سعید موحدی

Empowering the villagers in the field of rural tourism can bring about a successful transition to rural development as a strategic choice. The aim of this study was to explain the effects of empowerment of rural households on creative tourism in Shahrood. The statistical population of the study is 373 rural households that the questionnaire as a research tool was provided to the statistical population by sampling. The Cronbach's alpha value for creative tourism indicators is 0.80 and for empowerment indicators is 0.75. SPSS software version 24 and LISREL 8.8 were used for data analysis. Based on the obtained results, the components of creative tourism and empowerment of rural households with an average of 3.093 and 2.77, respectively, have been evaluated at the desired level and below the desired level, and the relations between the components of empowerment and creative tourism have been evaluated. Ali fitted with favorable indices (RMSEA = 0.079). According to the research findings, the promotion of education and awareness components, skills and development of human resources, finance and investment, competence and individual independence, and institutionalism and organization are effective in the development of creative tourism indicators.

The surface of the earth or the top of the earth at the level of cities and urban areas becomes significantly higher than the surrounding environment, which is known as the thermal component. If this phenomenon is higher than a certain limit, it will cause problems for the city residents. But one of the most important factors that can play a very important role in controlling this phenomenon is the presence of vegetation, which is mainly a significant part of this coverage in urban areas. In this study, the effect of vegetation cover on the amount of urban heat island in Isfahan City was investigated. For this purpose, the surface temperature of the city and its relationship with the surface cover were investigated using the land surface temperature (LST) products of the MODIS sensor in places with heat islands by applying the Normalized Difference Vegetation Index (NDVI) of Landsat satellite data during the period of 2001-2020. Examining the annual changes in temperature in Isfahan shows that during this period, the temperature is generally increasing, so 2011 with an average annual temperature of 37.21 degrees Celsius is the hottest year and 2012 is the coldest year with an average annual temperature of 33.7 degrees Celsius. The most apparent thermal islands were observed in areas with industrial use, high-traffic places with severe air pollution and population density, and places with weak vegetation, areas with dense and worn-out textures.

Water surface temperature in open seas and lakes is known as one of the indicators for measuring oceanographic and meteorological characteristics. In the present study, the data from the AVHRR sensor (1989-2019) have been used to analyze the temporal and spatial changes in water surface temperature of the Persian Gulf and Oman Sea. Average (monthly), seasonal, and annual temperatures were obtained from daily data in ArcGIS 10.8 software. Then, the trend of water temperature changes in the Persian Gulf and the Oman Sea compared to the Caspian Sea was investigated. The results of the water surface temperature data analysis showed that the water temperature in the Persian Gulf and the Oman Sea decreased in the northern part of the Persian Gulf in the spring, but in the summer, the temperature decreased in the central part of the Caspian Sea. In autumn and winter, the temperature drop is affected by the latitude. While the southern parts of the Caspian Sea, especially the southeast, are the hottest areas, the Middle Caspian has a lower temperature than the rest of the regions. In the spring season, the temperature decreased in the Middle Caspian, and an increase in temperature was observed towards the upper and lower latitudes. While in the autumn season, a decrease in the water surface temperature was observed in the Persian Gulf and in the winter, in the Oman Sea. The monthly temperature trends for the Persian Gulf and the Oman Sea are increasing for 12 months. In the Caspian Sea, the trend of temperature changes in January and December was decreasing and increasing in the rest of the months.

Agricultural products frost in spring imposes heavy financial losses to agriculture particularly in northwest of Iran’s orchards. Not only temperature is one of the most important climate parameters but also it is a very crucial element in the agricultural sector. Untimely temperature fluctuations and rise and fall which are usually unexpected will cause shock and heavy damages. Therefore taking into consideration the agricultural products frost and offering an approach would be of great importance for reducing relevant damages. In studies carried out by Omidvar and Dehghan Banadoki (2012) and Hesari et al. (2015) characteristics and different types of frosts have been considered in relation to the agricultural products. Different models were introduced to predict flowering date in different investigated regions. In more studies, in addition to determining the best model for predicting the date of occurance of flowering stage, probable date of last frost has been estimated as well. Investigating long term temperature changes is a method which applied by Martínez-Lüscher (2017) and Vitasse et al. (2018) to find out about established changes in flowering date and also changes in the last frost date. Nasr Esfahani and Yazdanpanah (2019) realized that 48-hour early warning for frost occurrence can be performed with adequate precision. Despite all studies in the field of products frost particularly during flowering date, it seems a rapid frost warning system must be established and provided to make early warning for each orchard. In this essay, since our goal is to make such early warning three days before frosting, so we have to investigate accuracy and validity of 72-hour minimum temperature simulation using WRF model. On the other hand, we must know phonological stage of each product in each orchard to inform the farmer about frost hazards based on critical temperature, therefore the second goal of this research is to detect phonological stages through Landsat 7 and Landsat 8 images.

