• Bender, M. A., 1997: The effects of relative flow on asymmetric structures in hurricanes. J. Atmos. Sci., 54 , 703724.

  • Chan, J. C. L., , and Williams R. T. , 1987: Analytical and numerical studies of the beta-effect in tropical cyclone motion. Part I: Zero mean flow. J. Atmos. Sci., 44 , 12571265.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., , and Kaplan J. , 1994a: A Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic basin. Wea. Forecasting, 9 , 209220.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., , and Kaplan J. , 1994b: Sea surface temperature and the maximum intensity of Atlantic tropical cyclones. J. Climate, 7 , 13241334.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., , and Kaplan J. , 1999: An updated Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic and eastern North Pacific basins. Wea. Forecasting, 14 , 326337.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., , Mainelli M. , , Shay L. K. , , Knaff J. A. , , and Kaplan J. , 2005: Further improvement to the Statistical Hurricane Intensity Prediction Scheme (SHIPS). Wea. Forecasting, 20 , 531543.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fiorino, M., , and Elsberry R. L. , 1989: Some aspects of vortex structure related to tropical cyclone motion. J. Atmos. Sci., 46 , 975990.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jarvinen, C., , Neumann J. , , and Davis M. A. S. , 1984: A tropical cyclone data tape for the North Atlantic basin, 1886–1983: Contents, limitations and uses. NOAA Tech Memo. NWS NHC 22, Coral Gables, FL, 21 pp. [Available from NTIS, 5285 Port Royal Rd., Springfield, VA 22161.].

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77 , 437471.

  • Knaff, J. A., , and Zehr R. M. , 2007: Reexamination of tropical cyclone wind–pressure relationships. Wea. Forecasting, 22 , 7188.

  • Knaff, J. A., , Kossin J. P. , , and DeMaria M. , 2003: Annular hurricanes. Wea. Forecasting, 18 , 204223.

  • Knaff, J. A., , Zehr R. M. , , and DeMaria M. , 2005: An operational Statistical Typhoon Intensity Prediction Scheme for the western North Pacific. Wea. Forecasting, 20 , 688699.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., , Zehr R. M. , , DeMaria M. , , Marchok T. P. , , Gross J. M. , , and McAdie C. J. , 2007: Statistical tropical cyclone wind radii prediction using climatology and persistence. Wea. Forecasting, 22 , 781791.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knapp, K. R., , and Kossin J. P. , 2007: A new global tropical cyclone data set from ISCCP B1 geostationary satellite observations. J. Appl. Remote Sens., 1 , 013505.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., 2002: Daily hurricane variability inferred from GOES infrared imagery. Mon. Wea. Rev., 130 , 22602270.

  • Kossin, J. P., , Knaff J. A. , , Berger H. I. , , Herndon D. C. , , Cram T. A. , , Velden C. S. , , Murnane R. J. , , and Hawkins J. D. , 2007: Estimating hurricane wind structure in the absence of aircraft reconnaissance. Wea. Forecasting, 22 , 89101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kwok, J. H. Y., , and Chan J. C. L. , 2005: The influence of uniform flow on tropical cyclone intensity change. J. Atmos. Sci., 62 , 31933212.

  • Lord, S. J., 1993: Recent developments in tropical cyclone track forecasting with the NMC Global Analysis and Forecast System. Preprints, 20th Conf. on Hurricanes and Tropical Meteorology, San Antonio, TX, Amer. Meteor. Soc., 290–291.

  • Maclay, K. S., 2006: A study of tropical cyclone structural evolution. M.S. thesis, Dept. of Atmospheric Sciences, Colorado State University, 110 pp. [Available from Atmospheric Branch, Morgan Branch, Colorado State University, Fort Collins, CO 80523.].

  • Mason, S. J., , and Graham N. E. , 1999: Conditional probabilities, relative operating characteristics, and relative operating levels. Wea. Forecasting, 14 , 713725.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, K. J., , DeMaria M. , , Knaff J. A. , , Kossin J. P. , , and Vonder Haar T. H. , 2006: Objective estimation of tropical cyclone wind structure from infrared satellite data. Wea. Forecasting, 21 , 9901005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peng, M. S., , Jeng B-F. , , and Williams R. T. , 1999: A numerical study on tropical cyclone intensification. Part I: Beta effect and mean flow effect. J. Atmos. Sci., 56 , 14041423.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., 1988: A real-time global sea surface temperature analysis. J. Climate, 1 , 7586.

