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Exploratory analysis of climate data using source separation methods
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DOI：10.1016/j.neunet.2006.01.011.png)
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Abstract
En 中文
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 stow 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. (c) 2006 Elsevier Ltd. All rights reserved.
Keywords:
independent component analysis
blind source separations
semiblind methods
denoising source separation
El Nino
global climate
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