• Aberson, S. D., A. Aksoy, K. J. Sellwood, T. Vukicevic, and X. Zhang, 2015: Assimilation of high-resolution tropical cyclone observations with an ensemble Kalman filter using HEDAS: Evaluation of 2008–11 HWRF forecasts. Mon. Wea. Rev., 143, 511523, https://doi.org/10.1175/MWR-D-14-00138.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bhatia, K. T., and D. S. Nolan, 2013: Relating the skill of tropical cyclone intensity forecasts to the synoptic environment. Wea. Forecasting, 28, 961980, https://doi.org/10.1175/WAF-D-12-00110.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cangialosi, J. P., and J. L. Franklin, 2014: 2013 National Hurricane Center Forecast verification report. NOAA/National Hurricane Center, 84 pp., https://www.nhc.noaa.gov/verification/pdfs/Verification_2013.pdf.

  • Cangialosi, J. P., E. Blake, M. DeMaria, A. Penny, A. Latto, E. N. Rappaport, and V. Tallapragada, 2020: Recent progress in tropical cyclone intensity forecasting at the National Hurricane Center. Wea. Forecasting, 35, 19131922, https://doi.org/10.1175/WAF-D-20-0059.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., M. Mainelli, L. K. Shay, J. A. Knaff, and J. Kaplan, 2005: Further improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS). Wea. Forecasting, 20, 531543, https://doi.org/10.1175/WAF862.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., J. A. Knaff, and J. Kaplan, 2006: On the decay of tropical cyclone winds crossing narrow landmasses. J. Appl. Meteor. Climatol., 45, 491499, https://doi.org/10.1175/JAM2351.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., C. R. Sampson, J. A. Knaff, and K. D. Musgrave, 2014: Is tropical cyclone intensity guidance improving? Bull. Amer. Meteor. Soc., 95, 387398, https://doi.org/10.1175/BAMS-D-12-00240.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doyle, J. D., and et al. , 2017: A view of tropical cyclones from above. Bull. Amer. Meteor. Soc., 98, 21132134, https://doi.org/10.1175/BAMS-D-16-0055.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K., and F. Zhang, 2016: On the predictability and error sources of tropical cyclone intensity forecasts. J. Atmos. Sci., 73, 37393747, https://doi.org/10.1175/JAS-D-16-0100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gall, R., J. Franklin, F. Marks, E. N. Rappaport, and F. Toepfer, 2013: The hurricane forecast improvement project. Bull. Amer. Meteor. Soc., 94, 329343, https://doi.org/10.1175/BAMS-D-12-00071.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hendricks, E. A., M. S. Peng, B. Fu, and T. Li, 2010: Quantifying environmental control of tropical cyclone intensity change. Mon. Wea. Rev., 138, 32433271, https://doi.org/10.1175/2010MWR3185.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, S. C., and et al. , 2003: The extratropical transition of tropical cyclones: Forecast challenges, current understanding, and future directions. Wea. Forecasting, 18, 10521092, https://doi.org/10.1175/1520-0434(2003)018<1052:TETOTC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaplan, J., and M. DeMaria, 1995: A simple empirical model for predicting the decay of tropical cyclone winds after landfall. J. Appl. Meteor., 34, 24992512, https://doi.org/10.1175/1520-0450(1995)034<2499:ASEMFP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaplan, J., and M. DeMaria, 2003: Large-scale characteristics of rapidly intensifying tropical cyclones in the North Atlantic basin. Wea. Forecasting, 18, 10931108, https://doi.org/10.1175/1520-0434(2003)018<1093:LCORIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaplan, J., M. DeMaria, and J. A. Knaff, 2010: A revised tropical cyclone rapid intensification index for the Atlantic and eastern North Pacific basins. Wea. Forecasting, 25, 220241, https://doi.org/10.1175/2009WAF2222280.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaplan, J., and et al. , 2015: Evaluating environmental impacts on tropical cyclone rapid intensification predictability utilizing statistical models. Wea. Forecasting, 30, 13741396, https://doi.org/10.1175/WAF-D-15-0032.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., and M. DeMaria, 2016: Reducing operational hurricane intensity forecast errors during eyewall replacement cycles. Wea. Forecasting, 31, 601608, https://doi.org/10.1175/WAF-D-15-0123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liang, J., L. Wu, G. Gu, and Q. Liu, 2016: Rapid weakening of Typhoon Chan-Hom (2015) in a monsoon gyre. J. Geophys. Res. Atmos., 121, 95089520, https://doi.org/10.1002/2016JD025214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martinez, J., M. M. Bell, R. F. Rogers, and J. D. Doyle, 2019: Axisymmetric potential vorticity evolution of Hurricane Patricia (2015). J. Atmos. Sci., 76, 20432063, https://doi.org/10.1175/JAS-D-18-0373.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Na, W., J. L. McBride, X.-H. Zhang, and Y.-H. Duan, 2018: Understanding biases in tropical cyclone intensity forecast error. Wea. Forecasting, 33, 129138, https://doi.org/10.1175/WAF-D-17-0106.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nystrom, R. G., and F. Zhang, 2019: Practical uncertainties in the limited predictability of the record-breaking intensification of Hurricane Patricia (2015). Mon. Wea. Rev., 147, 35353556, https://doi.org/10.1175/MWR-D-18-0450.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rappaport, E. N., J. L. Franklin, A. B. Schumacher, M. DeMaria, L. K. Shay, and E. J. Gibney, 2010: Tropical cyclone intensity change before U.S. Gulf Coast landfall. Wea. Forecasting, 25, 13801396, https://doi.org/10.1175/2010WAF2222369.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, R. F., and et al. , 2017: Rewriting the tropical record books: The extraordinary intensification of Hurricane Patricia (2015). Bull. Amer. Meteor. Soc., 98, 20912112, https://doi.org/10.1175/BAMS-D-16-0039.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rozoff, C. M., and J. P. Kossin, 2011: New probabilistic forecast models for the prediction of tropical cyclone rapid intensification. Wea. Forecasting, 26, 677689, https://doi.org/10.1175/WAF-D-10-05059.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Sang, N., R. K. Smith, and M. T. Montgomery, 2008: Tropical cyclone intensification and predictability in three dimensions. Quart. J. Roy. Meteor. Soc., 134, 563582, https://doi.org/10.1002/qj.235.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, K. M., and E. A. Ritchie, 2015: A definition for rapid weakening of North Atlantic and eastern North Pacific tropical cyclones. Geophys. Res. Lett., 42, 10 09110 097, https://doi.org/10.1002/2015GL066697.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Understanding Error Distributions of Hurricane Intensity Forecasts during Rapid Intensity Changes

