Articles | Volume 23, issue 11
https://doi.org/10.5194/acp-23-6319-2023
https://doi.org/10.5194/acp-23-6319-2023
Technical note
 | 
09 Jun 2023
Technical note |  | 09 Jun 2023

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

Daniel C. Anderson, Bryan N. Duncan, Julie M. Nicely, Junhua Liu, Sarah A. Strode, and Melanie B. Follette-Cook

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Opinion: Beyond global means – novel space-based approaches to indirectly constrain the concentrations of and trends and variations in the tropospheric hydroxyl radical (OH)
Bryan N. Duncan, Daniel C. Anderson, Arlene M. Fiore, Joanna Joiner, Nickolay A. Krotkov, Can Li, Dylan B. Millet, Julie M. Nicely, Luke D. Oman, Jason M. St. Clair, Joshua D. Shutter, Amir H. Souri, Sarah A. Strode, Brad Weir, Glenn M. Wolfe, Helen M. Worden, and Qindan Zhu
Atmos. Chem. Phys., 24, 13001–13023, https://doi.org/10.5194/acp-24-13001-2024,https://doi.org/10.5194/acp-24-13001-2024, 2024
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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
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OH, HO2, and RO2 radical chemistry in a rural forest environment: measurements, model comparisons, and evidence of a missing radical sink
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Atmos. Chem. Phys., 23, 10287–10311, https://doi.org/10.5194/acp-23-10287-2023,https://doi.org/10.5194/acp-23-10287-2023, 2023
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A machine learning methodology for the generation of a parameterization of the hydroxyl radical
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Ground-based investigation of HOx and ozone chemistry in biomass burning plumes in rural Idaho
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Subject: Gases | Research Activity: Remote Sensing | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
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Cited articles

AIRS Science Team and Teixeira, J.: AIRS/Aqua L3 Daily Standard Physical Retrieval (AIRS-only) 1 degree x 1 degree V006, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/Aqua/AIRS/DATA303, 2013. 
Anderson, D. C., Duncan, B. N., Fiore, A. M., Baublitz, C. B., Follette-Cook, M. B., Nicely, J. M., and Wolfe, G. M.: Spatial and temporal variability in the hydroxyl (OH) radical: understanding the role of large-scale climate features and their influence on OH through its dynamical and photochemical drivers, Atmos. Chem. Phys., 21, 6481–6508, https://doi.org/10.5194/acp-21-6481-2021, 2021. 
Anderson, D. C., Follette-Cook, M. B., Strode, S. A., Nicely, J. M., Liu, J., Ivatt, P. D., and Duncan, B. N.: A machine learning methodology for the generation of a parameterization of the hydroxyl radical, Geosci. Model Dev., 15, 6341–6358, https://doi.org/10.5194/gmd-15-6341-2022, 2022. 
Bedka, S., Knuteson, R., Revercomb, H., Tobin, D., and Turner, D.: An assessment of the absolute accuracy of the Atmospheric Infrared Sounder v5 precipitable water vapor product at tropical, midlatitude, and arctic ground-truth sites: September 2002 through August 2008, J. Geophys. Res.-Atmos., 115, D17310, https://doi.org/10.1029/2009JD013139, 2010. 
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Short summary
We describe a methodology that combines machine learning, satellite observations, and 3D chemical model output to infer the abundance of the hydroxyl radical (OH), a chemical that removes many trace gases from the atmosphere. The methodology successfully captures the variability of observed OH, although further observations are needed to evaluate absolute accuracy. Current satellite observations are of sufficient quality to infer OH, but retrieval validation in the remote tropics is needed.
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