In order to achieve the aim of current study, 72-hour minimum temperature simulation through the Weather Research and Forecasting (WRF) model was investigated and values of vegetation index were derived for a 30 meters pixel at an experimental orchard in Kahriz, West Azerbaijan Province, in 2016-2107. Computational grid for 2 meters temperature simulation using WRF model contains of three nested grid with horizontal resolution of 27, 9 and 3 kilometers. Horizontal resolution of terrain height and land use data is equal to 30 second (about 1 km). The initial and 3-h boundary conditions with 0.5º horizontal resolution from the Global Forecast System (GFS) were obtained from National Centers for Environmental Information (NCEI). Based on the previous research KFMYJ physical scheme configuration for WRF model were used in this research. Model's hindcasts at 03:00 UTC hour for each of 51 synoptic weather stations of northwest of Iran in internal computational grid were interpolated by MATLAB software with interp 3 function using linear method, then the obtained values were compared to minimum temperature observed in the stations by using MAE, MSE, RMSE and MSSS indicators. Phenological statistics, the time of beginning and end of growth stages were obtained from Iran Meteorological Organization. Besides, 77 Landsat 7 satellite images of ETM+ sensor, and 41 Landsat 8 images of OLI sensor were downloaded from United States Geological Survey website from March to September 2007-2016 with a spatial resolution of 30 meters. In this research, atmospheric and radiometric correction were performed with the FLAASH method on the metadata file in the ENVI software environment and then vegetation index was calculated using NDVI index.

Examining the evaluation indicators of the WRF model, results revealed a significant correlation and regression model between 2 meters temperature variable from WRF model output and minimum temperature variable observed in the entire stations for 72-hour simulation. As a result WRF model can be applied in 72-hour temperature simulation in the area of study. Another finding of this research indicated that in comparison to the field-recorded data, NDVI values gained from Landsat images properly indicates changes of phenology stages in the relevant apple orchard. In this study, the indicators used to evaluate the model error showed model hindcasts are more accurate for 24-hour and then 48-hour simulations than for 72-hour simulation, but the 72-hour simulation accuracy is not much different from 24-hour and 48-hour simulations. In northwestern Iran, which is a mountainous region, it is very difficult to simulate airflow in areas with complex topography, therefore the total correlation coefficient of all stations in all three simulations is in the range of 0.5, and the error rates of MAE and RMSE, respectively reaches about 2.8 and 3.8 Celsius. According to the second finding of this research, the NDVI indicator obtained from Landsat 7 and Landsat 8 satellite images can show the progress and changes in the phenological stages of apple trees.

 This study showed the efficiency of the WRF model for 72-hour simulation of the minimum temperature as well as the potential of Landsat 7 and Landsat 8 images in detecting apple phenological stages in the study area. Therefore, by using the WRF model for 72-hour minimum temperature simulation and recognizing the phenological stages from Landsat images, if the temperature in any orchard reaches a critical level in the next 72 hours due to the phenological stage, frost warning can be announced and then frost mitigation should be done by the farmer.

Concrete is the most widely used construction material in the world for many decades. Old existing structures are deteriorated and needed inspection and repair. Electrical methods, which are inexpensive and easy to handle, are well known as non-destructive inspection methods. They can give information about the position, size, and orientation of inclusions like bar and fiber, condition of corrosion, state of humidity and probable corrosive ions, and the degree of cracking in concrete. Both alternate and direct currents (AC & DC) can be used in electrical resistance measurement (ERT). A major problem of the DC method is the measurement error produced by a polarization of the specimen. In AC methods the frequency should be kept as low as possible to avoid the inductance effects of long connecting cables and also the frequency has to be high enough to avoid electrode polarization effects. In ERT, electric current is injected through electrodes, and the voltage produced on the object surface is recorded using several electrode pairs. Then an estimate of the spatial distribution of conductivity is mapped (Karhunen et al., 2010).The finite element method (FEM) has been widely applied for the numerical solution of governing physical-based partial differential equation of electric current flow (Hou and Lynch, 2009). FEM needs a mesh in the solution domain or on its boundary which makes some difficulty in highly irregular and complex geometry. The meshless method is an alternative solution that was developed to establish a system of (linear) algebraic equations for the entire domain of the problem without creating pre-defined meshes. In this study meshless method is used due to the following advantages (Nourani and Babakhani, 2012): 1) It doesn’t require a domain and boundary meshing; 2) there is no need for integration in domain and boundary; 3) point location is the only variable in RBF functions which makes it suitable for high dimensional problems; 4) RBF is easy to code and implement. Among various types of meshless methods, multi-quadratic radial basis function formulation (MQ-RBF) is mostly utilized. One challenging issue related to the MQ-RBF method is the calibration of shape coefficient which is a case-sensitive parameter. This research proposed a Bayesian statistical theorem for the calibration of shape coefficient.

Water surface temperature or surface temperature in open waters and seas is known as one of the indicators for measuring oceanographic and meteorological features. In this study, to analyze the spatio-temporal Sea Surface Temperature (SST) of the Caspian Sea, the AVHRR data (0.25 Degree) were selected for the period from 1989 to 2019. Evaluation of the trend of temperature changes in the Caspian. sea water level was prepared by calculating the average monthly, seasonal and annual temperatures from daily data.The results of data analysis of SST show that 2003 with an average annual temperature of 14.7 degrees Celsius as the coldest year and 2010 with an average annual temperature of 16.56 degrees Celsius as the warmest year. Average monthly, seasonal and annual temperatures were obtained from the daily data. The results indicated that the southern parts, especially the southeastern Caspian Sea, are the warmest regions and the middle Caspian sea has a lower temperature than the other regions, although in some seasons and months, the effect of latitude prevails and the northern parts show the lowest temperatures. The annual time series of SST in the Caspian Sea have a completely increasing trend. However, from a spatial point of view, it should be said that the northern parts of the Caspian Sea have the lowest temperature and the temperatures increase with decreasing latitude to the south. The overall average surface temperature over the 30 years is about 15.6 degrees Celsius. The result also indicated that SST of the Caspian Sea it is completely increasing in terms of annuals, and in terms of seasonality and the temperatures show an increasing trend. And it shows the exact state of the image, that is, the temperature increases with increasing latitude.

