Summary of Lucie: a Lightweight Uncoupled Climate Emulator with Long-term Stability and Physical Consistency For O(1000)-member Ensembles, by Haiwen Guan et al.
LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles
by Haiwen Guan, Troy Arcomano, Ashesh Chattopadhyay, Romit Maulik
First submitted to arxiv on: 25 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Atmospheric and Oceanic Physics (physics.ao-ph); Computational Physics (physics.comp-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 This paper introduces LUCIE, a lightweight and easy-to-train climate emulator that can be trained using as little as 2 years’ worth of ERA5 data. Unlike other state-of-the-art AI models, LUCIE remains physically consistent for 100 years of autoregressive simulation with 100 ensemble members. The model is able to accurately predict long-term mean climatology and variability in temperature, wind, precipitation, and humidity. Additionally, the paper discusses an improved training strategy that enables data-limited training without losing stability and physical consistency. The authors also provide a scaling experiment to compare the long-term bias of LUCIE with respect to the number of training samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LUCIE is a new way to predict the weather and climate using artificial intelligence. It’s special because it can learn from just 2 years’ worth of data and still be able to make accurate predictions for the next 100 years. This is important because we want to understand how our planet will change in the future due to things like climate change. The model is also easy to use, taking just 2.4 hours to train on a single computer chip. This makes it possible to run many different experiments and answer important scientific questions. |
Keywords
» Artificial intelligence » Autoregressive » Temperature
