Summary of Defining Error Accumulation in Ml Atmospheric Simulators, by Raghul Parthipan et al.
Defining error accumulation in ML atmospheric simulators
by Raghul Parthipan, Mohit Anand, Hannah M. Christensen, J. Scott Hosking, Damon J. Wischik
First submitted to arxiv on: 23 May 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 Machine learning models have made significant progress in predicting the weather, but they can be prone to error accumulation over time. To better understand this issue, we propose a definition and metric for measuring error accumulation in autoregressive models. Our approach distinguishes between errors caused by model deficiencies and those inherent to atmospheric systems like chaos or unobserved variables. We demonstrate the effectiveness of this definition by introducing a regularization loss penalty inspired by it, which shows performance improvements (measured by RMSE and spread/skill) in various atmospheric systems, including real-world weather prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Weather forecasting using machine learning models has gotten better, but there’s still a problem with errors adding up over time. We’re trying to fix this by defining what “error accumulation” means and how to measure it. We think of it as having two kinds of errors: ones we can try to fix by improving the model, and ones that are just part of the way weather works. We used this idea to create a new penalty for our models, which helped them do better (according to metrics like RMSE and spread/skill) in predicting the weather. |
Keywords
» Artificial intelligence » Autoregressive » Machine learning » Regularization