Introduction:

Climate change will affect all sectors of the economy to some extent, but the agricultural sector may be the most sensitive and most vulnerable part because agricultural products are highly dependent on climate resources, And according to scientific evidence, future climate change, especially the combined effects of elevated temperatures and elevated atmospheric CO2 concentrations, will have a significant impact on crops (droughts, floods, frosts) on crops( Chiottioud ,1995). The general effects of climate change on crop development vary depending on the plant and study area (Rawlins, 1991), and commenting on the response of different species to climate change requires case studies.

area of study The study area includes west and northwest of the country Uncovering Climate Change in Past Times Climatic data of maximum temperature, minimum temperature, 30-year historical rainfall (1988-2008) were obtained from 23 stations in the west and northwest of the country And Using two nonparametric tests Mann-Kendall and Estimator slop sen, the trend of precipitation and temperature changes was investigated in order to detect climate change phenomenon in the region. Generating climate scenarios in future periods To assess future climate change in the west and northwest of the country, the CCSM4 general circulation model under RCP4.5 scenario is one of a number of new RCP emission scenarios that the Climate Change Intervention Board will develop in its Fifth Assessment Report (AR5) as representative of the linesVarious concentrations of greenhouse gases have been used Predictive model of wheat phenology and yield unctional and phenological data for three years (90-93) with 40 climatic parameters (Table 2) from seven stations (Zanjan, Arak, Sararood, Maragheh, Ghamloo, Ardabil and Orumieh) containing performance and phenology data Was prepared Then, using these data, performance and phenology in the baseline and future period were predicted through simple linear regression, multiple regression. The results consisted of 20 regression models The best model was selected based on R-squard index using RMSE.

February, March, September, and the year are at 99 percent confidence levels, while January, June, July and August are at 95 percent confidence levels. As well as total rainfall, both the upward and downward trends have a significant and decreasing trend at 95% confidence level only in January and March. Changes in temperature and rainfall in the coming period The results of climate change assessment at each of the stations in the future climate show that the mean maximum temperature in the future climate has increased at 14 stations compared to the previous climate and decreased at the other stations. The mean minimum temperature in the future climate has increased in all the stations except for Ghamloo and Sanandaj stations compared to the previous climate. Average temperature also increased at all stations except Ahar, Zarineh, Sarab and Ghomloo stations in all stations compared to past climates Average mean precipitation in all stations excluding sarpolzahab station in future climates It increases with the past climate Impact of Climate Change on Phenology nder the climate change, the length of the flowering stage of the wheat in the future climate will be shorter than in the previous climate, so that the flowering stage length in all the studied stations with the exception of Zarrineh station in the next period (2039-2018 ) Is shorter than the average long-term flowering stage of wheat. he mean flowering stage duration in the basal climate is 136 days, whereas the mean flowering stage duration in the future climate is 118 days, ie the average flowering stage duration in the future climate is 18 days short. Increasingly, the shortening of the flowering stage in future climates is due to the increase in average temperature in May and April and average maximum temperature in December. Impact of Climate Change on PerformanceUnder climate change, wheat grain yields will increase in future climates than in the past, so that at all stations with the exception of Mahabad station, wheat grain yield is higher than the long-term average in the past. verage wheat yield in the past climate was 1863.3 kg / ha but in the future climate it would be 2529.9 kg / ha. Wheat yields are up 35 percent due to the favorable future climate in the region.

tudies show that precipitation in the west and northwest region of the country during the past period has been decreasing and the temperature is increasing in most months of the year. And in the coming period the temperature in all months of the year until the end of 2039 shows an increase of 2.5 to 3.5 ° C. The flowering period is also 136 days for the previous period but 118 days for the next period shortening of flowering stage of wheat plant is due to increase in average temperature in May and April and average maximum temperature in December as well as increase in precipitation in December and February. Comparison of wheat grain yields of the current and past periods showed that wheat grain yields will increase by 35% in the future, due to the increase in average March and April temperatures and average January and March minimum temperatures. It also saw an increase in the average precipitation of February and March in the next period compared to the previous period.

