A Monte Carlo model for estimating tornado impacts. (29th February 2016)

• Record Type: Journal Article
• Authors: Strader, Stephen M.; Pingel, Thomas J.; Ashley, Walker S.
• Is Part Of: Meteorological applications. Volume 23:Number 2(2016)
• Page Start: 269
• Page End: 281
• Publication Date: 2016-02-29
• Subjects: tornado -- Monte Carlo -- impacts -- exposure -- risk -- disaster; Meteorology -- Periodicals; Meteorological services -- Periodicals; 551.5
• DOI: 10.1002/met.1552
• Languages: English
• ISSNs: 1350-4827

Abstract

ABSTRACT: Determining the likelihood and severity of tornado disasters requires an understanding of the dynamic relationship between tornado risk and vulnerability. As population increases in the future, it is likely that tornado disaster frequency and magnitude will amplify. This study presents the Tornado Impact Monte Carlo (TorMC) model, which simulates tornado events atop a user‐defined spatial domain to estimate the possible impact on people, the built‐environment or other potentially vulnerable assets. Using a Monte Carlo approach, the model employs a variety of sampling techniques on observed tornado data to provide greater insight into the tornado disaster potential for a location. Simulations based on 10 000 years of significant tornado events for the relatively high‐risk states of Alabama, Illinois and Oklahoma are conducted to demonstrate the model processes, and its reliability and applicability. These simulations are combined with a fine‐scale (100 m), residential built‐environment cost surface to illustrate the probability of housing unit impact thresholds for a contemporary year. Sample results demonstrate the ability of the model to depict successfully tornado risk, residential built‐environment exposure and the probability of disaster. Additional outcomes emphasize the importance of developing versatile tools that capture better the tornado risk and vulnerability attributes in order to provide precise estimates of disaster potential. Such tools can provide emergency managers, planners, insurers and decision makers a means to advance mitigation, resilience and sustainability strategies.