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.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-8777-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review and perspective paper
| Highlight paper
| 
20 Nov 2025
Review and perspective paper | Highlight paper |
| 20 Nov 2025
| 20 Nov 2025
Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research
Sebastian H. M. Hickman
Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, Cambridge, UK
Makoto M. Kelp
Doerr School of Sustainability, Stanford University, Stanford, CA, USA
Paul T. Griffiths
CORRESPONDING AUTHOR
National Centre for Atmospheric Science, University of Cambridge, Cambridge, UK
now at: School of Chemistry, Bristol University, Bristol, UK
Kelsey Doerksen
Department of Computer Science, University of Oxford, Oxford, UK
Kazuyuki Miyazaki
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Elyse A. Pennington
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Gerbrand Koren
Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, the Netherlands
Fernando Iglesias-Suarez
Predictia Intelligent Data Solutions S.L., Santander, Spain
Martin G. Schultz
Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
Department of Mathematics and Computer Science, University of Cologne, Cologne, Germany
Kai-Lan Chang
NOAA Chemical Sciences Laboratory, Boulder, CO, USA
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA
Owen R. Cooper
NOAA Chemical Sciences Laboratory, Boulder, CO, USA
Alex Archibald
National Centre for Atmospheric Science, University of Cambridge, Cambridge, UK
Roberto Sommariva
School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
School of Chemistry, University of Leicester, Leicester, UK
David Carlson
Civil and Environmental Engineering, Duke University, Durham, NC, USA
Hantao Wang
Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC, USA
J. Jason West
Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC, USA
Zhenze Liu
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China
Data sets
TOAR Surface Observation Database; editing status 2025-03-12 re3data.org https://doi.org/10.17616/R3FZ0G
Model code and software
ML4O3/Applications-of-Machine-Learning-and-Artificial-Intelligence-in-Tropospheric-Ozone-Research: GMD P. Griffiths https://doi.org/10.5281/zenodo.17546216
Executive editor
I agree on publishing this manuscript as a Review and perspective paper.
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.
Machine learning is being more widely used across environmental and climate science. This work...