Lake Urmia is the largest interior lake in Iran. This lake, as an effective ecosystem unit in northwest of Iran, highly contribute in the ecology of the region. The water level of Lake Urmia has dropped between 6 and 7.40 meters that has reduced the area of water zone. This reduction has leaded to increase in salty marsh, since1995. By May 2017, the water level was 3.11 m lower than the ecological balance. Expansion of salt marshes is due to successive droughts and an increase in water harvesting from the surface and ground water sources. This phenomenon can promote dust and salt storm occurrence, and lead to the death of the lake ecosystem, compulsory migration and population health threats of more than 10 million people. In order to optimal long-term planning and management of the available water resources, a better understanding of the temporal and spatial variations of water balance components (especially actual evapotranspiration, surface runoff, and groundwater recharge) is essential. A review of various research findings around the world shows that the WetSpass-M model is a suitable model for the spatial simulation of surface runoff, actual evapotranspiration and groundwater recharge in basins. The main aim of this paper was to analyze spatial distribution of annual components of hydrology cycle in the basin of Lake Urmia, using WetSpass-M model during 25 years (1992-2017). The method adopted in this study was analytic. Climatic, hydrological and land use data were applied in this analysis. Climatic and hydrological data were provided from meteorological stations, hydrometric station information and observed wells respectively. In this study, the satellite images and field studies were applied to determine land use. These images were downloaded from the site of EarthExplorer. The original Wet Spass-M model is a quasi-steady state spatially distributed water balance model scripted in Avenue and used to predict hydrological processes at seasonal and annual time steps. Since the model is a distributed one, the water balance computation is performed at a raster-cell level. Individual raster water balance in this model was obtained by summing up independent water balances for the vegetated, bare soil, open- water, and impervious fraction of each raster cell. The total water balance of the given area was thus calculated as the summation of the water balance of each raster cell. Precipitation was taken as the starting point for the computation of the water balance by each of the above mentioned components of a raster cell. The rest of the processes (interception, runoff, evapotranspiration and recharge) follow in an orderly manner. To validate the results of Wet Spass-M model, the coefficients of Nash-Sutcliffe Efficiency and RMSE-Observations Standard Deviation Ratio were used. The basin of Lake Urmia has been affected by many climate and human changes that have caused Lake Urmia Crisis. During the study period, runoff declined but temperature and evaporation increased. Land use has also changed widely. These changes included dry farming and rangelands convert to settlement and increase in area of irrigated farming. About 3043 km2 of lake area has been reduced and added to salt marsh. The average depth of ground water has decreased by 7.4 meters. The analysis of simulation results indicated that Wets pass-M model works properly to simulate hydrological water budget components in the Urmia Lake Basin. According to the results, in 1992, the highest runoff occurred in the western part of the basin with the highest rainfall, and all groundwater in the southern and western parts of the basin was well recharge. In the year 2017 most of the runoff and groundwater recharge was confined to the southwestern part of the basin. In 2017, Lake Urmia experienced higher evapotranspiration than the year 1992. In 1992, 58.42% of the basin precipitation was spent on evapotranspiration, 7.20% for surface runoff, 31.18% for groundwater recharge and 3.2% for interception. In the year 2017, these changed to 55.49, 1.55, 39.77 and 3.19%, respectively. Among the simulated components during the study period, the runoff has the highest coefficient of variation and the lowest groundwater recharge. Also during the 25-year statistical period, eastern parts of Urmia Lake (including: Ajab Shir, Azarshahr, Maragheh and Shiramin) had the highest coefficient of variation in all studied components. The southwestern parts of the basin were in better condition.

The aim of this research is an analysis of tempo-spatial variations of Cloud cover at Iran. To achieve the goal, Cloud Fraction parameter has been used from cloud product of MODIS sensor on Terra satellite at level 2 and version 6 (MOD06) in the period of 2000 to 2013. Given that, the Available parameters in level 2 MODIS cloud products have not geographical coordinate’s regular network, At first, the Cloud Fraction parameter data were transferred to a regular network, due to this process, Climate analysis on the data is possible, Base on new database the results showed that In annual scale, the trend variations in cloud cover increase at a rate of 0.02% per annual (2% per century) in the country. Cloud cover in the seasonal scale showed that the decreasing trend occurs in autumn and increasing trend in other seasons. Cloud cover in the monthly scale, in July, October, December, and January decreased and there is an increasing trend in other months. The spatial distribution of cloud cover in the long-term annual trend showed that there is decreasing trend in the North East, North West and sporadically in the central regions and an increasing trend in the South, Southeast, and East of the country. Spatial distribution of seasonal trend showed that most parts of the country have been with the positive trend in autumn and the negative trend in winter.

