Frequency-based separation of climate signals

Authors:
Alexander Ilin;Harri Valpola
Affiliations:
Helsinki University of Technology, Neural Networks Research Centre, Espoos, Finland;Lab. of Computational Engineering, Helsinki University of Technology, Espoo, Finland
Venue:
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2005

Citing 2
Cited 1

Denoising Source Separation

The Journal of Machine Learning Research
Topographic Independent Component Analysis

Neural Computation

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

Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence

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Abstract

The paper presents an example of exploratory data analysis of climate measurements using a recently developed denoising source separation (DSS) framework. We analysed a combined dataset containing daily measurements of three variables: surface temperature, sea level pressure and precipitation around the globe. Components exhibiting slow temporal behaviour were extracted using DSS with linear denoising. These slow components were further rotated using DSS with nonlinear denoising which implemented a frequency-based separation criterion. 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.