Affiliation:
1. Department of Geographic and Atmospheric Sciences, Northern Illinois University, DeKalb, Illinois
2. Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
Abstract
AbstractPrevious studies have identified environmental characteristics that skillfully discriminate between severe and significant-severe weather events, but they have largely been limited by sample size and/or population of predictor variables. Given the heightened societal impacts of significant-severe weather, this topic was revisited using over 150 000 ERA5 reanalysis-derived vertical profiles extracted at the grid-point nearest—and just prior to—tornado and hail reports during the period 1996–2019. Profiles were quality-controlled and used to calculate 84 variables. Several machine learning classification algorithms were trained, tested, and cross-validated on these data to assess skill in predicting severe or significant-severe reports for tornadoes and hail. Random forest classification outperformed all tested methods as measured by cross-validated critical success index scores and area under the receiver operating characteristic curve values. In addition, random forest classification was found to be more reliable than other methods and exhibited negligible frequency bias. The top three most important random forest classification variables for tornadoes were wind speed at 500 hPa, wind speed at 850 hPa, and 0–500-m storm-relative helicity. For hail, storm-relative helicity in the 3–6 km and -10 to -30 °C layers, along with 0–6-km bulk wind shear, were found to be most important. A game theoretic approach was used to help explain the output of the random forest classifiers and establish critical feature thresholds for operational nowcasting and forecasting. A use case of spatial applicability of the random forest model is also presented, demonstrating the potential utility for operational forecasting. Overall, this research supports a growing number of weather and climate studies finding admirable skill in random forest classification applications.
Publisher
American Meteorological Society
Reference208 articles.
1. Trends in United States large hail environments and observations;Tang;npj Climate Atmos. Sci.,2019
2. andR The tornado severe thunderstorm database th on Climatology Meteor Soc https ams confex com ams older annual abstracts htm;Schaefer;Applied,1999
3. Measured severe convective wind climatology and associated convective modes of thunderstorms in the contiguous United States, 2003–09;Smith;Wea. Forecasting,2013
4. Observed and projected changes in United States tornado exposure;Strader;Wea. Climate Soc.,2017a
5. Statistical in the Atmospheric rd ed International Series Academic;Wilks;Methods Sciences Geophysics,2011
Cited by
15 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献