the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research
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.- Preprint
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Status: open (until 14 Apr 2025)
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RC1: 'Comment on egusphere-2024-3739', Anonymous Referee #1, 02 Apr 2025
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General comments
This is a comprehensive, well-written review written by experts in the field, and clearly deserves to be published in GMD. It will be useful for the customary purposes of a review paper (e.g. giving active participants in the reviewed field and in closely related fields entry points into the literature, providing nice summary graphics, and discussing methodologies and challenges that will underlie future research). As someone working on ML applications in a related geophysical field, I find that most of the broader themes in the text (e.g. heterogeneous datasets, end-to-end prediction, issues and challenges having to do with learning wide ranges of space and time scales and lots of correlated predictors, long-term emulator drift, explainability and PINNs, effective benchmarks and intercomparisons, foundation models) would apply just as well to my own area of research.
One thing I look for in a review article is to highlight some crisp, intellectually exciting problems that could launch a new student or postdoc into career-launching research directions. One could glean inspirations from the ‘Future Outlook’ subsections and Section 5 on ‘Challenges and Limitations’ and ‘Future Directions’, but the issues raised there mostly involve large coordinated efforts with a heavy software engineering focus. One could argue that such efforts are the primary path to further progress in ML for tropospheric ozone and related chemistry, but are there also relevant conceptual questions you’d like to highlight that are more accessible to academic researchers?
Specific comments
L181: Reference formatting
L199: Delete ‘so’
L243: What is an ‘NMB’?
L386: What is ‘MDA8’?
Citation: https://doi.org/10.5194/egusphere-2024-3739-RC1
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