• A Parameterization of Cirrus Cloud Formation: Revisiting Competing Ice Nucleation 

      Kärcher, B.ORCIDiD (Journal of Geophysical Research: Atmospheres, 2022-09-24)
      This study develops an advanced physically‐based parameterization of heterogeneous ice nucleation in cirrus clouds that includes an updated parameterization of stochastic homogeneous freezing of supercooled solution droplets. ...
    • Algorithmic Differentiation for Sensitivity Analysis in Cloud Microphysics 

      Hieronymus, M.ORCIDiD; Baumgartner, M.; Miltenberger, A.ORCIDiD; Brinkmann, A. (Journal of Advances in Modeling Earth Systems, 2022-07-12)
      The role of clouds for radiative transfer, precipitation formation, and their interaction with atmospheric dynamics depends strongly on cloud microphysics. The parameterization of cloud microphysical processes in weather ...
    • Deep Learning Based Cloud Cover Parameterization for ICON 

      Grundner, ArthurORCIDiD; Beucler, TomORCIDiD; Gentine, PierreORCIDiD; Iglesias‐Suarez, FernandoORCIDiD; Giorgetta, Marco A.ORCIDiD; Eyring, VeronikaORCIDiD (Journal of Advances in Modeling Earth Systems, 2022-12-14)
      A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm‐resolving model (SRM) simulations. The ICOsahedral ...
    • Non‐Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models 

      Behrens, GunnarORCIDiD; Beucler, TomORCIDiD; Gentine, PierreORCIDiD; Iglesias‐Suarez, FernandoORCIDiD; Pritchard, MichaelORCIDiD; Eyring, VeronikaORCIDiD (Journal of Advances in Modeling Earth Systems, 2022-08-13)
      Deep learning can accurately represent sub‐grid‐scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal ...