robert demaria. motivation objective data center-fixing method evaluation method results ...

Finding Tropical Cyclone Centers with the Circular

Hough TransformRobert DeMaria

Motivation Objective Data Center-Fixing Method Evaluation Method Results Conclusion

Overview

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

Motivation

sos.noaa.gov/Education/tracking.html

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

Motivation

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

Objective

2D Image of Temperature◦ Created every 15 minutes

Infrared 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

Storm Track Data

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

Center-Fixing

IR after subsect & thresholding:

Center Fixing Cont.

Laplacian of image performed to find edge pixels

Center-Fixing Cont.

Circular Hough Transform performed for a range of radii on image

Gaussian fit performed on accumulation space to produce center location

Center-Fixing Cont.

Circular Hough Transform

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

Evaluation Method

Eye Detection Examples

Katrina 08/29/00 2005 Earl 09/02/06 2010 Charley 08/13/18 2004

Katrina 08/25/18 2005 Ericka 09/02/18 2009 Sandy 10/19/18 2012

No Eye Cases

Eye 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

Hurricane Cases

Mean a-deck error: 42 km Mean CHT error: 91 km

Bias X: 6 km Bias Y: 8.5 km Bias Explained by Parallax

Results

Results by Storm:

Sandy: Earl: Erika Charley: Katrina:0

20

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60

80

100

120

140

160

180

Error (km)

Variability

Strong Circular Eye Greatly Improves Accuracy◦ Eye Mean Error: 54 km◦ No Eye Mean Error: 127 km◦ Strong circular eyes are fairly rare

Cyclone Eyes

Cloud Shield Shape Usually Not Ideal

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

Conclusions:

Use time-series information to improve Combine with information about vertical

shear Improve eye estimation technique

Future Work