Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 5
Articles | Volume 19, issue 5
https://doi.org/10.5194/gmd-19-1867-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-1867-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 5
Methods for assessment of models
 | 
04 Mar 2026
Methods for assessment of models |  | 04 Mar 2026

Data-driven discovery and model reduction methods for the atmospheric effects of high altitude emissions

Jurriaan A. van 't Hoff, Tom S. van Cranenburgh, Urban Fasel, and Irene C. Dedoussi

Viewed

Total article views: 562 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
375 141 46 562 24 25
  • HTML: 375
  • PDF: 141
  • XML: 46
  • Total: 562
  • BibTeX: 24
  • EndNote: 25
Views and downloads (calculated since 15 Oct 2025)
Cumulative views and downloads (calculated since 15 Oct 2025)

Viewed (geographical distribution)

Total article views: 562 (including HTML, PDF, and XML) Thereof 525 with geography defined and 37 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 04 Mar 2026
Download
Short summary
Chemistry transport models (CTMs) are critical in environmental assessments, but their computational cost often limits direct use in decision-making. We evaluate data-driven model discovery and reduction methods as reduced-order models for CTM simulations, showing they can reconstruct and forecast changes in global ozone distribution from supersonic aircraft emissions for several years at a fraction of the CTM cost while also being more accessible.
Read more
Share