Summary of Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-stokes and Atmospheric Dynamics, by Matthias Karlbauer et al.
Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics
by Matthias Karlbauer, Danielle C. Maddix, Abdul Fatir Ansari, Boran Han, Gaurav Gupta, Yuyang Wang, Andrew Stuart, Michael W. Mahoney
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 remarkable advancement in Deep Learning Weather Prediction (DLWP) models has made them competitive with traditional numerical weather prediction (NWP) models. Various DLWP architectures, including U-Net, Transformer, Graph Neural Network (GNN), and Fourier Neural Operator (FNO), have shown promise in forecasting atmospheric states. However, the most suitable method or architecture for weather forecasting and future model development remains unclear due to differences in training protocols, forecast horizons, and data choices. To address this, a detailed empirical analysis is provided, comparing and contrasting prominent DLWP models and their backbones. The results illustrate various tradeoffs, including accuracy, memory consumption, and runtime. For example, FNO shows favorable performance on synthetic data, while ConvLSTM and SwinTransformer are suitable for short-to-mid-ranged forecasts on the WeatherBench dataset. Long-ranged weather rollouts of up to 365 days favor spherical data representation architectures like GraphCast and Spherical FNO. All model backbones “saturate,” highlighting a direction for future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models can predict the weather better than traditional methods! Researchers compared different deep learning models to see which one is best for predicting the weather. They tested many models, including ones called U-Net, Transformer, Graph Neural Network, and Fourier Neural Operator. Each model did well on certain types of predictions, like forecasting the weather a few days in advance or up to a year from now. The results show that some models are better than others at predicting different parts of the weather. |
Keywords
* Artificial intelligence * Deep learning * Gnn * Graph neural network * Synthetic data * Transformer
