2006 Special issue: Exploratory analysis of climate data using source separation methods

Authors:
Alexander Ilin;Harri Valpola;Erkki Oja
Affiliations:
Laboratory of Computer and Information Science, Helsinki University of Technology, P.O. Box 5400, FI-02015 TKK, Espoo, Finland;Laboratory of Computational Engineering, Helsinki University of Technology, P.O. Box 9203, FI-02015 TKK, Espoo, Finland;Laboratory of Computer and Information Science, Helsinki University of Technology, P.O. Box 5400, FI-02015 TKK, Espoo, Finland
Venue:
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Year:
2006

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Abstract

We present an example of exploratory data analysis of climate measurements using a recently developed denoising source separation (DSS) framework. We analyzed a combined dataset containing daily measurements of three variables: surface temperature, sea level pressure and precipitation around the globe, for a period of 56 years. Components exhibiting slow temporal behavior were extracted using DSS with linear denoising. The first component, most prominent in the interannual time scale, captured the well-known El Nino-Southern Oscillation (ENSO) phenomenon and the second component was close to the derivative of the first one. The slow components extracted in a wider frequency range were further rotated using a frequency-based separation criterion implemented by DSS with nonlinear denoising. The rotated sources give a meaningful representation of the slow climate variability as a combination of trends, interannual oscillations, the annual cycle and slowly changing seasonal variations. Again, components related to the ENSO phenomenon emerge very clearly among the found sources.