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A new algorithm to generate a priori trace gas profiles for the GGG2020 retrieval algorithm

  • Joshua L. Laughner
  • , Sébastien Roche
  • , Matthäus Kiel
  • , Geoffrey C. Toon
  • , Debra Wunch
  • , Bianca C. Baier
  • , Sébastien Biraud
  • , Huilin Chen
  • , Rigel Kivi
  • , Thomas Laemmel
  • , Kathryn McKain
  • , Pierre Yves Quéhé
  • , Constantina Rousogenous
  • , Britton B. Stephens
  • , Kaley Walker
  • , Paul O. Wennberg
  • California Institute of Technology
  • University of Toronto
  • Harvard University
  • National Oceanic and Atmospheric Administration
  • University of Colorado Boulder
  • Lawrence Berkeley National Laboratory
  • University of Groningen
  • Finnish Meteorological Institute
  • Lab. Sci. du Climat et de l'Environ.
  • University of Bern
  • The Cyprus Institute

Research output: Contribution to journal › Article › peer-review

51 Scopus citations

Abstract

Optimal estimation retrievals of trace gas total columns require prior vertical profiles of the gases retrieved to drive the forward model and ensure the retrieval problem is mathematically well posed. For well-mixed gases, it is possible to derive accurate prior profiles using an algorithm that accounts for general patterns of atmospheric transport coupled with measured time series of the gases in questions. Here we describe the algorithm used to generate the prior profiles for GGG2020, a new version of the GGG retrieval that is used to analyze spectra from solar-viewing Fourier transform spectrometers, including the Total Carbon Column Observing Network (TCCON). A particular focus of this work is improving the accuracy of CO2, CH4, N2O, HF, and CO across the tropopause and into the lower stratosphere. We show that the revised priors agree well with independent in situ and space-based measurements and discuss the impact on the total column retrievals.

Original languageEnglish
Pages (from-to)1121-1146
Number of pages26
JournalAtmospheric Measurement Techniques
Volume16
Issue number5
DOIs
StatePublished - Mar 7 2023

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