Synoptic and Mesoscale Environment Variability of US Large Hail Events

Hail environments are increasingly becoming well understood for their thermodynamic properties, and relationship to both the hodograph and the vertical thermodynamic profile. However, nearly all these approaches focus on the near-storm environment, rather than taking a broader approach that contextualizes the background characteristics of that environment. While there has been ample investigation of the association of hail and tornadoes with their synoptic scale environment in Europe, such studies for the United States are comparatively rare. To explore these spatial relationships, we leverage a dataset of more than 80,000 reliable and spatially independent cases over the past 25 years derived from records from the Storm Prediction Center (SPC) Storm Data, the SPC Storm Mode Dataset, Community Collaborative Rain, Hail and Snow Network (CoCoRAHS), and Meteorological Phenomena Identification Near the Ground (MPING), partnered with ERA-5 reanalysis data. These data are representative of the range of situations that produce hail over the continental United States and include records for a variety of sizes ranging from 6.4 mm to >100.2 mm in maximum diameter.

In this presentation, we focus on contextualizing the relationship between hail and its antecedent mesoscale and synoptic background environment. To accomplish this, we characterize the wide variety of synoptic and mesoscale environments that could favor hail, to ensure that composites are consistent with the underlying environmental characteristics. A self-organizing map approach is used to characterize these broader scale patterns, with training to identify this set of nodes using a combination of vertical thermodynamic and kinematic profiles. Utilizing composites at the synoptic scale, mesoscale Skew-T and hodograph maps, together with varying fields of convective parameters, we will illustrate how these variations in antecedent environment may impact predictability and relate to driving mesoscale and synoptic processes.