نشریه پژوهش های اقلیم شناسی
پیاپی 35-36 (پاییز و زمستان 1397)

The results of model evaluation during training and testing demonstrated significant accuracy differences between various models. Results showed that in the ANFIS, minimum of R2 in SPI-3 was 0.59, in hyper-humid climates (Ramsar and Bandar-e-Anzali) and maximum of R2 in SPI-3 was 0.78, in hyper-arid climate (Zahedan) and humid climate (Yasuj). Also minimum of R2 in SPI-6 was 0.75, in semi-arid climate (Hamedan) and maximum of R2 in SPI-6 was 0.87, in hyper-arid and arid climates (Zahedan and Mashhad). In SPI-12, minimum of R2 was 0.88, in hype-arid and semi-arid climates (Zahedan and Hamedan) and minimum of R2 was 0.97 in arid climates (Mashhad). Also, results of ANFIS showed that membership functions type and climates type don't have effect on ANFIS performance and when model is using precipitation in two delay step and SPI in 3 delay step, it has acceptable and high accuracy results. In the GMDH, R2 is between 0.91-0.99 in all three SPI scales (SPI-3, SPI-6 and SPI-12) and in all climates which it indicates the acceptable accuracy of this model. In order to evaluate the results of GMDH models, the best models related to M4 and M9 that input variables are {SPI(t-1), SPI(t-2), SPI(t-3), SPI(t-4), SPI(t-5)} and {SPI(t-1), SPI(t-2), SPI(t-3), SPI(t-4), SPI(t-5), P(t-1), P(t-2)}. RMSE values indicated that it increases when climate type is changing. Hyper-humid and humid climates have RMSE more than other climates. It related to precipitation effect in models performance. M5 and M6 models that use just precipitation in the previous months have low performance in drought forecasting. Also results indicate that SPI is appropriate for 12-month scale. In fact, the performance of the models has direct relationship with the increasing of the SPI time scale. Finally, The results of the comparison of observed and calculated values of three SPI scales (SPI-3, SPI-6 and SPI-12) using the GMDH model in all climates showed that drought forecasting is reliable when this method used and it'll use possibility for future drought forecasting. In general, the results are accurate when using ANFIS and GMDH but the performance of the GMDH model is better than other model. Also, execution speed and GMDH calculations are far more than the ANFIS. Finally, in this study, GMDH propose as the best model for drought forecasting

Based on the results obtained in all of the studied centers in the semi-warm year, contrasting pressure systems are dominant in two levels. That is, in the sea level, thermal low pressure is dominant and at 500 hpa level, subtropical high pressure is dominant. The mean correlation coefficient in eight studied centers between the monthly surface cyclonicity indices (SCI) and the monthly upper cyclonicity indices (UCI) is -42.5, which this contrast is more evident in southwest of the country with the mean correlation coefficient of -0.55.
With regard to created cyclonicity indices, the initiation of thermal low pressure on activity on the surface of the earth in the south and southeast parts of the country is earlier than the southwest regions of Iran. The initiation of this thermal system is March (with a mean of + 0.7) in the south and southeast regions and May (with a mean of +0.7) in the southwest. The peak of this low-pressure activity was in July, which its value was + 1.5 in the south and southeast and + 1/2 in the southwest regions. The thermal low-pressure system in the south of Iran has been active until September, and this thermal system weakened and disappeared since the beginning of October. This thermal system is stronger in southeast of Iran than other regions in the warm half of year with a mean of +1.
In the warm season in the middle atmosphere (500 hpa level), we see subtropical high pressure system in south of Iran. The initiation of the system's activity has been around May and peak of its activity was in July. The system is gradually weakening in October and disappearing in November. The created cyclonicity indices show that subtropical high pressure system is stronger in south and southwest regions with mean of -1.0 compared to that in eastern regions in the warm half of year. In the cold half of year when the inter-tropical convergence zone displaces to lower latitudes, western flows are allowed to pass through south of Iran by disappearing of thermal low-temperature and subtropical high pressure at the above atmosphere. According to cyclonicity indices created on the surface of the earth and the above atmosphere, it was observed that anticyclone type of atmospheric flow has weak dominance to cyclone type of atmospheric flow in whole region in the cold half of year. On the surface of earth, this was more evident for southwest part of country (with mean of -0.6).
According to the results obtained, it was shown that in general, temperature shows better correlation cyclonicity indices, compared to precipitation (according to calculated coefficients). This result suggests that precipitation has high temporal and spatial variations compared to the temperature. Generally, temperature in the cold half season of the year is correlated better UCI indices and the effect of these two indices on the temperature in the warm half of year is almost the same. Given the fact that most of precipitations occur in the cold half of year in this region, in the region occurs in the cold season, results show that among these two indices, UCI indices  control the major part of precipitation in south of Iran. This index has a significant effect on precipitation with a positive effect and temperature with a negative effect. It means that cyclonic dominance at the 500 hpa level would lead to precipitation and reduced temperature.  Such a situation occurs during the winter with the presence of dynamic high pressure systems in the region.

