Likelihood Informed Dimension Reduction for Remote Sensing of Atmospheric Constituent Profiles

  • Otto LamminpääEmail author
  • Marko Laine
  • Simo Tukiainen
  • Johanna Tamminen
Part of the MATRIX Book Series book series (MXBS, volume 2)


We use likelihood informed dimension reduction (LIS) (Cui et al. Inverse Prob 30(11):114015, 28, 2014) for inverting vertical profile information of atmospheric methane from ground based Fourier transform infrared (FTIR) measurements at Sodankylä, Northern Finland. The measurements belong to the word wide TCCON network for greenhouse gas measurements and, in addition to providing accurate greenhouse gas measurements, they are important for validating satellite observations.

LIS allows construction of an efficient Markov chain Monte Carlo sampling algorithm that explores only a reduced dimensional space but still produces a good approximation of the original full dimensional Bayesian posterior distribution. This in effect makes the statistical estimation problem independent of the discretization of the inverse problem. In addition, we compare LIS to a dimension reduction method based on prior covariance matrix truncation used earlier (Tukiainen et al., J Geophys Res Atmos 121:10312–10327, 2016).


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We thank Dr. Rigel Kivi from FMI Arctic Research Centre, Sodankylä, Finland for the AirCore and TCCON data. We thank Dr. Tiangang Cui from Monash University and the mathematical research institute MATRIX in Australia for organizing a workshop where a part of this research was performed. This work has been supported by Academy of Finland (projects INQUIRE, IIDA-MARI and CoE in Inverse Modelling and Imaging) and by EU’s Horizon 2020 research and innovation programme (project GAIA-CLIM).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Otto Lamminpää
    • 1
    Email author
  • Marko Laine
    • 1
  • Simo Tukiainen
    • 1
  • Johanna Tamminen
    • 1
  1. 1.Finnish Meteorological InstituteHelsinkiFinland

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