Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 1
Articles | Volume 26, issue 1
https://doi.org/10.5194/acp-26-809-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-809-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 1
Research article
 | 
16 Jan 2026
Research article |  | 16 Jan 2026

Beyond binary maps from HCHO∕NO2: a deep neural network approach to global daily mapping of net ozone production rates and sensitivities constrained by satellite observations (2005–2023)

Amir H. Souri, Gonzalo González Abad, Bryan N. Duncan, and Luke D. Oman

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Short summary
We create long-term maps of PO3 magnitudes along with their corresponding sensitivity maps. This is achieved using a deep learning parameterization method that relies on satellite data, atmospheric models, and ground-based remote sensing. Our approach provides more quantitative information than commonly used methods that depend on ratio-based indicators (such as HCHO/NO2). Additionally, our method considers light and water vapor, making it suitable for applications with GEO satellites.
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