Drought is a natural and irreversible phenomenon that results from a reduction in rainfall over a given period of time. This phenomenon begins slowly and its impact gradually and over a relatively long period of time appears in different sectors, such as water resources, agriculture, the environment, and so on. Therefore, it is difficult to determine precisely the time of the onset and end of this phenomenon, due to the nature of the drought, it is difficult to detect the beginning and the end of the drought. Drought prediction in water resource systems plays an important role in reducing drought damage. Traditionally, in the last few decades, drought has been widely used to predict fit and mathematical models. For prediction of drought, a variety of approaches have been introduced in hydrology, in which intelligent models are the most important ones. In this study, monthly rainfall data of Nahrabad, Alshtar, Dorood and Boroujerd stations in Lorestan province were used to evaluate the accuracy of models in drought prediction. For modeling, wavelet network and artificial neural network models were used and the results were compared to each other for the accuracy of the studied models. In this research, four rain-impact stations of Nurabad, Alshatr, Dorood and Borujerd in Lorestan province were selected as the study area and drought analysis was carried out using SPI standard rainfall index at a 12-month time scale at these stations. . For this purpose, rainfall parameter was selected on monthly basis during the statistical period (1394-1374) as input and standard rainfall index as the output parameter of the models.
Wavenet called wavelet-based neural network which combined with wavelet theory and neural networks have been created.It also have supportive of the benefits and features of neural networks and charm and flexibility and strong mathematical foundations and analysis of multi-scale wavelet . a combination of wavelet theory with neural network concepts to the creation of wavelet neural network and feedforward neural shock  can be a good alternative for estimating approximate nonlinear functions .Feedforward neural network with sigmoid activation function is in the hidden layer While at the nerve shocked wavelet ,wavelet functions as activation function of hidden layer feedforward networks are considered, In both these networks and scale wavelet transformation parameters are optimized with their weight.
Artificial neural networks inspired by the brain's information processing systems, design and emerged intoTo help the learning process and with the use of processors called neurons trying to understand the inherent relationships between data mapping between input space and optimal space. Hidden layer or layers, the information received from the input layer and output layer are the processing and disposal.Based on the artificial neural network structure, its major features high processing speed, the ability to learn the pattern,The ability to extend the model after learning, flexibility against unwanted errorsNo disruption to   error on the part of the connection due to weight distribution network. The first practical application of synthetic networks with the introduction of Multilayer Perceptron network wasConsultants. for training this network back propagation algorithm is used.The basis of this algorithm is based on error correction learning ruleThat  consists of two main routes.By adjusting the parameters in the MLP model error signal and input signal occurs.Determine the number of layers and neurons is the most important issues in simulation with artificial neural network.
The criteria of correlation coefficient, root mean square error and of mean absolute error were used to evaluate and performance compare of models. The results showed that both models have a good ability to estimate the standard rainfall index, but in terms of accuracy, the wavelet neural network model has shown better performance than artificial neural network.
The results also showed that the wavelet neural network model has less error than the artificial neural network, and this model (wavelet neural network) has shown an acceptable accuracy in estimating most of the values. On the other hand, the results of the drought index test showed that in both models, the Drood station is more consistent with observational values. Overall, the results showed that the use of wavelet neural network model can be effective in drought estimation, which in turn is useful for facilitating the development and implementation of management strategies to prevent drought.

