A Random-Forest Model to Assess Predictor Importance and Nowcast Severe Storms Using High-Resolution Radar–GOES Satellite–Lightning Observations

Details

• Journal Title:
  Monthly Weather Review
• Personal Author:
  Mecikalski, John R. ; Sandmæl, Thea N. ; Murillo, Elisa M. ; Homeyer, Cameron R. ; Bedka, Kristopher M. ; Apke, Jason M. ; Jewett, Chris P.
• NOAA Program & Office:
  CIMMS (Cooperative Institute for Mesoscale Meteorological Studies)
• Description:
  Few studies have assessed combined satellite, lightning, and radar databases to diagnose severe storm potential. The research goal here is to evaluate next-generation, 60-s update frequency geostationary satellite and lightning information with ground-based radar to isolate which variables, when used in concert, provide skillful discriminatory information for identifying severe (hail ≥ 2.5 cm in diameter, winds ≥ 25 m s−1, and tornadoes) versus nonsevere storms. The focus of this study is predicting severe thunderstorm and tornado warnings. A total of 2004 storms in 2014–15 were objectively tracked with 49 potential predictor fields related to May, daytime Great Plains convective storms. All storms occurred when 1-min Geostationary Operational Environmental Satellite (GOES)-14 “super rapid scan” data were available. The study used three importance methods to assess predictor importance related to severe warnings and used random forests to provide a model and skill evaluation measuring the ability to predict severe storms. Three predictor importance methods show that GOES mesoscale atmospheric-motion-vector-derived cloud-top divergence and above-anvil cirrus plume presence provide the most satellite-based discriminatory power for diagnosing severe warnings. Other important fields include Earth Networks Total Lightning flash density, GOES estimated cloud-top vorticity, and overshooting-top presence. Severe warning predictions are significantly improved at the 95% confidence level when a few important satellite and lightning fields are combined with radar fields, versus when only radar data are used in the random-forest model. This study provides a basis for including satellite and lightning fields within machine-learning models to help forecast severe weather.
• Source:
  Monthly Weather Review, 149(6), 1725-1746
• DOI:
  https://doi.org/10.1175/MWR-D-19-0274.1
• Document Type:
  Journal Article
• Rights Information:
  Other
• Compliance:
  Submitted
• Download URL:
  https://repository.library.noaa.gov/view/noaa/31706/noaa_31706_DS1.pdf
• File Type:
  [PDF - 3.91 MB]
• Collection(s):
  NOAA Cooperative Institutes
• Main Document Checksum:
  urn:sha256:d26d6b9a5921b1b78649acad6ebfc9fe73eb184e1ace08bdf115d6fa5843e0eb