Articles | Volume 15, issue 11
https://doi.org/10.5194/gmd-15-4331-2022
https://doi.org/10.5194/gmd-15-4331-2022
Model description paper
 | 
03 Jun 2022
Model description paper |  | 03 Jun 2022

Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties

Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, and Scarlet Stadtler

Viewed

Total article views: 2,833 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,135 645 53 2,833 23 29
  • HTML: 2,135
  • PDF: 645
  • XML: 53
  • Total: 2,833
  • BibTeX: 23
  • EndNote: 29
Views and downloads (calculated since 19 Jan 2022)
Cumulative views and downloads (calculated since 19 Jan 2022)

Viewed (geographical distribution)

Total article views: 2,833 (including HTML, PDF, and XML) Thereof 2,477 with geography defined and 356 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Sep 2023
Download
Short summary
Ozone is a toxic greenhouse gas with high spatial variability. We present a machine-learning-based ozone-mapping workflow generating a transparent and reliable product. Going beyond standard mapping methods, this work combines explainable machine learning with uncertainty assessment to increase the integrity of the produced map.