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Enhancing Tropical Cyclone Center Detection Using Circular Hough Transform Techniques

This study explores the utilization of the Circular Hough Transform (CHT) for accurately locating tropical cyclone centers in infrared imagery. Motivated by the limitations of existing methods, particularly the reliance on subjective satellite data, the proposed method aims to automate the estimation process. By analyzing infrared images every 15 minutes and comparing CHT results to National Hurricane Center data, this research offers insights into improving cyclone tracking accuracy. The findings reveal that while CHT did not enhance real-time center fixes overall, it shows promise when distinct cyclone eyes are present.

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Enhancing Tropical Cyclone Center Detection Using Circular Hough Transform Techniques

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  1. Finding Tropical Cyclone Centers with the Circular Hough Transform Robert DeMaria

  2. Overview • Motivation • Objective • Data • Center-Fixing Method • Evaluation Method • Results • Conclusion

  3. Motivation • Only west Atlantic has routine hurricane hunter aircraft for finding storm centers • Satellite data used subjectively to find centers across the globe • Improvements to accuracy in real-time highly desirable sos.noaa.gov/Education/tracking.html

  4. Motivation • Geostationary satellites produce Infrared(IR) every 15 Minutes • Forecast produced every 6 hours • Due to time constraints, most of these images are unused • Automatic method for estimating tropical cyclone location is highly desirable

  5. Objective • Tropical cyclones are roughly circular • Use Circular Hough Transform (CHT) to produce estimate for tropical cyclone location by finding circles in IR imagery • Compare accuracy to National Hurricane Center real-time center-fix

  6. Infrared Data • 2D Image of Temperature • Created every 15 minutes

  7. Storm Track Data • A-Deck: Real-time estimate of position, velocity, wind speed, etc. • Updated every 6 hours • Best-Track: Improved a-deck data available after end of season

  8. Center-Fixing • Find a-deck position • Given the time an IR image was created, look up most recent a-deck information and extrapolate position to IR image time • Subset of IR image used • Center image on a-deck position • Image reduced to area around storm/area around eye • Background removed from cloud shield using temperature threshold

  9. Center Fixing Cont. • IR after subsect & thresholding:

  10. Center-Fixing Cont. • Laplacian of image performed to find edge pixels

  11. Center-Fixing Cont. • Circular Hough Transform performed for a range of radii on image • Gaussian fit performed on accumulation space to produce center location

  12. Circular Hough Transform

  13. Evaluation Method • For each time in best-track, find most recent IR image • Estimate if eye is present in image • If it is then perform center-fix searching for radii roughly the size of an eye • If not, perform center-fix searching for radii roughly the size of the entire storm • Error calculated as CHT center-fix distance from best-track location • Compare error to that of the a-deck position

  14. Eye Detection Examples Katrina 08/25/18 2005 Ericka 09/02/18 2009 Sandy 10/19/18 2012 No Eye Cases Eye Cases Katrina 08/29/00 2005 Earl 09/02/06 2010 Charley 08/13/18 2004

  15. Hurricane Cases • Charley 2004 – Very small but intense hurricane • Katrina 2005 – Classic large, intense hurricane • Ericka 2009 – Very disorganized weak tropical cyclone, did not make it to hurricane strength • Earl 2010 – Strong hurricane in higher latitudes • Sandy 2012 – Unusually large but only moderate strength, non-classical hurricane structure

  16. Results • Mean a-deck error: 42 km • Mean CHT error: 91 km • Bias X: 6 km • Bias Y: 8.5 km • Bias Explained by Parallax

  17. Results by Storm:

  18. Variability

  19. Cyclone Eyes • Strong Circular Eye Greatly Improves Accuracy • Eye Mean Error: 54 km • No Eye Mean Error: 127 km • Strong circular eyes are fairly rare

  20. Cloud Shield Shape Usually Not Ideal

  21. Conclusions: • Did not improve real-time center fix • Rotational center may not be in center of cloud features: CHT may not be well suited to large-scale images • CHT may be useful when an eye is present

  22. Future Work • Use time-series information to improve • Combine with information about vertical shear • Improve eye estimation technique

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