Understanding Biases in Tropical Cyclone Intensity Forecast Error

Wei Na State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

Search for other papers by Wei Na in
Current site
Google Scholar
PubMed
Close
,
John L. McBride School of Earth Science, University of Melbourne, and Research and Development Division, Bureau of Meteorology, Melbourne, Victoria, Australia

Search for other papers by John L. McBride in
Current site
Google Scholar
PubMed
Close
,
Xing-Hai Zhang State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, and Glarun Technology, Fourteenth Research Institute, China Electronic Technology Group Corporation, Nanjing, China

Search for other papers by Xing-Hai Zhang in
Current site
Google Scholar
PubMed
Close
, and
Yi-Hong Duan State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

Search for other papers by Yi-Hong Duan in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The characteristics of 24-h official forecast errors (OFEs) of tropical cyclone (TC) intensity are analyzed over the North Atlantic, east Pacific, and western North Pacific. The OFE is demonstrated to be strongly anticorrelated with TC intensity change with correlation coefficients of −0.77, −0.77, and −0.68 for the three basins, respectively. The 24-h intensity change in the official forecast closely follows a Gaussian distribution with a standard deviation only ⅔ of that in nature, suggesting the current official forecasts estimate fewer cases of large intensity change. The intensifying systems tend to produce negative errors (underforecast), while weakening systems have consistent positive errors (overforecast). This asymmetrical bias is larger for extreme intensity change, including rapid intensification (RI) and rapid weakening (RW). To understand this behavior, the errors are analyzed in a simple objective model, the trend-persistence model (TPM). The TPM exhibits the same error-intensity change correlation. In the TPM, the error can be understood as it is exactly inversely proportional to the finite difference form of the concavity or second derivative of the intensity–time curve. The occurrence of large negative (positive) errors indicates the intensity–time curve is concave upward (downward) in nature during the TC’s rapid intensification (weakening) process. Thus, the fundamental feature of the OFE distribution is related to the shape of the intensity–time curve, governed by TC dynamics. All forecast systems have difficulty forecasting an accelerating rate of change, or a large second derivative of the intensity–time curve. TPM may also be useful as a baseline in evaluating the skill of official forecasts. According to this baseline, official forecasts are more skillful in RW than in RI.

© 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: Dr. Duan Yi-Hong, duanyh@cma.gov.cn

Abstract

The characteristics of 24-h official forecast errors (OFEs) of tropical cyclone (TC) intensity are analyzed over the North Atlantic, east Pacific, and western North Pacific. The OFE is demonstrated to be strongly anticorrelated with TC intensity change with correlation coefficients of −0.77, −0.77, and −0.68 for the three basins, respectively. The 24-h intensity change in the official forecast closely follows a Gaussian distribution with a standard deviation only ⅔ of that in nature, suggesting the current official forecasts estimate fewer cases of large intensity change. The intensifying systems tend to produce negative errors (underforecast), while weakening systems have consistent positive errors (overforecast). This asymmetrical bias is larger for extreme intensity change, including rapid intensification (RI) and rapid weakening (RW). To understand this behavior, the errors are analyzed in a simple objective model, the trend-persistence model (TPM). The TPM exhibits the same error-intensity change correlation. In the TPM, the error can be understood as it is exactly inversely proportional to the finite difference form of the concavity or second derivative of the intensity–time curve. The occurrence of large negative (positive) errors indicates the intensity–time curve is concave upward (downward) in nature during the TC’s rapid intensification (weakening) process. Thus, the fundamental feature of the OFE distribution is related to the shape of the intensity–time curve, governed by TC dynamics. All forecast systems have difficulty forecasting an accelerating rate of change, or a large second derivative of the intensity–time curve. TPM may also be useful as a baseline in evaluating the skill of official forecasts. According to this baseline, official forecasts are more skillful in RW than in RI.

© 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: Dr. Duan Yi-Hong, duanyh@cma.gov.cn
Save
  • Cangialosi, J. P., and J. L. Franklin, 2014: 2013 National Hurricane Center Forecast verification report. NOAA/National Hurricane Center, 84 pp., http://www.nhc.noaa.gov/verification/pdfs/Verification_2013.pdf.

  • Cha, D., and Y. Wang, 2013: A dynamical initialization scheme for real-time forecasts of tropical cyclones using the WRF Model. Mon. Wea. Rev., 141, 964986, https://doi.org/10.1175/MWR-D-12-00077.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Curry, J. A., P. J. Webster, and G. J. Holland, 2006: Mixing politics and science in testing the hypothesis that greenhouse warming is causing a global increase in hurricane intensity. Bull. Amer. Meteor. Soc., 87, 10251037, https://doi.org/10.1175/BAMS-87-8-1025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., M. Mainelli, L. K. Shay, 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 C. Sampson, 2007: Evaluation of long-term trends 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
  • Emanuel, K., 2005: Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, 686, https://doi.org/10.1038/nature03906.

  • Emanuel, K., 2017: Will global warming make hurricane forecasting more difficult? Bull. Amer. Meteor. Soc., 98, 495501, https://doi.org/10.1175/BAMS-D-16-0134.1.

    • 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
  • Franklin, J. L., C. J. McAdie, and M. B. Lawrence, 2003: Trends in track forecasting for tropical cyclones threatening the United States, 1970–2001. Bull. Amer. Meteor. Soc., 84, 11971203, https://doi.org/10.1175/BAMS-84-9-1197.

