An Asymmetric Model of the Tropical Cyclone Surface Wind Field and Probabilistic Climatology of Structural Parameters

Eric W. Uhlhorn Verisk Analytics, Inc., Boston, Massachusetts

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Suz Tolwinski-Ward Verisk Analytics, Inc., Boston, Massachusetts

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Sylvie Lorsolo Verisk Analytics, Inc., Boston, Massachusetts

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Abstract

A new parametric model of a tropical cyclone’s (TC) surface wind field is proposed, in which parameters are calibrated on over 20 years of direct surface wind measurements from aircraft reconnaissance. The model extends previous formulations by representing structural asymmetries in the inner core and far field. The surface wind representation eliminates the need for uncertain aircraft flight (gradient) level-to-surface extrapolation or pressure gradient formulation conversions to wind speed. Parameters are expanded to wavenumber-1 asymmetric Fourier components around symmetric mean values; symmetric values are found to depend primarily on maximum wind speed and geographic location, while some asymmetric parameters show statistically significant relationships with motion and environmental vertical shear, consistent with recent studies. Finally, a probabilistic representation of the climatology of basin-wide TC wind field structure is constructed by fitting a multivariate statistical distribution to the optimized model parameters. The low-dimensional formulation is suitable for computationally efficient reconstruction of historical TC wind fields, from depression to Saffir–Simpson category-5 intensities, as well as for stochastic simulation in the context of catastrophe risk modeling.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Eric W. Uhlhorn, eric.uhlhorn@verisk.com

Abstract

A new parametric model of a tropical cyclone’s (TC) surface wind field is proposed, in which parameters are calibrated on over 20 years of direct surface wind measurements from aircraft reconnaissance. The model extends previous formulations by representing structural asymmetries in the inner core and far field. The surface wind representation eliminates the need for uncertain aircraft flight (gradient) level-to-surface extrapolation or pressure gradient formulation conversions to wind speed. Parameters are expanded to wavenumber-1 asymmetric Fourier components around symmetric mean values; symmetric values are found to depend primarily on maximum wind speed and geographic location, while some asymmetric parameters show statistically significant relationships with motion and environmental vertical shear, consistent with recent studies. Finally, a probabilistic representation of the climatology of basin-wide TC wind field structure is constructed by fitting a multivariate statistical distribution to the optimized model parameters. The low-dimensional formulation is suitable for computationally efficient reconstruction of historical TC wind fields, from depression to Saffir–Simpson category-5 intensities, as well as for stochastic simulation in the context of catastrophe risk modeling.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Eric W. Uhlhorn, eric.uhlhorn@verisk.com
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  • Bender, M. A., 1997: The effect of relative flow on the asymmetric structure in the interior of hurricanes. J. Atmos. Sci., 54, 703724, https://doi.org/10.1175/1520-0469(1997)054<0703:TEORFO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Berens, P., 2009: CircStat: A MATLAB toolbox for circular statistics. J. Stat. Software, 31 (10), 121, https://doi.org/10.18637/jss.v031.i10.

    • Search Google Scholar
    • Export Citation
  • Box, G. E. P., and D. R. Cox, 1964: An analysis of transformations. J. Roy. Stat. Soc., 26B, 211243, https://doi.org/10.1111/j.2517-6161.1964.tb00553.x.

    • Search Google Scholar
    • Export Citation
  • Brennan, M. J., C. C. Hennon, and R. D. Knabb, 2009: The operational use of QuikSCAT ocean surface vector winds at the National Hurricane Center. Wea. Forecasting, 24, 621645, https://doi.org/10.1175/2008WAF2222188.1.

    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., 2012: Effects of surface exchange coefficients and turbulence length scales on the intensity and structure of numerically simulated hurricanes. Mon. Wea. Rev., 140, 11251143, https://doi.org/10.1175/MWR-D-11-00231.1.

