ابراهیم فتاحی

To conduct this research, data on monthly synoptic and hydrometric precipitation observations from the National Meteorological Organization and the Ministry of Energy were obtained for a 30-year period (1976-2005). To assess future changes in rainfall, historical data from the period (1976-2005) and simulated climate data from the period (2021-2050) using two models (CM3 and CSIRO-Mk3.6) from the CMIP5 series were used. These simulations were based on four scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) with a spatial resolution of 0.5 x 0.5 using the BCSD method. A mean-based (MB) strategy was employed to correct any bias in the model outputs.  The results of the AOGCM models indicated that the CSIRO-Mk3.6 model had a lower error coefficient than the GFDL-CM3 model when simulating precipitation in the Large Karoun case. The average future rainfall (2021-2050) across the entire basin, compared to the average observed rainfall during the statistical period of 1976-2005, exhibited a significant decrease in both the amount and extent of precipitation in both basins for all models and scenarios. In the Great Karoun Basin, heavy rains were consistently concentrated east of the basin across all scenarios and models, with the central foothills experiencing the highest rainfall and the southwest and southeast regions receiving the lowest amounts.  The findings of this study estimate rainfall to range between 83-116 mm, with the highest rainfall expected in the Greater Karoun Basin under the rcp4.5 and rcp2.6 scenarios for both models.

The Persian Gulf and Gulf of Oman basin, due to its specific geographic-climatic position and location in the arid and semi-arid regions of the globe, is highly vulnerable to climatic anomalies, such that one of its most important climatic elements, precipitation, exhibits severe variability. This leads to prolonged droughts in some areas and causes floods and river overflows in other locations. Therefore, the study and prediction of climatic changes in this basin is crucial for reducing potential hazards and damages at various social, economic, and environmental levels.

In this research, daily precipitation data from 47 meteorological stations over 30 years (1993-2022) were obtained from the Iranian Meteorological Organization. After acquiring the data and creating a database, the daily Precipitation Concentration Index (PCI) was calculated for all studied stations. The PCI value is a number between zero and one. The closer the PCI is to one, the higher the concentration of precipitation within a limited number of days, increasing the likelihood of floods and heavy rainfall events in those areas. Ultimately, the long-term trends of the PCI were analyzed using Sen's slope estimator.

The long-term average of the daily PCI values for the Persian Gulf and Gulf of Oman basin indicates a high level of this index in the studied basin. The highest values of this index are observed along the southern to southeastern coastal strip from Bushehr to Chabahar, while the lowest values are found in the western and southwestern parts of the basin from Abadan to Piranshahr. The trend analysis results showed that, except for a few stations) Brujen, Jusk, Masjed Soleiman) with negative trends, the entire basin has been dominated by increasing trends. The increasing trend slope indicates that precipitation is concentrated within fewer rainy days, which could lead to an increase in heavy rainfall and flood events within this basin.

The PCI trend analysis results showed that most of the studied basin area has experienced increasing trends, meaning that precipitation has become concentrated within fewer rainy days. This increasing trend, which could be due to an increase in droughts in this basin, may exacerbate the occurrence of flood-like rainfall events within the basin. Therefore, the changes of this index in the sub-basins of Bandar Abbas-Sadij, South Baluchistan, Karun and Western Marzi have been statistically significant. This trend highlights the need for serious attention to flood, drought, and other hydroclimatological hazard management in this basin.

Forecasting monthly and seasonal rainfall is very important from a hydrological point of view, including the water resources management, as well as from climatic and atmospheric hazards perspective. Monthly and seasonal forecasts are widely used in sectors such as energy and agriculture. On the other hand, it has been proven that large-scale atmospheric teleconnections and indices have an effect on the atmospheric conditions of different parts of the earth. Machine learning methods are widely used to predict different atmospheric variables. The reason for using these methods is the high modeling ability of these algorithms when faced with a large amount of data. At the same time, these algorithms have many other capabilities such as the ability to model uncertainty.    In this article, three machine learning methods, namely artificial neural network, gradient boosting and random forest are used for prediction. The performance of these algorithms will be compared with each other in monthly and seasonal forecasting scales. The studied area is a part of northwestern Iran, including the area of Lake Urmia basin. Three evaluation criteria, namely Nash-Sutcliffe, correlation coefficient and root mean square error were used to evaluate the results. Moreover, by using the Nino 3.4, WP, and NAO indices, the regional average monthly rainfall in the region is predicted with the help of three mentioned machine learning methods. The reason for choosing these three indicators is their effectiveness on the rainfall of the studied area. Two monthly and seasonal modeling methods are compared. Since teleconnections may be effective on precipitation with a delay, different delays of zero to three months were considered for all three studied teleconnections and the best results were obtained. Three methods of machine learning, i.e., random forest, neural network and gradient boosting were used. In the monthly mode, the performance of the gradient boosting method (with a three-month delay for the WP index and the other two indices without delay) was better than the random forest method, and the neural network ranked last in terms of performance. In terms of estimating the extreme precipitation, the gradient boosting method has an acceptable performance. In seasonal modeling, for the three seasons of spring (March-April-May), autumn (September-October-November) and winter (December-January-February), the performance of random forest and for the summer season (June-July-August), the performance of the gradient boosting method were superior to other methods.

The Arzanfood area is located in southeast of Hamadan city, east of the Alvand granitoid. Based on of Iran geological structure zone, it is located in the Sanandaj - Sirjan zone. Quartz veins occur in the metamorphic rocks, including garnet-mica schist and phyllite, and are related to tectonic activity. The most of fault trends complied with N28W-N62W Zagros zone. In the field, three different types of quartz veins have been identified in the region including: 1) veins with a width of less than 10 cm and a length of several meters within the slate and hornfels rocks, 2) veins with a width of less than 2 meters and a length of less than 20 meters with the host rocks are garnet, micaschists and slate, and 3) veins and lenses with a width of more than 2 meters and a length of more than 20 meters that are present in micaschists rocks. Studies of fluid inclusions in quartz mineral show a homogenization temperature from 98 to 408 ° C and salinity ranges from 3.72 to 22.91 wt. % NaCl. Boiling and dilution with surface water are two processes that have been involved in mineralization. Finally, it can be concluded that quartz indices in the region are the product of hydrothermal activity related to metamorphic processes and quartz was deposited from mineralizing fluids in the empty spaces as a result of temperature and pressure reduction.

