Downscaling atmospheric chemistry simulations with physically consistent deep learning

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

Open source

DOI: 10.5194/gmd-15-6677-2022
Published: 2022-09-05
Container: Geoscientific Model Development
Publisher: Copernicus GmbH
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.5194/gmd-15-6677-2022,
  title = {Downscaling atmospheric chemistry simulations with physically consistent deep learning},
  author = {Andrew Geiss and Sam J. Silva and Joseph C. Hardin},
  year = {2022},
  journal = {Geoscientific Model Development},
  doi = {10.5194/gmd-15-6677-2022},
  url = {https://doi.org/10.5194/gmd-15-6677-2022}
}

RIS

TY  - JOUR
TI  - Downscaling atmospheric chemistry simulations with physically consistent deep learning
AU  - Andrew Geiss
AU  - Sam J. Silva
AU  - Joseph C. Hardin
PY  - 2022
JO  - Geoscientific Model Development
DO  - 10.5194/gmd-15-6677-2022
UR  - https://doi.org/10.5194/gmd-15-6677-2022
ER  -

APA

Geiss, A., Silva, S. J., & Hardin, J. C. (2022). Downscaling atmospheric chemistry simulations with physically consistent deep learning. Geoscientific Model Development. https://doi.org/10.5194/gmd-15-6677-2022

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crossref · retrieved 2026-09-26T04:44:04.023Z