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

TL;DR

The paper introduces AWT, a novel clustering algorithm for meteorological time series that automatically determines the number of clusters and detects outliers, applicable to large and heterogeneous datasets, with practical urban climate applications.

Contribution

AWT combines existing clustering ideas into a new algorithm that automatically selects cluster count and performs outlier detection for meteorological time series.

Findings

Effective outlier detection in meteorological data

Automatic determination of cluster number based on threshold

Successful application to urban temperature data revealing land-use patterns

Abstract

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.