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Performance of an Objective Model for Identifying Secondary Eyewall Formation in Hurricanes

Performance of an Objective Model for Identifying Secondary Eyewall Formation in Hurricanes. Matthew Sitkowski CIMSS – University of Wisconsin Jim Kossin NOAA/NESDIS/National Climatic Data Center. Acknowledgments. Thank$ Office of Naval Research NOAA GOES-R Risk Reduction Jeff Hawkins

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Performance of an Objective Model for Identifying Secondary Eyewall Formation in Hurricanes

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  1. Performance of an Objective Model for Identifying Secondary Eyewall Formation in Hurricanes Matthew SitkowskiCIMSS – University of Wisconsin Jim KossinNOAA/NESDIS/National Climatic Data Center

  2. Acknowledgments • Thank$ • Office of Naval Research • NOAA GOES-R Risk Reduction • Jeff Hawkins • NRL Webpage • Mark DeMaria • SHIPS Dataset • Dave Nolan, Chris Rozoff, John Knaff, Howard Berger, & Chris Velden

  3. Marked changes of the inner core structure • Broaden wind field – increase storm surge • Linked with rapid intensity changes • Landfall blessing/curse “SO SOME ADDITIONAL STRENGTHENING IS POSSIBLE... IF AN EYEWALL REPLACEMENT CYCLE DOES NOT INHIBIT THE INTENSIFICATION PROCESS.” “... AND WE HAVE NO SKILL IN FORECASTING EYEWALL REPLACEMENT CYCLES BEYOND ABOUT 6-12 HOURS...AT BEST • Rita (2005) NHC Discussion #18 (Stewart)

  4. Secondary Eyewall Formation (SEF) Objective Model Development • Algorithm requires the knowledge of prior SEF events • Developed an SEF climatology from 1997-2006 • Uses environmental features from SHIPS • Shear, MPI, relative humidity … • GOES IR features • Improve probability of detection from 22% to 30%

  5. SEF Algorithm • 2 classes (Cat 1+ over water) • SEF occurs within the next 12 hours • SEF does not occur within next 12 hours • 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 (~12% in the North Atlantic). “Leave-one-season-out” cross validated

  6. 8 Hurricanes Bertha Dolly Gustav Hanna Ike Kyle Omar Paloma 3 SEF Storms Algorithm ran smoothly Missing key IR features New forecast every 6 hours t=0,6,12,18, & 24 hr Greatest probability for non-SEF storm was 6% (excluding Gustav) Ike had 18 hr forecast of 65% that verified Sept 6 1737 UTC Sept 7 1145 UTC 2008 Season

  7. First major hurricane Never had probability >0% Classic eyewall replacement Features never acted in accord to produce a high probability Hurricane Bertha

  8. Hurricane Bertha • Some features were favorable at times • Shear somewhat high • Low MPI for SEF storm

  9. Cuba LA Intensity Probability

  10. Cuba TX SEF SEF Intensity Probability

  11. SEF Cuba Intensity Probability

  12. 2008 Beta Version • Run with key features missing • Improvement expected with the addition of the missing IR features • No major glitches • Automated - updates every 6 hr • Available online: • ftp://ftp.ssec.wisc.edu/pub/matts/ • A climatology of recon-based intensity changes associated with secondary eyewall formation would be of great benefit to forecasters

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