Non-stochastic long-term prediction model for US tornado level

Abstract

Last year, natural catastrophes caused US$ 160 bn in overall losses and US$ 65 bn in insured losses worldwide (according to Munich Re Group 2013). In the United States alone, Hurricane Sandy affected more than twenty states and caused severe damage and deadly flooding in the states of New York, New Jersey, and Connecticut. The tristate area suffered a mass loss of homes. These natural disasters underscore the importance of predictive modeling of catastrophic weather events. This article analyzes the trends and predictions of one type of catastrophic weather event—the USA tornado level (EF3–EF5). We created a model that predicts tornado level function as a function of frequencies from the Moon’s spectrum. Results from the predictive model were highly correlated with the historical data. Results were also compared to and exceeded those generated by a prior model developed by Isakov, Mezrin, and Suprun (presentation at the Global Derivatives Trading and Risk Management USA, 2011). This model suggests that tornadoes and periodicities of tornadoes are associated with the Sun–Earth–Moon gravitational/magnetic system.