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Nonlinear dimensionality reduction in climate data

  • Linear methods of dimensionality reduction are useful tools for handling and interpreting high dimensional data. However, the cumulative variance explained by each of the subspaces in which the data space is decomposed may show a slow convergence that makes the selection of a proper minimum number of subspaces for successfully representing the variability of the process ambiguous. The use of nonlinear methods can improve the embedding of multivariate data into lower dimensional manifolds. In this article, a nonlinear method for dimensionality reduction, Isomap, is applied to the sea surface temperature and thermocline data in the tropical Pacific Ocean, where the El Nino-Southern Oscillation (ENSO) phenomenon and the annual cycle phenomena interact. Isomap gives a more accurate description of the manifold dimensionality of the physical system. The knowledge of the minimum number of dimensions is expected to improve the development of low dimensional models for understanding and predicting ENSO

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Metadaten
Author:A. J. Gamez, Changsong Zhou, A. Timmermann, Jürgen KurthsORCiDGND
ISSN:1023-5809
Document Type:Article
Language:English
Year of first Publication:2004
Year of Completion:2004
Release Date:2017/03/24
Source:Nonlinear Processes in Geophysics. - ISSN 1023-5809. - 11 (2004), 3, S. 393 - 398
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie
Peer Review:Referiert
Publication Way:Open Access
Institution name at the time of publication:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik