Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 11
Articles | Volume 15, issue 11
https://doi.org/10.5194/gmd-15-4331-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-4331-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

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

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.
Read more
Special issue
Benchmark datasets and machine learning algorithms for Earth...