The clustering or clumping of particles appears to be nearly ubiquitous in clouds and rain. Clustering likely plays roles in a number of different physical processes from the growth of hail, to snow aggregation, to the growth of raindrops, to cloud radiation and even to what one means by the measurement of a size distribution. On the practical side, clustering may, at times, affect aircraft icing by introducing important fluctuations in the concentration of super-cooled cloud water of a magnitude sufficient to alter the density of the ice collected on the wings. In rain, clustering leads to broad rainfall rate distributions that not only enhance soil erosion but also cause the signal statistics of many remote sensing devices to deviate from the usual Rayleigh statistics. This slows converge to the proper mean values, increases estimate uncertainties and confounds comparisons among different instruments. Yet, the intensity of clustering in clouds and rain is variable and not easy to measure. Moreover, what few observations there are have been restricted to one dimension. It would be enlightening, for example, to observe clustering in two or more dimensions with good temporal resolution for studies of its anisotropy, origin and evolution. Practically speaking, it might also be helpful to have a device that could identify regions of significant clustering for aircraft to avoid during icing conditions. In this paper, a radar technique is derived for quantitatively characterizing clustering intensity in clouds and rain based upon a novel scanning procedure and the use of non-Rayleigh signal statistics. Such observations appear to be well within current technological capabilities.