Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 9
Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5373-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-5373-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 9
Model experiment description paper
 | 
01 Sep 2021
Model experiment description paper |  | 01 Sep 2021

Calibrating a global atmospheric chemistry transport model using Gaussian process emulation and ground-level concentrations of ozone and carbon monoxide

Edmund Ryan and Oliver Wild

Viewed

Total article views: 1,591 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
992 557 42 1,591 124 10 13
  • HTML: 992
  • PDF: 557
  • XML: 42
  • Total: 1,591
  • Supplement: 124
  • BibTeX: 10
  • EndNote: 13
Views and downloads (calculated since 25 Feb 2021)
Cumulative views and downloads (calculated since 25 Feb 2021)

Viewed (geographical distribution)

Total article views: 1,591 (including HTML, PDF, and XML) Thereof 1,386 with geography defined and 205 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 21 Sep 2023
Download
Short summary
Atmospheric chemistry transport models are important tools to investigate the local, regional and global controls on atmospheric composition and air quality. In this study, we estimate some of the model parameters using machine learning and statistics. Our findings identify the level of error and spatial coverage in the O2 and CO data that are needed to achieve good parameter estimates. We also highlight the benefits of using multiple constraints to calibrate atmospheric chemistry models.
Read more