{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/121548"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/121548","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Assessing large-scale climate influences on U.S. tornado outbreaks and potential insurance industry applications","abstract":"DSpace SAF Submission Ingestion Package generated from Vireo submission #19736 on 2023-12-04 at 17:03:22","abstract_html":"DSpace SAF Submission Ingestion Package generated from Vireo submission #19736 on 2023-12-04 at 17:03:22","abstract_has_math":false,"creators":["Elizondo, Antonio M"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Atmospheric Sciences","degree_department":null,"school":null,"contributors":["Sriver, Ryan L","Trapp, Robert J","Sime, Laura"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08","date_published":"2023-08","updated_at":"2026-07-22T22:25:00Z","subjects":["Enso","Nao","Climate","Tornado Outbreak Events/days/months/risks","Insurance Loss/risk/applications"],"languages":["en","eng"],"rights":["Copyright 2023 Antonio Elizondo"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/121548","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sriver, Ryan L","Trapp, Robert J","Sime, Laura"]},{"key":"dc:creator","label":"Author","values":["Elizondo, Antonio M"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-08","2023-07-21"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Atmospheric Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Enso","Nao","Climate","Tornado Outbreak Events/days/months/risks","Insurance Loss/risk/applications"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Antonio Elizondo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/121548"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["DSpace SAF Submission Ingestion Package generated from Vireo submission #19736 on 2023-12-04 at 17:03:22","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","The student, Antonio Elizondo, accepted the attached license on 2023-07-18 at 21:08.","The student, Antonio Elizondo, submitted this Thesis for approval on 2023-07-18 at 21:37.","This Thesis was approved for publication on 2023-07-21 at 09:10.","Devastating tornado outbreak events are a serious risk for substantial insurance losses in the United States. Improved assessments of these event risks are vital for mitigating future losses. Here, we aim to improve these risk assessments by exploring the influences of two large- scale climate signals, consisting of ENSO (Niño 3.4 region) and NAO, on tornado outbreaks in the conterminous United States. Tornado outbreak event probabilities were derived from both climate signals via multiple statistical analyses and a machine learning predictive model. A combination of negative ENSO and positive NAO conditions generated the largest event probabilities. Additional kernel density estimate maps created from tornado outbreak events found the highest concentration of tornado records over the Southeastern United States. We then utilize our research results to explore practical insurance risk applications such as hazard model integration, exposure visualization, and reinsurance purchasing. These risk applications show real promise of making meaningful contributions toward solving this major insurance industry issue."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Assessing large-scale climate influences on U.S. tornado outbreaks and potential insurance industry applications"]}]}],"canonical_facts":{"dc:contributor":["Sriver, Ryan L","Trapp, Robert J","Sime, Laura"],"dc:creator":["Elizondo, Antonio M"],"dc:date":["2023-08","2023-07-21"],"dc:description":["DSpace SAF Submission Ingestion Package generated from Vireo submission #19736 on 2023-12-04 at 17:03:22","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","The student, Antonio Elizondo, accepted the attached license on 2023-07-18 at 21:08.","The student, Antonio Elizondo, submitted this Thesis for approval on 2023-07-18 at 21:37.","This Thesis was approved for publication on 2023-07-21 at 09:10.","Devastating tornado outbreak events are a serious risk for substantial insurance losses in the United States. Improved assessments of these event risks are vital for mitigating future losses. Here, we aim to improve these risk assessments by exploring the influences of two large- scale climate signals, consisting of ENSO (Niño 3.4 region) and NAO, on tornado outbreaks in the conterminous United States. Tornado outbreak event probabilities were derived from both climate signals via multiple statistical analyses and a machine learning predictive model. A combination of negative ENSO and positive NAO conditions generated the largest event probabilities. Additional kernel density estimate maps created from tornado outbreak events found the highest concentration of tornado records over the Southeastern United States. We then utilize our research results to explore practical insurance risk applications such as hazard model integration, exposure visualization, and reinsurance purchasing. These risk applications show real promise of making meaningful contributions toward solving this major insurance industry issue."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/121548"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Antonio Elizondo"],"dc:subject":["Enso","Nao","Climate","Tornado Outbreak Events/days/months/risks","Insurance Loss/risk/applications"],"dc:title":["Assessing large-scale climate influences on U.S. tornado outbreaks and potential insurance industry applications"],"dc:type":["text"],"thesis:degree_discipline":["Atmospheric Sciences"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}