Assessing large-scale climate influences on U.S. tornado outbreaks and potential insurance industry applications

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Atmospheric Sciences

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Elizondo, Antonio M

Contributors dc:contributor

• Sriver, Ryan L
• Trapp, Robert J
• Sime, Laura

• Enso
• Nao
• Climate
• Tornado Outbreak Events/days/months/risks
• Insurance Loss/risk/applications

Statement dc:rights

• Copyright 2023 Antonio Elizondo

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/121548

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F121548