Articles | Volume 19, issue 6
https://doi.org/10.5194/gmd-19-2437-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-2437-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
26 Mar 2026
Model description paper |
| 26 Mar 2026
| 26 Mar 2026
Deep learning representation of the aerosol size distribution
Donifan Barahona
CORRESPONDING AUTHOR
NASA, Goddard Space Flight Center, Greenbelt, MD, USA
Katherine H. Breen
NASA, Goddard Space Flight Center, Greenbelt, MD, USA
Morgan State University, Baltimore, MD, USA
Karoline Block
Leipzig Institute for Meteorology, Faculty of Physics and Earth Sciences, University of Leipzig, Leipzig, Germany
Anton Darmenov
NASA, Goddard Space Flight Center, Greenbelt, MD, USA
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Short summary
Particulate matter impacts Earth's radiation, clouds, and human health, but modeling their size is challenging due to computational and observational limits. We developed a machine learning model to predict aerosol size distributions, which accurately replicates advanced models and field measurements.
Particulate matter impacts Earth's radiation, clouds, and human health, but modeling their size...
