جستجوی مقالات مرتبط با کلیدواژه "بارش ایران" در نشریات گروه "جغرافیا"

Geographical situation of Iran is a place for interacting many physical and human processes which lead to specific precipitation climatology in the country. The month to month variation of precipitation is one of  the features which the precipitation climatology may reflect due to tempo - spatial characteristics. In fact, monthly distribution of precipitation is one of precipitation normal features building up the climate structure. In order to recognize this fundamental characteristic three following questions have been raised:1) Have the month to month distribution of precipitation changed over recent four decades? 2) How is the pattern of relationship of month to month distribution of precipitation and spatio - topographical variables? 3) Is it possible to find a spatial pattern for decadal changes of precipitation of month to month distribution?

In order to find a responses for the abovementioned questions the distribution of month to month precipitation and its decadal changes was considered by adopting coefficients of variations (CV) for 46 years (1970-2016)  and using the third version of Asfazari dataset. The relationship of precipitation data and spatio-topographical variables calculated based on regression techniques. Moreover, the spatial pattern considered by using cluster analysis.  The CV calculated as follow: here ،،  are ith raw's and jth column's CV, standard deviation, and monthly mean, respectively. CV and its relationships with spatio-topographical variables were calculated in two temporal scale, for whole the under investigation period (1970-2016) and in decadal period for four decades (1977-1986, 1987-1996, 1997-2006, 2007-2016).

The results of current study proved that the month to month different in precipitation amounts have had spatial variations, whilst the temporal trends is not statistically significant. In addition, the minimum, maximum, and consequently, the range of values also the averages have not experienced significantly changes. However, the region experiencing the same values of precipitation illustrated oscillatory behavior. Accordingly, the decadal variations have happened in different areas. Although the there have been statistically significant relationships between monthly CV and spatio - topographical factors, the correlations were low. Based on cluster analysis, we found 5 regions according to CV and its anomalies in compares with normal CV for all under investigation period. These regions generally follow the latitudes from 32 N toward northern latitudes, whilst the region in the south of 32 N generally follow the longitude patterns.

Precipitation is known a chiastic and complicated climate element. One of chiastic behaviors which precipitation shows in its different time - scale behavior is its month to month distribution among a given year. In current research the decadal variation of  above-mentioned behavior among recent four decades and the variation of its relationships and the spatio - topographical features , as parts of climate structure of the country, have investigated in details.  Our finding illustrated that the month to month different in precipitation amounts have had tempo - spatial variations, whilst the temporal long - term trends is not statistically significant. Moreover, the values of minimum, maximum, and consequently, the range of month to month CV also the decadal averages have not experienced significantly changes over four under study decades. However, the region experiencing the same values of precipitation depicted oscillatory behavior. consequently, the decadal variations have happened in different areas. Although there have been statistically significant relationships between monthly CV and spatio - topographical variables, the correlations were not considerably high. Based on cluster analysis technique, we found 5 regions according to CV and its anomalies in compares to normal CV for all under study decades. These regions generally follow the latitudes from 32 N toward northern latitudes, whilst the region in the south of 32 N generally follow the longitude patterns. 

One of the important features of climate change is dry season persistence. Dry season continuing could effect the intensity and frequency of precipitation events as well as other climatic factors. Accordingly, the climatologists have recently been concentrating on this issue. According to the American Meteorological Society (AMS) a dry season is an annually recurring period of one or more months during which precipitation is at a minimum for that region. Changing in dry season length took a lot of expert's attention all over the world while it did not catch a lot of Iranian experts attention. In current study the changes in mean of length of dry season over Iran is considered. The objective of this research is to know and statistically analysis spatial pattern of length of dry season. Accordingly, daily database from 1961 to 2010 which have 15 15 km resolution are used. The spatial pattern of dry season follows the mountain chains pattern except in northwest and southeast of the country as well as some small parts of northern parts of Iran. Trend analysis showed that about 77.2 of the country experienced increasing in the length of dry season on average by 2 days per year. This event tends to pay a strong attention toward water resources managements.

The importance of precipitation forecasting as the most important climatologic element and the basis of all planning, especially in areas where there are significant changes in precipitation regimes, is considerable. The use of artificial neural networks is one of the forecasting methods that have been widely developed in recent years. In this research the data set of some climatologic elements, related to the cold seasons of previous year, were used to forecast the next year precipitations in Kermanshah and Nouje Hamedan synoptic stations. Therefore, time series of seven climatologic elements including mean temperature, precipitation, relative humidity, mixing ratio, vapor pressure, dew point temperature and sea level pressure were entered to the artificial neural networks as input, while next year precipitation was considered as the output of network. Due to nonlinear nature of selected climatologic elements of this research, the multi-layer perceptron (MLP) networks were applied. In fact, they are among the progressive networks with supervising training algorithms and suitable for nonlinear data. Other two categories of training algorithms including BP training algorithms and number normalization algorithm were used for training of networks. Eventually, the combination of these algorithms led to the production of 720 training networks at two stations, and finally the artificial neural network succeeded to proper forecasting of annual precipitation amounts. The best forecast for Kermanshah station is related to the traingd training function with mean and standard deviation normalization algorithm and testerror amount equal to 0.0195 in cold period of the year (overall autumn and winter), and in Nouje station is related to the traingdx training function with pca 0.06 normalization algorithm with testerror amount equal to 0.0047 in the winter.

