mohammad darand

Spatial variations of pressure patterns under the influence of large-scale atmospheric circulation create different conditions in terms of the intensity of synoptic systems affecting on extreme climatic phenomena. Regarding large-scale patterns, not only the changes of extreme events are considered, but also the changes and developments of synoptic systems on a regional scale are studied. Weather extremes have a great significance for society, as they pose a threat to human life and can result in enormous economic damage and disruption. The physical understanding and timely prediction of extreme weather events are of enormous importance to society regarding associated impacts. Extreme precipitation events (EPEs) is one of the most frequent natural hazards that affects the domain, eventually leading to floods. Synoptic systems in mid-latitudes such as blocking, ridge and troughs cause spatial changes in westerly waves. Spatial variations of atmospheric waves occur when, which normally move from west to east, change their main direction to the north or south. The occurrence of precipitation in the Middle East is related to the changes in waves and the position of the Mediterranean trough. Extreme precipitation events in the Middle East often result from tropical-extratropical interaction, whereby midlatitude forcing and poleward transport of tropical moisture are of central importance. The Mediterranean Sea region is considered to be one of the most important and major areas for circulation in the Northern Hemisphere, so that most of Iran's rain cyclones are formed over the Mediterranean Sea. In other words, Iran's rainfall in the cold period of the year is influenced by the Mediterranean circulation patterns. Evidence shows that the large-scale atmospheric circulation over the North Atlantic plays an important role in the Mediterranean circulation changes. We investigated the effect of mid-latitude atmospheric circulation changes on the position of the Mediterranean trough and the occurrence of super heavy precipitation in Iran. In this research, two category of upper atmosphere data were used. The first is daliy precipitation data for 350 stations that were made available by the Iranian Meteorological Organization (IRIMO). The second dataset is the geopotential height, relative vorticity, potential vorticity, U, V-component of wind and Relative humidity, extracted from the European Centre for Medium-Range Weather Forecasts (ECMWF-ERA5) reanalysis with grids at 0.25×0.25 resolution. In this regard, 150 extreme and widespread rains were identified, then using the sinuosity index, the changes of the most frequent contours (550, 565 and 575 decimeters) during the precipitations were identified over the North Atlantic and Mediterranean. The aim of this study was to find the relationship between the spatial variations of the upper level waves over the North Atlantic and the Mediterranean (-80° W to 70° E, 10° N to 60° N). According to sinuosity index, the minimum sinuosity value is about 1 indicating zonal flow. Any sinuosity value greater than 1 indicates an increase in atmospheric waves. The spatial changes of the Mediterranean trough are due to the meridian changes of currents over the North Atlantic, Europe. The blocking system over the Atlantic was responsible for both the amplification of the downstream trough, as well as the formation and expansion of the trough towards the Eastern Mediterranean. The results show that extremely heavy and widespread rains occur under the influence of the formation of the Mediterranean trough and the expansion of winds from the European side and the strengthening of the North Atlantic trough. The results showed that the position and changes of the Mediterranean trough are associated with the circulation pattern of the North Atlantic. Multiple Atlantic trough are associated with each case of Mediterranean trough downstream. The formation of the Atlantic trough causes an increase in the range of atmospheric waves and the formation of a ridge over Europe and the development of the Mediterranean trough. Under these conditions the Mediterranean Sea experiences 8 longitudinal and latitudinal changes compared to its average. Therefore, the spatial variations of the Mediterranean trough are due to the meridian changes of the European flows and the expansion of the high north side (tropical side) downstream of the western wind. Simultaneously with the formation of blocking over Europe in its eastern part, the dynamic ridge causes the transfer of cold winds over the Mediterranean, which strengthens and expands the Mediterranean trough to the east. These conditions, with the strengthening of the conditions of ascent and eastward movement of the Mediterranean trough, the Arabian Subtropical Anticyclone (ASA) also expands eastward. During the heavy rains of Iran, by strengthening the conditions of ascent and eastward expansion to the Mediterranean trough, the ASA also expands to the east. Therefore, with the location of the ASA over the northern Indian Ocean, the Arabian Sea, and the Oman Sea, more moisture is provided to the atmosphere, which is accompanied by the intensification of extremely intense and widespread precipitation. In general, the position of the Mediterranean trough during heavy rains is such that the western and southwestern regions of the country receive most of the precipitation. These conditions are more severe due to the orographic factor of ascent caused by the Zagros mountains in this region.

Albedo is one of the key parameter in climatic studies. Albedo climatology investigation can be a tool to recognize environmental changes. The MODIS continuously produces the land surface albedo on a global scale and with the appropriate spatial resolution and makes it available to researchers. In this research, in order to analyze the climatology albedo of Iran, firstly, the data produced by the MODIS sensor MCD43A4 product in the range of Iran in the period from 1/1/2001 to 12/30/2021 with a spatial resolution of 500 meters and a daily temporal resolution was taken from the NASA website. After the necessary pre-processing, the long-term average monthly, seasonal and annual albedo of Iran was calculated. The findings on a monthly scale showed that in the months of Jan, Feb, and Mar which are known as Iran's snow-covered months, Iran's albedo is maximum and is decreased in the transition months, and then in the warm months of the year (June, July, and Aug) it is increased again due to the dryness of the land and the increase in the land surface temperature. This two-way behavior is also evident in the seasonal scale. These calculations are made in the worst conditions (July) over 98% of the area of Iran and in the best conditions (Jan) on 99.97% of the area of Iran. In other words, in the July, the albedo time series data was complete for about 98% of Iran's area, and there was a statistical gap in about 2% of Iran's area. In the research of Kefayat Motlagh et al. (2021), the albedo data gap values of the MODIS sensor have been investigated in different seasons and annually. Results show that the maximum distribution of albedo in winter and autumn seasons corresponds to the snow-covered heights of Alborz, Zagros and the northwest of the country. But in the spring and especially in the summer, with the increase in air temperature and surface temperature, most of the wetlands dry up. With the drying of the bottom of Jazmurian, Hamon, Shadgan, Maharlo and salt lakes and Urmia, salt flats appear. These salt marshes also show a high albedo due to their white color. On the coastal of the Caspian Sea, low albedo is seen due to the decrease in land surface temperature and increase in soil moisture. This part of the research is in harmony with the findings of other studies conducted on the land surface albedo of Iran (Soltani Akmal, 1397; Kefayat Motlagh, 1400; Karbalaee, 1399). Also, the findings showed that the long-term average albedo of Iran is 12.5%, which is about half of the average planetary albedo (24%) (Zhang et al., 2010). This part of the research is also in line with the research of Karbalaee et al. (2021).

