational baselines reported for selected air-quality, wave, cyclone-track and high-resolution weather tests | Results apply to the paper’s datasets, metrics and forecast settings |
| Computational cost | Orders-of-magnitude lower cost in the reported comparisons; Microsoft cites about 5,000× faster than traditional techniques | The speed figure is comparison-specific, not a guarantee for every deployment |
| Model design | One pretrained foundation model that can be fine-tuned for several Earth-system tasks | Specialized fine-tuning and validation remain necessary |
Aurora 1.5: the later Microsoft update
Microsoft’s current Aurora project materials describe Aurora 1.5, an extension used by Microsoft Weather. The update adds 22 forecast variables, hourly resolution and probabilistic ensemble forecasting. Ensembles produce multiple plausible forecasts instead of one deterministic path, helping communicate uncertainty.
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Microsoft reports that Aurora 1.5 ensemble forecasts beat the ECMWF ensemble on 88.9% of evaluated targets for days 1–10. Microsoft also reports a 16% reduction in tropical-cyclone track error compared with the original Aurora. These are current Microsoft-reported figures from the Aurora 1.5 project page, not additional results from the 2025 Nature paper.
Can Aurora predict hurricanes and air pollution?
Yes, those are among the applications evaluated or described by Microsoft. The research includes tropical-cyclone tracking and air-pollution prediction, alongside ocean-wave and weather forecasts. “Can predict” means the model generates forecasts for those variables; it does not mean that every hurricane track or pollution event will be predicted accurately in every region. Users should rely on the responsible meteorological or environmental service issuing an operational forecast.
Is Aurora available to use?
Microsoft says the Aurora implementation and documentation are publicly available through its project materials and repository. The official documentation invites organizations interested in commercial applications to contact [email protected]. Public materials do not establish guaranteed commercial terms, service-level commitments, referral arrangements or a universally available hosted product.
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Organizations considering deployment should verify the current repository license, model weights, hardware requirements, supported data formats and any restrictions directly in Microsoft’s documentation. A benchmark result alone does not establish that a local forecasting operation can reproduce the same accuracy or speed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the study means for weather forecasting
Aurora’s significance is the combination of a broad, reusable model and relatively inexpensive inference. The 2025 Nature publication provides peer-reviewed evidence that this approach can compete with established operational forecasts in several specialized tests. Aurora 1.5 shows Microsoft continuing the work with more variables, hourly output and explicit uncertainty estimates.
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Frequently Asked Questions
When was Microsoft’s Aurora study published?
The paper “A Foundation Model for the Earth System” appeared in Nature on 21 May 2025, in volume 641, pages 1180–1187.
How large is the Aurora model?
Aurora has 1.3 billion parameters and was trained on more than one million hours of geophysical data.
What is the reported speed advantage?
Microsoft’s presentation reports that Aurora was approximately 5,000 times faster than traditional weather-forecasting techniques in the cited comparison; the figure is not a universal deployment guarantee.
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