Data Clustering Reveals Climate Impacts on Local Wind Phenomena

AbstractThe authors demonstrate the utility of k-means clustering for identifying relationships between winds at turbine heights and climate oscillations, thereby developing a method suited for predicting the impacts of climate change on wind resources. Fourteen years of data from an 80-m tower at the National Wind Technology Center (NWTC) in Colorado have been reduced to four dominant flow phenomena using k-means clustering. At this location, this method identifies two clusters of westerly inflow (strong and weak), another cluster of flow from the north, and one of flow from the south. Similar clusters are found for the data at all heights on the tower, and each follow distinct seasonal cycles. Time series of each cluster, as well as the mean wind speed at the NWTC, are retained for comparison with climate oscillations along with the local 500-hPa pressure gradient. The mean wind speed in the surface layer is strongly correlated with the local north–south pressure gradient. The frequency of strong wester...

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