add aurora

assets/models/system/aurora/description.md

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+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.
+
+## Resources
+
+* [Aurora Documentation for detailed instruction and examples](https://microsoft.github.io/aurora)
+* [Aurora Academic Paper](https://arxiv.org/abs/2405.13063)
+* [A full-fledged example that runs the model on ERA5](https://microsoft.github.io/aurora/example_era5.html).
+
+## Implementation
+
+_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._

assets/models/system/aurora/evaluation.md

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+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](https://arxiv.org/pdf/2405.13063).
+
+*Note: The documentation included in this file is for informational purposes only and is not intended to supersede the applicable license terms.*

assets/models/system/aurora/model.yaml

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+path:
+  container_name: models
+  container_path: microsoft/aurora/20250114/aurora_mlflow_pyfunc
+  storage_name: automlcesdkdataresources
+  type: azureblob
+publish:
+  description: description.md
+  type: mlflow_model

assets/models/system/aurora/notes.md

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+## Security
+
+See [SECURITY](https://github.com/microsoft/aurora/blob/main/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](https://www.microsoft.com/en-us/ai/responsible-ai).
+
+### 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](https://arxiv.org/pdf/2405.13063).
+The checkpoints include data from ERA5, CMCC, IFS-HR, HRES T0, GFS T0 analysis, 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](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
+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
+
+### Why are the fine-tuned versions of Aurora for air quality and ocean wave forecasting missing?
+
+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.

assets/models/system/aurora/spec.yaml

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+$schema: https://azuremlschemas.azureedge.net/latest/model.schema.json
+
+name: Aurora
+path: ./
+
+properties:
+  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
+
+tags:
+  industry: atmospheric-and-oceanic-physics
+  task: environmental-forecasting
+  Preview: ""
+  Featured: ""  
+  author: Microsoft
+  license: custom
+  hiddenlayerscanned: "true"
+  notes: "notes.md"
+  disable-batch: true
+  SharedComputeCapacityEnabled: ""
+  inference_compute_allow_list:
+    - Standard_NC24ads_A100_v4
+    - Standard_NC48ads_A100_v4
+    - Standard_NC96ads_A100_v4
+
+version: 1