35 citations · 81 across the 14 of their papers we have counts for
14 papers
Samudra 2: Scaling Ocean Emulators across Resolutions
Yuan Yuan, Jesse Rusak, Alexander Merose +5
Ocean general circulation models (OGCMs) are essential to climate science but computationally expensive, limiting ensemble size and forcing scenarios. Neural emulators promise orde…
Calibration of a neural network ocean closure for improved mean state and variability
Pavel Perezhogin, Alistair Adcroft, Laure Zanna
Global ocean models exhibit biases in the mean state and variability, particularly at coarse resolution, where mesoscale eddies are unresolved. To address these biases, parameteriz…
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…
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…
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…
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…
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…
Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation
Christian Pedersen, Laure Zanna, Joan Bruna +1
Integration of machine learning (ML) models of unresolved dynamics into numerical simulations of fluid dynamics has been demonstrated to improve the accuracy of coarse resolution s…
A data-driven framework for dimensionality reduction and causal inference in climate fields
Fabrizio Falasca, Pavel Perezhogin, Laure Zanna
We propose a data-driven framework to simplify the description of spatiotemporal climate variability into few entities and their causal linkages. Given a high-dimensional climate f…
Subgrid parameterizations of ocean mesoscale eddies based on Germano decomposition
Pavel Perezhogin, Andrey Glazunov
Ocean models at intermediate resolution (1/4 degree), which partially resolve mesoscale eddies, can be seen as Large eddy simulations (LES) of the primitive equations, in which the…
Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model
Cheng Zhang, Pavel Perezhogin, Cem Gultekin +3
We address the question of how to use a machine learned parameterization in a general circulation model, and assess its performance both computationally and physically. We take one…
Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
Pavel Perezhogin, Laure Zanna, Carlos Fernandez-Granda
Subgrid parameterizations of mesoscale eddies continue to be in demand for climate simulations. These subgrid parameterizations can be powerfully designed using physics and/or data…