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  • 1 Colorado State University, Fort Collins, Colorado
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Abstract

The characteristics of official National Hurricane Center (NHC) intensity forecast errors are examined for the North Atlantic and east Pacific basins from 1989 to 2018. It is shown how rapid intensification (RI) and rapid weakening (RW) influence yearly NHC forecast errors for forecasts between 12 and 48 h in length. In addition to being the tail of the intensity change distribution, RI and RW are at the tails of the forecast error distribution. Yearly mean absolute forecast errors are positively correlated with the yearly number of RI/RW occurrences and explain roughly 20% of the variance in the Atlantic and 30% in the east Pacific. The higher occurrence of RI events in the east Pacific contributes to larger intensity forecast errors overall but also a better probability of detection and success ratio. Statistically significant improvements to 24-h RI forecast biases have been made in the east Pacific and to 24-h RW biases in the Atlantic. Over-ocean 24-h RW events cause larger mean errors in the east Pacific that have not improved with time. Environmental predictors from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) are used to diagnose what conditions lead to the largest RI and RW forecast errors on average. The forecast error distributions widen for both RI and RW when tropical systems experience low vertical wind shear, warm sea surface temperature, and moderate low-level relative humidity. Consistent with existing literature, the forecast error distributions suggest that improvements to our observational capabilities, understanding, and prediction of inner-core processes is paramount to both RI and RW prediction.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Benjamin Trabing, btrabing@colostate.edu

Abstract

The characteristics of official National Hurricane Center (NHC) intensity forecast errors are examined for the North Atlantic and east Pacific basins from 1989 to 2018. It is shown how rapid intensification (RI) and rapid weakening (RW) influence yearly NHC forecast errors for forecasts between 12 and 48 h in length. In addition to being the tail of the intensity change distribution, RI and RW are at the tails of the forecast error distribution. Yearly mean absolute forecast errors are positively correlated with the yearly number of RI/RW occurrences and explain roughly 20% of the variance in the Atlantic and 30% in the east Pacific. The higher occurrence of RI events in the east Pacific contributes to larger intensity forecast errors overall but also a better probability of detection and success ratio. Statistically significant improvements to 24-h RI forecast biases have been made in the east Pacific and to 24-h RW biases in the Atlantic. Over-ocean 24-h RW events cause larger mean errors in the east Pacific that have not improved with time. Environmental predictors from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) are used to diagnose what conditions lead to the largest RI and RW forecast errors on average. The forecast error distributions widen for both RI and RW when tropical systems experience low vertical wind shear, warm sea surface temperature, and moderate low-level relative humidity. Consistent with existing literature, the forecast error distributions suggest that improvements to our observational capabilities, understanding, and prediction of inner-core processes is paramount to both RI and RW prediction.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Benjamin Trabing, btrabing@colostate.edu
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