Data for: A new paradigm for medium-range severe weather forecasts: Probabilistic random forest-based predictions

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
diminishing skill thereafter. The RF-based forecasts exhibit tendencies to
slightly 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
overall skill. The results highlight the utility of ML-generated products
to aid SPC forecast operations into the medium range.