Small-scale surface wind prediction using dynamic adaptation

Abstract

An efficient method for surface wind prediction based on dynamic adaptation of a low-resolutionmodel forecast towards a finer mesh is described. The method consists of: (1) interpolation ofthe data field predicted by the large-scale model onto the target grid; (2) dynamic adaptationof the fields onto the new grid by running the fine-mesh model for an appropriate period oftime. The time necessary for the adaptation is roughly estimated by scale analysis and by usinga linear model, and confirmed by some numerical simulations. This time was found to bebetween 30 and 45 min for adjusting general features of the wind field; the additional timeneeded to adjust small-scale wind phenomena can be several times longer. It was shown thatthis time period does not depend on the model configuration. The main advantage of thepresented method is its low computational cost. The adaptation with a simplified model iscapable of reproducing small-scale features in the wind field which develop due to the neworography in the fine grid. The method fails in cases of certain atmospheric conditions or awrong large-scale forecast.