most citedA non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics

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physics.ao-ph2024★ 1 cited

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…

physics.ao-ph2023★ 5 cited

Statistics of extreme events in coarse-scale climate simulations via machine learning correction operators trained on nudged datasets

Alexis-Tzianni Charalampopoulos, Shixuan Zhang, Bryce Harrop +2

This work presents a systematic framework for improving the predictions of statistical quantities for turbulent systems, with a focus on correcting climate simulations obtained by…