Editorial Type:
Article
Article Type:
Research Article

Evaluating Environmental Impacts on Tropical Cyclone Rapid Intensification Predictability Utilizing Statistical Models

John Kaplan * NOAA/AOML/Hurricane Research Division, Miami, Florida

Search for other papers by John Kaplan in
Current site
Google Scholar
PubMed Close
,
Christopher M. Rozoff CIMSS/University of Wisconsin–Madison, Madison, Wisconsin

Search for other papers by Christopher M. Rozoff in
Current site
Google Scholar
PubMed Close
,
Mark DeMaria NOAA/Center for Satellite Applications and Research, Fort Collins, Colorado

Search for other papers by Mark DeMaria in
Current site
Google Scholar
PubMed Close
,
Charles R. Sampson Naval Research Laboratory, Monterey, California

Search for other papers by Charles R. Sampson in
Current site
Google Scholar
PubMed Close
,
James P. Kossin NOAA/National Centers for Environmental Information, Asheville, North Carolina

Search for other papers by James P. Kossin in
Current site
Google Scholar
PubMed Close
,
Christopher S. Velden CIMSS/University of Wisconsin–Madison, Madison, Wisconsin

Search for other papers by Christopher S. Velden in
Current site
Google Scholar
PubMed Close
,
Joseph J. Cione * NOAA/AOML/Hurricane Research Division, Miami, Florida

Search for other papers by Joseph J. Cione in
Current site
Google Scholar
PubMed Close
,
Jason P. Dunion ** University of Miami/Cooperative Institute for Marine and Atmospheric Studies, Miami, Florida
* NOAA/AOML/Hurricane Research Division, Miami, Florida

Search for other papers by Jason P. Dunion in
Current site
Google Scholar
PubMed Close
,
John A. Knaff NOAA/Center for Satellite Applications and Research, Fort Collins, Colorado

Search for other papers by John A. Knaff in
Current site
Google Scholar
PubMed Close
,
Jun A. Zhang ** University of Miami/Cooperative Institute for Marine and Atmospheric Studies, Miami, Florida
* NOAA/AOML/Hurricane Research Division, Miami, Florida

Search for other papers by Jun A. Zhang in
Current site
Google Scholar
PubMed Close
,
John F. Dostalek Cooperative Institute for Atmospheres, Fort Collins, Colorado

Search for other papers by John F. Dostalek in
Current site
Google Scholar
PubMed Close
,
Jeffrey D. Hawkins Naval Research Laboratory, Monterey, California

Search for other papers by Jeffrey D. Hawkins in
Current site
Google Scholar
PubMed Close
,
Thomas F. Lee Naval Research Laboratory, Monterey, California

Search for other papers by Thomas F. Lee in
Current site
Google Scholar
PubMed Close
, and
Jeremy E. Solbrig Naval Research Laboratory, Monterey, California

Search for other papers by Jeremy E. Solbrig in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Oct 2015
DOI:
https://doi.org/10.1175/WAF-D-15-0032.1
Page(s):
1374–1396
Restricted access

Abstract

New multi-lead-time versions of three statistical probabilistic tropical cyclone rapid intensification (RI) prediction models are developed for the Atlantic and eastern North Pacific basins. These are the linear-discriminant analysis–based Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index (SHIPS-RII), logistic regression, and Bayesian statistical RI models. Consensus RI models derived by averaging the three individual RI model probability forecasts are also generated. A verification of the cross-validated forecasts of the above RI models conducted for the 12-, 24-, 36-, and 48-h lead times indicates that these models generally exhibit skill relative to climatological forecasts, with the eastern Pacific models providing somewhat more skill than the Atlantic ones and the consensus versions providing more skill than the individual models. A verification of the deterministic RI model forecasts indicates that the operational intensity guidance exhibits some limited RI predictive skill, with the National Hurricane Center (NHC) official forecasts possessing the most skill within the first 24 h and the numerical models providing somewhat more skill at longer lead times. The Hurricane Weather Research and Forecasting Model (HWRF) generally provides the most skillful RI forecasts of any of the conventional intensity models while the new consensus RI model shows potential for providing increased skill over the existing operational intensity guidance. Finally, newly developed versions of the deterministic rapid intensification aid guidance that employ the new probabilistic consensus RI model forecasts along with the existing operational intensity model consensus produce lower mean errors and biases than the intensity consensus model alone.

Current affiliation: NOAA/National Hurricane Center, Miami, Florida.

Retired.

