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@ARTICLE{BartolomGarca:1022543,
      author       = {Bartolomé García, Irene and Sourdeval, Odran and Spang,
                      Reinhold and Krämer, Martina},
      title        = {{T}echnical note: {B}imodal parameterizations of in situ
                      ice cloud particle size distributions},
      journal      = {Atmospheric chemistry and physics},
      volume       = {24},
      number       = {3},
      issn         = {1680-7316},
      address      = {Katlenburg-Lindau},
      publisher    = {EGU},
      reportid     = {FZJ-2024-01520},
      pages        = {1699 - 1716},
      year         = {2024},
      abstract     = {The cloud particle size distribution (PSD) is a key
                      parameter for the retrieval of microphysical and optical
                      properties from remote-sensing instruments, which in turn
                      are necessary for determining the radiative effect of
                      clouds. Current representations of PSDs for ice clouds rely
                      on parameterizations that were largely based on aircraft in
                      situ measurements where the distribution of small ice
                      crystals were uncertain. This makes current
                      parameterizations deficient to simulate remote-sensing
                      observations sensitive to small ice, such as from lidar and
                      thermal infrared instruments. In this study we fit the in
                      situ PSDs of ice crystals from the JULIA (JÜLich In situ
                      Aircraft data set) database, which consists of 11 campaigns
                      covering the tropics, midlatitudes and the Arctic,
                      consistently processed and considered more robust in their
                      measurements of small ice. For the fitting, we implement an
                      established approach to PSD parameterizations, which
                      consists of finding an adequate set of parameters for a
                      modified gamma function after normalization of both PSD
                      axes. These parameters are constrained to match in situ
                      measurements when predicting microphysical properties from
                      the PSDs, via a cost function minimization method. We
                      selected the ice water content and the ice crystal number
                      concentration, which are currently key parameters for modern
                      satellite retrievals and model microphysics schemes. We
                      found that a bimodal parameterization yields better results
                      than a monomodal one. The bimodal parameterization has a
                      lower spread for almost all ice crystal sizes over the
                      entire range of analyzed temperatures and fits better the
                      observations, especially for particles between 20 and about
                      110 µm at temperatures between −60 and −20 ∘C.
                      For this temperature range, the root mean square error for
                      the retrieved Nice is reduced from 0.36 to 0.20. This
                      demonstrates a clear advantage to considering the bimodality
                      of PSDs, e.g., for satellite retrievals.},
      cin          = {IEK-7},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IEK-7-20101013},
      pnm          = {2112 - Climate Feedbacks (POF4-211)},
      pid          = {G:(DE-HGF)POF4-2112},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001189421800001},
      doi          = {10.5194/acp-24-1699-2024},
      url          = {https://juser.fz-juelich.de/record/1022543},
}