Modeling of Minimum Temperature in East of Kermanshah
Message:
Abstract:
Introduction
The reality is that climate is one of the most interesting issuses in recent decades and many researches have been conducted in the world , regional and local scales and also temperature is one of the main and most essential elements in determining the role and scattering the rest of the climate elements and is one of the main indicators in zoning and classifying the climate. So, changes and fluctuations of this element have high scientific and practical importance. Although historical study of time series cause to identify the factors that may cause changes in the series, but the most important aim of study of time series is the ability to predict the unknown value of series. By using these information, conscious selections can be done about investment, decision making for production, inventories and etc.
Materials And Methods
In this study ,we have been investigated time changes of minimum temperature in the east of Kermanshah province by using monthly and annually minimum temperature data in synoptic stations of Kermanshah and Kangavar in time interval of January 1998 to December 2010 and ARIMA model. Firstly, Mann Kendall's non-parameterized test has been used for calculating the data procedure. Mann- Kendall test similar to other statistical tests is based on comparison 0 and 1 assumption and finally decide to accept or reject null assumption. null assumption of this test is based on being random and lack of series procedure and accepting assumption 1 (rejecting assumption 0) show existence of procedure in data series. Then for investigating data series and predicting minimum data in studied stations, ARIMA model has been used. ARIMA models are suitable for situations which according to order of model current amount of a climate element depends on amounts of it in past times, or instantaneous effects and randomized elements of it in past and present and has been written in form of (p,d,q)ARIMA. In this form, P presents the dependence of a climate element in present to past effective values. In this regression model, every element is determined according to its past values. q determine the average order of moving which mark dependence of climate series to present random element. To validate the predictions of ARIMA test, mean squared of error has been used.
Discussion of
Results
characteristics of descriptive statistics of annual and monthly temperatures in two mentioned stations show that in two stations of Kermanshah and Kangavar, the most changes for minimum temperature have been registered during January and February and the lowest minimum temperature is observed in these two monthes , but the lowest changes of minimum temperature in two studies stations is on April and the highest minimum temperature is observed in August and January. Mann- Kendall test investigated the procedure of annual data of two studied stations by using Mann- Kendall analysis and results showed that both stations of Kermanshah PACF) in station of Kermanshah show the season model of ARIMA (1,1,0) (4,1,1) and for station of Kangavar show the season model of ARIMA (1, 1, 0) (3, 1, 1) to model and fit. Results of analysis and modeling the monthly temperatures in studied stations by using ARIMA model and prediction of monthly minimum temperature to 2016 showed that minimum temperature in synoptic stations of Kangavar and Kermanshah have final model of ARIMA (1, 0, 0) ( 1,0,1) and have really a similar procedure minimum temperature changes that increases with fairly mild procedure. Results of this study showed that annual and monthly minimum temperature in semi east region of Kermanshah province is increasing with mild inclination. In finding the procedure of minimum temperature changes in studied stations, results of linear diagrams show an increasing procedure for minimum temperature , but kind of procedure doesnt present certain model.
Conclusion
Results of Mann – Kendall test showed that minimum temperature procedure for annual data is confirmed with ./95 possibility. Also results of modelling and analysis of monthly minimum temperature for two mentioned synoptic stations by using statistical models showed that seasonal ARIMA model or pattern of SARIMA (0,0,1) ( 0.1,1) with the least mean squared error amount can be selected as a suitable pattern for predicting the future values of monthly temperature of mentioned stations.
Language:
Persian
Published:
Geographic Space, Volume:16 Issue: 54, 2016
Pages:
47 to 67
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