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In this study we present a series of LES simulations employing theSuper-Droplet Method (SDM) for representing aerosol, cloud and rainmicrophysics. SDM is a particle-based and probabilistic approach in which aMonte-Carlo type algorithm is used for solving the particle collisions andcoalescence process. The model does not differentiate between aerosolparticles, cloud droplets, drizzle or rain drops. Consequently, it coversrepresentation of such cloud-microphysical processes as: CCN activation,drizzle formation by autoconversion, accretion of cloud droplets,self-collection of raindrops and precipitation including aerosol wetdeposition. Among the salient features of the SDM, there are: (i) therobustness of the model formulation (i.e. employment of basic principles ratherthan parametrisations) and (ii) the ease of comparison of the model resultswith experimental data obtained with particle-counting instruments.  The model set-up used in the study is based on observations from the Rain InCumulus over Ocean (RICO) field project (the GEWEX Cloud System Study BoundaryLayer Cloud Working Group RICO case). Cloud and rain droplet size spectrumfeatures obtained in the simulations are compared with previously publishedaircraft observations carried out during the RICO field project. The analysiscovers height-resolved statistics of simulated cloud microphysical parameterssuch as droplet number concentration, effective radius, and the width of thecloud droplet size spectrum. The sensitivity of the results to the gridresolution of the LES, as well as to the sampling density of the probabilistic(Monte-Carlo type) model is discussed.

Super-Droplet Approach to Simulate Precipitating Trade-Wind Cumuli - Comparison of Model Results with RICO Aircraft Observations