General circulation models (GCMs) provide valuable forecasts of world precipitation and temperature (Schepen et al., 2020). Through improved Seasonal forecasting in recent years several climates centers around the world provide operational climate Such as; the Climate Forecast System version 2 (CFSv2) by National Centers for Environmental Prediction (NCEP) (Saha et al., 2010), the European Centre for Medium-Range Weather Forecasts (ECMWF (Johnson et al., 2019), and the Geophysical Fluid Dynamics Laboratory (GFDL) (Delworth et al., 2020). These GCM outputs generally need to downscale to use in regional-scale relevant applications and more actionable end-user-oriented climate services. One way to transfer world predictions from GCMs to regional or local scales is dynamical downscaling with RCMs such as Weather Research and Forecasting model (WRF). The Initial and lateral boundary conditions from General Circulation Models (GCMs) drive these models. The mesoscale circulations, topography, and land use-land cover are displayed better by RCMs, and these models improve the extremes and regional climate variable compared to the coarse resolution GCMs. The WRF has been coupled with numerous parameterizations to resolve processes occurring within a grid box. Some research has indicated convective and planetary boundary layer (PBL) schemes have a significant influence on precipitation simulation (Li et al 2017; Njuki, S.M., et al 2021). The WRF Model version 4 provides more than 11 convective schemes and 13 planetary boundary layer (PBL) schemes. This study has attempted to assess a suitable combination of physics schemes of the Weather Research and Forecasting (WRF) model for seasonal precipitation simulation over the northeast of Iran. Using the CFSV2 as Initial and lateral boundary conditions data, simulation experiments from winter to spring in seven months (from November to May) have been performed for 2019-2020). Three nested domains have applied with the outer domain at 54 km resolution and two interdomains at 18 and 6 km resolution.

The study area is located in the northeast of Iran, and climatologically, most precipitation occurs from winter to spring (November to May). On average, the western part of this region receives approximately 60% of the annual precipitation, while the rest of the areas in the east receive lower precipitation. The real-time forecast data used in this study is the 6-hourly time series from the 9-month runs operational model for seasonal prediction at the NCEP operational CFSv2. The observed precipitation data is extracted from IRIMO. The new Weather Research and Forecasting model (WRF) is applied to determine how varying physical parameterization of PBL scheme configuration processes simulate seasonal (winter and spring) precipitation. For this purpose, four group configurations have been designed.Group1: convective schemes (KFT, BMJ, GF, KF), Yonsei University PBL (YSU) for the plenary boundary layer, surface layer scheme (Revised MM5), the shortwave radiation scheme (Dudhia), the longwave radiation scheme (RRTM) and land surface models (Noah).Group 2 all four convective schemes, PBL Mellor–Yamada–Janjic (MYJ), RRTMG for long –short radiation, 5-layer thermal diffusion, and Eta for land surface and surface layer. GROUP 3; include second-order Mellor-Yamada-Nakanishi-Niino (MYNN3) as PBL scheme, same shortwave and longwave radiation (New Goddard), the surface layer (MYNN), and land surface (RUC). Finally, group4 set by ACM2 for the plenary boundary layer, The surface layer (Pleim-Xiu), the shortwave and longwave radiation schemes (GFDL), the land surface (PX), four convective schemes have been fixed in all groups. For all WRF simulations, we used the WRF single-moment 6-class microphysics scheme. In this way, a total of 20 simulation sets in 4 groups have run, and one configuration set without any cumulus scheme in domain 3 in each group.The following statistics, the correlation (R), the root mean square error (RMSE), the mean absolute error (MAE), and bias and four verification skills are calculated from the total daily precipitation over the six months out of the seven-month integration time with the first month used as spin-up.

The WRF-CFSv2 model performance was evaluated against precipitation observations from Iran's Meteorological organization. The correlation scores between the observed and predicted 6- month and winter precipitation were moderately acceptable (0.3-0.5) however decreased to 0.36 in spring. In terms of bias, group 1 (PBL1,..) configuration have considerably structures than the group4 (PBL7,..), group2 (PBL2,…), and especially group3 (PBL6,..). All configurations showed a wet bias over the study area (-0.8 mm/d, -3.55mm/d) in the 6-month prediction. It is consistent with previous studies using GCMs in this region. The significant MAE of the 6-month precipitations simulated by group 1 and PBL1-CU2، PBL1-CU0, and PBL1-CU1 scenarios were the lowest among the configuration. Meanwhile, this group of configurations showed a similar RMSE score pattern by MAE, and the lowest RMSE showed in group 1 and group 2. In all configurations, the wet bias has been persistent in the study area.The WRF prediction captured the observed precipitation by groups 2 and 3 with MYJ and MYNN3 planetary boundary layer schemes. However, the false alarm (b) in group 1 and the number of missed events (c) in group 2 of configurations were finer low.

In this study, the WRF model performance was evaluated for various physical parameterizations in predicting precipitation for varying planetary boundary layer (PBL) schemes and Cumulus schemes over northeast Iran.Based on the sensitivity analysis, is concluded that the set that performs best for the region is YSU PBL, MM5 SL, Dudhia shortwave radiation, RRTM longwave radiation, and Noah LSM schemes.And using a cumulus scheme for grid resolutions between 3km and 10km is gray, as respects is not clear whether a cumulus scheme should be used or not. So, recommended testing a configuration set of no cumulus scheme mode to determine if using a cumulus scheme is ideal for your particular run.

The Great Khorasan in northeast Iran has a variety of surface structures and plains and high peaks, but due to its vicinity to the deserts of Afghanistan and Turkmenistan, it is affected by dust all the time, especially in summer. The purpose of this study was to simulate summer dust in this region by RegCM model. For this purpose, during the period 2000 to 2017, three extreme dust events were selected. The satellite image used to confirmed dust mass presence and then the synoptic structure was analyzed. Finally, the simulation results of RegCM 4.6 model were compared with the observational data including the horizontal visibility and aerosol optical depth (AOD) of Aqua satellite. The synoptic analysis showed that during the summer, low thermal pressures form in the southern Afghanistan and high pressure in the north. This structure lead to the development of north and northeast winds with speeds of 12 to 21 m / s and dust emission on the eastern border of Iran and western Afghanistan. Investigation of RegCM accuracy done by visibility, Aquas’s AOD showed that model performance in South Khorasan is better as Razavi Khorasan. The highest correlation coefficients of AOD of model and horizontal visibility were obtained at Khorasan central stations including Gonabad, Ferdows, Nahaband and Ghaen at -0.82, -0.77 and -0.44 respectively. RegCM model performed a better dust simulation in severe dust with a horizontal visibility down to less than 1000 m, high continuity and horizontal extension. Overall, the RegCM model underestimates the AOD value for the Aqua satellite algorithm.

