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

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Latest update: 22 Jul 2024
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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.