Abstract
Abstract. In recent papers (Alfonso et al., 2013; Alfonso and Raga, 2017) the sol–gel transition was proposed as a mechanism for the formation of large droplets required to trigger warm rain development in cumulus clouds. In the context of cloud physics, gelation can be interpreted as the formation of the “lucky droplet” that grows by accretion of smaller droplets at a much faster rate than the rest of the population and becomes the embryo for raindrops. However, all the results in this area have been theoretical or simulation studies. The aim of this paper is to find some observational evidence of gel formation in clouds by analyzing the distribution of the largest droplet at an early stage of cloud formation and to show that the mass of the gel (largest drop) is a mixture of a Gaussian distribution and a Gumbel distribution, in accordance with the pseudo-critical clustering scenario described in Gruyer et al. (2013) for nuclear multi-fragmentation.
Highlights
A fundamental, ongoing problem in cloud physics is associated with the discrepancy between the times modeled and observed for the formation of precipitation in warm clouds
In Göke et al (2007), an analysis of radar observations in the framework of the Small Cumulus Microphysics Study (SCMS), demonstrated that maritime clouds increased their reflectivity from − 5 to +7.5 dBZ in a characteristic time of 333 s
The most commonly accepted approach to modeling the collision coalescence process in cloud models with detailed microphysics relies upon the Smoluchowski kinetic equation or kinetic collection equation (KCE), governing the time evolution of the average number of particles
Summary
A fundamental, ongoing problem in cloud physics is associated with the discrepancy between the times modeled and observed for the formation of precipitation in warm clouds. One of the novelties of Telford’s approach was to recognize the shortcomings of the “continuous growth model” and took into account the statistical fluctuations inherent to the collision–coalescence process and its discrete nature He performed his analysis for a cloud consisting of identical 10 μm droplets together with collector drops with twice the volume (12.6 μm radius). The lucky droplet model was further developed by Kostinski and Shaw (2005), who presented numerical evidence that their model can lead to a rapid development of precipitation Their analysis was based on the derivation of the distribution of times for N collisions (which gave the result of an Erlang distribution).
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