فهرست مطالب محمدجعفر ناظم سادات

Aerosols play an important role in the climate system of the atmosphere. These atmospheric particles are directly affected by changing radiation exposure by diffusing and absorbing different wavelengths and indirectly by changing the optical properties and the durability of the clouds, and the optical properties of the aerosols, such as aerosols optical depth (AOD), are critical in understanding the effects of atmospheric aerosols on climate change and biogeochemical cycles. Therefore, in this research, the ability of radiation transmission model and MODIS images was evaluated to estimate the aeroeses optical depth in Persian Gulf. MATLAB was used for images processing of the images with resolution of 500 m at 5×5 networks. The evaluation of aerosoles optical depth data was performed by using AERONET stations with Pearson correlation, RMSE and RMSD indices. First results of the algorithm implementation showed the ability of model to simulate the aerosols optical depth. Modeling results showed that the algorithm is correct and according to spectral reflectance data for effective radius of the LUT tables, values simulated for aerosols optical depth. The results of evaluation showed that optical depth values obtained from the images has significantly correlation with the measured optical depth in AERONET stations. So that, amount of correlation calculated form 29 June 2013, 14 July 2014 and 6 October 2015 in the band of 1.243 is 0.96, 0.94 and 0.98 respectively, and for the band of 1.643 is 0.99, 0.98 and 0.98, respectively which shows a good trend between the measured data, RMSE and RMSD indices were calculated in these days less than 0.15 and less than 0.06, respectively. It can be concluded that simulations accuracy is appropriate in these bands.

The variability of sea surface temperature (SST) is used as a valuable climate index for the prediction of precipitation in far and near areas from the sea. The prediction of SST in the north western of the Indian Ocean is the main goal of this study. This water region including 81 gridpoints with 2˚×2˚ grid in the geographical location of 10-30N and 45-76˚E. The SST was extracted from the National Oceanic and Atmospheric Administration (NOAA) for the period 1951–2007. The principal components analysis technique was used to identify the main patterns of SST and data reduction. The PCA performed was based on the correlation matrix. The number of row and column of the input file for the correlation matrix was, respectively, the number of months and gridpoints. The four principal components that explained 98% of the SST total variance were extracted. The first, second, third and fourth principal components explained 79, 8/9, 5/9 and 4% of the SST total variance, respectively. These four principal components as four regions over the area of interest were studied. The first, second and third regions were geographically located in 16-24˚Nand 58-72˚E, 10-14˚N and 48-76˚E, and 14-16˚N and 50-74˚E. Also, the fourth region was the Persian Gulf. The spatial average of SST within each region was considered as a regional index. As the linear stochastic models, the “seasonal auto-regressive integrated moving average” (SARIMA) models were used to predict the monthly time series of the regional indices of SST patterns. The dataset for the monthly time scale for the 1951–2000 period was used to construct SARIMA models for each region. There is a linear trend in SST time series over three regions which indicate that the monthly SST over these regions is non-stationary. Since ARMA models prefer stationary time series data as their input files, a differencing procedure was considered as a smart approach for transforming these non-stationary series into the stationary ones. On the basis of the corrected Akaike information criterion (AIC) and significant coefficients, the best seasonal auto-regressive integrated moving average model was separately selected for each region. The auto-correlation function plots of the residuals for the selected models have indicated that the residuals are uncorrelated. The selected model for each region had a minimum value of AIC and its parameters were significantly different from zero. For example, SARIMA(1,1,0)×(1,1,0)12 model was identified for SST time series over the northwestern parts of the study area. As the independent data of training period, the SST time series for the 2001–2007 period was predicted at lead times ranging from one to 12 months and then was evaluated. For example, the value of Pearson correlation between the observed and the predicted SST over the northwestern parts of the study area with SARIMA(1,1,0)×(1,1,0)12 model for the test period (84 months) was 0.94. Also, the corresponding root mean square error was 0.46 degrees Celsius. In all of the regions, the correlation coefficient between the observed and the predicted SST for the independent dataset is higher than 0.9. Therefore, the time series models have a valuable ability in forecasting the monthly time series of SST in each region.

