An Objective Tool for Identifying Hurricane Secondary Eyewall Formation

1. An Objective Tool for Identifying Hurricane Secondary Eyewall Formation Jim Kossin and Matt Sitkowski Cooperative Institute for Meteorological Satellite Studies University of Wisconsin Madison, WI kossin@ssec.wisc.edu 62nd Interdepartmental Hurricane Conference Charleston, SC, March 2008

2. This work is supported by the National Oceanic and Atmospheric Administration under the GOES-R Risk Reduction program and the Office of Naval Research under Grant No. N00014-07-1-0163

3. Goal: • Create a tool that uses readily available data to estimate the probability of secondary eyewall formation events in tropical cyclones • Motivation: • These events are generally associated with marked changes in the intensity and structure of the inner core: • rapid intensity deviations • significant broadening of the surface wind field • changes in storm surge, sea-state, radius of 50 kt wind • Despite the importance of secondary eyewall formation in tropical cyclone forecasting, there is presently no objective guidance to diagnose or forecast these events.

4. Data and Method: • Our first step was to utilize the SHIPS developmental data. • ambient environmental features • geostationary satellite-derived features The features were then separated into 2 classes (using microwave, radar, recon reports, anything available): 1)a secondary eyewall formed at some time in the following 12 h 2)a secondary eyewall did not form at any time in the following 12 h Classes were limited to Category 1 hurricanes or greater, with centers over water. 10 years (1997–2006).

5. The algorithm is based on the Bayes probabilistic model • P (Cyes| F)estimates the probability of imminent secondary eyewall formation, given the setFof observed features. • P (Cyes) is the climatological probability (~10% in the North Atlantic). Based on class separation

6. “Leave-one-season-out” cross validated algorithm performance 20 (25) 22 (15) 12 (7) 970 (984) 52 (45) Inclusion of IR increases the confidence of the model skill skill

7. Cross validation hindcast example (hits, misses, false alarms)

8. 13 Sep 06Z (too early) Misses and false alarms? Ivan, 13 Sep 2004, 06Z ~12 h later 

9. The algorithm has been alerting us to secondary eyewall formation events that we had previously missed

10. False alarm? 27 Oct 1998, 00Z URNT12 KNHC 260508 DETAILED VORTEX DATA MESSAGE A. 26/0508Z B. 16 DEG 20 MIN N 81 DEG 53 MIN W C. 700 MB 2391 M D. NA E. NA F. 064 DEG 124 KT G. 342 DEG 9 NM H. 922 MB I. 14 C/ 3047 M J. 19 C/ 3024 M K. 15 C/ NA L. CLOSED WALL M. CO8-15 N. 16 DEG 20 MIN N 81 DEG 53 MIN W 26/0508Z O. 12345/7 P. .25/2 NM Q. AF966 1113A MITCH OB 09 KNHC DETAILED MAX FL WIND 124 KT NW QUAD 0500Z. GOOD RADAR PRESENTATION. DOUBLE EYEWALL, WIND CENTER 3 NM DIA.; Mitch, 27 Oct 1998, 00Z ~12 h later 

11. A miss and a false alarm

12. False Alarm

13. Hurricane Isabel (2003) 12 Sep 00Z  hit 17 Sep 00Z  miss

14. 22 Sep 00Z  hit 23 Sep 06Z  miss Hurricane Rita (2005)

15. Work in progress: • Extension beyond SHIPS features • Application to EPAC & WPAC tropical cyclones • Apply results to numerical simulations of secondary eyewall formation to better understand the physical mechanisms at work We hope to piggyback a beta-version onto SHIPS as soon as possible

16. End