    • 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
  • Gopalakrishnan, S. G., F. Marks, X. Zhang, J. Bao, K. Yeh, and R. Atlas, 2011: The experimental HWRF system: A study on the influence of horizontal resolution on the structure and intensity changes in tropical cyclones using an idealized framework. Mon. Wea. Rev., 139, 17621784, https://doi.org/10.1175/2010MWR3535.1.

    • 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., 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
  • Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone data. Bull. Amer. Meteor. Soc., 91, 363376, https://doi.org/10.1175/2009BAMS2755.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kowch, R., and K. Emanuel, 2015: Are special processes at work in the rapid intensification of tropical cyclones? Mon. Wea. Rev., 143, 878882, https://doi.org/10.1175/MWR-D-14-00360.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, C.-Y., M. Tippett, A. H. Sobel, and S. J. Camargo, 2016: Autoregressive modeling for tropical cyclone intensity climatology. J. Climate, 29, 78157830, https://doi.org/10.1175/JCLI-D-15-0909.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, H., and Z. Tan, 2016: A dynamical initialization scheme for binary tropical cyclones. Mon. Wea. Rev., 144, 47874803, https://doi.org/10.1175/MWR-D-16-0176.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McAdie, C. J., and M. B. Lawrence, 2000: Improvements in tropical cyclone track forecasting in the Atlantic basin, 1970–98. Bull. Amer. Meteor. Soc., 81, 989997, https://doi.org/10.1175/1520-0477(2000)081<0989:IITCTF>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mei, W., and S. Xie, 2016: Intensification of landfalling typhoons over the northwest Pacific since the late 1970s. Nat. Geosci., 9, 753757, https://doi.org/10.1038/ngeo2792.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peng, X., J. Fei, X. Huang, and X. Cheng, 2017: Evaluation and error analysis of official forecasts of tropical cyclones during 2005–14 over the western North Pacific. Part I: Storm tracks. Wea. Forecasting, 32, 689712, https://doi.org/10.1175/WAF-D-16-0043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Powell, M. D., and S. D. Aberson, 2001: Accuracy of United States tropical cyclone landfall forecasts in the Atlantic basin (1976–2000). Bull. Amer. Meteor. Soc., 82, 27492767, https://doi.org/10.1175/1520-0477(2001)082<2749:AOUSTC>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, R., S. Lorsolo, P. Reasor, J. Gamache, and F. Marks, 2012: Multiscale analysis of tropical cyclone kinematic structure from airborne Doppler radar composites. Mon. Wea. Rev., 140, 7799, https://doi.org/10.1175/MWR-D-10-05075.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ruf, C. S., and Coauthors, 2016: New ocean winds satellite mission to probe hurricanes and tropical convection. Bull. Amer. Meteor. Soc., 97, 385395, https://doi.org/10.1175/BAMS-D-14-00218.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schubert, W. H., and J. J. Hack, 1982: Inertial stability and tropical cyclone development. J. Atmos. Sci., 39, 16871697, https://doi.org/10.1175/1520-0469(1982)039<1687:ISATCD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tallapragada, V., and Coauthors, 2016: Forecasting tropical cyclones in the western North Pacific basin using the NCEP operational HWRF model: Model upgrades and evaluation of real-time performance in 2013. Wea. Forecasting, 31, 877894, https://doi.org/10.1175/WAF-D-14-00139.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vigh, J. L., and W. H. Schubert, 2009: Rapid development of the tropical cyclone warm core. J. Atmos. Sci., 66, 33353350, https://doi.org/10.1175/2009JAS3092.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webster, P. J., G. J. Holland, J. A. Curry, and H.-R. Chang, 2005: Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309, 18441846, https://doi.org/10.1126/science.1116448.

    • 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
  • Yamaguchi, M., T. Nakazawa, and S. Hoshino, 2012: On the relative benefits of a multi‐centre grand ensemble for tropical cyclone track prediction in the western North Pacific. Quart. J. Roy. Meteor. Soc., 138, 20192029, https://doi.org/10.1002/qj.1937.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamaguchi, M., and Coauthors, 2015: Global distribution of the skill of tropical cyclone activity forecasts on short- to medium-range time scales. Wea. Forecasting, 30, 16951709, https://doi.org/10.1175/WAF-D-14-00136.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., and Y. Weng, 2015: Predicting hurricane intensity and associated hazards: A five-year real-time forecast experiment with assimilation of airborne Doppler radar observations. Bull. Amer. Meteor. Soc., 96, 2533, https://doi.org/10.1175/BAMS-D-13-00231.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, K., Q. Lin, W. Lee, Y. Y. Qiang Sun, and F. Zhang, 2016: Doppler radar analysis of triple eyewalls in Typhoon Usagi (2013). Bull. Amer. Meteor. Soc., 97, 2530, https://doi.org/10.1175/BAMS-D-15-00029.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, K., F. Vitart, S. T. Lang, L. Magnusson, R. L. Elsberry, G. Elliott, M. Kyouda, and T. Nakazawa, 2017: Doppler radar analysis of a tornadic miniature supercell during the landfall of Typhoon Mujigae (2015) in south China. Bull. Amer. Meteor. Soc., 98, 18211831, https://doi.org/10.1175/BAMS-D-15-00301.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 878 298 24
PDF Downloads 552 132 15