Skeleton-Based Tornado Hook Echo Detection

We propose and evaluate a method to identify tornadoes automatically in Doppler radar imagery by detecting hook echoes, which are important signatures of tornadoes, in Doppler radar precipitation density data. Our method uses a skeleton to represent 2D storm shapes. To characterize hook echoes, we propose four shape features of skeletons: curvature, curve orientation, thickness variation, boundary proximity, and two shape properties of tornadoes: southwest localization and the ratio of storm size to model hook echoe size. To evaluate the hook echo detection algorithm, the hook echoes detected in several radar datasets by the algorithm are compared to those proposed by an expert. The effectiveness of the algorithm is quantified using a Critical Success Index (CSI) analysis.

• Type: Conference
• Year: 2007
• Where: ICIP
• Authors: Hongkai Wang, Robert E. Mercer, John L. Barron, Paul Joe
• Keywords: Doppler Radar Imagery, Doppler Radar Precipitation, Hook Echo Detection, Hook Echoe Size, Hook Echoes, ICIP 2007, Image Processing