Machine learning models recover most warm-rain and ice microphysical process rates from standard ICON model outputs for accumulation intervals of 10 minutes or less using a two-step classification-regression approach with calibrated uncertainty.
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz ’96 Model
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In the Lorenz '96 system, stochastic parameterizations with temporal persistence improve early ensemble spread growth and spread-error consistency without increasing long-term variance.
Explores theoretical and data-driven closures for ocean mesoscale eddies and examines their connections using analytical and data-driven methods.
citing papers explorer
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PRecover 1.0: Process Rate Recovery with Machine Learning
Machine learning models recover most warm-rain and ice microphysical process rates from standard ICON model outputs for accumulation intervals of 10 minutes or less using a two-step classification-regression approach with calibrated uncertainty.
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Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations
In the Lorenz '96 system, stochastic parameterizations with temporal persistence improve early ensemble spread growth and spread-error consistency without increasing long-term variance.
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Towards bridging the gap between data-driven and theoretical turbulence closures in stratified flows
Explores theoretical and data-driven closures for ocean mesoscale eddies and examines their connections using analytical and data-driven methods.