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Extratropical cyclones according to the frequency, duration, and intensity, the major cause of mid-and high-latitude precipitation across the Mediterranean during winter and autumn. For this research using network data of ECMWF climatic variables with 6-hour time resolution and 0.25 × 0.25 spatial resolution from1979-2016 and were used of 4 Basin stations precipitation data from the Asfezari database from 1979-2016. The results showed that the first pattern is the Mediterranean trough pattern which has the highest frequency of 42%. In this pattern, the low-level due to the presence of a high-level that acts as a barrier have caused the deepening of the Mediterranean low-level and its axis extends to the Red Sea, and due to the collision of low-level and high-level on the region, instability is intensified and has caused the most rainfall among the patterns.  In contrast to the first pattern, The fourth pattern has the lowest frequency of 10% Which is the trough pattern of western winds which is located on the Caspian Sea but due to high-level in the south of the region has prevented the entry of low-level and is located in the northern part of the study area due to this, the isobar in the north of the region have become orbital as a result, fewer cyclones enter the area, resulting in less rainfall between patterns. The results also showed that the frequency of cold-core cyclones was 60% in winter and 40% in autumn, but the frequency of hot core cyclones was 62% in winter and 38% in autumn, which in winter, the frequency of hot core cyclones has increased compared to cold-core cyclones, while in the fall the frequency of hot-core cyclones has decreased compared to cold-core. In the last decade, both the frequency of occurrence of cyclones and their intensity has decreased compared to the last two decades. In terms of cyclogenesis places, the western part of the study area has always been active, And with the onset of the cold season from autumn to winter, cyclogeneses places are gradually becoming more active.

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sudden stratospheric warming has an obvious effect on the Earth's surface climate. In this research, the changes in precipitation during the occurrence of this phenomenon have been investigated. For this purpose, after revealing the warmings that occurred during the studied period (1986-2020), 18 warmings were identified. The 5th decile and 9th decile of precipitation were calculated for the precipitation data of 117 stations. And the size of the difference from the normal rainfall was checked in two ways. First, the precipitation at the time of warming was compared with the long-term average, and then the trend of changes in precipitation at three times before thewarming, at the same time as the warming, and after the warming was finished. Finally, these results were obtained. Warmings according to the month in which they occur; They have a different effect on the amount of precipitation. In the sudden stratospheric warming that occurred in December, January and February, the northwest experiences the most rainfall changes and is above normal, and the probability of rainfall above the 9th decile increases up to 65%. Western and southwestern regions also have higher than average rainfall and the probability of heavy rainfall is high. Precipitation on the shores of the Caspian Sea shows an inverse relationship with sudden stratospheric warming, so in all the investigations of this research, the lack of precipitation at the time of warming in these areas is significant. Southern regions have less than normal rainfall in all sudden stratospheric warming events. The center of Iran has higher than average rainfall in the sudden stratospheric warming months of March. Eastern Iran also has heavy rains compared to normal during the sudden stratospheric warming months of March.

 

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The aim of the current research is to show the potential areas for autumn sugar beet cultivation in Golestan province according to temperature and precipitation parameters. For this purpose, temperature (daily) and precipitation (annual) statistics were used for a statistical period of 15 years (2006-2020). In order to analyze the data, the methods of thermal potential diagrams, deviation from optimal conditions, phenology and zoning of Golestan province in terms of the amount of irrigation during growth were used from the annual rainfall data. The results of temperature evaluation using the thermal potential method based on the thermal threshold showed zero, 4 and 10 degrees Celsius at the level of stations in Golestan province; Inche Brun station has the highest cumulative thermal unit and Aliabad Katul station has the lowest cumulative thermal unit. Examining the date of the probability of late spring frost at the level of 95% showed that due to its occurrence at the end of April in the center, east, north, northeast and west of Golestan province, it coincides with the phenological stage of root bulking and sugar accumulation. Also, based on the deviation from the optimal conditions in Golestan province, Inche Brun station was calculated to have the lowest deviation (-20.64). Also, in terms of phenology, in the stage of budding, six leaves, root bulking and sugar accumulation and full ripening of the autumn sugar beet plant in Golestan province, the favorable areas for autumn sugar beet cultivation in Golestan province were determined, including Gonbadkavus, Bandar Turkman, Kalaleh, Inche Brun and Bandar Gaz. Also, the evaluations in terms of rainfall according to the annual rainfall zoning map of Golestan province, it can be concluded that in terms of rainfall and water supply required for autumn sugar beet, there is not much limitation towards Golestan province, however, considering the recent multi-year droughts, the cultivation of this plant in the province needs additional irrigation for growth.

