Statistical–dynamical downscaling of wind fields using self-organizing maps

In this work a temporally and spatially consistent method for the efficient long-term assessment of the wind resource is presented. It contributes to the field of statistical–dynamical downscaling of the wind resource by combining stratified sampling of long-term mean Sea Level Pressure (SLP) fields with a neural-network method called self-organizing maps (SOM). The objective of the method is to construct a synthetic year which can be considered representative of the long-term period (typically 30 years) in terms of its wind resource. Validation is performed in two ways. (1) A comparison of the long-term against the synthetic SLP field was conducted showing that the proposed approach is capable of reproducing the overall SLP long-term mean with an error of less than 1 hPa. (2) The wind representativeness of the selected year was verified against 10 years of measured wind data from 22 automatic stations in Navarra (Northeastern Spain), covering a variety of different climate and terrain conditions. The error found in the prediction of a variety of wind speed parameters is of the order 1% for most stations.