Abstract:

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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