Wavelet-Based Clustering of Sea Level Records Articles uri icon

publication date

  • February 2016

start page

  • 149

end page

  • 162

issue

  • 2

volume

  • 48

international standard serial number (ISSN)

  • 1874-8961

electronic international standard serial number (EISSN)

  • 1874-8953

abstract

  • The classification ofmultivariate 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.

keywords

  • wavelets; clustering; sea level; time series; multivariate time series; baltic sea; forecast densities; variability; pressure