The study of the simultaneous occurrence of cyclones and the changes of Tropopause Pressure's level (TPL) can provide useful insights into the characteristics of the precipitation, especially the widespread precipitation (WP) over Iran; as mid-latitude cyclones are one of the most critical factors associated with WP in Iran. Understanding the mechanisms and the features associated with the cyclones can be crucial for estimating and predicting cyclones and their consequences with precision. To this end, in the current study, we underlined the relationship between tropopause and cyclones affecting WP in the country.In the current study, two data sets were adopted. These data sets include daily precipitation data of Asfazary national data set (version 3) and atmospheric data (including temperature and geopotential height (GH) data of ERA-Interim base from the European Centre for Medium-Range Weather Forecasts (ECMWF)) with spatial resolution of 0.25 degrees for an area comprised 0 to 80° N and -10 to 120° E. The main aim of selecting the aforementioned area and the data was to identify all the cyclones which are originated from or pass through the Mediterranean Sea and are associated with WP over Iran. Accordingly, the associated pressure levels of the tropopause were examined.The Asfazary database from 1979 to 2015 was adopted to identify days with WP based on precipitation anomalies covering more than 10% of the country. Accordingly, a total of about 1189 days with WP was extracted for the intended period.In this study, regional variations of GH at the level of 1000 hPa have been used to identify cyclone centers. To this end, the GH of the pixel was evaluated in relation to the eight neighboring pixels; when the GH was lower than the neighboring ones, and the gradient of the GH was at least 100 geopotential meters per thousand kilometers, the pixel was considered as the center of the cyclone. Cyclones were tracked with respect to the days with WP, and their characteristics were investigated based on the day of cyclone activity and the day of WP.Using the thermal criterion defined by the World Meteorological Organization (WMO, 1957)), the tropopause was identified.The 1189 days with WP have been studied visually. Since it is not feasible to present all the days in this brief paper, a few samples were selected to identify the association of tropopause with cyclones on days with WP. The days were selected based on the highest percentage of the area covered for different months. Accordingly, for the entire period, 8 days were selected to represent January, February, March, April, June, October, November, and December. In May, July, August, and September, days with WP were not observed. In the present study, to investigate the relationship between tropopause and cyclones in eight WP samples, the features of tropopause and cyclones on the starting days and on the days with WP were considered.The spatial distribution of the TPL on the day of cyclone activity and the day with WP showed that on the day of cyclone activity, tropopause had certain characteristics; at this time, the tropopause pressure level showed larger values than those in the surrounding areas. Even on days when WP was observed in Iran and within the cyclone activity range, this anomaly was observed in the TPL. The tropospheric condition of the country compared to the day of the cyclone activity had significant differences; at the time of precipitation, tropopause level showed a larger numerical value in most areas compared to the beginning of the cyclone, especially in areas with heavy precipitation intensity. Tropopause at the time of the formation of the cyclone with WP on April 7, 2013, was different from other under study cases. In this case, at the beginning of the cyclone activity on the cyclone formation area, the tropopause did not have a significant anomaly; while on the day of WP in the south of Iran, the anomaly was significantly prominent. It seems that this difference can be due to the differences in the origin and the mechanism of cyclones in different areas. This probably explains the difference in the characteristics of tropopause on the day of cyclone activity. In the whole area under study, at latitudes above 30 degrees, in geographic locations where the cyclones emerged at the 1000 hPa, tropopause was broken. At this time, tropopause pressure levels showed larger values than the surrounding areas. Given this fact, it seems that there is a relationship between the two phenomena, cyclones and TPL.Based on the findings, in all eight samples of WP days, tropopause had special characteristics in the same area of cyclone; in addition, tropopause pressure levels in these areas were higher than their counterparts at the same geographical situation.

In this study, the trend of tropopause changes in the Iranian atmosphere in the spring and autumn months was analyzed using ECMWF database data from 1979 to 2018. The results of studying the trend of tropopause changes in spring and autumn showed that in large parts of the country the observed trend was without statistical significance and only in March in parts of the west and northwest of the country and in September in the Zagros Mountains was positive and statistically significant. The results of studying the temperature trend of two levels around the tropopause also showed that in all months of spring and the first two months of autumn, except for very limited areas of the country, the observed trend is without statistical significance. The two-level temperature trend around the tropopause in November was significantly different from other months under the study; this month, large parts of the country were statistically significant in the two levels around the tropopause. Examination of the temperature difference trend of the two levels around the tropopause also showed that in areas with a significant trend, the trend of the difference between the two levels of high and low tropopause was negative. Analysis of Variance, Skewness and Kurtosis Tropopause trends also showed that in the three months of spring in most areas the observed trend is not statistically significant, but in autumn (especially September) in some parts of the country, the observed trend is significant. According to the results, it can be said that during the last forty years, the effect of climate change on the tropopause layer on the Iranian atmosphere in spring and autumn has been very small and changes in spring have been much more limited than in autumn.

Climate change is considered as one of the environmental challenges in recent decades. Climatologists have evaluated the behavior and change of climatic elements to identify weather change and its importance to the structure of the Earth's weather in recent years. Trend is one of the most critical components of a series, which is very practical in climate to investigate the long-term orientation of time series. Outgoing long Wave Radiation (OLR) is one of the basic variables of weather, as well as the core component of the earth radiation budget (Whitburn et al., 2021: 1. Scherrick et al., 2018: 1), which is known as an essential parameter in applications for cloud identification and precipitation estimation. Therefore, it is necessary to study Outgoing long Wave Radiation trends at different temporal and spatial scales. Mann-Kendall test is one of the widely used non-parametric tests, which has been applied in climate studies, especially in examining the trend significance (Gauss and Trajkovic, 2013: 172. Nelson, 2001: 57). Kefayat Motlagh et al. (2018: 128) indicated that the trend of earthlight radiation is increasing by 0.4 watts per square meter in each decade, while the trend of Iran's earthlight radiation is more than three times (1.4 watts per square meter) the global trend in the same period. Sari Sarraf et al. (2015: 33) investigated the effects of global warming on the cities’ climate in the Urmia Lake basin using the Kendall method and the least-squares error. They concluded that the average rainfall in the whole region decreased by about 4 mm per year. Chu and Wang (1997: 636) examined the trend through Mann-Kendall statistics to find climate change in convective precipitation in the western Pacific and Indian Oceans from Outgoing long Wave Radiation. They found a significant decrease in OLR in the tropical central and western Pacific and a large part of the Indian Ocean, while the largest increase in OLR over time was in North Australia. In this study, the trend and changes were investigated using the non-parametric Man Kendall method, the amount of changes was determined by Sen's Slope method, and hot spots analysis was performed by Jay statistical method given. In addition, spatial and cluster analysis was performed on the average data and seasonal variation coefficient due to the importance of Outgoing long Wave Radiation at different temporal and spatial scales.

Iran, with an area of 1648195 square kilometers, is located between 25 to 40 degrees north latitude and 44 to 63 degrees east longitude. The data of the Atmospheric Infrared Sounder (AIRS), Aqua satellite, were used to measure Outgoing long Wave Radiation for a statistical period of 17 years (01/07/2002 to 01/07/2019). MATLAB, ArcGIS, and SPSS software were used for calculations and maps. First, the average monthly data maps of Outgoing long Wave Radiation were prepared, and then, the standard deviation parameter was used to show the data dispersion. Moreover, the Mann-Kendall test was used to determine the trend of outgoing long wave radiation on each cell in Iran, and the slope of the data series trend line was calculated by Sen's Slope estimator method. Spatial variations of outgoing long wave radiation were calculated over time as spatial behavior using hot spot map analysis.

According to Stefan–Boltzmann law, the Earth's surface and atmosphere emit energy in waves in proportion to their temperature. These waves propagate in the range of long wavelengths, i.e., between 4 and 100 microns given the normal surface temperature and atmosphere (Kaviani and Alijani, 2000: 94). According to the maps, the average long-wave radiation fluctuates between wet and dry seasons as well as geographical offerings, and its amount is higher in the dry season and the southern regions of the country. One of the reasons for the maximum outgoing long wave radiation of southern Iran in early spring is the angle of vertical radiation of the sun and the clear sky, which receives more energy than the latitudes, and the amount of energy output is more in the south of Iran than in the north. The outgoing long wave radiation increases gradually due to the decrease in cloudiness, and more energy is received in longer geographical areas gradually, in May and June, with the onset of summer as the day length increases. The ingoing and outgoing radiation becomes more uniform throughout the regions of Iran, except for the mountainous areas and the coasts. According to the results of cluster diagrams in the three seasons of spring, autumn, and winter, the radiation patterns of the basins are similar to the latitude and mountainous areas in Iran. The largest cluster in spring belongs to the south of the Alborz Mountains and the west of the Middle Zagros Mountains depending on latitude and sunny slopes. The highest uniformity in all seasons is located in the southwestern quarter of Iran, and Haraz heights in southern Iran are distinguished as a cold spot among the surrounding basins. Examination of the trend by the Mann-Kendall method showed no significant trend on an annual scale, but monthly and seasonal anomalies are quite evident. The descending trend of long wave radiation can be confirmed only in May and September in some parts of the country, and the dominant trend in most months of summer, autumn, and winter is increasing in most parts of the country, including northern offerings. The results of the G-statistic study also show the changes of hot spots towards northern offerings.

The trend of 17 years of outgoing long wave radiation of the earth (2003-2019) was investigated in monthly, seasonal, and annual time scales using non-parametric Men-Kendall test and hot spot statistics (G). Changes and abnormalities of long wave radiation were observed on a monthly and seasonal time scale in most parts of the country. These changes can be due to changes in the amount of energy input, cloud cover, and the type of clouds, aerosols, atmospheric compounds, such as moisture from global warming and other greenhouse gases. In addition, changes in land cover such as vegetation, forests, water resources, salinities, and sand dunes can influence the sensible heat and change of ground wave radiation due to the amount of moisture, which needs further investigation in this regard.

