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

Mecikalski, John R.; Sandmæl, Thea N.; Murillo, Elisa M.; Homeyer, Cameron R.; Bedka, Kristopher M.; Apke, Jason M.; Jewett, Chris P.

doi:10.1175/MWR-D-19-0274.1

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

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

2021
By Mecikalski, John R. ; Sandmæl, Thea N. ; Murillo, Elisa M. ; ...
Source: Monthly Weather Review, 149(6), 1725-1746

[PDF-3.91 MB]

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Journal Title:

Monthly Weather Review
Personal Author:

Mecikalski, John R. ; Sandmæl, Thea N. ; Murillo, Elisa M. ; Homeyer, Cameron R. ; Bedka, Kristopher M. ; ... More +

Mecikalski, John R. ; Sandmæl, Thea N. ; Murillo, Elisa M. ; Homeyer, Cameron R. ; Bedka, Kristopher M. ; Apke, Jason M. ; Jewett, Chris P. Less -
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.
Keywords:

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Convective-scale Processes Convective Storms Nowcasting Probability Forecasts Radars/Radar Observations Satellite Observations
Source:

Monthly Weather Review, 149(6), 1725-1746
DOI:

https://doi.org/10.1175/MWR-D-19-0274.1
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Journal Article
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urn:sha256:d26d6b9a5921b1b78649acad6ebfc9fe73eb184e1ace08bdf115d6fa5843e0eb
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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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