    • Search Google Scholar
    • Export Citation
  • Byrd, R. H., P. Lu, J. Nocedal, and C. Zhu, 1995: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput., 16, 11901208, https://doi.org/10.1137/0916069.

    • Search Google Scholar
    • Export Citation
  • Chang, D., S. Amin, and K. Emanuel, 2020: Modeling and parameter estimation of hurricane wind fields with asymmetry. J. Appl. Meteor. Climatol., 59, 687705, https://doi.org/10.1175/JAMC-D-19-0126.1.

    • Search Google Scholar
    • Export Citation
  • Chen, Y., and M. K. Yau, 2003: Asymmetric structures in a simulated landfalling hurricane. J. Atmos. Sci., 60, 22942312, https://doi.org/10.1175/1520-0469(2003)060%3C2294:ASIASL%3E2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Davis, C., and Coauthors, 2008: Prediction of landfalling hurricanes with the advanced hurricane WRF model. Mon. Wea. Rev., 136, 19902005, https://doi.org/10.1175/2007MWR2085.1.

    • Search Google Scholar
    • Export Citation
  • Demuth, J. L., M. DeMaria, and J. A. Knaff, 2006: Improvement of Advanced Microwave Sounder Unit tropical cyclone intensity and size estimation algorithms. J. Appl. Meteor. Climatol., 45, 15731581, https://doi.org/10.1175/JAM2429.1.

    • Search Google Scholar
    • Export Citation
  • Depperman, C. E., 1947: Notes on the origin and structure of Philippine typhoons. Bull. Amer. Meteor. Soc., 28, 399404, https://doi.org/10.1175/1520-0477-28.9.399.

    • Search Google Scholar
    • Export Citation
  • Frank, W. M., and E. A. Ritchie, 1999: Effects of environmental flow upon tropical cyclone structure. Mon. Wea. Rev., 127, 20442061, https://doi.org/10.1175/1520-0493(1999)127%3C2044:EOEFUT%3E2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Frank, W. M., and E. A. Ritchie, 2001: Effects of vertical wind shear on the intensity and structure of numerically simulated hurricanes. Mon. Wea. Rev., 129, 22492269, https://doi.org/10.1175/1520-0493(2001)129<2249:EOVWSO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gopalakrishnan, S. G., S. Goldenberg, T. Quirino, X. Zhang, F. Marks Jr., K.-S. Yeh, R. Atlas, and V. Tallapragada, 2012: Toward improving high-resolution numerical hurricane forecasting: Influence of model horizontal grid resolution, initialization, and physics. Wea. Forecasting, 27, 647666, https://doi.org/10.1175/WAF-D-11-00055.1.

    • Search Google Scholar
    • Export Citation
  • Hendricks, E. A., J. C. Knievel, and D. S. Nolan, 2021: Evaluation of boundary layer and urban canopy parameterizations for simulating wind in Miami during Hurricane Irma (2017). Mon. Wea. Rev., 149, 23212349, https://doi.org/10.1175/MWR-D-20-0278.1.

    • Search Google Scholar
    • Export Citation
  • Hill, K. A., and G. M. Lackmann, 2009: Analysis of idealized tropical cyclone simulations using the weather research and forecasting model: Sensitivity to turbulence parameterization and grid spacing. Mon. Wea. Rev., 137, 745765, https://doi.org/10.1175/2008MWR2220.1.

    • Search Google Scholar
    • Export Citation
  • Hock, T. F., and J. L. Franklin, 1999: The NCAR GPS dropwindsonde. Bull. Amer. Meteor. Soc., 80, 407420, https://doi.org/10.1175/1520-0477(1999)080<0407:TNGD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Holland, G. J., 1980: An analytic model of the wind and pressure profiles in hurricanes. Mon. Wea. Rev., 108, 12121218, https://doi.org/10.1175/1520-0493(1980)108<1212:AAMOTW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Holland, G. J., J. I. Belanger, and A. Fritz, 2010: A revised model for radial profiles of hurricane winds. Mon. Wea. Rev., 138, 43934401, https://doi.org/10.1175/2010MWR3317.1.

