Computational modeling demonstrates that droplet growth rate depends on collection kernel magnitude and size derivatives, indicating rapid transition to self-preserving distributions.
Calculations of cloud droplet growth over the radius range from 4 to 200 μ for collection kernels representing hydrodynamic capture, electric field capture, and geometric sweep-out show that the rate of droplet growth is proportional to the magnitude of the kernel, and the pattern of growth depends upon a derivative of the kernel with respect to droplet size. Below 60 μ a large kernel derivative causes the distribution to spread. Above 6O μ the derivative of each kernel decreases to a common value that causes water to accumulate on large drops. This leads to a self-preserving distribution, similar to Golovin's, asymptotic solution, in about 5 min when the liquid water content is 1 gm m−3. The stochastic model produces a growth rate nearly equal to the continuous model but transfers much more water to larger drops.
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Elizabeth Berry (1967) studied this question.
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