models Aurora

Aurora

Overview

Aurora is a machine learning model that can predict general environmental variables, such as temperature and wind speed. 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 environmental 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. Please see the documentation of the Aurora Foundry Python API.

Please email [email protected] if you are interested in using Aurora for commercial applications. For research-related questions or technical support with the open-source version of the model, please open an issue in the GitHub repository or reach out to the authors of the paper.

Version: 4

Tags

task : environmental-forecasting Preview Featured author : Microsoft license : MIT `licenseDescription : Microsoft. Copyright (c) Microsoft Corporation.

MIT License

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. hiddenlayerscanned : true notes : ## Resources

• Documentation of the Aurora Foundry Python API
• A full-fledged example that runs the model on Foundry.
• Implementation of the Aurora model
• Documentation of the Aurora implementation
• Paper with detailed evaluation

Quickstart

First install the model:

pip install microsoft-aurora

Then you can make predictions with a Azure Foundry AI endpoint as follows:

from aurora import Batch

from aurora.foundry import BlobStorageChannel, FoundryClient, submit


initial_condition = Batch(...)  # Create initial condition for the model.

for pred in submit(
    initial_condition,
    model_name="aurora-0.25-finetuned",
    num_steps=4,  # Every step predicts six hours ahead.
    foundry_client=FoundryClient(
        endpoint="https://endpoint/",
        token="ENDPOINT_TOKEN",
    ),
    # Communication with the endpoint happens via an intermediate blob storage container. You
    # will need to create one and generate an URL with a SAS token that has both read and write
    # rights.
    channel=BlobStorageChannel(
        "https://storageaccount.blob.core.windows.net/container?<READ_WRITE_SAS_TOKEN>"
    ),
):
    pass  # Do something with `pred`, which is a `Batch`.

License

This model and the associated model weights are released under the MIT licence.

Security

See SECURITY.

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.

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.

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, CMCC, IFS-HR, HRES T0, GFS T0, and GFS forecasts.

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. evaluation : 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 here is for informational purposes only and is not intended to supersede the applicable license terms. disable-batch : True SharedComputeCapacityEnabled inference_compute_allow_list : ['Standard_NC24ads_A100_v4'] sku_to_num_replicas_map : ordereddict({'Standard_NC24ads_A100_v4': 1, 'Default': 1}) displayName : Aurora summary : Aurora is a machine learning model that can predict general environmental variables. inputModalities : data outputModalities : data freePlayground : false`

View in Studio: https://ml.azure.com/registries/azureml/models/Aurora/version/4

License: MIT

Properties

languages: EN

inference-min-sku-spec: 24|1|220|64

inference-recommended-sku: Standard_NC24ads_A100_v4

SharedComputeCapacityEnabled: True