A Cluster Space Calibration Scheme for Synthetic Wind Statistics Calculated with a Mesoscale Model and its Application to Regional Wind Climate Studies

Summary

¶A scheme for calibrating an ensemble of wind fields computed by a mesoscale model in order to generate synthetic wind statistics is described. It is based on two main points. The first is to exploit the power of a mesoscale model to determine wind fields over complex terrain for different weather situations classified by cluster analysis. The second is to use all the information in the cluster analysis, i.e., the centroid values as well as the internal standard deviations of the clusters, to determine a cluster space distribution at each grid point in the model domain. The latter makes calibration possible if reliable measurements are available at the position of one of the grid points. The accuracy of the calibration is increased by splitting the cluster spaces into several parts. Combining both the modelled mesoscale wind fields and the method of split cluster spaces leads to a spatial transformation of the calibration from the calibration point to each grid point in the model domain. A validation of the scheme is carried out with measurements at grid points other than the calibration point and reveals remarkable improvements in the accuracy of the model wind statistics, especially with regard to wind speed distributions.