Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 22
Articles | Volume 18, issue 22
https://doi.org/10.5194/gmd-18-8777-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-18-8777-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 22
Review and perspective paper
 | Highlight paper
 | 
20 Nov 2025
Review and perspective paper | Highlight paper |  | 20 Nov 2025

Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research

Sebastian H. M. Hickman, Makoto M. Kelp, Paul T. Griffiths, Kelsey Doerksen, Kazuyuki Miyazaki, Elyse A. Pennington, Gerbrand Koren, Fernando Iglesias-Suarez, Martin G. Schultz, Kai-Lan Chang, Owen R. Cooper, Alex Archibald, Roberto Sommariva, David Carlson, Hantao Wang, J. Jason West, and Zhenze Liu

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Short summary
Machine learning is being more widely used across environmental and climate science. This work reviews the use of machine learning in tropospheric ozone research, focusing on three main application areas in which significant progress has been made. Common challenges in using machine learning across the three areas are highlighted, and future directions for the field are indicated.
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Tropospheric Ozone Assessment Report Phase II (TOAR-II) Community...