Minimalistic particle-based model of scalar variability in a cloud chamber

Cloud chamber measurements at the laboratory scale can provide valuable information on turbulence-microphysics interactions occurring at small scales in real clouds. We use a minimalistic, stochastic, particle-based model to represent scalar fluctuations at length scales in the inertial range of Rayleigh-Bénard (RB) turbulence in a cloud chamber. The scalars of interest are temperature, vapor mixing ratio of moist air, and the resulting supersaturation affecting droplet growth by condensation. The turbulent flow in the chamber is represented by an ensemble of notional particles that carry a set of Lagrangian attributes such as position, velocity and the scalars of interest. Notional particles represent either fluid particles (in dry and moist RB turbulence) or tracer liquid droplets (in cloudy RB turbulence). The model maintains scalar fluctuations through stationary exchange of temperature and vapor mixing ratio on the chamber walls. No external random scalar forcing, which is usually based on the assumption of equilibrium scalar fluctuation spectrum, is imposed. The statistical analysis of results for moist and cloudy conditions enables direct comparison with experimental data for the Eulerian scalar fields. The results show both qualitative and quantitative agreement with measurements by fitting a single model parameter, the velocity‐to‐scalar time-scale ratio. Despite its intentional simplicity, the model captures essential features previously accessible only through direct numerical simulations (DNS) and provides a practical framework for particle-based  modeling of subgrid-scale scalar variance in large-eddy simulations (LES) of clouds.