[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124962-en":3,"doc-seo-124962-105":30,"detail-sidebar-cat-0-en-105":95},{"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},124962,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Unsupervised machine learning of tornado-producing storms in the southeastern United States - Master of Science thesis","The east-southeastern United States experiences storm and tornado-related damages, injuries, and fatalities in distinct ways. This thesis uses doppler radar, satellite, and modeled data to study storm varieties that generate strong tornadoes, addressing the limits of time-intensive and literature-dependent classification methods. It investigates the radar-derived data structure and the spread of mesoscale strong-tornado events, applies K-Means unsupervised clustering to identify storm-type clusters and attributes, and evaluates K-Means for storm typing. Convective strength and length best explain variance and reduce cluster overlap. Testing 2–8 clusters yields three storm types—Weaker Linear, Stronger Cellular, and Weaker Cellular—with the highest Silhouette score (0.41). Only 54% of storms are assigned with under 80% certainty, indicating a continuum rather than strict groupings. Climatological analyses for 2000–2020 show that these types reproduce spatial and temporal signals consistent with key storm-classification literature, supporting future storm taxonomy frameworks and data-driven alternatives to subjective manual typing.","University of Tennessee, Knoxville  \nTRACE: Tennessee Research and Creative Exchange  \n\n| Masters Theses | Graduate School |\n| --- | --- |\n| 8-2023\u003Cbr>Unsupervised machine learning of tornado-producing storms in the southeastern United States\u003Cbr>Morgan R. Steckler[msteckle@vols.utk.edu](msteckle@vols.utk.edu)\u003Cbr>Follow this and additional works at: [https://trace.tennessee.edu/utk_gradthes](https://trace.tennessee.edu/utk_gradthes)\u003Cbr> Part of the Climate Commons, Databases and Information Systems Commons, Data Science Commons, Meteorology Commons, and the Other Oceanography and Atmospheric Sciences and Meteorology Commons |  |\n\nRecommended Citation  \nSteckler, Morgan R., \"Unsupervised machine learning of tornado-producing storms in the southeastern United States. \" Master's Thesis, University of Tennessee, 2023.  \n[https://trace.tennessee.edu/utk_gradthes/9927](https://trace.tennessee.edu/utk_gradthes/9927)  \nThis Thesis is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Masters Theses by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [trace@utk.edu](trace@utk.edu).  \nTo the Graduate Council:  \nI am submitting herewith a thesis written by Morgan R. Steckler entitled \"Unsupervised machine learning of tornado-producing storms in the southeastern United States.\" I have examined the final electronic copy of this thesis for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Master of Science, with a major in Geography.  \nKelsey N. Ellis, Major Professor  \nWe have read this thesis and recommend its acceptance: Qiusheng Wu, Hannah V. Herrero  \nAccepted for the Council: Dixie L. Thompson  \nVice Provost and Dean of the Graduate School  \n(Original signatures are on file with official student records.)  \nUnsupervised machine learning of tornado-producing storms in the southeastern United  \nStates  \nA Thesis Presented for the  \nMaster of Science  \nDegree  \nThe University of Tennessee, Knoxville  \nMorgan R. Steckler  \nAbstract  \nThe east-southeastern US is uniquely affected by storm and tornado-related damages, costs, injuries, and deaths. Based on doppler radar, satellite, and modeled data, previous research sought to understand these different types of storms that produce strong tornadoes. Many approaches to storm classification are time intensive, complex, and vary significantly across the literature. The purpose of this work is to (1) explore the radar-derived data structure and spread of strong tornado-producing mesoscale storms in the east-southeastern US; (2) use K-Means unsupervised machine learning methods to elucidate clusters (storm types) and clustering attributes; and (3) assess the utility ofK-Means as a storm typing algorithm. Convective and stratiform strength, length, width, shape, and area, as well as the position of stratiform rain relative to convection, were evaluated using principal component analysis and K-Means clustering. The results show that convective strength and length attributes best explained variance in the dataset and minimized cluster overlap. While other attributes wielded explanatory power, they did not separate into distinct clusters. After testing K-Means on 2 through 8 clusters, the intensity and shape attributes generated three strong tornado-producing storm types: Weaker Linear (n=146), Stronger Cellular (n=222), and Weaker Cellular (n=292) . These storm types were most like those in the literature and yielded the highest K-Means Silhouette score (0.41) . It was found that 54% of storms were placed with less than 80% certainty, which emphasizes that storm types belong on a continuum and are not easily divided into groups. However, climatological analyses of the storms from 2000–2020 reveal that Weaker Linear, Stronger Cellular, and Weaker Cellular storm types produc","cbCaiioBsuACpRO2","https://ap.wps.com/l/cbCaiioBsuACpRO2","pdf",6508891,1,63,"English","en",105,"# CHAPTER 1. INTRODUCTION AND LITERATURE REVIEW\n## 1.1 Storm Environments\n## 1.2 Storm Types\n## 1.3 Classification\n## 1.4 Research Questions\n# CHAPTER 2. DATA AND METHODS\n## 2.1 Data\n## 2.2 Methods\n## 2.2.1 Pre-Processing\n## 2.2.2 Clustering\n# CHAPTER 3. RESULTS AND DISCUSSION","[{\"question\":\"What problem does this thesis address in tornado storm classification?\",\"answer\":\"It addresses that existing storm classification approaches are time intensive, complex, and vary across the literature, making comparisons and typing difficult.\"},{\"question\":\"Which machine learning method is used to identify storm types?\",\"answer\":\"The thesis uses K-Means unsupervised machine learning to cluster radar-derived storm attributes into storm-type groups.\"},{\"question\":\"How many tornado-producing storm types are identified, and what are they?\",\"answer\":\"After testing K-Means with 2–8 clusters, it identifies three types: Weaker Linear, Stronger Cellular, and Weaker Cellular.\"},{\"question\":\"What does the uncertainty in cluster assignment imply about storm types?\",\"answer\":\"It shows that 54% of storms have less than 80% certainty, suggesting storm types form a continuum rather than cleanly separable groups.\"}]","Unsupervised machine learning of tornado-producing storms in the southeastern United States - Master of Science thesis | PDF",1785895646,159,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"unsupervised-machine-learning-of-tornado-producing-storms-in-the-southeastern-united-states-master-of-science-thesis","",{"@graph":36,"@context":89},[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/unsupervised-machine-learning-of-tornado-producing-storms-in-the-southeastern-united-states-master-of-science-thesis/124962/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this thesis address in tornado storm classification?","Question",{"text":75,"@type":76},"It addresses that existing storm classification approaches are time intensive, complex, and vary across the literature, making comparisons and typing difficult.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning method is used to identify storm types?",{"text":80,"@type":76},"The thesis uses K-Means unsupervised machine learning to cluster radar-derived storm attributes into storm-type groups.",{"name":82,"@type":73,"acceptedAnswer":83},"How many tornado-producing storm types are identified, and what are they?",{"text":84,"@type":76},"After testing K-Means with 2–8 clusters, it identifies three types: Weaker Linear, Stronger Cellular, and Weaker Cellular.",{"name":86,"@type":73,"acceptedAnswer":87},"What does the uncertainty in cluster assignment imply about storm types?",{"text":88,"@type":76},"It shows that 54% of storms have less than 80% certainty, suggesting storm types form a continuum rather than cleanly separable groups.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]