The Total Carbon Column Observing Network's GGG2020 Data Version: Data Quality, Comparison with GGG2014, and Future Outlook

New atmospheric gas retrievals using the GGG2020 version of the Total Carbon Column Observing Network (TCCON) dataset are significantly improved compared with the previous GGG2014 data version. The GGG2020 retrievals have lower variability across the network, better spectral fits, and smaller biases. The exception to this is XCH4 , which is, on average across the network, 5.3 ppb lower in GGG2020 than in GGG2014, a difference we mostly attribute to the a priori profiles used to perform the scaling. We discuss the remaining biases and corrections, the TCCON approach of identifying, quantifying, and minimizing biases, and suggest future work to further minimize the biases. The GGG2020 TCCON data are available from the TCCON archive at http://tccondata.org.

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Publication Details

Journal
Caltech Library
Published
2025-02-07
DOI
https://doi.org/10.14291/tccon.ggg2020.documentation.r1
Primary Topic
Mycorrhizal Fungi and Plant Interactions
Type
article
Field-Weighted Citation Impact
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article

The Total Carbon Column Observing Network's GGG2020 Data Version: Data Quality, Comparison with GGG2014, and Future Outlook

Caltech Library
Mycorrhizal Fungi and Plant Interactions
article

The Total Carbon Column Observing Network's GGG2020 Data Version: Data Quality, Comparison with GGG2014, and Future Outlook

article en

Abstract

New atmospheric gas retrievals using the GGG2020 version of the Total Carbon Column Observing Network (TCCON) dataset are significantly improved compared with the previous GGG2014 data version. The GGG2020 retrievals have lower variability across the network, better spectral fits, and smaller biases. The exception to this is XCH4 , which is, on average across the network, 5.3 ppb lower in GGG2020 than in GGG2014, a difference we mostly attribute to the a priori profiles used to perform the scaling. We discuss the remaining biases and corrections, the TCCON approach of identifying, quantifying, and minimizing biases, and suggest future work to further minimize the biases. The GGG2020 TCCON data are available from the TCCON archive at http://tccondata.org.

Caltech Library
Karlsruhe Institute of Technology (DE), California Institute of Technology (US), Agencia Estatal de Meteorología (ES), Ames Research Center (US), Finnish Meteorological Institute (FI), Centre National de la Recherche Scientifique (FR), Environmental Defense Fund (US), Jet Propulsion Laboratory (US), Environment and Climate Change Canada (CA), Los Alamos National Laboratory (US), Cyprus Institute (CY), Science and Technology Facilities Council (GB), Deutscher Wetterdienst (DE), University of Bremen (DE), University of Toronto (CA), Chinese Academy of Sciences (CN), Royal Belgian Institute for Space Aeronomy (BE), University of Wollongong (AU), Japan Aerospace Exploration Agency (JP), Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE), Sorbonne Université (FR), Laboratoire d’Etudes du Rayonnement et de la Matière en Astrophysique et Atmosphères (FR), National Institute of Meteorological Sciences (KR), National Institute of Water and Atmospheric Research (NZ), National Institute of Environmental Research (KR)
Openalex Percentile: Top 45%
Mycorrhizal Fungi and Plant Interactions
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