Advertisement

On a Temporal Investigation of Hurricane Strength and Frequency

  • Moinak BhaduriEmail author
  • Chih-Hsiang Ho
Article
  • 47 Downloads

Abstract

Hurricanes originating in the West Atlantic often have devastating consequences on the cities in the US east coast, both monetary and otherwise, and hence pose a source of considerable concern to several authorities. The possibility of a connection between global warming in general and an increased frequency of these strong hurricanes is well researched, but is still actively debated. The present work tries to promote the use of a smoothing statistic termed empirical recurrence rates and to advocate the use of another, termed empirical recurrence rates ratio in a bid to better understand the rich history of these storms on one hand and to make appropriate inferences on the other, so that some light can be shed on the acceptability of conjectures held by renowned climate scientists. The methods introduced are intuitive and simple to implement and should find wide applications in diverse disciplines.

Keywords

Hurricanes Tropical cyclones Global warming Empirical recurrence rates Empirical recurrence rates ratio Time series 

References

  1. 1.
    Bhaduri, M., & Zhan, J. (2018). Using empirical recurrence rates ratio for time series data similarity. IEEE Access, 6, 30855–30864.  https://doi.org/10.1109/ACCESS.2018.2837660.CrossRefGoogle Scholar
  2. 2.
    Brillinger, D. R. (1994). Time series, point processes and hybrids. Canadian Journal of Statistics, 22(2), 177–206.CrossRefGoogle Scholar
  3. 3.
    Chen, J., & Gupta, A. K. (2014). Parametric statistical change point analysis: With applications to genetics, medicine, and finance. Birkhauser Boston.  https://doi.org/10.1007/978-0-8176-4801-5.CrossRefGoogle Scholar
  4. 4.
    Damsleth, E., & El-Shaarawi, A. (1989). Arma models with double-exponentially distributed noise. Journal of the Royal Statistical Society: Series B Methodological, 51(1), 61–69.Google Scholar
  5. 5.
    Elsner, J. B., Kara, A. B., & Owens, M. A. (1999). Fluctuations in North Atlantic hurricane frequency. Journal of Climate, 12, 427–437.CrossRefGoogle Scholar
  6. 6.
    Emanuel, K. (2003). Tropical cyclones. Annual Review of Earth and Planetary Sciences, 31, 75–104.CrossRefGoogle Scholar
  7. 7.
    Emanuel, K. (2006). Hurricanes: tempests in a greenhouse. Physics Today, 59, 74–75.CrossRefGoogle Scholar
  8. 8.
    Emanuel, K. (2007). Environmental factors affecting tropical cyclone power dissipation. Journal of Climate, 20, 5497–5509.CrossRefGoogle Scholar
  9. 9.
    Evan, A. T., Dunion, J., Foley, J. A., Heidinger, A. K., & Velden, C. S. (2006). New evidence for a relationship between Atlantic tropical cyclone activity and African dust outbreaks. Geophysical Research Letters, 33, L19813.CrossRefGoogle Scholar
  10. 10.
    Guolo, A., & Varin, C. (2014). Beta regression for time series analysis of bounded data, with applications to Canada Google flu trends. Ann. Appl. Stat., 8(1), 74–88.CrossRefGoogle Scholar
  11. 11.
    Hamilton, J. D. (1994). Time series analysis (Vol. 2). Princeton: Princeton University Press.Google Scholar
  12. 12.
    Hansen, J., Nazarenko, L., Ruedy, R., Sato, M., Willis, J., Del Genio, A., Koch, D., Lacis, A., Lo, K., Menon, S., Novakov, T., Perlwitz, J., Russell, G., Schmidt, G. A., & Tausnev, N. (2005). Earth’s energy imbalance: confirmation and implications. Science, 308, 1431–1435.CrossRefGoogle Scholar
  13. 13.
    Henschel, K., Hellwig, B., Amtage, F., Vesper, J., Jachan, M., Lucking, C. H., Timmer, J., & Schelter, B. (2008). Multivariate analysis of dynamical process. European Physical Journal Special Topics, 165, 25–34.CrossRefGoogle Scholar
  14. 14.
    Hoyos, C. D., Agudelo, P. A., Webster, P. J., & Curry, J. A. (2006). Deconvolution of the factors contributing to the increase in global hurricane intensity. Science, 312, 94–97.CrossRefGoogle Scholar
  15. 15.
    Ho, C.-H. (2010). Hazard area and recurrence rate time series for determining the probability of volcanic disruption of the proposed high-level radioactive waste repository at Yucca Mountain, Nevada, USA. Bulletin of Volcanology, 72, 205–219.CrossRefGoogle Scholar
  16. 16.
    Ho, C.-H. (2008). Empirical recurrence rate time series for volcanism: application to Avachinsky Volcano, Russia. Journal of Volcanology and Geothermal Research, 173, 15–25.CrossRefGoogle Scholar
  17. 17.
    Ho, C.-H., & Bhaduri, M. (2015). On a novel approach to forecast sparse rare events: applications to Parkfield earthquake prediction. Natural Hazards, 78(1), 669–679.CrossRefGoogle Scholar