Corresponding author address: John Kaplan, NOAA/AOML/Hurricane Research Division, 4301 Rickenbacker Cswy., Miami, FL 33149. E-mail: john.kaplan@noaa.gov

Abstract

New multi-lead-time versions of three statistical probabilistic tropical cyclone rapid intensification (RI) prediction models are developed for the Atlantic and eastern North Pacific basins. These are the linear-discriminant analysis–based Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index (SHIPS-RII), logistic regression, and Bayesian statistical RI models. Consensus RI models derived by averaging the three individual RI model probability forecasts are also generated. A verification of the cross-validated forecasts of the above RI models conducted for the 12-, 24-, 36-, and 48-h lead times indicates that these models generally exhibit skill relative to climatological forecasts, with the eastern Pacific models providing somewhat more skill than the Atlantic ones and the consensus versions providing more skill than the individual models. A verification of the deterministic RI model forecasts indicates that the operational intensity guidance exhibits some limited RI predictive skill, with the National Hurricane Center (NHC) official forecasts possessing the most skill within the first 24 h and the numerical models providing somewhat more skill at longer lead times. The Hurricane Weather Research and Forecasting Model (HWRF) generally provides the most skillful RI forecasts of any of the conventional intensity models while the new consensus RI model shows potential for providing increased skill over the existing operational intensity guidance. Finally, newly developed versions of the deterministic rapid intensification aid guidance that employ the new probabilistic consensus RI model forecasts along with the existing operational intensity model consensus produce lower mean errors and biases than the intensity consensus model alone.

Current affiliation: NOAA/National Hurricane Center, Miami, Florida.

Retired.

Corresponding author address: John Kaplan, NOAA/AOML/Hurricane Research Division, 4301 Rickenbacker Cswy., Miami, FL 33149. E-mail: john.kaplan@noaa.gov
Save
Cover Weather and Forecasting
Weather and Forecasting

  • Bosart, L. F., Velden C. S. , Bracken W. E. , Molinari J. , and Black P. G. , 2000: Environmental influences on the rapid intensification of Hurricane Opal (1995) over the Gulf of Mexico. Mon. Wea. Rev., 128, 322–352, doi:10.1175/1520-0493(2000)128<0322:EIOTRI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Carrasco, C. A., Landsea C. W. , and Lin Y. L. , 2014: The influence of tropical cyclone size on its intensification. Wea. Forecasting, 29, 582–590, doi:10.1175/WAF-D-13-00092.1.

    • Search Google Scholar
    • Export Citation

  • Chen, H., and Zhang D. L. , 2013: On the rapid intensification of Hurricane Wilma (2005). Part II: Convective bursts and the upper-level warm core. J. Atmos. Sci., 70, 146–162, doi:10.1175/JAS-D-12-062.1.

    • Search Google Scholar
    • Export Citation

  • Cione, J. J., and Uhlhorn E. W. , 2003: Sea surface temperature variability in hurricanes: Implications with respect to intensity change. Mon. Wea. Rev., 131, 1783–1796, doi:10.1175//2562.1.

    • Search Google Scholar
    • Export Citation

  • DeMaria, M., 2009: A simplified dynamical system for tropical cyclone intensity prediction. Mon. Wea. Rev., 137, 68–82, doi:10.1175/2008MWR2513.1.

    • 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, 326–337, doi:10.1175/1520-0434(1999)014<0326:AUSHIP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • DeMaria, M., DeMaria R. T. , Knaff J. A. , and Molenar D. , 2012: Tropical cyclone lightning and rapid intensity change. Mon. Wea. Rev., 140, 1828–1842, doi:10.1175/MWR-D-11-00236.1.

    • Search Google Scholar
    • Export Citation

  • DeMaria, M., Sampson C. R. , Knaff J. A. , and Musgrave K. D. , 2014: Is tropical cyclone intensity guidance improving? Bull. Amer. Meteor. Soc., 95, 387–398, doi:10.1175/BAMS-D-12-00240.1.

    • Search Google Scholar
    • Export Citation

  • Dowdy, S., and Wearden S. , 1991: Statistics for Research. 2nd ed. Wiley-Interscience, 555 pp.

  • Dunion, J. D., 2011: Rewriting the climatology of the tropical North Atlantic and Caribbean Sea. J. Climate, 24, 893–908, doi:10.1175/2010JCLI3496.1.

    • Search Google Scholar
    • Export Citation

  • Elsberry, R. L., Lambert T. D. B. , and Boothe M. A. , 2007: Accuracy of Atlantic and eastern North Pacific tropical cyclone intensity forecast guidance. Wea. Forecasting, 22, 747–762, doi:10.1175/WAF1015.1.