The tropopause is a thin layer separating the stratosphere from the troposphere and is often characterized by a large change in the thermal, mass and chemical structure of the atmosphere.Compared to global studies on the tropopause and its various features, studies conducted in Iran are very few and the methods used are often less inclusive or the length of the statistical period is limited. For this reason, and considering the importance of the tropopause and its effect on exchanges between the troposphere and the stratosphere, and also due to the lack of information about it in Iran, accurate knowledge of the height of the tropopause in the country using more reliable data sources is a fundamental necessity. To calculate the tropopause, we used daily temperatures of ECMWF reanalysis datasets from January 1979 until December 2018. Gridded data witha spatial resolution of 0.25*0.25 were used. In vertical levels, we used 10 standard isobaric surfaces from 700 to 50 hPa.

The location of the tropopause thermally and dynamically was defined. According to the WMO (World Meteorological Organization), the tropopause is defined as the lowest level at which the lapse rate decreases to 2°C/km or less, provided that the average lapse rate between this level and all higher levels within 2 km does not exceed 2°C/km.In this study, this index was used to identify the tropopause.In this study, to identify the factors affecting the tropopause, the relationship between the tropopause and spatial variables (latitude and longitude) and altitude was evaluated by general and partial correlations.

The results of this study showed that in the months of cold season, the tropopause pressure level on Iran is followed by latitude, and the tropopause height decreases with increasing latitude, but in the months of the warm season (June, July, and August), the tropopause pressure level is different from the months of the winter season.In these months, the changes in the tropopause pressure levels do not follow the latitude; on the Zagros and Kerman heights, the tropopause height is at its lowest, while the highest tropopause elevation is in these months at higher latitudes than in other months.The temperature of the upper and lower levels of tropopause also showed that the temperature of the lower levels of the tropopause in all seasons was below the temperature of the upper levels of the tropopause and the temperature of the two levels changed with the changes in the levels of tropopause pressure in different months.The study of low and high levels of tropopause showed that during the cold season, the temperature of the two levels around the tropopause, following the tropopause pressure levels, follows the latitude, and with increasing latitude, temperature increases in the two levels around the tropopause.In two studied seasons, the lowest temperature of the two levels of the tropopause is consistent with the highest level of the tropopause, but the highest two-level temperature is only consistent with the lowest tropopause pressure level during the warm season months, and in other months, this observation coordination failed.Investigating the thermal difference between two levels of tropopause showed that the temperature difference between the two levels of the tropopause in the warm season is more significant than that of the cold season, while in the cold season, the temperature difference in most regions of the latitude is obeyed. Slowly, the difference in temperature decreases with increasing latitude.

Examination of the characteristics of the tropopause and its related factors for summer and winter showed that in each season due to local conditions and changes in large-scale factors, the height of the tropopause changes, and therefore the tropopause in each season has completely different characteristics from the other season.Examination of the characteristics of the tropopause and its related factors for summer and winter showed that in each season due to local conditions and changes in large-scale factors, the height of the tropopause changes, and therefore the tropopause in each season has completely different characteristics from the other season.

Urban heat island monitoring with remote sensing data is increasing and one of the most important reasons is to provide more spatial information of urban temperature than terrestrial data. The heat island resulting from this data is called the surface urban heat island. Different methods can show the intensity of Surface urban heat island in a city differently. Furthermore, consider of the temporal and spatial variations in temperature can cause error in calculating urban heat island. The relationship between factors such as vegetation index, land use, altitude and meteorological factors with urban heat island has been investigated and proven in previous researches. In this regard, predicting the land surface temperature in and around the cities to simulate the intensity of the urban heat island in the coming years has been of interest to researchers because reliable predictions of the difference between urban and rural areas are essential for planning about cities. Different cities may affected by different factors depending on the climate in which they are established. Therefore, in the study of the heat island of Tabriz and Urmia, land use is investigated as a determining factor. In addition, temporal variations in the area of Lake Urmia will be studied to assess the relationship between the extent changes of this water body and the intensity of surface urban heat island in Tabriz and Urmia.

In this study, MODIS land surface temperature data (MOD11A1) in tile No. h21, v06 has been used to investigate the urban heat island. This tile covers northwestern Iran. The common time series used in MODIS Terra and Aqua is from 2003 to 2019. Terra and Aqua each monitor the entire earth twice a day. In this study, all four observations have been used in order to evaluate the diurnal variations of the urban heat island. Second MODIS data that used in this study is the land cover type (MCD12Q1). To identify the types of land cover, the FAO Land Cover Classification System has been selected among the existing classification layers, which has been generated by applying the supervised classification method to the MODIS reflectance data. Another MODIS product has been used to study the changes in the area of Lake Urmia. This product provides a time series of the world's lakes extent, depth and reservoir variations. The data were obtained from the detection of water and land pixels using a machine-learning algorithm.Urban area and type of pervasive land cover around the city has been obtained using MODIS land cover type data. The pixels that belonged to a specific land cover in more than 75% of the study period (temporal frequency of land use species) were considered as the representative pixels of that land cover. To determine the urban and rural area, an area equal to the size of the urban extent around it has been selected as rural area. The land covers were examined among the rural pixels. The pixels that cover more than two thirds of the rural area have been identified (spatial frequency of land covers). The intensity of the heat island has been estimated according to each of the dominant land covers. Then, the intensity of surface urban heat island in relation to each of the land covers of rural district has been compared. This process also has been done once for the whole area of the rural pixels.

Evaluations of land cover type in rural area of Urmia and Tabriz cities showed that land cover type of cropland and natural herbaceous has the largest area with more than 75% of land cover frequency. The surface urban heat island in Tabriz has annual cycles. In the warm period of the year, the cropland shows a more intense heat island rather than natural herbaceous and all rural area. In addition, at this time of the year, the estimate of urban heat island in relation to the area of natural herbaceous in most cases indicates the cold heat island in Tabriz. These conditions are inverted during the cold period of the year and the urban heat island in cropland and natural herbaceous shows the cold and heat island, respectively. The intensity of the urban heat island of Urmia in the land cover of cropland and natural herbaceous is well separated and show completely different annual cycles, but its annual variations is the same as in Tabriz. The use of natural herbaceous as a rural land cover in Urmia shows a more severe cold island in the warm period of the year than Tabriz.The urban heat island of Urmia at night shows obvious differences compared to Tabriz. First, the annual in the Urmia heat island cycles are well seen, which indicates the increase of the night heat island in the warm period of the year and its decrease in the cold period. The second major difference is the urban heat island values related to different rural land cover type. The heat island of Urmia, although in smaller numbers, often shows more intensity than Tabriz. It may be due to the smaller size of Urmia city compared to Tabriz and its shorter distance from the lake that cause to more affect by water extent variation of the Urmia Lake. Because of this condition, the daytime urban heat island in Urmia occurred more frequently. In addition, there is a significant difference between urban heat island of rural land covers in Urmia. While this difference is less in Tabriz and less in nighttime than daytime.

Calculation of the surface urban heat island with MODIS data showed that in some cases, especially during the daytime cold island occurs in some parts of the two cities of Tabriz and Urmia. The calculated heat or cold island was determined by selected type of land covers in rural area. In addition, the selected type of land cover in rural area has a great effect on estimating the intensity of the urban heat island. Cropland as a rural area during the night shows more intense heat island than natural herbaceous while during the daytime the opposite condition was happened. The use of all type land covers as rural area shows the intensity of the heat island between cropland and natural herbaceous as rural area.Due to the large effect of heterogeneous surfaces on the measurement of surface temperature during the daytime, measurements at nighttime can provide the intensity of the urban heat island with better accuracy. In this regard, nighttime observations of MODIS land surface temperature, especially in Aqua, which is the closest observation to minimum temperatures, can be useful in monitoring the intensity of the urban heat island and its temporal-spatial changes, especially in warm period of the year.

