Application of Distribution Extreme Value for Analyzing of Extreme Temperatures in Kermanshah Province

Introduction
Changes in occurrence and frequency of extreme events can have more severe and damage effects than changes in the average climatic characteristics (Choi et al, 2008). Therefore, it is important to study the variability and change the behavior of extreme atmospheric events. The main purpose of this paper is to investigate the temperature extreme events using the distribution of generalized extreme value distribution (GEV) and non-parametric methods in Kermanshah province. The results of this study can be effective in providing the necessary context for assessing the extent of vulnerability and adaptation methods and strategies to deal with it.
Methodology
The study area in the present study is Kermanshah province. Because to study the extreme events, the length of the statistical period should be long-term, so in this study, the data of Kermanshah synoptic station, which has a statistical period of 56 years (1961-2016), was used. First, the maximum and minimum daily temperature data for the study period were obtained from the Meteorological Organization of the country and after reconstructing the incomplete data, the quality of the data was checked. The data series were first analyzed by trend and then analyzed by frequency of boundary events. To study and analyze the trend of marginal events, the indicators presented by the National Climate Committee of the World Meteorological Organization and the Climate Change and Prediction Research Program, called ETCCDMI, have been used. In total, the group provided 16 main indices with a major emphasis on temperature limits that can be extracted from a series of recorded daily data (Zhang et al., 2006: 2014.(
Results and Discussion
Generalized Extreme Value Distribution
The present study aimed to analyze the changes in temperature extreme events in the study period using generalized extreme value distribution in Kermanshah province. According to the statistics and information of meteorological stations, this region has a drastic change in terms of climate and is affected every year by dry days without successive rains on the one hand or sudden heavy rains on the other, with a sharp rise or fall in temperature. The results of the Maxima block methods showed that in the study area, the intensity and frequency of cold border events decreased and the intensity and frequency of hot border events increased. Warm nights mean an increase in the percentage of days when the minimum daily temperature is above 90 and hot days mean a percentage of days when the maximum daily temperature is above 90 ‌. The incremental trend is the highest annual value of the minimum daily temperature at the 95% level. The slope of the trend line for the index is 0.04 C in the decade.
Conclusion
The results showed that concerning cold extreme indices such as frost days, ice days, cold days and nights, the direction of change is negative and with hot extreme indices such as summer days, tropical nights, nights and Hot days the direction of change is positive with a confidence level of 99 percent. Since the rate of increase of the minimum temperature was higher than the maximum temperature, the range of the day and night temperature in the region has decreased. Also, graphs of the values ​​of minimum and maximum temperature polynomials in years of return T with a 95 percent confidence interval were plotted. According to the above diagrams, we can estimate the extreme values ​​of the desired parameter for the specified return period.