Articles | Volume 15, issue 11
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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2022-2', Juan Antonio Añel, 23 Feb 2022
    • AC1: 'Reply on CEC1', Clara Betancourt, 03 Mar 2022
  • RC1: 'Comment on gmd-2022-2', Anonymous Referee #1, 25 Feb 2022
    • AC2: 'Reply on RC1', Clara Betancourt, 14 Apr 2022
  • RC2: 'Comment on gmd-2022-2', Anonymous Referee #2, 13 Mar 2022
    • AC3: 'Reply on RC2', Clara Betancourt, 14 Apr 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Clara Betancourt on behalf of the Authors (14 Apr 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (11 May 2022) by Fiona O'Connor
AR by Clara Betancourt on behalf of the Authors (12 May 2022)  Author's response    Manuscript
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.