Objective, Satellite-Based Tropical
Cyclone Center-Fixing
Tony Wimmers and Chris Velden
University of Wisconsin - Cooperative Institute for
Meteorological Satellite Studies (CIMSS)
Sponsored by the Oceanographer of the Navy through the PEO C4I PMW-120 program office
and the Naval Research Laboratory
Principal Goal
• Create
an objective,
automated algorithm to
locate the rotational
centers of TCs evident in
microwave, IR, or visible
images
• Should be resilient to
identifying “false eyes” (moats)
but robust enough to detect
partial eyes and convergent
cloud spiral signatures
• Should rely only loosely on a
first-guess (forecast) position
estimate
• Should provide position
estimate uncertainty indices
“First guess”
Center fix
ARCHER: Automated Rotational Center Hurricane Eye Retrieval
Wimmers, A. J., and C. S. Velden, 2010: Objectively determining the
rotational center of tropical cyclones in passive microwave satellite imagery.
Journal of App. Meteor and Clim., e-View doi: 10.1175/2010JAMC2490.1
ARCHER research/development status
• Returns a numerical score that relates to an estimated center position, a
2-D score field that corresponds to the likelihood of having found the correct
position, and eyewall diameter if an eyewall is identified
• Currently operates on the following:
MW: 85GHz
MW: 37GHz
Geo IR
Geo Vis
Frequency
~ 2-6 hrs
~ 12-24 hrs
(AMSRE)
~30 min
~30 min,
daytime
Advantages
No cirrus
obstruction
No cirrus
obstruction
Continuous
Higher Res.
Status
Mature
Experimental
Near Maturity Experimental
Basic ARCHER Approach
1) Produce a
2D field
(contoured)
that expresses
how well a
location
registers as the
center of the
large-scale
spiral pattern
2) Produce a
separate 2D
field that rates
how well a
location is
centered inside
a circular ring
of convection
“Spiral Score”
3) Combine the two 2D
fields as a weighted sum
into a single score field
“Ring Score”
“Combined Score”
Spiral-fitting concept
Circulation is indicated by spirally-oriented bands
caused by convergent flow and horizontal shearing
Spiral-fitting concept
Algorithm: determine the alignment of the
image gradients with a spiral vector field
“Spiral Score” component
spiral unit vector
field centered on (,)
(lon, lat)
gradient of the
log of the image
• Calculated at every sample point (dot) on
the TC image, then interpolated to the
resolution of the image (contour plot)
• High values occur where the vector field
lines up with the image gradients
“Ring score” component
• The scores are proportional to the
average dot product of the image
gradient and a radial unit vector on a ring
• Ring scores are assigned to the center
of a ring of points inside an eyewall
Combined (final) ARCHER score
Center fix
“First guess”
Best track
CS(f,q ) = wSSSS(f,q ) + RS(f,q)
• wSS is the “relative weight” (next slide)
• Lat/Lon of max(CS) point is final
ARCHER estimate of TC center fix
Additional Procedures
• Images are pre-processed to compensate for any parallax shift, and
then resampled to a 0.05 rectangular grid
• We introduce a small “penalty” for positions that stray far from the
first guess, to help mitigate gross errors
• If the Combined Score does not exceed a fixed threshold value, then
the algorithm can default back to the first guess (OFC forecast) position
• The relative weight and the combined score thresholds are dependent
on sensor type, and for MW are calibrated for three modes that each
behave differently under the ARCHER scheme: Tropical Storm, Cat 1,
and Cat 2-5 strength TCs
Example: Unresolved eye (Dennis 2005)
1) Wider domain
Example: Unresolved eye (Dennis 2005)
2) Zoomed view
Example: Unresolved eye (Dennis 2005)
3) With best track position
Example: Unresolved eye (Dennis 2005)
4) With simulated forecast position (first guess)
Example: Unresolved eye (Dennis 2005)
5) Spiral score
Example: Unresolved eye (Dennis 2005)
6) Ring score
Example: Unresolved eye (Dennis 2005)
7) Combined score
Example: Unresolved eye (Dennis 2005)
“First guess”
Center fix
Best track
8) Compare to best track position
Example: Evolving eyewall (Chaba 2010)
Center-fix
1. Complete eyewall
Example: Evolving eyewall
Center-fix
2. Shearing leads to asymmetric eyewall pattern
Example: Evolving eyewall
Center-fix
3. Eyewall only evident on the western side
Example: Evolving eyewall
Center-fix
4. Sub-pixel core and banding only on the west
Example: Evolving eyewall
Center-fix
5. Sub-pixel eye and a developing secondary eyewall
Example: Evolving eyewall
Center-fix
6. Completed eyewall replacement cycle
Validation: 2005 season, North Atlantic
• Independent
from calibration sample, 85GHz only
• 40% tropical storm, 20% Cat 1, 40% Cat 2-5
• Simulated forecast position errors of 0.1, 0.4 and 0.7 for each image, weighted
to match the distribution of typical forecast fix errors in the NATL
• Validation uses cases that are < 3 hours from an aircraft position fix. NHC Best
Track is used as “truth.”
