Using Routinely Available Information to Estimate Tropical Cyclone Wind Structure

John A. Knaff NOAA/Center for Satellite Applications and Research, Fort Collins, Colorado

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Christopher J. Slocum Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Kate D. Musgrave Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

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Charles R. Sampson Naval Research Laboratory, Monterey, California

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Brian R. Strahl Joint Typhoon Warning Center, Pearl Harbor, Hawaii

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Abstract

A relatively simple method to estimate tropical cyclone (TC) wind radii from routinely available information including storm data (location, motion, and intensity) and TC size is introduced. The method is based on a combination of techniques presented in previous works and makes an assumption that TCs are largely symmetric and that asymmetries are based solely on storm motion and location. The method was applied to TC size estimates from two sources: infrared satellite imagery and global model analyses. The validation shows that the methodology is comparable with other objective methods based on the error statistics. The technique has a variety of practical research and operational applications, some of which are also discussed.

Corresponding author address: John Knaff, NOAA/RAMMB, CIRA/CSU, Campus Delivery 1375, Fort Collins, CO 80523-1365. E-mail: john.knaff@noaa.gov

Abstract

A relatively simple method to estimate tropical cyclone (TC) wind radii from routinely available information including storm data (location, motion, and intensity) and TC size is introduced. The method is based on a combination of techniques presented in previous works and makes an assumption that TCs are largely symmetric and that asymmetries are based solely on storm motion and location. The method was applied to TC size estimates from two sources: infrared satellite imagery and global model analyses. The validation shows that the methodology is comparable with other objective methods based on the error statistics. The technique has a variety of practical research and operational applications, some of which are also discussed.

Corresponding author address: John Knaff, NOAA/RAMMB, CIRA/CSU, Campus Delivery 1375, Fort Collins, CO 80523-1365. E-mail: john.knaff@noaa.gov
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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, 703–724, doi:10.1175/1520-0469(1997)054<0703:TEORFO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bender, M. A., I. Ginis, R. Tuleya, B. Thomas, and T. Marchok, 2007: The operational GFDL coupled hurricane–ocean prediction system and summary of its performance. Mon. Wea. Rev., 135, 3965–3989, doi:10.1175/2007MWR2032.1.

    • Search Google Scholar
    • Export Citation
  • Bender, M. A., M. Morin, T. Marchok, I. Ginis, B. Thomas, and R. E. Tuleya, 2015: Upgrades to the GFDL/GFDN Operational Hurricane Models Planned for 2015. 69th Interdepartmental Hurricane Conf., Jacksonville, FL, Office of the Federal Coordinator for Meteorological Services and Supporting Research, S3b-01. [Available online at http://www.ofcm.gov/ihc15/presentations/Session3b/S3b-01-Mbender2015IHC.Tuesday.pptx.]

  • Brown, D. P., 2015: Hurricane Gonzalo, 12–19 October 2014. National Hurricane Center Tropical Cyclone Rep. AL082014, 30 pp. [Available online at http://www.nhc.noaa.gov/data/tcr/AL082014_Gonzalo.pdf.]

  • Chan, K. T. F., and J. C. L. Chan, 2013: Angular momentum transports and synoptic flow patterns associated with tropical cyclone size change. Mon. Wea. Rev., 141, 3985–4007, doi:10.1175/MWR-D-12-00204.1.

    • Search Google Scholar
    • Export Citation
  • Chan, K. T. F., and J. C. L. Chan, 2014: Impacts of initial vortex size and planetary vorticity on tropical cyclone size. Quart. J. Roy. Meteor. Soc., 140, 2235–2248, doi:10.1002/qj.2292.

    • Search Google Scholar
    • Export Citation
  • Chavas, D. R., and K. A. Emanuel, 2010: A QuikSCAT climatology of tropical cyclone size. Geophys. Res. Lett., 37, L18816, doi:10.1029/2010GL044558.

    • Search Google Scholar
    • Export Citation
  • Chou, K.-H., C.-C. Wu, and S.-Z. Lin, 2013: Assessment of the ASCAT wind error characteristics by global dropwindsonde observations. J. Geophys. Res. Atmos., 118, 9011–9021, doi:10.1002/jgrd.50724.

    • Search Google Scholar
    • Export Citation
  • Courtney, J., and J. A. Knaff, 2009: Adapting the Knaff and Zehr wind–pressure relationship for operational use in tropical cyclone warning centres. Aust. Meteor. Oceanogr. J., 58, 167–179.

    • Search Google Scholar
    • Export Citation
  • DeMaria, M., M. Mainelli, L. K. Shay, J. A. Knaff, and J. Kaplan, 2005: Further improvements in the Statistical Hurricane Intensity Prediction Scheme (SHIPS). Wea. Forecasting, 20, 531–543, doi:10.1175/WAF862.1.

