• OpenAccess
  • A Statistical Model for Long-Term Forecasts of Strong Sand Dust Storms  [ICGG 2014]
  • DOI: 10.4236/gep.2014.23003   PP.16 - 26
  • Author(s)
  • Siqi Tan, Moinak Bhaduri, Chih-Hsiang Ho
  • Historical evidence indicates that dust storms of considerable ferocity often wreak havoc, posing a genuine threat to the climatic and societal equilibrium of a place. A systematic study, with emphasis on the modeling and forecasting aspects, thus, becomes imperative, so that efficient measures can be promptly undertaken to cushion the effect of such an unforeseen calamity. The present work intends to discover a suitable ARIMA model using dust storm data from northern China from March 1954 to April 2002, provided by Zhou and Zhang (2003), thereby extending the idea of empirical recurrence rate (ERR) developed by Ho (2008), to model the temporal trend of such sand dust storms. In particular we show that the ERR time series is endowed with the following characteristics: 1) it is a potent surrogate for a point process, 2) it is capable of taking advantage of the well developed and powerful time series modeling tools and 3) it can generate reliable forecasts, with which we can retrieve the corresponding mean number of strong sand dust storms. A simulation study is conducted prior to the actual fitting, to justify the applicability of the proposed technique.

  • ARIMA Model, Empirical Recurrence Rate, ERR Plot, Point Process, Time Series
  • References
  • [1]
    Amei, A., Fu, W., & Ho. C-H. (2012). Time Series Analysis for Predicting the Occurrences of Large Scale Earthquakes. International Journal of Applied Science and Technology, 2, 64-75.
    Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.
    Brockwell, P. J., & Davis, R. A. (2002). Introduction to Time Series and Forecasting (2nd ed.). New York: Springer-Verlag.
    Cowpertwait, P. S. P., & Metcalfe, A. V. (2009). Introductory Time Series with R. New York: Springer.
    Cryer, J. D., & Chan, K. S. (2008). Time Series Analysis with Applications in R (2nd ed.). New York: Springer.
    Goudie, A. S., & Middleton, N. J. (1992). The Changing Frequency of Dust Storms through Time. Climatic Change, 20, 197-225.
    Ho, C.-H. (2008). Empirical Recurrent Rate Time Series for Volcanism: Application to Avachinsky Volcano, Russia. Journal of Volcanology and Geothermal Research, 173, 15-25.
    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.
    Li, W. K. (2004). Diagnostic Checksin Time Series. Florida: Chapman& Hall/CRC.
    Shumway, R. H., & Stoffer, D. S. (2006). Time Series Analysis and Its Applications with R Examples. New York: Springer.
    Tao, G., Jingtao, L., Xiao, Y., Ling, K., Yida, F., & Yinghua, H. (2002). Objective Pattern Discrimination Model for Dust Storm Forecasting. Meteorological Applications, 9, 55-62.
    Warner, T. T. (2004). Desert Meteorology. New York: Cambridge University Press.
    Yang, B., Brauning, W., Zhang Z. Y., Dong Z. B., & Esper, J. (2007). Dust Storm Frequency and Its Relation to Climate Changes in Northern China during the Past 1000 Years.Atmospheric Environment, 41, 9288-9299.
    Zhang, Q. Y., Zhao, X. Y., Zhang, Y., & Li, L. (2002). Preliminary Study on Sand-Dust Storm Disaster and Countermeasures in China. Chinese Geographical Science, 12, 9-13.
    Zhou, Z. J., & Zhang, G. C. (2003). Typical Strong Sand Storm Events in the Northern China. Chinese Science Bulletin, 48, 1224-1228.

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