Modeling collision–coalescence in particle microphysics: numerical convergence of mean and variance of precipitation in cloud simulations using the University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1

Zmijewski, Piotr; Dziekan, Piotr; Pawlowska, Hanna

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 and multi-box simulations of collision–coalescence. Parameters studied are the time step for coalescence and the number of super-droplets (SDs) per cell. A time step of inline-formula0.1 s 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 the time step. In box simulations, mean DSD converges for inline-formula103 SDs per cell, but variance of the DSD does not converge as it decreases with an increasing number of SDs. Fewer SDs per cell are required for convergence of the mean DSD in multi-box simulations, probably thanks to mixing of SDs between cells. In CC simulations, more SDs are needed for convergence than in box or multi-box simulations. Mean precipitation converges for inline-formula5×103 SDs, but only in a strongly precipitating cloud. In cases with little precipitation, mean precipitation does not converge even for inline-formula105 SDs per cell. Variance in precipitation between independent CC runs is more sensitive to the resolved flow field than to the stochasticity in collision–coalescence of SDs, even when using as few as 50 SDs per cell.

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Zmijewski, Piotr / Dziekan, Piotr / Pawlowska, Hanna: Modeling collision–coalescence in particle microphysics: numerical convergence of mean and variance of precipitation in cloud simulations using the University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1. 2024. Copernicus Publications.

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