Automated Objective Tropical Cyclone Eye Detection

Eye formation is often associated with rapid intensification (RI) of tropical cyclones (TCs), so the presence or recent appearance of an eye in satellite imagery is valuable information for forecasters. Currently, eye-detection is performed manually and thus a very large volume of available satellite imagery is underutilized. Data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Imager Channel 5 (11.5 µm) on board the Joint Polar Satellite System (JPSS) Suomi National Polar-Orbiting Partnership (S-NPP) satellite have been used together with Geosynchronous Operational Environmental Satellite (GOES) Imager Channel 4 (10.7 µm) to develop a preliminary version of the automated objective eye-detection algorithm. 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. Input to the algorithm includes infrared (IR) imagery in a 320 km by 320 km region around each storm. Additionally, the basic storm information that is routinely available to forecasters, including the maximum wind speed, latitude and longitude of the storm center, and the storm motion vector are used as an input. The input sample included 4109 GOES cases at 6 hr intervals for Atlantic TCs from 1995 to 2012 and available matching VIIRS cases. Principal Component Analysis (PCA) 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 TC had an eye, obtained from Dvorak intensity fixes made by National Hurricane Center (NHC) Tropical Analysis and Forecast Branch (TAFB).

Results showed that the LDA and QDA algorithms successfully classified about 90% of the test cases. The QDA version performed a slightly better than LDA using a Peirce Skill Score, which measures the ability to correctly classify cases. The LDA version performed slightly better using the Brier Skill Score, which measures the utility of the class probabilities. Work continues on improving the algorithm by including VIIRS data as well as other inputs. The high success rate indicates that the algorithm can reliably reproduce what forecasters are currently doing subjectively. This algorithm can be used as a standalone application that will provide forecasters with an objective way to determine if a TC has or is becoming more likely to form an eye. The algorithm output could be also modified to be used as input to other algorithms, such as the NHC operational Rapid Intensification Index (RII).

Disclaimer: The views, opinions, and findings contained in this article are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration (NOAA) or U.S. Government position, policy, or decision.