Summary of Recommendations For Comprehensive and Independent Evaluation Of Machine Learning-based Earth System Models, by Paul A. Ullrich et al.
Recommendations for Comprehensive and Independent Evaluation of Machine Learning-Based Earth System Models
by Paul A. Ullrich, Elizabeth A. Barnes, William D. Collins, Katherine Dagon, Shiheng Duan, Joshua Elms, Jiwoo Lee, L. Ruby Leung, Dan Lu, Maria J. Molina, Travis A. O’Brien, Finn O. Rebassoo
First submitted to arxiv on: 24 Oct 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 proposed framework aims to develop Earth-system models (ESMs) using machine learning (ML), building on successful applications in weather forecasting. To overcome the challenges of modeling the complex Earth system, this paper suggests five recommendations for evaluating ML-based ESMs, focusing on comprehensive, standardized, and independent assessments to enhance credibility and promote broader adoption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to create models that can predict how the Earth will change in the future. It’s a big job because the Earth is made up of many different parts that work together. The paper suggests ways to test these new models so we can trust them when making predictions about our planet. |
Keywords
* Artificial intelligence * Machine learning
