Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 16
Articles | Volume 15, issue 16
https://doi.org/10.5194/gmd-15-6341-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6341-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 16
Model description paper
 | 
17 Aug 2022
Model description paper |  | 17 Aug 2022

A machine learning methodology for the generation of a parameterization of the hydroxyl radical

Daniel C. Anderson, Melanie B. Follette-Cook, Sarah A. Strode, Julie M. Nicely, Junhua Liu, Peter D. Ivatt, and Bryan N. Duncan

Viewed

Total article views: 2,582 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,972 563 47 2,582 126 20 23
  • HTML: 1,972
  • PDF: 563
  • XML: 47
  • Total: 2,582
  • Supplement: 126
  • BibTeX: 20
  • EndNote: 23
Views and downloads (calculated since 09 Mar 2022)
Cumulative views and downloads (calculated since 09 Mar 2022)

Viewed (geographical distribution)

Total article views: 2,582 (including HTML, PDF, and XML) Thereof 2,353 with geography defined and 229 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Apr 2024
Download
Short summary
The hydroxyl radical (OH) is the most important chemical in the atmosphere for removing certain pollutants, including methane, the second-most-important greenhouse gas. We present a methodology to create an easily modifiable parameterization that can calculate OH concentrations in a computationally efficient way. The parameterization, which predicts OH within 5 %, can be integrated into larger climate models to allow for calculation of the interactions between OH, methane, and other chemicals.
Read more