In this article, offshore wind energy potential is measured around the Iberian Mediterranean coast and the Balearic Islands using the WRF meteorological model without 3DVAR data assimilation (the N simulation) and with 3DVAR data assimilation (the D simulation). Both simulations have been checked against the observations of six buoys and a spatially distributed analysis of wind based on satellite data (second version of Cross-Calibrated Multi-Platform, CCMPv2), and compared with ERA-Interim (ERAI). Three statistical indicators have been used: Pearson’s correlation, root mean square error and the ratio of standard deviations. The simulation with data assimilation provides the best fit, and it is as good as ERAI, in many cases at a 95% confidence level. Although ERAI is the best model, in the spatially distributed evaluation versus CCMPv2 the D simulation has more consistent indicators than ERAI near the buoys. Additionally, our simulation’s spatial resolution is five times higher than ERAI. Finally, regarding the estimation of wind energy potential, we have represented the annual and seasonal capacity factor maps over the study area, and our results have identified two areas of high potential to the north of Menorca and at Cabo Begur, where the wind energy potential has been estimated for three turbines at different heights according to the simulation with data assimilation.

在本文中，使用WRF气象模型在没有3DVAR数据同化（N模拟）和3DVAR数据同化（D模拟）的情况下，对伊比利亚地中海沿岸和巴利阿里群岛周围的海上风能潜力进行了测量。根据卫星数据（交叉校准的多平台第二版，CCMPv2）对六个浮标的观测结果和风的空间分布分析进行了检查，并与ERA-Interim（ERAI）进行了比较。使用了三个统计指标：皮尔森相关性，均方根误差和标准偏差的比率。具有数据同化功能的模拟提供了最佳拟合，并且在许多情况下具有95％的置信度，它与ERAI一样好。尽管ERAI是最好的模型，与CCMPv2相比，在空间分布评估中，与浮标附近的ERAI相比，D模拟具有更一致的指标。此外，我们仿真的空间分辨率比ERAI高出五倍。最后，关于风能潜力的估算，我们代表了研究区域的年度和季节性容量因子图，我们的结果确定了梅诺卡岛北部和卡波贝格两个具有高潜力的地区，在该地区风能潜力根据具有数据同化的模拟，已针对不同高度的三台涡轮机进行了估算。