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Microsoft Research’s Aurora team published a peer-reviewed study of its artificial-intelligence weather model in Nature on 21 May 2025. The paper presents Aurora as a 1.3-billion-parameter foundation model trained on more than one million hours of geophysical data, then adapted for weather, air quality, ocean waves and tropical-cyclone tracking. The reported results show higher forecast skill than several operational systems in the evaluated tasks, with much lower computational cost.
What Microsoft published in Nature
The paper, “A Foundation Model for the Earth System,” appears in Nature, volume 641, pages 1180–1187, dated 21 May 2025. It describes Aurora as a general Earth-system model rather than a single-purpose weather network. Microsoft pretrains one model on varied atmospheric and geophysical inputs, then fine-tunes it for particular forecasting jobs.
That design matters because conventional forecasting workflows often rely on separate systems for separate variables or domains. Aurora’s foundation-model approach aims to reuse learned physical patterns across tasks while allowing specialized fine-tuning for a target such as pollution or cyclone tracks.
How Aurora works
A large pretrained model
- Model size: 1.3 billion parameters.
- Training material: more than one million hours of diverse geophysical data.
- Base role: a general atmospheric model that can be adapted to different Earth-system predictions.
The training volume and variety are intended to help Aurora learn relationships among atmospheric states instead of optimizing for only one forecast product. The published work then evaluates specialized versions for medium- and high-resolution weather, air-pollution prediction, ocean-wave prediction and tropical-cyclone tracking.
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One model, several forecasting tasks
| Capability | What the study or Microsoft reports |
|---|---|
| Medium-range weather | Forecasting for periods extending out to roughly a couple of weeks, according to Microsoft researcher Megan Stanley. |
| High-resolution weather | Aurora outperformed the operational forecasts used in the paper’s comparison. |
| Air quality | The Nature abstract reports better performance than operational forecasts in the evaluated air-quality task. |
| Ocean waves | The Nature abstract reports better performance than operational forecasts in the evaluated ocean-wave task. |
| Tropical cyclones | The Nature abstract reports improved track predictions in the evaluated comparisons. |
How accurate is Microsoft’s AI weather model?
The strongest claim supported by the publication is comparative: Aurora outperformed operational forecasts for air quality, ocean waves, tropical-cyclone tracks and high-resolution weather in the benchmarks reported by the Nature paper. Those results describe the tested datasets, regions, variables and lead times; they are not a guarantee that every local forecast will be more accurate than a national weather service’s operational product.
Computational efficiency is a second part of the result. Microsoft’s research presentation says Aurora was approximately 5,000 times faster than traditional weather-forecasting techniques in the cited comparison. That ratio is a Microsoft-reported result tied to the comparison’s specific hardware, implementation and forecasting setup, so it should not be interpreted as a universal speed multiplier for every deployment.
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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 reports that Aurora correctly predicted the storm’s path and maximum wind speed in that case. A single storm example illustrates the model’s behavior but cannot establish performance across all storms, coastlines or warning thresholds.
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What changed with Aurora 1.5
Microsoft’s later Aurora 1.5 project update extends the original research model. It adds 22 forecast variables, hourly output and probabilistic ensemble forecasting. An ensemble produces multiple plausible forecasts, allowing users to estimate uncertainty instead of receiving only one deterministic trajectory.
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| Aurora version | Key information | Reported result |
|---|---|---|
| Aurora, Nature study (2025) | 1.3 billion parameters; more than one million hours of geophysical training data; multiple fine-tuned Earth-system tasks. | Outperformed operational forecasts in the evaluated air-quality, ocean-wave, tropical-cyclone and high-resolution-weather comparisons. |
| Aurora 1.5 project update (2026) | 22 additional forecast variables, hourly resolution and probabilistic ensembles. | Microsoft reports that the ensembles beat the ECMWF ensemble on 88.9% of evaluated targets for days 1–10 and reduced tropical-cyclone track error by 16% versus the original Aurora. |
The 88.9% and 16% figures come from Microsoft’s Aurora 1.5 project page, not from the 2025 Nature paper. They are current Microsoft-reported evaluation results and should be read with the same care as any benchmark: performance depends on the targets, period, baselines and evaluation protocol.
Can Aurora predict hurricanes or air pollution?
Yes, those are among the applications Microsoft reports evaluating. The original Aurora work includes tropical-cyclone tracking and air-pollution prediction, alongside weather and ocean-wave forecasts. “Predict” does not mean that the model replaces official warnings. Emergency decisions still depend on national meteorological agencies, local observations, data-assimilation systems and human forecasters.
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Is Aurora available to use?
Microsoft has made the Aurora implementation and documentation publicly available through its project materials and repository. The official documentation invites organizations interested in commercial applications to contact Microsoft at AIWeatherClimate@microsoft.com. Public materials do not establish a universal hosted service, pricing schedule, service-level agreement or guaranteed commercial access terms.
Organizations evaluating Aurora should confirm the applicable license, model-download requirements, compute hardware, input-data format, forecast domain and support arrangements directly in Microsoft’s current documentation. A benchmark result alone does not show that a local operational pipeline will achieve the same accuracy or cost.
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What the Nature study means for weather forecasting
Aurora’s significance is the combination of scale, reuse and efficiency. Instead of building an isolated model for each output, Microsoft demonstrates a foundation model that can be adapted across several Earth-system problems. The Nature results indicate that this approach can be competitive with operational systems while requiring substantially less computation in the reported tests.
The limits are equally important: the comparisons are benchmark-specific, the Storm Ciarán result is a case study rather than a broad validation, and Aurora 1.5’s newer percentages come from Microsoft’s later update. Users should treat Aurora as a research and deployment option to validate against local observations and official forecasts, not as an automatic replacement for established warning services.
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