Frequency-Based Separation of Climate Signals
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.
- 7.Särelä, J., Valpola, H.: Denoising source separation. Journal of Machine Learning Research 6, 233–272 (2005)Google Scholar
- 8.Ilin, A., Valpola, H., Oja, E.: Semiblind source separation of climate data detects El Niño as the component with the highest interannual variability. In: Proc. of Int. Joint Conf. on Neural Networks (IJCNN 2005), Montreal, Quebec, Canada (2005) (accepted)Google Scholar