Summary of Exploring the Design Space Of Deep-learning-based Weather Forecasting Systems, by Shoaib Ahmed Siddiqui et al.
Exploring the design space of deep-learning-based weather forecasting systems
by Shoaib Ahmed Siddiqui, Jean Kossaifi, Boris Bonev, Christopher Choy, Jan Kautz, David Krueger, Kamyar Azizzadenesheli
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper aims to understand the design space of deep-learning-based weather forecasting systems by analyzing various design choices, including architecture, problem formulation, pretraining scheme, and more. The study explores different architectures, such as UNet, transformer-based models, and grid-invariant models like neural operators. Results show that fixed-grid architectures outperform grid-invariant ones, highlighting the need for further development in grid-invariant models. A hybrid system combining fixed-grid and grid-invariant architectures is proposed to leverage their strengths. The study also emphasizes the importance of multi-step fine-tuning, pretrained objectives, and using larger datasets when training smaller models. Keywords: deep learning, weather forecasting, UNet, transformer-based models, neural operators, pretraining scheme. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make better weather forecasts using computers. It tries to figure out what works best by testing different ways of designing the computer program. The study finds that one type of design is better than others and proposes a new way to combine different designs to get even better results. It also shows that doing more training on larger datasets can improve performance, but only if you’re using a smaller model. This research will help make weather forecasting systems better in the future. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Pretraining » Transformer » Unet
