README.md

Welcome to Eagle!

This repository contains various configurations to guide users through a full machine learning pipeline for weather prediction!

You will find multiple directories showcasing various model configurations ranging from a "hello world" setup to operational quality models.

The key steps to this pipeline include:

1. Data preprocessing using ufs2arco to create training, validation, and test datasets
2. Model training using anemoi-core modules to train a graph-based model
3. Creating a forecast with anemoi-inference to run inference from a model checkpoint
4. Verifying your forecast (or multiple) with wxvx to verify against gridded analysis or observervations

Throughout this process you will also use a eagle-tools library that provides various utilites for tasks such as executing certain modules or post-processing needs.

For more information about model configurations or the various steps of the pipeline, please see our documentation.

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Acknowledgments

ufs2arco: Tim Smith (NOAA Physical Sciences Laboratory)

• Github
• Documentation

Anemoi: European Centre for Medium-Range Weather Forecasts

• anemoi-core github
• anemoi-inference github
• Documentation: anemoi-models, anemoi-graphs, anemoi-training, anemoi-inference

wxvx: Paul Madden (NOAA Global Systems Laboratory/Cooperative Institute for Research In Environmental Sciences)

• Github

eagle-tools: Tim Smith (NOAA Physical Sciences Laboratory)

• Github