B1630
Title: Regridding uncertainty for statistical downscaling of solar radiation
Authors: Soutir Bandyopadhyay - Colorado School of Mines (United States) [presenting]
Douglas Nychka - Colorado School of Mines (United States)
Maggie Bailey - Colorado School of Mines (United States)
Abstract: As the photovoltaic (PV) industry moves to extend plant lifetimes to 50 years, the changing climate may have an effect on PV production and assumptions that current solar radiation patterns are representative of the future may not be appropriate. A key step in aiding the prediction of PV production is projecting solar radiation for future years based on a changing climate. This involves downscaling future climate projections for solar radiation to spatial and temporal resolutions that are useful for building PV plants. Initial steps in downscaling involve being able to closely predict observed data from regional climate models (RCMs). This prediction requires (1) regridding RCM output from their native grid on differing spatial resolutions to a common grid in order to be comparable to observed data and (2) bias correcting solar radiation data, via quantile mapping, for example, from climate model output. The uncertainty associated with (1) is not always considered for downstream operations in (2). This uncertainty, which is not often shown to the user of a regridded data product, is examined. 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 in California is presented.
