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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/amt-2020-403
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-403
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  10 Nov 2020

10 Nov 2020

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This preprint is currently under review for the journal AMT.

The Adaptable 4A Inversion (5AI): Description and first XCO2 retrievals from OCO-2 observations

Matthieu Dogniaux1, Cyril Crevoisier1, Raymond Armante1, Virginie Capelle1, Thibault Delahaye1, Vincent Cassé1, Martine De Mazière2, Nicholas M. Deutscher3,4, Dietrich G. Feist5,6,7, Omaira E. Garcia8, David W. T. Griffith3, Frank Hase9, Laura T. Iraci10, Rigel Kivi11, Isamu Morino12, Justus Notholt4, David F. Pollard13, Coleen M. Roehl14, Kei Shiomi15, Kimberly Strong16, Yao Té17, Voltaire A. Velazco3, and Thorsten Warneke4 Matthieu Dogniaux et al.
  • 1Laboratoire de Météorologie Dynamique/IPSL, CNRS, École polytechnique, Institut Polytechnique de Paris, Sorbonne Université, École Normale Supérieure, PSL Research University, Palaiseau, 91120, France
  • 2Royal Belgian Institute for Space Aeronomy, Brussels, Belgium
  • 3Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, Australia
  • 4University of Bremen, Bremen, Germany
  • 5Max Planck Institute for Biogeochemistry, Jena, Germany
  • 6Ludwig-Maximilians-Universität München, Lehrstuhl für Physik der Atmosphäre, Munich, Germany
  • 7Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 8Izaña Atmospheric Research Center (IARC), State Meteorological Agency of Spain (AEMET), Spain
  • 9Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe, Germany
  • 10NASA Ames Research Center, Moffett Field, CA, USA
  • 11Finnish Meteorological Institute, Sodankylä, Finland
  • 12National Institute for Environmental Studies (NIES), Tsukuba, Japan
  • 13National Institute of Water and Atmospheric Research Ltd (NIWA), Lauder, New Zealand
  • 14Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
  • 15Japan Aerospace Exploration Agency (JAXA), Tsukuba, Japan
  • 16Department of Physics, University of Toronto, Toronto, Canada
  • 17Laboratoire d'Etudes du Rayonnement et de la Matière en Astrophysique et Atmosphères (LERMA-IPSL), Sorbonne Université, CNRS, Observatoire de Paris, PSL Université, 75005 Paris, France

Abstract. A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Spaceborne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on Bayesian optimal estimation relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) from measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission, and uses an empirically corrected absorption continuum in the O2 A-band. For airmasses below 3.0, XCO2 retrievals successfully capture the latitudinal variations of CO2, as well as its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a difference of 1.33 ± 1.29 ppm, which is similar to the standard deviation of the Atmospheric CO2 Observations from Space (ACOS) official products. We show that the systematic differences between 5AI and ACOS results can be fully removed by adding an average calculated – observed spectral residual correction to OCO-2 measurements, thus underlying the critical sensitivity of retrieval results to forward modelling. These comparisons show the reliability of 5AI as a Bayesian optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry-air mole fractions of greenhouse gases.

Matthieu Dogniaux et al.

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Matthieu Dogniaux et al.

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Short summary
In this work we present the Adaptable 4A Inversion (5AI), an implementation of the Optimal Estimation (OE) algorithm, relying on the 4A/OP radiative transfer model, that enables to retrieve greenhouse gas atmospheric weighted columns from infrared measurements. It is tested on a sample of Orbiting Carbon Observatory-2 (OCO-2) observations and its results satisfactorily compare to several reference products, thus showing the reliability of 5AI OE implementation.
In this work we present the Adaptable 4A Inversion (5AI), an implementation of the Optimal...
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