92 citations · 216 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Machine learning for online sea ice bias correction within global ice-ocean simulations
William Gregory, Mitchell Bushuk, Yongfei Zhang +2
In this study we perform online sea ice bias correction within a GFDL global ice-ocean model. For this, we use a convolutional neural network (CNN) which was developed in a previou…
Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks
Aakash Sane, Brandon G. Reichl, Alistair Adcroft +1
Vertical mixing parameterizations in ocean models are formulated on the basis of the physical principles that govern turbulent mixing. However, many parameterizations include ad ho…
Deep learning of systematic sea ice model errors from data assimilation increments
William Gregory, Mitchell Bushuk, Alistair Adcroft +2
Data assimilation is often viewed as a framework for correcting short-term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, 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…