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Seasonal prediction of hurricane activity reaching the coast of the United States

Abstract

Much of the property damage from natural hazards in the United States is caused by landfalling hurricanes1,2,3—strong tropical cyclones that reach the coast. For the southeastern Atlantic coast of the US, a statistical method for forecasting the occurrence of landfalling hurricanes for the season ahead has been reported4, but the physical mechanisms linking the predictor variables to the frequency of hurricanes remain unclear. Here we present a statistical model that uses July wind anomalies between 1950 and 2003 to predict with significant and useful skill the wind energy of US landfalling hurricanes for the following main hurricane season (August to October). We have identified six regions over North America and over the east Pacific and North Atlantic oceans where July wind anomalies, averaged between heights of 925 and 400 mbar, exhibit a stationary and significant link to the energy of landfalling hurricanes during the subsequent hurricane season. The wind anomalies in these regions are indicative of atmospheric circulation patterns that either favour or hinder evolving hurricanes from reaching US shores.

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Figure 1: Tropospheric height-averaged wind anomalies linked significantly to above-median seasonal US landfalling hurricane activity 1950–2003.

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References

  1. Benfield Industry Analysis and Research Team, Catastrophe Losses (Benfield, London, 2004)

    Google Scholar 

  2. Diaz, H. F. & Pulwarty, R. S. Hurricanes: Climate and Socioeconomic Impacts (Springer, Berlin, 1997)

    Book  Google Scholar 

  3. Pielke, R. A. Jr & Landsea, C. W. Normalised hurricane damage in the United States: 1925–1995. Weath. Forecast. 13, 621–631 (1998)

    Article  ADS  Google Scholar 

  4. Lehmiller, G. S., Kimberlain, T. B. & Elsner, J. B. Seasonal prediction models for North Atlantic basin hurricane location. Mon. Weath. Rev. 125, 1780–1791 (1997)

    Article  ADS  Google Scholar 

  5. Gray, W. M., Landsea, C. W., Mielke, P. W. Jr & Berry, K. J. Predicting Atlantic basin seasonal tropical cyclone activity by 1 August. Weath. Forecast. 8, 73–86 (1993)

    Article  ADS  Google Scholar 

  6. Gray, W. M. Atlantic seasonal hurricane frequency. Part II: Forecasting its variability. Mon Weath. Rev. 112, 1669–1683 (1984)

    Article  ADS  Google Scholar 

  7. Klotzbach, P. J. & Gray, W. M. Forecasting September Atlantic basin tropical cyclone activity. Weath. Forecast. 18, 1109–1128 (2003)

    Article  ADS  Google Scholar 

  8. Bove, M. C., Elsner, J. B., Landsea, C. W., Niu, X. & O'Brien, J. J. Effect of El Niño on U.S. landfalling hurricanes revisited. Bull. Am. Meteorol. Soc. 79, 2477–2482 (1998)

    Article  ADS  Google Scholar 

  9. Pielke, R. A. Jr & Pielke, R. A. Sr. La Niña, El Niño and Atlantic hurricane damages in the United States. Bull. Am. Meteorol. Soc. 80, 2027–2034 (1999)

    Article  ADS  Google Scholar 

  10. Saunders, M. A., Chandler, R. E., Merchant, C. J. & Roberts, F. P. Atlantic and NW Pacific typhoons: ENSO spatial impacts on occurrence and landfall. Geophys. Res. Lett. 27, 1147–1150 (2000)

    Article  ADS  Google Scholar 

  11. Lyons, S. W. U. S. tropical cyclone landfall variability 1950–2002. Weath. Forecast. 19, 473–480 (2004)

    Article  ADS  Google Scholar 

  12. Waple, A. M. et al. Climate assessment for 2001. Bull. Am. Meteorol. Soc. 83, S1–S62 (2001)

    Google Scholar 

  13. Neumann, C. J., Jarvinen, B. R., McAdie, C. J. & Hammer, G. R. Tropical Cyclones of the North Atlantic Ocean 1871–1998 (Historical Climatology Series 6–2, National Oceanic and Atmospheric Administration, Asheville, 1999)

    Google Scholar 

  14. Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis. Bull. Am. Meteorol. Soc. 77, 437–471 (1996)

    Article  ADS  Google Scholar 

  15. Dong, K. & Neumann, C. J. The relationship between tropical cyclone motion and environmental geostrophic flows. Mon. Weath. Rev. 114, 115–122 (1986)

    Article  ADS  Google Scholar 

  16. Franklin, J. L., Feuer, S. E., Kaplan, J. & Aberson, S. Tropical cyclone motion and surrounding flow relationships: searching for beta gyres in Omega dropwindsonde datasets. Mon. Weath. Rev. 124, 64–84 (1996)

    Article  ADS  Google Scholar 

  17. Collins, D. J. & Lowe, S. P. A macro validation dataset for U.S. hurricane models. 217–252 (Casualty Actuarial Society, Winter Forum, 2001); available from CAS at 〈http://www.casact.org/pubs〉.

  18. Davis, R. E. Predictability of sea surface temperatures and sea level pressure anomalies over the North Pacific Ocean. J. Phys. Oceanogr. 6, 249–266 (1976)

    Article  ADS  CAS  Google Scholar 

  19. Chen, W. Y. Fluctuations in northern hemisphere 700 mb height field associated with the Southern Oscillation. Mon. Weath. Rev. 110, 808–823 (1982)

    Article  ADS  Google Scholar 

  20. Elsner, J. B. & Schmertmann, C. P. Assessing forecast skill through cross-validation. Weath. Forecast. 9, 619–624 (1994)

    Article  ADS  Google Scholar 

  21. Standardised verification system (SVS) for long-range forecasts (LRF). New Attachment II-9 to the Manual on the GDPS (WMO-No. 485), Vol. 1 (WMO, Geneva, 2002)

    Google Scholar 

  22. Wilks, D. S. Statistical Methods in the Atmospheric Sciences (Academic, San Diego, 1995)

    Google Scholar 

  23. Freund, R. J. & Wilson, W. J. Regression Analysis: Statistical Modeling of a Response Variable (Academic, San Diego, 1998)

    MATH  Google Scholar 

  24. Hilti, N., Saunders, M. A. & Lloyd-Hughes, B. Forecasting stronger profits. Glob. Reinsurance 6–7 (July/August 2004).

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Acknowledgements

We thank B. Lloyd-Hughes for help with Table 3 and other aspects of the work. J. B. Elsner, C. W. Landsea, F. Vitart, J. Heming and I. M. Mason are thanked for comments on the manuscript. This work is supported by the TSR (Tropical Storm Risk) venture sponsored by Benfield (an independent reinsurance intermediary), Royal & SunAlliance (an insurance group), and Crawford & Company (a claims management solutions company). We acknowledge NOAA-CIRES, Climate Diagnostics Center, Boulder, Colorado, for the NCEP/NCAR Global Reanalysis Project data, and NOAA's Hurricane Research Division for the HURDAT North Atlantic hurricane database.

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Correspondence to Mark A. Saunders.

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Saunders, M., Lea, A. Seasonal prediction of hurricane activity reaching the coast of the United States. Nature 434, 1005–1008 (2005). https://doi.org/10.1038/nature03454

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