    • Search Google Scholar
    • Export Citation
  • Hu, K., Q. Chen, and S. K. Kimball, 2012: Consistency in hurricane surface wind forecasting: An improved parametric model. Nat. Hazards, 61, 10291050, https://doi.org/10.1007/s11069-011-9960-z.

    • Search Google Scholar
    • Export Citation
  • Jazwinksi, A. H., 1970: Stochastic Processes and Filtering Theory. Mathematics in Science and Engineering, Vol. 64, Academic Press, 376 pp.

  • Jelesnianski, C. P., 1965: A numerical calculation of storm tides induced by a tropical storm impinging on a continental shelf. Mon. Wea. Rev., 93, 343358, https://doi.org/10.1175/1520-0493(1993)093<0343:ANCOS>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kepert, J., 2001: The dynamics of boundary layer jets within the tropical cyclone core. Part I: Linear theory. J. Atmos. Sci., 58, 24692484, https://doi.org/10.1175/1520-0469(2001)058<2469:TDOBLJ>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kimball, S. K., and J. L. Evans, 2002: Idealized numerical simulations of hurricane–trough interaction. Mon. Wea. Rev., 130, 22102227, https://doi.org/10.1175/1520-0493(2002)130%3C2210:INSOHT%3E2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Klein, P. M., P. A. Harr, and R. L. Elsberry, 2002: Extratropical transition of western North Pacific tropical cyclones: Midlatitude and tropical cyclone contributions to reintensification. Mon. Wea. Rev., 130, 22402259, https://doi.org/10.1175/1520-0493(2002)130%3C2240:ETOWNP%3E2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Klotz, B. W., and E. W. Uhlhorn, 2014: Improved stepped-frequency microwave radiometer tropical cyclone surface winds in heavy precipitation. J. Atmos. Oceanic Technol., 31, 23922408, https://doi.org/10.1175/JTECH-D-14-00028.1.

    • Search Google Scholar
    • Export Citation
  • Klotz, B. W., and H. Jiang, 2016: Global composites of surface wind speeds in tropical cyclones based on a 12 year scatterometer database. Geophys. Res. Lett., 43, 10 48010 488, https://doi.org/10.1002/2016GL071066.

    • Search Google Scholar
    • Export Citation
  • Klotz, B. W., and H. Jiang, 2017: Examination of surface wind asymmetries in tropical cyclones. Part I: General structure and wind shear impacts. Mon. Wea. Rev., 145, 39894009, https://doi.org/10.1175/MWR-D-17-0019.1.

    • Search Google Scholar
    • Export Citation
  • Klotz, B. W., and D. S. Nolan, 2019: SFMR surface wind undersampling over the tropical cyclone life cycle. Mon. Wea. Rev., 147, 247268, https://doi.org/10.1175/MWR-D-18-0296.1.

    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 35763592, https://doi.org/10.1175/MWR-D-12-00254.1.

    • Search Google Scholar
    • Export Citation
  • Loridan, T., R. P. Compton, and E. Dubossarsky, 2017: A machine learning approach to modeling tropical cyclone wind field uncertainty. Mon. Wea. Rev., 145, 32033221, https://doi.org/10.1175/MWR-D-16-0429.1.

    • Search Google Scholar
    • Export Citation
  • Mattocks, C., and C. Forbes, 2008: A real-time, event-triggered storm surge forecasting system for the state of North Carolina. Ocean Modell., 25, 95119, https://doi.org/10.1016/j.ocemod.2008.06.008.

    • Search Google Scholar
    • Export Citation
  • Melhauser, C., F. Zhang, Y. Weng, Y. Jin, H. Jin, and Q. Zhao, 2017: A multiple-model convection-permitting ensemble examination of the probabilistic prediction of tropical cyclones: Hurricanes Sandy (2012) and Edouard (2014). Wea. Forecasting, 32, 665688, https://doi.org/10.1175/WAF-D-16-0082.1.

