A Random-Forest Model to Assess Predictor Importance and Nowcast Severe Storms Using High-Resolution Radar–GOES Satellite–Lightning Observations
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.
 
 
Help
Clear All

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



Public Access Version Available on: December 02, 2021, 12:00 AM
Please check back on the date listed above.

Details:

  • 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
  • Document Type:
  • Rights Information:
    Other
  • Main Document Checksum:
  • File Type:
  • Supporting Files:
    No Additional Files

More +

Related Documents

You May Also Like

Checkout today's featured content at

Version 3.16