Snow reservoirs are one of the most important sources of water supply in Iran. This study aims to investigate the changes in snow cover in Sefidrood basin and its sub-basins. To achieve this goal, snow cover images of MODIS's MOD10cm product were used over a period of 19 years (2000 to 2019). After analyzing the images in ENVI software, the snow area of each image was calculated and, consequently, monthly values for all years of the study period were extracted and transferred to ArcGIS software. The modified Mann-Kendall test was applied to examine the annual and seasonal trends of snow cover. Then, the Sen’s slope estimator test was used to determine the rate of change. Based on the findings, it can be seen that the time of beginning and end of snow in Sefidrood basin and its sub-basins has undergone very little change. The snow cover of the basin starts in October and reaches its minimum in April after increasing towards the cold period of the year. January is the month with maximum snow cover in all sub-basins. During the period under review, the range of snow cover was maximum in the water year 2006-2007 and minimum in 2009-2010. Findings from the fit of the modified non-parametric Mann-Kendall test on the annual time series of the snow cover of the basin indicate a decrease and of course insignificant trend in most of the basin area, although sometimes slight increasing trends are also seen. In terms of spatial distribution, in the central and eastern regions of the basin, there is a significant decreasing trend of snow cover; its annual decreasing rate in some areas is 17.77% per decade. Seasonal trends are also decreasing. The highest rate of decrease is related to winter, which in some areas reaches about 33% per decade.

The aim of the current study is spatiotemporal analysis of changes in minimum, maximum and mean air temperature over Iran based on Representative Concentration Pathway (RCP) of the fifth report of IPCC. To do this, three datasets have been used: 1) Daily maximum, minimum and mean air temperature data for 42 synoptic stations during 1/1/1979 to 31/12/2005, 2) National Center for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) data for 26 variables, and 3) Representative Concentration Pathway (RCP) scenarios data during 1/1/2006 to 31/12/2100. The model was calibrated using predictors from the NCEP/NCAR reanalysis datasets over the base period 1979–2005. The selected downscaling model structure has been done by a recommended method by Mahmood and Babel (2013) which is a combination of the correlation matrix, partial correlation, and P-value was used. Due to the bias, downscaled temperature data forced by CanESM2 model was corrected using a bias correction technique for each station.  Finally, changes in the characteristics of temperature were estimated for the future period (2006–2100) based on the future scenarios RCP2.6, RCP4.5 and RCP8 compared to the base period (1979–2005). The projected changes were assessed with the nonparametric modified Mann-Kendal trend test and Sen Slope estimator at 95% confidence level. The results showed great reliability of SDSM downscaled model structure and screening of variables. The findings illustrated that minimum, maximum and mean air temperature were projected to increase. The projected increases was larger in semi western part of country. Minimum air temperature projected to increase 0.35, 0.6 and over 1 ℃ based on the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. Maximum air temperature increases rapidly after 2070 decade and projected to increase more than 1℃ rather than base period (1979-2005). The projected changes showed that warming of Iran’s air temperature more over 0.5 ℃ after 2040 and reached to 1.5 ℃ by the end of twenty first century.

In this study, the relationship between mean zonal and meridional variations of westerlies with wet and dry periods in the western Iran was investigated. Therefore, the data cumulative precipitation values of the study area from Eropean Center for Medium-Range Weather Forecasts (ECMWF) version (ERA-Interim) with spatial resolution of 0.125*0.125 and also the geopotential height data of the version ERA5 with a spatial resolution of 0.25*0.25 degrees was received from the same center for the years 1979 to 2019.The Standard Precipitation Index (SPI) was used to determine the monthly wet and dry periods. So, to investigate the relationship between mean zonal and meridional variations of the westerlies with wet and dry periods in western Iran, the Middle East Synoptic Index (MESI) was defined based on variations of the westerlies from 25 to 45 degrees north latitude at atmospheric levels of 500, 600 and 700 hPa. The results indicated, that when wet periods occur in western Iran, MESI upper than the threshold and westerlies on the Middle East (Mediterranean and Red Seas) have a meridional flow. Whereas in dry periods, MESI is lower than the threshold and westerlies have a zonal flow. According to the results, no wet and dry periods was found, the value of which MESI was lower and upper than the threshold. Therefore, it seems that variations of the westerlies flow can be effective as one of the factors in formation wet and dry periods in the western Iran.

The aim of this study is to identify the spatial distribution of Vertically Integrated Moisture Flux Convergence (Vertically Integrated) Moisture Flux Convergence) on Iran’s atmosphere. To achieve this aim, the monthly ECMWF gridded data used during the period from 1/1979-12/2013. First, based on the specific humidity content in the atmosphere, troposphere divided into three layers (850-1000hPa), mid (700-775hPa) and upper (500-600hPa). In order to achieve VIMFC spatial variations on Iran, spatial self-correlation methods   of globular moron and hot spots used at 90, 95, 99 and 99/99 percent significance levels. The results of this study showed that the spatial distribution of VIMFC in Iran during the first layer of troposphere and especially during warm months of year has a high cluster pattern and in cold months of the year and in the third layer of troposphere cluster pattern decrease. Based on the hot spots index in the first layer of troposphere low height regions, in the second layer of troposphere the  high regions of the Alborz, zagros and central mountains and in the third layer of troposphere alpine regions of central and eastern Iranchr('39')s mountains has positive spatial self-correlation (hot spots). The results show that in winter and autumn during the second period (1999-2013), the range of hot spots of the VIMFC show a significant reduction compared to the first period (1979-1998) on Iran.

The purpose of this study is to investigate and analyze turbulence, fluctuations and jumps of Iranian regions. For this purpose, environmental data has been gathered in two parts. In the first part of the data, the results of the interpolation of the daily precipitation observations of 1434 stations of climate and climate were used from the beginning of 1340 to 1383. After the formation of a database to identify the Iranian regions, a cluster analysis was used on average data and annual and monthly rainfall variation coefficients. Silhouette analysis has been used to validate the Iranian rainy areas. In order to investigate disturbances, mutations and fluctuations in Iranchr('39')s rainy areas, this study was carried out. The results of cluster analysis indicate that Iranchr('39')s peripheral areas are divided into six classes. In the meantime, the Caspian region (area 4) has the highest rainfall and the lowest coefficient of variation. The distribution of rainfall regime in each of the six areas shows that Iranchr('39')s precipitation regime is more frequent in winter and spring and sometimes in autumn. Investigation and analysis of rainfall turmoil has shown that rainfall, except in the 4th district (Caspian region), in other areas of distribution of rainfall occurred along with disturbance. Although most disturbances occur in the zagros area, the highest sequence of disturbances is related to the fifth load region. The least sequence of disturbances occurred in the central and eastern part of the country. The results of mutation analysis and fluctuations indicate that rainfall disturbances, except in the early years of precipitation regions two and five in other regions of the other regions, have no significant mutation, while short-term fluctuations of 3-5 years on rainfall Each of the six domains has dominated.

Monitoring the tropopause features over a geographic area is important for a number of interrelated reasons. From a climatological point of view, it is important to investigate the behaviour of the tropopause charactreristics for a long term, so that to detect any increased or decreased trends. From a dynamic point of view, it is essential to define the tropopause hieght, in order to explore the stratosphere—troposphere exchange taking place over a geographic area that may be responsible for changes in the chemical composition of the atmosphere. Over the past two decades, there has been growing interest in the tropopause charactreristics among the atmospheric scientific community. It is now broadly accepted that the tropopause plays a key role in a variety of atmospheric and climatic phenomena.

Compared to studies performed globally, in Iran a limited number of studies concerning the tropopause have been conducted. Moreover, the methods have been used and the length of the dataset were often inadequate. Therefore, in the present study, for the detection of tropopause, the daily data of Temperature, and Geopotential Height from the European Centre for Medium-Range Weather Forecasts (ECMWF) for 700 to 50 hpa with a spatial resolution of 0.25 × 0.25 longitude/latitude from 1979 to 2018 was adopted. Accordingly, 2491 cells have been covered across Iran. The LRT was used to detect tropopause. the tropopause is defined as ‘‘the lowest level at which the lapse-rate decreases to 2 /km or less, provided that the average lapse-rate between this level and all higher levels within 2 km does not exceed 2C/km. In this study, in addition to the tropopause pressure level, the tropopause height (m) was also evaluated during this period.To obtain better cognitive knowledge about tropopause, the factors that were likely to be related to the spatial changes in height of tropopause were investigated. To achieve this goal, the relationship between tropopause pressure and spatial variables (e.g. longitude, latitude, and elevation) was assessed by general and partial linear correlation ceofeicents. In addition to the characteristics of temperature at the lower and upper levels of the tropopause, the difference in temperature between these two levels on the atmosphere of Iran and the characteristics of the minimum, maximum, average, as well as the temperature range of the earth's surface were evaluated and their relationship with tropopause pressure levels and height levels was assessed.

