Abstract A new model for mapping the near-surface wind speed L-moments on a high spatial resolution scale (250 m × 250 m) is introduced (GloWiSMo). The target variables are the first five L-moments of 6146 globally distributed wind speed time series. ERA5 reanalysis wind speed available on a 0.25° × 0.25° grid was used as predictor representing the large-scale wind field. Eleven predictors derived from a land cover model and a digital elevation model were applied to integrate the influence of small-scale surface properties on the wind field. The model is based on a least-squares boosting approach which is a machine learning algorithm. The parameters of the Kappa and Wakeby distribution were estimated based on the modeled L-moments. By applying the power law, the near-surface wind speed distribution can be extrapolated to any hub height. Here, we selected a typical wind turbine hub height of 120 m to demonstrate the potential of GloWiSMo. It was found that the relevance of a predictor on the spatial variability of the wind resource changes with the size of the investigation area. While the roughness length is a decisive factor for the large-scale spatial variability of the wind resource, the relative elevation is an important factor for the small-scale spatial variability. Rigorous model evaluation was performed using a validation dataset containing 598 globally distributed wind speed time series. The coefficient of determination calculated for the first L-moment was found to be 0.83. Based on the evaluation results, we argue that the developed model enables accurate and spatially explicit wind resource estimates at a very high spatial resolution.

中文翻译：

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在新的高分辨率全球风速模型中整合小尺度表面特性

摘要 介绍了一种在高空间分辨率尺度（250 m × 250 m）上绘制近地表风速 L 矩的新模型（GloWiSMo）。目标变量是 6146 个全球分布的风速时间序列的前五个 L 矩。在 0.25° × 0.25° 网格上可用的 ERA5 再分析风速被用作代表大尺度风场的预测器。应用源自土地覆盖模型和数字高程模型的 11 个预测器来整合小尺度表面特性对风场的影响。该模型基于最小二乘提升方法，这是一种机器学习算法。Kappa 和 Wakeby 分布的参数是基于建模的 L 矩估计的。通过应用幂律，近地表风速分布可以外推到任何轮毂高度。这里，我们选择了 120 m 的典型风力涡轮机轮毂高度来展示 GloWiSMo 的潜力。结果表明，风资源空间变异性预测因子的相关性随着调查区域的大小而变化。粗糙度长度是风资源大尺度空间变异的决定性因素，相对高程是小尺度空间变异的重要因素。使用包含 598 个全球分布的风速时间序列的验证数据集进行了严格的模型评估。发现为第一个 L 力矩计算的决定系数为 0.83。基于评估结果，我们认为开发的模型能够以非常高的空间分辨率实现准确和空间明确的风资源估计。