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models Aurora
Aurora: A Foundation Model for the Earth System
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.
- Aurora Documentation for detailed instruction and examples
- Aurora Academic Paper
- A full-fledged example that runs the model on ERA5.
The package currently includes the pretrained model and the fine-tuned version for high-resolution weather forecasting. We are working on the fine-tuned versions for air pollution and ocean wave forecasting, which will be included in due time.
Version: 1
task : environmental-forecasting Preview Featured author : Microsoft license : custom hiddenlayerscanned : true `notes : ## Security
See SECURITY.
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.
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.
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, CMCC, IFS-HR, HRES T0, GFS T0 analysis, and GFS forecasts.
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.
The package currently includes the pretrained model and the fine-tuned version for high-resolution weather forecasting.
We are working on the fine-tuned versions for air pollution and ocean wave forecasting, which will be included in due time.
disable-batch : True SharedComputeCapacityEnabled inference_compute_allow_list : ['Standard_NC24ads_A100_v4', 'Standard_NC48ads_A100_v4', 'Standard_NC96ads_A100_v4']`
View in Studio: https://ml.azure.com/registries/azureml/models/Aurora/version/1
License: custom
languages: EN
inference-min-sku-spec: 24|1|220|64
inference-recommended-sku: Standard_NC24ads_A100_v4, Standard_NC48ads_A100_v4, Standard_NC96ads_A100_v4
SharedComputeCapacityEnabled: True