robert demaria. motivation objective data center-fixing method evaluation method results ...
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Finding Tropical Cyclone Centers with the Circular
Hough TransformRobert DeMaria
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Motivation Objective Data Center-Fixing Method Evaluation Method Results Conclusion
Overview
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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
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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
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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
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2D Image of Temperature◦ Created every 15 minutes
Infrared Data
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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
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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
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IR after subsect & thresholding:
Center Fixing Cont.
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Laplacian of image performed to find edge pixels
Center-Fixing Cont.
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Circular Hough Transform performed for a range of radii on image
Gaussian fit performed on accumulation space to produce center location
Center-Fixing Cont.
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Circular Hough Transform
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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
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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
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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
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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
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Results by Storm:
Sandy: Earl: Erika Charley: Katrina:0
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80
100
120
140
160
180
Error (km)
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Variability
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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
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Cloud Shield Shape Usually Not Ideal
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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:
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Use time-series information to improve Combine with information about vertical
shear Improve eye estimation technique
Future Work