  18. 18.
    Ho, C.-H., Zhong, G., Cui, F., & Bhaduri, M. (2016). Modeling interaction between bank failure and size. Journal of Finance and Bank Management, 4(1), 15–33.Google Scholar
  19. 19.
    Ho, C.-H., & Bhaduri, M. (2017). A quantitative insight into the dependence dynamics of the Kilauea and Mauna Loa volcanoes, Hawaii. Mathematical Geosciences, 49(7), 893–911.CrossRefGoogle Scholar
  20. 20.
    Ke, F. (2009). Linkage between the Atlantic tropical hurricane frequency and the Antarctic oscillation in the Western Hemisphere. Atmospheric and Oceanic Science Letters, 2(3), 159–164.CrossRefGoogle Scholar
  21. 21.
    Killick, R., & Eckley, I. A. (2014). Changepoint: an R package for change-point analysis. Journal of Statistical Software, 58(3).  https://doi.org/10.18637/jss.v058.i03.
  22. 22.
    Knutson, T. R., Mcbride, J. L., Chan, J., Emanuel, K., Holland, G., Landsea, C., Held, I., Kossin, J. P., Srivastava, A. K., & Sugi, M. (2010). Tropical cyclones and climate change. Nature Geoscience, 3, 157–163.CrossRefGoogle Scholar
  23. 23.
    Lin, Y.-C., Chang, T.-J., Lu, M.-M., & Yu, H.-L. (2015). A space-time typhoon trajectories analysis in the vicinity of Taiwan. Stochastic Environmental Research and Risk Assessment, 29, 1857–1866.CrossRefGoogle Scholar
  24. 24.
    Pettitt, A. N. (1979). A non-parametric approach to the change-point problem. Journal of the Royal Statistical Society C, 28(2), 126–135.Google Scholar
  25. 25.
    Przyborowski, J., & Wilenski, H. (1940). Homogeneity of results in testing samples from Poisson series with an application to testing clover seed for dodder. Biometrika, 31(3–4), 313–323.Google Scholar
  26. 26.
    Ross, G. J. (2014). Sequential change detection in the presence of unknown parameters. Statistics and Computing, 24(6), 1017–1030.CrossRefGoogle Scholar
  27. 27.
    Ross, G. J. (2015). Parametric and nonparametric sequential change detection in R: the cpm package. Journal of Statistical Software, 66(3).  https://doi.org/10.18637/jss.v066.i03.
  28. 28.
    Santer, B.D., Wigley, T.M.L., Geckler, P.J., Bonfils, C., Wehner, M.F., AchutaRao, K., Barnett, T.P., Boyle, J.S., Brüggemann, W., Fiorino, M., Gillett, N., Hansen, J.E., Jones, P.D., Klein, S.A., Meehl, G.A., Raper, S.C.B., Reynolds, R.W., Taylor, K.E., & Washington, W.M. (2006). Forced and unforced ocean temperature changes in Atlantic and Pacific tropical cyclogenesis regions. Proceedings of the National Academy of Sciences, 103(38), 13905–13910.CrossRefGoogle Scholar
  29. 29.
    Shumway, R. H., & Stoffer, D. S. (2006). Time series analysis and its applications with R examples. New York: Springer.Google Scholar
  30. 30.
    Sisson, S. A., Pericchi, L. R., & Coles, S. G. (2006). A case for a reassessment of the risks of extreme hydrological hazards in the Caribbean. Stochastic Environmental Research and Risk Assessment, 20, 296–306.CrossRefGoogle Scholar
  31. 31.
    Sriver, R., & Huber, M. (2006). Low frequency variability in globally integrated tropical cyclone power dissipation. Geophysical Research Letters, 33.  https://doi.org/10.1029/2006GL026167.
  32. 32.
    Tan, S., Bhaduri, M., & Ho, C.-H. (2014). A statistical model for long-term forecasts of strong sand dust storms. Journal of Geoscience and Environment Protection, 2, 16–26.CrossRefGoogle Scholar
  33. 33.
    Trenberth, K. (2005). Uncertainty in hurricanes and global warming. Science, 308, 1753–1754.CrossRefGoogle Scholar
  34. 34.
    Trenberth, K. E., & Shea, D. J. (2006). Atlantic hurricanes and natural variability in 2005. Geophysical Research Letters, 33.  https://doi.org/10.1029/2006GL026256.
  35. 35.
    Vanem, E. (2011). Long-term time-dependent stochastic modelling of extreme waves. Stochastic Environmental Research and Risk Assessment, 25, 185–209.CrossRefGoogle Scholar
  36. 36.
    Wallis, K. F. (1987). Time series analysis of bounded economic variables. Journal of Time Series Analysis, 8(1), 115–123.CrossRefGoogle Scholar
  37. 37.
    Xie, M., Sandels, C., Zhu, K., & Nordström, L. (2013). A seasonal ARIMA model with exogenous variables for elspot electricity prices in Sweden. European Energy Market (EEM) 2013 10th International Conference, May 2013.Google Scholar
  38. 38.
    Zhang, Y., & Lam, J. S. L. (2015). Reliability analysis of offshore structures within a time varying environment. Stochastic Environmental Research and Risk Assessment, 29, 1615–1636.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Mathematical SciencesBentley UniversityWalthamUSA
  2. 2.Department of Mathematical SciencesUniversity of Nevada, Las VegasLas VegasUSA

Personalised recommendations