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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.

SUBMITTER: Murillo EM

PROVIDER: S-EPMC8050948 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Severe Hail Fall and Hailstorm Detection Using Remote Sensing Observations.

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