1. TC Intensity Estimation: SATellite CONsensus (SATCON)
Derrick Herndon and Chris Velden
University of Wisconsin - Madison
Cooperative Institute for Meteorological Satellite Studies
Interdepartmental Hurricane Conference
Savannah, GA
01-04 March 2010
Research supported by
the ONR Marine Meteorology and Atmospheric Effects Program

2. Motivation Importance of getting current TC intensity right
Intensification trends > forecasts
Predictor for statistical forecast models
Climatology (Basin Best Tracks)
Initial conditions for numerical models
Contemporary methods to estimate TC intensity can vary by more than 40 knots
Several objective TC intensity methods exist, but the goal of SATCON is to assist forecasters in assessing current intensity by combining the confident aspects of the individual objective estimates into a single “best” estimate

3. Motivation

4. Motivation

5. ADT (Advanced Dvorak Technique)
SATCON Members
ADT (Advanced Dvorak Technique)
Uses IR imagery to objectively assess storm cloud patterns and
structure to infer intensity
Latest version uses information from MW to make adjustments
Clear Eye
Pinhole Eye
Large Eye
Shear
Curved Band
Uniform

6. SATCON Members: CIMSS AMSU
Channel 8
150 mb
Channel 7
250 mb
55 Knots
Channel 6
350 mb
AMSU Tb Anomaly vertical cross section for Katrina 2005
Scattering corrected Tb cross-section for a typical strong hurricane. Panels to the left are for Hurricane Rita.
70 Knots
TC Pressure Anomaly Magnitude
125 knots
AMSU Channel 8 Tb Anomaly Magnitude

7. SATCON Members: CIRA AMSU
AMSU-A Tb are used to produce a statistical temperature retrieval at 23 pressure levels. Estimates of Vmax are then determined from the thermal warm core structure.
IR image from NRL TC Page

8. SATCON
The strengths and weaknesses of each method are assessed based on statistical analysis, and that knowledge is used to assign weights to each method in the consensus algorithm based on situational performance to arrive at a single intensity estimate

9. Another component of SATCON is cross-method information sharing
What relationships might exist between the parameters of
the member algorithms?
Can some of the unique information from these
parameters be shared between the algorithms to improve
the individual members?
Corrections can be made to improve the performance of
each algorithm, then the weights re-derived to produce an
improved weighted consensus
Each of the methods contain a number of parameters that are part of the algorithm or predictors used in the algorithm.

10. SATCON cross-method information sharing
ADT Estimate of Eye Size
Example: ADT to AMSU
In eye scenes, IR can be used to estimate eye size
CIMSS AMSU uses eye size information to correct
resolution sub-sampling
Compare to AMSU-A
FOV resolution
Adjust AMSU
pressure if
needed

11. Information Sharing
Example: Objective estimates of eye size from CIMSS ‘ARCHER’ method (using MW imagery)
Currently, AMSU uses IR-based eye size or values from op center if no eye in IR.
MW imagery (MI) often depicts eyes when IR/ADT cannot
ARCHER method (Wimmers and Velden, 2010) uses objective analysis of MI and accounts for eyewall slope
Notes:
Verification Process Status and Issues: (How well does the product/software fulfill its functional requirement or intended use – AMOP VTR Status and Issues)
Validation Process Status and Issues: (How well does the product/system/hardware “as built” fulfill its intended Operational performance goal - Operational Test Status and Issues)
Measures of Performance: (principal metrics or test criteria used or plan to use in the V&V Process)
ARCHER eye = 33 km
Information can be input to AMSU
method

12. SATCON Weighting Scheme
Weights are based on situational analysis for each member
Separate weights for MSW and MSLP estimates
Example criteria: scene type (ADT)
scan geometry/sub-sampling (AMSU)
Example: ADT Scene type vs. performance
CDO
SHEAR
EYE
RMSE 14 knots
RMSE 12 knots
RMSE 18 knots

13. Examples ADT determines scene is an EYE
CIMSS AMSU: Good, near nadir pass. Eye is well resolved by AMSU resolution
CIRA is sub-sampled by FOV offset with TC center
SATCON Weighting:
ADT = 28 % CIMSS AMSU =47 % CIRA AMSU = 25 %

14. Examples ADT determines scene is a SHEAR scene
CIMSS AMSU indicates no sub-sampling present
CIRA AMSU: little sub-sampling due to position offset from FOV center
Center of TS Chris
SATCON Weighting:
ADT = 18 % CIMSS AMSU =41 % CIRA AMSU = 41 %

15. 1999-2009 performance stats (Vmax) - Atlantic
CIMSS
AMSU
ADT
CIRA AMSU
SATCON
BIAS
4.0
- 5.0
-8.6
-1.0
AVG ERROR
9.1
11.5
12.3
7.2
RMSE
10.2
13.5
14.6
8.3
Dependent sample. Values in knots. Validation is best track Vmax coincident with
aircraft recon +/- 3 hours from estimate time. Negative bias = method was too weak.

