IntroductionPrecipitation is one of the most important meteorological variables in comparison with other climatic parameters. It varies extremely over time and space. The occurrence of this climate phenomenon requires specific circumstances in environment. Its accurate measurement is important to a wide range of decision makers including hydrologists, agriculturalists, industrialists and etc. The density of rain gauges and meteorological radars is often too poor to satisfactorily capture rainfall characteristics at fine spatial resolutions. To overcome this problem gridded precipitation data base was developed based on interpolation of the daily precipitation data. ERA-Interim is the latest gridded global atmospheric reanalysis produced by the European Center for Medium-Range Weather Forecasts (ECMWF). This data covers the period from 1 January 1979 to the present. The precipitation analysis and estimation is based on obtained precipitation data from rain gauge stations, meteorological radars and satellite sensors. The forecasted precipitation is available at 3-hourly based on applying different models.

Esthetic is a natural necessity among all human beings, and the reconstruction of the natural features of prosthesis is considered an important aspect in dentistry. Reconstruction of the natural features in removable prosthesis by characterization of denture base is one of the most important factors. In this regard esthetic, in removable prostheses, is achieved through different techniques such as: tooth selection and arrangement, designing the denture base and acrylic base color. The final goal for all these effects is to achieve a natural appearance for denture. However, other factors such as treatment limitations, different personal needs and general health principles, should be taken into consideration. This article not only reviews the natural appearance of oral mucosa that is replaced by the denture base, but also discusses about the materials used in tinting acrylic denture base and their application techniques.

Introduction: Air temperature is one of the most important climate measurements in the human environment, which directly affects the physical and biological processes of the ecosystem. Understanding this climate measure can be the basis for understanding many climatic processes, especially evapotranspiration (due to the climate of Iran). Reanalysis have been used in recent years in many studies, including the studies about climate trends, climate modeling, and the assessment of renewable resources, and their high accuracy. The purpose of this present study is to evaluate the accuracy of the open-baseline base temperature data of the European Centre for Medium-Range Weather Forecasts (ECMWF) of the ERA-Interim version with a 0. 125 × 0. 125 arc-spatial resolution in a survey with observational data from the weather stations and the National Bassoon database. In this regard, we have evaluated the temporal changes of the temperature of Iran.

Stream flow forecasting on a monthly time scale is essential for optimal water resources management and planning. In this paper using the predictions obtained from the ECMWF climate model, monthly stream flow forecast was made in Shahroud river Subbasin, part of Sefidrood basin northwest of Iran. To this end, using monthly precipitation forecasts from ECMWF climate model in tandem with SVR data-driven modelling, as a rainfall-runoff model, the stream flow was predicted based on the predicted precipitations. First, the results of precipitation forecast for the desired historical period and up to a 3-month forecast horizon for the study area were obtained from the Climate Data Store. Then, by using the SVR driven model, a linked Climate-Data-driven model was developed to predict the flow up to a 3-month forecast horizon. The results showed that flow forecasting based on climate forecasting models for the forecast horizon of the next month is more accurate than that of two and three months. The forecast horizon of the next month had the highest Nash-Sutcliffe coefficients of 0. 77 and 0. 48 in calibration and validation, respectively. It alo had the highest correlation coefficients in calibration (0. 87) and validation (0. 69), the lowest root mean square error in calibration (6. 8 million cubic meters) and validation (6. 3 million cubic meters) and moreover had the best relative bias value for calibration (0. 96) and validation (1. 1). Furthermore, based on the POD and FAR probabilistic indices, the results showed that the developed predictive model has a high ability to detect different states of stream flow events, especially for extreme flows event.

