The 10 members of the mixed-physics sub-ensemble (also known as the core members), as well as data from the 8 members of the High Resolution Ensemble Forecast version 2 (HREFv2) ensemble, were used as the input for machine learning models designed for hail prediction – specifically an object-based system using random forests, using Hagelslag (a publically-available Python package for object tracking, classification, and machine learning). Machine learning forecast systems were trained separately for the CAPS SSEF and HREFv2 members, using a set of 2D (x-y) storm and environmental variable fields.
An overview of the machine learning system used will be presented, and the results of hail forecasts produced using SSEF and HREFv2 data during the 2018 HWT SFE will be summarized and verified using both subjective and objective methods. Preliminary results suggest that while both the SSEF and HREFv2 forecasts suffered from over-prediction of hail, forecasts using data from the CAPS SSEF system exhibited this error to a lesser extent. Preliminary results also suggest that both the SSEF and HREFv2-based forecasts exhibit ability to discriminate between hail and non-hail severe weather hazards.
![](images/flag.png)
![](images/blue_ribbon.png)