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Activity Number: 73 - SPEED: Statistical Computing and Statistics in Genomics Part 2
Type: Contributed
Date/Time: Sunday, August 7, 2022 : 5:05 PM to 5:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #323733
Title: Uncertainty in Regridding for Statistical Downscaling of Solar Radiation
Author(s): Maggie Bailey* and Soutir Bandyopadhyay and Douglas Nychka
Companies: Colorado School of Mines and Colorado School of Mines and Colorado School of Mines
Keywords: statistical downscaling; solar radiation; uncertainty propogation; climate model output; regridding

As the photo-voltaic (PV) industry moves to extend plant lifetimes to 50 years, assumptions that current solar radiation patterns and PV production 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 from climate model output. The uncertainty associated with (1) is not always considered for downstream operations in (2). This talk examines this uncertainty which is not often shown to the user of a regridded data product. This analysis is applied to data from the National Solar Radiation Database housed at the National Renewable Energy Lab and a case study over California is shown.

Authors who are presenting talks have a * after their name.

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