[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122795-en":3,"doc-seo-122795-105":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},122795,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Tornado outbreak false alarm probabilistic forecasts with machine learning - Thesis for Master of Science","Tornado outbreaks occur annually and can cause fatalities and severe property damage, making improved forecasting essential for public protection. False alarms (FAs) can reduce public trust and willingness to act, motivating the development of a probabilistic FA forecasting method. This thesis applies machine learning to predict FA likelihood using Storm Prediction Center (SPC) tornado outbreak forecasts. Using a 2010–2020 hit/FA outbreak database, Weather Research and Forecasting (WRF) simulations characterize meteorological environments, and SVM training forecasts FA outcomes. Results are encouraging and support potential operational severe-weather applications.","Mississippi State University  \nScholars Junction  \n\n| Theses and Dissertations | Theses and Dissertations |\n| --- | --- |\n| 5-12-2023\u003Cbr>Tornado outbreak false alarm probabilistic forecasts with machine learning\u003Cbr>Kirsten Reed Snodgrass\u003Cbr>Mississippi State University, [kirstensnodgrass13@gmail.com](kirstensnodgrass13@gmail.com)\u003Cbr>Follow this and additional works at: [https://scholarsjunction.msstate.edu/td](https://scholarsjunction.msstate.edu/td)\u003Cbr> Part of the Artificial Intelligence and Robotics Commons, and the Meteorology Commons |  |\n\nRecommended Citation  \nSnodgrass, Kirsten Reed, \"Tornado outbreak false alarm probabilistic forecasts with machine learning\"(2023) . Theses and Dissertations. 5804.  \n[https://scholarsjunction.msstate.edu/td/5804](https://scholarsjunction.msstate.edu/td/5804)  \nThis Graduate Thesis-Open Access is brought to you for free and open access by the Theses and Dissertations at Scholars Junction. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of Scholars Junction. For more information, please contact scholcomm@msstate. libanswers.com.  \nTornado outbreak false alarm probabilistic forecasts with machine learning  \nBy  \nKirsten Reed Snodgrass  \nApproved by:  \nAndrew E. Mercer (Major Professor/Graduate Coordinator)  \nJamie L. Dyer  \nMichael E. Brown  \nRick Travis (Dean, College of Arts & Sciences)  \nA Thesis  \nSubmitted to the Faculty of  \nMississippi State University in Partial Fulfillment of the Requirements for the Degree of Master of Science  \nin Geosciences  \nin the Department of Geosciences  \nMississippi State, Mississippi  \nMay 2023  \nCopyright by Kirsten Reed Snodgrass 2023  \nName: Kirsten Reed Snodgrass  \nDate of Degree: May 12, 2023  \nInstitution: Mississippi State University  \nMajor Field: Geosciences  \nMajor Professor: Andrew E. Mercer  \nTitle of Study: Tornado outbreak false alarm probabilistic forecasts with machine learning  \nPages in Study: 66  \nCandidate for Degree of Master of Science  \nTornadic outbreaks occur annually, causing fatalities and millions of dollars in damage. By improving forecasts, the public can be better equipped to act prior to an event. False alarms (FAs) can hinder the public’s ability (or willingness) to act. As such, a probabilistic FA forecasting scheme would be beneficial to improving public response to outbreaks.  \nHere, a machine learning approach is employed to predict FA likelihood from Storm Prediction Center (SPC) tornado outbreak forecasts. A database of hit and FA outbreak forecasts spanning 2010 – 2020 was developed using historical SPC convective outlooks and the SPC Storm Reports database. Weather Research and Forecasting (WRF) model simulations were done for each outbreak to characterize the underlying meteorological environments. Parameters from these simulations were used to train a support vector machine (SVM) to forecast FAs. Results were encouraging and may result in further applications in severe weather operations.  \nDEDICATION  \nThis thesis is wholeheartedly dedicated to my beloved parents, who have been my source of inspiration, who have given me strength when I thought of giving up, and who continually provide their moral, spiritual, and emotional support. I would also like to dedicate this work tomy sister, my partner, and my dear friends who shared their words of advice and encouragement to finish this study. And lastly, I dedicate this thesis to God. Thank you for the guidance, strength, and wisdom to surpass all trials encountered and for giving me determination to pursue this study and for ultimately making this opportunity possible.  \nACKNOWLEDGEMENTS  \nI would like to express the deepest appreciation to my committee chair, Dr. Andrew Mercer, as without his guidance, patience, and persistent help this thesis would not have been possible. I would also like to thank my committee members Dr. Jamie Dyer and Dr. Mike Brown whose assistance, perspective, and helpful suggestions were param","cbCaickgzHYug8GA","https://ap.wps.com/l/cbCaickgzHYug8GA","pdf",1851720,1,76,"English","en",105,"# Dedication\n# Acknowledgements\n# List of Tables\n# List of Figures\n# Chapter I. Introduction\n# Chapter II. Background\n## Overview of Tornado Outbreaks\n## Tornado Outbreak Meteorological Characteristics\n## Tornado Outbreak Forecasts\n# Chapter III. Data & Methods\n## Datasets\n## Methodology\n## Feature Selection Methodologies\n## Optimal SVM Configuration","[{\"question\":\"Why are probabilistic false alarm forecasts important for tornado outbreaks?\",\"answer\":\"False alarms can hinder the public’s ability or willingness to act. Probabilistic FA forecasting aims to improve public response prior to outbreaks.\"},{\"question\":\"What data and time span are used to build the hit and false alarm database?\",\"answer\":\"A database of hit and false alarm outbreak forecasts spanning 2010–2020 is built from historical SPC convective outlooks and SPC Storm Reports.\"},{\"question\":\"How does the thesis use machine learning to predict false alarm likelihood?\",\"answer\":\"WRF model simulations characterize the meteorological environment for each outbreak, simulation parameters are used to train a support vector machine (SVM), and the trained model forecasts FA likelihood.\"}]","Tornado outbreak false alarm probabilistic forecasts with machine learning - Thesis for Master of Science | PDF",1785812935,192,{"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},"tornado-outbreak-false-alarm-probabilistic-forecasts-with-machine-learning-thesis-for-master-of-science","",{"@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/tornado-outbreak-false-alarm-probabilistic-forecasts-with-machine-learning-thesis-for-master-of-science/122795/",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 are probabilistic false alarm forecasts important for tornado outbreaks?","Question",{"text":75,"@type":76},"False alarms can hinder the public’s ability or willingness to act. Probabilistic FA forecasting aims to improve public response prior to outbreaks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and time span are used to build the hit and false alarm database?",{"text":80,"@type":76},"A database of hit and false alarm outbreak forecasts spanning 2010–2020 is built from historical SPC convective outlooks and SPC Storm Reports.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis use machine learning to predict false alarm likelihood?",{"text":84,"@type":76},"WRF model simulations characterize the meteorological environment for each outbreak, simulation parameters are used to train a support vector machine (SVM), and the trained model forecasts FA likelihood.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]