    • Search Google Scholar
    • Export Citation

  • Guimond, S. R., Heymsfield G. M. , and Turk F. J. , 2010: Multiscale observations of Hurricane Dennis (2005): The effects of hot towers on rapid intensification. J. Atmos. Sci., 67, 633–654, doi:10.1175/2009JAS3119.1.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., 1999: Hypothesis tests for evaluating numerical precipitation forecasts. Wea. Forecasting, 14, 155–167, doi:10.1175/1520-0434(1999)014<0155:HTFENP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hanley, D., Molinari J. , and Keyser D. , 2001: A composite study of the interactions between tropical cyclones and upper-tropospheric troughs. Mon. Wea. Rev., 129, 2570–2584, doi:10.1175/1520-0493(2001)129<2570:ACSOTI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hendricks, E. A., Peng M. S. , Fu B. , and Li T. , 2010: Quantifying environmental control on tropical cyclone intensity change. Mon. Wea. Rev., 138, 3243–3271, doi:10.1175/2010MWR3185.1.

    • Search Google Scholar
    • Export Citation

  • Hong, X., Chang S. W. , Raman S. , Shay L. K. , and Hodur R. , 2000: The interaction between Hurricane Opal (1995) and a warm core ring in the Gulf of Mexico. Mon. Wea. Rev., 128, 1347–1365, doi:10.1175/1520-0493(2000)128<1347:TIBHOA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Jiang, H., 2012: The relationship between tropical cyclone intensity change and the strength of inner-core convection. Mon. Wea. Rev., 140, 1164–1176, doi:10.1175/MWR-D-11-00134.1.

    • Search Google Scholar
    • Export Citation

  • Judt, F., Chen S. S. , and Berner J. , 2015: Predictability of tropical cyclone intensity: Scale-dependent forecast error growth in high-resolution stochastic kinetic-energy backscatter ensembles. Quart. J. Roy. Meteor. Soc., doi:10.1002/qj.2626, in press.

  • Kaplan, J., and DeMaria M. , 2003: Large-scale characteristics of rapidly intensifying tropical cyclones in the North Atlantic basin. Wea. Forecasting, 18, 1093–1108, doi:10.1175/1520-0434(2003)018<1093:LCORIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kaplan, J., DeMaria M. , and Knaff J. A. , 2010: A revised tropical cyclone rapid intensification index for the Atlantic and eastern North Pacific basins. Wea. Forecasting, 25, 220–241, doi:10.1175/2009WAF2222280.1.

    • Search Google Scholar
    • Export Citation

  • Kidder, S. Q., and Jones A. S. , 2007: A blended satellite total precipitable water product for operational forecasting. J. Atmos. Oceanic Technol., 24, 74–81, doi:10.1175/JTECH1960.1.

    • Search Google Scholar
    • Export Citation

  • Kieper, M., and Jiang H. , 2012: Predicting tropical cyclone rapid intensification using the 37 GHz ring pattern identified from passive microwave measurements. Geophys. Res. Lett., 39, L13804, doi:10.1029/2012GL052115.

    • Search Google Scholar
    • Export Citation

  • Knaff, J. A., 2008: Rapid tropical cyclone transitions to major hurricane intensity: Structural evolution of infrared imagery. Preprints, 28th Conf. on Hurricanes and Tropical Meteorology, Orlando, FL, Amer. Meteor. Soc., 15A.1. [Available online at http://ams.confex.com/ams/pdfpapers/137929.pdf.]

  • Knaff, J. A., DeMaria M. , Sampson C. R. , and Gross J. M. , 2003: Statistical 5-day tropical cyclone intensity forecasts derived from climatology and persistence. Wea. Forecasting, 18, 80–92, doi:10.1175/1520-0434(2003)018<0080:SDTCIF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Knaff, J. A., Longmore S. P. , and Molenar D. A. , 2014: An objective satellite-based tropical cyclone size climatology. J. Climate, 27, 455–476, doi:10.1175/JCLI-D-13-00096.1.

    • Search Google Scholar
    • Export Citation

  • Kossin, J. P., and Schubert W. H. , 2001: Mesovortices, polygonal flow patterns, and rapid pressure falls in hurricane-like vortices. J. Atmos. Sci., 58, 1079–1090, doi:10.1175/1520-0469(2001)058<1079:TDRITK>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kurihara, Y., Tuleya R. E. , and Bender M. A. , 1998: The GFDL Hurricane Prediction System and its performance in the 1995 hurricane season. Mon. Wea. Rev., 126, 1306–1322, doi:10.1175/1520-0493(1998)126<1306:TGHPSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Landsea, C. W., and Franklin J. L. , 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 3576–3592, doi:10.1175/MWR-D-12-00254.1.

