Increased Accuracy in Statistical Seasonal Hurricane Forecasting

Hurricanes are among the costliest and most destructive natural hazards in the U.S. Accurate hurricane forecasts are crucial to optimal preparedness and mitigation decisions in the U.S. where 50 percent of the population lives within 50 miles of the coast. We developed a flexible statistical approach to forecast annual number of hurricanes in the Atlantic region during the hurricane season. Our model is based on the method of Random Forest and captures the complex relationship between hurricane activity and climatic conditions through careful variable selection, model testing and validation. We used the National Hurricane Center's Best Track hurricane data from 1949-2011 and sixty-one candidate climate descriptors to develop our model. The model includes information prior to the hurricane season, i.e., from the last three months of the previous year (Oct. through Dec.) and the first five months of the current year (January through May). Our forecast errors are substantially lower than other leading forecasts such as that of the National Oceanic and Atmospheric Administration (NOAA).