Will hail be severe? Elusive Environmental Predictors Generating Large Hail

Expected hail size is frequently poorly forecast. Hail predictions have been explored on a variety of timescales through both modeling and statistical approaches, though relatively few skillful predictors have been identified. The lack of applicable predictors has led to challenges in understanding why a given parameter may inform the expected occurrence of hail, or its absolute diameter. Previous approaches have focused around bulk metrics or coarse parameterizations of storm processes. However, these efforts have been hampered by a failure to focus on the underlying quality control of the data, including lack of  consideration of the representativeness of hail size observations, inappropriate use of nulls, and failure to look beyond existing parameters. This presentation will focus on new insights and parameters identified through modeling, discriminant analyses, self-organising maps and clustering, and leverages a data-informed approach to better characterise the relationship between the ambient storm environment and hail characteristics. We find that key to addressing this problem is the addition of consideration of synoptic regime, regional differences, seasonality and storm mode.

 

Parameter importance is tested through ERA-5 pseudo-proximity soundings to hail profiles from multiple datasets, including 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). Through use of buddy-checking, and a KD-tree approach for spatial independence, these sources in combination yield 80,000 reliable and independent cases over the past 25 years. Using these data allows assessment of the relationship between hail and its environment for a variety of sizes ranging from 6.4mm to >100.2 mm in maximum diameter. Results show that the null dataset being used to predict hail is important, and prior approaches have likely failed due to strong parameter overlaps for existing parameters. Through this analysis, we find that predictability exists for whether hail will be severe (>2.5 cm) or not, but with existing parameters the predictability becomes more challenging for discriminating larger categories. These results also highlight that many common associations between parameters (e.g. CAPE and hail size) are improperly-posed, as hail production exhibits multiple environmental pathways or requires different parameters, or can be driven by storm dynamics. These suggest that a multi-model and profile-aware approach is necessary to obtain reliable environmental-based predictions of hail size.