Improved wind power assessments by bias adjusted reanalysed data with applications near Morocco's coast

The wind energy requirements for high temporal and spatial observations, unavailable in many countries, are met by reanalysed wind data, which are increasingly used in wind power assessments. However, these data also include unrealistic features, making their validation compulsory. The validation analysis of ERA5 and MERRA-2 wind data, two of the most successful wind reanalyses, with observations near Morocco's coast, highlighted the existence of bias in both datasets, and other shortcomings. The study was conducted at eight selected sites in the period 2011-2020 using linear and probability density function based statistical tests. The local ERA5-BA and MERRA-2 BA velocity fields were created using Bias Adjustment (BA) Techniques. Only part of the observed and modelled datasets (training period) were used to identify the transfer function. The BA fields were verified against observations during the remaining period (verification period). They outperformed their respective non-adjusted fields and also the WFDE5 (the global bias-corrected ERA5 wind field) in the wind potential assessments, represented by capacity factor and low-wind day. At every location, the relative errors in the latter's estimates using ERA5-BA were lowered to less than 10 %, and the same was true for MERRA-2-BA. Thus, an observational dataset of reduced length can help overcome some reanalysed wind data limitations for wind power estimations. Additionally, this methodology can be applied to the correction of each reanalysis update.