Objective Identification of Annular Hurricanes

Abstract

Annular hurricanes are a subset of intense tropical cyclones that have been shown in previous work to be significantly stronger, to maintain their peak intensities longer, and to weaken more slowly than average tropical cyclones. Because of these characteristics, they represent a significant forecasting challenge. This paper updates the list of annular hurricanes to encompass the years 1995?2006 in both the North Atlantic and eastern?central North Pacific tropical cyclone basins. Because annular hurricanes have a unique appearance in infrared satellite imagery, and form in a specific set of environmental conditions, an objective real-time method of identifying these hurricanes is developed. However, since the occurrence of annular hurricanes is rare (?4% of all hurricanes), a special algorithm to detect annular hurricanes is developed that employs two steps to identify the candidates: 1) prescreening the data and 2) applying a linear discriminant analysis. This algorithm is trained using a dependent dataset (1995?2003) that includes 11 annular hurricanes. The resulting algorithm is then independently tested using datasets from the years 2004?06, which contained an additional three annular hurricanes. Results indicate that the algorithm is able to discriminate annular hurricanes from tropical cyclones with intensities greater than 84 kt (43.2 m s?1). The probability of detection or hit rate produced by this scheme is shown to be ?96% with a false alarm rate of ?6%, based on 1363 six-hour time periods with a tropical cyclone with an intensity greater than 84 kt (1995?2006).