[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121985-en":3,"doc-seo-121985-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121985,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","A Machine Learning Approach to Improve the Usability of Severe Thunderstorm - Wind Reports","Many concerns affect thunderstorm wind reports in the National Center for Environmental Information Storm Events Database, including wind-speed overestimation, changes in report frequency linked to population density, and differences driven by damage tracers. These issues are most evident for estimated reports, which constitute nearly 90% of the database. Machine learning models predict the probability that a severe wind report reflects winds at or above 50 kt, using measured reports and supporting meteorological and geospatial variables. Skill metrics indicate a stacked generalized linear model with strong discrimination and calibrated reliability, enabling uses in forecast verification and quality control.","Article  \nA Machine Learning Approach to Improve the Usability of Severe Thunderstorm  \nWind Reports  \nElizabeth Tirone, Subrata Pal, William A. Gallus Jr., Somak Dutta, Ranjan Maitra, Jennifer Newman, Eric Weber, and Israel Jirak  \nKEYWORDS:  \nWind gusts; Machine learning; Forecast verification/skill; Damage assessment; Severe storms  \nABSTRACT: Many concerns are known to exist with thunderstorm wind reports in the National Center for Environmental Information Storm Events Database, including the overestimation of wind speed, changes in report frequency due to population density, and differences in reporting due to damage tracers. These concerns are especially pronounced with reports that are not associated with a wind speed measurement, but are estimated, which make up almost 90% of the database. We have used machine learning to predicthe probability that a severe wind report was caused  \nby severe intensity wind, or wind ≥ 50 kt ( 25 m s−1). A total of six machine learning models were trained on 11 years of measured thunderstorm wind reports, along with meteorological parameters, population density, and elevation. Objective skill metrics such as the area under the ROC curve (AUC), Brier score, and reliability curves suggest that the best performing model is the stacked generalized linear model, which has an AUC around 0.9 and a Brier score around 0.1. The outputs from these models have many potential uses such as forecast verification and quality control for implementation in forecast tools. Our tool was evaluated favorably at the Hazardous Weather Testbed Spring Forecasting Experiments in 2020, 2021, and 2022.  \n[https://doi.org/10.1175/BAMS-D-22-0268.1](https://doi.org/10.1175/BAMS-D-22-0268.1)  \nCorresponding author: Elizabeth Tirone, [elizabeth.tirone@noaa.gov](elizabeth.tirone@noaa.gov)[ ](elizabeth.tirone@noaa.gov)In final form 5 February 2024  \n© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy ([www.ametsoc.org/PUBSReuseLicenses](www.ametsoc.org/PUBSReuseLicenses)).  \nAMERICAN METEOR LrO GoughIAtLo SO CyouI TyYIowa State University Library | UnauthenticatedM |A wHn o0a2d4ed 0E633/24 05:14 PM UTC  \nAFFILIATIONS: Tirone, Pal, Gallus, Dutta, Maitra, Newman, and Weber—Iowa State University, Ames, Iowa; Jirak—NOAA/Storm Prediction Center, Norman, Oklahoma  \nS  \ntraight-line winds from thunderstorms are one of the most destructive types of weather, as was evident in the August 2020 Midwestern derecho that caused over $12 billion in damage (NOAA 2020). Each year around 15,000 reports of severe wind or wind damage  \nappear in the National Center for Environmental Information Storm Events Database (NCEI 2023) . Severe thunderstorm wind reports include winds from thunderstorms that are 50 kt (1 kt ≈ 0.51 m s−1) or more, or winds less than 50 kt that cause fatalities, injuries, and/or damage (NOAA 2021a) .  \nThere are two different types of thunderstorm wind reports (SRs)—measured SRs, which are assigned a wind speed based on a measurement from an approved weather station, and estimated SRs, which are assigned an estimated value based on damage or radar information. Issues with estimated SRs include the overestimation of wind speeds (Edwards et al. 2018), changes in report frequency due to population density (Trapp et al. 2006), and differences in reporting due to differences in damage tracers (Weiss 2002). We have also noted evidence of subjectivity in the assigned values, finding that there was a peak in the number of estimated SRs occurring with an estimate of exactly 50 kt, the threshold to be considered a severe thunderstorm, that was not present with measured SRs (Fig. 1). While there may be a philosophical argument for alerting the public to thunderstorms that are producing damage at wind speeds less than the 50 kt threshold, this de","cbCaicI1ZSXmcSMx","https://ap.wps.com/l/cbCaicI1ZSXmcSMx","pdf",1981257,1,16,"English","en",105,"# Introduction\n## Background on severe thunderstorm wind reports\n## Limitations of estimated reports\n# Data\n## Data sources for model development\n# Machine Learning Approach\n## Model inputs and training\n## Evaluation metrics and results\n# Applications and Evaluation\n## Hazardous Weather Testbed experiments","[{\"question\":\"Why do thunderstorm wind reports in the NCEI Storm Events Database raise usability concerns?\",\"answer\":\"Concerns include overestimation of wind speed, changes in report frequency related to population density, and reporting differences caused by damage tracers.\"},{\"question\":\"What is the focus of the machine learning tool described in the paper?\",\"answer\":\"The tool assigns a probability that a severe wind report was caused by winds greater than or equal to 50 kt, helping interpret severe-intensity events more consistently.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Performance is assessed with objective skill metrics such as AUC, Brier score, and reliability curves, with the best model showing strong discrimination and good calibration.\"}]","A Machine Learning Approach to Improve the Usability of Severe Thunderstorm - Wind Reports | PDF",1785808157,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-approach-to-improve-the-usability-of-severe-thunderstorm-wind-reports","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-approach-to-improve-the-usability-of-severe-thunderstorm-wind-reports/121985/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do thunderstorm wind reports in the NCEI Storm Events Database raise usability concerns?","Question",{"text":75,"@type":76},"Concerns include overestimation of wind speed, changes in report frequency related to population density, and reporting differences caused by damage tracers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the focus of the machine learning tool described in the paper?",{"text":80,"@type":76},"The tool assigns a probability that a severe wind report was caused by winds greater than or equal to 50 kt, helping interpret severe-intensity events more consistently.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated?",{"text":84,"@type":76},"Performance is assessed with objective skill metrics such as AUC, Brier score, and reliability curves, with the best model showing strong discrimination and good calibration.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]