1 CIRA, Colorado State University, Fort Collins, CO 2 NOAA/NESDIS, Fort Collins, CO

1. Operational Implementation of an Objective Annular Hurricane Index Andrea B. Schumacher1, John A. Knaff2, Thomas A. Cram1, Mark DeMaria2, James P. Kossin3 1CIRA, Colorado State University, Fort Collins, CO 2NOAA/NESDIS, Fort Collins, CO 3CIMSS, University of Wisconsin-Madison, Madison, WI

2. Overview • Annular hurricanes: structural characteristics • General environmental conditions • Intensity characteristics: Motivation for objective prediction scheme • Objectively determining annular structure: Annular Hurricane Index • Step 1: Screening • Step 2: Linear discriminant analysis • Verification • Operational example: Daniel 2006 (EP05)

3. Annular Hurricane Structure • Distinctly axisymmetric • Large circular eyes • Greatly reduced rainband activity • Lasts at least 3 hours • Rare, occur ~4% of the time Isabel (2003)

4. Environmental Conditions • Weak Easterly/Southeasterly Wind Shear • Weak Relative Eddy Flux Convergence • 200 hPa Easterlies • SSTs in a range 25.4 to 28.6 °C steady or decreasing. OR • Weak Easterly shear, under an upper ridge, over SST <28.6 °C ALSO • Intensity > 85 kt (Knaff et al. 2003)

5. Annular Hurricane Intensity Characteristics • Do not weaken rapidly after max intensity • Intensity is very close to 85% MPI wrt SST • Have large intensity biases & larger than normal intensity errors

6. Determining Annular Structure Yellow = Structure Blue = Environment

7. Annular Hurricane Index (AHI)

8. AHI Step 1: Screening +/- 3 standard deviations from means of AH’s (1995-2003) 976 (54 AH) cases (6h) > 84 kt intensity  241 cases after screening (53 AH) Hit Rate = 100%, False Alarm Rate = 19%

9. AHI Step 2: Linear Discriminant Analysis (Overview) • Graphical Interpretation of LDA for Case With 2 Predictors (x,y) and 2 Groups DF=0 DF = c0 + c1x + c2y Coordinate transformation that provides maximum separation of groups (From www.doe-mbi.ucla.edu) Refs: Wilks (2006), Hennon & Hobgood (MWR, 2003)

10. AHI Step 2: Linear Discriminant Analysis (cont…) • Only predictors with significant annular vs. non-annular differences in means were used** • SST • U200 – 200 hPa zonal winds • σc– azimuthal standard deviation of BTs at Rc • VAR –variance of azimuthally-averaged BTs from TC center to 600 km • ΔT eye- max difference between Rc and any azimuthally-averaged BT at smaller radius ** exceeds 95% confidence level using Student’s T test

11. Verification • Dependent Years (1995-2003) • Independent Years (2004-2006) STEP 1: Screening Reduced 941 (54) cases to 241 (53) FAR = 19% STEP 2: LDA “N” LDA “Y” Hit Rate ~ 87 % FA Rate ~ 6 % NAH AH STEP 1: Screening Reduced 387 (7) cases to 82 (7) FAR = 19% STEP 2: LDA “N” LDA “Y” Hit Rate ~ 100 % FA Rate ~ 4 % NAH AH

12. AHI Output & Interpretation • If case doesn’t pass screening, AHI set to 0. • If screening is passed, LDA function value is linearly scaled to obtain the Annular Hurricane Index, which ranges between 1 & 100. • AHI is displayed at the end of the SHIPS model output file. AHI = 0  No annular structure AHI = 1  Worst match to annular structure AHI = 100  Best match to annular structure

13. Example: Daniel 2006 7/20/06 0Z, vmax=95 kt AHI = 0 7/22/06 0Z vmax = 130 kt AHI = 100 7/21/06 0Z, vmax=120 kt AHI = 50

14. EP05 (Daniel 2006), 7/22/06 0Z

15. Summary • AHI is an objective algorithm that determines the likelihood of annular structure in an existing hurricane using SHIPS environmental predictors and 6-hr storm-centered GOES IR imagery • For the period 1995-2006, the AHI algorithm had a hit rate of 96% and a false alarm rate of 4% • The AHI will be tested in a real-time operational setting, running concurrently with the SHIPS model, at the National Hurricane Center during the 2007 hurricane season. • After the 2007 season, if evaluation of the algorithm is favorable the transition to an operational product will be pursued