Summary of Huge Ensembles Part Ii: Properties Of a Huge Ensemble Of Hindcasts Generated with Spherical Fourier Neural Operators, by Ankur Mahesh et al.
Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators
by Ankur Mahesh, William Collins, Boris Bonev, Noah Brenowitz, Yair Cohen, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis OBrien, Michael Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, Jared Willard
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Atmospheric and Oceanic Physics (physics.ao-ph)
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 novel ensemble-based approach for weather forecasting is presented, leveraging Spherical Fourier Neural Operators and bred vectors as initial condition perturbations. The ensemble demonstrates comparable performance to operational weather forecasting systems while requiring several orders of magnitude fewer computational resources. To generate a massive ensemble (HENS) with 7,424 members, initialized each day of summer 2023, the authors provide technical requirements for running huge ensembles at this scale. HENS precisely samples the tails of the forecast distribution and presents detailed sampling of internal variability. The approach improves skill in extreme climate statistics, reducing the probability of outlier events by enhancing coverage of possible future trajectories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict the weather is developed using a special type of artificial intelligence called Spherical Fourier Neural Operators. This method uses smaller versions of itself as starting points and trains them separately. The result is an ensemble that does just as well as current forecasting systems but uses much less computer power. To make this work, the team created a huge version with 7,424 copies, one for each day in summer 2023. They explain how to run huge ensembles like this on computers. This approach helps predict extreme weather events more accurately and gives a better idea of what might happen. |
Keywords
* Artificial intelligence * Probability
