Preprints
https://doi.org/10.5194/egusphere-2024-3739
https://doi.org/10.5194/egusphere-2024-3739
06 Jan 2025
 | 06 Jan 2025
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research

Sebastian H. M. Hickman, Makoto 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, Alexander T. Archibald, Roberto Sommariva, David Carlson, Hantao Wang, J. Jason West, and Zhenze Liu

Abstract. Machine learning (ML) is transforming atmospheric chemistry, offering powerful tools to address challenges in tropospheric ozone research, a critical area for climate resilience and public health. As in adjacent fields, ML approaches complement existing research by learning patterns from ever-increasing volumes of atmospheric and environmental data relevant to ozone. We highlight the rapid progress made in the field since Phase 1 of the Tropospheric Ozone Assessment Report, focussing particularly on the most active areas of research, namely short-term ozone forecasting, emulation of atmospheric chemistry and the use of remote sensing for ozone estimation. Despite these advances, many challenges in the field remain, including the quality of data, benchmarks, and limited model generalisation and explainability. This review provides a comprehensive synthesis of recent advancements, highlights critical challenges, and proposes actionable pathways to further advance ML applications in ozone research. Achieving this potential will require close collaborations across atmospheric chemistry, ML and computational science, aimed at addressing key challenges such as the development of global benchmark datasets and robust, explainable models.

Competing interests: Some authors are involved in editorial work for Copernicus Journals, and ORC is an editor for the TOAR2 special issue

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Machine learning is being more widely used across environmental and climate science. This work...
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