Energy usage leads to environmental problems especially climate change and air pollution. In urban areas, extensive consumption of fossil energy sources deteriorates urban air quality and causes health problems such as asthma and respiratory diseases. The usage of renewable energy sources mainly solar energy instead of fossil energy sources provide the healthier and cleaner environment. Some statistical approaches have been used to predict the solar radiation for many years. The formula which is named as Angström-Prescott is widely used for the estimation. This approach uses the value of relative sunshine duration which is computed by using the daily measurement of sunshine duration and the daylength. Sunshine duration has been measured at meteorological stations for a long time. But in some cases such as remote areas or non-exist stations, sunshine duration is forecasted by using statistical methods. In atmospheric environment, sunshine duration is affected from other climate parameters such as cloud cover, wind speed, relative humidity, precipitation, air temperature, pressure. This paper offers to gain the relationships for sunshine duration via precipitation and relative humidity. To construct the statistical models, cloud cover was entered the models firstly because of its association with sunshine duration. Many studies were shown that sunshine duration was highly correlated to the cloud cover. The observed daily mean values of sunshine duration and climate elements were the data of Samsun meteorological station measured by Turkish State Meteorological Service. To acquire the statistical models for sunshine duration, data was arranged as monthly mean values and then linear regression analysis method was operated. Also the graphs of time series of climate variables were visually created for a clear interpretation. According to the findings, sunshine duration can be estimated bu using statistical models over cloud cover, precipitation and relative humidity. The contributions of precipitation and relative humidity change for different time scales.

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The monthly air quality index (AQI) derived from ground observation stations that obtained daily air pollutants information for 1990- through 2010 was analyzed in this study. AQI was evaluated using the common comparative index method presented by the U.S. Environmental Protection Agency (USEPA), and a statistically based approach was used for predicting the AQI value. With the first method, AQI was predicted using the USEPA subindex formula for different pollutants, such as particulate matter and sulfur dioxide, which contribute the most to air pollution. A combination of the principal component analysis (PCA) and multiple linear regression (MLR) methods were used with the measured values of climate variables obtained from the ground stations for the most effective contributors and a prediction was modelled. The results of these two methods were compared and evaluated for consistency. Two methods were presented for determining the AQI value. According to the findings, the common comparative index method was consistent with the statistical prediction models, and the best results were obtained using PCA models with varimax rotation.

In recent decades, studies on atmospheric circulations indicate that those patterns have influences on meteorological variables. This paper investigates the comparative statistical analysis of atmospheric oscillations with climatological elements. Based on analysis of the climate data obtained from observed values of meteorological station in Antalya, it was pointed that atmospheric elements such as meteorological variables were associated with atmospheric oscillations such as North Atlantic Oscillation, Arctic Oscillation, Antarctic Oscillation and Pacific-North American pattern. Spearman’s rho and Kendall’s tau statistics were employed to reveal the relations between atmospheric variables and atmospheric oscillations as statistically significant. Both coefficients were compared in interpreting the direction and strength of the relationships. It was seen that Spearman’s rho coefficients presented more suitable values generally.

This study estimates cloudiness data using meteorological parameters which include climatic variables and air quality index. Daily average observed values of all meteorological parameters used in this study were transformed to monthly mean data for 1990-2015 period. The monthly mean values of cloudiness were estimated by using the other climatic elements and the value air quality index at urban area in Kayseri. Multiple Linear Regression model was built to determine the mathematical relationships for predicting cloudiness. It has been shown that meteorological parameters affect cloudiness the most in May and October, and the least in September and January. Additionally, according to the estimated models, air quality index value has effect on cloudiness data on January, July, October and November as statistically significant.