    • Search Google Scholar
    • Export Citation

  • Molinari, J., and Vollaro D. , 1990: External influences on hurricane intensity. Part II: Vertical structure and response of the hurricane vortex. J. Atmos. Sci., 47, 1902–1918, doi:10.1175/1520-0469(1990)047<1902:EIOHIP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Montgomery, M. T., and Kallenbach R. J. , 1997: A theory for vortex Rossby waves and its application to spiral bands and intensity changes in hurricanes. Quart. J. Roy. Meteor. Soc., 123, 435–465, doi:10.1002/qj.49712353810.

    • Search Google Scholar
    • Export Citation

  • Nolan, D. S., and Grasso L. D. , 2003: Three-dimensional perturbations to balanced, hurricane-like vortices. Part II: Symmetric response and nonlinear simulations. J. Atmos. Sci., 60, 2717–2745, doi:10.1175/1520-0469(2003)060<2717:NTPTBH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Rappaport, E. N., Jiing J.-G. , Landsea C. W. , Murillo S. T. , and Franklin J. L. , 2012: The Joint Hurricane Test Bed: Its first decade of tropical cyclone research-to-operations activities reviewed. Bull. Amer. Meteor. Soc., 93, 371–380, doi:10.1175/BAMS-D-11-00037.1.

    • Search Google Scholar
    • Export Citation

  • Reasor, P. D., Eastin M. D. , and Gamache J. F. , 2009: Rapidly intensifying Hurricane Guillermo (1997). Part I: Low-wavenumber structure and evolution. Mon. Wea. Rev., 137, 603–631, doi:10.1175/2008MWR2487.1.

    • Search Google Scholar
    • Export Citation

  • Riemer, M., Montgomery M. T. , and Nicholls M. E. , 2013: Further examination of the thermodynamic modification of the inflow layer of tropical cyclones by vertical wind shear. Atmos. Chem. Phys., 13, 327–346, doi:10.5194/acp-13-327-2013.

    • Search Google Scholar
    • Export Citation

  • Rogers, R. F., Reasor P. D. , and Lorsolo S. , 2013: Airborne Doppler observations of the inner-core structural differences between intensifying and steady-state tropical cyclones. Mon. Wea. Rev., 141, 2970–2991, doi:10.1175/MWR-D-12-00357.1.

    • Search Google Scholar
    • Export Citation

  • Rogers, R. F., Reasor P. D. , and Zhang J. , 2015: Multiscale structure and evolution of Hurricane Earl (2010) during rapid intensification. Mon. Wea. Rev., 143, 536–562, doi:10.1175/MWR-D-14-00175.1.

    • Search Google Scholar
    • Export Citation

  • Rozoff, C. M., and Kossin J. P. , 2011: New probabilistic forecast models for the prediction of tropical cyclone rapid intensification. Wea. Forecasting, 26, 677–689, doi:10.1175/WAF-D-10-05059.1.

    • Search Google Scholar
    • Export Citation

  • Rozoff, C. M., Velden C. S. , Kaplan J. , Kossin J. P. , and Wimmers A. J. , 2015: Improvements in the probabilistic prediction of tropical cyclone rapid intensification with passive microwave observations. Wea. Forecasting, doi:10.1175/WAF-D-14-00109.1, in press.

  • Sampson, C. R., and Schrader A. J. , 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., Kaplan J. , Knaff J. A. , DeMaria M. , and Sisko C. A. , 2011: A deterministic rapid intensification aid. Wea. Forecasting, 26, 579–585, doi:10.1175/WAF-D-10-05010.1.

    • Search Google Scholar
    • Export Citation

  • Shay, L. K., Goni G. J. , and Black P. G. , 2000: Effects of a warm oceanic feature on Hurricane Opal. Mon. Wea. Rev., 128, 1366–1383, doi:10.1175/1520-0493(2000)128<1366:EOAWOF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Tallapragada, V., Kieu C. , Kwon Y. , Trahan S. , Liu Q. K. , Zhang Z. , and Kwon I.-H. , 2014: Evaluation of storm structure from the operational HWRF during 2012 implementation. Mon. Wea. Rev., 142, 4308–4325, doi:10.1175/MWR-D-13-00010.1.

    • Search Google Scholar
    • Export Citation

  • Wang, H., and Wang Y. , 2014: A numerical study of Typhoon Megi (2010). Part I: Rapid intensification. Mon. Wea. Rev., 142, 29–48, doi:10.1175/MWR-D-13-00070.1.

    • Search Google Scholar
    • Export Citation

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

  • Willoughby, H. E., 1990: Temporal changes of the primary circulation in tropical cyclones. J. Atmos. Sci., 47, 242–264, doi:10.1175/1520-0469(1990)047<0242:TCOTPC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2986 868 59
PDF Downloads 1830 431 30