Can Ingredients-Based Forecasting Be Learned? Disentangling a Random Forest’s Severe Weather Predictions

Details

• Journal Title:
  Weather and Forecasting
• Personal Author:
  Mazurek, Alexandra C. ; Hill, Aaron J. ; Schumacher, Russ S. ; McDaniel, Hanna J.
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; WPO (Weather Program Office)
• Description:
  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. TI feature contributions are analyzed in time and space for CSU-MLP day 2 and day 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 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 storm climatology. A discussion on how these insights could benefit forecast operations more broadly is also provided.
• Source:
  Weather and Forecasting, 40(2), 237-258
• DOI:
  https://doi.org/10.1175/WAF-D-23-0193.1
• ISSN:
  0882-8156 ; 1520-0434
• Format:
  PDF
• Publisher:
  American Meteorological Society
• Document Type:
  Journal Article
• Funding:
  Grant no. NA20OAR4590350
• Rights Information:
  Other
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:7b06fa9aa60baea59d2bdf14c82f07f91382d1746c4f6f750675c654bdd5c89b438ca2134539ac70f2e74652b852bbbcca8eaaa0ba6cadc02877ac5d2e8e0516
• Download URL:
  https://repository.library.noaa.gov/view/noaa/70212/noaa_70212_DS1.pdf
• File Type:
  [PDF - 7.05 MB ]