One of the main sources of air pollution in metropolitan areas is the traffic and the associated problems in the transportation system. Traffic network development and increasing the number of different types of vehicles with different speeds, leading possibility of incomplete fuel burning, can increase air pollutant concentration which is often inevitable; moreover, has endangered the health of civilian in cities. Accordingly, air pollution modeling to predict spatial concentration of air pollutants across the cities is required. In this research, a case study of carbon monoxide emission, near Niayesh expressway in Tehran megacity, for duration of 23rd October 2015 to 21st November 2015, has been conducted. This research is carried out by using CALINE4 software, presenting the results upon GIS-based methods. The CO concentration across the selected domain is predicted at different points by the distance of 500 m at the sides of expressway axis considering different atmospheric stability classes. The results are affected by path geometric of expressway, traffic data and weather conditions during the study period. The proposed model calibration has been obtained by predicting model results with some measured point values. The results of the proposed model have presented the CO pollutant concentration increase in the areas affected along the wind direction, especially in the north side of the expressway while there is stabilized atmosphere and wind speed is decreasing. Furthermore, the CO concentration is remained at minimum level along opposite wind direction. The predicted pollution concentrations with increasing distance from the expressway show  exponentially reduction trend and have been decreased at distances of 25, 100, 150, and 300 meters from expressway axis as 18%, 48%, 57%, 73%, and 96%, respectively.  In this study, the CO concentration in GIS system was predicted, for duration of 23rd October 2015 to 21st November 2015, which is the most stable period of atmospheric condition. The obtained results represent that the CO concentration along two zones of Valiasr street to Seoul street and Farahzadi Boulevard to Ashrafi Esfahani highway are more than the rest. Accordingly, the conditions of two named zones are introduced as high hazardous zones which are in need of pollutant reduction by the changes in boundary conditions.

The tidal phenomenon leaves the most important effect on the coastline range. By the movement of coastline tension due to tide, waves and coastal currents find the opportunity to exert changes on coastline by the formation of geomorphic shapes in the coast.
As mentioned earlier, three factors including pressure, wind force, and temperature have been considered in the field of presenting a model for forecasting sea level and sea level model is presented according to these variables. The results show that there is a high correlation between monthly parameters of sea level, temperature and atmosphere pressure. On average, monthly sea level in the stations of Jask and Chabahar in the research periods indicated very high seasonal changes with climate parameters such as temperature, pressure and wind rate. Increasing trend of sea level and temperature and decreasing trend of pressure in Oman Sea show gradual changes of climate of this research. Multi-correlation coefficient between parameters of MSL, P, W and T shows that there is a significant correlation between these parameters, and as we expected, there is a reverse correlation between MSL and P that is related to reverse effect of barometric pressure on the sea level.
During 20 years period in the stations of Jask and Chabahar increasing trend of sea level in Jask and Chabahar are 80 mm and 30mm respectively. Yet, the highest range of sea level changes in Jask (about 460mm) in comparison to Chabahar (about 400mm) can be related to the changes of wind blow. The highest rate of Wind direction for Jask port is from south and west. . In multi variable correlation between the variables of temperature, pressure and wind rate with sea level in both stations, correlation between temperature and wind with sea level is direct and reverse between pressure and sea level. The rate of increase for tidal gauge datum is about 0.33 mm in every month. So, we can anticipate about 396 mm uprising of water in northern coast of Oman for the next century. According to tidal gauge information in Chabahar, the amount of sea level rise is about 0.12 mm every month. So, it is necessary to have a concise and exact investigation about the procedures related to environments of coastlines and research about tidal condition of region and parameters affecting climate.

Results show that the Persian Gulf's coastline structures and features (e.g. mountains) play key roles in determining wind characteristics in northern side of the Persian Gulf. By advancing from west to east part of the Persian Gulf, general wind pattern is controlled by various wind components (systematic or land-sea breeze components). While, systematic wind are govern in western head's wind structure, land-sea breeze play a key role for determining wind features in the eastern sections, where systematic component is impact inconsiderable. Decreasing of mountains distance from beach in eastern rather (Daier station to Bandar Abbas) than western coasts (Abadan station to Daier) is main reasons to decrease the systematic wind energy by its friction with mountains. The land-sea breeze effects on local wind structures increase due to positive impact of mountains that intensified it in eastern parts. In the other hand, land and sea breezes influence intensify when systematic wind is calm. Land and sea breezes speed decrease slightly about 2ms-1 to 3ms-1 from west to the east. Through the seasonal analysis, it is deduced that summer's sea breeze is more intense and the possibility of its occurrence is much more frequently than other seasons. Also it is more sharply rather than land breeze during all seasons. Since nighttime is longer than daytime, land breeze occurred more than sea breeze with less speed values during winter.

