The random forest models were trained using NCAR ensemble runs from May through July 2015. Observations of hail size were gathered from the NOAA NSSL Multi-Radar Multi-Sensor radar mosaic Maximum Expected Size of Hail (MESH) product. Individual hailstorm tracks were identified in each ensemble member and were matched with MESH tracks. Information about storm intensity and environmental conditions was extracted from within each storm track. Separate random forest models predicted whether or not hail would occur and the parameters of each storm’s hail size distribution. Hail forecasts were generated in real-time for May through September 2016 and were displayed on the NCAR ensemble website.
Results have shown that the machine learning hail forecasts can discriminate reliably between storms that produce hail and those that do not. Hail size distribution parameter forecasts showed little bias and good sharpness. Storm-surrogate ensemble probabilities derived from the machine learning models generated fewer false alarms and higher probabilities of detection than other hail forecasting methods, particularly at the 50 mm threshold. Case studies and regional trends are also investigated.