Mohammad Khaledi; Ghasem Zarei
Abstract
In order to be able to make decisions and manage in the field of sustainable development of natural resources, knowledge of climatic parameters is one of the most important and effective topics of this matter. Meanwhile, the temperature parameter, as one of the most influential climate parameters, plays ...
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In order to be able to make decisions and manage in the field of sustainable development of natural resources, knowledge of climatic parameters is one of the most important and effective topics of this matter. Meanwhile, the temperature parameter, as one of the most influential climate parameters, plays a fundamental role in related studies and research. It is very important to have consistent, high-quality and error-free information available, Because the existence of a statistical gap or an incorrect assessment of information leads to wrong decisions and a deviation from the goal and reality. Daily temperature information is among the most important and useful climate data used in industrial, agricultural and social fields. Since time series inevitably always have problems and statistical discontinuities, in this investigation, for the first time and using classical statistical methods, in relation to the temperature data at the country scale including geographic (graphical) coordinates, normal ratios, the weighted correlation coefficient and the arithmetic mean, which commonly used in completing meteorological and climatic statistical information, were used to evaluate the methods and to determine the most reliable method to solve and estimate the missing daily-scale temperature data. According to the average values obtained from the evaluation of the results, the normal ratio method, the weighted correlation coefficient, the geographic coordinates and the arithmetic mean are prioritized with the RMSE value of 3.05, 3.28, 3.30 and 3.51 degrees Celsius, respectively. Therefore, the normal ratio method is more acceptable among other studied methods and this method can be used to solve problems such as the lack of information, the error in the data and as well as the extension of the study period.
