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| 001 | 1052339 | ||
| 005 | 20260123203315.0 | ||
| 024 | 7 | _ | |a 10.5194/gmd-18-8777-2025 |2 doi |
| 024 | 7 | _ | |a 1991-959X |2 ISSN |
| 024 | 7 | _ | |a 1991-9603 |2 ISSN |
| 024 | 7 | _ | |a 10.34734/FZJ-2026-00942 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2026-00942 |
| 082 | _ | _ | |a 550 |
| 100 | 1 | _ | |a Hickman, Sebastian H. M. |0 0000-0001-7437-0846 |b 0 |
| 245 | _ | _ | |a Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research |
| 260 | _ | _ | |a Katlenburg-Lindau |c 2025 |b Copernicus |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a 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 (TOAR), 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. This review provides a comprehensive synthesis of recent advancements, highlights critical challenges, and proposes actionable pathways to develop ML in ozone research. Further advances hinge on addressing domain-specific issues such as the dependence of ozone concentrations on several poorly observed precursor species, as well as making progress on generic ML challenges such as the definition of suitable benchmarks and developing robust, explainable models. Reaping the full potential of ML for ozone research and operational applications will require close collaborations across atmospheric chemistry, ML and computational science and vigilant pursuit of the rapid developments in adjacent fields. |
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| 536 | _ | _ | |a IntelliAQ - Artificial Intelligence for Air Quality (787576) |0 G:(EU-Grant)787576 |c 787576 |f ERC-2017-ADG |x 2 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Kelp, Makoto M. |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Griffiths, Paul T. |0 0000-0002-1089-340X |b 2 |e Corresponding author |
| 700 | 1 | _ | |a Doerksen, Kelsey |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Miyazaki, Kazuyuki |0 0000-0002-1466-4655 |b 4 |
| 700 | 1 | _ | |a Pennington, Elyse A. |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Koren, Gerbrand |0 0000-0002-2275-0713 |b 6 |
| 700 | 1 | _ | |a Iglesias-Suarez, Fernando |0 0000-0003-3403-8245 |b 7 |
| 700 | 1 | _ | |a Schultz, Martin G. |0 P:(DE-Juel1)6952 |b 8 |
| 700 | 1 | _ | |a Chang, Kai-Lan |0 0000-0001-5812-3183 |b 9 |
| 700 | 1 | _ | |a Cooper, Owen R. |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Archibald, Alex |0 P:(DE-HGF)0 |b 11 |
| 700 | 1 | _ | |a Sommariva, Roberto |0 0000-0002-2728-5814 |b 12 |
| 700 | 1 | _ | |a Carlson, David |0 0000-0003-1005-6385 |b 13 |
| 700 | 1 | _ | |a Wang, Hantao |0 P:(DE-HGF)0 |b 14 |
| 700 | 1 | _ | |a West, J. Jason |0 0000-0001-5652-4987 |b 15 |
| 700 | 1 | _ | |a Liu, Zhenze |0 0000-0001-8326-3698 |b 16 |
| 773 | _ | _ | |a 10.5194/gmd-18-8777-2025 |g Vol. 18, no. 22, p. 8777 - 8800 |0 PERI:(DE-600)2456725-5 |n 22 |p 8777 - 8800 |t Geoscientific model development |v 18 |y 2025 |x 1991-959X |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1052339/files/gmd-18-8777-2025.pdf |y OpenAccess |
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