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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-350
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-350
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: review article 14 Oct 2019

Submitted as: review article | 14 Oct 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Estimating and Reporting Uncertainties in Remotely Sensed Atmospheric Composition and Temperature

Thomas von Clarmann1, Douglas A. Degenstein2, Nathaniel J. Livesey3, Stefan Bender4, Amy Braverman3, André Butz5, Steven Compernolle6, Robert Damadeo7, Seth Dueck2, Patrick Eriksson8, Bernd Funke9, Margaret C. Johnson3, Yasuko Kasai10, Arno Keppens6, Anne Kleinert1, Natalya A. Kramarova11, Alexandra Laeng1, Vivienne H. Payne3, Alexei Rozanov12, Tomohiro O. Sato10, Matthias Schneider1, Patrick Sheese13, Viktoria Sofieva14, Gabriele P. Stiller1, Christian von Savigny15, and Daniel Zawada2 Thomas von Clarmann et al.
  • 1Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Karlsruhe, Germany
  • 2University of Saskatchewan, Saskatoon, Canada
  • 3NASA Jet Propulsion Laboratory and California Institute of Technology, Pasadena, CA, USA
  • 4Department of Physics, Norwegian University of Science and Technology (NTNU), NO-7491 Trondheim, Norway
  • 5Institut für Umweltphysik, Department of Physics and Astronomy, Universität Heidelberg, Heidelberg, Germany
  • 6Department of Atmospheric Composition, Royal Belgian Institute for Space Aeronomy (BIRA-IASB), 1180 Brussels, Belgium
  • 7NASA Langley Research Center, Hampton, VA
  • 8Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
  • 9Instituto de Astrofísica de Andalucía, CSIC, Spain
  • 10National Institute of Information and Communications Technology (NICT), 4-2-1 Nukui-kita, Koganei, Tokyo 184-8795, Japan
  • 11NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 12Institute of Environmental Physics (IUP), University of Bremen, Germany
  • 13Department of Physics, University of Toronto, Canada
  • 14Finnish Meteorological Institute, Helsinki, Finland
  • 15Greifswald University, Greifswald, Germany

Abstract. Remote sensing of atmospheric state variables typically relies on the inverse solution of the radiative transfer equation. An adequately characterized retrieval provides information on the uncertainties of the estimated state variables as well as on how any constraint or a priori assumption affects the estimate. Reported characterization data should be intercomparable between different instruments, empirically validatable, grid-independent, usable without detailed knowledge of the instrument or retrieval technique, traceable, and still have reasonable data volume. The latter condition of adequacy may force one to work with representative rather than individual characterization data. The main sources of uncertainty are measurement noise, calibration errors, simplifications and idealizations in the radiative transfer model and retrieval scheme, auxiliary data errors, and uncertainties in atmospheric or instrumental parameters. Some of these errors affect the result in a random way, while others chiefly cause a bias or are of mixed character. Beyond this, it is of utmost importance to know the influence of any constraint and prior information on the solution. While different instruments or retrieval schemes may require different error estimation schemes, we provide a list of recommendations which shall help to unify retrieval error reporting.

Thomas von Clarmann et al.
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Status: open (until 18 Dec 2019)
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Short summary
Remote sensing of atmospheric state variables typically relies on the inverse solution of the radiative transfer equation. An adequately characterized retrieval provides information on the uncertainties of the estimated state variables as well as on how any constraint or a priori assumption affects the estimate. This paper summarizes related techniques and provides recommendations for unified error reporting.
Remote sensing of atmospheric state variables typically relies on the inverse solution of the...
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