Summary of Efficient Fine-tuning Of 37-level Graphcast with the Canadian Global Deterministic Analysis, by Christopher Subich
Efficient fine-tuning of 37-level GraphCast with the Canadian global deterministic analysis
by Christopher Subich
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Atmospheric and Oceanic Physics (physics.ao-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for fine-tuning the GraphCast data-driven forecast model is presented in this study, allowing it to simulate another analysis system, specifically the Global Deterministic Prediction System (GDPS) of Environment and Climate Change Canada (ECCC). By leveraging two years of training data (July 2019 – December 2021) and utilizing 37 GPU-days of computation, the tuned GraphCast model outperforms both its unmodified version and operational forecast in terms of forecast skill over lead times from 1 to 10 days in the troposphere. The fine-tuning process involves abbreviating DeepMind’s original training curriculum for GraphCast, employing a shorter single-step forecast stage to accomplish the bulk of adaptation work, and consolidating autoregressive stages into separate 12hr, 1d, 2d, and 3d stages with larger learning rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how to improve the GraphCast model’s ability to predict weather. Researchers took two years of data and used a special type of computer called a GPU to make the model better. The new version did a great job predicting the weather, especially in the short-term (1-10 days). To make this happen, they shortened the way the model was trained and broke it down into smaller parts that could learn from each other. |
Keywords
» Artificial intelligence » Autoregressive » Fine tuning
