Summary of Analyzing and Exploring Training Recipes For Large-scale Transformer-based Weather Prediction, by Jared D. Willard et al.
Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction
by Jared D. Willard, Peter Harrington, Shashank Subramanian, Ankur Mahesh, Travis A. O’Brien, William D. Collins
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper explores the role of deep learning (DL) in numerical weather prediction (NWP), where DL models have achieved comparable or superior skill to traditional physics-based NWP. However, there is significant variability in training settings and architectures among leading DL models. The authors show that high forecast skill can be attained using off-the-shelf architectures, simple training procedures, and moderate compute budgets. They train a minimally modified SwinV2 transformer on ERA5 data and achieve superior forecast skill compared to IFS. The paper presents ablation studies on key aspects of the training pipeline, including loss functions, model sizes, depths, and multi-step fine-tuning. Additionally, it examines model performance using metrics beyond ACC and RMSE, as well as scalability with model size. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how deep learning can be used to predict the weather. Right now, there are many different models that all do a good job of predicting the weather. But they’re all a bit different from each other. The authors tried taking one of these models and making some simple changes to see if it could still do a great job of predicting the weather. They found that by using a special type of model called SwinV2, they could get better results than some other methods. They also looked at how different parts of the process worked together to make the predictions. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Transformer
