Verification of the Hail Differential Reflectivity using the Severe Hazards Analysis and Verification Experiment (SHAVE) Dataset

Hail is responsible for billions of dollars in damages in the United States annually. Despite the significant societal impact it has, many challenges still exist in nowcasting the size of anticipated hail fall within a given storm. Part of these challenges can be attributed to gaps in our understanding of signals retrieved from weather radars; polarimetric radar products do not have a straightforward relationship with hail size owing to various properties of hail such as canting angle. Another challenge can be ascribed to a dearth of ground truth observations available to robustly corroborate any potential signature found in polarimetric radar data. The upgrade to the national network of WSR-88D radars from single polarization to dual-polarization, finished in 2013, helped alleviate the former challenge by affording researchers the opportunity to interrogate years of polarimetric radar data as it relates to hail. This has contributed to the discovery of new relationships and signatures that relate to severe hazards (e.g., hail), while also allowing previously established relationships to be explored more robustly. One such previous relationship that has benefitted from the dual-polarization upgrade is the hail differential reflectivity (HDR). Defined as a simple reflectivity (ZH)-ZDR relationship, the HDR has received increased attention in recent years, but its discriminatory power has yet to be verified with a large dataset of ground truth observations. In this study, we aim to elucidate the relationship between HDR and the size of hail fall through the use of the Severe Hazards Analysis and Verification Experiment (SHAVE) dataset. Utilizing ground truth observations from the SHAVE dataset helps address the second aforementioned challenge and provides an avenue to robustly explore HDR as it relates to hail size. Vertical profiles of HDR are examined to determine if HDR within a certain layer provides a better indicator of the size of hail expected at the surface. Additionally, the HDR is compared to the operational Hydrometeor Classification Algorithm (HCA) and Hail Size Discrimination Algorithm (HSDA) to evaluate its potential use operationally. Finally, maximum estimated size of hail (MESH)-HDR combinations are analyzed to highlight the performance of combinations of hail size proxies in determining the size of hail fall at the surface.