    • Search Google Scholar
    • Export Citation
  • DeMaria, M., J. A. Knaff, R. Knabb, C. Lauer, C. R. Sampson, and R. T. DeMaria, 2009: A new method for estimating tropical cyclone wind speed probabilities. Wea. Forecasting, 24, 1573–1591, doi:10.1175/2009WAF2222286.1.

    • Search Google Scholar
    • Export Citation
  • DeMaria, M., and Coauthors, 2013: Improvements to the operational tropical cyclone wind speed probability model. Wea. Forecasting, 28, 586–602, doi:10.1175/WAF-D-12-00116.1.

    • Search Google Scholar
    • Export Citation
  • Demuth, J., M. DeMaria, J. A. Knaff, and T. H. Vonder Haar, 2004: Validation of an Advanced Microwave Sounder Unit (AMSU) tropical cyclone intensity and size estimation algorithm. J. Appl. Meteor. Climatol., 43, 282–296, doi:10.1175/1520-0450(2004)043<0282:EOAMSU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Demuth, J., M. DeMaria, and J. A. Knaff, 2006: Improvement of Advanced Microwave Sounding Unit tropical cyclone intensity and size estimation algorithms. J. Appl. Meteor. Climatol., 45, 1573–1581, doi:10.1175/JAM2429.1.

    • Search Google Scholar
    • Export Citation
  • Developmental Testbed Center, 2015: Gridpoint Statistical Interpolation advanced user’s guide version 3.4.0.0. Developmental Testbed Center, 143 pp. [Available online at http://www.dtcenter.org/com-GSI/users/docs/index.php.]

  • Dvorak, V. F., 1984: Tropical cyclone intensity analysis using satellite data. NOAA Tech. Rep. 11, 45 pp. [Available from NOAA/NESDIS, NOAA/Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740.]

  • Emanuel, K., S. Ravela, E. Vivant, and C. Risi, 2006: A statistical deterministic approach to hurricane risk assessment. Bull. Amer. Meteor. Soc., 87, 299–314, doi:10.1175/BAMS-87-3-299.

    • 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, 2249–2269, doi:10.1175/1520-0493(2001)129<2249:EOVWSO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Herndon, D., and C. Velden, 2004: Upgrades to the UW-CIMSS AMSU-based tropical cyclone intensity estimation algorithm. 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 4D.1. [Available online at https://ams.confex.com/ams/pdfpapers/75933.pdf.]

  • Herndon, D., C. Velden, and J. Hawkins, 2012: Update on SATellite-based CONsensus (SATCON) approach to TC intensity estimation. 30th Conf. on Hurricanes and Tropical Meteorology, Ponte Vedra Beach, FL, Amer. Meteor. Soc., 7C.2. [Available online at https://ams.confex.com/ams/30Hurricane/webprogram/Paper205129.html.]

  • Holmlund, K., C. Velden, and M. Rohn, 2001: Enhanced AUTOMATED quality control applied to high-density satellite-derived winds. Mon. Wea. Rev., 129, 517–529, doi:10.1175/1520-0493(2001)129<0517:EAQCAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Jones, W. L., W. L. Grantham, L. C. Schroeder, J. W. Johnson, C. T. Swift, and J. L. Mitchell, 1975: Microwave scattering from the ocean surface. IEEE Trans. Microwave Theory Tech., 23, 1053–1058, doi:10.1109/TMTT.1975.1128742.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge University Press, 341 pp.

  • Knaff, J. A., and R. M. Zehr, 2007: Reexamination of tropical cyclone wind–pressure relationships. Wea Forecasting, 22, 71–88, doi:10.1175/WAF965.1.

    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., and B. A. Harper, 2010: KN1: Tropical cyclone surface wind structure and wind-pressure relationships. Proc. WMO Seventh Int. Workshop on Tropical Cyclones, La Reunion, France, WMO, 35 pp. [Available online at http://www.wmo.int/pages/prog/arep/wwrp/tmr/otherfileformats/documents/KN1.pdf.]

  • Knaff, J. A., and C. R. Sampson, 2015: After a decade are Atlantic tropical cyclone gale force wind radii forecasts now skillful? Wea. Forecasting, 30, 702–709, doi:10.1175/WAF-D-14-00149.1.

    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., C. R. Sampson, M. DeMaria, T. P. Marchok, J. M. Gross, and C. J. McAdie, 2007: Statistical tropical cyclone wind radii prediction using climatology and persistence. Wea. Forecasting, 22, 781–791, doi:10.1175/WAF1026.1.

