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.

Data sets

Gridded data for the AQ-Bench dataset Clara Betancourt, Ann-Kathrin Edrich, and Martin G. Schultz https://doi.org/10.23728/b2share.9e88bc269c4f4dbc95b3c3b7f3e8512c

Global average ozone map 2010-2014 Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, and Scarlet Stadtler https://doi.org/10.23728/b2share.a05f33b5527f408a99faeaeea033fcdc

Model code and software

Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties - Source Code Clara Betancourt, Timo Stomberg, Ann-Kathrin Edrich, Ankit Patnala, and Scarlet Stadtler https://doi.org/10.34730/af084443e1c444feb12d83a93a65fa33

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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.