An Operational Rapid Intensification Prediction Aid for the Western North Pacific

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

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Charles R. Sampson Naval Research Laboratory, Monterey, California

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

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Abstract

This work describes tropical cyclone rapid intensification forecast aids designed for the western North Pacific tropical cyclone basin and for use at the Joint Typhoon Warning Center. Two statistical methods, linear discriminant analysis and logistic regression, are used to create probabilistic forecasts for seven intensification thresholds including 25-, 30-, 35-, and 40-kt changes in 24 h, 45- and 55-kt in 36 h, and 70-kt in 48 h (1 kt = 0.514 m s−1). These forecast probabilities are further used to create an equally weighted probability consensus that is then used to trigger deterministic forecasts equal to the intensification thresholds once the probability in the consensus reaches 40%. These deterministic forecasts are incorporated into an operational intensity consensus forecast as additional members, resulting in an improved intensity consensus for these important and difficult to predict cases. Development of these methods is based on the 2000–15 typhoon seasons, and independent performance is assessed using the 2016 and 2017 typhoon seasons. In many cases, the probabilities have skill relative to climatology and adding the rapid intensification deterministic aids to the operational intensity consensus significantly reduces the negative forecast biases.

© 2018 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: John Knaff, john.knaff@noaa.gov

Abstract

This work describes tropical cyclone rapid intensification forecast aids designed for the western North Pacific tropical cyclone basin and for use at the Joint Typhoon Warning Center. Two statistical methods, linear discriminant analysis and logistic regression, are used to create probabilistic forecasts for seven intensification thresholds including 25-, 30-, 35-, and 40-kt changes in 24 h, 45- and 55-kt in 36 h, and 70-kt in 48 h (1 kt = 0.514 m s−1). These forecast probabilities are further used to create an equally weighted probability consensus that is then used to trigger deterministic forecasts equal to the intensification thresholds once the probability in the consensus reaches 40%. These deterministic forecasts are incorporated into an operational intensity consensus forecast as additional members, resulting in an improved intensity consensus for these important and difficult to predict cases. Development of these methods is based on the 2000–15 typhoon seasons, and independent performance is assessed using the 2016 and 2017 typhoon seasons. In many cases, the probabilities have skill relative to climatology and adding the rapid intensification deterministic aids to the operational intensity consensus significantly reduces the negative forecast biases.

© 2018 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: John Knaff, john.knaff@noaa.gov
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  • Atkinson, G. D., and C. R. Holliday, 1977: Tropical cyclone minimum sea level pressure/maximum sustained wind relationship for the western North Pacific. Mon. Wea. Rev., 105, 421427, https://doi.org/10.1175/1520-0493(1977)105<0421:TCMSLP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnes, S. L., 1964: A technique for maximizing details in numerical weather map analysis. J. Appl. Meteor., 3, 396409, https://doi.org/10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bates, J., and C. Granger, 1969: The combination of forecasts. Oper. Res. Quart., 20, 451468, https://doi.org/10.1057/jors.1969.103.

  • Brand, S., 1973: Rapid intensification and low-latitude weakening of tropical cyclones of the western North Pacific Ocean. J. Appl. Meteor., 12, 94103, https://doi.org/10.1175/1520-0450(1973)012<0094:RIALLW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Callaghan, J., and K. Tory, 2014: On the use of a system-scale ascent/descent diagnostic for short-term forecasting of tropical cyclone development, intensification and decay. Trop. Cyclone Res. Rev., 3, 7890.

    • Search Google Scholar
    • Export Citation
  • Carrasco, C. A., C. W. Landsea, and Y. Lin, 2014: The influence of tropical cyclone size on its intensification. Wea. Forecasting, 29, 582590, https://doi.org/10.1175/WAF-D-13-00092.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CAWCR, 2017: Forecast verification - Issues, methods and FAQ. Collaboration for Australian Weather and Climate Research, http://www.cawcr.gov.au/projects/verification/verif_web_page.html.

  • Chavas, D. R., K. A. Reed, and J. A. Knaff, 2017: Physical understanding of the tropical cyclone wind–pressure relationship. Nat. Commun., 8, 1360, https://doi.org/10.1038/s41467-017-01546-9.

    • Crossref
    • 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, 167179, https://doi.org/10.22499/2.5803.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CSIRO, 2017: Software from Alan J. Miller. Commonwealth Scientific and Industrial Research Organisation, http://wp.csiro.au/alanmiller/.

