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Cloud Droplet Growth by Collection
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EngineeringLiquid Water ContentCloud DatabaseLiquid-liquid FlowStochastic AnalysisStochastic PhenomenonCloud Resource ManagementData ScienceCloud AnalyticsStochastic ProcessesCapillarity PhenomenonTransport PhenomenaCollection KernelsData ManagementDisperse FlowBrownian MotionStochastic ModelingWater ResourcesEnvironmental EngineeringCloud ComputingCloud Droplet GrowthBig Data
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.