Droplet positions in atmospheric clouds are random but possibly correlated on some scales. This ‘clustering’ must be quantified in order to account for it in theories of cloud evolution and radiative transfer. Tools as varied as droplet concentration power spectrum, Fishing test, and fractal correlation analysis have been used to describe the small‐scale nature of clouds, and it has been difficult to compare conclusions systematically. Here we show, by using the correlation‐fluctuation theorem and the Wiener–Khinchin theorem, that all of these measures can be related to the pair‐correlation function. It is argued that the pair‐correlation function is ideal for quantifying droplet clustering because it contains no scale memory and because of its quantitative link to the Poisson process. Copyright © 2002 Royal Meteorological Society

Marine stratocumulus clouds are the “global reflectors,” sharply contrasting with the underlying dark ocean surface and exerting a net cooling on Earth’s climate. The magnitude of this cooling remains uncertain in part owing to the averaged representation of microphysical processes, such as the droplet-to-drizzle transition in global climate models (GCMs). Current GCMs parameterize cloud droplet size distributions as broad, cloud-averaged gammas. Using digital holographic measurements of discrete stratocumulus cloud volumes, we found cloud droplet size distributions to be narrower at the centimeter scale, never resembling the cloud average. These local distributions tended to form pockets of similar-looking cloud regions, each characterized by a size distribution shape that is diluted to varying degrees. These observations open the way for new modeling representations of microphysical processes.

The spatial clustering of drops is a defining characteristic of rain on all scales from centimeters to kilometers. It is the physical basis for much of the observed variability in rain. The authors report here on the temporal–spatial 1-min counts using a network of 21 optical disdrometers over a small area near Charleston, South Carolina. These observations reveal significant differences between spatial and temporal structures (i.e., clustering) for different sizes of drops, which suggest that temporal observations of clustering cannot be used to infer spatial clustering simply using by an advection velocity as has been done in past studies. It is also shown that both spatial and temporal clustering play a role in rain variability depending upon the drop size. The more convective rain is dominated by spatial clustering while the opposite holds for the more stratiform rain. Like previous time series measurements by a single disdrometer but in contradiction with widely accepted drop size distribution power-law relations, it is also shown that there is a linear relation between 1-min averages of the rainfall rate R over the network and the average total number of drops Nt. However, the network (area) R–Nt relation differs from those derived strictly from time series observations by individual disdrometers. These differences imply that the temporal and spatial size distributions and their variabilities are not equivalent.

The extent of droplet clustering in turbulent clouds has remained largely unquantified, and yet is of possible relevance to precipitation formation and radiative transfer. To that end, data gathered by an airborne holographic instrument are used to explore the three-dimensional spatial statistics of cloud droplet positions in homogeneous stratiform boundary-layer clouds. The three-dimensional radial distribution functions $g(r)$ reveal unambiguous evidence of droplet clustering. Three key theoretical predictions are observed: the existence of positive correlations, onset of correlation in the turbulence dissipation range, and monotonic increase of $g(r)$ with decreasing $r$. This implies that current theory captures the essential processes contributing to clustering, even at large Reynolds numbers typical of the atmosphere.

A network of optical disdrometers (including laser precipitation monitors and a two‐dimensional video disdrometer) was utilized to determine whether the recent reports of “superterminal” raindrops were spurious results of drop breakup occurring on instrumentation. Results unequivocally show that superterminal raindrops at small (less than 1 mm) sizes are ubiquitous, are measurable over an extended area, and appear in every rain event investigated. No evidence was found to suggest that superterminal drops are the result of drop breakup due to impact with the measurement instrument; thus, if the superterminal drops are the result of drop fragmentation, this fragmentation happens in the ambient atmosphere during all rain events measured in this study. The ubiquity of superterminal drops at small drop sizes raises natural questions regarding rain accumulation estimations, estimates of drop size distributions, and erosion characterization.

Reliable measurements of the three-dimensional radial distribution function for cloud droplets are desired to help characterize microphysical processes that depend on local drop environment. Existing numerical techniques to estimate this three-dimensional radial distribution function are not well suited to in situ or laboratory data gathered from a finite experimental domain. This paper introduces and tests a new method designed to reliably estimate the three-dimensional radial distribution function in contexts in which (i) physical considerations prohibit the use of periodic boundary conditions and (ii) particle positions are measured inside a convex volume that may have a large aspect ratio. The method is then utilized to measure the three-dimensional radial distribution function from laboratory data taken in a cloud chamber from the Holographic Detector for Clouds (HOLODEC).