An important issue in polarimetric rainfall estimation is determining which method to employ for a given set of polarimetric observables. For example, under what circumstances does phase information become important and reliable such that it should be used instead of horizontal and differential reflectivity to make a rainfall estimate? At Colorado State University, a blended algorithm has been developed and utilized for a number of years to estimate rainfall based on ZH, ZDR, and KDP. The rainfall estimators for each sampling volume are chosen on the basis of fixed thresholds, which maximize the measurement capability of each polarimetric variable and combinations of variables. The inherent limitation of this approach is that it is assumed that the rain rate estimators are mutually exclusive for a given set of radar observations.
In this study, a new blended algorithm is developed based on a combination of hydrometeor identification (HID) and fuzzy logic that allows the appropriate rain rate estimator to be determined based on the overlapping characteristics of the polarimetric observables (i.e., the measurement set is not assumed to be mutually exclusive for each rain rate estimator). Data collected from the CSU-CHILL radar in northeast Colorado and a network of rain gauges are used to test the algorithms performance in a variety of precipitation environments (warm season convection with and without the presence of hail as well as cold frontal rain) and compare the results with existing polarimetric rainfall algorithms in the literature.