Tropospheric ozone column dataset from OMPS-LP/OMPS-NM limb-nadir matching

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https://doi.org/10.5194/amt-17-1791-2024
 http://hdl.handle.net/11603/28170

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UMBC Joint Center for Earth Systems Technology (JCET)
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https://orcid.org/0000-0002-7829-0920

Date

2024-03-28

Type of Work

journal articles
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Citation of Original Publication

Orfanoz-Cheuquelaf, A., Arosio, C., Rozanov, A., Weber, M., Ladstätter-Weißenmayer, A., Burrows, J. P., Thompson, A. M., Stauffer, R. M., and Kollonige, D. E.: Tropospheric ozone column dataset from OMPS-LP/OMPS-NM limb-nadir matching, Atmos. Meas. Tech. Discuss. https://doi.org/10.5194/amt-17-1791-2024

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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract

A Tropospheric Ozone Column (TrOC) dataset from the Ozone Mapping and Profiler Suite (OMPS) observations was generated by combining the retrieved total ozone column from OMPS - Nadir Mapper (OMPS-NM) and limb profiles from OMPS - Limb Profiler (OMPS-LP) data. All datasets were generated at the University of Bremen, and the TrOC product was obtained by applying the Limb-Nadir Matching technique (LNM). The retrieval algorithm and a comprehensive analysis of the uncertainty budget are presented here. The OMPS-LNM-TrOC dataset (2012–2018) is analysed and validated by comparing with ozonesondes, tropospheric ozone residual (TOR) data from the combined Ozone Monitoring Instrument/Microwave Limb Sounder (OMI/MLS) observations, and the TROPOspheric Monitoring Instrument (TROPOMI) Convective Cloud Differential technique (CCD) dataset. The OMPS-LNM TrOC is generally lower than the other datasets. The average bias with respect to ozonesondes is −1.7 DU with no significant latitudinal dependence identified. The mean difference with respect to OMI/MLS TOR and TROPOMI CCD is −3.4 and −1.8 DU, respectively. The seasonality and inter-annual variability are in good agreement with all comparison datasets.