The amount of precipitation plays an essential role in the occurrence of floods. The more accurate the rainfall forecast is, the better the flood can be predicted. Numerical weather forecasting models such as WRF usually do not have suitable outputs for predicting the amount of precipitation in the first hours of implementation; This is intensified in the summer season on the southern shores of the Caspian Sea in the provinces of Gilan and Mazandaran. Because most of the heavy rains in this season occur in the form of convection and in small spatial and temporal dimensions. Using the extrapolation of GPM, TRMM satellite products to predict short-term rainfall up to 24 hours can be a suitable method to achieve the amount of rainfall with more appropriate resolution and accuracy. In this research, the occurrence of two daily precipitation systems of more than 40 mm in summer on the southern shores of the Caspian Sea was investigated. The results showed that the use of GPM, TRMM satellite products for short-term rainfall forecast up to 24 hours is more accurate compared to the model output. Statistical analysis showed a good correlation between model prediction and ground data and satellite data

Changes in the land biosphere since 1970 are consistent with global warming: climate zones have shifted poleward in both hemispheres, and the growing season has on average lengthened by up to two days per decade since the 1950s in the Northern Hemisphere extratropic. In this research, projected change of growing season length (GSL) and the start date of the growing season over Yazd province are investigated using three GCMs (GFDL, HadGEM and MIROC5) under the RCP8.5 scenario during 2021-2040. The results indicate Yazd province will experience extended GSL in a warmer world. The thermal growing season has been projected to increase owing to the earlier onset of growth in late winter and spring. Therefore, the prolongation of GSL under the RCP8.5 is attributed to earlier onset. Future temperature and precipitation scenarios based on GFDL and HadGEM models under the RCP8.5 scenario show more unfavorable conditions for this province. Because of the sensitivity of agriculture to weather and climate conditions, these impacts can have substantial direct and indirect effects on production and profitability.

During the rainfall, the intensity of precipitation varies. Changes in the amount of precipitation during an event of rainfall are effective in the resulting of flood and its intensity. Knowledge of how rainfall changes over time during rainfall is determined by temporal distribution pattern of rainfall. For this purpose, availability of short-term time scales rainfalls data are important that obtained by rain gauge stations. However, the low density of the rain gauge network and the lack of sufficient data from the time pattern of rainfall have always been a problem in determining storm patterns for executive plans. Therefore, the simulation of WRF numerical weather models can be used. The WRF model is one of the most responsive models for predicting precipitation, temperature and atmospheric elements that used in this study. In this paper, three great storm events on 15 December 2003, 24 - 26 December 2006 and 6-7 March 2007 have been selected in the Parsian dam basin and surrounding areas in south west of Iran. The result of WRF numerical weather prediction model for these great storms compared with data loggers. It showed that the WRF model was able to performance the heavy rainfall and simulates the rainfall pattern in these dates.

The dust has affected most parts of Khorasan Razavi in July 1, 2014. The purpose of this study is ‎to simulate this event by RegCM model and validate its results with observation and remote sensing ‎data. For this purpose, horizontal visibility were prepared from the Iran Meteorological ‎Organization and AOD of Aqua and Merra-2 satellites from Giovanni web.‎ The result shows that the development of low heat pressure on Pakistan along with north and ‎northeast winds of 20 m/s in the east of the Razavi province leads to the transfer of dust to the ‎region. The HYSPLIT model and the output of the RegCM model show the dust source in ‎Turkmenistan and western Afghanistan.‎ The optical depth of aerosol obtained from the RegCM model is in good agreement with the ‎visibility in the east and south stations of the Razavi province, So that the correlation coefficient in ‎Gonabad was -0.98, Torbat Jam -0.66 and Sarakhs -0.61. In addition, the correlation coefficient of ‎the RegCM model and the Merra-2 optical thickness of aerosol obtained in Sarakhs and Torbat-e ‎Jam located in in the east of the province, including, were -0.45 and -0.78, respectively.‎ The result of comparing the time series showed the model output in three-hour intervals is in good ‎consistency with the visibility, the Aqua optical depth aerosol, and the Merra-2 optical thickness ‎aerosol in the east and south stations of the Razavi province. However, the RegCM model does not ‎estimate dust and has a time delay of 6 to 9 hours in the central and western stations.‎

In this paper, using reanalysis ERA5 data and observational data, the effect of Low-Level Jet (LLJ) on dust in the west and south-west of Iran during the period 2007-2017 is studied. The vertical wind shear transfers the momentum from the jet level to the surface and then transfers dust from the sources, so to identify the LLJ besides the maximum wind speed, the vertical wind shear is also considered. Moreover, the horizontal and vertical range of LLJ, average of long term frequency of LLJ and monthly and seasonal frequency of dust are calculated in this study. The result of the comparison of observational data with ERA5 data shows that 10 m wind speed of ERA5 is larger than of observational data. Strong north to south pressure gradient due to the high pressure system over Iraq and Zagros and the low pressure system over the south-west of Iran, is the most important synoptic forcing that causes strong north and north-westerly and LLJ winds over the study area. Furthermore, region topography, land-sea breeze, weak surface tension over the Persian Gulf, temperature differences and special heat capacities between land and sea are other factors that form and affect the intensity of LLJ. The monthly wind speed distribution shows the maximum average wind speed 10 m/s in July and its minimum 2 m/s in January. The average height of the boundary layer varies from 2400 meters by days to 150 meters at nights, which is one of the main causes of LLJ. The highest frequency of dust in Ahvaz was in July 2009 with 30 days of dust

One of the flooding areas in Iran is the Chaldoran area in the northwest of West Azarbaijan province. Accordingly, the goal of this project is to implement a heavy precipitation forecasting and alert system in the Chaldoran Basin using a numerical model of weather forecasting with various configurations. Therefore, in this study, 10 precipitation systems with a lifetime of 24, 48, 72 and 96 hours in West Azerbaijan province have been selected based on drawing of iso-storm curves and determining rainfall volume and Depth-Area-Duration curves of rainfall. Precipitation in the Chaldoran area has been predicted using 12 different configurations including three convection schemes, two boundary layer schemes, and two microphysical schemes to obtain a suitable configuration, and outputs of the WRF model were verification. The results of the verification in the Chaldoran region show the Grell-Freitas convective scheme more appropriately. Also, the MRF boundary layer scheme and the Goddard microphysical scheme have been more successful in predicting the amount and distribution of precipitation. Then, by selecting the best output of the WRF model, with NET-based technology, the design of a heavy precipitation warning system with easy access and according to the thresholds required by the user based on the amount of rainfall, rainfall intensity, and the temperature has been done.

Investigating the components of general atmospheric circulation is important to discover the rules governing the country's rainfall. The relative vorticity as a key variable of synoptic motions is one of the best components in this regard. Relative vorticity is a measure of the intensity and direction of spin in a circular movement which is performed by a unit volume of air around the vertical axis perpendicular to the plane over which this rotation occurs. This research is looking for the relationship between spatiotemporal variations of relative vorticity and precipitation in Iran to improve forecasting skills.In this study the connection between the 500 hPa relative vorticity field and precipitation over Iran is investigated using canonical correlation analysis (CCA). Using of CCA statistical method is usual in climatological studies but it has not yet entered into the Iran climatological studies.