In this study, the effects of some leading Teleconnection Patterns of atmospheric circulation, on regional-scale for Middle East, along with precipitation over Iran have been investigated. Different types of data including Teleconnection Indices and monthly precipitation data weather stations have been used. One of important parameters is Precipitation that has change from a year to other year. Teleconnection Patterns and Indices are remote controller of precipitation amount variation in Iran and the entire world. So in this paper on of main goal is relationship between teleconnection patterns and precipitation data in main meteorological station of IRAN that have long term data base.
Material and By using Statistical analysis (Correlation and linear regression) over Precipitation data via 25 Weather Station from 1951-2005 in whole Country and Teleconnection Indices from NOAA, we analysis and notice to archive component that have up most impact on Precipitation variation in Iran.
According to the relationship between major teleconnection indices and Iran's rainfall, we investigate to recognition of their effects and phenomena on climate of Iran. As a results the Sum of 24 stations, the precipitation of Iran have decrease trend. Eventually changes in some Teleconnection indices cause decreasing rainfall as SCAND index, GlobLandTA, NH sstaⶩ, NH ta Land, NTA and EA in spite of intensifying of some indices that increasing rainfall as SST4, MEI, PDO, NOI.
In resent decade, Global increasing temperature causes change over most atmospheric parameters in whole world and the other hand these atmospheric parameters themselves have impact as feedback over other branches of Climate and weather machine. One of important results in current paper is showing effect of Global Warming that cause decreasing in average of Iran's precipitation by means of change in some teleconnection pattern in time series. Also the results indicate that ENSO is the most effective factor and it can influence on variation of precipitation, temporally and spatially, on all type of climate regimes in Iran.

Identification of heavy rainfall events are important in the design of water-related structures, agriculture, weather modification, policy making and planning and in monitoring climate change. Generally the change in rainfall in the world was examined in both time and space. Hence, much attention has been paid to different methods of analysis of extreme precipitations during recent years.Karl and Knight (1998), studied the twentieth century trends of precipitation are examined by a variety of methods to more fully describe how precipitation has changed or varied. They believed that since 1910, precipitation has increased by about 10% across the contiguous United States. The increase in precipitation is reflected primarily in the heavy and extreme daily precipitation events. For example, over half 53% of the total increase of precipitation is due to positive trends in the upper 10 percentiles of the precipitation distribution.

