Articles | Volume 15, issue 17
https://doi.org/10.5194/gmd-15-6677-2022
https://doi.org/10.5194/gmd-15-6677-2022
Development and technical paper
 | 
05 Sep 2022
Development and technical paper |  | 05 Sep 2022

Downscaling atmospheric chemistry simulations with physically consistent deep learning

Andrew Geiss, Sam J. Silva, and Joseph C. Hardin

Data sets

Chem. Downscaling Models Andrew Geiss https://doi.org/10.5281/zenodo.6784614

Model code and software

Chem. Downscaling GMD Supplemetary Code Andrew Geiss https://doi.org/10.5281/zenodo.6502897

Video supplement

Ozone Super Resolution Andrew Geiss, Sam J. Silva, and Joseph C. Hardin https://youtu.be/QL_onStfd90

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Short summary
This work demonstrates the use of modern machine learning techniques to enhance the resolution of atmospheric chemistry simulations. We evaluate the schemes for an 8 x 10 increase in resolution and find that they perform substantially better than conventional methods. Methods are introduced to target machine learning methods towards this type of problem, most notably by ensuring they do not break known physical constraints.