Tornadoes and related damage costs: statistical modeling with a semi-Markov approach

TL;DR

This paper introduces a semi-Markov statistical model for predicting tornado occurrences and associated economic damages, capturing duration effects better than traditional Markov models.

Contribution

It demonstrates the effectiveness of semi-Markov models over Markov models in modeling tornado intensity and damage costs, with practical application to economic risk assessment.

Findings

Semi-Markov models outperform Markov models in reproducing tornado duration effects.

The model accurately estimates expected tornado damage costs.

Statistical tests confirm the superiority of semi-Markov over Markov models.

Abstract

We propose a statistical approach to tornadoes modeling for predicting and simulating occurrences of tornadoes and accumulated cost distributions over a time interval. This is achieved by modeling the tornadoes intensity, measured with the Fujita scale, as a stochastic process. Since the Fujita scale divides tornadoes intensity into six states, it is possible to model the tornadoes intensity by using Markov and semi-Markov models. We demonstrate that the semi-Markov approach is able to reproduce the duration effect that is detected in tornadoes occurrence. The superiority of the semi-Markov model as compared to the Markov chain model is also affirmed by means of a statistical test of hypothesis. As an application we compute the expected value and the variance of the costs generated by the tornadoes over a given time interval in a given area. he paper contributes to the literature by demonstrating that semi-Markov models represent an effective tool for physical analysis of tornadoes as well as for the estimation of the economic damages to human things.