Summary of Codicast: Conditional Diffusion Model For Global Weather Prediction with Uncertainty Quantification, by Jimeng Shi et al.
CoDiCast: Conditional Diffusion Model for Global Weather Prediction with Uncertainty Quantification
by Jimeng Shi, Bowen Jin, Jiawei Han, Sundararaman Gopalakrishnan, Giri Narasimhan
First submitted to arxiv on: 9 Sep 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 The authors propose CoDiCast, a conditional diffusion model that generates accurate global weather predictions while quantifying uncertainty with ensemble forecasts and modest computational cost. The model is trained on ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and outperforms several existing data-driven methods in accuracy. CoDiCast can generate 6-day global weather forecasts, at 6-hour steps and 5.625^latitude-longitude resolution, for over 5 variables, in about 12 minutes on a commodity A100 GPU machine with 80GB memory. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoDiCast is a new way to make better weather predictions that also shows how certain it is. Right now, making good weather forecasts takes a lot of computer power and doesn’t show how uncertain the forecast is. CoDiCast uses a special kind of math called diffusion models to create many possible weather scenarios for the future. It then chooses the most likely one and shows how uncertain it is. This helps make better predictions that also take into account how certain they are. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
