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Both clustering and outlier detection play an important role formeteorological measurements. We present the AWT algorithm, a clusteringalgorithm for time series data that also performs implicit outlier detectionduring the clustering. AWT integrates ideas of several well-known K-Meansclustering algorithms. It chooses the number of clusters automatically based ona user-defined threshold parameter, and it can be used for heterogeneousmeteorological input data as well as for data sets that exceed the availablememory size. We apply AWT to crowd sourced 2-m temperature data with an hourlyresolution from the city of Vienna to detect outliers and to investigate if thefinal clusters show general similarities and similarities with urban land-usecharacteristics. It is shown that both the outlier detection and the implicitmapping to land-use characteristic is possible with AWT which opens newpossible fields of application, specifically in the rapidly evolving field ofurban climate and urban weather.

AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree