Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 9
Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5583-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-5583-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 9
Development and technical paper
 | 
10 Sep 2021
Development and technical paper |  | 10 Sep 2021

Efficient ensemble generation for uncertain correlated parameters in atmospheric chemical models: a case study for biogenic emissions from EURAD-IM version 5

Annika Vogel and Hendrik Elbern

Viewed

Total article views: 3,031 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,090 856 85 3,031 73 99
  • HTML: 2,090
  • PDF: 856
  • XML: 85
  • Total: 3,031
  • BibTeX: 73
  • EndNote: 99
Views and downloads (calculated since 23 Feb 2021)
Cumulative views and downloads (calculated since 23 Feb 2021)

Viewed (geographical distribution)

Total article views: 3,031 (including HTML, PDF, and XML) Thereof 2,819 with geography defined and 212 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Nov 2025
Download
Short summary
While atmospheric chemical forecasts rely on uncertain model parameters, their huge dimensions hamper an efficient uncertainty estimation. This study presents a novel approach to efficiently sample these uncertainties by extracting dominant dependencies and correlations. Applying the algorithm to biogenic emissions, their uncertainties can be estimated from a low number of dominant components. This states the capability of an efficient treatment of parameter uncertainties in atmospheric models.
Read more
Share