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@PHDTHESIS{Krasauskas:1008186,
      author       = {Krasauskas, Lukas},
      title        = {{E}xamining transport in the {U}pper {T}roposphere –
                      {L}ower {S}tratosphere with the infrared limb imager
                      {GLORIA}},
      volume       = {606},
      school       = {Univ. Wuppertal},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2023-02232},
      isbn         = {978-3-95806-691-5},
      series       = {Schriften des Forschungszentrums Jülich Reihe Energie $\&$
                      Umwelt / Energy $\&$ Environment},
      pages        = {-},
      year         = {2023},
      note         = {Dissertation, Univ. Wuppertal, 2023},
      abstract     = {The Gimballed Limb Observer for Radiance Imaging of the
                      Atmosphere (GLORIA) is an airborne infrared limb imager that
                      can measure temperature and trace gas concentration data in
                      the Upper Troposphere and Lower Stratosphere (UTLS) with
                      high vertical resolution (upo to 200 m). In addition to
                      standard 1-D retrievals, a unique 3-D data set can be
                      obtained by flying around the observed air mass and
                      performing a tomographic retrieval. Such data sets have high
                      horizontal resolution (up to 20 km×20 km) as well and can
                      give insight into many important small-scale processes in
                      UTLS, such as mixing, filamentation and internal gravity
                      wave propagation. A 3-D tomographic retrieval is a highly
                      challenging and computationally expensive inverse modelling
                      problem. It typically requires an introduction of some
                      general knowledge of the atmosphere (regularisation) due to
                      its underdetermined nature. The quality of 3-D data strongly
                      depends on regularisation. In this thesis, a consistent,
                      physically motivated (no ad-hoc parameters) regularisation
                      scheme based on spatial derivatives of first order and
                      Laplacian is introduced. As shown by a case study with
                      synthetic data, this scheme, combined with newly developed
                      irregular grid retrieval methods, improves both upon the
                      quality and the computational cost of 3D tomography. It also
                      eliminates grid dependence and the need to tune parameters
                      for each use case. The few physical parameters required can
                      be derived from in situ measurements and model data. Tests
                      show that an $82\%$ reduction in the number of grid points
                      and a $50\%$ reduction in total computation time, compared
                      to previous methods, could be achieved without compromising
                      results. An efficient Monte Carlo technique was also adopted
                      for accuracy estimation of the new retrievals.},
      cin          = {IEK-7},
      cid          = {I:(DE-Juel1)IEK-7-20101013},
      pnm          = {2112 - Climate Feedbacks (POF4-211)},
      pid          = {G:(DE-HGF)POF4-2112},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      doi          = {10.34734/FZJ-2023-02232},
      url          = {https://juser.fz-juelich.de/record/1008186},
}