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In this research, the frequency of merging of the polar-front and subtropical jet streams and its effect on the amount of precipitation received from the atmospheric pattern in western Iran during the 10-year statistical period of 2010-2019 was investigated. Then, by coding in Grads, the maps of jet stream level 300 hPa were drawn with a time interval of 6 hours. During the statistical period, these two jet streams merged along their axis. By examining the frequency of the merging of two jet streams, it was found that until 2015, the frequency of the merging of two jet streams in December was an increasing trend and in 2016 and 2017, there was a decreasing trend. It has increased again in 2018 and 2019. In all cases, the merging of two jet streams has not resulted in heavy precipitation events (December 2011, 2014 and 2017). So, considering the heavy rainfall of 110 mm from Dehloran station, the date of 12th to 15th of December was chosen to analyze and understand the system that led to the occurrence of rainfall. From December 12 to 15, 2010, the decrease in air temperature over Europe and Southwest Asia led to a large meridional movement of the polar-front jet stream, which resulted in merging with the subtropical monsoon. On the 12th of December 2010, along with the medicalization of the polar-front jet stream and its penetration into the tropical regions, its velocity core merged with the core of the subtropical jet stream over the northern Arabian Peninsula, the Red Sea and northeastern Africa. The merging of these two jet streams has led to the vertical expansion of the jet to lower levels, and in the middle level of the atmosphere, very low meridional movement is observed. As a result, the Sudan low moves to higher latitudes and merges with the Mediterranean low pressure.

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The aim of this study is synoptic analysis of heavy precipitations in Kurdistan Province during 1/1/1961 to 31/12/2010(18263days). By using two thresholds of extensively and intensity of precipitation, 107 days have been recognized. In temporal view, the selected days occurrences in wet seasons of year that start from October to June. By applying the principle component analysis on the sea level pressure during the 107 days, the results showed that 12 components explain about 93 percent of sea level pressure variation. The results of the applying cluster analysis on the component score showed that two circulation patterns result in occurrence of extreme and heavy precipitations in Kurdistan province. In order to recognize heavy precipitation in dry season (June to October), the area mean precipitation for the province have been calculated. According to the 99 percentile threshold and extensively of precipitation the heavy phenomena recognized. By two thresholds 32 days selected and same as to the wet season precipitation the sea level pressure data from NOAA extracted. The data analyzed by Principle Component Analysis and 11 components extracted that explain the 93 percent of variation sea level pressure in the frame study. By doing cluster analysis on the 11 components, one pattern recognized for the sea level pressure.  The results showed that in lower atmospheric levels (1000, 925 and 850 hpa) occurred highest convergence on the study region during wet season. The source of humidity in the lower levels is Arabian, Red, Oman, Persian Gulf and Mediterranean Seas. In the high level atmosphere, the value of convergence is low. The sources of humidity in high levels atmosphere are Red and Mediterranean Seas. In the mid level atmosphere (500 hpa) the presence of trough results in nutrition of the instability on the study region. Also during dry season, the highest moisture flux convergence occurred in the low levels but the resource of the moisture is Caspian Sea. In the upper levels (from 700 hpa to up) in addation to Caspian Sea, Mediterranean and Red sea are the resource of heavy precipitations.

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In this study, by using the Mann-Kendall nonparametric method and Sen' s Estimator slope test, the trend of some elements including  precipitation, average of maximum and minimum temperature and the  number of snowy days Chaharmahal and Bakhtiari Province covers part of Zagros and Zardkohe-Bakhtiari highlands, from which three major rivers including Zayandehrud, Dez and Karun originate. in an annual and monthly scale, was evaluated in  the stations of the province during a period of 30 years (1986-2015). The output was presented in the form of tables, graphs and iso-trend maps as drawn in the Arc_GIS. The results showed that although changes in rainfall did not follow any specific trend in most months of the year, the amount of precipitation in the stations of Koohrang as the rainiest station in the Province, Lordegan and Yan-Cheshme had a decreasing trend at the significance level of 99%; also, the the number of snowy days during March showed a decreasing trend in Koohrang station. However, the average minimum and maximum temperature in most areas of the province, in both monthly and yearly scales, except for the months of November and December, had a significantly increasing trend.