The aim of this study is to assess some synoptic characteristics of heavy precipitations in southwestern parts of Iran and evaluate the relationship between them with the Madden-Julian Oscillation (MJO). Research is conducted with regard to distribution of precipitation per month and identifying their steam sources. Daily records of the November-April precipitation data in Abadan, Ahwaz, Bandar-Abbas, Bushehr, Shahr-e-kord and Shiraz stations for the 1975- 2011 period are collected as well as same panel data for Yasuj station from 1990 to 2011. Rainfall data are sorted in descending order and precipitation values that were fallen within the 5% and 10% of highest records are categorized as the heavy precipitation. The most frequent precipitations occurred in January, February and December. The most frequent heavy precipitations in Ahwaz, Bandar-Abbas, Bushehr, Shahr-e-kord and Shiraz stations occurred in phase 8, while in Abadan station occurred in phases 7 and 8. Apparently, due to the short duration precipitations data at Yasuj station, the most frequent heavy precipitation observed in phase 2.Synoptic maps show that harmonized with eastward movement of convective precipitation in Indian or pacific oceans.Heavy precipitation forms in the west region of Iran and moves toward southwest and south Central of Iran and then appears to Afghanistan.Formation of a cyclonic circulation that encompasses the Mediterranean Sea, Red Sea and Persian Gulf plays an important role for moisture supplement of these storm activities. The synoptic maps have indicated that main sources of these heavy rainfalls are moisture produced at the Arab sea and western parts of the Indian Ocean.

In this study, the artificial neural networks (ANNs) and regression models were used to downscale the simulated outputs of the general circulation models (GCMs). The simulated precipitation for 25.18 º N to 34.51 º N and 45 º E to 60 º E, geopotential height at 850 mb and zonal wind at 200 mb for 12.56° N to 43.25° N and 19.68° E to 61.87° E data sets as the predictors were extracted from ECHAM5 GCM for the period 1960-2005. The observed monthly precipitation data of Abadan, Abadeh, Ahwaz, Bandar Abbas, Bushehr, Shiraz and Fasa stations as the predictand were extracted for the period 1960-2005. The principal components (PCs) of the simulated data sets were extracted and then six PCs were considered as the input file of the ANN and multiple regression models. Also the combinations of the simulated data sets were used as the input file of these models. The periods 1960-2000 and 2001-2005 were considered as the train and test data in the ANN, respectively. The Pearson correlation coefficient and normalized root mean square error results indicated that ANN predicts precipitation more accurate than multiple regression. For the monthly time scale, the geopotential height is the best predictor and for the seasonal time scale (winter) the simulated precipitation is the best predictor in ANN based standardized precipitation principal components.

IntroductionInvestigating the trends of hydroclimatic components as a result of climate change has been an interesting task for the scientists and water resources managers. So far, numerous case studies have been conducted to exploit the possible trends in temperature and precipitation which imposed by climate change events. Unfortunately, there is a few research works to address the long term variations in some hydrometeorological components such as evaporation and evapotranspiration, perhaps for their complexity. Majority of the recent studies in America, China, India and Australia have reported a decline (reducing) trend in reference evapotranspiration (ET0). The downward trend in pan evaporation and evapotranspiration over most of the United States and former Soviet Union implies that, for large regions of the globe, the terrestrial evaporation component of the hydrological cycle has been decreasing. One explanation is that increased global cloudiness, especially low cloud cover, would be an expected consequence of higher global temperatures. Some increases in annual mean cloudiness have been observed over Europe, Australia, the Indian sub–continent and North America. When cloudiness over the oceans is also considered, it is not possible to be confident that average global cloudiness has really increased. The main purpose of this article is to explore the possible trend in daily ET0 in some selected sites located in warm regions of Iran. The assessment of trend in daily ET0 would be an important tool for the decision makers in water resources engineering and agriculture sectors.Data and MethodsThe authors have used a 50-year (1957-2006) dataset of the observed meteorological variables (mean of daily maximum temperature, mean of daily minimum temperature, relative humidity, wind speed, water vapor pressure, dew point temperature, and air pressure) which recorded in 13 synoptic sites (IRIMO, 2007) during the period of study. Prior to the analysis, all datasets were tested for quality check and gaps. The annual and seasonal trends of ET0 were derived by Mann-Kendall Test and Sen's estimator as non-parametric methods. To compare the daily means of ET0 (50-year and 16-year), Mann-Whitney test was applied. For each selected site, the 50-year ET0 trends were compared against the ET0 trends of 16-year (1991-2006) period. The trend analysis from Sen's method was capable to perform better results, because of eliminating the effect of repeating data in ET0 time series.Results and DiscussionThe either tests (Mann-Kendall and Sen) confirmed that the maximum and minimum significant trends (annual and seasonal) have occurred in summer (47% of total stations) and winter (7% of total stations), respectively. Therefore, the first hypothesis of the study (the existence of significant trend in ET0 values was confirmed. The comparison of the trends in different climates showed that the slope of the ET0 trend during the recent 16 years (1991-2006), which is slightly greater than that of 50-year (1957-2006), has the same overall sign (negative) for most of the stations. As a result, the second hypothesis of the study (the existence of similar trends in recent decades) was also confirmed.Main Findings and ConclusionsIn general, 65% of the stations revealed a negative (decline) trend in ET0 time series at the specified significant level (P< 0.05). The highest and lowest slopes of ET0 were observed for summer (-4 mm/year) and winter (2.56 mm/year), respectively. The comparison of the ETo trends for 50-year period with those of 16-year period presented a good consistency. The results obtained for 16-year analysis showed that 67% of the study sites have experienced negative trends. This suggests that a higher number of sites had the chance to experience reduction in annual ET0 during the recent years. Trend analysis of Mann-Kendall and Sen methods were generally in good agreement. It was shown that 47% of the case studies have no significant trend (positive and negative) in reference evapotranspiration values. In seasonal scale, summer seasons experienced higher number of significant ET0 trends, in comparison to other seasons. This result is in good agreement with most findings from other research works reported from outside Iran. The detection of trends in ET0 for other climate types is required for a national comprehensive work. Further study is also required to find out the reason for different trend signs.