  • DeMaria, M., and J. Kaplan, 1999: An updated Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic and eastern North Pacific basins. Wea. Forecasting, 14, 326337, https://doi.org/10.1175/1520-0434(1999)014<0326:AUSHIP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., J. A. Knaff, and C. R. Sampson, 2007: Evaluation of long-term trend in tropical cyclone intensity forecasts. Meteor. Atmos. Phys., 97, 1928, https://doi.org/10.1007/s00703-006-0241-4.

    • 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
  • Dvorak, V. F., 1984: Tropical cyclone intensity analysis using satellite data. NOAA Tech. Rep. 11, 47 pp., http://severe.worldweather.wmo.int/TCFW/RAI_Training/Dvorak_1984.pdf.

  • Elliot, G., and Coauthors, 2014: Topic 1, Motion - Recent advances. Rep. on the Eighth International Workshop on Tropical Cyclones, WMO, http://www.wmo.int/pages/prog/arep/wwrp/new/documents/Topic1_AdvancesinForecastingMotion.pdf.

  • Finocchio, P. M., and S. J. Majumdar, 2017: A statistical perspective on wind profiles and vertical wind shear in tropical cyclone environments of the Northern Hemisphere. Mon. Wea. Rev., 145, 361378, https://doi.org/10.1175/MWR-D-16-0221.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fisher, R. A., 1936: The use of multiple measurements in taxonomic problems. Ann. Eugen., 7, 179188, https://doi.org/10.1111/j.1469-1809.1936.tb02137.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fitzpatrick, P. J., 1997: Understanding and forecasting tropical cyclone intensity change with the Typhoon Intensity Prediction Scheme (TIPS). Wea. Forecasting, 12, 826846, https://doi.org/10.1175/1520-0434(1997)012<0826:UAFTCI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Graefe, A., J. S. Armstrong, R. J. Jones, and A. G. Cuzán, 2014: Combining forecasts: An application to elections. Int. J. Forecasting, 30, 4354, https://doi.org/10.1016/j.ijforecast.2013.02.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holliday, C. R., 1977: On the rapid intensification of typhoons. M.S. thesis, Department of Meteorology, Texas A&M University, 87 pp., http://hdl.handle.net/1969.1/ETD-TAMU-1977-THESIS-H739.

  • Holliday, C. R., and A. H. Thompson, 1979: Climatological characteristics of rapidly intensifying typhoons. Mon. Wea. Rev., 107, 10221034, https://doi.org/10.1175/1520-0493(1979)107<1022:CCORIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IMSL, 2017: IMSL Fortran Numerical Stat Library. International Mathematics and Statistics Library, http://docs.roguewave.com/imsl/fortran/7.0/stat/stat.htm.

  • Jones, R. C., 2014: Making better (investment) decisions. J. Portfolio Manage., 40, 128143, https://doi.org/10.3905/jpm.2014.40.2.128.

  • 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 east Pacific basins. Wea. Forecasting, 25, 220241, https://doi.org/10.1175/2009WAF2222280.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaplan, J., and Coauthors, 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
  • Knaff, J. A., and R. M. Zehr, 2007: Reexamination of tropical cyclone wind–pressure relationships. Wea. Forecasting, 22, 7188, https://doi.org/10.1175/WAF965.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., and R. T. DeMaria, 2017: Forecasting tropical cyclone eye formation and dissipation in infrared imagery. Wea. Forecasting, 32, 21032116, https://doi.org/10.1175/WAF-D-17-0037.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., C. R. Sampson, and M. DeMaria, 2005: An operational statistical typhoon intensity prediction scheme for the western North Pacific. Wea. Forecasting, 20, 688699, https://doi.org/10.1175/WAF863.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., D. P. Brown, J. Courtney, G. M. Gallina, and J. L. Beven II, 2010: An evaluation of Dvorak technique–based tropical cyclone intensity estimates. Wea. Forecasting, 25, 13621379, https://doi.org/10.1175/2010WAF2222375.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., M. DeMaria, S. P. Longmore, and R. T. DeMaria, 2014a: Improving tropical cyclone guidance tools by accounting for variations in size. 31st Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Amer. Meteor. Soc., 51, https://ams.confex.com/ams/31Hurr/webprogram/Paper244165.html.