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Knowing of precipitation values in different regions is always of main and strategic issues of human which has important role in short- term and long-term decisions. In order to determine of precipitation model and forecasting it, there are different models, but given that the precipitation data have a spatial autocorrelation, the spatial statistic is a powerful tool to recognition of spatial behaviors. In this research, for determine of precipitation model and predicting of it with geographical factors e.g. altitude, slope and view shade and latitude- longitude by using spatial regressions analysis such as ordinary least squares (OLS) and geographical weighted regressions(GWR), 13 synoptic stations of Khuzestan province from establishment to 2010 were used. Results showed a powerful correlation between precipitations with geographical factors. Also results of modeling through OLS and GWR representative that forecasting of GWR is close to reality, so that in GWR, the sum of errors of residuals is less, the  is more and there aren't any spatial autocorrelation in residuals and the residuals are normal. The of OLS can only justify 75 percent of precipitation variations with spatial factors while in GWR this quantity is 82- 97 percent. Accordingly, it was found that, in east, northeast and north of province the altitudes, in east and northeast and Zagros Mountains the view shade and slope are the most important spatial factors, respectively.

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In this research, the relationship between NCPI and CACO indices with autumn precipitation of Southern Coast of Caspian Sea (SCCS) was investigated. In this regard, two sets of data were used (Aphrodite and Station). And the days with more rainfall than long-term average rainfall station and on condition that the rainfall is more than 70% of the region rainfall, were chosen as a day of widespread rainfall. The sea level pressure data was extracted and by cluster analysis and coalition method was clustered. Then, a representative of the widespread precipitation days from station dataset was selected, investigated and analyzed accordingly. The results state that within all patterns there exists a high pressure on the upper side of the Caspian Sea, or a margin of high pressure is extended on to the sea itself. These high pressure regions have relatively cold nature that can cause currents in the northern direction while intersecting with the relatively warm water during the summer. These currents can absorb moisture during their motion towards south which can lead to their instability. In addition, one should not forget the fact that in each three investigated patterns, dynamic factors at high levels have intensified the abovementioned phenomenon and enhanced the instability, which as a result brought about widespread precipitation. Continuously, the abovementioned Remote bond indices were extracted on a daily basis and their relation to north coast widespread rainfalls was studied, which came to a meaningful relationship between these index sets and fall index sets. The relationship is direct with NCPI or surveyed stations, and it’s an inverse relationship with CACO. On the other hand, the study of indexes anomalies on the days without rainfall and with rainfall was done by One Way ANOVA and Tukey test. The result was a meaningful index anomaly on the days with and without rainfall.

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In this study, precipitation simulated annual and seasonal in East and North-East of Iran ,in 1987-2011, by using RegCM4 dynamic model in two case; with and without using post-processing technique. The required data for RegCM4 model with NetCDf format, received from ICTP center. For the implementation of the main dynamic model, Convective precipitation test scheme and the horizontal resolution, performed for 2007. According to the test, Kuo Schema had less error than Emmanuel and Gurl schemes in Precipitation and region temperature modeling. Horizontal resolution selected 30 Km. After model implementation with Gurl schema and 30 Km horizontal resolution, Precipitation and temperature output post- processed using MA model. According to results, in the study area, during 2006-2011 verification period, average annual rainfall raw bias of RegCM4 model was calculated and post-processed equal to 8.3 millimeter and 61.04 respectively. Briefly in the annual time scale, in 75% of studied stations, post-processing is effective and MA model is more efficient. In seasonal scale, bias error of average precipitation is equal to 54.99 millimeter in the winter, 27011 millimeters in the spring, -3.6 millimeter in the summer and 7.21 millimeter in the fall. Simulation of the temperature data in the stations using RegCM4 and MA model in north-east of Iran, revealed high performance. Bias error of average temperature is equal to -2.78 for RegCM4 model and post-processed equal to -0.05. In all stations, modeled Annual temperature and observational data has difference less than 0/1 ° C. In seasonal scale, the mean bias error range according ° C is equal to -4.1 in the winter, -4.09 in the spring, -1.8 in the summer and -1.5 in the fall.
 

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Estimating and predicting precipitation and achieving its runoff play an important role to correct management and exploitation of basins, management of dams and reservoirs, minimizing the flood damages and droughts, and water resource management, so they are considered by hydrologists. The appropriate performance of intelligent models leads researchers to use them for predicting hydrological phenomena more and more. Therefore, in this study, the Gene Expression Programming (GEP) and Support Vector Regression (SVR) models were used to model monthly precipitation of Nahavand City. In this study, precipitation, temperature, and relative humidity data were used in a 32-year period (from 1983 to 2014). The results showed that the same and good performance of both models (R²= 0.92), but according to different evaluation criteria, GEP model showed a little better performance (RMSE= 0.0478 and 0.0486), while the running GEP model is so easier than the SVM model. Totally, it can be said that GEP model had been suitable for modeling monthly precipitation of Varayeneh station in Nahavand City. Finally, the monthly precipitation was predicted the GEP which showed a decrease in precipitation in compared with previous months.
 

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