  All studies in the field of climate change impact assessment needs climate data with different spatial and temporal scales. The lack of temperature and precipitation data with high spatial resolution is a major limitation to analyzing future climate change. In addition, the output of the models has error that needs to be corrected; otherwise they will make a significant bias for assessing effect of climate change. Therefore, identifying the best regional climate model for downscale the global climate models is essential to better understanding of climate conditions in the local and regional scale. In the last few years, using various regional climate models in order to produce a multi-member set of the downscaled data in the CMIP5 project by World Climate Research Program (WCRP) in action with Coordinated Regional climate Downscaling Experiment (CORDEX) with the aim of producing regional climate change forecasts, was established as an input to researches on the impacts of climate change and ways for adaptation to it. The main objective of this research is accuracy evaluation of different model outputs of the CORDEX project with different domain and resolution in Iran. In the CORDEX project there are two domain that covering Iran. These two domains are North Africa-Middle East (CORDEX-MNA) including latitude of 7° S to 45° N and longitude 27° W to 76° E and South Asia (CORDEX-WAS) that includes latitude 13° S to 44° N and longitude is 27° E to 107° E (Figure 1). To do this research, first daily output of precipitation, maximum and minimum temperatures in the period of 1990-2005 for three regional climate models with a special resolution of 0.22 ° and 0.44 ° that performed by three international meteorology institutes, available at ESGF web site (Table 1). Daily observation data that recorded in 304 synoptic stations in Iran for the three variables were collected from Iran Meteorology Organization and transferred to a matrix with 3044×5844 dimensions. Then, several scripts were written in the MATLAB software for extract the model data in the domain of Iran and compare output model and observational data with two conditions. The first condition is in the output models resolution of 0.44° (spatiotemporal matrix with dimensions of 5844×740), the observation station should have a distance of less than 25 km, and the next condition is in the resolution of 0.22 ° (spatiotemporal matrix with dimensions of 5844×3218) should have a distance of less than 12 km. The difference between observation data and its corresponding estimated cell were investigated with statistical method such as Mean Error (ME), Pearson Correlation Coefficient, Root Mean Square Error (RMSE) and Standard Deviation (SD). Also, we were used Box-Whisker plots and Taylor Diagram to find the best regional climate model.
Result and The precipitation accuracy of regional climate models output presented by different meteorological institutes (Table 1) was evaluated with observational data in two domain, CORDEX-MENA and CORDEX-WAS, in Iran (Fig. 4). The calculation of the outputs mean error of different models showed that none of the models have a suitable estimation of precipitation values in research domain. The HadRM3P model shows the lowest RMSE calculated with comparing to observational data for the maximum temperature across Iran except the central parts. However, for the minimum temperature RegCM4.1 model shows the lowest difference with compare to observation data in most parts of the research domain. For annual precipitation using the Box-Whisker plot, which compares the correlation coefficients between the observed data and the corresponding cells in the northern and southern half of Iran, in general, none of the models have an accurate estimate of precipitation in Iran (Fig. 8a). This plot for different models showed that the outputs of the HadRM3P and RegCM4.1 models, respectively, for maximum and minimum temperatures in most cells, have more than 0.8, correlation coefficient (Fig. 8b and c). The correlation of rainfall data shows that most models in the central and mountainous regions of Iran do not have high correlation coefficient with observational data. Spatial distribution of correlation between maximum temperature model outputs and observational data in Iran shows that the two HadRM3P and RCA4-WAS0.44 models have a strong correlation coefficient, respectively and changes in the correlation coefficient in the HadRM3P model are low in both the northern half and the southern half of Iran. The RegCM4.1 model had the stronger correlation in the northern half in compare to the southern parts of Iran. Also, the mean difference of estimated model output with observation data of this variable in the whole of Iran is less than 1°C and this model is the most appropriate model among the available models for minimum temperature in Iran

A cloud is a hydrometeor consisting of minute particles of liquid water or ice, or of both, suspended in the free air and usually not touching the ground. It may also include larger particles of liquid water or ice as well as on-aqueous liquid or solid particles such as those present in fumes, smoke or dust (WMO, 1975). Clouds, which display great variability in space and time, as well as in type, can influence climate through many complex interactions involving the hydrological cycle; however, their dominant role is in controlling the 3-dimensional field of radiative fluxes in the atmosphere. In turn, these radiative fluxes drive the thermally forced general circulations of the earth's atmosphere and oceans. These circulations then form clouds and a major climate feedback loop is joined (Rossow and Schiffer, 1983). Many researches have been done about sky conditions classifications abroad of Iran such as, Nakumura (1985), Qian et al (2012), Stove et al (1989) and Filipiak et al (2009). Bannayan et al, 2012 classified sky condition in northeast of Iran, they classified cloud amounts of synoptic meteorology stations in cloudy day (7-8 octas), partly cloudy (3-6 octas) and clear sky (0-2 octas).
Methods and Material: We have applied Cloud Fraction Parameter from Cloud product of MODIS sensor on board Terra satellite (MOD06). Data were downloaded from site of ftp://ladsweb.nascom.nasa.gov/allData/ 6/MOD06_L2. Statistical period is from 2000 to 2013 solar year (2000 to 2014), so, the research has been done based on solar year. Cloud products of MODIS (MOD06) do not have neatly geographic data network on daily, so, we have used new method for gridding of data, At first, a network of geographical coordinates with the spatial resolution arrays of 5 × 5 km, based on the framework of Iran (25-40 degrees north latitude and 44-64 degrees east longitude) was prepared as a regular network or reference with dimensions of 618 × 353 (618 columns and 353 rows). Using geographic coordinates latitude and longitude of each cell in regular network, a framework was prepared 1.5 times the distance between each pixel dimensions (a radius of less than 7.5 km) and was spread on the granules geographical coordinates and the CF data within the framework of the granules were transferred to target cells in terms of latitude closest to the regular network. To transfer CF data parameters in each path to a regular network on a daily basis, this process was carried out for each individual cell in a regular network. So, CF data were ready for statistical computing and As a new database, next steps of research was carried out based on this database. Then, Sky conditions classified based on clear sky days (0 - 25%), partly cloudy days (25% - 62.5%) and cloudy days (>62.5% to 100%). Daily data composite to sessional and monthly data. Then, spatial distribution and long-term average of sky condition classifications were identified in monthly and seasonal scales on Iran.

