Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1

Abstract

Abstract. Numerical convergence of the collision-coalescence algorithm used in Lagrangian particle-based microphysics is studied in 2D simulations of an isolated Cumulus Congestus (CC) and in box simulations of collision-coalescence. Parameters studied are the time step for coalescence and the number of super-droplets per cell. Time step of 0.1s gives converged droplet size distribution (DSD) in box simulations and converged mean precipitation in CC. Variances of the DSD and of precipitation are not sensitive to time step. In box simulations mean DSD converges for 103 super-droplets per cell, but variance of the DSD does not converge. In CC simulations mean precipitation converges for 5 × 103, but only in a strongly precipitating case. In cases with less precipitation, mean precipitation does not converge even for 105 super-droplet per cell. The result that more super-droplets are needed in CC simulations than in box simulations indicates that too large differences in the DSD between cells can reduce precipitation in cloud simulations. Variance in precipitation between independent CC runs is not affected by the number of super-droplets. This study suggests that parameters typically used in large-eddy simulations (LES) with particle microphysics can lead to underestimation of rain in lightly precipitating clouds.