In this paper, we examine the processes of the meteorological organization and how they are categorized as organizational items. These items in each organization map the processes and the way in which the organization performs its tasks and tasks. At the macro level, processes are dominated by the processes of the central processes the main categories of support processes are categorized and at lower levels, they will briefly describe how they are conducted and the flow of information in the organization. Public policy is a set of decisions that are tied up by experts to achieve specific goals or to obtain the appropriate means to achieve those goals. These decisions are made according to how the processes are performed and how they are done. To be the core function of the strategy is to create a competitive advantage for the organization, and it can be used to create value (by defining the key factors of the success factor) for users. Strategies can be defined from the standpoint of its characteristics or how it is shaped or its role in the success of the organization. The process, strategy is intended to create distinctive competencies in the organization to create value. The process of conducting and reviewing the paper was initially initiated from the design of the organization's process map in order to transfer the organization from task-oriented to processor, then each of the process metrics are linked to the key performance indicators in the strategy map. Then, the relationship of each of the processes in the process map with the strategy map in the operational sample of the knowledge management project for managing the performance of processes based on a balanced scorecard has been identified in the meteorological organization The balanced scorecard, compiled by Kaplan and Norton from four main perspectives, was studied by many organizations from a different perspective, and ultimately this model, developed by the original model of the balanced scorecard, is a model for assessing the Meteorological Organization This paper describes how to produce and generate data through this model and ultimately have been successful in collecting intelligent data in the Meteorological Organization of the country so that we have been able to sample the data from an hour to Minutes increase.

Temperature is the most important climatic element and also one of the main factors in climatic zonation and classification and accordingly fluctuations and significant variations in the temperature of the globe or global warming have been considered as the most important phenomena of climate change in the present century. Therefore, prediction of climatic elements will necessary give planners more time to plan and provide necessary measures. To predict data series, stochastic statistical models have been used extensively in hydrological, weather and climate themes, including models such as Time Series or Box-Jenkins models. In this research, SARIMA seasonal stochastic model is used for modeling and forecasting the average monthly temperature of 5 synoptic stations from 4 different climates of Iran, to examine the model accuracy for estimating average monthly temperature of different climates. Five synoptic stations were selected in the cities Abadan, Isfahan, Anzali, Tabriz and Mashhad which were placed in 4 climatic classes; warm and super dry, cold and super dry, temperate and humid, cold and semi-arid, cold and semi-arid by De Martonne method’s evaluating and had long-term data on monthly temperature over the years 1951-2014.
The time series model, which is also called as the Box-Jenkins model, is a model commonly used to measure time-based data. This model which was introduced for the first time by Box and Jenkins in 1976, is intended for numerical simulation as well as prediction of the variables sorted by time that are recorded at the same time intervals. Among the time series models, the SARIMA model has been used in this research, which can be used to simulate the stochastic behavior of seasonal time series.
Autocorrelation function (ACF): This function is a very important function in the analysis of time series modeling, especially periodic time series. Among ACF’s usages, displaying and analyzing seasonal trends in data, and assessing return period of the series can be mentioned.
Model evaluation criteria: In order to ensure the accuracy of modeling and prediction, the outputs of the model should be compared with the same times’ actual values. For this, Schwarz Bayesian criterion, Root Mean Squared Error and coefficient of determination () have been used in this study. The temperature series were measured by ACF and a seasonal trend was confirmed with a 12 return period in each series and after 4 degrees of seasonal differencing, it was found that the best removing of the seasonal trend, is in the first degree in all series. Data were divided into two sections: 61 years old for calibration and 3 years old for validation that the first 61 years, by entering seasonal and non-seasonal autoregressive and moving average model from 0 to 3, in total 256 models for temperature series of each synoptic station were extracted, their outputs were measured by the evaluation criteria and the best models of each series were used to predict a long step in the next 3 years or 36 months. Best model for Abadan station was SARIMA(1,0,1)(1,1,1)12, Isfahan station was SARIMA(2,0,2)(3,1,1)12, Anzali station SARIMA(1,0,0)(1,1,1)12, Tabriz station SARIMA(1,0,2)(1,1,1)12 and Mashhad station was SARIMA(0,0,1)(0,1,1)12 and their errors was evaluated during 6, 12, 18, 24, 30 and 36 months forecasting horizons which expressed the remarkable accuracy of these models to forecast the monthly temperature time series. During the evaluations, SARIMA model in order to accuracy, in Abadan synoptic station with modeling Root Mean Squared Error=1.23 and predicting Root Mean Squared Error =0.97 degrees of centigrade had the best performance among the 5 synoptic stations’ temperature series and after that the stations Anzali, Isfahan, Tabriz and Mashhad had the best results with the Root Mean Squared Error in order 1.36, 1.44, 1.81 and 1.90 degrees of centigrade for modeling, and 1.58, 1.06, 1.86, 1.46 degrees of centigrade for predicting. Estimating average monthly temperature during the similar statistical period, At these stations, the model shows that the model has the highest accuracy in estimating and predicting the temperature of hot and super dry climate of Khuzestan province, then in the temperate and humid climate of the north of the country, then in the cold and dry climate of Isfahan province, after that in the cold and semi-arid climate of the northwest and at last in the cold and semi-arid climate of Iran’s northwest.