An Investigation of the Inter-Annual Wind Changes In Iran

Authors(2) :-Mokhtar Karami, Mehdi Asadi

In this study, an attempt has been made to evaluate long-term average variation and fluctuation of wind in Iran. For this purpose, wind database network was initially formed over Iran. Then, data from the base of a 30-year period, the daily period of 1/01/1982 to 31/12/2012, was supposed as the basis of the present study, and a cell with dimensions of 15 × 15 km of the studied area was spread. In order to achieve the wind three past decade's changes in Iran modern methods of spatial statistics such as, Moran global spatial autocorrelation, Moran Local insulin index and Hot spots, by using of programming in GIS environment, were accomplished. The results of this study showed that the spatial distribution of wind in Iran has the cluster pattern. In the meantime, based on Moran local index and Hot spots, wind patterns in the South, South-East, East, South West and West, have spatial autocorrelation positive pattern (too much windy pattern), and parts of the Caspian Sea coast, north and center of the country have negative spatial autocorrelation (low wind pattern). During the study period, a large part of the country (almost half of the total area) had a significant pattern or spatial autocorrelation.

Authors and Affiliations

Mokhtar Karami
Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran
Mehdi Asadi
Ph.D. Student Agricultural Meteorology, Hakim Sabzevari University, Sabzevar, Iran

Wind, Spatial Autocorrelation, Local Moran, Global Moran, Iran.

  1. Ageena, I., Macdonald, N., & Morse, A. P. 2013. Variability of maximum and mean average temperature across Libya (1945–2009). Theoretical and Applied Climatology, 1-15.
  2. Allard, D., & Soubeyrand, S. 2012. Skew-normality for climatic data and dispersal models for plant epidemiology: when application fields drive spatial statistics. Spatial Statistics, 1, 50-64.
  3. Anselin L, Syabri I, Kho. Y. 2009. GeoDa: an introduction to spatial data analysis. In Fischer MM, Getis A (Eds) Handbook of applied spatial analysis. Springer, Berlin, Heidelberg and New York, pp.73-89.
  4. Anselin L. 1995, Local indicators of spatial association: LISA. Geogr Anal, 27(2): 93-115.
  5. B – Stine; A,Godar, 2006, Climatology, translator Abdul hamid Rajaee, Niknami publication, 592 pages.
  6. Chao-bing, H. L. M. D., &Ning, L. I. 2011. A review on the Hot Spotissues of urban heat island effect. Journal of Meteorology and Environment, 4, 011.
  7. Del Río, S., Herrero, L., Pinto-Gomes, C., & Penas, A. 2011. Spatial analysis of mean temperature trends in Spain over the period 1961–2006. Global and Planetary Change, 78(1), 65-75.
  8. Diffenbaugh, N. S., Giorgi, F., & Pal, J. S. 2008. Climate change Hot spots in the United States. Geophysical Research Letters, 35(16).
  9. Diggle, P. J. 2003. Statistical Analysis of Spatial Point Patterns. Arnold, London, second edition.
  10. Entezari, Alireza. Amirahmadi, Abolghasem. Borzooyi, Akram. Erfani, Atefe. 2012. Wind energy potential evaluation and assessing the possibility of wind plant construction in Sabzevar, dry areas geographical studies periodical, third year, No. 9 & 10 33-46.
  11. Getis A, Ord JK. 1992, the analysis of spatial association by use of distance statistics. Geogr Anal 24 (3): 189-206.
  12. Getis, A, Aldstadt, J. 2004. Constructing the spatial weights matrix using a local statistic. Geogr Anal 36 (2): 90-104.
  13. Griffith, D., 1987, spatial Autocorrelation: A Primer. Resource Publication in Geography, Association of American geographers.
  14. Homar V, Ramis C, Romero R, Alonso, S. 2010. Recent trends in temperature and precipitation over the Balearic Islands (Spain). Clim Change 98:199–211.
  15. Illian, J., Penttinen, A., Stoyan, H., and Stoyan, D. 2008. Statistical Analysis and Modelling of Spatial Point Patterns. John Wiley and Sons, Chichester.
  16. Killeen, T. J., Douglas, M., Consiglio, T., Jorgensen, P. M., & Mejia, J. 2007. Dry spots and wet spots in the Andean Hot spot. Journal of Biogeography, 34(8), 1357-1373.
  17. Kim, S., & Singh, V. P. 2014. Modeling daily soil temperature using data-driven models and spatial distribution. Theoretical and Applied Climatology, 1-15.
  18. Levine, N. 1996. Spatial statistics and GIS: software tools to quantify spatial patterns. JAm Plann Assoc 62(3): 381-391.
  19. Masoudian, A. 2011, weather of Iran, Mashhad Sharia publications, first printing, Mashhad.
  20. Mitchell, A. 2005. The ESRI guide to GIS analysis, volume 2: spatial measurements and statistics. ESRI, Redlands [CA].
  21. Mohammadi, Hossein. Rostami Jalilian, Nima. Taghavi, Farnaz and Shamsipoor, Aliakbar. 2012. Wind energy potential in Kermanshah province, natural geography researches, 44th year, No. 2, 19-23.
  22. Nemec, J., Gruber, C., Chimani, B., & Auer, I. 2013. Trends in extreme temperature indices in Austria based on a new homogenized dataset. International Journal of Climatology, 33(6), 1538-1550.
  23. Noorollahi, Yoones. Ashraf, Seyyedmohammadali and Zamani, Mohsen. 2010. Wind power energy potential in west using GIS, Moshaveran and Eghtesad-e Shaygan Co.
  24. Ohayon, B. 2011. Statistical Analysis of Temperature Changes in Israel: An Application of CHange Point Detection and Estimation Techniques.
  25. Ord JK, Getis, A. 1995. Local spatial autocorrelation statistics: distributional issues and an application. Geogr Anal 27(4): 287-306.
  26. Robeson, S. M., Li, A., & Huang, C. 2014. Point-pattern analysis on the sphere. Spatial Statistics.
  27. Rogerson, P.A., 2006, Statistics Methods for Geographers: students Guide, SAGE Publications. Los Angeles, California.
  28. Scott, L. M., & Janikas, M. V. 2010. Spatial statistics in ArcGIS. In Handbook of applied spatial analysis (pp. 27-41). Springer Berlin Heidelberg.
  29. Waagepetersenand, R., and Schweder, T. 2006. Likelihood-based inference for clustered line transect data. Journal of Agricultural, Biological, and Environ- mental Statistics, 11: 264–279.
  30. Wheeler D, Paéz, A. 2009. Geographically Weighted Regression. In Fischer MM, Getis A (eds) Handbook of applied spatial analysis. Springer, Berlin, Heidelberg and New York, pp.461-486.
  31. Zhang C, Luo L, Xu W, Ledwith. V. 2008. Use of local Moran’s I and GIS to identify pollution Hot spots of Pb in urban soils of Galway, Ireland. Sci Total Environ 398 (1-3):

Publication Details

Published in : Volume 2 | Issue 2 | March-April 2016
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 21-29
Manuscript Number : IJSRSET162173
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Mokhtar Karami, Mehdi Asadi, " An Investigation of the Inter-Annual Wind Changes In Iran, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 2, pp.21-29, March-April-2016. Citation Detection and Elimination     |     
Journal URL : https://ijsrset.com/IJSRSET162173

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