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 contrib..., Forecast data: These data include publically available local storm reports (from NOAA), publically available Storm Prediction Center (SPC) outlooks, and forecasts generated from the machine learning prediction system detailed in the manuscript. The local storm reports were retrieved from an online public-facing archive and gridded to NCEP grid 4. The SPC outlooks were originally in a shapefile format and ArcGIS was used to convert the shapefiles to a netCDF format. Then, the netCDF gridded SPC outlooks were regridded to NCEP grid 4 to conduct verification with local storm reports. Lastly, the machine learning-based forecasts are generated on the NCEP grid. Each of these datasets are then combined in a 'master' netCDF file for each forecast leadtime examined in the study (day 2, day 3, and day 4) for easy compression and storage. The master netCDF files additionally have metadata associated with the latitude and longitude points of the grid and forecast day strings. Forecasts span Octobe..., , # Data for: Can Ingredients-Based Forecasting be Learned? Disentangling a Random Forest's Severe Weather Predictions

[https://doi.org/10.5061/dryad.0rxwdbs7w](https://doi.org/10.5061/dryad.0rxwdbs7w)

Data for: \"Can Ingredients-Based Forecasting be Learned? Disentangling a Random Forest's Severe Weather Predictions\"

Mazurek, Alexandra C., Aaron J. Hill, Russ S. Schumacher, and Hanna J. McDaniel: \"Can Ingredients-Based Forecasting be Learned? Disentangling a Random Forest's Severe Weather Predictions\", Weather and Forecasting.

Day 2, 3, and 4 forecasts from the machine learning-based prediction system detailed in the associated manuscript (cited above) as well as those from the Storm Prediction Center (SPC) and observations (local storm reports) of severe thunderstorm hazards are included in this dataset. Forecasts, outlooks, and observations for each forecast day (day 2, day 3, day 4) are contained in a single netCDF file. For the day 2 and 3 forecasts, the netCDF files contain three...