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November 20, 2025Geoscientific model developmentOpen Access

Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research

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Authors

SHSebastian H. M. Hickman MKMakoto M. Kelp PGPaul T. Griffiths

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Implication

This review demonstrates advances in remote sensing and machine learning for tropospheric ozone, indicating improved climate resilience and public health outcomes.

Key Points

To explore the applications of machine learning and artificial intelligence in tropospheric ozone research.
Review of recent advancements in tropospheric ozone research utilizing machine learning and artificial intelligence.
Analysis of challenges and solutions in ozone forecasting and remote sensing methodologies.
Synthesis of findings from the Tropospheric Ozone Assessment Report (TOAR).
Identified significant progress in machine learning applications for ozone forecasting and emulation of atmospheric chemistry.
Highlighted the importance of addressing challenges related to precursor species and the need for robust models.
Proposed pathways for enhancement of collaborations across atmospheric chemistry, ML, and computational science.

Cite This Study

Hickman et al. (2025) studied this question.

synapsesocial.com/papers/6924f07fc0ce034ddc35014e https://doi.org/10.5194/gmd-18-8777-2025

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Implication

This review demonstrates advances in remote sensing and machine learning for tropospheric ozone, indicating improved climate resilience and public health outcomes.

Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research

Discussion

Member takes

Implication

Key Points

Cite This Study

Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research

Discussion

Member takes

Implication

Key Points

Cite This Study

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