Two main data sets were used in this study; the time series of 500 hPa monthly relative vorticity fields and monthly precipitation for 97 Iran stations during the rainy season (November to February). The study area extends from latitude of 10 to 70 degrees north and a longitude of 10 to 70 degrees east over a region affecting Iran's precipitation.Relative vorticity monthly values (1981-2017) using the U and V component wind values obtained from NCEP-DOE reanalysis databases at grid points spaced by 2.5° at the pressure level of 500 hPa was calculated. NCEP-DOE Reanalysis II that is an improved version of the NCEP-NCAR Reanalysis I model that fixed errors and updated parameterizations of physical processes.The monthly precipitation data (1981-2017) were received from 97 synoptic stations of Iran and were used after standardization. To achieve the purpose of this study, at first the activity centers of vorticity and precipitation were identified by applying S mode principle component analysis (PCA). Then canonical correlation analysis (CCA) was performed on factor scores of these centers. The 30 years base period (1981-2010) was selected to applying CCA method while the years 2010 to 2017 were used as evidence.This multivariate statistical method was originally developed by Hotelling in 1936 from an interdependence model and first applied in climatology during the 1980s by Barnett and Preisendorfer and also by Nicholls in 1987.Canonical correlation analysis is a multivariate statistical technique for analyzing internal relations between a set of multiple independent variables (predictors) and a set of multiple dependent variables (predictants).This method is often used in atmospheric sciences to identify predictors within the datasets. Relationships between variables are highlighted through CCA.

The application of PCA led to 6 factors for the precipitation over Iran and 18 factors for the 500 hPa relative vorticity, accounting for 72% and 80% of the total variance respectively. The first meaningful factor of Iran precipitation is located in northwest of Iran.Second factor is extended from east to south and the fifth factor is in the Caspian Sea southern coasts.PCA factor scores time series of the each two sets were used for the subsequent CCA.CCA yielded three physically reasonable relevant pairs of patterns that describe the simultaneous responses of the precipitation field to the relative vorticity changes accounting for 82.5% of the common variance of both fields.The first CCA pair exhibits a correlation between the precipitation over east of Iran and the 500 hPa relative vorticity changes in the eastern Mediterranean and the Middle East. The second CCA pair reveals a negative correlation between the precipitation over the Caspian Sea southwest coasts and the relative vorticity activities, centered over Eastern Europe. The third CCA pair shows a correlation between the rain of northwest Iran and the relative vorticity activities over Turkey, Cyprus, and the Black sea.

The influence of the relative vorticity centers of activity in different regions of the Middle East, eastern Mediterranean, and Europe that appear in the form of the positive or negative relative vorticity on the Iran precipitation anomalies was investigated.In this case, three linear combinations were found by the canonical correlation analysis technique.According to the first pattern,  negative vorticity centered over the eastern Mediterranean and the Middle East is associated with below normal precipitation over the east and southern regions of Iran. In fact, the negative vorticity region in the eastern Mediterranean and the Middle East indicates the location of the ridge and the east of Iran locates in front of this ridge that is the area of descending movements that result in weather stability in this part of Iran.The second pattern reveals that however the relative vorticities will go up to the negative values in Eastern Europe; the rainfall will be higher on the southwest margin of the Caspian Sea. In the winter when Eastern Europe is located in ascending region of western winds (ridge), the Caspian Sea would be located in descending region of mid-level (trough) which will increase weather instability and increase precipitation in the southern coast.The third pattern says that as far as the relative vorticity tends to positive values in the Cyprus and Turkish cyclogenesis areas, the rainfall will be higher in the northwest of Iran. That’s because the extension of western winds in this area in winter could bring Mediterranean humidity to the northwest and west of Iran with no mountainous barrier.Therefore, these results can be used to increase rainfall forecasting skills in different parts of the country. The results of this study confirm the results of the study of Rezaei et al. (2012 and 2013).

Dust and sand storms are meteorological phenomena that have harmful effects on the environment, society, public health and natural resources. Climate systems and land’s structure are the main factors in creating dust storms. In general, variations of physical and dynamical specification of atmospheric systems have very significant role on the production and transport of dust in these areas. Dust storms are most commonly caused by strong pressure gradients, which increase the wind velocity over a wide area. Since most of Iran (including the northeastern and eastern regions) has an arid and semi-arid climate, it is conducive to the occurrence of dust events. In recent years, the intensity and frequency of dust storms in the east and northeast of Iran has increased. Due to the importance of this issue, synoptic analyzes and temporal and spatial distributions of dust events in the northeastern and eastern region of Iran have been performed in separate studies such as Karkon Systani,2012; Omidvar et al.,2016; Boroghni et la., 2016; Doostan., 2017; Poorhashemi et al., 2019. Despite the several studies conducted by various researchers for this region, but a comprehensive study of atmospheric pressure patterns affecting on dust production and transportation, temporal and spatial distributions of dust, the trend of horizontal visibility changes, backward tracking and determining dust sources and determining the prevailing wind in spring and summer for a long period has not been done. Therefore, the main purpose of this article is to investigation these issues for the study area.

The study area ( Great Khorasan) is located in the east and northeast of Iran. This region includes the provinces of Khorasan Razavi, North Khorasan and South Khorasan, which have 17, 7 and 11 synoptic stations, respectively.In this study, horizontal visibility, 10-meter wind speed and weather codes related to dust (codes 06, 07 and 30 to 35) were investigated between 2000 to 2017 in 35 meteorological stations of Great Khorasan. This data set were obtained from Iran Meteorological Organization. Due to lack of data, only 5 stations including Sarakhas, Gonabad, Tabas, Birjan and Nehbandan were selected for more investigation.In addition, the grid point data (Sea level Pressure and 500mb) were extracted from the Era-Interim reanalysis products. The grid data with 0.75°×0.75° resolution selected for the area between 40-70°E and 20-45°N. Throughout this study, it is attempted to investigation of temporal and spatial distributions and trend of dust in Khorasan, and to determine characteristics of effective pressure patterns and main dust emission's sources by using backward trajectory technique. Accordingly, the study method consists of three main parts:The first part of this study is statistical investigations and determination of temporal and spatial distributions of dust events using observation data in meteorological stations. The second part is the study and identification of circulations and pressure patterns affecting on production and transport of dust using grid data for selected cases. The third part is to determine the active dust sources for the studied cases using the backward trajectory technique by HYSPLIT model.

The number of dusty days in Razavi and South Khorasans in 2003, 2008, 2013, 2014 and 2017 was almost the same. This implies the predominance of large-scale circulations and pressure patterns rather than small-scale local factors.Monthly distributions showed that the highest number of dusty days in South Khorasan was in May, July, and June respectively and in Khorasan Razavi were in June and May. North Khorasan also had the highest number of dusty days in June. Since the South Khorasan stations are located on the western slopes of the north-south Mountains, they had more dust events than Khorasan Razavi province. Gonabad had fewer days of dust events than Sarakhs, Tabas, and Birjand, but the intensity of dust is higher in Gonabad station. The highest number of dust events occurred in 2008 and 2014, indicating that dust was widespread, but the least visibility recorded in 2012, that detected as the severe dusty year.

