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The occurrence of torrential rains in April 2017 in Lorestan province was a clear example of heavy rains that left very heavy damage to agricultural, urban, transportation and communications infrastructure. The purpose of this study is to investigate and reveal the relationship between the physical structure of clouds producing two waves of heavy rainfall in April 2017 in the Doroud catchment area of ​​Boroujerd. In this regard, the statistical characteristics of two precipitation waves on March 25 and April 1, 2019 were analyzed. The supernatural properties of the clouds producing these two heavy rainfall waves were investigated using the Madis superconductor product, MOD06. 4 Microphysical factors of generating clouds These two waves of heavy rainfall in the Doroud-Borujerd basin, including cloud peak temperature (CTT), cloud peak pressure (CTO), optical cloud thickness (COT) and cloud cover ratio (CF) were analyzed. Statistics of these two waves of heavy rainfall showed that in the first wave of heavy rainfall, ie the wave of March 25, 2019, (5 April 1398) 15% of the total annual rainfall and in the second wave, the wave of April 1, 2019 (April 12, 1398) 20% of the total The total average annual rainfall of the region was recorded in these two days. The results of analyzing the microphysical structure of the generating clouds of these two precipitation waves using the MODSI cloud sensor product data showed that the four microphysical factors of the cloud showed a significant spatial correlation with the recorded precipitation values ​​of these two heavy precipitation waves. The two factors of temperature and pressure of cloud peak, which show a vertical expansion of clouds in the area, showed a significant inverse relationship with the amount of precipitation in the basin, while the two factors of cloud ratio and cloud optical thickness have a direct and significant spatial correlation with values. Recorded rainfall showed. The results of this study showed that in these two events of heavy rainfall, a significant and strong relationship was established between the microphysical structure of the cloud and the amount of rainfall recorded in the region.
 

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  Climate control centers in each area are diverse and understanding how they relate to the atmospheric components of the Earth's surface contribute to prediction of climate fluctuations.   In this study, by using Pearson's correlation and multivariate regression in a thirty-year period (1961-2010), the relationship between widespread rainfall anomalies in entire of Iran west with temperature and pressure of atmospheric centers in East and West of Mediterranean Sea in 5 atmospheric levels (SLP, 850, 500 and 300 Hpa) were analyzed and modeled. Based on the results, the correlation of atmospheric control centers in the East and West Mediterranean Sea with anomalies of rainfall in West of Iran is inverse and meaningful in 95% level. In this study, statistical indicators such as temperature differences and standardized  pressure between West and East Mediterranean sea were identified as the most important indicators in relation to changes of rainfall in the study area.   Based on the designed indicators, whenever indicators DT and DH is positive, this means higher temperature and higher atmospheric standardized pressure in the Western parts of Mediterranean sea in compare with its East and therefore the wet spells (Monthly) occur in the study area, and If the above mentioned index is negative, means that the occurrence of drought in West Iran. As for the indicators introduced for lower levels of the atmosphere, especially in the case of temperature, meaningful strong and direct correlation is seen with rainfall abnormalities in entire West of Iran. Modeling provided some indicator for Mediterranean region using multivariate regression that they showed a relatively strong correlation in this regard of the selected components that include the pressure difference in sea level, the temperature difference in 925 and 850 hPa level in the West (Compared to its East) Mediterranean sea. Also check the regression model using real data confirm the accuracy of the relative performance of the model.  

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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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Rainfall is one of the most important components of the water cycle and plays a very important role in the measurement of climate characteristic in any area. Limitations such as lack of sufficient information about the amount of rainfall in time and space scale and complexity of the relationship between meteorological elements related to rainfall, causes the calculation of these parameters using the conventional method not to be implemented. One method of evaluating and forecasting of rainfall in each region is time series models. In this research, to predict the average annual rainfall synoptic station at Mahabad, Uromiya and Mako in West Azarbayejan provience during 1984-2013, linear time series ARIMA was used. To investigate model static, Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) was applied and with differencing method, the non-static data transformed to static data. In next step, stochastic models to estimate the annual rainfall average were used. With regard to the evaluation criterion such as T, P-VALUE < 0.05 and Bayesian Information Creterion (BIC), ARIMA (1,0,0), ARIMA (0,1,1) and ARIMA (0,1,1) models was determined as a suitable model for predicting annual rainfall in the three selected stations at Uromiya, Makoo and Mahabad. In the following, the annual rainfall for 3 (2013-2016) years is forecasted which based on rainfall data in that time, the adjusted model was acceptable.

