A comparison of polarimetric radar rainfall algorithms: Application in Colorado precipitation events

The efficacy of dual polarimetric radar for quantitative precipitation estimation (QPE) is firmly established. Specifically, rainfall retrievals using combinations of reflectivity (ZH), differential reflectivity (ZDR), and specific differential phase (KDP) have demonstrated advantages over traditional Z-R methods because more information about the drop size distribution and hydrometeor type are available, as well as the fact that they are less sensitive to the DSD. In addition, dual-polarization based rain rate estimators are generally less susceptible to error and biases due to the presence of ice in the sampling volume, as well as to absolute calibration.

A number of methods have been developed to estimate rainfall from dual-polarization radar measurements. However, the robustness of these techniques in different precipitation regimes is unknown. Because the National Weather Service (NWS) will soon upgrade the WSR 88-D radar network to dual-polarization capability, it is important to test retrieval algorithms in different meteorological environments in order to better understand the limitations of the different methodologies.

An important issue in dual-polarimetric rainfall estimation is determining which method to employ for a given set of polarimetric observables. For example, under what circumstances does differential phase information provide superior rain estimates relative to methods using reflectivity and differential reflectivity? At Colorado State University (CSU), a “blended” algorithm has been developed and used for a number of years to estimate rainfall based on ZH, ZDR, and KDP (Cifelli et al. 2002). 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. Tests have shown, however, that the retrieval is sensitive to the calculation of ice fraction in the radar volume via the difference reflectivity (ZDP – Golestani et al. 1989) methodology such that an inappropriate estimator can be selected in situations where radar echo is relatively weak (< 40 dBZ).

In this study, a new blended rainfall algorithm is developed using hydrometeor identification (HID) to drive the rainfall estimation algorithm. HID discrimination for rainfall application namely, (1) all rain, (2) mixed precipitation, and (3) all ice, is used to guide the selection of the most appropriate rainfall estimator. Data collected from the CSU-CHILL radar and a network of rain gauges are used to test the performance of the new algorithm in a variety of precipitation situations. The results are compared to similar results using the algorithm from the National Severe Storm Laboratory (NSSL), derived from Oklahoma precipitation events (Ryzhkov et al. 2005). The applicability of the method derived from Oklahoma observations to Colorado precipitation events is also explored.