Global solar radiation (Rs) has wide applications in several disciplines. The data of measured or predicted Rs are widely applied by solar engineers, architects, agriculturists and hydrologists. Due to the importance of Rs, several empirical models have been developed to predict its values all over the world. In this study, Angstrom model was calibrated based on the ratio of actual and possible sunshine hours n/N by using measured daily data of Rs at Bajghah meteorological station in Fars province during 2003-2004. The model was modified by using air temperature for considering the effect of cloudy conditions as well as n/N ratios. The results showed that using both the air temperatures and the ratios of n/N led to a higher accuracy. In regard to estimation of the Rs values, the results showed that mean air temperatures have a higher accuracy compared with differences between maximum and minimum air temperatures. Also, a new local model with higher accuracy was developed based on a number of daily meteorological parameters such as deficit vapor pressure, relative humidity, precipitation, mean air temperature, maximum and minimum air temperatures difference and n/N. This new local model that used different meteorological parameters had the highest accuracy in comparison with the other models. Also, a number of models developed by other investigators for estimation of Rs were calibrated for the study area. Finally, different selected models were validated by using the measured data of Rs in 2005. The results showed that the developed local multi-variable model provided higher accuracy results in comparison with the other radiation models.

Zabol region is one of the places that is exposed to severe wind erosion, which is located in southeast Iran Due to the fact that, some stations like Zabol suffers from missing data (having five observations instead of eight), thus, the effects of missing data on prediction of wind speed and direction also needed to be considered. This study aim to determine the minimum years of data required in predicting hourly wind speed and direction, and predicts hourly wind speed and direction, also to verify suitability of Weibull distribution in predicting hourly wind speed and direction and finally to analyze the erosive winds in Zabol region. In this study, the regression coefficients (r) of probability of wind occurrence in various speeds for 16 cardinal directions in two different periods (1986- 1990 and 1986-1995) were separately calculated and compared. To predict hourly wind speed and direction by Weibull distribution, at first its scale and shape parameters (c and k) were determined using the least square method. Then, wind direction distribution, the ratio of maximum to minimum of wind speed, and the hours with maximum wind speed during any month were determined. Using these parameters along with generation of random numbers, the hourly wind speed and direction were simulated. The results indicated five years instead of ten years of data can be used to predict wind speed and direction with a confidence level at 99%. Weibull distribution provided best fit during the months that both the probability of calm periods or standard deviation of probability of wind occurrence in different directions were low. The maximum and minimum wind speed occurred at about 6:00 AM and 6:00 PM, respectively. The probability of occurrence of erosive winds (V≥8m/s) were maximum in June, July, August, and September. The analyses of wind data indicated that the most erosive winds were from North-Northwest, Northwest, and North. The wind speed and direction were predicted by Weibull distribution in the region with 99% accuracy. The results obtained from this research can help researchers and soil conservationists to predict and control wind erosion.