Wavelet-Based Clustering of Sea Level Records
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The classification of multivariate time series in terms of their corresponding temporal dependence patterns is a common problem in geosciences, particularly for large datasets resulting from environmental monitoring networks. Here a wavelet-based clustering approach is applied to sea level and atmospheric pressure time series at tide gauge locations in the Baltic Sea. The resulting dendrogram discriminates three spatially-coherent groups of stations separating the southernmost tide gauges, reflecting mainly high-frequency variability driven by zonal wind, from the middle-basin stations and the northernmost stations dominated by lower-frequency variability and the response to atmospheric pressure.
KeywordsWavelets Clustering Sea level Time series
Tide gauge data kindly provided by DMI (K. Madsen), SMHI (T. Hammarklint) and UHSLC. S.M. Barbosa acknowledges support of the FCT—Fundação para a Ciência e a Tecnologia (contract under programme IF2013 and project UID/EEA/50014/2013). This work was supported by the European Regional Development Fund (FEDER) through the COMPETE programme and by the Portuguese Government through the FCT, in the scope of the project UID/MAT/04106/2013 (Centro de I&D em Matemática e Aplicações, http://cidma.mat.ua.pt/) and projects PEst-OE/EEI/UI0127/2014 and UID/CEC/00127/2013 (Instituto de Engenharia Electrónica e Informática de Aveiro, IEETA/UA, http://www.ieeta.pt). S. Gouveia acknowledges the postdoctoral grant by FCT (ref. SFRH/BPD/87037/2012). A.M. Alonso acknowledges support of the Ministerio de Economía y Competitividad projects ECO2011-25706 and ECO2012-38442.
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