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The aim of this study is to retrieve land surface temperature (LST), air temperature (AT) and precipitation and to study their relationship with vegetation in rang lands of Karun watershed of Khuzestan province. For this purpose, land surface temperature (LST) and NDVI was drived from NOAA-AVHRR for maximum amount of greenness (April) for a period of 27 years. In order to extract LST, Price algorithm was used. Also air temperature and precipitation were interpolated for selected weather stations using IDW method. Spatial correlation outcomes (on 0.05) between NDVI with LST and air temperature show a reversed relation. This spatial relation is stronger for LST, so that this coefficient is often upper than 0.6, while seldom is 0.4 for air temperature and precipitation. Spatial regression models show that 62 percent of NDVI changes is determined by LST (R²=0.62) and air temperature and precipitation determine very limited amount of NDVI dynamics.

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Thunderstorms are major climatic events due to the significant effects and catastrophic consequences on humans and the natural environment. The researches have shown that the elevation and latitude factors are two variables that can affect the occurrence of this phenomenon. Therefore, the main aim of this study is to investigate the spatial analysis of the effects of lightning and its effects on the components such as elevation and geographic extent in Iran. Apart from this fact, firstly, the monthly data of thunderstorms occurrence in 118 synoptic stations of Iran, from 1991 to 2010 on a basis from the country's meteorological organization were obtained and GIS software was produced by the annual and seasonal maps of Iran. Then, for the spatial analysis of this climatic phenomenon, the method of landing statistics of the Kriging (Universal) method was to examine its seasonal and annual status. In order to better understand the effect of Thunder hurricanes from altitude and latitude using Curve Expert software, seasonal and annual charts, along with the correlation of each production, were analyzed. The results show that the highest annual thunderstorms occur in the northwest of Iran, and the least amount is consistent with the central and eastern parts of the country. In addition, according to seasonal analysis, although the station has the highest rate at 800 to 1,300 meters, the maximum occurrence of this phenomenon varies from 0 to 2200 meters in different seasons of the stations. The overall result shows that the factor of height is slightly correlated with the occurrence of the Thunder storm phenomenon and the highest correlation is due to the latitude factor.

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In this research, applied synoptic model for determining the average daily temperature and its relationship with the Geopotential Height in middle level (500 HPa). Therefore, two database were used: database of atmospheric circulations, includes the data of geopotential height at 500 HPa and its data were extracted from the NCEP/DOE(US National Oceanic and Atmospheric Administration) in hours 00:00; 03:00; 06:00; 09:00; 12:00; 15:00; 18:00; and 21:00 in Zulu and other, database of environmental (surface) events. Contain of average daily temperature in the Mashhad, Torbat-Heydarieh and Sabzevar stations in Khorasan Razavi Province. The maximum and minimum of these stations in the time interval from 01/01/1987 to 01/01/2014 equal as 9862 days from the meteorological organization of Iran. Then, was calculated the correlation of the average daily temperature of selected stations with high atmospheric data (500 HPa level) with the northern hemisphere in Surfer Software. The result shown, four regions in the northern hemisphere which had high correlations with selected stations. The correlation results suggest that the United States has 25 pixels, Northern China 25 pixels, Africa 45 pixels and Japan with 65 pixels. Then, weighted average of pixels in heights by multiple regression equation station. The results of diagnostic models indicate that, per geopotential height increase in the profile, the average daily temperatures of selected stations in the Sabzevar 1.4, Torbat-Heydarieh 1.3 and Mashhad 1.3 degrees Celsius will increase.
 

