عنوان مقاله [English]
The Modeling and Prediction of Annual Mean
Temperature Changes in Tehran
1*Halabian, A.H., 2Helforosh Salmasi, A.
1 Assoc. Profe. Geography Department, Payame-Noor University, Tehran, Iran
2 M.A in Climatology, Payame-Noor University, Tehran, Iran
(Received: 2017/01/07; Accepted: 2018/10/02)
The influence of temperature in climate conditions of each region and importance of its prediction in environmental planning made it necessary to use statistical methods to study prediction and changes of the temperature. Statistical method are useful tools to perceive and analysis climate behavior. Nowadays, modeling and prediction of climatic parameters due to climate change, global warming and the recent droughts is inevitable. Temperature is including climatic parameters that is important in environmental planning, water resources management and agriculture. In this research, using Mann- Kendall graphical- statistical test, which is one of the proposed method of world meteorological organization(WMO) for time series analysis, Sen's slope estimator method and Box- Jenkins ARIMA model, the change trend of long- term mean of annual temperature in Tehran will be studied. To analysis this trend and access a suitable pattern in this situation, temperature annual mean from Tehran synoptic station during 1951- 2014 has been collected from the meteorological organization of Iran. The results of trend analysis showed that there are significant ascending trends in temperature annual mean. Trend gradient calculated by Sen's slope estimator is equal to 0.037 per year. The ARIMA (0,1,1)con was identified as final pattern base on common modeling. The first order of difference degree shows a linear trend what is approved by a constant term in co model. According to the ARIMA (0,1,1) con, the amount of this trend is 0.033 C per year. According to the adjusted model, can be expected that the temperature will increase to 18.88 C in 2024.
Keywords: Modeling, Annual temperature, Mann- Kendall, Sen's slope estimator, ARIMA, Trend, Autocorrelation, Partial autocorrelation, Tehran
*Corresponding author: Email: firstname.lastname@example.org
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