In Current research, the sky condition has been investigated based on Cloud Fraction parameter in cloud product of MODIS Terra satellite, at first, sky condition was classified to clear sky, partly cloudy and cloudy days based on thresholds considered. After calculating the long-term average and spatial distribution of sky condition classifications were analyzed. Due to the irregular geographical coordinate of daily granules of MODIS on country, first, the Cloud Fraction (CF) data were transferred to a 5× 5 km regular network to be able to analyze long term climatology of cloud cover. The findings of this study reveal that the highest frequency of cloudy days is in winter with 36 days and the lowest is seen in summer with 7.8 days. The highest (lowest) frequency of clear sky days is seen in summer season with 79.6 (45.2) days. On monthly time scale, the highest (lowest) frequency of cloudy days is in the February (September) with 12.8 (1.8) days and the highest (lowest) frequency of clear days is in the September (February) with 27.9 (14.1) days. The spatial distribution of cloudiness indicated that the maximum of cloudy days in spring, summer and fall is seen over the south and west parts of the Caspian shores, however, in winter the maximum of cloudy days is over mountainous regions of the north. The minimum of cloudy days in spring, fall and winter is seen over south-east of the country but in spring it is far away from the south and south-east areas of the country and it is seen over central parts of Iran. The maximum of partly cloudy days is seen over mountainous areas and the minimum is over southern, central areas and also the hills and the plains. In general, the frequency of cloudy (clear) days decreases (increases) from north to the south-east but in summer it increases over south-east and in the north of Hormuz Strait and in winter over the mountainous regions of the north it has an increasing tendency and the decreasing (increasing) of cloudy (clear) days is no longer seen.

The aim of this research is identification of spatial distribution and frequency monthly and seasonal sky condition classifications on Iran, according to this, it was found that the spatial distribution obtained from the study method showed that the maximum frequent cloudy days is seen in the southern and western shores of the Caspian Sea in the Summer, Spring, and Autumn, and its minimum is seen in the southeast of country in spring, winter, autumn. these results are correspond with Rasooli et al (2014), but the maximum (minimum) territory of cloudy days and clear sky days vary over Iran in summer and Winter Compared with other seasons and this situation have also seen in some months that it differ with Rasooli et al (2014) results. Generally, the frequency cloudy days (clear sky days) decrease (increase) from north to south of country, but, this path is impaired in north of Strait of Hormuz and southeast of country in summer (July, August, September) and over the mountain heights of north of country in winter (December, January, February). The most frequent cloudy days occur in the winter and the lowest it occur in the summer. The most frequent clear sky days occur in the summer and the lowest it occur in the winter. In the monthly scale, the most frequent cloudy days occur in the February and the lowest it occur in the September.

Clouds and water vapor are important modulators of climate and are involved in feedbacks that strongly affect global circulation and energy balance. Typically, 50% of the earth surface is covered by clouds, at any given time. A cloud is defined as a visible mass of condensed water droplets or ice crystals suspended in the atmosphere above the earth surface. The cloud phenomenon plays an important role in the water cycle, radiation, temperature, precipitation, predictors of climate, and etc. As there are not adequate information on the spatial distribution of clouds in Iran, except for synoptic stations data, it is necessary to conduct researches on the physical properties of cloud by remote sensing data. Therefore, these kinds of data can provide us with a more accurate and comprehensive understanding of cloud phenomenon. This study examines the spatial distribution of cloud Fraction (cover) over Iran in 2007 using remotely sensed data. The results of this fundamental research could be applied in practical knowledge such as weather forecasting, climate modeling, seeding clouds, site selection of solar panels, and etc.
The aim of this research is to survey the spatial distribution of Cloud Fraction (CF) over Iran in annual and seasonal timescales. To do this, daily data of cloud product of MODIS Terra (MOD06_L2.A) over Iran was used in 2007. The data was obtained from the ftp linkftp://ladsftp.nascom.nasa.gov/allData/6/MOD06_L2/. In this research, we have used spatial resolution of CF in 5 km×5 km scripting in MATLAB. In the first step, overlapping images have been removed and, then, the data within the Iranian border have been extracted from daily data. The data have been transferred into regular network of Iran for doing statistical computations.
The investigation on the annual mean percent of CF indicates that the value is 25.3% for the morning and 29.7%for afternoon. Thus, the amount of CF in the afternoon is increased compared to the morning times. In winter, the amount of CF for the morning is 48.2% but for the afternoon it is reduced to 44.1% and this reduction condition is not seen in other seasons. In spring, fall and winter seasons the amount of CF is increased in the afternoon compared to the morning. The spatial distribution of annual percent of CF indicates that the maximum is seen over the southern shores of the Caspian Sea and the minimum is observed in south east part of the country. The spatial distribution of seasonal percent of CF shows that the maximum amount of CF is over the highlands of Zagrous and Alborz mountains and the minimum is in south and south-east regions of the country. In this season, the maximum percent of CF is not seen over Caspian shores like other seasons. In spring, the maximum percent of CF can be seen over the southern shores of the Caspian Sea and parts of north-west but the minimum percent of CF can be observed over central areas of south and south-east regions of the country. In fall season, the maximum percent of CF is seen over the southern shores of the Caspian Sea and parts of north-east and the minimum is observed in south and south-east regions. In summer, the maximum percent of CF is in the southern shores of the Caspian Sea and the minimum can be seen over east and west parts of the country. In summer, the extent of minimum percent of CF is changed to other seasons and is far away from south-east regions.
In this investigation, the CF parameter of MODIS Terra was applied in the daily temporal resolutions for the year 2007 to explore the spatial distribution of cloud cover over Iran. As the data did not have a regular geographical coordinated grid, a regular coordinate was initially constructed and CF data were trasnferred to this regular grid. This process was conducted to analyze the climatology of cloud cover. The results from MODIS Terra data for the pass of morning and afternoon times revealed that the maximum annual percent of CF is seen over the southern shores of the Caspian Sea and the minimum is occurred over south-east part of the country which is consistent with the results of Rasooli et al. (2013) and Masoodian and Kaviani (2008). For the seasonal time scales, the maximum percent of CF is occurred over the southern shores of the Caspian Sea for spring, fall and summer seasons but in winter it is seen over the elevations of Alborz and north-west parts of the country. The minimum percent of CF is seen in south-east and east parts of the country for spring, fall and winter seasons. In summer, it is observed over east and west regions where shows that the formation process of cloud is different in winter and summer compared to the other seasons. The validation of CF values in the annual time scale indicates that MODIS overestimes CF by 3% compared to the synoptic stations. This is acceptable when the results are compared with the findings of Bisoolli and Pahl (2001) in Germany with the erros of 6% and Kotarba (2009) in July and January months with the error of 4.38% and 7.28% for the year 2004. In general, the estimation of cloud cover is identical for the two data sets.