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owledge of spatial rainfall behavior in environmental, land planning is effective. These changes in the later place in the form of time later and in the climate of the area. The Target of this study was to reveal the presence or absence of precipitation trend in the ratio of the height of local precipitation behavior and identify province mazandarn. Therefore, the purpose of the rainfall data station 32 (Meteorological Agency and Department of energy), the statistical period 1988-2010. To get the regression analysis of precipitation process was used to identify the local behavior of precipitation, the method of spatial statistics were used. The results obtained from the behavior of precipitation, the existence of the process within the scope of the study and the emphasis is most consistent with the Be modified regression model at adjustment indicate. According to the regional behavior of precipitation, using local spatial statistics, spatial Moran well hot spots check this behavior. The results showed that precipitation in the province of Mazandaran has the pattern of clusters with high value. According to the local hot spots and methods Moran, West Coast up to a height of 700 m has positive z score and clusters with high value, 99% confidence level. This range includes 15% of the total of the province. The range of the Southern Highlands as well as the negative z score and clusters with low value with a confidence level shows 99%. This range is also about 20 per cent of the province's total. About 65 percent of the total area of the province as well as the lack of a significant trend show.

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Considering the vast application of sea surface temperature in climatic and oceanic investigations, this parameter was studied in Oman Sea from 1986 to 2015. The SST was surveyed using trend analysis and Global and local Moran’s I spatial autocorrelation. In trend analysis, the Mann-Kendall test was used to determine the trend of SST changes and the Sen's Estimator method was used to examine the slope of the changes. Using these methods, it was found that during January, February and December, there was no significant ascending trend in SST values, and only parts of the Strait of Hormuz had a significance descending trend. On the other hand, there was no significant descending trend in March, and the ascending trend in the SST was seen in the southern part of the Oman Sea. Other months of the year had a significant ascending and descending trend in different parts of the Oman Sea, which October had the highest ascending trend. In the annual time scale, it was also found that the southern parts of the Oman Sea had ascending trend in the SST value and Western parts had a descending trend. The occurred changes in the high amounts (positive and negative) were corresponding to the significance ascending and descending trends. The results of Global Moran for the annual time scale indicated an ascending trend of autocorrelation values and cluster patterns of SST data over time, using the local Moran analysis, it was found that warm clusters of SST are increasing in the Oman Sea, and on the other hand, cold clusters of this parameter have been reduced over 30 years. According to the results of trend and spatial autocorrelation analysis, it has been found that SST have been increasing in different parts of the Oman Sea during 30 years, so climate change and global warming may have affected this region.

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In this Research, the maximum temperature of selected stations in Khuzestan province and the numerical values of 8 extreme climatic indicators belonging to the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI) were used in the statistical period of 1987-2017. To analyze the trend of extreme climatic indices, the Man-Kendall test was used and to estimate the slope of the trend line, the Sen’s estimator was used. In this study, given the importance of global warming that severely affected all aspects of life, the authors explore the relationship between climatic factors and maximum temperature in Khuzestan province until to rely on it, and ones can predict and forecast air temperature at this region. For this purpose, the temperature of selected stations in Khuzestan province and numerical values of  8 climate indicators in the period 1987-2014 have been used. To understand the relationship between climate indicators and maximum temperature at 1 to 12 months of delays, Pearson’s correlation coefficient was used. The results showed that most of the extreme climatic indicators in the study period had a significant trend. The TX10 and TN10 indices have had negative trend in most stations and the TX90, TN90, TXx, TXn, TNx and TNn indices have had positive trend. According to the results of correlation coefficients can be concluded that all studied signals have a significant effect on the province's maximum temperature. The correlation between maximum temperature and indices PNA, TSA, WHWP, WP and NAO, was more than the other climate indicators. Results also showed that the entire indices except NAO have significant positive correlation with maximum temperature of the province. PNA index with a delay of 10 months has the highest positive correlation with maximum temperature of study area.

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