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Francisco J. Tapiador, Ziad S. Haddad, and Joe Turk


The raindrop size distribution (RDSD) is defined as the relative frequency of raindrops per given diameter in a volume. This paper describes a mathematically consistent modeling of the RDSD drawing on probability theory. It is shown that this approach is simpler than the use of empirical fits and that it provides a more consistent procedure to estimate the rainfall rate (R) from reflectivity (Z) measurements without resorting to statistical regressions between both parameters. If the gamma distribution form is selected, the modeling expresses the integral parameters Z and R in terms of only the total number of drops per volume (N T), the sample mean [m = E(D)], and the sample variance [σ 2 = E(mD)2] of the drop diameters (D) or, alternatively, in terms of N T, E(D), and E[log(D)]. Statistical analyses indicate that (N T, m) are independent, as are (N T, σ 2). The ZR relationship that arises from this model is a linear R = T × Z expression (or Z = T −1 R), with T a factor depending on m and σ 2 only and thus independent of N T. The ZR so described is instantaneous, in contrast with the operational calculation of the RDSD in radar meteorology, where the ZR arises from a regression line over a usually large number of measurements. The probabilistic approach eliminates the need of intercept parameters N 0 or , which are often used in statistical approaches but lack physical meaning. The modeling presented here preserves a well-defined and consistent set of units across all the equations, also taking into account the effects of RDSD truncation. It is also shown that the rain microphysical processes such as coalescence, breakup, or evaporation can then be easily described in terms of two parameters—the sample mean and the sample variance—and that each of those processes have a straightforward translation in changes of the instantaneous ZR relationship.

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