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Situations with high (low) wind speeds receiving low (high) diagnosed probabilities were compared with valid cases where diagnosed probability matched observed wind severity. The comparison showed that high-wind/low-probability cases occurred in less favorable environments for severe wind production, while low-speed/high-probability cases had more favorable storm-related characteristics.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/exploring-the-explainability-of-a-machine-learning-tool-to-improve-severe-thunderstorm-wind-reports/290709/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/exploring-the-explainability-of-a-machine-learning-tool-to-improve-severe-thunderstorm-wind-reports/290709.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does the machine learning tool predict for severe thunderstorm wind reports?","Question",{"text":112,"@type":113},"It assigns a probability that a given severe thunderstorm wind report was caused by winds reaching severe intensity (≥50 kts).","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why are “counterintuitive” cases important in this study?",{"text":117,"@type":113},"They involve situations where wind speeds and diagnosed probabilities do not align as expected, which helps evaluate whether model outputs reflect physically meaningful relationships and identifies potentially misleading circumstances.",{"name":119,"@type":110,"acceptedAnswer":120},"What environmental differences explain high-wind/low-probability cases versus low-speed/high-probability cases?",{"text":121,"@type":113},"High-wind/low-probability cases occur in less conducive environments for severe wind production, whereas low-speed/high-probability cases show environmental characteristics more favorable for producing severe wind.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290709,1789642414,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":36,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Article  \nExploring the Explainability of a Machine Learning Tool to Improve Severe Thunderstorm Wind Reports  \nElizabeth Tirone *, William A. Gallus, Jr. and Alexander J. Hamilton  \nDepartment of Earth, Atmosphere, and Climate, Iowa State University, Ames, IA 50011, USA;  \n[wgallus@iastate.edu](wgallus@iastate.edu) (W.A.G.J.); [ajh3@iastate.edu](ajh3@iastate.edu) (A.J.H.)  \n* Correspondence: [elizabeth.tirone@noaa.gov](elizabeth.tirone@noaa.gov)  \nAbstract  \nOutput from a machine learning tool that assigns a probability that a severe thunderstorm wind report was caused by severe intensity wind was evaluated to understand counterintuitive cases where reports that had a high (low) wind speed received a low (high) diagnosed probability. Meteorological data for these cases was compared to that for valid cases where the machine learning probability seemed consistent with the observed severity of the winds. The comparison revealed that the cases with high winds but low probabilities occurred in less conducive environments for severe wind production (less instability, greater low-level relative humidity, weaker lapse rates) than in the cases where high winds occurred with high probabilities. Cases with a low speed but a high probability had environmental characteristics that were more conducive to producing severe wind. These results suggest that the machine learning model is assigning probabilities based on storm modes that more often have measured severe wind speeds (i.e., clusters of cells and bow echoes), and counterintuitive values may reflect events where storm interactions or other smaller-scale features play a bigger role. In addition, some evidence suggests improper reporting maybe common for some of these counterintuitive cases.  \nKeywords: machine learning; thunderstorms; severe wind  \nAcademic Editor: Stephan Havemann  \nReceived: 2 June 2025  \nRevised: 11 July 2025  \nAccepted: 15 July 2025  \nPublished: 18 July 2025  \nCitation: Tirone, E.; Gallus, W.A., Jr.; Hamilton, A.J. Exploring the Explainability of a Machine Learning Tool to Improve Severe Thunderstorm Wind Reports. Atmosphere 2025, 16, 881 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)atmos16070881  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1. Introduction  \nMachine learning (ML) has been used in meteorology since the 1950s, but its use has increased much more rapidly in recent years [1–3] . Recent work has focused on topics such as hazard prediction [4–7], bias correction in physics-based modelling [8,9], and storm mode classification [10,11] . Despite its increasingly widespread applications, there is still a general hesitation from end users in trusting the output from machine learning models [2] . To improve both the understanding of ML and the performance of ML tools, there is a pushto improve the explainability and interpretability of the setup of the ML models as well as to evaluate their output [2,3] . This includes evaluating the machine learning algorithms to determine which features were most important in the prediction and to investigate the output of the models to determine if their predictions are consistent with the laws of physics.  \nRecently, a ML-based tool was created to improve the usability of severe thunderstorm wind reports (SRs) in the National Center for Environmental Information’s Storm Events Database [12] . Thunderstorms have been shown to produce damaging winds from a range of different modes and mechanisms. Ref. [13] found that significant severe wind events (≥65 kts) generally occurred evenly between supercells, mesoscale convective systems,  \nand disorganized convection. Within these convective events, se","cbCaiezU91OLeoW2","https://ap.wps.com/l/cbCaiezU91OLeoW2","pdf",1985198,16,"English","# Introduction\n## Explainability motivation for ML in meteorology\n## ML tool for severe thunderstorm wind report usability\n## Counterintuitive probability cases and study focus","[{\"question\":\"What does the machine learning tool predict for severe thunderstorm wind reports?\",\"answer\":\"It assigns a probability that a given severe thunderstorm wind report was caused by winds reaching severe intensity (≥50 kts).\"},{\"question\":\"Why are “counterintuitive” cases important in this study?\",\"answer\":\"They involve situations where wind speeds and diagnosed probabilities do not align as expected, which helps evaluate whether model outputs reflect physically meaningful relationships and identifies potentially misleading circumstances.\"},{\"question\":\"What environmental differences explain high-wind/low-probability cases versus low-speed/high-probability cases?\",\"answer\":\"High-wind/low-probability cases occur in less conducive environments for severe wind production, whereas low-speed/high-probability cases show environmental characteristics more favorable for producing severe wind.\"}]","Exploring the Explainability of a Machine Learning Tool to Improve Severe Thunderstorm Wind Reports | PDF"]