The study explores the methodologies used to create wind and solar resource profiles for capacity expansion modeling. It assesses the accuracy of both meteorological datasets and "weather to power" conversion methods in developing these profiles for electric system planning tools. Observations from installed U.S. solar and wind farms are used to evaluate performance, focusing on hub-height wind speed and irradiance data for the former, and generation output for the latter. This inter-comparison shows ERA5's skill over MERRA2, and that use of reanalysis data in python libraries effectively captures wind and solar generation variability. The combined ERA5 and open source windpowerlib and pvlib python model approach is shown to be accurate, efficient, and adaptable for future enhancements.