Scale-dependent droplet clustering in turbulent clouds

The current understanding of fundamental processes in atmospheric clouds, s uch as nucleation, droplet growth, and the onset of precipitation (collisio n-coalescence), is based on the assumption that droplets in undiluted cloud s are distributed in space in a perfectly random manner, i.e. droplet posit ions are independently distributed with uniform probability. We have analys ed data from a homogeneous cloud core to test this assumption and gain an u nderstanding of the nature of droplet transport. This is done by examining one-dimensional cuts through clouds, using a theory originally developed fo r x-ray scattering by liquids, and obtaining statistics of droplet spacing. The data reveal droplet clustering even in cumulus cloud cores free of ent rained ambient air. By relating the variance of droplet counts to the integ ral of the pair correlation function, We detect a systematic, scale-depende nt clustering signature. The extracted signal evolves from sub- to super-Po issonian as the length scale increases. The sub-Poisson tail observed below mm-scales is a result of finite droplet size and instrument resolution. Dr awing upon an analogy with the hard-sphere potential from the theory of liq uids, this sub-Poisson part of the signal can be effectively removed. The r emaining Dart displays unambiguous clustering at mm- and cm-scales. Failure to detect this phenomenon until now is a result of the previously unapprec iated cumulative nature, or 'memory,' of the common measures of droplet clu stering.