  Both clustering and outlier detection play an important role for
meteorological measurements. We present the AWT algorithm, a clustering
algorithm for time series data that also performs implicit outlier detection
during the clustering. AWT integrates ideas of several well-known K-Means
clustering algorithms. It chooses the number of clusters automatically based on
a user-defined threshold parameter, and it can be used for heterogeneous
meteorological input data as well as for data sets that exceed the available
memory size. We apply AWT to crowd sourced 2-m temperature data with an hourly
resolution from the city of Vienna to detect outliers and to investigate if the
final clusters show general similarities and similarities with urban land-use
characteristics. It is shown that both the outlier detection and the implicit
mapping to land-use characteristic is possible with AWT which opens new
possible fields of application, specifically in the rapidly evolving field of
urban climate and urban weather.
