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20232025
most citedA stable implementation of a data-driven scale-aware mesoscale parameterization

14 citations · 17 across the 4 of their papers we have counts for

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Showing 2024Show all

2 papers · 1 filter

physics.ao-ph2024★ 2 cited

An Analysis of Deep Learning Parameterizations for Ocean Subgrid Eddy Forcing

Cem Gultekin, Adam Subel, Cheng Zhang +5

Due to computational constraints, climate simulations cannot resolve a range of small-scale physical processes, which have a significant impact on the large-scale evolution of the…

physics.geo-ph2024

Addressing out-of-sample issues in multi-layer convolutional neural-network parameterization of mesoscale eddies applied near coastlines

Cheng Zhang, Pavel Perezhogin, Alistair Adcroft +1

This study addresses the boundary artifacts in machine-learned (ML) parameterizations for ocean subgrid mesoscale momentum forcing, as identified in the online ML implementation fr…