In recent years, pseudo-spectral direct numerical simulations (DNS) have emerged as an important research tool for studying statistics, structure and dynamics of small-scale turbulence and transport of the dispersed phase. Such studies are used, among others, to characterize the effects of air turbulence on the growth of cloud droplets during warm rain initiation. Modeling of these processes is a quite challenging task due to the wide range of scales involved (both spatial and temporal).

In this study, the effects of air turbulence on the growth of cloud droplets during warm rain initiation is quantified. Turbulence can enhance the rate of collision-coalescence and as such provides a mechanism to overcome the gap between the diffusional growth and the gravitational collision-coalescence mechanism. Several specific issues related to geometric collisions (without droplet–droplet aerodynamic interaction) of the same-size particles will be discussed. These include: the effect of the large-scale forcing mechanisms, the effect of the flow Reynolds number or equivalently the range of flow scales represented in DNS and the role of gravity. A thorough analysis of these effects is necessary for developing better parameterizations for numerical weather prediction models which, in turn, will allow to develop more accurate weather forecasts and deepen our knowledge of the climate change.

The research tool employed for modeling cloud processes is an innovative DNS code that allows to integrate the Navier-Stokes equations using pseudo-spectral method. The code is massively parallel MPI application, designed to run on supercomputers with distributed memory. It enables performing high-resolution DNS of turbulent collisions so the simulation results can be used to address the question of Reynolds number dependence of pair and collision statistics. Moreover, larger domains (equivalently larger Reynolds number) make the simulations closer to the physical conditions.