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 4393-4436Subject Categories
Geophysics
Probability Theory and Statistics
Signal Processing
DOI
10.5194/amt-13-4393-2020