Fluctuation properties of precipitation. Part VI: Observations of hyperfine clustering and drop size distribution structures in three-dimensional rain

In past work it is argued that rain consists of patches of coherent, physic al drop size distributions passing in an unpredictable fashion for an unkno wn duration over a sensor. This leads to the detection both of correlations among drops and of clustering. While the analyses thus far support this ch aracterization, in this final paper in this series, techniques are develope d demonstrating that clustering of drops of a specific size in rain is occu rring even on scales as small as a few centimeters. Moreover, using video d isdrometer data processed to achieve high temporal resolution, it is shown that drops of different sizes are also cross correlated over rimes from 0.0 1 to several seconds. It is then shown that physical patches of drop size distributions (often ex ponential in form) exist and can be measured even over time periods as smal l as 2-3 s. Such distributions may be the result of enhanced drop interacti ons due to clustering or perhaps simply stochastic "accidents" brought abou t by some "clustering" mechanism. Since most drop spectra are measured over considerably longer intervals, however, observed distributions are likely probability mixtures of many short duration spectra. Such mixed distributio ns exhibit enhanced variance and curvatures reminiscent bf gamma spectra of ten described in the literature. Thus, as measurement intervals increase, t he form of the observed drop distributions apparently changes from an expon ential-like distribution, to a mixture of distributions, finally returning once again to an exponential when the averaging is over very long intervals and a wide variety of conditions. It is also shown that for these data, much of the variability in rainfall r ate arises due to concentration fluctuations rather than to changes in the average drop size. For completeness, it is also shown that the dimensionali ty of drop counts and rainfall rate are consistent with Euclidean scaling o ver distances from centimeters to kilometers. Finally, a specific example of drop clustering in wide sense statistically stationary rain is also given. These observations cannot be explained in te rms of a nonhomogeneous Poisson process. Consequently, it appears most appr opriate to characterize clustering and the structure of rain in terms of co rrelations and probability ruling discussed here and in previous papers in this series. This approach can be used to simulate rain numerically in orde r to explore not only the statistical properties of the rain itself, but al so to achieve a better understanding of the effect of raindrop clustering a nd rainfall variability on a variety of topics, such as signal statistics a nd interpretations of remote sensing measurements.