Articles | Volume 15, issue 16
https://doi.org/10.5194/gmd-15-6341-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6341-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Aug 2022
Model description paper |
| 17 Aug 2022
| 17 Aug 2022
A machine learning methodology for the generation of a parameterization of the hydroxyl radical
Daniel C. Anderson
CORRESPONDING AUTHOR
GESTAR II, University of Maryland, Baltimore County, Baltimore, MD,
USA
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA
Melanie B. Follette-Cook
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA
GESTAR II, Morgan State University, Baltimore, MD, USA
now at: Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Sarah A. Strode
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA
GESTAR II, Morgan State University, Baltimore, MD, USA
Julie M. Nicely
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Junhua Liu
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA
GESTAR II, Morgan State University, Baltimore, MD, USA
Peter D. Ivatt
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Bryan N. Duncan
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA
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Cited
14 citations as recorded by crossref.
- Climate and Tropospheric Oxidizing Capacity A. Fiore et al.
- Enhancing long-term trend simulation of the global tropospheric hydroxyl (TOH) and its drivers from 2005 to 2019: a synergistic integration of model simulations and satellite observations A. Souri et al.
- Advancing Aerosol Chemistry with Machine Learning: A Short Review Y. Wang et al.
- Technical note: Constraining the hydroxyl (OH) radical in the tropics with satellite observations of its drivers – first steps toward assessing the feasibility of a global observation strategy D. Anderson et al.
- Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al.
- The application of machine learning to air pollution research: A bibliometric analysis Y. Li et al.
- Enhancing 72-Hour air quality forecasting with an observation-driven deep learning chemistry transport model S. Li & J. Xing
- Decoupling in the vertical shape of HCHO during a sea breeze event: The effect on trace gas satellite retrievals and column-to-surface translation A. Souri et al.
- Emulating chemistry-climate dynamics with a linear inverse model E. Mei et al.
- Opinion: Beyond global means – novel space-based approaches to indirectly constrain the concentrations of and trends and variations in the tropospheric hydroxyl radical (OH) B. Duncan et al.
- Interannual changes in atmospheric oxidation over forests determined from space J. Shutter et al.
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al.
- The impact of internal climate variability on OH trends between 2005 and 2014 Q. Zhu et al.
- Feasibility of robust estimates of ozone production rates using a synergy of satellite observations, ground-based remote sensing, and models A. Souri et al.
14 citations as recorded by crossref.
- Climate and Tropospheric Oxidizing Capacity A. Fiore et al.
- Enhancing long-term trend simulation of the global tropospheric hydroxyl (TOH) and its drivers from 2005 to 2019: a synergistic integration of model simulations and satellite observations A. Souri et al.
- Advancing Aerosol Chemistry with Machine Learning: A Short Review Y. Wang et al.
- Technical note: Constraining the hydroxyl (OH) radical in the tropics with satellite observations of its drivers – first steps toward assessing the feasibility of a global observation strategy D. Anderson et al.
- Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al.
- The application of machine learning to air pollution research: A bibliometric analysis Y. Li et al.
- Enhancing 72-Hour air quality forecasting with an observation-driven deep learning chemistry transport model S. Li & J. Xing
- Decoupling in the vertical shape of HCHO during a sea breeze event: The effect on trace gas satellite retrievals and column-to-surface translation A. Souri et al.
- Emulating chemistry-climate dynamics with a linear inverse model E. Mei et al.
- Opinion: Beyond global means – novel space-based approaches to indirectly constrain the concentrations of and trends and variations in the tropospheric hydroxyl radical (OH) B. Duncan et al.
- Interannual changes in atmospheric oxidation over forests determined from space J. Shutter et al.
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al.
- The impact of internal climate variability on OH trends between 2005 and 2014 Q. Zhu et al.
- Feasibility of robust estimates of ozone production rates using a synergy of satellite observations, ground-based remote sensing, and models A. Souri et al.
Saved (final revised paper)
Latest update: 04 May 2026
Short summary
The hydroxyl radical (OH) is the most important chemical in the atmosphere for removing certain pollutants, including methane, the second-most-important greenhouse gas. We present a methodology to create an easily modifiable parameterization that can calculate OH concentrations in a computationally efficient way. The parameterization, which predicts OH within 5 %, can be integrated into larger climate models to allow for calculation of the interactions between OH, methane, and other chemicals.
The hydroxyl radical (OH) is the most important chemical in the atmosphere for removing certain...
