Improving wind energy estimates in mountainous terrain using optimal ERA5 model level heights

Mountainous regions worldwide offer substantial yet underutilized wind energy potential. A key challenge limiting the expansion of wind energy in such areas is the difficulty of obtaining accurate wind resource estimates in complex terrain. Traditionally, long-term wind speed series are derived from short-term site observations combined with reanalysis products. Conventional reanalysis products such as the ERA5 single levels at 10 m and 100 m often misrepresent local orography, resulting in biased wind speed predictions and unreliable inputs for Measure-Correlate-Predict (MCP) methods used in wind resource assessment. Our study addresses this challenge by employing high-quality mast observations at high-elevation sites in the tropical Andes and by leveraging ERA5 model level wind fields, which remain largely unexplored in wind energy research and industry. We compared wind speed estimates at different atmospheric heights of ERA5 model level data with hourly wind speed observations at 80 m from four meteorological masts (2829–3796 m a.s.l.) in the tropical Andes of southern Ecuador. We developed site-specific Random Forest (RF) models to calibrate ERA5 wind speeds. Our results indicate that wind speeds extracted from upper ERA5 model levels (approximately 1000–1500 m for most sites) are stronger correlated with mast measurements than those at the hub-heights (near the surface). Relative to single level inputs, RF estimates driven by model level data show mean improvements of 59% in the Perkins Skill Score, 40% in R², and 23% in MAE and RMSE. In addition, the bias in annual energy production is reduced to below 7%, compared to 22% when ERA5 single level data are used. The largest gains are observed at sites located on exposed ridgelines and peaks, typical targets for wind farm development, where upper model levels more effectively represent the local atmospheric flow. Our results demonstrate that selecting optimal ERA5 model level offers a strategy for generating reliable site-specific wind time series in complex terrain providing useful information for wind resource assessment studies accelerating the development of wind energy projects in mountainous regions.