جستجوی مقالات مرتبط با کلیدواژه « spectrum analysis » در نشریات گروه « جغرافیا »

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.

Identification and analysis of time-series behavior of the climatic elements and processes upon them are the basic factors of climatology science and has an important role in management and environmental planning. In general، the behavior of climatic elements are observed in 3 form: Trend، Oscillation and Fluctuation. These behaviors will help us to identify more the climate change. Today by changing more the trend of climatic elements، climate change has become more important than previous years. But in between the climatic elements، it has been noticed less than the other elements such as water vapor pressure. The importance of water vapor is that this climatic parameter is has an important role in explaining the climate change، because of 1) It is the main source of rainfall in all weather systems، 2) It supplies the latent heat in this process and controls the heat in the troposphere and 3) It is the intensify severe storms. Given the significant role of atmospheric moisture in the Earth''s energy equilibrium، it can be helpful in explaining climate change. Therefore، this article tries to survey two time series behaviors of climate data; the trends and fluctuations in water vapor pressure. The Mann-Kendall test is one of the popular nonparametric techniques for explaining the trend analyzing in hydrological and climatic data. The spectrum analysis is one of the best methods for extracting and analyzing visible and invisible climatic oscillations with different wavelength. In fact، spectrum show the distribution of variance along the entire range of wavelengths in time series. So in this article، we want to analyze the trend by using the Mann-Kendall test and detect and identify the fluctuations and cycles of water vapor pressure time series by using the spectrum analysis. Study area: The study area، with about 360،200 km2 area، is located in the south and southwest of Iran and approximately between 25° 00''N and 34° 25''N latitudes and between 45° 38''E and 59° 17''E longitudes. Southern and southwestern parts of the study area is located in sides of two massive sources of moisture، Persian Gulf and Oman Sea.

In this study، water vapor pressure data from 28 synoptic stations، during a period of more than 20 years in south and southwest of Iran were used that were collected by the Iranian Meteorological data website (http: //www. weather. ir). It should be noted that to survey the Mann-Kendall test and Spectral analysis، the stations with 44-years period (2010-1967) and stations that have data since their establishment were used، respectively. The methodology of this study is detecting trends and oscillation of water vapor pressure in south and southwest of Iran by Mann-Kendal test and Spectral analysis، respectively based on ground station. Tests for the detection of significant trends in climatological time series can be classified as parametric and non-parametric methods. Parametric trend tests require data to be independent and normally distributed، while non-parametric trend tests require only that the data be independent. In this study، a non-parametric method، Mann-Kendal، was used to detect trends. To analyze the Oscillation of water vapor pressure، the spectral analysis has been applied. In this function، frequency and cycles indicate the time scale and amount of variance in the time scale، respectively. After extracting all waves، in second step the proportion of each waves in explaining the total variance are specified and Finally، the significance of each waves are tested. For performing the Mann-Kendall test and spectrum analysis technique، the capability of programming in MATLAB software was used.

The results showed significant trend in most of the stations. As the results of this article، the downward significant trend in the stations of Ahwaz، Bandarabas، Shahrekord، Boroujen، Abadeh and Khorramabad، the upward significant trend in Lengeh، Bushehr، Dezful and non-significant trend in Abadan، Fasa and Shiraz was seen. By applying the spectrum analyzing in 95% confidence level on the water vapor pressure data، the multiple cycles were obtained; So that the significant sinusoidal cycles 3-2، 4 and 15-7 years were more common. Results show that the cycles of 3-2 years was most repeated in the water vapor pressure time series. 4-year cycle was the other cycle of events occurred in the study area that researchers have attributed this cycle to ENSO. The survey showed that more cycles are traceable in the southern part of the Zagros. Another cycle in the region was 15-7 year cycle that most scientists have attributed this cycle to sunspot activity. The final survey showed that the plurality of cycles in the West and South West of the study area.

In general it can be concluded that the downward significant trend of water vapor pressure was observed in the Zagros and the upward significant trend was observed in nearby the Persian Gulf. By applying the spectrum analyzing on the water vapor pressure data، the multiple cycles were obtained. Results of spectral analysis showed that the cycles of 3-2 years was most repeated in the water vapor pressure time series. The spatial distribution of this cycle was observed in the near of Persian Gulf coasts. Most scientists have attributed this cycle as a result of El Niño - Southern Oscillation (ENSO) and Quasi-biennial oscillation (QBO).

Climate is a multivariate system. Every change in every climate variable could result in different changes in climate. Recently، experts have paid extensive attention to frequency distribution sequence of climate elements. Accordingly، many different indices have been defined. Precipitation extremes can exhibit the phase space of climate system. A technique that can be used to analyze climate phase space is multivariate statistics. In order to analyze precipitation extremes simultaneity، 26 indices of daily precipitation of Zanjan city during 1961-2006 had been chosen. Principle Components Analysis has been used to find the most important components in extreme precipitation. Accordingly، 6 components could justify about 86. 4% of variation in precipitation extremes indices. Spectral analyses on principle components have shown a 3- year cycle in second components as well as a 4-year cycle in forth component and 2-3 years cycles in the fifth. The second component that shows frequency and spells 95 percentile of precipitation has a significant decreasing trend. Also، there is a dropped jump in first component as the first percentile.