A statistical model of solar radiation absorption in the United States.

The transitivity of solar radiation in the atmosphere varies greatly depending on location, time of day, earth-to-sun distance, angle of incidence, and other variables. Solar radiation has an impact on climate change and can be used as energy. So, its modelling will help plan and design policies for climate change and the sustainable use of energy. This study aimed to investigate solar energy patterns and trends on the Earth's surface via solar radiation absorption by cloud cover. Data on solar radiation absorption from 133 stations between the years 1998 and 2020 across the United States were downloaded from the National Solar Radiation Database (NSRDB) website. A linear regression model was used to model solar absorption by cloud and factor analysis was used to group the regions by reducing the spatial correlation of solar radiation absorption. After that, a multivariate regression model was utilized to investigate average changes. There were seven regions obtained from factor analysis. All regions showed a seasonal pattern, with the peak in December to January and the lowest level in June to July. The north, north-east, or south-east of the country experienced an increase in solar radiation absorption, while the north-west, central, and south of the country experienced a decrease. The overall average absorption increased by 0.015%. The patterns and trends of solar radiation by location and time help climate scientists make better decisions. It is also useful to manage renewable energy sources, which will lead policymakers to make better policies. [ABSTRACT FROM AUTHOR]