  In this study we present a series of LES simulations employing the
Super-Droplet Method (SDM) for representing aerosol, cloud and rain
microphysics. SDM is a particle-based and probabilistic approach in which a
Monte-Carlo type algorithm is used for solving the particle collisions and
coalescence process. The model does not differentiate between aerosol
particles, cloud droplets, drizzle or rain drops. Consequently, it covers
representation of such cloud-microphysical processes as: CCN activation,
drizzle formation by autoconversion, accretion of cloud droplets,
self-collection of raindrops and precipitation including aerosol wet
deposition. Among the salient features of the SDM, there are: (i) the
robustness of the model formulation (i.e. employment of basic principles rather
than parametrisations) and (ii) the ease of comparison of the model results
with experimental data obtained with particle-counting instruments.
  The model set-up used in the study is based on observations from the Rain In
Cumulus over Ocean (RICO) field project (the GEWEX Cloud System Study Boundary
Layer Cloud Working Group RICO case). Cloud and rain droplet size spectrum
features obtained in the simulations are compared with previously published
aircraft observations carried out during the RICO field project. The analysis
covers height-resolved statistics of simulated cloud microphysical parameters
such as droplet number concentration, effective radius, and the width of the
cloud droplet size spectrum. The sensitivity of the results to the grid
resolution of the LES, as well as to the sampling density of the probabilistic
(Monte-Carlo type) model is discussed.
