Mark DeMaria , NOAA/NCEP/NHC Robert DeMaria, CIRA/CSU NCAR-CSU Tropical Cyclone Workshop

1. Application of the Computer Vision Hough Transform for Automated Tropical Cyclone Center-Fixing from Satellite Data Mark DeMaria, NOAA/NCEP/NHC Robert DeMaria, CIRA/CSU NCAR-CSU Tropical Cyclone Workshop January 8, 2014 Boulder, CO

2. Outline • Tropical cyclone center fixing from satellites • The circular Hough transform • Application to tropical cyclones • Preliminary results • Improvement through multi-spectral analysis

3. Aircraft Data Availability • Only west Atlantic and around Hawaii have routine aircraft center fixes • Satellite data used subjectively to find centers across the globe • Improvements to accuracy in real-time highly desirable

4. Center location (fixing) • Center Location = surface center • Center of circulation • Usually close to the lowest sea-level pressure • Visible and IR methods – Dvorak • Eye • Distinct and inferred center with shear pattern and low-level clouds • Spiral bands and curved cloud lines • Wedge method • Using animation • Low-level cloud motions • Deep layer cloud motions • Ignore cirrus layer cloud motions • Mid-level centers tilted from surface center • Using microwave images • Thick cirrus clouds in visible and IR images obscure features below, used for center location • Thick cirrus clouds in microwave images are more transparent, and the microwave images may often provide better views of features, for improved center locations • Using 3.9-micrometer images at night • New Day-Night band from VIIRS

5. Center Location • Nearly all methods subjective • Exception is CIMSS ARCHER method that fits spiral patterns to microwave imagery from LEO satellites • Many more geostationary images than center fixes • Automated methods would allow use of high temporal resolution of IR and visible data

6. Circular Hough Transform • Hough transform developed for computer vision applications to detect features • Originally developed for lines and edges • Later generalized to shapes • Circular Hough transform applied to accurately detecting centers of breeder reactors • Application to finding tropical cyclone centers

7. Circular Hough Transformfor case with known radius

8. Generalization for Unknown Radius • Estimate range of possible radii • Perform CHT for range of test radii • Calculate 2-D accumulation matrix for each test radius • Scale accumulation matrices by radius • Average scaled matrices • Center is point with the most votes or some weighted average around the maximum • Can be generalized to multiple circles • Automated coin identification

9. Application to TC Center Fixing from IR Data • Apply cold threshold to IR image to isolate cold clouds • Apply edge detection method • Take Laplacian of IR brightness temperature • Apply threshold to |Laplacian| • Perform CHT for a range of radii • 10 to 300 km in 1 km intervals • Use combined accumulation matrix to find the center

10. Hurricane Katrina Example

11. Accumulation Matrix for Radii from 45 to 112 Pixels

12. Tropical Cyclone 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 • IR images every 6 hr for lifecycle of each storm • 135 images

13. 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

14. Results • Mean CHT error: 91 km • Storms with eyes: 54 km • Bias X: 6 km • Bias Y: 8.5 km • Bias Explained by Parallax

15. Results by Storm:

16. Primary Error Sources • Sheared storms • Circulation center displaced from cold cloud shield • Storms with eyes • Radii on scale of outer cloud shield gets more “votes” than radii on the eye scale

17. Tropical Storm Erika X

18. Eye Center Out-Voted

19. Next Steps • Accumulation matrices may be useful for eye detection • Multiple solutions for centers • Use CHT from IR data as first guess to visible algorithm • Combine with other information • Shear vector • Microwave imagery, day-night band • Time continuity of displacement