Automated tropical cyclone eye detection using discriminant analysis

Abstract

Eye formation is often associated with rapid intensification of tropical cyclones, so this information is very valuable to hurricane forecasters. Linear and Quadratic Discriminant Analysis (LDA and QDA) were utilized to develop a method for objectively determining whether or not a tropical cyclone has an eye. The input to the algorithms included basic storm information that is routinely available to forecasters, including the maximum wind, latitude and longitude of the storm center, and the storm motion vector. Infrared imagery from geostationary satellites in a 320 km by 320 km region around each storm was also used as input. Principal Component Analysis was used to reduce the dimension of the IR dataset. The ground truth for the algorithm development was the subjective determination of whether or not a tropical cyclone had an eye made by hurricane forecasters. The input sample included 4109 cases at 6 hr intervals for Atlantic tropical cyclones from 1995 to 2013. Results showed that the LDA and QDA algorithms successfully classified about 90% of the test cases. The best algorithm used a combination of basic storm information and principal components from the IR imagery. These included the maximum winds, the storm latitude and components of the storm motion vector, and 10 PCs from eigenvectors that primarily represented the symmetric structures in the IR imagery. The QDA version performed a little better using a Peirce Skill Score, which measures the ability to correctly classify cases. The LDA and QDA algorithms also provide the probability that each case contains an eye. The LDA version performed a little better using the Brier Skill Score, which measures the utility of the class probabilities. The high success rate indicates that the algorithm can reliably reproduce what forecasters are currently doing subjectively. This algorithm would have a number of applications, including providing forecasters with an objective way to determine if a tropical cyclone has or is becoming more likely to form an eye. The probability information and its time trends could be used as input to other algorithms, such as existing operational forecast methods for estimating tropical cyclone intensity changes.