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Reanalysis data are widely used for simulating renewable energy and inparticular wind power generation. While MERRA-2 has been a de-facto standard inmany studies, the newer ERA5- reanalysis recently gained importance. Here, weuse these two datasets to simulate wind power generation and evaluate therespective quality in terms of correlations and errors when validated againsthistorical wind power generation. However, due to their coarse spatialresolution, reanalyses fail to adequately represent local climatic conditions.We therefore additionally apply mean bias correction with two versions of theGlobal Wind Atlas (GWA) and assess the respective quality of resultingsimulations. Potential users of the dataset can also benefit from our analysisof the impact of spatial and temporal aggregation on simulation qualityindicators. While similar studies have been conducted, they mainly coverlimited areas in Europe. In contrast, we look into regions, which globallydiffer significantly in terms of the prevailing climate: the US, Brazil,South-Africa, and New Zealand. Our principal findings are that (i) ERA5outperforms MERRA-2, (ii) no major improvements can be expected by usingbias-correction with GWA2, while GWA3 even reduces simulation quality, and(iii) temporal aggregation increases correlations and reduces errors, whilespatial aggregation does so only consistently when comparing very low and veryhigh aggregation levels.

Towards a global dynamic wind atlas: A multi-country validation of wind  power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the  Global Wind Atlas