Adapting Aurora for Basin-Scale Scalar Prediction
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- Dominant language
- Python
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Description
Really impressed by Aurora, it looks like a fantastic step forward for Earth system modeling. For my use case, I was wondering how should I adopt it for basin-scale scalar prediction tasks (eg. River Discharge)? Is it best to use the encoder as a frozen feature extractor on gridded atmospheric inputs and pool spatial tokens before a regression head, or is fine-tuning typically necessary? Also, should inputs match the full pretraining variable stack and temporal resolution?
Thank you so much for making your amazing work available to use!
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
No files, tests, or entry points are mentioned. First clarify whether this is a request for documentation, an example workflow, or model changes for basin-scale scalar prediction; completion would require an agreed approach for pooling inputs, fine-tuning, and required variables and temporal resolution.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100