    • Search Google Scholar
    • Export Citation
  • Moyer, A. C., J. L. Evans, and M. D. Powell, 2007: Comparison of observed gale radius statistics. Meteor. Atmos. Phys., 97, 4155, https://doi.org/10.1007/s00703-006-0243-2.

    • Search Google Scholar
    • Export Citation
  • Nolan, D. S., J. A. Zhang, and D. P. Stern, 2009a: Evaluation of planetary boundary layer parameterizations in tropical cyclones by comparison of in situ observations and high-resolution simulations of Hurricane Isabel (2003). Part I: Initialization, maximum winds, and the outer-core boundary layer. Mon. Wea. Rev., 137, 36513674, https://doi.org/10.1175/2009MWR2785.1.

    • Search Google Scholar
    • Export Citation
  • Nolan, D. S., D. P. Stern, and J. A. Zhang, 2009b: Evaluation of planetary boundary layer parameterizations in tropical cyclones by comparison of in situ observations and high-resolution simulations of Hurricane Isabel (2003). Part II: Inner-core boundary layer and eyewall structure. Mon. Wea. Rev., 137, 36753698, https://doi.org/10.1175/2009MWR2786.1.

    • Search Google Scholar
    • Export Citation
  • Powell, M. D., S. H. Houston, L. R. Amat, and N. Morisseau-Leroy, 1998: The HRD real-time hurricane wind analysis system. J. Wind Eng. Ind. Aerodyn., 7778, 5364, https://doi.org/10.1016/S0167-6105(98)00131-7.

    • Search Google Scholar
    • Export Citation
  • Rodríguez-Yam, G. A., R. A. Davis, and L. L. Scharf, 2002: A Bayesian model and Gibbs sampler for hyperspectral imaging. Sensor Array and Multichannel Signal Processing Workshop Proc., Rosslyn, VA, Institute of Electrical and Electronics Engineers, 105–109, https://doi.org/10.1109/SAM.2002.1191009.

  • Rogers, R., and E. Uhlhorn, 2008: Observations of the structure and evolution of surface and flight-level wind asymmetries in Hurricane Rita (2005). Geophys. Res. Lett., 35, L22811, https://doi.org/10.1029/2008GL034774.

    • Search Google Scholar
    • Export Citation
  • Ross, R. J., and Y. Kurihara, 1992: A simplified scheme to simulate asymmetries due to the beta effect in barotropic vorticies. J. Atmos. Sci., 49, 16201628, https://doi.org/10.1175/1520-0469(1992)049<1620:ASSTSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151058, https://doi.org/10.1175/2010BAMS3001.1.

    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 21852208, https://doi.org/10.1175/JCLI-D-12-00823.1.

    • Search Google Scholar
    • Export Citation
  • Schloemer, R. W., 1954: Analysis and Synthesis of Hurricane Wind Patterns over Lake Okeechobee, Florida. U.S. Department of Commerce, Weather Bureau, 49 pp.

  • Shapiro, L. J., 1983: The asymmetric boundary layer flow under a translating hurricane. J. Atmos. Sci., 40, 19841998, https://doi.org/10.1175/1520-0469(1983)040<1984:TABLFU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Takahashi, T., D. S. Nolan, and B. D. McNoldy, 2024: The vortex structure and near-surface winds of Typhoon Faxai (2019) during landfall. Part-II: Evaluation of WRF simulations. Quart. J. Roy. Meteor. Soc., 150, 16431667, https://doi.org/10.1002/qj.4663.

    • Search Google Scholar
    • Export Citation
  • Ueno, M., and M. Kunii, 2009: Some aspects of azimuthal wavenumber-one structure of typhoons represented in the JMA operational mesoscale analyses. J. Meteor. Soc. Japan, 87, 615633, https://doi.org/10.2151/jmsj.87.615.

