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

California Institute of Technology (Caltech)

Jet Propulsion Laboratory, California Institute of Technology

Stefan Bender

Norges teknisk-naturvitenskapelige universitet

Amy Braverman

Jet Propulsion Laboratory, California Institute of Technology

Andre Butz

Universität Heidelberg

Steven Compernolle

Royal Belgian Inst Space Aeron 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

Natl Inst Informat & Commun Technol NICT

Arno Keppens

Royal Belgian Inst Space Aeron 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

Royal Belgian Inst Space Aeron BIRA IASB

Vivienne H. Payne

Jet Propulsion Laboratory, California Institute of Technology

California Institute of Technology (Caltech)

Alexei Rozanov

Universität Bremen

Tomohiro O. Sato

Natl Inst Informat & Commun Technol NICT

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 4393-4436

Ämneskategorier

Geofysik

Sannolikhetsteori och statistik

Signalbehandling

DOI

10.5194/amt-13-4393-2020

Mer information

Senast uppdaterat

2020-10-16