Exploratory analysis of climate data using source separation methods

Neural Networks : the Official Journal of the International Neural Network Society
Alexander IlinErkki Oja

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 Niño-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.

References

Jul 7, 2001·Neural Computation·A HyvärinenM Inki
Apr 9, 2002·Neural Computation·Laurenz Wiskott, Terrence J Sejnowski

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Citations

Jan 1, 2008·IEEE Reviews in Biomedical Engineering·Ricardo Vigario, Erkki Oja
Oct 12, 2012·Journal of Neurophysiology·Alain de CheveignéBoris Gourévitch

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