Forecasting temperature as one of the important climatic parameters plays a major role in climate change research. Therefore, in this study, the surface data of the average daily temperature of selected stations in the Caspian Sea (Anzali, Gorgan, Rasht, Babolsar and Ramsar) and the data of Geopotential height of level of 500 hPa whose data were extracted from the website NCEP/DOE under the US National Oceanic and Atmospheric Organization in hours 00:00, 03:00, 06:00, 09:00, 12:00, 15:00, 18:00, and 21:00 in the Zulu. During the period from 01/01/1979 to 01/01/2011 AD, four observation days per day were extracted from the NCEP / DOE website for the whole of the northern hemisphere, and then the correlation between the average daily temperature and geopotential data of the 500 hPa equilibrium was calculated throughout the northern hemisphere. The results of the correlation showed that the US with 28, the northern China with 30, Africa with 53 and at last, Japan with 69 pixels have the most pixels. In general, in the northern hemisphere, there are 180 points whose correlation with the temperature of selected station is very high. Also, the forecasting model of stations shows that for each geopotential meter increase, the average daily temperatures of the stations of Anzali, Gorgan, Rasht, Babolsar, and Ramsar increases as 0.1, 1.1, 0.1, 0.1, and 0.1 respectively.

Global climate change is relatively depending on human activities which have important impacts on the environment. So the climate change is a very important issue in the recent age. Climate change is the impact on human lives so the climate change increase their temperature and also is very important global problem and monitoring of the climate conditions in early region is a good solution for combating the national housing regarding to climate change large floods and other disasters. There are different climate models and in the CORDEX projects there are regional climate were included in some domains. The main objective of the present work is to evaluate CORDEX-WAS with a spatial resolution of 50 kilometer compared with observational data over Isfahan province. At the end we are evaluating the creation the variation of the minimum and maximum temperature during 2017 -2050 according to model outputs for three models with 2, RCP4.5 and RCP8.5 emissions scenarios.

Knowledge of past climate needs long-term and accurate climatic data for future planning and predicting. In this study, we reconstructed the average maximum temperature in spring and minimum temperatures in fall and winter by applying the width of annual ringsofQuercusPersica through multiple- regression. With this goal in mind, two growth heights were selected in Dena Forests and 40 growth samples from 20 bases in two geographical directions of southwest and northeast were extracted at breast height and measured with AutoCAD software with an accuracy of 3 microns. After cross dating stage, to eliminate non-climate effects, all climatic parameters and tree rings time series were standardized. The calculated Residual Chronology (RES) was calibrated with climatic variations of the period 1881-2011 and positive and significant correlation with the width of growth rings was confirmed. Based on the relations and correlation between the calculated chronology and joint statistical climatic data the reconstruction of annual rainfall was performed and it was found that the average rainfall of the last three decades had a 4 percent increase in comparison to the average rainfall of the last century.