Investigating the characteristics of tropopause on the atmosphere of the studied area in the under investigation period (1979 to 2018) and its related factors in the autumn and spring months showed that in the months of these two seasons, various factors affecting height and pressure levels of tropopause are diffrnt and in both seasons have different characteristics. In all spring and autumn except for September, the height of the tropopause is decreasing as the latitude increases, but in October and November, the rate of change was greater than in the other months. In all the months of these two seasons (except September), the highest level of the tropopause taking place in the south-east of the country, whilst the lowest level of pressure occured in the north-west of the country. Investigating the changes in tropopause height and tropopause pressure levels also showed that they were not consistent in the under investigation seasons, so that in places with similar pressure levels, observed elevations were different. the tropopause pressure levels have a strong relation with the latitude, but changes in the tropopause height did not show a regular relationship with latitude.Tropopause height changes are mostly irregular in spring and autumn, and in parts of the country, it is almost dependent on longitude. In the spring and autumn periods, the high and low tropopause levels are among the most influential factors on tropopause. Among the cases that were related to the tropopause were surface temperatures and their characteristics in the spring and autumn seasons. During these two seasons, it was found that the potantial relationship of surface temperatures with tropopause pressure and elevation levels, especially at high latitudes, is low, but in lower latitudes, due to limited variation in the surface temperature, the potantial connection of tropopause and surface temperatures are higher than other parts of the country. In the months of the spring and October and November, it was revealed that the potantial correlation between tropopause pressure and elevation levels with local factors was low, but in September in parts of the country, the effects of surface elevations on the levels of tropopause pressure is much more significent.

The results of the study of the tropopause and its related factors showed that the trend of tropopause pressure changes in the vicinity of latitude is decreasing with increasing the latitude. But the tropopause height is not aligned with its pressure levels, and in most areas, it is in the vicinity of Longitude. Among the studied factors, the low and high levels of tropopause have the highest impact on the tropopause, and the effects of surface temperature and other examined cases have not a noticable impact.he results of the study of the tropopause and its related factors showed that the trend of tropopause pressure changes in the vicinity of latitude is decreasing with increasing the latitude. But the tropopause height is not aligned with its pressure levels, and in most areas, it is in the vicinity of Longitude. Among the studied factors, the low and high levels of tropopause have the highest impact on the tropopause, and the effects of surface temperature and other examined cases have not a noticable impact.he results of the study of the tropopause and its related factors showed that the trend of tropopause pressure changes in the vicinity of latitude is decreasing with increasing the latitude. But the tropopause height is not aligned with its pressure levels, and in most areas, it is in the vicinity of Longitude. Among the studied factors, the low and high levels of tropopause have the highest impact on the tropopause, and the effects of surface temperature and other examined cases have not a noticable impact.

Today, air pollution is one of the main and most harmful problems in human societies, which has caused many environmental problems. Air quality is changing daily, even when the amount of pollutants entering the air is constant, factors that determine climate change, such as wind speed, wind direction, air mass thermal profile, amount of solar energy to perform photochemical reactions, wind duration or rainfall, alter air quality specifically. The air has a limited capacity and does not tolerate the discharge of various wastes and toxins that humans enter today. Multi- collinearity test was performed to remove additional input variables in SPSS software. The correlation between the independent and dependent variables is measured by two indicators of Variance Inflation Factor (VIF) and tolerance. The Variance Inflation Factor (VIF) indicates how much the variance of the estimated regression coefficients has increased since there are no correlated variables in the model. If the value of this index is close to one, there is no linearity. The relative tolerance factor is the relative scatter of a variable. If its value is close to one, it means that in an independent variable a small part of its scattering is justified by other independent variables, and if this value is close to zero, it means that one The variable is almost a linear combination of other independent variables. If the VIF values of the independent variables are more than 10 and tolerance is less than 0.2, then it can be said that the model is suffering from multi-collinearity. To quantify the severity of the multi- collinearity, tolerance and Variance Inflation Factor (VIF) results were used. The Forward Selection (FS) method is based on regression and was used to select the best subsets of input variables. The Forward Selection (FS) technique has been used by many researchers to build powerful predictive models. This method is based on the degree of dependence of the independent variables with the dependent. After that, the variable that creates the most dependence with the dependent variable is considered as the first input and the variables with less dependence constitute a set of subsequent inputs. This step is repeated n-1 to evaluate the effect of each of the variables on the model output, and a subset of the input variables is obtained to predict the outputs. The linear relation between variables created several models for each pollutant. Then the application of Multi-Layer Perceptron (MLP) network was used to predict pollutants in Matlab software. To reduce errors and increase accuracy in forecasting, both independent and dependent data were normalized between zero and one. Neural networks are nonlinear models that are widely used to identify systems, predict time periods, and pattern. These networks can be tools for the flexibility of nonlinear regressions, which are generally composed of one or more layers with different neurons. The structure of the neural network typically consists of three layers, the input layer that distributes the data in the network, the hidden layer that processes the data, and the output layer that extracts the results for specific inputs. For each pollutant, seven models were evaluated, but for O3 pollutant, eight models were calculated due to the effect of pollutant NO2. The results of multilayer perceptron neural network analysis show that, MODEL 1 with FB = 0.0170, IOA = 0.967, NMSE = 0.100 and the highest R2 = 0.7341 was suitable for same-day predicting of CO. To predict one-day advance of CO, MODEL 2 and MODEL 5 have the highest R2 values, but IOA statistics in MODEL 2 is more than MODEL 5, and the values of FB and NMSE in MODEL 2 is lower than that of MODEL 5, so MODEL 2 is more suitable for one-day advance of CO pollutants. In predicting the PM10 pollutant, the MODEL 4 has a maximum value of IOA= 0.960 and FB = 0.00151 with one of the lower value than other models in same day predicting of PM10, and the MODEL 4 has the lowest amount of NMSE = 0.487 and RMSE = 0.0718 in one-day advance predicting of PM10, so MODEL 4 is selected for prediction of PM10 pollutant as the optimal model. The prediction results of SO2 pollutant indicate that the MODEL 3 has the lowest FB = -0.00302 and NMSE = 0.135 and the highest IOA = 0.943 and R2 = 0.6118, respectively. Therefore, this MODEL is perfect for same-day prediction of SO2 concentration. Based on the result MODEL 6 with lowest values of NMSE = 0.105, FB = -0.0048, and the highest IOA = 0.972 is suitable MODEL for predicting one-day advance of SO2. In predicting NO2 the MODEL 2 and MODEL 3 represents the highest performance compared to other models in same-day and one-day advance in prediction of NO2 pollutant. Comparing the two models mentioned for NO2 shows, that both models have the same conditions in minimum and maximum values of the statistics, so considering the RMSE of the test phase, which is less in model 2 than model 6, indicate that model 2 it is a more appropriate model in predicting NO2. The prediction results of the O3 pollutant indicate that the MODEL 7 in the same day forecasting and the MODEL 5 in one-day forecasting in terms of the IOA index have the same value, and the indexes NMSE = 0.00120 and FB = 0.00137 in the MODEL 5 have the minimum values and in model 7, the value of R2 = 0.711 is highest, so the input composition of MODEL 5 is considered as the optimal model. The results of this study showed that for the prediction of any pollutants, no need to use all seven variables from the output of the multi- collinearity test. The optimal number of independent variables for the prediction of each pollutant was obtained differently. Therefore, we can conclude that the selection of effective independent variables by FS method will reduce the analysis cost and time, as well as increase the accuracy of the pollutant predictions.

