A data-driven ABL flux parameterization using convolution operators on mean profiles, trained and tested on LES, improves on standard K-profile closures while remaining interpretable.
and Zanna, Laure , title =
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physics.flu-dyn 2years
2026 2verdicts
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Explores theoretical and data-driven closures for ocean mesoscale eddies and examines their connections using analytical and data-driven methods.
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Data-Driven Flux Parameterization for the Atmospheric Boundary Layer
A data-driven ABL flux parameterization using convolution operators on mean profiles, trained and tested on LES, improves on standard K-profile closures while remaining interpretable.
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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.