Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature
Artikel i vetenskaplig tidskrift, 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.
Författare
Thomas von Clarmann
Karlsruher Institut für Technologie (KIT)
Douglas A. Degenstein
University of Saskatchewan
Nathaniel J. Livesey
Jet Propulsion Laboratory, California Institute of Technology
California Institute of Technology (Caltech)
Stefan Bender
Norges teknisk-naturvitenskapelige universitet
Amy Braverman
Jet Propulsion Laboratory, California Institute of Technology
Andre Butz
Universität Heidelberg
Steven Compernolle
Belgian Institute for Space Aeronomy (BIRA-IASB)
Robert Damadeo
NASA Langley Research Center
Seth Dueck
University of Saskatchewan
Patrick Eriksson
Chalmers, Rymd-, geo- och miljövetenskap, Mikrovågs- och optisk fjärranalys
Bernd Funke
Consejo Superior de Investigaciones Científicas (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
Karlsruher Institut für Technologie (KIT)
Natalya A. Kramarova
NASA Goddard Space Flight Center
Alexandra Laeng
Karlsruher Institut für Technologie (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
Karlsruher Institut für Technologie (KIT)
Patrick Sheese
University of Toronto
Viktoria Sofieva
Finnish Meteorological Institute
Gabriele P. Stiller
Karlsruher Institut für Technologie (KIT)
Christian von Savigny
Universität Greifswald
Daniel Zawada
University of Saskatchewan
Atmospheric Measurement Techniques
1867-1381 (ISSN) 1867-8548 (eISSN)
Vol. 13 8 4393-4436Ämneskategorier
Geofysik
Sannolikhetsteori och statistik
Signalbehandling
DOI
10.5194/amt-13-4393-2020