| Paper: | WP-P4.7 |
| Session: | Image and Video Storage and Retrieval IV |
| Time: | Wednesday, September 19, 14:30 - 17:10 |
| Presentation: |
Poster
|
| Title: |
SKELETON-BASED TORNADO HOOK ECHO DETECTION |
| Authors: |
Hongkai Wang; University of Western Ontario | | |
| | Robert Mercer; University of Western Ontario | | |
| | John Barron; University of Western Ontario | | |
| | Paul Joe; Meteorological Service of Canada | | |
| Abstract: |
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. |