    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., M. DeMaria, D. A. Molenar, C. R. Sampson, and M. G. Seybold, 2011: An automated, objective, multisatellite platform tropical cyclone surface wind analysis. J. Appl. Meteor. Climatol., 50, 2149–2166, doi:10.1175/2011JAMC2673.1.

    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., S. P. Longmore, and D. A. Molenar, 2014: An objective satellite-based tropical cyclone size climatology. J. Climate, 27, 455–476, doi:10.1175/JCLI-D-13-00096.1; Corrigendum, 28, 8648–8651, doi:10.1175/JCLI-D-15-0610.1.

    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., S. P. Longmore, R. T. DeMaria, and D. A. Molenar, 2015: Improved tropical cyclone flight-level wind estimates using routine infrared satellite reconnaissance. J. Appl. Meteor. Climatol., 54, 463–478, doi:10.1175/JAMC-D-14-0112.1.

    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., J. A. Knaff, H. I. Berger, D. C. Herndon, T. A. Cram, C. S. Velden, R. J. Murnane, and J. D. Hawkins, 2007: Estimating hurricane wind structure in the absence of aircraft reconnaissance. Wea. Forecasting, 22, 89–101, doi:10.1175/WAF985.1.

    • Search Google Scholar
    • Export Citation
  • Lazarus, S. M., S. T. Wilson, M. E. Splitt, and G. A. Zarillo, 2013: Evaluation of a wind-wave system for ensemble tropical cyclone wave forecasting. Part I: Winds. Wea. Forecasting, 28, 297–315, doi:10.1175/WAF-D-12-00054.1.

    • Search Google Scholar
    • Export Citation
  • Lee, C. S., K. K. W. Cheung, W.-T. Fang, and R. L. Elsberry, 2010: Initial maintenance of tropical cyclone size in the western North Pacific. Mon. Wea. Rev., 138, 3207–3223, doi:10.1175/2010MWR3023.1.

    • Search Google Scholar
    • Export Citation
  • Loridan, T., E. Scherer, M. Dixon, E. Bellone, and S. Khare, 2014: Cyclone wind field asymmetries during extratropical transition in the western North Pacific. J. Appl. Meteor. Climatol., 53, 421–428, doi:10.1175/JAMC-D-13-0257.1.

    • Search Google Scholar
    • Export Citation
  • Maclay, K. S., M. DeMaria, and T. H. Vonder Haar, 2008: Tropical cyclone inner core kinetic energy evolution. Mon. Wea. Rev., 136, 4882–4898, doi:10.1175/2008MWR2268.1.

    • Search Google Scholar
    • Export Citation
  • Merrill, R. T., 1984: A comparison of large and small tropical cyclones. Mon. Wea. Rev., 112, 1408–1418, doi:10.1175/1520-0493(1984)112<1408:ACOLAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mueller, K. J., M. DeMaria, J. A. Knaff, J. P. Kossin, and T. H. Vonder Haar, 2006: Objective estimation of tropical cyclone wind structure from infrared satellite data. Wea. Forecasting, 21, 990–1005, doi:10.1175/WAF955.1.

    • Search Google Scholar
    • Export Citation
  • Musgrave, K. D., R. K. Taft, J. L. Vigh, B. D. McNoldy, and W. H. Schubert, 2012: Time evolution of the intensity and size of tropical cyclones. J. Adv. Model. Earth Syst., 4, M08001, doi:10.1029/2011MS000104.

    • Search Google Scholar
    • Export Citation
  • Olander, T. L., and C. S. Velden, 2007: The advanced Dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery. Wea. Forecasting, 22, 287–298, doi:10.1175/WAF975.1.

    • Search Google Scholar
    • Export Citation
  • Ooyama, K., 1969: Numerical simulation of the life cycle of tropical cyclones. J. Atmos. Sci., 26, 3–40, doi:10.1175/1520-0469(1969)026<0003:NSOTLC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Probst, P., and G. Franchello, 2012: Global storm surge forecast and inundation modeling. EUR—Scientific and Technical Research Series, EUR 25233 EN-2012, Joint Research Centre, European Commission, 47 pp., doi:10.2788/14951.

  • Reasor, P. D., M. T. Montgomery, and L. D. Grasso, 2004: A new look at the problem of tropical cyclones in vertical shear flow: Vortex resiliency. J. Atmos. Sci., 61, 3–22, doi:10.1175/1520-0469(2004)061<0003:ANLATP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • RAMMB/CIRA, 2015: SHIPS: Statistical tropical cyclone intensity forecast technique development. Colorado State University, Fort Collins, CO. [Available online at http://rammb.cira.colostate.edu/research/tropical_cyclones/ships/.]

