To verify the three hail forecasting methods, neighborhood ensemble probabilities are calculated for a 24-hour period for both 25 mm and 50 mm hail. These hail forecasting methods are verified against data from the NSSL Multi-Radar Multi-Sensor (MRMS) radar mosaic using the Maximum Expected Size of Hail (MESH) method. Relative Operating Characteristic (ROC) curves as well as Attribute Diagrams were created along with calculating the ROC Area Under the Curve (ROC AUC) and Brier Skill Score. A case study of May 26, 2016 was performed; on this day a large complex of storms moved over Nebraska, Kansas, Oklahoma, and Texas, producing 204 reports of severe hail, 183 reports of severe wind, and 21 tornado reports.
Overall, the Gagne Machine Learning Method has greater skill, in terms of the Brier Skill Score, than the other two hail forecasting methods. The Gagne Machine Learning Method also exhibits better discrimination for 25 mm hail in terms of the ROC AUC score. Lastly, the Gagne Machine Learning Method consistently performs well across all microphysics schemes because it is calibrated on each microphysics scheme. For the May 26, 2016 case study, the Gagne Machine Learning method exhibited greater capability to predict hail exceeding 25 mm in diameter while producing relatively few false alarms.