1. TC Intensity Estimation: SATellite CONsensus (SATCON) Derrick Herndon, Chris Velden, Tony Wimmers, Tim Olander International Workshop on Tropical Cyclone Analysis and Intensity Honolulu, HI 13-16 April 2011 University of Wisconsin - Madison Cooperative Institute for Meteorological Satellite Studies Jeff Hawkins Naval 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.

2. 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

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

4. Even taking an average of five expert Dvorak intensity estimates can lead to significant differences.

5. 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 EyePinhole EyeLarge Eye ShearCurved Band Uniform

6. SATCON Members: CIMSS AMSU Channel 6 Channel 7 Channel 8 350 mb 250 mb 150 mb AMSU Tb Anomaly vertical cross section for Katrina 2005 70 Knots 125 knots 55 Knots AMSU Channel 8 Tb Anomaly Magnitude TC Pressure Anomaly Magnitude

7. SATCON Members: CIRA AMSU IR image from NRL TC Page Similar to CIMSS approach, however the AMSU-A T b 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.

8. 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

9. 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

10. 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 correct resolution sub-sampling Example: ADT to CIMSS AMSU

11. 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

12. SATCON cross-method information sharing CIMSS AMSU position with bracketing correction can be applied to correct CIRA AMSU estimate

13. 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 knotsRMSE 12 knotsRMSE 18 knots CDO EYE SHEAR

14. CIRA RMSE 12 knots CIMSS RMSE 10 knots CIRA RMSE 15 knots CIMSS RMSE 12 knots CIRA RMSE 18 knots CIMSS RMSE 15 knots ABC Example: AMSU scan geometry vs. performance SATCON Weighting Scheme

15. 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

16. 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

17. 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

18. 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 = 289 CIMSS AMSU CIMSS ADT CIRA AMSU SATCONDvorak BIAS0.6- 2.0-7.1- 0.5- 1.9 AVG ERROR 8.710.511.7 7.1 7.7 RMSE11.114.315.68.99.9 Cases: ATL = 263 EPAC = 8 WPAC = 18

19. 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 = 18 CIMSS AMSU CIMSS ADT CIRA AMSU SATCONDvorak BIAS- 3.7- 2.0-7.2- 1.5- 4.9 AVG ERROR 7.3710.514.0 8.4 10.8 RMSE9.014.317.09.912.5

20. 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 = 289 CIMSS AMSU CIMSS ADT CIRA AMSU SATCONDvorak BIAS0.3- 2.5-2.60.1-2.0 AVG ERROR 5.4 8.9 6.84.6 6.8 RMSE7.312.510.46.5 9.3 Cases: ALT = 263 EPAC = 8 WPAC = 18

21. 1999-2010 SATCON Performance Comparison 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 = 289 SIMPLE MSW SATCON MSW SIMPLE MSLP SATCON MSLP BIAS- 3.0- 0.3-1.60.1 AVG ERROR 8.1 7.6 5.04.6 RMSE10.59.67.56.5 Cases: ALT = 263 EPAC = 8 WPAC = 18

29. SATCON Web Site http://cimss.ssec.wisc.edu/tropic2/real-time/satcon

30. 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

31. Analysis of Sat-Based TC Intensity Estimation in the WNP During TCS-08 N=14 ‘ Blind ’ Dvorak Consensus Oper Dvorak Consensus (w/Koba) ADT w/MW CIMSS AMSU SATCON Bias3.62.0-3.62.9-0.1 Abs Error 9.312.013.68.69.0 RMSE11.914.917.410.110.6 Positive Bias indicates method estimates are too strong Comparison of All Satellite-based Estimates – Vmax (Kts)

32. Analysis of Sat-Based TC Intensity Estimation in the WNP During TCS-08 N=14 ‘ Blind ’ Dvorak Consensus Oper Dvorak Consensus (w/Koba) ADT w/MW CIMSS AMSU SATCON Bias0.70.1-1.9-1.3 Abs Error 5.27.510.74.96.0 RMSE6.68.912.86.37.2 Positive Bias indicates method estimates are too strong. 2mem SATCON RMSE= 4.7 Blind and Oper Dvorak conversion is Knaff/Zehr Comparison of All Satellite-based Estimates – MSLP (mb)