Most of the dusty days in the study area occurred in June. Strong north and northeast winds in summer have played an important role in the production and transfer of dust in the provinces of Razavi and South Khorasans, and therefore the dustiest days have occurred in this season. While in North Khorasan, the wind pattern was variable and most of the dusty days occurred in spring.The results of long-term statistical study of changes in horizontal visibility in Razavi and South Khorasans indicated that the trend change patterns were different in these provinces. So that this trend increased from 2005 to 2016 in Khorasan Razavi, while in South Khorasan it decreased. The trend of horizontal visibility changes, in addition to being different seasonally, has not been the same in different stations. For example, the line trend slope of horizontal visibility at Nehbadan station in spring and summer were +0.34 and +0.27 but in Tabas -0.28 and -0.3. Most of the dusty days in the study area occurred in June. Strong north and northeast winds in summer have played an important role in the production and transfer of dust in the provinces of Razavi and South Khorasans, and therefore the dustiest days have occurred in this season. While in North Khorasan, the wind pattern was variable and most of the dusty days occurred in spring.The results of synoptic analysis of the atmospheric pressure patterns which leading to the occurrence of intense and extensive dust events in spring and summer showed that depending on the pressure gradient created between the two pressure (low pressure and high pressure) systems, threshold wind speed, sources and transport path varied in the study area. In the spring of the development of low-pressure system on the eastern border and the high-pressure system on the western border of Iran leads to the creation of pressure gradients in the west-east direction in the central regions of Iran and as a result, the creation of westerly winds in the study area. The strong westerly winds on dusty sources create the favorable conditions for dust production. In summer, the development of heat low-pressure system and two high-pressure systems, one in northern Afghanistan and the other on the Caspian Sea, leads to north-south pressure gradients and creates northeasterly and northly winds with an average speed of 18 ms-1.The dusty paths simulated by the HYSPLIT model are corresponded with the prevailing wind direction so that dust sources and transport can be determined. The results showed that in spring the central deserts of Iran were considered as dust sources of the study area and in summer the deserts of Turkmenistan and Afghanistan were considered as dust sources of Great Khorasan.

Heavy rains are among the natural hazards that knowledge of their temporal and spatial distribution helps to reduce potential damage. For this purpose, the existence of a significant relationship between widespread precipitation and topographic indices of Alborz mountainous region has been investigated. In this study, Pearson correlation test was used and in this model, the dependent variable of total daily precipitation of the study area (precipitation occurred in more than 70% of synoptic stations in the area) and the independent variable of data related to topographic indicators of the study area (station height Synoptic, station slope and direction, longitude and latitude of the station, distance from the northern baseline, distance from the ridge, average height of the station in a radius of 2.5 km, average height of eight blocks fifty km in eight geographical directions to the center of the synoptic station, height difference Its average is eight blocks from the average height of the station in a radius of 2.5 km). First, the correlation between precipitation (129 days) and topographic indices based on common synoptic stations in three seasons of winter, spring and autumn was identified and then the correlation coefficients with 95% confidence interval at a significance level less than 0.05 were investigated. Each season, a sample day with the highest correlation coefficients in the majority of topographic indices was selected as the representative of that season. The study area, which is the vast Alborz mountain range, has been divided into areas with topographic and climatic similarities due to the complexity of topography and the diversity of its climatic conditions in each area. The highest number of significant statistics in terms of temporal and spatial scale between widespread precipitation with topographic indices is related to widespread precipitation in spring with 18 cases and the lowest is related to widespread precipitation in winter with 9 cases in autumn 14 cases of significant linear relationship have been identified. Among the topographic indices, the strongest index is related to the difference between the average height of blocks 50 km from the average height of the station in a radius of 2.5 km in different directions according to different seasons of the year and Evidence of the effect of direction on inclusive rainfall in the study area and that the area is in a certain direction to receive more rain , meaning that the mass of humid air entering from the northern parts of the country, including Siberian high pressure (Babaei Fini, 1393) due to low altitude and proximity to the ground is affected The surface roughness is located and the mass of moist air entering from the northwestern and western parts of the country, including the western migratory high pressures (Qashqaei, 1375), (Moradi, 1385)) due to higher altitude and more power is less affected by surface roughness. And the region's precipitation is affected by these high-level atmospheric systems. On the other hand, widespread precipitation in the northwestern and south-central Alborz region has the highest and the lowest south-western Alborz region has a significant linear relationship with the values of topographic indices. The results of this study have a significant linear relationship with most topographic indices (22 indices out of 24 Index) with widespread precipitation to be able to estimate and predict the most effective indices affecting precipitation in the region. The two topographic indices for the station and the distance from the ridge did not enter the correlation model on any day and in any area of the study area and did not establish any significant linear relationship, albeit weakly, with the total precipitation of the area.

The main idea of ​​atmospheric circulation patterns and their classification is to convert a set of atmospheric parameters into a univariate catalog, with cases of similar characteristics grouped together. Easier interpretation of weather conditions would be the advantage of the classification.Many researchers to classify atmospheric circulation patterns have used the principal component analysis (PCA) method. Esteban (2006) in a study of atmospheric circulation patterns in Western Europe, categorized daily synoptic circulation patterns into 20 groups and showed sea level pressure and 500-hPa geopotential height configurations throughout the year. Smith and Sheridan (2018) clustered geopotential height anomalies at levels of 100 and 10-hPa and sea level pressure to study the patterns of tropospheric and stratospheric potential vorticity. In Iran, Raziei (2007) by PCA classification and clustering identified 18 atmospheric circulation patterns for the Middle East and Iran. He showed that patterns with meridional and northwest flow often cause drought and patterns with southwest flow cause wetting. Hanafi (1399) identified the air types in the northwestern region of the country (Maragheh station), and showed that the high geopotential systems of Saudi Arabia and North Africa governed by hot and dry conditions and the Mediterranean and polar cold air masses are involved in creating cold periods.Atmospheric circulation patterns identified for Iran are based on monthly average atmospheric data and with a resolution of 2.5°. Often due to the importance of winter as the main rainy season in Iran, the identification of atmospheric circulation patterns of other seasons has less studied. On the other hand, most studies have conducted regionally in a province/small part of the country. In this study, we have classified the synoptic patterns affecting Iran and the Middle East in the period of 1990-2019, using the PCA method for all seasons and with a higher accuracy using data with 0.5° resolution on a daily time scale. Then, composite maps of the MSLP (Mean Sea Level Pressure) and 500-hPa geopotential height were prepared and analyzed to obtain the frequency of monthly and seasonal occurrence of each investigated pattern. Previous studies have focused mainly on 500-hPa and MSLP parameters. However, in the present study we have used the 850-hPa moisture flux besides the already applied quantities, which can be considered a main and important quantity for the rain occurrence.