One of the consequences of global warming and climate change is shifts in tempospatial of important atmospheric parameter precipiation. The aim of this study is detection changes in precipitation waiting time duration in Kurdistan province during recent decades. To doing this research daily precipitation data from 162 synoptic, climatic and rain gauge stations in and out of province during 1/1/1340 to 11/10/1391 extracted from Kurdistan Regional Water Company and meteorology organizations. By geostatistic Kriging method daily precipitation interpolated on 6 6 kilometer and one digital map has been created for each days. Then data over province on the 811 pixels that covers whole of province extracted. One data base in 18914*811 dimensions has been created that locate days on the rows and on the columns pixels. The average waiting time duration calculated for each pixels during different months. The Mann Kendal nonparametric statistics test fitted on the average waiting time duration time series at 90% confidence level. The chages rate estimated by linear least square method. The results showed that waiting time duration has been longer in cold seasons while shorter in warm seasons. The decrease rate of waiting time duration is twice waiting time duration increse. The distribution of significant trend over Kuridstan province show that waiting time duration is longer in semiwestern parts. In contrast in semieastern parts precipitation waiting time duration is shorter.

In this study, we will be addressing Iran’s thermal seasons and its variance during 1/1/1370 to 29/12/1392 of the solar calendar. At first daily data from 49 stations were gathered all over Iran in the mentioned period of time. The difference between yearly standard deviation with deviation (Dev) for each station was calculated and therefore seasonal advancing and receding was detected. Finally, three matrices namely SWS (Start Warm season), EWS (End Warm season) and SWL (Season Warm Length) all with dimensions of 63 × 49 were developed. Afterwards, to understand homogeneous arena, cluster-analysis with merger method was executed on matrices. Ultimately, by using a non-parametric modified Mann Kendall test, meaningful variability, start variability rate, final and warm period in the studied period were measured. Findings indicate that thermal seasons in Iran aren’t conformed to official calendar in the country and the country’s arena ought to be divided into four homogeneous thermal seasons. Latitude, longitude, altitude, distance from water resources as well as atmospheric humidity have an important effect on the known arena. Iran’s thermal seasons have been significantly altered during the start and end of studied period. Overall, thermal period in most parts of Iran have increased. In the studied stations, the decreasing rate of warm period are significantly more than decreasing and increasing rate of the ending warm period. It seems that the findings of this study are strong evidences concerning the effects of dominant heat all over Iran and altering the seasons as well as thermal situation.

  Rainfall is the most important meteorological factor driving the hydrology of river basins. For the development and management of water resources, it is required to have a reliable coverage of rain gauges, rainfall satellite data and weather radars. Well-maintained ground-based rainfall stations give the best rainfall estimation with high accuracy over time for a small area. The spatial sampling error becomes higher in estimating rainfall when using interpolation techniques. This issue becomes particularly critical in data scarce regions with unevenly distributed rain gauge stations. This is recognized as one of the principal sources of uncertainty in hydrological modeling. Currently, with more and more global precipitation datasets developed, a decline is seen in the application of reanalysis products in hydrological modelling. As a hot research field, many studies focus on the application of directly measured precipitation data on flood risk evaluation at basin scales and discuss their potential for hydrological prediction of ungauged/poorly gauged basins. In this study, we used PERSIANN-CDR gridded database precipitation for modeling runoff in SWAT model in Maharlu Lake basin. The PERSIANN-CDR was used as a gridded database of precipitation for modeling runoff in SWAT model in Maharlu Lake basin. The PERSIANN-CDR is initially compared with rain gauged data and after that it was used as input to SWAT model. SWAT model was calibrated by rain gauge data during 1983 to 2013. The Warmup period set to 3 years. Three discharge stations were used for calibration. Correlation coefficient of, Nash-Sutcliff, POD, CSI, FAR, RMSE, ME and BIAS had been assessed to determine the accuracy of PERSIANN-CDR. SWAT model uncertainty and sensitivity were calculated in SWAT-CUP by SUFI2 method. Comparing the PERSIANN-CDR in monthly scales, we found that this satellite wheatear database less estimates the variables in all months. The results showed average of correlation coefficient is 0.6 and RMSE showed a high error in rainy seasons. In SWAT model, calibration period was set to 1983 to 2010 with validation from 2011 to 2013. Calibration with gauged data showed satisfactory Nash-Sutcliff and R2 statistical indices about 0.6 for the area. The best result was occurred in Chenar-Sokhte-khosh discharge station, R2 was about 72% in calibration and 81% in validation. Calibration with PERSIANN-CDR database showed that this database is not good enough to be used in this semi-distributed model. In Chenar-Sokhte-khosh discharge station, R2 is calculated about 0.59 and Nash-Sutcliff about 0.21. R-factor and P-factor was presented about 0.5 in all discharge stations. These factors show that uncertainty calculation was occurred in good form. The simulation of annual runoff showed that the average runoff simulated using observation database was 1.68 m3/s, the mean runoff simulated by PERSIANN-CDR is 0.84 m3/s, and mean runoff of discharge stations were 1.77 cubic meters per second. On monthly scale, PERSIANN-CDR estimated less runoff like rainfall over all months. Both databases simulate runoff values relative to those recorded in the autumn months less than actual values. The results of this study, which were conducted using the PERSIANN-CDR satellite product, unlike the other studies with global exploratory bases, displayed that in the simulation with the SWAT model, this base cannot be accurately high in simulation. The error of estimating precipitation has been entered directly into the model and caused an error. In this study, with the accuracy of precipitation data, PERSIANN-CDR satellite data on rainfall estimation revealed that this database estimated precipitation values less than real values in all months of the year. Runoff simulation using this satellite product expresses the explanatory factor and the efficiency of Nash-Sutcliff about 0.59 and 0.21.
Despites the time series of precipitation values, this satellite database has a high correlation with the actual values observed on rain-fed stations, but as the findings show the estimated rainfall values are always lower than actual recorded values. Based on the findings from this study, the PERSIANN-CDR satellite is not very accurate on the area of the Maharlou Lake Basin, located in eastern Zagros. In the semi-distributed SWAT model, it cannot simulate runoff. Therefore, it is suggested that before applying estimated rainfall data, this satellite database will have its error and bias values compared with the observed data on rain gauge stations and, then, the estimated precipitation values are corrected based on the bias