  • Ritchie, E. A., 2004: Tropical cyclones in complex vertical wind shears. Preprints, 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 88–89.

  • Ritchie, E. A., , and Frank W. M. , 2007: Interactions between simulated tropical cyclones and an environment with a variable Coriolis parameter. Mon. Wea. Rev., 135 , 18891905.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., , and Holland G. J. , 1996a: The beta drift of baroclinic vortices. Part I: Adiabatic vorticies. J. Atmos. Sci., 53 , 411427.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., , and Holland G. J. , 1996b: The beta drift of baroclinic vortices. Part II: Diabatic vortices. J. Atmos. Sci., 53 , 37373756.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., , and Holland G. J. , 1996c: Tropical cyclone motion and evolution in vertical shear. J. Atmos. Sci., 53 , 33133332.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 467 pp.

  • Wu, L., , and Braun S. A. , 2004: Effect of convective asymmetries on hurricane intensity: A numerical study. J. Atmos. Sci., 61 , 30653081.

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Objective Identification of Annular Hurricanes

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  • 1 NOAA/NESDIS/Center for Satellite Applications and Research, Fort Collins, Colorado
  • 2 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
  • 3 Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado
  • 4 Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin
  • 5 NOAA/NESDIS/Center for Satellite Applications and Research, Fort Collins, Colorado
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Abstract

Annular hurricanes are a subset of intense tropical cyclones that have been shown in previous work to be significantly stronger, to maintain their peak intensities longer, and to weaken more slowly than average tropical cyclones. Because of these characteristics, they represent a significant forecasting challenge. This paper updates the list of annular hurricanes to encompass the years 1995–2006 in both the North Atlantic and eastern–central North Pacific tropical cyclone basins. Because annular hurricanes have a unique appearance in infrared satellite imagery, and form in a specific set of environmental conditions, an objective real-time method of identifying these hurricanes is developed. However, since the occurrence of annular hurricanes is rare (∼4% of all hurricanes), a special algorithm to detect annular hurricanes is developed that employs two steps to identify the candidates: 1) prescreening the data and 2) applying a linear discriminant analysis. This algorithm is trained using a dependent dataset (1995–2003) that includes 11 annular hurricanes. The resulting algorithm is then independently tested using datasets from the years 2004–06, which contained an additional three annular hurricanes. Results indicate that the algorithm is able to discriminate annular hurricanes from tropical cyclones with intensities greater than 84 kt (43.2 m s−1). The probability of detection or hit rate produced by this scheme is shown to be ∼96% with a false alarm rate of ∼6%, based on 1363 six-hour time periods with a tropical cyclone with an intensity greater than 84 kt (1995–2006).

Corresponding author address: John Knaff, NOAA/NESDIS/Office of Research and Applications, CIRA/Colorado State University, Foothills Campus Delivery 1375, Fort Collins, CO 80523-1375. Email: john.knaff@noaa.gov

Abstract

Annular hurricanes are a subset of intense tropical cyclones that have been shown in previous work to be significantly stronger, to maintain their peak intensities longer, and to weaken more slowly than average tropical cyclones. Because of these characteristics, they represent a significant forecasting challenge. This paper updates the list of annular hurricanes to encompass the years 1995–2006 in both the North Atlantic and eastern–central North Pacific tropical cyclone basins. Because annular hurricanes have a unique appearance in infrared satellite imagery, and form in a specific set of environmental conditions, an objective real-time method of identifying these hurricanes is developed. However, since the occurrence of annular hurricanes is rare (∼4% of all hurricanes), a special algorithm to detect annular hurricanes is developed that employs two steps to identify the candidates: 1) prescreening the data and 2) applying a linear discriminant analysis. This algorithm is trained using a dependent dataset (1995–2003) that includes 11 annular hurricanes. The resulting algorithm is then independently tested using datasets from the years 2004–06, which contained an additional three annular hurricanes. Results indicate that the algorithm is able to discriminate annular hurricanes from tropical cyclones with intensities greater than 84 kt (43.2 m s−1). The probability of detection or hit rate produced by this scheme is shown to be ∼96% with a false alarm rate of ∼6%, based on 1363 six-hour time periods with a tropical cyclone with an intensity greater than 84 kt (1995–2006).

Corresponding author address: John Knaff, NOAA/NESDIS/Office of Research and Applications, CIRA/Colorado State University, Foothills Campus Delivery 1375, Fort Collins, CO 80523-1375. Email: john.knaff@noaa.gov

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