Using Images of Naturally Falling Hailstones Observed with High-Speed, High-Resolution Stereographic Cameras to Re-Examine Radar-Hail Relations

The accurate detection and quantification (e.g., size estimation) of hail using radar data require a robust understanding of the fall behavior and scattering properties of hail (both individual hailstones and populations of hailstones). Unfortunately, the variability in the properties of naturally occurring hailstones can be very large, which widens the expected ranges of the commonly used radar quantities (e.g., radar reflectivity factor, differential reflectivity, correlation coefficient, etc.) that are associated with hail. Although scientific field projects have collected a large number of fallen hailstones from which size-shape-density relations have been established, comparatively little data exists of large hail in natural freefall, which is problematic since the fall behavior of hailstones (to include gyrating or tumbling behavior quantified as canting angle distributions) and its impacts on the water distribution on mixed-phased hailstones add uncertainty to the aforementioned hail-radar relations. To this end, we have developed a high-resolution, high-speed, dual-camera observing package designed to image hailstones and other large hydrometeors in natural freefall. Images collected in several severe convective storms in 2022 show a wide array of canting angles and fall behaviors, including highly prolate hailstones with very high rotation rates (>= 50 Hz) and non-horizontally aligned water tori on melting hailstones. In addition, the system has shown the ability to observe complicated microphysical interactions between hydrometeors (e.g., drop-hailstone collisions) and within hydrometeors (drop shedding from melting hail and large drop breakup). These ground-level observations will be combined with simulated and observed radar data to validate and improve our understanding of the radar-representation of hail (with intended downstream improvements on hail algorithm performance).