Accelerating Radiation Computations for Dynamical Models With Targeted Machine Learning and Code Optimization

Accelerating Radiation Computations for Dynamical Models With Targeted Machine Learning and Code Optimization

Abstract Atmospheric radiation is the main driver of weather and climate, yet due to a complicated absorption spectrum, the precise treatment of radiative transfer in numerical weather and climate models is computationally unfeasible. Radiation parameterizations need to maximize computational effici...

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Bibliographic Details
Main Authors: Peter Ukkonen, Robert Pincus, Robin J. Hogan, Kristian Pagh Nielsen, Eigil Kaas
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2020-12-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
machine learning
radiation
atmospheric model
parameterization
Online Access:https://doi.org/10.1029/2020MS002226

Internet

https://doi.org/10.1029/2020MS002226

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