Accurate predictions of the wind field are key for better wind power forecasts. Wind speed forecasts from numerical weather models present differences with observations, especially in places with complex topography, such as the north of Chile. The present study has two goals: (a) to find the WRF model boundary layer (PBL) scheme that best reproduces the observations at the Totoral Wind Farm, located in the semiarid Coquimbo region in north‐central Chile, and (b) to use an artificial neural network (ANN) to postprocess wind speed forecasts from different model domains to analyze the sensitivity to horizontal resolution. The WRF model was run with three different PBL schemes (MYNN, MYNN3, and QNSE) for 2013. The WRF simulation with the QNSE scheme showed the best agreement with observations at the wind farm, and its outputs were postprocessed using two ANNs with two algorithms: backpropagation (BP) and particle swarm optimization (PSO). These two ANNs were applied to the innermost WRF domains with 3‐km (d03) and 1‐km (d04) horizontal resolutions. The root‐mean‐square errors (RMSEs) between raw WRF forecasts and observations for d03 and d04 were 2.7 and 2.4 ms⁻¹, respectively. When both ANN models (BP and PSO) were applied to Domains d03 and d04, the RMSE decreased to values lower than 1.7 ms⁻¹, and they showed similar performances, supporting the use of an ANN to postprocess a three‐nested WRF domain configuration to provide more accurate forecasts in advance for the region.

中文翻译：

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通过智利中部半干旱的科金博地区的风电场的“天气研究与预报”模型改进风速预报

对风场的准确预测对于更好地预测风能至关重要。数值天气模型的风速预报与观测结果存在差异，尤其是在地形复杂的地方，例如智利北部。本研究有两个目标：（a）找到WRF模型边界层（PBL）方案，该方案最能重现位于智利中北部半干旱Coquimbo地区的Totoral风电场的观测结果，以及（b）使用人工神经网络（ANN）对来自不同模型域的风速预测进行后处理，以分析对水平分辨率的敏感性。在2013年使用三种不同的PBL方案（MYNN，MYNN3和QNSE）运行WRF模型。使用QNSE方案进行WRF模拟显示，与风电场的观测结果具有最佳的一致性，其输出使用具有两种算法的两个人工神经网络进行后处理：反向传播（BP）和粒子群优化（PSO）。这两个人工神经网络应用于水平分辨率为3 km（d03）和1 km（d04）的最里面的WRF域。原始WRF预测与d03和d04观测值之间的均方根误差（RMSE）为2.7和2.4 ms^-1。当将两个ANN模型（BP和PSO）都应用于域d03和d04时，RMSE降低到低于1.7 ms ^-1的值，并且它们表现出相似的性能，支持使用ANN对三嵌套WRF域配置进行后处理以便提前提供该地区的更准确的预测。