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Detecting Oscillations Hidden in Noise: Common Cycles in Atmospheric, Geomagnetic and Solar Data

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Nonlinear Time Series Analysis in the Geosciences

Part of the book series: Lecture Notes in Earth Sciences ((LNEARTH,volume 112))

Abstract

In this chapter we present a nonlinear enhancement of a linear method, the singular system analysis (SSA), which can identify potentially predictable or relatively regular processes, such as cycles and oscillations, in a background of colored noise. The first step in the distinction of a signal from noise is a linear transformation of the data provided by the SSA. In the second step, the dynamics of the SSA modes is quantified in a general, non-linear way, so that dynamical modes are identified which are more regular, or better predictable than linearly filtered noise. A number of oscillatory modes are identified in data reflecting solar and geomagnetic activity and climate variability, some of them sharing common periods.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod vodárenskou věží 2, 182 07 Prague 8, Czech Republic

    Milan Paluš

  2. Institute of Atmospheric Physics, Academy of Sciences of the Czech Republic, Boční II/1401, 141 31 Prague 4, Czech Republic

    Dagmar Novotná

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  1. Milan Paluš
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  2. Dagmar Novotná
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Editors and Affiliations

  1. TU Dresden Institut für Wirtschaft und Verkehr, Andreas-Schubert-Str. 23, 01062 Dresden, Germany

    Reik V. Donner (Dr) (Dr)

  2. Depto. Matematica Aplicada, Universidade do Porto Fac. Ciencias, Rua do Campo Alegre 687, 4169-007 Porto, Portugal

    Susana M. Barbosa (Dr) (Dr)

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Paluš, M., Novotná, D. (2008). Detecting Oscillations Hidden in Noise: Common Cycles in Atmospheric, Geomagnetic and Solar Data. In: Donner, R.V., Barbosa, S.M. (eds) Nonlinear Time Series Analysis in the Geosciences. Lecture Notes in Earth Sciences, vol 112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78938-3_15

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