Several approaches towards normalizing raindrop size distributions have been proposed in the scientific literature. The aim of normalizing (or “scaling”) raindrop size distributions is to collapse many individual and highly variable empirical distributions into (ideally) one single “general” (or “intrinsic”) distribution, which is typical for a particular type of rain or climatic setting. This provides a statistically more robust manner to adjust analytical parameterizations to raindrop size distributions than the traditional approach where a particular parametric form is fitted to each empirical distribution separately. An additional advantage of normalizing raindrop size distributions is that it becomes easier to establish connections between the shape of raindrop size distributions and the physical processes producing them. This will ultimately lead to improved rainfall retrieval algorithms for ground-based and space-borne microwave remote sensors (both active and passive). We present a non-parametric adjustment method to estimate radar reflectivity - rain rate relationships and normalized raindrop size spectra (and their associated uncertainties) based on Parzen densities.