16. 1999-2009 SATCON compared to a simple straight consensus (Atlantic)
MSLP
SIMPLE
SATCON Vmax
Vmax
BIAS
0.3
-2.5
-1.0
- 4.0
AVG ERROR
5.2
5.7
7.2
8.1
RMSE
6.4
7.7
8.3
9.3
Dependent sample. Vmax validation in knots vs. BT. MSLP validation in hPa vs. recon.
Negative bias = method was too weak. SIMPLE is simple average of the 3 members

17. 1999-2009 SATCON compared to operational Dvorak (Atlantic)
MSLP
Dvorak
SATCON Vmax
Vmax
BIAS
0.3
-2.7
-1.0
-3.0
AVG ERROR
5.2
7.6
7.2
8.1
RMSE
6.4
9.1
8.3
9.0
AFWA was not included since they no longer do fixes.
Dependent sample. Vmax validation in knots vs. BT. MSLP validation in hPa vs. recon.
Neg. bias = method was too weak. Dvorak is average of TAFB and SAB estimates

18. SATCON Web Site

19. Summary
A weighted consensus of three objective satellite-based methods to estimate TC intensity (SATCON) shows skill compared to conventional Dvorak-based methods.
Independent trials during 2008 and 2009 in the Atlantic support the dependent sample results.
SATCON also showed skill vs. other methods in the WestPac during TPARC/TCS-08 in 2008 (small sample of validated cases).
SATCON is run experimentally on all global TCs in real-time, with the information available on the CIMSS TC web site.

20. References
Brueske K. and C. Velden 2003: Satellite-Based Tropical Cyclone Intensity Estimation Using the
NOAA-KLM Series Advanced Microwave Sounding Unit (AMSU). Monthly Weather Review
Volume 131, Issue 4 (April 2003) pp. 687–697
Demuth J. and M. DeMaria, 2004: Evaluation of Advanced Microwave Sounding Unit Tropical-Cyclone Intensity and Size Estimation Algorithms. Journal of Applied Meteorology Volume 43, Issue 2 (February 2004) pp. 282–296
Herndon D. and C. Velden, 2004: Upgrades to the UW-CIMSS AMSU-based TC intensity algorithm.
Preprints, 26th Conference on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc.,
Olander T. and C. Velden 2007: The Advanced Dvorak Technique: Continued Development of an
Objective Scheme to Estimate Tropical Cyclone Intensity Using Geostationary Infrared Satellite Imagery. Wea. and Forecasting Volume 22, Issue 2 (April 2007) pp. 287–298
Velden C. et al., 2006: The Dvorak Tropical Cyclone Intensity Estimation Technique: A Satellite-Based Method that Has Endured for over 30 Years. Bulletin of the American Meteorological Society Volume 87, Issue 9 (September 2006) pp. 1195–1210
Wimmers, A., and C. Velden, 2010: Objectively determining the rotational center of tropical cyclones in passive microwave satellite imagery. Submitted to JAMC.

24. Analysis of Sat-Based TC Intensity Estimation in the WNP During TCS-08
Comparison of All Satellite-based Estimates – Vmax (Kts)
N=14
‘Blind’
Dvorak
Consensus
Oper
(w/Koba)
ADT
w/MW
CIMSSAMSU
SATCON
Bias
3.6
2.0
-3.6
2.9
-0.1
Abs Error
9.3
12.0
13.6
8.6
9.0
RMSE
11.9
14.9
17.4
10.1
10.6
Notes:
Verification Process Status and Issues: (How well does the product/software fulfill its functional requirement or intended use – AMOP VTR Status and Issues)
Validation Process Status and Issues: (How well does the product/system/hardware “as built” fulfill its intended Operational performance goal - Operational Test Status and Issues)
Measures of Performance: (principal metrics or test criteria used or plan to use in the V&V Process)
Positive Bias indicates method estimates are too strong

25. Analysis of Sat-Based TC Intensity Estimation in the WNP During TCS-08
Comparison of All Satellite-based Estimates – MSLP (mb)
N=14
‘Blind’
Dvorak
Consensus
Oper
(w/Koba)
ADT
w/MW
CIMSSAMSU
SATCON
Bias
0.7
0.1
-1.0
-1.9
-1.3
Abs Error
5.2
7.5
10.7
4.9
6.0
RMSE
6.6
8.9
12.8
6.3
7.2
Notes:
Verification Process Status and Issues: (How well does the product/software fulfill its functional requirement or intended use – AMOP VTR Status and Issues)
Validation Process Status and Issues: (How well does the product/system/hardware “as built” fulfill its intended Operational performance goal - Operational Test Status and Issues)
Measures of Performance: (principal metrics or test criteria used or plan to use in the V&V Process)
Positive Bias indicates method estimates are too strong. 2mem SATCON RMSE= 4.7
Blind and Oper Dvorak conversion is Knaff/Zehr