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Abstract
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Article Information:
Statistical Analysis of Fluctuations in Kermanshah’s Maximum Temperature and its Time Series Forecast
Dariush Yarahmadi, Parsa Gholami, Saeid Olfati and Khadijeh Moradi
Corresponding Author: Parsa Gholami
Submitted: August 02, 2013
Accepted: August 21, 2013
Published: October 20, 2013 |
Abstract:
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Fluctuations maximum temperature of Kermanshah province of Iran and world nowadays one of the most important discussions in the scientific circle is the discussion about climatic changes’ increase in the average temperature of the globe and its consequence that more or less has been paid attention to in our country as well. In this research the attempt was to pay attention to the survey of increase data in the maximum temperature of Kermanshah. Thus the annual absolute maximums of date in the specific station during a 50-year period (1961-2010) have been used. The correlation method (logarithmic’ poly nominal) and Mann- Kendall statistical test have been used for testing the meaningfulness of fluctuation direction of series temperature and its diagram has been drawn by using the MATLAB software. The results show that the temperatures of two decades of 1970 and 1980 have been lower and three decades of 1990 and 2000 have been higher than the long-term average and the decade of 1980 has been the coldest of the decades. The general result of this research shows that there are seasonal fluctuations in maximum temperature of Kermanshah which are not meaningful. The model of halt winter has been recognized as the best model for predicating the temperature of maximum extreme of Kermanshah which the diagram in a Minitab was drawn and the results of this predication shows that there is an abnormal increase in the temperature of 2014 and 2019.
Key words: Decade’s direction, halt-winters, kermanshah, mann-kendal, maximum temperature, ,
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Cite this Reference:
Dariush Yarahmadi, Parsa Gholami, Saeid Olfati and Khadijeh Moradi, . Statistical Analysis of Fluctuations in Kermanshah’s Maximum Temperature and its Time Series Forecast . Research Journal of Environmental and Earth Sciences, (10): 631-637.
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ISSN (Online): 2041-0492
ISSN (Print): 2041-0484 |
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