Climatology
Bohlul Alijani; Ali Bayat; Mehdi Doostkamian; yadollah Balyani
Abstract
Precipitation is one of the most essential and variable climate components whose understanding has long been a concern for climatologists. The main objective of the current paper is to investigate and analyze the precipitation cycles in Iran. In order to realize this objective, the annual precipitation ...
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Precipitation is one of the most essential and variable climate components whose understanding has long been a concern for climatologists. The main objective of the current paper is to investigate and analyze the precipitation cycles in Iran. In order to realize this objective, the annual precipitation data of isometric station of Iran were extracted. These data have been collected by the country’s meteorological organization since the establishment of the station until 2008 which adds up to more than 40 years of statistics. Then, in order to investigate and analyze the precipitation cycles, spectral analysis (co-structural analysis) was utilized. Regarding the calculations, the programming utilities of Matlab were used and the Surfer software application was exploited for drawing operations. The results obtained from analyzing the cycles show that there are significant 2 to 3 year cycles, 3 to 5-year cycles, 2 to 6 year cycles and sometimes 11 or more- year cycles governing Iran’s precipitation patterns. Hence, in east and southeast of Iran, 3 to 5-year cycles are prevailing and in west and northwest 2 to 3-year cycles are dominant and finally in north east 2 to 6-year cycles are customary. The most numerous and the most variable cycles happen in south and south east, mainly due to the mountainous regions of Zagros as well as the proximity to Persian Gulf. The north western regions, much like the southwestern regions, indicate variable cycles due to the mammoth mountains of Sabalan and Sahand. Moreover, the presence of those cycles which have a return period equal to the statistical period has been seen in various parts of Iran, which indicates a precipitation trend in this country.
Hossein Asakareh; Ali Bayat
Volume 17, Issue 45 , November 2013, , Pages 121-142
Abstract
Principal Component Analysis (PCA) is an optimum mathematical method to decrease variables into some limited components in order to justify the highest variance of primary variables. In this study some statistical characteristics of annual precipitation of Zanjan city including sum of annual precipitation, ...
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Principal Component Analysis (PCA) is an optimum mathematical method to decrease variables into some limited components in order to justify the highest variance of primary variables. In this study some statistical characteristics of annual precipitation of Zanjan city including sum of annual precipitation, number of rainy days, extreme daily precipitation in a year, the ratio of extreme precipitation to the sum of annual precipitation and some characteristics such as Standard Deviation (SD), Skewness (Sk), Kurtosis (Ku), Absolute Mean Deviation (AMD) and Mean Absolute Interannual Variability (MAIV) were was calculated from monthly precipitation for each year, and were introduced principal component analysis technique. The results show that 95% percent of annual precipitation variations can be explained through 4 components. The first component which indicates the highest data variance (42.6%), represents annual precipitation and absolute variability indices including SD, AMD and MAIV. The second component represents the shape of frequency distribution indices (Sk, Ku), the third component represents extreme precipitations and finally the fourth component represents the number of rainy days. The analysis of the trend of components scores show that first and fourth components scores have a significant decreasing and increasing trend, respectively. Round a lines show a precipitation decrease during the period under study from one hand and having uniform temporal distribution on the other hand.
