Simulation
of a convective cloud from a Lagrangian cloud model, and its
application to the parameterization of cloud microphysics
Yign Noh
Yonsei University
Abstract:
A Lagrangian cloud model (LCM), in which the flow field is
simulated by LES, and the droplets are treated as Lagrangian particles
responding to the simulated flow field, is applied to simulate a
precipitating convective cloud. The LCM is based on the concept of an
ensemble of super-droplets with each super-droplet representing a large
number of real droplets of a given size. A statistical method is used
to calculate the collision/coalescence of droplets. It is shown that
the model is capable of reproducing the general features of the cloud
and precipitation process, including the evolutions of mixing ratio,
relative humidity, vertical velocity, dissipation rate, droplet
spectrum, and the initiation of precipitation. The LCM is then applied
to study the impact of turbulent collision/coalescence on the
precipitation process, revealing that precipitation starts earlier and
becomes stronger, if the effect of turbulence is included in the
collection kernel. Furthermore, it has been demonstrated that the LCM
provides various information on the motion of droplets, which plays a
crucial role in the parameterization of cloud microphysics, such as the
vertical velocity of droplets, the raindrop size distribution, and the
precipitation flux.
Besides, two other topics from the lecturer’s
previous works for NWP are briefly introduced; the parameterization of
PBL and the production of SST from satellite data assimilation to a
NWP-ocean mixed layer coupled model.