Cloud Droplet Collisions in Turbulent Environment: Collision Statistics and Parameterization

Abstract

The purpose of this paper is to quantify the influence of turbulence in collision statistics by separately studying the impacts of computational domain sizes, eddy dissipation rates (EDRs), and droplet sizes and eventually to develop an accurate parameterization of collision kernels. Direct numerical simulations (DNS) were performed with a relatively wide range of EDRs and Taylor microscale Reynolds numbers R l . EDR measures the turbulence intensity levels. DNS model studies have simulated homogeneous turbulence in a small domain in the cloud's adiabatic core. Clouds clearly have much larger scales than current DNS can simulate. For this reason, it is emphasized that R l obtained from current DNS is fundamentally only a measure of the computational domain size for a given EDR and cannot completely describe the physical properties of cloud turbulence. Results show that the collision statistics are independent of the domain sizes and hence of the computational R l for droplet sizes no bigger than 25 mm as long as the droplet separation distance, which is on the order of the Kolmogorov scale in real clouds, is resolved. Instead, they are found to be highly correlated with EDRs and droplet sizes, and this correlation is used to formulate an improved parameterization scheme. The new scheme well represents the turbulent geometric collision kernel with a relative uncertainty of 14%. A comparison between different parameterizations is made, and the formulas proposed here are shown to improve the fit to the collision statistics.

Bridging the condensation–collision size gap: a direct numerical simulation of continuous droplet growth in turbulent clouds

“…Franklin et al (2005) resolved the droplet collisions using an efficient collision detection technique. Chen et al (2016) made changes to allow simulation in larger domain sizes and introduced a new forcing scheme to achieve a statistically steady turbulent dissipation rate. Chen et al (2018) added the local disturbance flow field induced by droplets to obtain accurate turbulent collision efficiencies and droplet collisional growth affected by both the disturbance flow and the turbulence flow.…”

Effects of Wintertime Polluted Aerosol on Clouds over the Yangtze River Delta: Case Study

“…7). Many studies have revealed other factors on CDR variation, such as functions of different aerosol components and cloud physical dynamics (Sardina et al, 2015;Chen et al, 2016). Furthermore, the relationship between aerosol and precipitation is complex as well.…”

Bridging the condensation-collision size gap: a direct numerical simulation of continuous droplet growth in turbulent cloud

“…Franklin et al (2005) resolved the droplet collisions using an efficient collision detection technique. Chen et al (2016) made changes to allow simulation in 20 larger domain sizes and introduced a new forcing scheme to achieve a statistically steady turbulent dissipation rate. Chen et al (2018) added the local disturbance flow field induced by droplets to obtain accurate turbulent collision efficiencies and droplet collisional growth affected by both the disturbance flow and the turbulence flow.…”

“…The domain size of each simulation is about 10 cm in each direction, with grid space ≈ 0.1 cm determined by the dissipation rate as explained in Chen et al (2016). It is recognized that droplet condensation in still-air leads to a narrow DSD and DSD broadening by condensation impacted by small-scale turbulence is insignificant.…”

“…We used the forcing method of Sullivan et al (1994) to maintain the turbulence. A simple modification to the forcing scheme by Chen et al (2016) is introduced to improve the efficiency in obtaining the desired mean dissipation rates. The droplet motion is governed by fluid drag force and gravity:…”