A Comparison of Continuous and Stochastic Methods for Modeling Rain Drop Growth in Clouds
Abstract
Two models for raindrop growth in clouds are developed and compared. A continuous accretion model is solved numerically for drop growth from 20-50 microns, using a polynomial approximation to the collection kernel, and is shown to underestimate growth rates. A Monte Carlo simulation for stochastic growth is also implemented to demonstrate discrete drop growth. The approach models the effect of decreased average time between captures as the drop size increases. It is found that the stochastic model yields a more realistic growth rate, especially for larger drop sizes. It is concluded that the stochastic model showed faster droplet accumulation and hence shorter times for drop growth.
Keywords
Cite
@article{arxiv.1309.1682,
title = {A Comparison of Continuous and Stochastic Methods for Modeling Rain Drop Growth in Clouds},
author = {Rehan Siddiqui and Brendan M. Quine},
journal= {arXiv preprint arXiv:1309.1682},
year = {2020}
}
Comments
10 pages and 6 figures with good Model Sensitivity: To check the sensitivity of the model to the capture probability, average growth-times Tavg (q) were computed for 100 values of q in the range [0.01, 1.0]