Introduction Among climate variables, wind has a skewing role due to its high spatio-temporal variability and its role in other parameters such as air temperature. It is important to study wind changes in different ways so as wind speed decreases its energy and consequently increases urban pollution. Reducing the wind speed also reduces heat transfer, viscosity between the earth's surface and the atmosphere, and ultimately increases the temperature. Decreasing wind speeds at night, especially in winter nights, cause the Earth to radiate inversion. Increasing wind speeds will also result in high winds, tornadoes and damage to affected areas. Also, wind speed is one of the important components in combinatorial equations to estimate evapotranspiration and any trends in wind speed will also affect the water requirement of plants. As discussed, wind is a very important climatic parameter, but its study in particular is changing its course with limitations such as inaccessibility of homogeneous time series and long term data with inadequate stations. Station data, on the other hand, can also be affected by discontinuities associated with changes in measuring equipment, station location, or different measurement methods. To overcome these limitations, the re-analyzed global meteorological dataset, available for a long period, is useful for meteorological studies. In recent years, these databases have also been used for various wind energy applications. The purpose of this study was to evaluate wind speed changes and trends in central Iran and since most of the area is arid and insufficient stations, ECMWF database data were used. The results of this study can be useful for studies on climate change, agriculture and renewable energies. Research Methodology The study area is Central Iran. Central Iran is said to be bounded on the north by the Alborz Mountains, on the west and south by the Zagros Mountains, and on the east by the dispersed Khorasan Mountains. Much of central Iran has warm and dry climates that are milder and humid in the highlands. In this study, four provinces of central Iran were selected and evaluated for wind changes. Two groups of data were used in this study. 1-Wind speed data from Synoptic stations and 2-Wind speed data from ECMWF ERA-Interim version with spatial resolution of 0. 75 × 0. 75 °, . Kolmogorov-Smirnov (K –,S) test confirmed the normality of the data and the missing data were reconstructed using linear interpolation method. Synoptic station data were also used to validate the ERA-Interim ECMWF database data. Coefficient of determination (R2), Mean bias error (MBE) and root-mean-square error (RMSE) of open data analysis of ECMWF database ERA-Interim were used for wind speed trend in central Iran with nonparametric Mann-Kendall test. Was evaluated. Results and discussion Minimum wind clock speeds are only less than 2 meters in November (1. 98) and December (1. 96). In other months this fluctuates between 2. 01 and 2. 59 meters. The maximum wind speeds were also between 3. 43 and 5. 90 meters, respectively, from November to July, respectively. During the warmer months of June (Jun, July and August) the maximum wind speed is more than 5 meters. The average wind speed is also presented in this table, based on the results of the long-term minimum wind speed in central Iran with a mean of 2. 83 meters in January and its maximum with a value of 3. 95 meters in July. On this basis, it can be said that during the cold period of the year in central Iran, the wind speed is slower, as the hot months of the year ahead, the wind speed will increase. The average annual wind speed was 3. 19 meters. Among the seasons and months studied, winter showed the highest intensity of the trend of increasing wind speed (Z-score of 4. 916 Mann-Kendall test), which is significant at 99% level. The focus of the maximum wind speed increase trend is in Semnan province, and as we move from January to March, the intensity of the trend increases. The highest percentage of incremental trend zones is in February, with 92. 20% of central Iran showing an increasing trend of wind speed this month. June with 80. 52% of the upward trend zones after March accounted for most of the areas with upward wind speeds in the spring. In contrast to the upward trend zones that peaked in January but the maximum upward trend intensities in April reached the Mann-Kendall Z test score of 4. 031, which was statistically significant at 99%. Conclusion The results showed that the ECMWF database is well suited for wind clock evaluation. The Shahroud, Yazd and Kerman stations showed maximum coefficient of determination (R2) and minimum error. Yazd and Kerman also showed less deviation from synoptic stations. Minimum wind speeds in November and December and maximum wind speeds were calculated in July and June. The mean wind speed was calculated based on the ECMWF results of 19. 1 m / s. The average wind speed in central Iran is directly related to the air temperature and season. Generally, during the cold season of the year the wind speed from south to north and during the warm season from north to south of central Iran is increased due to the location of arid regions such as Dasht-e Kavir in the north and Dasht-e Lut in the south of the study area. The trend of the wind clock in central Iran has shown that the maximum intensity of the trend of increasing wind speed is in the winter of March (Mann-Kendall Z test score of 4. 916) which is significant at 99% level. Also, the maximum decreasing trend with the Z-score of Man-Kendall test is-2. 73 in December. The upward trend of wind speeds in more than 50 percent of central Iran in 10 months of the year, while only in October and February, is the decline observed in more than 50 percent of the study area. Since the most important factor in reducing or increasing wind speed is pressure gradient changes, wind speed variations can be a sign of climate change.

This study statistical approach-analytical, with the aim of analyzing change was spatiotemporal height of the boundary layer (BLH) were the order of the height of the boundary layer of the European Centre for Medium-Range Weather Forecasts (ECMWF) version of the ERA-Interim with spatial resolution 0/125 ° × 0/125 ° arc period 1979 to 2015 were used. The results showed that the pace of change when boarding height of the boundary layer influenced by the pace of change and seasonal temperature pattern on the one hand and the components of latitude and altitude is. When the maximum height of the boundary layer in June and a minimum height of boundary layer occurs in January. The furnishing and rugged Zagros highlands and desert areas Sawwah has a minimum thickness of the boundary layer and smooth inner regions, especially South-East and Central regions of Iran, the thickness of the boundary layer is at the highest level. Changes in the boundary layer height space in the cold months is more consistent warmer months. Adjacent coastal areas and the sea because the sea moderating role in the temperature always have the minimum thickness of the boundary layer. In the cold months the maximum height of the boundary layer in the southern part of East and especially during the hot months of June and July in the western region and northern and southern Kerman province to be drawn.