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

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  • 1 a Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, Alabama
  • | 2 b School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • | 3 c Science Directorate, NASA Langley Research Center, Hampton, Virginia
  • | 4 d Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado
  • | 5 e Earth Systems Science Center, Huntsville, Alabama
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Abstract

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.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: John R. Mecikalski, johnm@nsstc.uah.edu

Abstract

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.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: John R. Mecikalski, johnm@nsstc.uah.edu
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