  Reanalysis data are widely used for simulating renewable energy and in
particular wind power generation. While MERRA-2 has been a de-facto standard in
many studies, the newer ERA5- reanalysis recently gained importance. Here, we
use these two datasets to simulate wind power generation and evaluate the
respective quality in terms of correlations and errors when validated against
historical wind power generation. However, due to their coarse spatial
resolution, reanalyses fail to adequately represent local climatic conditions.
We therefore additionally apply mean bias correction with two versions of the
Global Wind Atlas (GWA) and assess the respective quality of resulting
simulations. Potential users of the dataset can also benefit from our analysis
of the impact of spatial and temporal aggregation on simulation quality
indicators. While similar studies have been conducted, they mainly cover
limited areas in Europe. In contrast, we look into regions, which globally
differ significantly in terms of the prevailing climate: the US, Brazil,
South-Africa, and New Zealand. Our principal findings are that (i) ERA5
outperforms MERRA-2, (ii) no major improvements can be expected by using
bias-correction with GWA2, while GWA3 even reduces simulation quality, and
(iii) temporal aggregation increases correlations and reduces errors, while
spatial aggregation does so only consistently when comparing very low and very
high aggregation levels.
