Finding Tropical Cyclone Centers with the Circular Hough Transform

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