  Atmospheric aerosols have different sources that we can refer to volcanic activities, dust, salt particles in the seas and oceans, or they due to human activities that we can refer to activities that such as industrial activities, transportation, fuel costs and …. aerosols have very important role in transitive radiation and chemical process that they are the earth’s climate controller. Among the internationally-conducted works in this area can refer to the Olcese et al. , 2015 which have been done based on the use of the artificial neural network model (MLP). They used previous values of the AOD at two stations as input of artificial neural network model to estimate the AOD under cloudy conditions and in situations where little data is available. This method was used to predict the values of AOD on nine stations with 440nm wavelengths on the east coast of the United States during the 1999 to 2012. The calculated R2=0. 85 between the observed and predicted AOD indicate a good performance of this model. To date, there is no research to estimate AOD by using different models such as Multiple linear Regression, Principal Component Regression, Artificial Neural Networks and Autoregressive integrated moving average model in Iran. Therefor in this research estimation AOD examined in two cases including estimate for areas with no Pyranometer stations and long- lasting estimation in stations with solar radiation detector for the future under.
Material and In this study related data to Pyranometer were collected for understudied are though the Meteorology office in center of Kurdistan province ranged 2005/01/01 until 2016/12/31. Thus, the total number of available data for the mentioned time period was 4382 data in the study area, and since there was no solar radiation for some days of the year, the total number of data used for Sanandaj city was reduced to 3956.
Study area:Sanandaj is the capital of Kurdistan province. About geographic location this city is located in within limits 35 degree and 20 minutes north latitude and 47 degree east longitude from Greenwich Hour circle and in the 1373/4 meters height above sea level.
Multiple linear Regression Model:In the Multiple linear Regression turn to check the relation between a dependent variable and several independent by earned relationship for them in the SPSS software, in the Multiple linear Regression the measure of AOD serve as dependent variable and meteorology numeral quantity such as temperature, relative humidity, wind speed and also altitude atmosphere were considered as independent variable. The general formula for the MLR model is as follows:Y=β_0+β_1 x_1+⋯+β_n x_n+ε
In this case, y is dependent variable. X1, …, Xn denote the independent variables, and also nβ0, …, β report the fixed constants. Ԑ also indicates the remaining values.
Principal Component Regression Model:Principal Component Regression Model is a combination of Principal Component Analysis (PCA) and Multiple Linear Regression (MLR). These calculations are as follows:Y=φβ_PCR+e
Where φ is the matrix of base components, which is obtained as n * k, and βPCR represents the first of the components of the K score. The vector of e is a random error which defined as n٭1. Mark and scores for the components are based on the original version of the OLS method as follows:β_PCR= (φ^' 〖φ) 〗^ (-1) φ^' y= (L^2) ^ (-1) φ^' y
In this case, L2 is the amount of slice of the matrix, which is based on the Kth parameter, which also indicates the slip of the parameter k⅄. Finally, the following equation was reached. β_PCR=∑_ (K=1) ^K▒ (υ_k u_k^') /d_k y, K<min⁡ (n, p) in this model primary variable changed to new components and Independent from each, that both of the two components have Zero correlation coefficient, finally these used as primary variables.
Autoregressive Integrated Moving Average Model:Autoregressive integrated moving average model is one of the important method in anticipation time series which presented by Box and Jenkins in 1970. ARIMA model is a Data- driven model, it means the mentioned model use of the structure of data and this model facet. Limitation if data have any meaningful nonlinearities relationships. ARIMA model is able in this way present the forecasts related to the time series. This model is a forecasting method with Statistical theory and because of having advantages such as high attention and strong adaptability ability is able to have a good usage in many bases.
Artificial Neural Networks Model:Multilayer perceptron (MLP) is the most well know and mostly the most used among different kind of neural networks and in most cases act as signals that transfer input to output in the network. In these kind of multilayer network layers are joined as outputs of first layer act as second layer inputs, and output from second layer are the third layer inputs and it will be continued till last layer output, that they are the main outputs and the certain and real answer.
Discussion of Results & The first model, Multiple linear Regression according to the made result for this model, the measure of the AOD in understudied city has a direct connection with temperature and wind speed parameters out the level 850 hectopascal, but also this have an opposite connection with relative moisture and atmospheric layer altitude also the measure of got determination factor by this model allocated itself less numerical value and it is used because of linear structure in the data. The equation presented for it is as follows:AOD=458/0+039/0T_850-127/0〖RH〗_850+021/0〖Speed〗_850-064/0 BLH
The R^2=0. 071, RMSE=0. 1698 and MAE=0. 1498 were obtained for training phase and R^2=0. 096, RMSE=0. 1703 and MAE=0. 1494 were acquired for testing phase. The results of the training and testing phases of the MLR model indicate the low accuracy of this model in predicting the AOD in Sanandaj city. The second used model in this research was Principal Component Regression model. In this model AOD have direct connection with temperature and wind speed but it has a negative connection with the other parameters such as relative moisture and atmospheric layer altitude. The extracted equation for PCR model as follows:AOD=457/0+041/0T_850-126/0〖RH〗_850+021/0〖Speed〗_850-065/0BLH
In this section, the R^2=0. 071, RMSE=0. 1699 and MAE=0. 15 were obtained for training phase and R^2=0. 069, RMSE=0. 1694 and MAE=0. 1484 were acquired for testing phase. According to the result, got out puts by MLR and PCR models have a close result to estimate the AOD for stations with no Pyranometer. Autoregressive Integrated Moving Average Model was the third used model. This model had the best function to estimate AOD in the station with no Pyranometer. The obtained equation for ARIMA model as follows:AOD=0061/0+7084/0y_ (t-1) +0572/0 y_ (t-2) +2189/0y_ (t-3)
In this section, the R^2=0. 91, RMSE=0. 0501 and MAE=0. 033 were obtained for training phase and R^2=0. 89, RMSE=0. 086 and MAE=0. 0374 were acquired for testing phase. Artificial Neural Networks model was the fourth used model. In the research two hidden layers were used in this model. The number of optimized neurons for the understudied area was different with available data. The number of optimized neurons determined for Sanandaj city were 24 and 33 neurons to estimate the AOD in the long time (a year) in the station with no Pyranometer. In this section, the R^2=0. 75, RMSE=0. 1162 and MAE=0. 0921 were obtained for training phase and R^2=0. 63, RMSE=0. 14 and MAE=0. 113 were acquired for testing phase. It can be concluded that for estimate AOD in the area with Pyranometer instrument is better using the autoregressive stage instead follows the training and testing phases of the different models. Because, as it has been showed, the data required for the autoregressive stage is only the data of the AOD at the station. In general, the results of this research showed that use of different and efficient models can be a suitable solution for estimating AOD for regions with Pyranometer, as well as the area without a Pyranometer.

In order to doing this study, daily precipitation data from 188 synoptic, climatology and rain gauge over and out of Kurdistan province during 1/1/1340 to 30/10/1389 have been used. For every day one digital map in dimension 6*6 km has been created by Kriging method. Then data of 811 pixels that covers whole of the province extracted from daily maps. One data base 18203*811 created that days located on the rows and pixels on the columns. The results of this study showed that precipitation persistency over province is between 1 to 37 days. Persistency of 1 to 9 days over whole province has been observed. The long persistency in semi western parts of province is more frequent. The short persistency (1 to 2 days) assigns to high percent of rainy days and is more partnership in amount of precipitation in semi eastern parts of province. The spatial importance of persistency shows significant importance of this precipitation for semi eastern parts of province. Increase in persistency result in decrease partnership in total rainy days. In semi western parts of province long time persistency (4 to 9 days) have high partnership in total rainy days and amount of precipitation while spatial precipitation persistency is maximum for other parts of province. This shows that long time precipitations persistency are regular for semi western parts of province and extreme for central and semi eastern parts of province.

Due to high spatiotemporal resolution of gridded data, reanalysis precipitation databases, have many applications in climate prediction, climate change modeling, water resources management and hydrological modeling, especially in areas without observational data. Therefore, in the present study, hydrological simulation was evaluated by SWAT model and spatial data mining for drought monitoring with SPI and SDI indexes over Maharlu Lake and assessing the temporal sense of Asfazari national database by observational stations as a reference on the spatial extent. The results showed high accuracy of Asfazari database in simulating of runoff in comparison with simulated runoff with observation database. The coefficient of determination and Nash efficiency presented the average accuracy of 0.6 in the simulation. During the cold and rainy seasons, the performance of this database is higher than is the warm season. During rainy months of year, the correlation coefficient between observation and Asfazari is about 0.85, and the POD index is more than 0.9. Furthermore, subsequently the accuracy of monitoring drought by Asfazari is too high, so it can be said that the Asfazari database can be used as a reliable database in runoff simulating and drought monitoring, especially in areas with poor or no sufficient precipitation data.

In order to dong this study, daily data of 34 meteorological variables during 1/1/1961 to 30/6/2013 from Urmiah synoptic station has been used. Cluster Analysis with Ward method applied on the Euclidean distance. The numbers of clusters determined by quality control inter group’s variance Indices. The results show that clustering air mass to seven groups is the optimum. The second air mass is the most rainiest air mass which its rainy occurrence probability equal to 65.7% and the amount of precipitation per day equal to 6.6 mm. The fourth air mass is the hottest and drier air mass which has observed in the one third of year’s day. The fourth air mass with the occurrence index of 0.89 and with persistency of 9 days is the most stable air mass. While the third air mass with the occurrence index of 0.43 and with persistency of 1.7 days is the most unstable air mass. The third air mass with sequantality of 21.1% is the most agreeable air mass which occurs after the second air mass subsequently. Daily variety of air mass index get highest value during winter season (69%) while it gets to zero in some days of July and August which indicates a certain air mass (the fourth).

