The current understanding of fundamental processes in atmospheric clouds, such as
nucleation, droplet growth, and the onset of precipitation (collision–coalescence), is
based on the assumption that droplets in undiluted clouds are distributed in space in
a perfectly random manner, i.e. droplet positions are independently distributed with
uniform probability. We have analysed data from a homogeneous cloud core to test
this assumption and gain an understanding of the nature of droplet transport. This
is done by examining one-dimensional cuts through clouds, using a theory originally
developed for x-ray scattering by liquids, and obtaining statistics of droplet spacing.
The data reveal droplet clustering even in cumulus cloud cores free of entrained
ambient air. By relating the variance of droplet counts to the integral of the pair
correlation function, we detect a systematic, scale-dependent clustering signature. The
extracted signal evolves from sub- to super-Poissonian as the length scale increases.
The sub-Poisson tail observed below mm-scales is a result of finite droplet size and
instrument resolution. Drawing upon an analogy with the hard-sphere potential from
the theory of liquids, this sub-Poisson part of the signal can be effectively removed.
The remaining part displays unambiguous clustering at mm- and cm-scales. Failure
to detect this phenomenon until now is a result of the previously unappreciated
cumulative nature, or ‘memory,’ of the common measures of droplet clustering.