  With an increasing share of the electricity grid relying on wind to provide
generating capacity and energy, there is an expanding global need for
historically accurate high-resolution wind data. Conventional downscaling
methods for generating these data have a high computational burden and require
extensive tuning for historical accuracy. In this work, we present a novel deep
learning-based spatiotemporal downscaling method, using generative adversarial
networks (GANs), for generating historically accurate high-resolution wind
resource data from the European Centre for Medium-Range Weather Forecasting
Reanalysis version 5 data (ERA5). We achieve results comparable in historical
accuracy and spatiotemporal variability to conventional downscaling by training
a GAN model with ERA5 low-resolution input and high-resolution targets from the
Wind Integration National Dataset, while reducing computational costs over
dynamical downscaling by two orders of magnitude. Spatiotemporal
cross-validation shows low error and high correlations with observations and
excellent agreement with holdout data across distributions of physical metrics.
We apply this approach to downscale 30-km hourly ERA5 data to 2-km 5-minute
wind data for January 2000 through December 2023 at multiple hub heights over
Eastern Europe. Uncertainty is estimated over the period with observational
data by additionally downscaling the members of the European Centre for
Medium-Range Weather Forecasting Ensemble of Data Assimilations. Comparisons
against observational data from the Meteorological Assimilation Data Ingest
System and multiple wind farms show comparable performance to the CONUS
validation. This 24-year data record is the first member of the super
resolution for renewable energy resource data with wind from reanalysis data
dataset (Sup3rWind).
