Improving wind speed forecasts from the Weather Research and Forecasting model at a wind farm in the semiarid Coquimbo region in central Chile

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.