    • Search Google Scholar
    • Export Citation
  • Ueno, M., and K. Bessho, 2011: A statistical analysis of near-core surface wind asymmetries in typhoons obtained from QuikSCAT data. J. Meteor. Soc. Japan, 89, 225241, https://doi.org/10.2151/jmsj.2011-304.

    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E. W., and P. G. Black, 2003: Verification of remotely sensed sea surface winds in hurricanes. J. Atmos. Oceanic Technol., 20, 99116, https://doi.org/10.1175/1520-0426(2003)020<0099:VORSSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E. W., and D. S. Nolan, 2012: Observational undersampling in tropical cyclones and implications for estimated intensity. Mon. Wea. Rev., 140, 825840, https://doi.org/10.1175/MWR-D-11-00073.1.

    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E. W., P. G. Black, J. L. Franklin, M. Goodberlet, J. Carswell, and A. S. Goldstein, 2007: Hurricane surface wind measurements from an operational stepped frequency microwave radiometer. Mon. Wea. Rev., 135, 30703085, https://doi.org/10.1175/MWR3454.1.

    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E. W., B. W. Klotz, T. Vukicevic, P. D. Reasor, and R. F. Rogers, 2014: Observed hurricane wind speed asymmetries and relationships to motion and environmental shear. Mon. Wea. Rev., 142, 12901311, https://doi.org/10.1175/MWR-D-13-00249.1.

    • Search Google Scholar
    • Export Citation
  • Vickery, P., D. Wadhera, M. D. Powell, and Y. Chen, 2009: A hurricane boundary layer and wind field model for use in engineering applications. J. Appl. Meteor. Climatol., 48, 381405, https://doi.org/10.1175/2008JAMC1841.1.

    • Search Google Scholar
    • Export Citation
  • Vickery, P. J., and D. Wadhera, 2008: Statistical models of Holland pressure profile parameter and radius to maximum winds of hurricanes from flight-level pressure and H*Wind data. J. Appl. Meteor. Climatol., 47, 24972517, https://doi.org/10.1175/2008JAMC1837.1.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., and G. J. Holland, 1996: Tropical cyclone motion and evolution in vertical shear. J. Atmos. Sci., 53, 33133332, https://doi.org/10.1175/1520-0469(1996)053<3313:TCMAEI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Elsevier, 676 pp.

  • Willoughby, H. E., R. W. R. Darling, and M. E. Rahn, 2006: Parametric representation of the primary hurricane vortex. Part II: A new family of sectionally continuous profiles. Mon. Wea. Rev., 134, 11021120, https://doi.org/10.1175/MWR3106.1.

    • Search Google Scholar
    • Export Citation
  • Winterbottom, H., and E. Chassignet, 2011: A vortex isolation and removal algorithm for numerical weather prediction model tropical cyclone applications. J. Adv. Model. Earth Syst., 3, M11003, https://doi.org/10.1029/2011MS000088.

    • Search Google Scholar
    • Export Citation
  • Wood, V. T., L. W. White, H. E. Willoughby, and D. P. Jorgensen, 2013: A new parametric tropical cyclone tangential wind profile model. Mon. Wea. Rev., 141, 18841909, https://doi.org/10.1175/MWR-D-12-00115.1.

    • Search Google Scholar
    • Export Citation
  • Xie, L., S. Bao, L. J. Pietrafesa, K. Foley, and M. Fuentes, 2006: A real-time hurricane surface wind forecasting model: Formulation and verification. Mon. Wea. Rev., 134, 13551370, https://doi.org/10.1175/MWR3126.1.

    • Search Google Scholar
    • Export Citation
  • Yan, D., and T. Zhang, 2022: Research progress on tropical cyclone parametric wind field models and their application. Reg. Stud. Mar. Sci., 51, 102207, https://doi.org/10.1016/j.rsma.2022.102207.

    • Search Google Scholar
    • Export Citation
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