Articles | Volume 15, issue 11
Geosci. Model Dev., 15, 4331–4354, 2022
https://doi.org/10.5194/gmd-15-4331-2022

Special issue: Benchmark datasets and machine learning algorithms for Earth...

Geosci. Model Dev., 15, 4331–4354, 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 et al.

Viewed

Total article views: 2,242 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,721 475 46 2,242 20 25
  • HTML: 1,721
  • PDF: 475
  • XML: 46
  • Total: 2,242
  • BibTeX: 20
  • EndNote: 25
Views and downloads (calculated since 19 Jan 2022)
Cumulative views and downloads (calculated since 19 Jan 2022)

Viewed (geographical distribution)

Total article views: 2,242 (including HTML, PDF, and XML) Thereof 1,921 with geography defined and 321 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Feb 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.