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Maren Brast, Fellowship Grant 2017

„Scale-adaptive Parameterization of Shallow Cumulus Convection“

The representation of shallow cumulus clouds in large-scale numerical weather and climate models
is important, because shallow cumulus clouds cover large parts of the Earth's surface. The
representation of this type of clouds is mainly done by parameterization, since those clouds are
often too small to be resolved by the model. However, when the model resolution increases to a
degree where parts of a shallow cumulus cloud field becomes resolved, the parameterization
scheme should adapt, meaning that it should only represent those clouds that are not resolved by the
large-scale model. In my work, this scale-adaptivity of a shallow cumulus parameterization scheme
was tested. The main work during the time of the GSGS fellowship Grant encompassed the
simulation of a semi-realistic case of a shallow cumulus case over land and the comparison with
observations. It was found that the simulations could represent the measured characteristics of three
days of a measurement campaign fairly well. Furthermore, the parameterization scheme is scaleadaptive
for these cases and represents only those clouds smaller than the grid size. With the help of
the GSGS grant, I could complete this study and therefore finish my PhD thesis. Also, a publication
based on another study included in the thesis could be prepared. This study is a preliminary study
and investigates the scale-adaptivity of the parameterization in an idealized case of shallow cumulus
over the ocean.
Publication prepared during the grant period, to be submitted soon:
Brast, M., V. Schemann, and R. A. J. Neggers, 2017: Using LES as an interactive testing ground for
developing convective parameterizations in the gray zone. J. Atmos. Sci.

Maren Brast

Institute for Geophysics and Meteorology

Title of PhD thesis: „Scale-adaptive Parameterization of Shallow Cumulus Convection“
Supervisor: Prof. Dr. Roel Neggers (Integrated Scale-Adaptive Parameterization and Evaluation)
Defense: 30.06.2017