A simple Monte Carlo method for estimating the chance of a cyclone impact

Cyclones endanger life and cause great financial impact on interior and coastal regions through the destruction of buildings and land. Governments need to have a way of estimating the chance of different regions being impacted by a cyclone. The goal of this paper is to use big data to better predict future cyclone impacts. Large cyclone data sets from the CMA Tropical Cyclone Data Center are used in the analysis. By using big data analysis techniques, long-term patterns in cyclone locations and size can be revealed. The Hausdorff distance is used to determine overall changes in cyclone positions decade by decade. Monte Carlo techniques estimate the probability of a region being impacted by a cyclone any given year. This is done by creating random data sets that mimic long-term patterns in cyclone position and radii. It will be shown that any region can be assigned a probability of cyclone impact purely on large historical data sets.