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Location where to get the Complete Article --> Vol 3, No 1 (2016): 43-48

ISSN- 2348-5191 (Print version); 2348-8980 (Online)

Estimation of Dusty Days Using the Model of Time Series: A Case Study of Hormozgan Province

Mohsen Farahi, Hossein Jehantigh


Dust storm is one of the climatic hazards in the arid and semi-arid regions. Southern Iran with its hot and dry climate is more likely affected by the adverse consequences of dust storms due to the proximity to the dusty deserts of Saudi Arabia and Iraq, on one hand, and the synoptic situation for the occurrence of the dust storms in the Persian Gulf, on the other hand. In this study, the frequency of dusty days in Hormozgan Province was investigated and predicted. To this end, data were collected from the three synoptic stations in Bandar Abbas, Bandar Lengeh and Bandar-e Jask from the Iran Meteorological Organization during the statistical period of 1968-2008. Then, using the non-seasonal ARIMA (p, d, q), were analyzed in 16Minitab and the frequency of the dusty days in the region were predicted. Results of the study show that the ARIMA (1, 1, 1)noc was the most appropriate pattern for predicting the frequency of dusty days in Hormozgan Province. The results showed that the predictions for Bandar-e Jask, compared to those of Bandar Abbas and Bandar Lengeh are more accurate in terms of continuous increasing trend and the interval stability of the time series prediction and the smaller difference between the observed values with the predicted values.


  • Aghapour, S.M., Yazdani, S., & Salami, H. (2010): Patterning and predicting of the rainfall rate and determining the obtainable water of the agriculture sector in Kabudarahang plain. J.Agricul. Econom., 1: 9-30.
  • Asakareh, H. (2009): Patterning of ARIMA for annual average temperature of Tabriz. J. Geographic.Res., 93: 3-24.
  • Azizi, Gh., Shamsipour, A.A. & Yarahmadi, D. (2008): Detection of climate change in half-west of Iran, using multivariable regression analysis. Physical Geog. Res., 66: 19-35.
  • Azizi, Gh., Shamsipour, A., Miri, M., & Safarrad, T. (2012): Statistic and Synoptic Analysis of Dust Phenomena in West of Iran. J. Environ. Stud. Fall, 38(3): 31-33.
  • Chattopadhyay, P., Chakraborthy, P., & Chattopadhyay, S. (2012): Mann-Kendall Trend Analysis of Tropospheric Ozone and its Modelling Using ARIMA. Theor. Appl. Climatol., 110: 321-328.
  • Dodangeh, E., Abedi Koupai, J., & Gohari, S. A.R. (2012): Application of Time Series Modeling to Investigate Future Climatic Parameters Trend for Water Resources Management Purposes. J. Sci. Technol. Agricul. Natural Resources. 59: 59-74.
  • Feidas, H., Makrogiannis, T., & Bora-Senta, E. (2004): Trend Analysis of Air Tempreture Time Series in Greece and their Relationship with Circulation Using Surface and Atellite Data: 1955-2001. Theor. Appl. Climatol., 79(3): 185-208.
  • Gil-Alana, L. A. (2012): Long memory, Seasonality and Time Trends in the Average Monthly Temperatures in Alaska. Theor. Appl. Climatol., 108(3): 385-396.
  • Goudiee, A.S., & Middleton, N. J. (1992): The Changing Frequency of Dust Storms Through Time. Climatic Change, 20:197-225.
  • Indoitu, R., Orlovsky, L., & Orlovsky, N. (2012): Dust storms in Central Asia: Spatial and temporal variations. J. Arid Environ., 85: 62-70.
  • Jalali, M., & Kargar, H. (2011): Analysis and modeling for temperature of Bushehr station. J. Geographic. Space, 39:149-173.
  • Levy, R.C., Remer, L.A., & Dobovik, O. (2007): Global aerosol optical properties and application to Moderate Resolution Imaging spectroradiometer aerosol retrieval over land. J. Geophys. Res. 112: D13210, doi:10. 1029/2006JD007815.
  • Rabani, F. & Karami, F. (2009): Frequency trend of frost days in Northern Khorasan province. J. Physic. Geography, 4: 85-94.
  • Taei Samiromi, S., Moradi, H., Khadagholi, M., & Ahmadi, M. (2014): Study of factors affecting dust phenomenon in west of Iran. Human & Environment, (27): 1-10.
  • Tajabadi, J.M.R., Moghadamnia, A., Piri, J., & Ekhtesasi, M.R. (2010): Application of artificial neural networks in dust storm prediction (case study: Zabol city). Iranian J. Range Desert Res., 17(2):205-220.
  • Tarazkar, M.H., & Sedghamiz, A. (2008): Comparing monthly discharge forecasting for Karkheh river by using time series and artificial Intelligent traits. J.Res. Construction, Natural Resources, 80:51-58.
  • Veisipour, H., Masompour Samakoosh, J., Sahneh, B., & Yusefi, Y. (2010): Analysis of precipitation and temperature trend prediction with using time series models (ARIMA) (case study: Kermanshah Township). J. Geography, 12: 63-77.
  • Yusof, F. & Lawal Kane, I. (2013): Volatility modeling of rainfall time series. Theor. Appl. Climatol., 113(1):247-258.

  • DOI:10.21276/ambi.2016.03.1.ra06

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    Published by: National Cave Research and Protection Organization, India

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