  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 coarse to assess local impacts. Within the context of solar
radiation, the changing climate may have an effect on photovoltaic (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. Although
temporal and spatial downscaling of solar radiation data is widely studied, we
present a novel method to downscale solar radiation data from daily averages to
hourly profiles, while maintaining spatial correlation of parameters
characterizing the diurnal profile of solar radiation. The method focuses on
the use of a diurnal template which can be shifted and scaled according to the
time or year and location and the use of thin plate splines for spatial
downscaling. 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 several sub-regions of continental United States is
presented.
