Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 5
Articles | Volume 14, issue 5
https://doi.org/10.5194/gmd-14-3067-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-3067-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 5
Development and technical paper
 | 
28 May 2021
Development and technical paper |  | 28 May 2021

Physically regularized machine learning emulators of aerosol activation

Sam J. Silva, Po-Lun Ma, Joseph C. Hardin, and Daniel Rothenberg

Viewed

Total article views: 3,057 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,139 864 54 3,057 48 52
  • HTML: 2,139
  • PDF: 864
  • XML: 54
  • Total: 3,057
  • BibTeX: 48
  • EndNote: 52
Views and downloads (calculated since 21 Dec 2020)
Cumulative views and downloads (calculated since 21 Dec 2020)

Viewed (geographical distribution)

Total article views: 3,057 (including HTML, PDF, and XML) Thereof 2,735 with geography defined and 322 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (final revised paper)

Latest update: 13 Dec 2024
Download
Short summary
The activation of aerosol into cloud droplets is an important but uncertain process in the Earth system. The physical and chemical interactions that govern this process are too computationally expensive to explicitly resolve in modern Earth system models. Here, we demonstrate how hybrid machine learning approaches can provide a potential path forward, enabling the representation of the more detailed physics and chemistry at a reduced computational cost while still retaining physical information.
Read more