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physics.ao-ph2026
Hierarchical Testing of a Hybrid Machine Learning-Physics Global Atmosphere Model
Ziming Chen, L. Ruby Leung, Wenyu Zhou +9
Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predi…
physics.ao-ph2024
A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics
Benedikt Barthel Sorensen, Alexis Charalampopoulos, Shixuan Zhang +3
Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully-resolved climate simulations remain com…