The Utility of Vertically Integrated Graupel as a Max Hail Size Predictor

A few very recent studies have begun to explore the utility of vertically integrated graupel derived from high-resolution convection allowing models as a predictor for both the occurrence and approximate size of hail. Results are still somewhat mixed, which may be at least partially related to the limitations of the hail data used to verify model results. As the most spatially and temporally comprehensive dataset available to researchers, Storm Data are traditionally used to verify scientific studies; however, these data are often inadequate for such studies due to inconsistencies and a generally low resolution. The Severe Hazards Analysis and Verification Experiment (SHAVE) and Meteorological Phenomena Identification Near the Ground (mPing) address many of Storm Data's shortcomings, but are still limited by population density, potential melting between the time of hail fall and the time of observation, and possible error in size estimation. In contrast, high-resolution hail data collected by A Hail Spatial and Temporal Observing Network Effort (HailSTONE) are measured and documented in real time, rectifying many of the aforementioned limitations.

This study compares the maximum hail size in six unique storms sampled by HailSTONE to maximum values of vertically integrated graupel in 1 km WRF-ARW simulations. The six storms have maximum hail sizes ranging from 1.5 inches (3.8 cm) to 6 inches (15.2 cm) in diameter, and represent multiple storm modes, providing a varied dataset for model verification. A comparison of vertically integrated graupel and hail size will be presented, as will a comparison to other model-derived parameters such as updraft helicity and maximum updraft velocity.