Temporal downscaling for solar radiation

Abstract

Global and regional climate model projections are useful for gauging future patterns of climate variables, including solar radiation, but data from these models is often too spatio-temporally course for local use. Within the context of solar radiation, the changing climate may have an effect on photo-voltaic (PV) production, especially as the PV industry moves to extend plant lifetimes to 50 years. Predicting PV production while taking into account a changing climate requires data at a resolution that is useful for building PV plants. Temporal and spatial downscaling of solar radiation data is widely studied. We present a novel method to downscale global horizontal irradiance (GHI) data from daily averages to hourly profiles, while maintaining spatial correlation of parameters characterizing the diurnal profile of GHI. The method focuses on the use of a diurnal template which can be shifted and scaled according to the time or year and location. Variability in the profile is later added to account for clouds if the daily average value indicates a cloudy day. This analysis is applied to data from the National Solar Radiation Database housed at the National Renewable Energy Lab and a case study of the mentioned methods over California is presented. This method will later be applied to future projections of solar radiation from bias-corrected regional climate models to create a massive dataset that projects solar radiation for future years across the United States.