TC Intensity Estimation: SATellite TC Intensity Estimation: SATellite CONsensus (SATCON)CONsensus (SATCON)

Derrick Herndon, Chris Velden, Tony Wimmers, Tim Olander

International Workshop on Tropical Cyclone Analysis and IntensityInternational Workshop on Tropical Cyclone Analysis and IntensityHonolulu, HI 13-16 April 2011Honolulu, HI 13-16 April 2011

University of Wisconsin - Madison

Cooperative Institute for Meteorological Satellite Studies

Jeff HawkinsNaval Research Laboratory Monterey, CA

The support of the research sponsors, the Oceanographer of the Navy through the program office at the PEO C4I&Space/PMW-120, under program element PE-0603207N and the Office of Naval Research under program element PE-0602435N is gratefully acknowledged.

Motivation

• 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 assess the current intensity by combining the confident aspects of the individual objective estimates into a single “best” estimate

TCS-08 result showing the spread of 5 expert Dvorak analysts for TY Sinlaku (15W) who were blind to recon obs

Recon vs Dvorak for 15W (MSW)

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Even taking an average of five expert Dvorak intensity

estimates can lead to significant differences.Recon vs Dvorak for 15W (MSW)

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Recon B1 B5 B3 B4 B2 Blind Mean

SATCON MembersADT (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

ShearCurved Band Uniform

SATCON Members: CIMSS AMSU

Channel 6

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AMSU Tb Anomaly vertical cross section for Katrina 2005

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AMSU Channel 8 Tb Anomaly MagnitudeTC

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SATCON Members: CIRA AMSU

IR image from NRL TC Page

Similar to CIMSS approach, however the AMSU-A Tb are used to retrieve a temperature profile at 23 pressure levels. Estimates of Vmax are then determined from the thermal warm core structure and non-linear balance equation.

SATCON Strategy

The strengths and weaknesses of each objective 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 superior

intensity estimate

Another important component of SATCON is cross-method information sharing

• Utilize relationships that exist between the output parameters of the individual member algorithms

• Unique information from each of these parameters can be shared between the algorithms to improve the performance of the individual members

• Situational corrections can be made to each algorithm’s intensity output, then the member weights re-derived to produce an improved consensus estimate

Adjust AMSU pressure if

needed

SATCON cross-method information sharing

ADT Estimate of Eye Size

Compare to AMSU-A FOV resolution

In clear eye scenes, IR can be used to estimate eye size

CIMSS AMSU uses eye size information to correctresolution sub-sampling

Example: ADT to CIMSS AMSU

Example: Objective estimates of eye size from CIMSS ‘ARCHER’ method (using MW imagery)

Currently, CIMSS AMSU method 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

SATCON cross-method information sharing

ARCHER eye = 33 km Information can be input to CIMSS AMSU method

SATCON cross-method information sharing

CIRA MSW Error Compared to AMSU-B Tb Near TC Center

R2 = 0.1791

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AMSU-B 89 Ghz Tb For FOV Used for Estimate

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CIMSS AMSU position with bracketing correction can be applied to correct CIRA AMSU estimate

SATCON Weighting Scheme

Example: ADT Scene type vs. performance

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)

RMSE 14 knots RMSE 12 knots RMSE 18 knots

CDO EYE SHEAR

CIRA RMSE 12 knotsCIMSS RMSE 10 knots

CIRA RMSE 15 knotsCIMSS RMSE 12 knots

CIRA RMSE 18 knotsCIMSS RMSE 15 knots

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Example: AMSU scan geometry vs. performance

SATCON Weighting Scheme

• Use ARCHER scores to determine how much motion component to add. A greater component is added to storms with strong well developed eyewalls, and less component for storms with poor inner core structure

• Use the statistically superior SATCON MSLP estimate to estimate MSW using TC structure information. Take an average of this P-W derived MSW estimate and the SATCON MSW weighted estimate to get final MSW

• ARCHER TC eye size is used to adjust MSW upward for for small eyes and downward for large eyes

Additional SATCON Adjustments

SATCON Examples

ADT determines scene is an EYE scene

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 %

B

SATCON Examples

ADT determines situation is a SHEAR scene

CIMSS AMSU indicates no sub-sampling present

CIRA AMSU: little/no sub-sampling error due to position offset from FOV center

SATCON Weighting:ADT = 18 % CIMSS AMSU =41 % CIRA AMSU = 41 %

Center of TS Chris

1999-2010 SATCON Performance (Vmax)

Independent sample. Values in knots. Validation is Best Track Vmax coincident with aircraft recon +/- 3 hours from estimate time. Negative bias = method was too weak.

