Validation of the wind climatology from the ALARO-0 model at different resolutions over Ethiopia

The establishment of wind-energy resources in Ethiopia has seen tremendous growth in the past few years. Ethiopia, however, features a complex topography rendering a detailed wind-resource assessment essential. This requires detailed modeling and a reliable observational dataset for validation. In this study, we compare the ability of a regional climate model ALARO-0 at various resolutions (4, 12, and 40-km) in reproducing the near-surface summer wind climatology by comparison with long-term (1990-2010) ground observations at 35 sites. Despite a consistent model underestimation, both the model and observations show higher wind speeds along and east of the Great Rift Valley than in the central regions of the Ethiopian highlands with 4.12 m/s and 2.12 m/s on average for the observations. Evaluation scores indicate better performance at higher resolutions especially for those stations with high wind-energy potential. The overall model underestimation decreases from -1.34 m/s to -0.45 m/s and further to -0.24 m/s for the 40-km, 12-km, and 4-km resolutions, respectively. The observed wind distribution reveals a variety of prevailing wind directions, with the most common being approximately 30% southwesterly, around 20% southeasterly, and roughly 10% for both northerly and easterly directions while the modeled wind direction was predominantly southwest. Clustering based on principal-component analysis identifies two distinct regions of wind speed variability: one in the Ethiopian highlands and another along and east of the Great Rift Valley and both also feature strongly-different validation results. The high-resolution ALARO-0 model run at 4-km resolution clearly outperforms the 12-km and 40-km runs, thereby providing an added value for the identification of areas with significant wind-energy potential. These findings underscore the advantages of high-resolution climate simulations and the benefits of mapping wind-energy resources in regions with complex orography.