Wind Speed Downscaling of the WRF Model at Subkilometer Scale in Complex Terrain for Wind Power Applications

Abstract

Mesoscale numerical weather prediction models are frequently utilized for wind speed analysis and forecasting in the planning and support of wind power generation. However, high computational costs only allow for routine use up to a kilometer scale, which is sometimes too coarse to support onshore wind power generation in areas with complex orography. To address this, an algorithm was developed in southern Italy to downscale the wind fields output using the weather research and forecasting (WRF) model for the first 250 m above ground level. The algorithm is based on artificial neural networks (ANNs) and uses the WRF model outputs on a 1.2 km regular grid, and the land surface height and orientation on a 240 m regular grid to downscale wind fields to a 240 m regular grid. To train the ANNs, a WRF simulation dataset in large eddy simulation (LES) mode was developed. Particular attention was paid to defining the ANN architectures and analyzing inputs to mitigate overfitting risk while maintaining manageable computation costs. The evaluation of outcomes conducted using independent test datasets from WRF-LES simulations reveals that the wind speed root-mean-square difference (RMSD) is 0.5 m/s over land and 0.2 m/s over the sea surface, respectively, at a spatial resolution of approximately 800 m. These figures are lower than the RMSD values of 1.6 m/s over land and 1.0 m/s over the sea surface, accompanied by a spatial resolution of 1.8 km, which were obtained through comparison with the spline interpolation method.

Keywords

• Artificial neural network (ANN)
• downscaling
• remote sensing
• resolution enhancement
• wind speed
• weather research and forecasting (WRF)