Resumen
Physical-approach-based wind forecasts have the merit of a heavily reduced uncertainty in predictions, but very often suffer from a prohibitively lengthy numerical computation time, if high spatial resolutions are required. To tackle this hurdle, proper orthogonal decomposition (POD) has manifested extraordinary power in reducing the number of computation grids and hence the computation time. However, POD itself suffers from difficulties in extracting basis vectors when the snapshots contain large amounts of data, when considering large areas using high spatial resolution. By means of computational simulations and inverse process analyses, in this study the authors developed a new method for rapid wind field reconstruction with high spatial resolution, while reducing the computation load to a minimum. The strategy is to establish snapshots of velocity fields in a large area, but only using a much smaller subset of the large area to extract the basis vectors. The basis vectors are then used to reconstruct the wind field of the large area with a high spatial resolution. The method can dramatically reduce the overall computation work due to the much smaller grid size in the subset area. The new method can be applied to situations where the velocity distributions for a large area need to be known with high spatial resolution.