Articles | Volume 16, issue 9
https://doi.org/10.5194/gmd-16-2355-2023
https://doi.org/10.5194/gmd-16-2355-2023
Development and technical paper
 | 
05 May 2023
Development and technical paper |  | 05 May 2023

Emulating aerosol optics with randomly generated neural networks

Andrew Geiss, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin

Viewed

Total article views: 1,609 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,258 322 29 1,609 20 16
  • HTML: 1,258
  • PDF: 322
  • XML: 29
  • Total: 1,609
  • BibTeX: 20
  • EndNote: 16
Views and downloads (calculated since 08 Jul 2022)
Cumulative views and downloads (calculated since 08 Jul 2022)

Viewed (geographical distribution)

Total article views: 1,609 (including HTML, PDF, and XML) Thereof 1,529 with geography defined and 80 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Mar 2024
Download
Short summary
Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.