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

TL;DR

This work addresses geographic biases in reanalysis wind speeds used for UK wind energy modelling. It introduces a high-resolution cluster-based bias-correction framework that operates on ERA5 and MERRA-2 data and uses observed wind-power CFs to derive location-specific correction factors, which are spatially mapped and interpreted against topography. The methodology yields substantial reductions in wind-power simulation errors (over 32–34% depending on the dataset) and reveals systematic bias patterns tied to elevation, terrain complexity, and coastal proximity, illustrating the importance of geographic heterogeneity in bias correction. The approach provides a robust, transferable tool for improving wind-resource assessments and has practical implications for siting, planning, and downscaling in wind energy systems.

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.

Figures (14)

• Figure 1: Overall workflow of the proposed bias correction methodology.
• Figure 2: Distribution of UK wind farms contained within the observed data. Left: 303 wind farms used during the training phase (2015–2018, four years). Right: 319 wind farms used during the testing phase (2019, one year).
• Figure 3: Multiscale K-means clustering of UK wind turbines (left to right the number of clusters $K$ = 2, 5, 10).
• Figure 4: Fine-scale k-means clustering at $K = 50$ with zoomed-in panels A $-$ D. Insets show (A) central–western Scotland, (B) the eastern Scottish coast, (C) eastern England, and (D) Northeast England
• Figure 5: Impact of clustering and temporal resolution on three error metrics (RMSE, MAE, and MBE) for ERA5.