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Abstract:
Modeling of collision-coalescence is one of the main differences between various Lagrangian particle-based cloud microphysics models. Most of these models use the all-or-nothing (AON) algorithm of the super-droplet method. This algorithm gives the correct mean number of collisions, but too large variance in the number of collisions. Variance decreases with the number of super-droplets (SDs) used. It is not well understood how the increased variance affects precipitation. The goal of our study is to understand convergence of AON, with respect to the number of SDs and to the time step, in cloud simulations.
We perform box simulations of collision-coalescence and 2D and 3D simulations of a cumulus congestus (CC) cloud. Box simulations show that mean droplet size distribution (DSD) converges for a 0.1s time step and 1000 SDs. Variance of the DSD is not sensitive to the time step and is inversely proportional to the number of SDs. Simulations of CC are done dynamically, i.e., with a resolved flow field, and kinematically, i.e., for a predefined flow-field. Mean precipitation in CC varies with the number of SDs per cell in a non-trivial way. It does not converge even for 100000 per cell. This suggests that the increased variance in AON may affect mean precipitation. Variance in the amount of rain in CC decreases with the number of SDs, but only in kinematic simulations. In dynamic simulations the variance in rain is more strongly affected by differences in realization of the flow field than by differences in realization of the AON algorithm.
