1. 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

2. Principal Goal Create an objective, automated algorithm to locate the rotational centers of TCs evident in microwave, IR, or visible images 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 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 rely only loosely on a first-guess (forecast) position estimate Should provide position estimate uncertainty indices Should provide position estimate uncertainty indices First guess Center fix

3. 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 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: Currently operates on the following:

4. 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 Spiral ScoreSpiral Score 2) Produce a separate 2D field that rates how well a location is centered inside a circular ring of convection Ring ScoreRing Score 3) Combine the two 2D fields as a weighted sum into a single score field Combined ScoreCombined Score

5. Circulation is indicated by spirally-oriented bands caused by convergent flow and horizontal shearing Spiral-fitting concept

6. Algorithm: determine the alignment of the image gradients with a spiral vector field Spiral-fitting concept

7. Spiral Score componentSpiral Score component Calculated at every sample point (dot) on the TC image, then interpolated to the resolution of the image (contour plot) 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 High values occur where the vector field lines up with the image gradients (lon, lat) gradient of the log of the image spiral unit vector field centered on (, )

8. Ring score componentRing score component The scores are proportional to the average dot product of the image gradient and a radial unit vector on a ring 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 Ring scores are assigned to the center of a ring of points inside an eyewall

9. Combined (final) ARCHER score w SS is the relative weight (next slide) w SS is the relative weight (next slide) Lat/Lon of max(CS) point is final ARCHER estimate of TC center fix Lat/Lon of max(CS) point is final ARCHER estimate of TC center fix First guess Center fix Best track

10. Additional Procedures Images are pre-processed to compensate for any parallax shift, and then resampled to a 0.05 rectangular grid 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 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 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 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

11. Example: Unresolved eye (Dennis 2005) 1) Wider domain

12. Example: Unresolved eye (Dennis 2005) 2) Zoomed view

13. Example: Unresolved eye (Dennis 2005) 3) With best track position

14. Example: Unresolved eye (Dennis 2005) 4) With simulated forecast position (first guess)

15. Example: Unresolved eye (Dennis 2005) 5) Spiral score

16. Example: Unresolved eye (Dennis 2005) 6) Ring score

17. Example: Unresolved eye (Dennis 2005) 7) Combined score

18. Example: Unresolved eye (Dennis 2005) 8) Compare to best track position First guess Center fix Best track

19. Example: Evolving eyewall (Chaba 2010) 1. Complete eyewall Center-fix

20. Example: Evolving eyewall 2. Shearing leads to asymmetric eyewall pattern Center-fix

21. Example: Evolving eyewall 3. Eyewall only evident on the western side Center-fix

22. Example: Evolving eyewall 4. Sub-pixel core and banding only on the west Center-fix

23. Example: Evolving eyewall 5. Sub-pixel eye and a developing secondary eyewall Center-fix

24. Example: Evolving eyewall 6. Completed eyewall replacement cycle Center-fix

25. Validation: 2005 season, North Atlantic Independent from calibration sample, 85GHz only Independent from calibration sample, 85GHz only 40% tropical storm, 20% Cat 1, 40% Cat 2-5 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 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 uses cases that are < 3 hours from an aircraft position fix. NHC Best Track is used as truth.

26. Validation Results, 85-92 GHz The RMS errors have been adjusted to account for the displacement between the rotational center at the surface and at the image level 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 are ~5% larger in other basins due to greater OFC forecast position error Errors improve with increasing Vmax (organization/structure) Errors improve with increasing Vmax (organization/structure) Trop. StormCat 1Cat 2-5All Default rate83%15%0.01%37% % worsened0.8%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

27. ARCHER: Adapting to IR imagery The ADT (next presentation by Olander) employs a forerunner version of ARCHER that operates on IR data. It 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 Latest scheme still under development (Low organization) Organization score = 0.91 (Medium organization) Organization score = 1.55 (High organization) Organization score = 2.60

28. ARCHER-IR: Example output Numerical output: 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 Graphical output: Graphical output: Center-fix Spiral grid Combined grid Often ~95% for more organized TCs

29. Cal/Val results, All IR imagery The independent validation showed probabilities greater than or equal to these benchmark certainty values 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.) It is not necessary to vary any ARCHER parameters with TC intensity. (Not so with MW.) % of sampleProb. error < 0.2°Prob. error < 0.4°Prob. error < 1.0° No center-fix26%-- Lowest bin of Organization Score 4%8%23%66% Second bin23%16%41%87% Third bin27%41%77%99% Highest bin19%67%95%100%

30. ARCHER MW intensity estimates are now integrated into the Advanced Dvorak Technique algorithm (further details in next presentation on the ADT)ARCHER MW intensity estimates are now integrated into the Advanced Dvorak Technique algorithm (further details in next presentation on the ADT) Method: Method: Score is based on the robustness of the eyewall structure as depicted in 85GHz brightness temperatures, and is objectively determined from two parameters: Score is based on the robustness of the eyewall structure as depicted in 85GHz brightness temperatures, and is objectively determined from two parameters: 1)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) 2)Completeness component: Add 15 points to the score if … o>85% of the points on the eyewall are 85% of the points on the eyewall are <232K (the absolute measure) OR o>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) 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 Conversion to ADT Vmax: Scores >20 Vmax 72 kts, Scores >60 Vmax 90 kts Example: Image for TC 26W (2009) [24 Nov 1052 UTC] [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 Microwave Intensity Application

31. 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 methods 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). ARCHER-ADT Methodology Summary

32. Summary A fully automated and objective TC center finding routine based solely on satellite imagery has been developed, called ARCHER. 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. 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 end- user. A new version still under development for geostationary IR imagery yields a center fix along with a certainty estimate, allowing more options for the end- user. ARCHER can also diagnose intensity estimates from 85GHz imagery in developing TC cases. These estimates are now passed to the ADT. 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. Other ARCHER applications not discussed here include TC visualization, eyewall diameter retrieval, rapid intensification, 37GHz and Vis apps.

33. 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. 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. 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. 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. 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!