  • Rozoff, C. M., D. S. Nolan, J. P. Kossin, F. Zhang, and J. Fang, 2012: The roles of an expanding wind field and inertial stability in tropical cyclone secondary eyewall formation. J. Atmos. Sci., 69, 2621–2643, doi:10.1175/JAS-D-11-0326.1.

    • Search Google Scholar
    • Export Citation
  • Sampson, C. R., and A. J. Schrader, 2000: The Automated Tropical Cyclone Forecasting System (version 3.2). Bull. Amer. Meteor. Soc., 81, 1231–1240, doi:10.1175/1520-0477(2000)081<1231:TATCFS>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sampson, C. R., and J. A. Knaff, 2015: A consensus forecast for tropical cyclone gale wind radii. Wea. Forecasting, 30, 1397–1403, doi:10.1175/WAF-D-15-0009.1.

    • Search Google Scholar
    • Export Citation
  • Sampson, C. R., P. A. Wittmann, and H. L. Tolman, 2010: Consistent tropical cyclone wind and wave forecasts for the U.S. Navy. Wea. Forecasting, 25, 1293–1306, doi:10.1175/2010WAF2222376.1.

    • Search Google Scholar
    • Export Citation
  • Sampson, C. R., and Coauthors, 2012: Objective guidance for use in setting tropical cyclone conditions of readiness. Wea. Forecasting, 27, 1052–1060, doi:10.1175/WAF-D-12-00008.1.

    • Search Google Scholar
    • Export Citation
  • Schubert, W. H., and J. J. Hack, 1982: Inertial stability and tropical cyclone development. J. Atmos. Sci., 39, 1687–1697, doi:10.1175/1520-0469(1982)039<1687:ISATCD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Shapiro, L. J., and H. E. Willoughby, 1982: The response of balanced hurricanes to local sources of heat and momentum. J. Atmos. Sci., 39, 378–394, doi:10.1175/1520-0469(1982)039<0378:TROBHT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sitkowski, M., J. P. Kossin, and C. M. Rozoff, 2011: Intensity and structure changes during hurricane eyewall replacement cycles. Mon. Wea. Rev., 139, 3829–3847, doi:10.1175/MWR-D-11-00034.1.

    • Search Google Scholar
    • Export Citation
  • Smith, R. K., C. W. Schmidt, and M. T. Montgomery, 2011: An investigation of rotational influences on tropical-cyclone size and intensity. Quart. J. Roy. Meteor. Soc., 137, 1841–1855, doi:10.1002/qj.862.

    • Search Google Scholar
    • Export Citation
  • Tallapragada, V., and Coauthors, 2014: Hurricane Weather Research and Forecasting (HWRF) model: 2014 scientific documentation. Tech. Doc. HWRFv3.6a, Developmental Testbed Center, 105 pp. [Available online at http://www.dtcenter.org/HurrWRF/users/docs/scientific_documents/HWRFv3.6a_ScientificDoc.pdf.]

  • 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, 1290–1311, doi:10.1175/MWR-D-13-00249.1.

    • Search Google Scholar
    • Export Citation
  • Velden, C., and Coauthors, 2005: Recent innovations in deriving tropospheric winds from meteorological satellites. Bull. Amer. Meteor. Soc., 86, 205–223, doi:10.1175/BAMS-86-2-205.

    • Search Google Scholar
    • Export Citation
  • Velden, C., and Coauthors, 2006: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years. Bull. Amer. Meteor. Soc., 87, 1195–1210, doi:10.1175/BAMS-87-9-1195.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. International Geophysics Series, Vol. 91, Academic Press, 627 pp.

  • Wu, L., W. Tian, Q. Liu, J. Cao, and J. A. Knaff, 2015: Implications of the observed relationship between tropical cyclone size and intensity over the western North Pacific. J. Climate, 28, 9501–9506, doi:10.1175/JCLI-D-15-0628.1.

    • Search Google Scholar
    • Export Citation
  • Xu, J., and Y. Wang, 2010: Sensitivity of the simulated tropical cyclone inner-core size to the initial vortex size. Mon. Wea. Rev., 138, 4135–4157, doi:10.1175/2010MWR3335.1.

    • Search Google Scholar
    • Export Citation
  • Xu, J., and Y. Wang, 2015: A statistical analysis on the dependence of tropical cyclone intensification rate on the storm intensity and size in the North Atlantic. Wea. Forecasting, 30, 692–701, doi:10.1175/WAF-D-14-00141.1.

    • Search Google Scholar
    • Export Citation
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