Materials and methods  :

Geopotential height data of 500-hPa received from the ECMWF (ERA5) at one hour intervals during the period 1990-2019 in the area of 20E-65°E longitude and 10N-55°N latitude. Due to the data volume and software and hardware limitations, data with 0.5° resolution extracted. By calculating the daily average in each grid, the data matrix consisted of 8281 columns (grids) and 10957 rows (number of days). Then the MSLP, 10m wind speed, and 850-hPa temperature and relative humidity maps/data were provided/extracted.The PCA analysis is a method for extracting important variables from a large set of variables in a data set. The main goal is to reduce number of components by finding the ones that explain correlations between the variables. We selected the first nine components covering a large portion of the described variances and ignored other minor components. In the present study, the R language programming were used to analyze the principal components with S array and select the components. To classify air types, K-means clustering was used, so that it presents the most alternating patterns of atmospheric circulation in the study area during the year. After selecting the main components and determining the scores of the components, all days (10957 days) were classified into 18 groups. Sea level pressure and 500-hPa geopotential height maps were prepared and interpreted. Monthly and seasonal frequency of occurrence of each pattern and relationship of spatial distribution of moisture flux at 850-hPa were investigated.

Based on the results obtained from principal component analysis, the first 9 components were selected, which explained 95.93% of the total variance of the data. According to the monthly and seasonal distribution of each pattern, it was observed that cp1, cp7 and cp18 circulation patterns occur in summer and early autumn and cp13 occur in late spring, summer and early autumn. Cp2, cp4, cp6, cp8, cp11 and cp16 occur in late autumn to early spring of the following year and cp3, cp5, cp9, cp10, cp12, cp14, cp15 and cp17 circulation patterns occur in late autumn to spring of the following year. These situations correspond to the synoptic conditions of each pattern at sea level and the level of 500-hPa.In the circulation patterns of the warm period of the year, thermal low pressure of sea level pressure map below 35° N is associated with subtropical high at mid-level of atmosphere. The maximum moisture flux in 850-hPa located in the eastern parts of Iran and the maritime borders of Yemen and Oman which is compatible with southern and southeastern flow. In cp2, cp4, cp6, cp8, cp11 and cp16 circulation patterns that occur in the cold period of the year; south and southwest flow of low pressure in the south of the Red Sea are associated with mid-level of atmosphere. There is convergence of moisture flux in the south of the Red Sea and in parts of the south and southwest of Iran, east of the Black Sea, northeast of the Mediterranean Sea and south of Turkey. In other patterns that occur in the cold period of the year as well as in spring, low pressure in south and center of the Red Sea is associated with trough in the mid-level of the atmosphere. Convergence of moisture flux was in the west, southwest, south an northwest of Iran, northeast of the Mediterranean Sea, southwest of Turkey and south of the Red Sea.

In the present study, principal component analysis with S array and K-means clustering was used to classify circulation patterns in Middle East and Iran. The results show significant differences in the arrangement of patterns, the frequency of air types and their path to Iran.

Roads are part of the development of civilization and support economic activities and the foundation of new life, but unfortunately in the last decade due to low driving culture, non-standard vehicles and roads, environmental factors and increasing traffic volume, number of road accidents increased dramatically. Four human factors, vehicle, road and environmental factors, are always involved in the occurrence of accidents, among which environmental, climatic and climatic factors that occur due to the special geographical conditions of Iran and its mountainous nature, along with other factors play a significant role in accidents. Major road meteorological tasks include continuous preparation of statistics and information, Changes and evolution of meteorological elements in the area of roads covered by the relevant station, Issuance of specific meteorological forecasts along the route in relation to wind intensity and speed, Various storms with lightning and the occurrence of destructive phenomena, Publication of notices and warnings in the event of dangerous weather phenomena on the way, Wind intensity on various stairs and phenomena that are effective in reducing vision, Land and rail transportation is one of the most important issues at the national level. The aim of this study is to locate the meteorological stations of the Alborz mountain axes (Chalous and Haraz) using the Location-Allocation method.

The present study is conducted in the mountainous roads of Alborz (Chalous axis and Haraz axis). Criteria used to locate road meteorological stations include climatic (rainfall of more than 30 mm, avalanche, snow cover, fog, minimum and maximum temperature of -10 and 30 degrees Celsius), geomorphological (rock fall, Landslides and active faults), traffic (accidental points and environmental criteria) and economic-security (traffic). Then, using the Analytical Hierarchy Process (AHP) method, based on the determination of the variable comparison matrix and weighting of each criterion, the final prioritization map is prepared according to the final weight. In the following, based on the Location-Allocation analysis, the proposed meteorological stations on the mountainous axes of Alborz will be determined.

The three meteorological stations of Karaj (from Karaj to km 43), Siah Bisheh (km 43 to 110) and Nowshahr (110 km onwards) reflect the climatic conditions of each section. The number of days recorded for the occurrence of fog at Siah Bisheh station is approximately 844 days in 10-year statistics. Nowshahr and Karaj stations had 70 and 50 foggy days, respectively, in the study of available statistics and information. Therefore, the approximate kilometer of 43 to 110 km in terms of the phenomenon of fog, according to the statistics of Siah Bishe station, has the highest probability of fog days. 60 to 100 km from the beginning of Karaj (middle part of the axis) has a high risk of frost. From the beginning of the axis to 60 km and the approximate area between Vali-Abad and Marzanabad, the risk of frost is moderate. In other parts of the axis, the intensity of frost in the classroom is low and very low. Chalous axis in Mazandaran province from the approximate area of Vali-Abad village to Chalous city includes high and very high risk classes in rainfall of more than 30 mm and Chalous axis in Alborz province experiences low rainfall risk conditions.Due to the minimum threshold temperature in the middle part of Chalous axis, which includes two provinces of Alborz and Mazandaran, it is in moderate danger. This part of the axis includes the range of Nesa, Gachsar, Siah Bisheh and Harijan. Other parts of the axis are in the low risk class. Also, due to the maximum threshold temperature in Chalous axis, the hazardous conditions of the axis in Alborz province are low and in Mazandaran province it is very low.Examining the average of snow cover by the desired months in this study, it can be seen that the middle parts of the Chalus axis experience the highest frequency of snowfall. As we approach the warmer months of the year, a gradual trend of snowmelt is observed. In avalanche risk, the area of Asara village has a moderate risk. The central sections range from Garmab village to Zangoleh bridge in high and very high classes. From the Black Forest area to the end of the Chalous axis, the avalanche falls to the low-lying class.Ranks 74, 78, 82, 84-85, 88-89 have been reported to be affected by the landslide phenomenon. In terms of point density, 78 km to Chalous in Mazandaran province have the highest amount.In sunny weather, km 20-17 and 41, in cloudy weather, km 62, during rainfall, km 40 and 70, in snowy weather, km 40, 60 and 62, and during foggy weather, km 60, 62 and 65 have been the maximum number of accidents.The importance of each of the criteria andsub-criteria was determined according to library studies, installation guidelines for road meteorological stations and expert opinions. The uncertainty coefficient was also 0.6, which is less than the defined 0.1, andaccording to this result, the weighting process is approved. According to global standards and studies conducted, the distance between meteorological stations on the road varies between 30 and 50 square kilometers. In general, each meteorological station can cover an area of about 30 km.Then, using AHP method, the final weight of climatic, geomorphological, traffic and economic-security criteria and sub-criteria in Haraz and Chalous axis were determined. After prioritizing the new stations in terms of need, in the last step, by analyzing LocationAllocation and examining the optimal distances of the axis from the highway, fuel stations, surveillance cameras and villages around the axis, the final stations in priority-oriented are introduced.

