What is already known:
What this study adds:
Hail can result in billions of dollars with of damage every year. The ability to forecast for significant hail events even just a day in advance can greatly mitigate severe hail risk. Machine-learning (ML) algorithms have already shown skill in producing skillful hail forecasts, as they can identify the areas that hail will be a threat. Using output from the High Resolution Ensemble Forecast version 2 (HREFv2) model, new forecasts were produced during the Hazardous Weather Testbed (HWT) Spring 2018 experiment for days April 30th to June 1st. Verification is necessary to identify weaknesses in these algorithms in order to make improvements. The ultimate goal of verification of these forecasts is to show that these ML algorithms can skillfully forecast for hail to increase trust to eventually implement them into operational forecasting. By verifying these forecasts using reliability diagrams, it was discovered that there was a bias of over-forecasting. Isotonic regression was used to correct for the HREFs tendency to over-forecasting. The raw HREFv2 data was calibrated to both the SPC practically perfect forecasts and to the observations. When calibrated to the observations, the corrected HREFv2 produced more reliable forecasts.