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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 6
Nonlin. Processes Geophys., 17, 753–764, 2010
https://doi.org/10.5194/npg-17-753-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 17, 753–764, 2010
https://doi.org/10.5194/npg-17-753-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  14 Dec 2010

14 Dec 2010

Embedding reconstruction methodology for short time series – application to large El Niño events

H. F. Astudillo1, F. A. Borotto1, and R. Abarca-del-Rio2,3 H. F. Astudillo et al.
  • 1Departamento de Física, Universidad de Concepción, Casilla 160-C, Concepción, Chile
  • 2Departamento de Geofísica, Universidad de Concepción, Chile
  • 3LEGOS (UMR5566), 14 Av Ed Belin, 31400 Toulouse, France

Abstract. We propose an alternative approach for the embedding space reconstruction method for short time series. An m-dimensional embedding space is reconstructed with a set of time delays including the relevant time scales characterizing the dynamical properties of the system. By using a maximal predictability criterion a d-dimensional subspace is selected with its associated set of time delays, in which a local nonlinear blind forecasting prediction performs the best reconstruction of a particular event of a time series. An locally unfolded d-dimensional embedding space is then obtained. The efficiency of the methodology, which is mathematically consistent with the fundamental definitions of the local nonlinear long time-scale predictability, was tested with a chaotic time series of the Lorenz system. When applied to the Southern Oscillation Index (SOI) (observational data associated with the El Niño-Southern Oscillation phenomena (ENSO)) an optimal set of embedding parameters exists, that allows constructing the main characteristics of the El Niño 1982–1983 and 1997–1998 events, directly from measurements up to 3 to 4 years in advance.

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