Due to the importance of optimal development of the road meteorological network, which reduces road damages and losses and destroys the surrounding environment and economic savings, the optimal location on the Chalous and Haraz axis was examined. The results showed that the required stations on Chalus Road are in the area of Kiasar, Marzan abad, Khargoosh Darreh and Vali abad and Haraz road are in the Polur, Abali, Rahdari and Rineh.

Rainfall prediction plays an important role in flood management and flood alert. With rainfall information, it is possible to predict the occurrence of floods in a given area and take the necessary measures. Due to the fact that the three months of January, February and March are most floods and most precipitation is occurring this quarter, this study aimed to investigate the factors affecting precipitation and modeling of this quarter. For precipitation modeling, the monthly rainfall data of the Hamadid and Baranzadeh station in the statistical period (1984-2014) for 30 years as a dependent variable and climatic indexes, large-scale climatic signals including sea surface temperatures and 1000 millimeter temperatures Altitude of 500 milligrams, 200 milligrams of omega and climatic elements have been used as independent variables. Due to the nonlinear behavior of rainfall, artificial neural networks were used for modeling. Factor analysis was used to determine the best architecture for entering the neural network. For prediction of precipitation, the data that showed the most relationship with precipitation was used in four patterns, in January the fourth pattern with entropy error was 045/0, the number of input layers was 91, the best makeup was 15-1, and the correlation coefficient was 94% Was. In February, the third pattern with a correlation coefficient of 97%, entropy error, was 0.36. Percentage, number of input units was 8 units, and the best type of latency layout was 10-1. The precipitation of March with all patterns was high predictive coefficient. The first pattern with entropy error was 0.038, the number of input units was 67, the hidden layer arrangement was 17-1, the correlation coefficient was 98%. 

Different regions of Iran are affected by internal or external dust sources. Satellite data can be ‎used to examine the temporal and spatial distribution and detected the source of dust. The aim ‎of this study was to analyze the concentration of surface dust, dust in air column and aerosol ‎optical depth (AOD) using MERRA-2 model and Aqua satellite data in Iran. Data were prepared ‎between 2007 and 2017 and the figures were plotted by Grads and OriginPro software. The ‎research findings showed three cross-border dust source including western, eastern and ‎northeastern Iran and two internal area in the desert plain and southeastern Iran. The dust ‎source located in southeastern Iraq and Kuwait have the highest concentration of surface dust. ‎In Abadan, the highest concentration of surface dust with 552 (μg⁄m^3 ) in summer, dust in ‎air column 709 (mg⁄m^2 ) in spring and the AOD of MERRA-2 with 0.58 in the spring took ‎placed. The correlation coefficient between surface dust and air column dust was obtained in ‎internal area including Tabas, Kahnooj, Khor Biabank and Nikshahr with 0.94 and 0.93, 0.89 and ‎‎0.89, which can indicate the effect of local conditions on development. Time series of air ‎column dust in three stations of Bushehr, Abadan and Ahvaz shows the highest decreasing line ‎trend with -0.05 and -0.035, which shows the decrease in the effect of dust external dust ‎sources.In the monthly distribution, the years 2008 and 2012 were determined by the most ‎severe and widespread dust in Iran. The maximum dust values was obtained in April and July ‎‎2008, May 2012 and July 2016 in Ahvaz.‎

Coordinating buildings with climatic conditions and saving energy is one of theimportant goals of climate design. Jolfa city has a relatively cold and dry climate dueto being located in a special geographical location, topographic conditions and climatesystems affecting the region. Is. So that the air temperature in the coldest month of theyear reaches -20 degrees Celsius and in the warmest month of the year sometimesexceeds 44 degrees Celsius. Therefore, bioclimatic conditions have a special status, so it is necessary to study the climatic conditions in relation to design to establish buildings in the cold seasons of the year to reduce the relevant problems. The mainquestion of this article is what effect do the specific climatic elements of Jolfa cityhave on the architecture of this city? The method of this article is a descriptive-analytical one in which an attempt will be made to study and evaluate the humanbioclimate of Oshbin Jolfa using the bioclimatic index of tissue comfort criteria (Pen Warden) and Algi. Using the synoptic meteorological data, the bioclimatic situation ofJolfa city was examined. To do this, meteorological statistics were used over a 27-yearperiod (from 1985 to 2012). The data were analyzed using experimental methods suchas Mahan, Gioni, Pen Warden, Algi and the Climate Need Calendar with the aim ofachieving the correct principles of climate design, the results of which provide solutions based on the study of the lunar climate. The year of the year and the comfortof human beings have been according to these indicators, including the establishmentof buildings in the direction of south, southwest and southeast

Climatic conditions is one of the most important factors affecting different aspects of human life, especially in urban areas and affects social and economic sectors. Establishing a thermal balance between the human body and the environment is one of the primary needs for health and comfort. Human thermal comfort conditions can be evaluated by using various indices based either on simple empirical approaches or on more complex and reliable human-biometeorological approaches. Human thermal comfort conditions with a single parameter or thermal indices derived from experimental equations, cannot fully evaluate thermal comfort conditions. In this study, we tried to investigate the temporal distribution of heat and cold stress events based on the physiological equivalent temperature (PET) index of Tehran in 9th area.

Determining suitable weather conditions for outdoor presence (in terms of heat and cold stresses) can be an important factor in reducing mortality and providing human comfort in these areas. Therefore, the focus of this paper is to examine the weather conditions in Tehran 9 area. The establishment of Tehran's Mehrabad Airport in this area as well as the presence of the western terminus on the northwest side of Azadi Square has made it one of the busiest areas in Tehran. The evaluation was based on statistics and information from Tehran's Mehrabad Airport synoptic station located on the southwestern side of zone 9. In this paper, to determine the time range of occurrence of heat and cold stresses, temperature data, relative humidity, water vapor pressure, wind speed, and cloudiness on a scale of hourly during 2008 to 2017 were obtained from the Meteorological Organization  and PET index was calculated using RayMan Model and analyzed on annual, seasonal, monthly, daily and hourly scales.

