811 citations · 811 across the 1 of their papers we have counts for
5 papers
Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision
Janni Yuval, Paul A. O'Gorman, Chris N. Hill
A promising approach to improve climate-model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms tha…
Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
Janni Yuval, Paul A. O'Gorman
Global climate models represent small-scale processes such as clouds and convection using quasi-empirical models known as parameterizations, and these parameterizations are a leadi…
Response of Vertical Velocities in Extratropical Precipitation Extremes to Climate Change
Ziwei Li, Paul O'Gorman
Precipitation extremes intensify in most regions in climate-model projections. Changes in vertical velocities contribute to the changes in intensity of precipitation extremes but r…
Using machine learning to parameterize moist convection: potential for modeling of climate, climate change and extreme events
Paul A. O'Gorman, John G. Dwyer
The parameterization of moist convection contributes to uncertainty in climate modeling and numerical weather prediction. Machine learning (ML) can be used to learn new parameteriz…
Precipitation extremes under climate change
Paul A. O'Gorman
The response of precipitation extremes to climate change is considered using results from theory, modeling, and observations, with a focus on the physical factors that control the…