Important Note: “Dvorak” performance values are derived from a consensus of available estimates (the consensus is usually superior to individual OFC estimates

N = 289CIMSS

AMSU

CIMSS

ADT

CIRA

AMSUSATCON Dvorak

BIAS 0.6 - 2.0 -7.1 - 0.5 - 1.9

AVG ERROR

8.7 10.5 11.7 7.1 7.7

RMSE 11.1 14.3 15.6 8.9 9.9

Cases: ATL = 263 EPAC = 8 WPAC = 18

1999-2010 SATCON Performance (Vmax) WPAC

Independent sample. Values in knots. Validation is Best Track Vmax coincident with aircraft recon +/- 3 hours from estimate time. Negative bias = method was too weak.

Important Note: “Dvorak” performance values are derived from a consensus of available estimates (the consensus is usually superior to individual OFC estimates

N = 18CIMSS

AMSU

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AMSUSATCON Dvorak

BIAS - 3.7 - 2.0 -7.2 - 1.5 - 4.9

AVG ERROR

7.37 10.5 14.0 8.4 10.8

RMSE 9.0 14.3 17.0 9.9 12.5

1999-2010 SATCON Performance (MSLP)

Independent sample. Values in millibars. Validation is coincident with aircraft recon +/- 3 hours from estimate time. Negative bias = method was too weak.

Important Note: “Dvorak” performance values are derived from a consensus of available estimates (the consensus is usually superior to individual OFC estimates

N = 289CIMSS

AMSU

CIMSS

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AMSUSATCON Dvorak

BIAS 0.3 - 2.5 -2.6 0.1 -2.0

AVG ERROR

5.4 8.9 6.8 4.6 6.8

RMSE 7.3 12.5 10.4 6.5 9.3

Cases: ALT = 263 EPAC = 8 WPAC = 18

1999-2010 SATCON PerformanceComparison to simple ave/consensus (un-weighted)

Independent sample. Validation is coincident with aircraft recon +/- 3 hours from estimate time. Negative bias = method was too weak.

N = 289SIMPLE

MSWSATCON

MSWSIMPLE MSLP

SATCON MSLP

BIAS - 3.0 - 0.3 -1.6 0.1

AVG ERROR

8.1 7.6 5.0 4.6

RMSE 10.5 9.6 7.5 6.5

Cases: ALT = 263 EPAC = 8 WPAC = 18

SATCON and Dvorak MSW Stats for 1999-2010 Independent SATCON SampleBinned by Category

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DVK ABS ERR SATCON RMSE DVK RMSE

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satcon new recon dvk satcon_old CIMSS AMSU ADT CIRA AMSU

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SATCON Web Site

http://cimss.ssec.wisc.edu/tropic2/real-time/satcon

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., 118-119

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.

References

Analysis of Sat-Based TC Intensity Analysis of Sat-Based TC Intensity Estimation in the WNP During TCS-08Estimation in the WNP During TCS-08

N=14‘Blind’

DvorakConsensus

OperDvorakConsensus

(w/Koba)

ADTw/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

Positive Bias indicates method estimates are too strong

Comparison of All Satellite-based Estimates – Vmax (Kts)

Analysis of Sat-Based TC Intensity Analysis of Sat-Based TC Intensity Estimation in the WNP During TCS-08Estimation in the WNP During TCS-08

N=14‘Blind’

DvorakConsensus

OperDvorakConsensus

(w/Koba)

ADTw/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

Positive Bias indicates method estimates are too strong. 2mem SATCON RMSE= 4.7Blind and Oper Dvorak conversion is Knaff/Zehr

Comparison of All Satellite-based Estimates – MSLP (mb)