14 citations · 17 across the 4 of their papers we have counts for
4 papers
A Framework for Hybrid Physics-AI Coupled Ocean Models
Laure Zanna, William Gregory, Pavel Perezhogin +23
Climate simulations, at all grid resolutions, rely on approximations that encapsulate the forcing due to unresolved processes on resolved variables, known as parameterizations. Par…
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…
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…
A stable implementation of a data-driven scale-aware mesoscale parameterization
Pavel Perezhogin, Cheng Zhang, Alistair Adcroft +2
Ocean mesoscale eddies are often poorly represented in climate models, and therefore, their effects on the large scale circulation must be parameterized. Traditional parameterizati…