Introduction Iran has very complicated topography and climate including two mountain chains of Zagros and Elborz, two wide deserts of Kevir-e-lut and Dasht-e-kevir, forest lands and three large water bodies of Caspian Sea, Persian Gulf and Oman Sea. Climatically, Iran has a variable climate. Winters are cold with heavy snowfall in the northwest and below freezing temperatures during December and January. Spring and fall are relatively mild, while summers are dry and hot. In the south, winters are mild and the summers are very hot. On the Khuzestan plain, summer heat is accompanied by high humidity (Alijani, 2003). The intergovernmental Panel on Climate Change (IPCC) reported that the global mean temperature has been increased 0.6o C during 20th century while the atmospheric concentration of carbon dioxide also increased from 280ppm to 370 ppm in third Assessment Report (TAR) published in 2001(Kwon, 2005). Validation of PRECIS regional climate model in Bangladesh is performed with the surface observational data of rainfall and temperature (maximum and minimum) at 26 observational sites throughout the country from 1961-1990. It is found that regional analysis provides overestimation of PRECIS values in Bangladesh whereas data extracted at some particular locations provide better performance of PRECIS. For baseline, the performance of PRECIS is about 90% for rainfall.PRECIS can detect about 96% and 100.3% of maximum and minimum temperature respectively (Islam et. al., 2005). Climate change in the past decade in Jianghuni valley is studied by using statistical techniques. Both frequency and strength of extreme climate events such as hot weather, droughts and floods have increased remarkably since 1990s. Also, the regional climate model of PRECIS is used to provide a prediction of future climate in the valley. The results give an average surface warming of 2.9oC under the SRES B2 emission scenario by the end of this century (2071-2100). Precipitation may increase on the same period (Tian, et. al., 2006). Methodology Under Article of the United Nation Framework Convention on Climate Change (UNFCC), all Parties must study the impact of climate change in their countries using regional climate models. To do this, a PC-based regional climate model named PRECIS has been developed at the Hadley Center of United Kingdom Meteorology Office. PRECIS is based on the atmospheric component of the HadCM3 general circulation model. The atmospheric dynamics module of PRECIS is a hydrostatic version of the full `primitive equations and uses a regular longitude-latitude grid in the horizontal and a hybrid vertical coordinate. In this research monthly to seasonal precipitation of Iran has been modeled using PRECIS regional climate model with HadAM3P boundary condition data. PRECIS has a horizontal resolution of 50 km with 19 levels in the atmosphere (from the surface to 30 km in the stratosphere) and four levels in the soil. The present version of PRECIS has the option to downscale to 25 km horizontal resolution. In addition to a comprehensive representation of the physical processes in the atmosphere and land-surface, it also includes the sulphur cycle. The validation of model has been done by comparing observation data and model output data with two different methods of region to region and station to station. For this study, the PRECIS model domain has been set up with a horizontal resolution of 50 x 50 km. The domain is roughly stretched over the latitude 23 to 45?N and longitude 43 to 68?E. The HadAM3P global data set is used to drive the PRECIS model. The horizontal resolution of the HadAM3P boundary data is 150 km and for the present and future climate, it covers the period 1960-1990 and 2070-2100 respectively (Wilson et al., 2005). For the future climate, both SRES A2 and B2 greenhouse gas emission scenario is selected. Results and Discussion Seasonal to annual error and bias of the model outputs have been calculated using to different approach of region to region and station to station methods. We considered Masoudian's zoning (Masoudian, 1387) approach of the precipitation of Iran for computing regional error and bias of the model. In this approach precipitation region of Iran have been categorized in 12 regimes of South Central, Farsi, Kordi, Sistani, West Khorasan, East-Khorasan, North Central, Khuzi, Hormozi, Azari, Baluchi and Khazari. It is found that overall error of model is 5.3%. Maximum error has occurred over Farsi, Hormozi, Khuzi and Khazari regions with errors of 24.9, 16.9, 12.2 and -10.2 respectively. Minimum errors occurred over Kordi, Azari, and Northern central and eastern Khorasan regions. Maximum monthly errors occurred in September, the transition month between summer and autumn and minimum monthly precipitation has happened in May. It seems that wet bias of simulations in Southern regions can be due to transferring of high amount of humidity from large-scale GCM’s cells into RCM’s fine cells. Also dry bias of simulations in Caspian region is because of low ability of PRECIS in parameterizations of convective precipitations. Results show the regional errors are found in Farsi and Hormozi regions and minimum errors are in Kordi and North-Central regions. Maximum and minimum monthly errors are found in September and December, respectively. Conclusion As a main result, PRECIS skill in modeling regional precipitation, especially over the regions with high amount of convective and local precipitation is low, but it can model well the total precipitation of Iran. Average error of modeling over the country is less than 2%, but maximum regional error of modeling is 10% in Farsi region. Maximum precipitation errors are found in transition months. We found that PRECIS can model overall precipitation of Iran well, but it has some deficiency in modeling convective precipitation in Caspian region and southern part of Iran. There is no significant difference between PRECIS-modeled data and actual data retrieved from weather stations. So, as a powerful regional model, PRECIS can be used for regional climate modeling over Iran and future climate change projections.

In this research, temporal and spatial behavior of Azores high pressure was studied at 700 hPa level. This study has been done using daily data of geopotential height at 12 GMT from NCEP/NCAR database with spatial resolution of 2.5*2.5 degree in a 55 years period including 20089 days from January, 1st, 1951(Dey, 11th, 1329) to December, 31st,2005(Dey, 10th,1384). At First, mean geopotential height at 12 GMT is extracted for all days of solar calendar to determine spatial behavior of Azores high pressure and its relation to the contiguous patterns. Then, an agglomerative hierarchical cluster analysis using ward linkage method applied to it. According to this analysis two circulation patterns in the Northern hemisphere were recognized. The first pattern which is coincided with the cold half of year, shows weakening of Azores high pressure and predominance of westerlies with Rossby waves.The second pattern which is coincided with the warm half of year shows maximum activity of Azores high pressure. Occurrence of this pattern is coincided with precipitation cease in most parts of Iran. To identify temporal behavior of Azores high pressure, by selecting a suitable framework, mean of geopotential height whit in this framework at 700 hPa level was calculated and standardized for each day of solar calendar from 1330 to 1384. Thus, a matrix of size 19724*1 was obtained that shows positive and negative anomalies (strengthening and weakening of Azores high pressure) in each day. The mean intensity of Azores index is 0.84 during positive phase and -0.79 during negative phase. Applying contingency table method and calculating χ 2 statistic during statistical period(1330-1383) showed that occurrence of precipitation in about 4% of studied stations(40 out of 1066) is independent of influence of Azores high pressure. These stations are mainly encompass southrn coast of Caspian sea and North west of Iran. According to this analysis, the occurrence of precipitation in 96% of studied stations(1026 out of 1066) weren’t independent of influence of Azores high pressure and are under the control of Azores high pressure at the time of predominance of this system over Iran, intensively.

EI Nino Southern Oscillation (ENSO) is an important teleconnection pattern of Southern Hemisphere. Although a significant difference between temperature and precipitation of various parts of the globe has been reported during warm and cold phases of ENSO, the response of local climates to ENSO is not uniform and happens with complex lag times. In this paper all precipitation records of Iran during the last half century have been analyzed by employing a linear regression model fitted to precipitation time series and Multivariate ENSO Index (MEI). The analysis showed that there is generally a positive relationship between ENSO and precipitation in Iran in October, November and June, especially in its eastern parts.