Abstract. The LiDAR Statistical Barnes Objective Analysis (LiSBOA), presented in Letizia et al., is a procedure for the optimal design of LiDAR scans and calculation over a Cartesian grid of the statistical moments of the velocity field. The LiSBOA is applied to LiDAR data collected in the wake of wind turbines to reconstruct mean and turbulence intensity of the wind velocity field. The proposed procedure is firstly tested for a numerical dataset obtained by means of the virtual LiDAR technique applied to the data obtained from a large eddy simulation (LES). The optimal sampling parameters for a scanning Doppler pulsed wind LiDAR are retrieved from the LiSBOA, then the estimated statistics are calculated showing a maximum error of about 4 % for both the normalized mean velocity and the turbulence intensity. Subsequently, LiDAR data collected during a field campaign conducted at a wind farm in complex terrain are analyzed through the LiSBOA for two different configurations. In the first case, the wake velocity fields of four utility-scale turbines are reconstructed on a 3D grid, showing the capability of the LiSBOA to capture complex flow features, such as high-speed jet around the nacelle and the wake turbulent shear layers. For the second case, the statistics of the wakes generated by four interacting turbines are calculated over a 2D Cartesian grid and compared to the measurements provided by the nacelle-mounted anemometers. Maximum discrepancies as low as 3 % for the normalized mean velocity and turbulence intensity endorse the application of the LiSBOA for LiDAR-based wind resource assessment and diagnostic surveys for wind farms.

中文翻译：

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LiSBOA：LiDAR统计Barnes客观分析，用于LiDAR扫描和风统计的最佳设计。第二部分：风力机尾流在合成和真实LiDAR数据中的应用

摘要。Letizia等人介绍的LiDAR统计巴恩斯客观分析（LiSBOA）是优化LiDAR扫描和在笛卡尔网格上计算速度场统计矩的程序。LiSBOA应用于在风力涡轮机尾流中收集的LiDAR数据，以重建风速场的均值和湍流强度。首先对通过虚拟LiDAR技术获得的数值数据集测试提出的程序，该技术应用于从大型涡流模拟（LES）获得的数据。从LiSBOA中检索出扫描多普勒脉冲风LiDAR的最佳采样参数，然后计算估计的统计数据，显示归一化平均速度和湍流强度的最大误差约为4％。后来，通过LiSBOA对两种不同配置的LiSBOA分析了在复杂地形的风电场进行的野外活动期间收集的LiDAR数据。在第一种情况下，在3D网格上重建了四个公用事业级涡轮机的尾流速度场，显示了LiSBOA捕获复杂流动特征的能力，例如机舱周围的高速射流和尾流湍流剪切层。对于第二种情况，由四个相互作用的涡轮机产生的尾流的统计数据在二维笛卡尔网格上进行计算，并与机舱安装的风速计提供的测量结果进行比较。归一化平均速度和湍流强度的最大差异低至3％，支持LiSBOA在基于LiDAR的风电场评估和风电场诊断调查中的应用。