Submitted as: model evaluation paper
12 Apr 2023
Submitted as: model evaluation paper |  | 12 Apr 2023
Status: this preprint is currently under review for the journal GMD.

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

Piotr Zmijewski, Piotr Dziekan, and Hanna Pawlowska

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.

Piotr Zmijewski et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Piotr Zmijewski et al.

Data sets

Dataset to "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations" by Zmijewski, Dziekan & Pawlowska Piotr Zmijewski and Piotr Dziekan

Model code and software

igfuw/UWLCM: Convergence paper Piotr Dziekan, Clare Singer, Maciej Waruszewski, Anna Jaruga, Piotr GlazerMann, and Codacy Badger

Piotr Zmijewski et al.


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Short summary
In computer simulations of clouds it is necessary to model the myriad of droplets that constitute a cloud. A popular method for this is to use so-called super-droplets (SDs), each representing many real droplets. It has remained a challenge to model collisions of SDs. We study how precipitation in a cumulus cloud depends on the number of SDs. Surprisingly, we do not find convergence in mean precipitation even for number of SDs much larger than typically used in simulations.