Geographic variability in reanalysis wind speed biases: A high-resolution bias correction approach for UK wind energy

TL;DR

This study develops and applies a high-resolution, cluster-based bias correction method to reanalysis wind speeds in the UK, significantly improving wind power simulation accuracy by accounting for geographic variability and topography.

Contribution

It introduces a novel spatially resolved bias correction framework that effectively reduces wind speed biases in reanalysis data, tailored for high-resolution wind energy applications.

Findings

Reduced monthly wind power errors by over 32% with the correction method.

Demonstrated robustness of the approach across different reanalysis datasets.

Identified significant spatial variability in wind speed biases, especially in mountainous regions.

Abstract

Reanalysis datasets have become indispensable tools for wind resource assessment and wind power simulation, offering long-term and spatially continuous wind fields across large regions. However, they inherently contain systematic wind speed biases arising from various factors, including simplified physical parameterizations, observational uncertainties, and limited spatial resolution. Among these, low spatial resolution poses a particular challenge for capturing local variability accurately. Whereas prevailing industry practice generally relies on either no bias correction or coarse, nationally uniform adjustments, we extend and thoroughly analyse a recently proposed spatially resolved, cluster-based bias correction framework. This approach is designed to better account for local heterogeneity and is applied to 319 wind farms across the United Kingdom to evaluate its effectiveness. Results show that this method reduced monthly wind power simulation errors by more than 32% compared to the uncorrected ERA5 reanalysis dataset. The method is further applied to the MERRA-2 dataset for comparative evaluation, demonstrating its effectiveness and robustness for different reanalysis products. In contrast to prior studies, which rarely quantify the influence of topography on reanalysis biases, this research presents a detailed spatial mapping of bias correction factors across the UK. The analysis reveals that for wind energy applications, ERA5 wind speed errors exhibit strong spatial variability, with the most significant underestimations in the Scottish Highlands and mountainous areas of Wales. These findings highlight the importance of explicitly accounting for geographic variability when correcting reanalysis wind speeds, and provide new insights into region-specific bias patterns relevant for high-resolution wind energy modelling.