Abstract
The seasonal variation in wind resources has a great impact on wind energy generation, affecting the maintenance planning, operational strategies, and economic benefits of wind farms. Therefore, effective seasonal prediction of wind resources is crucial for the wind power industry. This study evaluates the seasonal prediction skill for global onshore wind resources using the SIDRI-ESS V1.0 dynamic prediction system. High prediction skill for 10 m wind speed (ws10m) is observed mainly in six regions: southern North America, northern South America, western and eastern Europe, and South and East Asia. These regions already have a substantial wind power industry or possess rich wind resources and will need wind power industry deployment in the future. Prediction skill is the highest at a 1-month lead time for most regions and decays as the lead time increases. The highest skill emerges in East Asia, with a temporal correlation coefficient (TCC) reaching 0.7, and persists with a 1-month to 5-month lead time. However, the highest skill for southern North America is at a 6-month lead time. Additionally, ensemble prediction effectively reduces uncertainty, such that a multi-member ensemble mean always matches or even exceeds the individual ensemble member with the best performance. Ensemble size analysis shows that increasing the number of ensemble members generally enhances the prediction skill, with 24 members being sufficient for most regions and lead times. However, further increasing the ensemble size is essential to improve the prediction skill at a 6-month lead time. Meanwhile, we also indicate that ws10m can be used as a proxy in evaluating seasonal prediction of wind resources over most regions, while direct seasonal prediction of wind power density is more effective for northern South America. The high seasonal prediction skill of SIDRI-ESS V1.0 highlights its potential for providing valuable seasonal climate prediction services to the wind power industry.
Published Version
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