Stochastic Track Model for Tornado Risk Assessment in the U.S.

Fanfu Fan,Weichiang Pang

doi:10.3389/fbuil.2019.00037

Abstract

This paper presents the development of a stochastic tornado simulation model for the United States (US). The continental of the US is subjected to more than 1,000 tornadoes each year, causing significant financial losses and social disruption. Compared to hurricanes, the damage region of a tornado is relatively small and the probability of occurrence at a given location is extremely low. Therefore, it is not feasible to use solely the observed data or tracks to quantify the tornado risk for a given structure or a city that has not been affected by historical tornadoes. In this paper, a methodology for performing stochastic simulation of tornado tracks for the US is presented. The stochastic simulation framework consists of a genesis model, which utilizes the kernel density estimation to simulate the spawn locations of tornadoes. Statistical models for tornado parameters such as track length, path width and intensity, were calibrated using the tornado database maintained by the US National Oceanic and Atmospheric Administration (NOAA) Storm Prediction Center (SPC). The developed statistical models were used to simulate 1,000,000 years of tornado tracks. The simulated tornado parameters include the tornado occurrence rate, intensity (EF-scale), location, touchdown time, path length and path width. All these parameters are geographic dependent, meaning the parameters vary depending on the tornado spawn locations. The simulated spawn rates and other key parameters for the continental of the US are compared to the observations. Good agreements are observed between simulations and observations. To illustrate a potential use of the simulated tornado track database, a probabilistic tornado hazard analysis was performed for Moore, Oklahoma. The 50-year tornado hazard curves for three domain sizes are developed to assess the influence of the domain size on tornado risk.

Highlights

On average, the continental United States is subjected to more than 1,000 tornadoes every year, causing significant financial losses and social disruption
One of the key contributions of the tornado simulation methodology developed in this study is the use of kernel density estimation (KDE) and Monte Carlo Simulation (MCS) methods to generate geographic dependent tornado parameters, which include the enhanced Fujita (EF)-scale, path length, maximum path width, path direction, spawn month, date, and hours
The main procedure used to simulate the geographic dependent tornado tracks is present in Figure 2, and discussed in this paper according to the following organization: 1) Data pre-processing: the historical tornado database was pre-processed to remove incomplete data set and reconcile inconsistent entries

Summary

INTRODUCTION

The continental United States is subjected to more than 1,000 tornadoes every year, causing significant financial losses and social disruption. Boruff et al (2003) found that while the number of reported tornado events were almost doubled from 1950 to 2000, there has been a steady reduction in tornado induced fatalities and injuries in recent years This is likely attributed to the advancement made in forecasts and warning times of tornado outbreaks. A more recent study by Suckling and Ashley (2006) examined more than 6,000 tornado tracks from 1980 to 2002 They found that while tornadoes generally travel in paths from the southwest toward the northeast direction, in central and northern region of the US, a more westerly tornado paths preponderates during late spring and summer. One of the key contributions of the tornado simulation methodology developed in this study is the use of kernel density estimation (KDE) and MCS methods to generate geographic dependent tornado parameters, which include the EF-scale, path length, maximum path width, path direction, spawn month, date, and hours. It is very important to have a model that can explicitly simulate geographic dependent tornado parameters such as EFscale, path length, path width, spawn month, and spawn time in a day, in particular, when the model is intended for use in estimating occupant risk or casualty

METHOD OF ANALYSIS Analysis Procedure

Findings

SUMMARY AND CONCLUSION