Precipitation is a vital component of the global water and energy cycle with large variations in space and time. The observational datasets that are based on meteorological stations data usually serve as the main sources of precipitation data. However, because of uneven distribution in space, such datasets may not be directly applicable to some problems. Furthermore, there are gaps in the data as there may be times for which the precipitation has not been recorded by some metrological stations for various technical reasons. In the last decades, several gridded precipitation databases have been developed by researchers or institutes. The main aim of creating these databases is to serve user requirements and solve the problems mentioned above. The even distribution in space of the gridded precipitation data and their availability are two very important factors. These databases are critical for many studies including climate change and numerical weather prediction (NWP) applications, management of water resources, agriculture, and disaster management.
The Global Precipitation Climatology Centre (GPCC) has been established in the year 1989 at the request of the World Meteorological Organization (WMO). It is constructed by the Deutscher Wetterdienst (DWD, National Meteorological Service of Germany) as a German contribution to the World Climate Research Programme (WCRP). The precipitation data of GPCC are freely available via the website http://gpcc.dwd.de at 2.5º × 2.5º, 1º×1º, and 0.5 º × 0.5 º resolutions.
The aim of this research is to evaluate the accuracy of GPCC database over Iran by comparing it with two national databases, the Asfezari and that of the synoptic stations called Stations hereafter. The monthly precipitation data from the three databases including GPCC, Asfezari and Stations have been used from January 1962 to the end of December 2010. To evaluate the accuracy of the estimated GPCC precipitation data, first the spatial resolutions of the three databases have been synchronized by the nearest neighbor algorithm. The high-resolution database is converted to the low-resolution database in order to select spatial pixels and carry out the comparisons. Seven accuracy evaluation indices have been used.
The results indicate a high temporal correlation between the estimated precipitation of GPCC and the observed precipitation by the Asefazri and the Stations databases. The results of applying accuracy evaluation indices to the precipitation time series show that in addition to high temporal correlation, quantitatively the estimated precipitations are also very similar to the observed precipitations. Although in some regions the estimated precipitation values are contaminated with bias, but overall the estimated precipitation error is low compared to the total precipitation received. In a spatial viewpoint, the highest accuracy is observed over the western parts of Zagros mountain range and the northeast of the country. Over these regions, the index of agreement and the coefficient of determination are close to unity. The highest relative root mean square (rms) is observed over the dry interior regions and the Lut desert. The relative rms is low in the regions with high precipitation when compared with the rest of the regions. In a temporal viewpoint, the highest correlation between the precipitation time series is observed in rainy months. Based on the results from the Nash–Sutcliffe efficiency index, over most regions of the country, using the estimated precipitations by GPCC is preferable to applying the mean precipitation amounts. The results of this study confirmed the finding of other researches about the accuracy of the estimated precipitation by GPCC database.

It is clear to all that update precipitation data with adequate spatiotemporal resolution is step one for each research on precipitation. To doing this research Iran daily precipitation data with spatial resolution 0.25 degree extracted from Aphrodite data base during 1/1/1951 to 31/12/2007. One time spatial matrix in dimension 20818× 2491 has been created that located days on the rows and pixels on the columns. For each calendar days precipitation long term mean has been calculated and one new matrix in dimension 2491 × 366. By applying cluster analysis with Ward merging method regionalization of precipitation has been done. To detection optimum numbers of clusters, different inter groups variance quality control indices used. Amount of indices for different clusters have been calculated. The results show that regionalization of Iran precipitation into 8 regions is optimum. Amount of precipitation for each months of year in different regions calculated. Disperse of precipitation regions are corresponds with reality. It seems that Aphrodite precipitation data base has adequate accuracy to analysis of spatiotemporal precipitation on Iran.

Precipitation is one of the most important meteorological variables in comparison with other climatic parameters. It varies extremely over time and space. The occurrence of this climate phenomenon requires specific circumstances in environment. Its accurate measurement is important to a wide range of decision makers including hydrologists, agriculturalists, industrialists and etc. The density of rain gauges and meteorological radars is often too poor to satisfactorily capture rainfall characteristics at fine spatial resolutions. To overcome this problem gridded precipitation data base was developed based on interpolation of the daily precipitation data. ERA-Interim is the latest gridded global atmospheric reanalysis produced by the European Center for Medium-Range Weather Forecasts (ECMWF). This data covers the period from 1 January 1979 to the present. The precipitation analysis and estimation is based on obtained precipitation data from rain gauge stations, meteorological radars and satellite sensors. The forecasted precipitation is available at 3-hourly based on applying different models. The horizontal spatial resolution is available at 3, 2.5, 1.5, 1.125, 1, 0.75, 0.5, 0.25 and 125 Gaussian degrees over globe. Information about the current status of ERA-Interim production, availability of data online, and near-real-time updates of various climate indicators derived from ERA-Interim data, can be found at http://www.ecmwf.int/research/era. The purpose of this research is to evaluate temporal-spatial accuracy of gridded precipitation data of ERA-Interim version from European Center for Medium-Range Weather Forecasts (ECMWF) data base over Iran country. In order to conduct this research the 3-hourly gridded precipitation data from ECMWF version ERA-Interim over Iran country has been extracted during 1/1/1979 to 31/12/2013. The high spatial resolution data with 0.125 degree has been selected. By accumulation of 3-hourly data the daily, monthly and yearly time series have been created. A Matrix with dimension 12784×9965 has been created that located time (Days) on the rows and location (Pixels in Iran’s country political boundary) on the columns. During the same period, Iran’s daily precipitation data of the synoptic stations have been extracted from Iranian Meteorological Organization. The national precipitation gridded data base of ASFEZARI with 15 km spatial resolution was also prepared. By nearest neighbor algorithm and conversion of high density to low density approach, the spatial resolutions were even. To evaluate temporal-spatial accuracy of the estimated ECMWF precipitation we applied different indices. The results of this research indicate that not only there is a high temporal correlation between estimated ECMWF precipitations and two national data bases but also there is high correlation between amounts of precipitations. At spatial view, the high correlation observed over Zagros Mountains is covered over southwestern and northeastern parts of country. Over these regions correlation coefficient (R) and Index of Agreement (IA) are over 0.94 and close to 1, respectively. The Bias index rate (Bias) of estimated precipitation relevant to this data base is negative over very rainy regions at southern parts of Caspian Sea and northern parts of Persian Gulf. While the Bias rate on the other regions is positive. The bias and Root Mean Square Error (RMSE) rate are considerable rather than other regions but the estimated precipitation error is very low to total observation precipitation. Thus, the Relatively Root Mean Square Error (RRMSE) show low rate over these regions rather than other regions. In other words, it can be said that the bias and error rate of estimated precipitation over dry regions including southeastern, some regions in northwestern and central parts is higher than very rainy regions cores in southwestern parts of Caspian Sea and Zagros mountain ranges. The results of probabilities of detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI) indices imply high ability of ECMWF data base to isolation of these days over Zagros mountain ranges, southern parts of Caspian Sea and northeastern parts of country. The results show that the accuracy of this data base is higher during rainy months rather than dry and low rainy months. This research has provided further evidence of the capability of ECMWF precipitation data base to capture precipitation characteristics at fine temporal-spatial resolutions. Agreement between ECMWF precipitation data base and synoptic stations data (Stations) is as good as with gridded national ASFEZARI data base. This provides confidence in the quality of ECMWF precipitation data base and confirm the findings of other researches.

Monthly data of numbering trend of thunderstorm days connected to 50 synoptic stations of Iran during 1/1980 to 12/2010 were used to do this research. One matrix with dimensions 372×50 was created where there was time (months) on the rows and columns were located with place or analyzing synoptic stations. Nonparametric Mann Kendal test was used to analyze variance of numbering trend of thunderstorm days. The significance of trend at confidence level was 95% of test. The Sen Estimator was applied to estimate variance rate. The results of this study showed that the frequency of thunderstorms days of Iran experienced a significant variance during analyzing period. The frequency of thunderstorms increased on synoptic stations during most of the months of the year. The increasing trend of most stations located in northwest, west, north and northeast was significant at 95% confidence level. The extent and increasing rate of frequency of thunderstorm days in spring was more than other seasons. In contrast to other months most of western stations experienced significant decreasing trend in March. It seems that the reason of these variances in frequency of occurrence of this atmospheric phenomenon resulted from variance of reaction of synoptic systems of effective scale on climate of Iran. The increasing reaction of Siberian anticyclone results in other conditions over northern parts of country during spring and summer and it results in increase of thunderstorms occurrence over northern parts of country. Also decreasing trend in March is resulted by decreasing trend in Sudan and Mediterranean cyclonic systems activity.