Knowledge of past climate for forecasting and planning for future needs long-term and accurate climate data. Researchers have found that over certain protests caused by the climatic factors, the nature of past climate can be realized (Kaviani and Alijani, 1998, p.388). One of such sources is biological evidence. By studying the physical parameters of the tree, climate change during the life of the tree can be reconstructed (loder et al. 2007, Ch.3). Pan et al. (1997) found that a variety of oak species has the value dendroclimatoligical analysis. Extensive studies have been conducted in the world and Iran in connection with the reconstruction of climatic variables using dendroclimatology knowledge. Among such studies one can refer to: Köse et al. (2011), Köse and Güner (2012), Jalilvand and Kazemi (2008), Safari et al (2011), Karamzade et al. (2011), Najafi et al. (2011), Amirchakhmaghi and Sohrabi (2010), Azizi et al (2012), Arsalani et al. (2012), Portahmasebi et al. (2008), Balapour et al (2008) and Soosani et al. (2008). Knowledge of past climate of the biggest habitat of Quercus Persica in Dena region due to the lack of long-term data on climate, poverty of dendroclimatology studies in the region requires an extensive study of past climate parameters and this study is in line with these objectives.
Dena region is 51.12° to 51.88° along the eastern longitude and 30.51° to 31.51° along the north latitude with an area of approximately 4,500 km2 and is located in the central Zagros, parts of Esfahan, Chahar Mahal and Bakhtiari, Fars province and Kohkiluyeh and Boyer-Ahmad province. To reconstruct seasonal maximum and minimum temperature, data of four weather stations of the region, i.e. Yasooj, Pataveh, Dashte Room and Hana were selected in the region and 30-year (1982-2011) data were analyzed. This study is categorized in two sections. First tree rings response to climatic variables was examined and in the second part, the seasonal temperature was reconstructed applying the growth tree rings. With this aim in mind, two dominant sites of Quercus Persica species in region were selected and a total of 36 growth samples in two Southwest and Northeast directions were extracted at breast height of 1.30 m perpendicular to the tree trunk using increment borer at the time of maximum plant growth at growth peak season in summer 2011.After scanning the growth sample, numerating and measuring annual rings were done with an accuracy of 0.01 millimeter by LINTAB5 which is equipped with TSAP software (a new method of reading growth rings) from the skin to the kernel. Time consistency of the two species of each tree and all the trees of the site were performed using TSAP software. The GLK sign test was applied to check synchrony and homogeneity level of the growth trends of trees.
To eliminate non-climate tendencies, growth rings were standardized applying ARSTAN software (Cook, 1985). The residual chronology (RES) was used among the four constructed chronologies (RES, STD, RAW, ARS). For the constructed chronology, mean sensitivity (MS), the signal-to-noise ratio (SNR) and Expressed Population Signal (EPS) were also calculated. After constructing the appropriate chronology, in calibration stage applying the SAS software and Pearson correlation, the 30-year data (1982-2011) of regional climate stations were used to find the relation between the residual chronology of stations, with maximum average and minimum seasonal temperature of 4 meteorological stations. Then considering the gained results, the maximum and minimum seasonal temperature of the region were constructed applying the growth rings width. Then, the validity of the constructed data was tested using a meteorological station outside the region.
Discussion of Results and Considering the results, it seems that there is a good coordination between growth samples of each tree and all the trees together. This correlation was between the minimum amount of 75.4 and the maximum of 92.6% (75.4

n this study investigated the synoptic and 3-d dimensional cyclone and cyclogenesis centers in different levels at theatmosphere in 1372. Improvement forecast of climatic systems for preventing of damages and disease cause of them and or better planning for use of precipitation, necessities for the identifying cyclone centers and which cyclones that included our country.The cyclones identified and detected through the designed algorithms. Case study area included -30 western degree, 80 eastern degrees and 0 to 80 northern degrees. For this purpose used of reanalyzed NCEP/NCAR data, 6-hour intervals(00, 06, 12, 18z) and 2.5*2.5 degrees of spatial resolution for detection of cyclones that affected in the middle east and Iran regions. Grads software was used for the spatial detection of cyclones. Matlab, Surfer and Excel software was used for the analysis and map designing. Results show that in 500 and 1000 hpa levels had more cyclones than 925, 850, 700 and 600 hpa levels. Autumn season had the highest cyclone except 500 hpa level through all level studied. Summerseason had the lowest cyclone through all levels was studied. Also, main cyclogenesis centers were located in the Gulf of Genoa , Southern Italy, and Spanish-Portugal.

Introduction The climate is such that large system interaction between the four large-scale climatic: hydrosphere, cry sphere, lithosphere and Biosphere. If a change occurs in one of these systems, other systems quickly or slowly make their coordinators. The consequence of this arrangement, the skirt has been instrumental in changing the initiator includes an endless chain links, the device will tie together. Because this is the most important link between climates scientists in the fields of concern in recent years as climate change. Why the climate change caused by global warming and subsequently climate of the planet for future decades, the rapidly growing world system has been disturbed. 2- Methodology In this study, were used the data of mid-level atmospheric pressure, geopotential height pressure from the center's database based on National Centers for Environmental Prediction / National Center for Atmospheric Research(NCEP/NCAR). This data are available for 6 hours GMT time and period 1948 to 2007 on the website Climate Diagnostics Center. National Oceanographic & Atmospheric Administration Government (www.esrl.noaa.gov). The spatial resolution of the data is 2/5 degrees of arc and the area from 0 - 90 degrees north and 0 - 360 degrees meridian and contains 5184 pixels. For comparison, the t - test was used for paired samples and to achieve this goal, the 30-year period was divided into two periods. Diagnosis and detection of positive or negative trend (increase or decrease) during the period of the subtropical high pressure system in the area used the f Kendall's tau trend test and a significance level SPSS software 0/05 and 0/01. 3– Discussion Results of Kendall's tau trend test calculation showed, increases in all months of the area insubtropical high pressure system at significance level of 0/01. August has highest and April has lowest area rate. The system is based on semi-warm and generally higher latitude and has the lowest area. With the passing of spring, and the system is still moving higher latitude, until in July to reach its highest latitude. But with the start of the transition period and beginning of cold period of the year, this contour is pulled toward the low latitudes and most of the area is covered and In February to reach its lowest latitude. 4– Conclusion The results showed that all the months of the study period, with an increasing trend (positive) area are subtropical high pressure system. August has the highest and April has the lowest area rate. The northern limit of the system (contour of 5840 geopotential) in the semi-warm temperate latitudes generally is based as parts of the North, Central Mediterranean, central Europe and North America and is area lowest in July. But with the onset of cold periods, the contour is pulled down latitude and occupies the most area in February.