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ProbSevere version 3: Improved Exploitation of Data Fusion and Machine Learning for Nowcasting Severe Weather



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  • Journal Title:
    Weather and Forecasting
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    NOAA/CIMSS ProbSevere, or the “probability of severe” models, provide next-hour probabilistic guidance on severe weather in the United States (large hail, strong wind gusts, and tornadoes) using machine learning and meteorological data from remotely sensed platforms and short-term numerical weather models. ProbSevere has been widely used by NOAA’s National Weather Service (NWS) since 2016. ProbSevere version 3 employs multi-platform, multi-scale storm-object identification and tracking to collect storm features, and then uses gradient-boosted decision trees to make predictions of convective hazards from the extracted predictors. ProbSevere v3 was trained, validated, and tested on data from 2018-2023. Results demonstrate improved performance over ProbSevere v2 for the majority of the U.S. across all seasons, spanning a wide range of meteorological regimes. The greatest improvements are for the hail, wind, and any-severe models, whereas the tornado model demonstrated modest improvement. ProbSevere v3 models were evaluated by NWS forecasters in simulated real-time experiments from 2021 to 2023. The majority of forecasters preferred ProbSevere v3 over v2 due to improved confidence in predictions and greater lead times for the issuance of severe weather warnings. An analysis of predictor importance revealed several radar, lightning, satellite, and numerical model fields that strongly contribute to the model predictions, which is consistent with past research and knowledge of severe thunderstorm forecasting. This reinforces the importance of environmental data fusion for nowcasting severe weather associated with mid-latitude convection.
  • Source:
    Weather and Forecasting (2024)
  • DOI:
  • ISSN:
    0882-8156 ; 1520-0434
  • Format:
    pdf
  • Publisher:
    American Meteorological Society
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA15NES4320001
  • Rights Information:
    Other
  • Compliance:
    Library
  • Main Document Checksum:
    urn:sha-512:00a27ec48ec62bad403b348c972fb328492387ac1ffdb7e98c5c606f00a7471df2b2a74b713acf0aef2bb69cfb9d49c406de896e7ce3e389273e0fbb2485afa8
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  • File Type:
    Filetype[PDF - 11.03 MB ]
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