Validation Results, 85-92 GHz
Trop. Storm
Cat 1
Cat 2-5
All
Default rate
83%
15%
0.01%
37%
% worsened
0.8%
0.1%
0.0%
0.4%
RMSE (w/o
defaults)
0.17
0.060
0.070
0.064
RMSE (all)
0.22
0.10
0.070
0.15
Defaults are defined as those cases where ARCHER did not
exceed thresholds and the first guess forecast position is retained
• The RMS errors have been adjusted to account for the displacement
between the rotational center at the surface and at the image level
• Errors are ~5% larger in other basins due to greater OFC forecast
position error
• Errors improve with increasing Vmax (organization/structure)
ARCHER: Adapting to IR imagery
(Low organization)
Organization score = 0.91
(Medium organization)
Organization score = 1.55
(High organization)
Organization score = 2.60
• The ADT (next presentation by Olander) employs a forerunner version of ARCHER that operates
on IR data. It uses an ad hoc rules-based approach and is very resilient to big false-positives.
• We are developing a more effective ARCHER-IR method with better cal/val, good theoretical
connections to ARCHER-MW, and including uncertainty information.
• Latest scheme still under development
ARCHER-IR: Example output
• Graphical output:
Spiral grid
Combined grid
Center-fix
• Numerical output:
Forecast position (lon, lat): -61.10, 36.70
Center-fix position (lon, lat): -60.81, 36.47
Eye confidence (%) = 31
Error distribution parameter (alpha) = 6.39
Probability of error < 0.2° (%) = 36.5
Probability of error < 0.4° (%) = 72.4
Probability of error < 0.6° (%) = 90.0
Probability of error < 1.0° (%) = 98.8
Often ~95% for more organized TCs
Cal/Val results, All IR imagery
% of sample
Prob. error < 0.2°
Prob. error < 0.4°
Prob. error < 1.0°
No center-fix
26%
--
--
--
Lowest bin of
Organization Score
4%
8%
23%
66%
Second bin
23%
16%
41%
87%
Third bin
27%
41%
77%
99%
Highest bin
19%
67%
95%
100%
• The independent validation showed probabilities greater than or equal
to these benchmark certainty values
• It is not necessary to vary any ARCHER parameters with TC intensity.
(Not so with MW.)
ARCHER Microwave Intensity Application
• ARCHER MW intensity estimates are now integrated into the Advanced Dvorak
Technique algorithm (further details in next presentation on the ADT)
Method:
 Score is based on the robustness of the eyewall structure as depicted in 85GHz
brightness temperatures, and is objectively determined from two parameters:
1)
2)
“Difference component”: Measure the difference between the warmest pixel in the
eye and the warmest pixel on the eyewall ring (similar to the Dvorak Technique)
“Completeness component”: Add 15 points to the score if …
o
o
>85% of the points on the eyewall are <232K (“the absolute measure”) OR
>85% of the points on the eyewall are >20K colder than the warmest pixel in the eye
(“the relative measure)
 MW scores are generally between 0 (no structure, weak TC) and 100 (powerful TC)
Conversion to ADT Vmax: Scores >20  Vmax ≥ 72 kts, Scores >60  Vmax ≥ 90 kts
Example: Image for
TC 26W (2009)
[24 Nov 1052 UTC]
Difference component: 268K (eye pixel) - 251K (warmest
eyewall pixel) = 17 points
Completeness component: 93% of eyewall pixels >20K
colder than warmest pixel in eye  +15 points
Result: Score: 17+15 = 32  ADT Vmax = 72 kts (JTWC
estimate was 65 kts at this time)
ARCHER-ADT Methodology Summary
Application of the MW intensity scores to the ADT
1. If the MW score exceeds 20, the ADT current intensity value is reset
to 72kts IF the ADT has not yet identified an EYE scene type.
2. If the MW score exceeds 60, the ADT current intensity value is reset
to 90kts IF the ADT has not yet identified an EYE scene type.
3. If the ADT analyzes an EYE scene before the MW scores exceed the
20 threshold, then the MW is not used.
4. Currently, the MW scores are only employed in the ADT for TC
formative stages. The ARCHER method’s estimates are not robust
enough in other TC stages, as indicated by validation studies. An
approach being developed at NRL may be promising (later
presentation by Hawkins).
Summary
• A fully automated and objective TC center finding routine based solely on
satellite imagery has been developed, called ARCHER.
• The primary version that operates on 85GHz passive microwave imagery
has been fully tested and validated, with an overall RMS center-fix position
error of 0.15. In general, accuracy increases with TC intensity.
• A new version still under development for geostationary IR imagery yields a
center fix along with a certainty estimate, allowing more options for the enduser.
• ARCHER can also diagnose intensity estimates from 85GHz imagery in
developing TC cases. These estimates are now passed to the ADT.
• Other ARCHER applications not discussed here include TC visualization,
eyewall diameter retrieval, rapid intensification, 37GHz and Vis apps.
Final Remarks
Distribution
• A free, licensed version of ARCHER is available for distribution (Matlab code). The
user must have a source for the input satellite data and OFC track forecasts.
HURSAT
• ARCHER has been tested by NCDC on the IBTrACS HURSAT dataset (Knapp) and
yields good results, and will be further employed in the IBTrACS project.
Ongoing work
• Current research involves a cross-comparison of ARCHER accuracy from microwave,
IR and Visible imagery, and deriving an optimal approach to combine coincident
multispectral results into a single “best” estimate.
• Looking into the possibility of extending ARCHER to operate on 37GHz imagery.
Acknowledgments
• Special thanks to Jeff Hawkins of the Naval Research Laboratory, and the Office of
Naval Research for the support towards the development and continued advancement
of the ARCHER!