Evaluation of potential sources of a priori ozone profiles for TEMPO tropospheric ozone retrievals

Creators: Johnson, Matthew S.; Liu, Xiong; Zoogman, Peter; Sullivan, John; Newchurch, Michael J.; Kuang, Shi; Leblanc, Thierry; McGee, Thomas

Abstract

Potential sources of a priori ozone (O_3) profiles for use in Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite tropospheric O_3 retrievals are evaluated with observations from multiple Tropospheric Ozone Lidar Network (TOLNet) systems in North America. An O_3 profile climatology (tropopause-based O_3 climatology (TB-Clim), currently proposed for use in the TEMPO O_3 retrieval algorithm) derived from ozonesonde observations and O_3 profiles from three separate models (operational Goddard Earth Observing System (GEOS-5) Forward Processing (FP) product, reanalysis product from Modern-era Retrospective Analysis for Research and Applications version 2 (MERRA2), and the GEOS-Chem chemical transport model (CTM)) were: (1) evaluated with TOLNet measurements on various temporal scales (seasonally, daily, and hourly) and (2) implemented as a priori information in theoretical TEMPO tropospheric O_3 retrievals in order to determine how each a priori impacts the accuracy of retrieved tropospheric (0–10 km) and lowermost tropospheric (LMT, 0–2 km) O_3 columns. We found that all sources of a priori O_3 profiles evaluated in this study generally reproduced the vertical structure of summer-averaged observations. However, larger differences between the a priori profiles and lidar observations were calculated when evaluating inter-daily and diurnal variability of tropospheric O_3. The TB-Clim O_3 profile climatology was unable to replicate observed inter-daily and diurnal variability of O_3 while model products, in particular GEOS-Chem simulations, displayed more skill in reproducing these features. Due to the ability of models, primarily the CTM used in this study, on average to capture the inter-daily and diurnal variability of tropospheric and LMT O_3 columns, using a priori profiles from CTM simulations resulted in TEMPO retrievals with the best statistical comparison with lidar observations. Furthermore, important from an air quality perspective, when high LMT _O3 values were observed, using CTM a priori profiles resulted in TEMPO LMT O_3 retrievals with the least bias. The application of near-real-time (non-climatological) hourly and daily model predictions as the a priori profile in TEMPO O_3 retrievals will be best suited when applying this data to study air quality or event-based processes as the standard retrieval algorithm will still need to use a climatology product. Follow-on studies to this work are currently being conducted to investigate the application of different CTM-predicted O_3 climatology products in the standard TEMPO retrieval algorithm. Finally, similar methods to those used in this study can be easily applied by TEMPO data users to recalculate tropospheric O_3 profiles provided from the standard retrieval using a different source of a priori.

Additional Information

© Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Received: 27 Dec 2017 – Discussion started: 30 Jan 2018. Revised: 24 May 2018 – Accepted: 04 Jun 2018 – Published: 19 Jun 2018. This work is supported by the TOLNet program within NASA's Science Mission Directorate. Xiong Liu and Peter Zoogman were supported by the NASA Earth Venture Instrument TEMPO project (NNL13AA09C). The authors would also like to thank the Harvard University Atmospheric Chemistry Modeling Group for providing the GEOS-Chem model and the NASA GMAO for providing the GEOS-5 FP and MERRA2 products used during our research. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at NASA Ames Research Center. All the authors express gratitude for the support from NASA's Earth Science Division at Ames Research Center. Finally, the views, opinions, and findings contained in this report are those of the authors and should not be construed as an official NASA or United States Government position, policy, or decision. Author contributions. MJ, XL, PZ, JS, and MN designed the methods and experiments presented in the study and MJ carried them out. XL, JS, SK, TL, and TM were instrumental in providing TEMPO and TOLNet data and assisting MJ in the application of these data. MJ prepared the manuscript with contributions from all listed coauthors. Data availability. All the data and models used during this study are publically available or can be provided through personal communication with the corresponding author (matthew.s.johnson@nasa.gov). The tropospheric O_3 lidar data can be downloaded from the TOLNet website: http://www-air.larc.nasa.gov/missions/TOLNet/ (last access: 23 May 2018). NASA GMAO model products can be downloaded from: GEOS5_FP: http://portal.nccs.nasa.gov/cgi-lats4d/opendap.cgi?&path=GEOS-5/fp/0.25_deg/assim (last access: 1 June 2018) and MERRA2: https://disc.gsfc.nasa.gov/daac-bin/FTPSubset2.pl (last access: 1 June 2018). Instructions for downloading the public GEOS-Chem model can be found here: http://wiki.seas.harvard.edu/geos-chem/index.php/Downloading_GEOS-Chem_source_code_and_data (last access: 1 June 2018). The authors declare that they have no conflict of interest. Edited by: Mark Weber Reviewed by: two anonymous referees

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