The PET index for Tehran 9 area was calculated from January 2008 to December 2017 using data from Tehran Mehrabad Synoptic Station. The results showed that in this decade, the thermal comfort, frequency of PET index accounted for only 12.91% of the whole period. Much of the analyzed data belong to cold stress classes of less or higher, with 54.62% being the most abundant. The rest of the data are in the range that according to the PET index classification, are slightly warm to hot and 27% of the whole period is affected by different heat stresses. The results showed that extreme cold stresses began in the second decade of October and continued until the first decade of April. This class of cold stresses completely disappears between the second decade of April to the first decade of November. Initial surveys showed that the highest percentage of very severe cold stress was distributed in the first 10 days of January. This percentage is slowly declining as temperatures rise in the coming days, reaching their lowest level in the first decade of April. The percentage of severe, moderate and mild cold stresses decreased by 11% over the whole period from 23% to 12%. The range of severe cold stresses started from the second decade of October and was steadily stable in the study area until the third decade of April. Maximum and minimum percentages of severe cold stress were in the second decade of March (35%) and the first decade of October (2.5%). Moderate cold stresses began 20 days earlier than severe cold stresses and continued until the second decade of May. According to the figures obtained from the PET index, it peaked at 46% in the second decade of April. Slight cold stresses that started in the third decade of April and continued until the third decade of June. Extreme heat stress is distributed only in June, July, August and September. Frequency of heat stress increases significantly in July, especially in the second decade. The frequency of this stress in the first 10 days of June increased by 1.3% and increased to 37.5% in the second decade of July. The convergence frequency of the PET index is observed in different percentages between the third decade of March to the second decade of October.

Area 9 of Tehran, 86% of the total year was outside the range of thermal comfort conditions, with 59% related to cold stresses (PET <18 ° C) and 27% related to varying degrees of heat stress(PET>23C°). Extremely severe cold stresses in January, February and December from 6 pm to 6 am and severe to moderate heat stress from late May to late September between 9 am and 3 pm.

This research aims to study the impact of temperature and wind in the southern low-pressure system and its associated precipitation in the southern regions of Iran. As The southern low pressure system moves eastward, it crosses the southern regions of Iran, causing medium and heavy rainfall in these areas. In this study, two southern low-pressure systems that caused heavy rainfall on March 11, 2015 and January 17, 2000 in southern Iran were selected, analyzed and simulated using the numerical Weather Research and Forecasting (WRF) model. Since the wind and temperature fields play a significant role in the southern low-pressure systems, four experiments were performed for investigating the effects of temperature and wind on the intensification and weakening of the southern system. The simulation results showed that the simulation for the increased (decreased) temperature caused the weakened (intensified) the southern low pressure in the studied area. This result showed that the vertical structure of the southern low-pressure and its physical characteristics are similar to the mid-latitudes cyclones, and these systems were different from the thermal low pressures. The results of wind speed changes showed that the increased (decreased) wind speed simulation caused an increase (decrease) in relative vorticity, thus the southern low pressure was intensified (weakened). In both cases, the rainfall was decreased by the increased temperature simulation, and decreased temperature caused an increase in rainfall. It was also seen that the increase in wind speed caused the special humidity advection to be increased and then the rainfall increased. Also the amount of rainfall decreased when conditions did not provide for the advection of specific humidity or the wind speed reduced.

Climate systems are complex, yet organized, systems that, in some years, exhibit definitive behavior and in others, random behavior. In the meantime, the subtropical high-pressure system, by shifting its location, creates occasional and sometimes random environmental events (droughts or wetlands, etc.) in one area. These environmental events are at different levels of balance, threshold and risk. In this article, in response to this issue, namely, identifying the behavior of tropical high-pressure systems from equilibrium to hazard, it was attempted to identify and evaluate this process in the form of systematic thinking. Since in the behavior of climatic systems, quantitative and qualitative studies interaction is essential. For this purpose, the research method in this study consists of two main parts: In the statistical research method, the data of 500 hPa pressure during the period 1948-2018 and the monthly precipitation data of 84 meteorological stations were analyzed and analyzed. Also, in the qualitative research method, the types of equilibrium, thresholds, hazards and environmental disasters in the behavior of high-pressure systems as well as its management type were studied. The results showed that in the warmest months of the study, the most significant impact on Iran has revealed several levels of equilibrium, in particular static, instantaneous, dynamic and transient equilibrium, which in some years face a threshold effect, which threatens to initiate risks. Is the environment (severe and very severe droughts). Which will be subject to environmental disasters if disturbed and drought-prone behavior is managed. Therefore, it can be said that combining quantitative and qualitative perspectives (in the form of active management) can be effective as a practical approach in the field of environmental management and predicting the behavior of systems to reduce risks.

In this study, in order to Dezful precipitation modeling were Used from the monthly precipitation data of Dezful Joint Station in the statistical period (2014-1961) for 53 years as a dependent variable and climatic indicators and climatic elements as independent variable. Factor analysis was used to apply the most important climatic elements affecting the study area, and varieties of regression analysis methods were used to identify the most important climatic signals affecting the dependent variable.
Due to the nonlinear behavior of precipitation, artificial neural networks were used for modeling. To enter the neural network, precipitation data were applied to regression analysis. The results of the study revealed that the prediction of climatic signals had a higher correlation coefficient. For example, the correlation coefficient with the gradual data removal method was 100% and the step-by-step method was 99.88%. Between of 144 units (climatic signal per month), 16 units or indicators were selected in 18 months with 18 networks for precipitation with a correlation coefficient of 99.88% for forecasting. To predict precipitation with climatic elements, among the 7 effective components, the second component of precipitation-temperature was 100% probably successful in regression and 99.7% probably in nervous system.

Recognizing the interactions of the large-scale components of atmospheric circulation creates an appropriate capacity to examine the regional climate variability and improve description of land-atmosphere connections. Assessment Iran vulnerability to climate fluctuations is very important. Part of this recognition will be achieved by assessing the components of the atmospheric circulation. The relative vorticity distribution is an important index of synoptic motion in mid latitudes. Regions of positive relative vorticity are associated with cyclonic storms in the Northern Hemisphere. Thus the distribution of relative vorticity is an excellent diagnostic for weather analysis. Vorticity, the microscopic measure of rotation in a fluid, is a vector field defined as the curl of velocity. Relative vorticity is a measure of the intensity and direction of spin in a circular movement, which is performed by a unit volume of air around the vertical axis perpendicular to the plane over which this rotation occurs. Relative vorticity is a good quantity for studying atmospheric changes, because it presents the main order of magnitude of daily cyclonicity or anticyclonicity.