Can ingredients based forecasting be learned? Disentangling a random forest's severe weather predictions

Machine learning (ML)-based models have been rapidly integrated into
forecast practices across the weather forecasting community in recent
years. While ML tools introduce additional data to forecasting operations,
there is a need for explainability to be available alongside the model
output, such that the guidance can be transparent and trustworthy for the
forecaster. This work makes use of the algorithm tree interpreter (TI) to
disaggregate the contributions of meteorological features used in the
Colorado State University Machine Learning Probabilities (CSU-MLP) system,
a random forest-based ML tool that produces real-time probabilistic
forecasts for severe weather using inputs from the Global Ensemble
Forecast System v12. TI feature contributions are analyzed in time and
space for CSU-MLP day-2 and 3 individual hazard (tornado, wind, and hail)
forecasts and day-4 aggregate severe forecasts over a 2-yr period. For
individual forecast periods, this work demonstrates that feature
contributions derived from TI can be interpreted in an ingredients-based
sense, effectively making the CSU-MLP probabilities physically
interpretable. When investigated in an aggregate sense, TI illustrates
that the CSU-MLP system's predictions use meteorological inputs in
ways that are consistent with the spatiotemporal patterns seen in
meteorological fields that pertain to severe storms climatology. This work
concludes with a discussion on how these insights could be beneficial for
model development, real-time forecast operations, and retrospective event
analysis.