activity
20242026
most citedAn Analysis of Deep Learning Parameterizations for Ocean Subgrid Eddy Forcing

2 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

cond-mat.mtrl-sci2026

Universal effect of ammonia pressure on synthesis of colloidal metal nitrides in molten salts

Ruiming Lin, Vikash Khokhar, Ningxin Jiang +11

Metal nitrides represent a large class of materials with extensive applications in optoelectronics, energy, and healthcare technologies. For example, GaN and related nitride semico…

physics.ao-ph2025

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…

physics.ao-ph2025

Generalizable neural-network parameterization of mesoscale eddies in idealized and global ocean models

Pavel Perezhogin, Alistair Adcroft, Laure Zanna

Data-driven methods have become popular to parameterize the effects of mesoscale eddies in ocean models. However, they perform poorly in generalization tasks and may require retuni…

physics.ao-ph2025

Data-Driven Probabilistic Air-Sea Flux Parameterization

Jiarong Wu, Pavel Perezhogin, David John Gagne +4

Accurately quantifying air-sea fluxes is important for understanding air-sea interactions and improving coupled weather and climate systems. This study introduces a probabilistic f…

physics.ao-ph20242 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…