  • Knaff, J. A., S. P. Longmore, and D. A. Molenar, 2014b: An objective satellite-based tropical cyclone size climatology. J. Climate, 27, 455476, https://doi.org/10.1175/JCLI-D-13-00096.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., C. J. Slocum, K. D. Musgrave, C. R. Sampson, and B. R. Strahl, 2016: Using routinely available information to estimate tropical cyclone wind structure. Mon. Wea. Rev., 144, 12331247, https://doi.org/10.1175/MWR-D-15-0267.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mundell, D. B., 1990: Prediction of tropical cyclone rapid intensification events. M.S. thesis, Dept. of Atmospheric Science, Colorado State University, 186 pp. [Available from Dept. of Atmospheric Science, Colorado State University, 3915 W. Laporte Ave., Fort Collins, CO 80523.]

  • Murphy, A. H., 1973: A new vector partition of the probability score. J. Appl. Meteor., 12, 595600, https://doi.org/10.1175/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Musgrave, K. D., 2011: Tropical cyclone inner core structure and intensity change. Ph.D. dissertation, Colorado State University, 103 pp. [Available from Dept. of Atmospheric Science, Colorado State University, 3915 W. Laporte Ave., Fort Collins, CO 80523.]

  • Neumann, C. J., and M. B. Lawrence, 1975: An operational experiment in the statistical-dynamical prediction of tropical cyclone motion. Mon. Wea. Rev., 103, 665673, https://doi.org/10.1175/1520-0493(1975)103<0665:AOEITS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • RAMMB, 2017: SHIPS developmental data. Regional and Mesoscale Meteorology Branch, Cooperative Institute for Research in the Atmosphere, Colorado State University, http://rammb.cira.colostate.edu/research/tropical_cyclones/ships/developmental_data.asp.

  • Sampson, C. R., and A. J. Schrader, 2000: The Automated Tropical Cyclone Forecasting system (version 3.2). Bull. Amer. Meteor. Soc., 81, 12311240, https://doi.org/10.1175/1520-0477(2000)081<1231:TATCFS>2.3.CO;2.

    • Crossref
    • 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, 13971403, https://doi.org/10.1175/WAF-D-15-0009.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sampson, C. R., J. L. Franklin, J. A. Knaff, and M. DeMaria, 2008: Experiments with a simple tropical cyclone intensity consensus. Wea. Forecasting, 23, 304312, https://doi.org/10.1175/2007WAF2007028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sampson, C. R., J. Kaplan, J. A. Knaff, M. DeMaria, and C. Sisko, 2011: A deterministic rapid intensification aid. Wea. Forecasting, 26, 579585, https://doi.org/10.1175/WAF-D-10-05010.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sanders, F., 1973: Skill in forecasting daily temperature and precipitation: Some experimental results. Bull. Amer. Meteor. Soc., 54, 11711178, https://doi.org/10.1175/1520-0477(1973)054<1171:SIFDTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shay, L. K., G. J. Goni, and P. G. Black, 2000: Effects of a warm oceanic feature on Hurricane Opal. Mon. Wea. Rev., 128, 13661383, https://doi.org/10.1175/1520-0493(2000)128<1366:EOAWOF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shu, S., J. Ming, and P. Chi, 2012: Large-scale characteristics and probability of rapidly intensifying tropical cyclones in the western North Pacific basin. Wea. Forecasting, 27, 411423, https://doi.org/10.1175/WAF-D-11-00042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., and C. Snyder, 2012: Uncertainty of tropical cyclone best-track information. Wea. Forecasting, 27, 715729, https://doi.org/10.1175/WAF-D-11-00085.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velden, C. S., and J. Sears, 2014: Computing deep-tropospheric vertical wind shear analyses for tropical cyclone applications: Does the methodology matter? Wea. Forecasting, 29, 11691180, https://doi.org/10.1175/WAF-D-13-00147.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., Y. Rao, Z. Tan, and D. Schönemann, 2015: A statistical analysis of the effects of vertical wind shear on tropical cyclone intensity change over the western North Pacific. Mon. Wea. Rev., 143, 34343453, https://doi.org/10.1175/MWR-D-15-0049.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weatherford, C., 1989: The structural evolution of typhoons. Dept. of Atmospheric Science Paper 446, Colorado State University, Fort Collins, CO, 198 pp., https://tropical.colostate.edu/media/sites/111/2016/10/446_Weatherford.pdf.

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences: An Introduction. 2nd ed. Academic Press, 627 pp.

  • 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, 692701, https://doi.org/10.1175/WAF-D-14-00141.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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