Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature
Journal article, 2020

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 may force one to work with representative rather than individual characterization data. Many errors derive from approximations and simplifications used in real-world retrieval schemes, which are reviewed in this paper, along with related error estimation schemes. 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 should help to unify retrieval error reporting.

Author

Thomas von Clarmann

Karlsruhe Institute of Technology (KIT)

Douglas A. Degenstein

University of Saskatchewan

Nathaniel J. Livesey

Jet Propulsion Laboratory, California Institute of Technology

California Institute of Technology (Caltech)

Stefan Bender

Norwegian University of Science and Technology (NTNU)

Amy Braverman

Jet Propulsion Laboratory, California Institute of Technology

Andre Butz

Heidelberg University

Steven Compernolle

Belgian Institute for Space Aeronomy (BIRA-IASB)

Robert Damadeo

NASA Langley Research Center

Seth Dueck

University of Saskatchewan

Patrick Eriksson

Chalmers, Space, Earth and Environment, Microwave and Optical Remote Sensing

Bernd Funke

Spanish National Research Council (CSIC)

Margaret C. Johnson

California Institute of Technology (Caltech)

Jet Propulsion Laboratory, California Institute of Technology

Yasuko Kasai

Japan National Institute of Information and Communications Technology

Arno Keppens

Belgian Institute for Space Aeronomy (BIRA-IASB)

Anne Kleinert

Karlsruhe Institute of Technology (KIT)

Natalya A. Kramarova

NASA Goddard Space Flight Center

Alexandra Laeng

Karlsruhe Institute of Technology (KIT)

Bavo Langerock

Belgian Institute for Space Aeronomy (BIRA-IASB)

Vivienne H. Payne

California Institute of Technology (Caltech)

Jet Propulsion Laboratory, California Institute of Technology

Alexei Rozanov

Universität Bremen

Tomohiro O. Sato

Japan National Institute of Information and Communications Technology

Matthias Schneider

Karlsruhe Institute of Technology (KIT)

Patrick Sheese

University of Toronto

Viktoria Sofieva

Finnish Meteorological Institute (FMI)

Gabriele P. Stiller

Karlsruhe Institute of Technology (KIT)

Christian von Savigny

University of Greifswald

Daniel Zawada

University of Saskatchewan

Atmospheric Measurement Techniques

1867-1381 (ISSN) 1867-8548 (eISSN)

Vol. 13 8 4393-4436

Subject Categories

Geophysics

Probability Theory and Statistics

Signal Processing

DOI

10.5194/amt-13-4393-2020

More information

Latest update

10/13/2021