  Historical observations of severe weather and simulated severe weather
environments (i.e., features) from the Global Ensemble Forecast System v12
(GEFSv12) Reforecast Dataset (GEFS/R) are used in conjunction to train and test
random forest (RF) machine learning (ML) models to probabilistically forecast
severe weather out to days 4--8. RFs are trained with 9 years of the GEFS/R and
severe weather reports to establish statistical relationships. Feature
engineering is briefly explored to examine alternative methods for gathering
features around observed events, including simplifying features using spatial
averaging and increasing the GEFS/R ensemble size with time-lagging. Validated
RF models are tested with ~1.5 years of real-time forecast output from the
operational GEFSv12 ensemble and are evaluated alongside expert human-generated
outlooks from the Storm Prediction Center (SPC). Both RF-based forecasts and
SPC outlooks are skillful with respect to climatology at days 4 and 5 with
degrading skill thereafter. The RF-based forecasts exhibit tendencies to
underforecast severe weather events, but they tend to be well-calibrated at
lower probability thresholds. Spatially averaging predictors during RF training
allows for prior-day thermodynamic and kinematic environments to generate
skillful forecasts, while time-lagging acts to expand the forecast areas,
increasing resolution but decreasing objective skill. The results highlight the
utility of ML-generated products to aid SPC forecast operations into the medium
range.
