Severe Hail Fall and Hailstorm Detection Using Remote Sensing Observations

Abstract

Severe hail days account for the vast majority of severe weather-induced property losses in the United States each year. In the United States, real-time detection of severe storms is largely conducted using ground-based radar observations, mostly using the operational Next Generation Weather Radar network (NEXRAD), which provides three-dimensional information on the physics and dynamics of storms at ;5-min time intervals. Recent NEXRAD upgrades to higher resolution and to dual-polarization capabilities have provided improved hydrometeor discrimination in real time. New geostationary satellite platforms have also led to significant changes in observing capabilities over the United States beginning in 2016, with spatiotemporal resolution that is comparable to that of NEXRAD. Given these recent improvements, a thorough assessment of their ability to identify hailstorms and hail occurrence and to discriminate between hail sizes is needed. This study provides a comprehensive comparative analysis of existing observational radar and satellite products from more than 10 000 storms objectively identified via radar echo-top tracking and nearly 6000 hail reports during 30 recent severe weather days (2013-present). It is found that radar observations provide the most skillful discrimination between severe and nonsevere hailstorms and identification of individual hail occurrence. Single-polarization and dual-polarization radar observations perform similarly at these tasks, with the greatest skill found from combining both single-and dual-polarization metrics. In addition, revisions to the ''maximum expected size of hail'' (MESH) metric are proposed and are shown to improve spatiotemporal comparisons between reported hail sizes and radar-based estimates for the cases studied.

Trends in United States large hail environments and observations

“…MESH is then computed from a revised empirical fit between the SHI and the 95th percentile of observed hail size, following Murillo and Homeyer. 58 MESH days are then computed identically to LHR days, using a MESH threshold of 6.4 cm and a 50-km neighborhood. The MESH threshold was determined by maximizing the critical success index of a predictor metric and verifying metric.…”

Understanding Hailstone Temporal Variability and Contributing Factors over the U.S. Southern Great Plains

“…This study used newly developed MESH relationships to improve hail size discrimination. In particular, we used a power-law relationship fit to the 95th percentile hail size from approximately 6000 hail reports, which had better agreements with hail reports than a power-law fit to the 75th percentile hail size (Murillo and Homeyer 2019). The underlying data used to calculate MESH (GridRad) are on a regular longitude-latitude Cartesian grid with a horizontal resolution of 0.028 3 0.028 (about 2 km 3 2 km), 1-km vertical resolution, and an hourly temporal resolution.…”

“…However, due to the uncertainties relating radar observables to hail characteristics, it can have large biases in terms of the hailstone sizes (Ortega et al 2009;Ortega 2018). Very recently, the algorithms for MESH were improved by Murillo and Homeyer (2019) and applied to the Gridded NEXRAD WSR-88D Radar (GridRad; Bowman and Homeyer 2017) dataset during the time period of 2004-16 in the central United States.…”

Understanding Hail in the Earth System

“…Comparing MESH to VIL, both have similar skill in discriminating hail size categories as the parameters are highly correlated; however, MESH is advantageous as it is only related to convective echoes, while VIL can instead be present for any echo (Ortega, ). Recent analyses have also suggested that recalibration of MESH thresholds using a larger sample size of hailstorms than the original (Witt et al, ) sample can result in increases to overall skill in identifying severe hail (Murillo & Homeyer, ).…”