Embedding reconstruction methodology for short time series - application to large El Nino events

H. F. Astudillo (1) , F. A. Borotto (2) , R. Abarca-Del-Rio (3)

1 Departamento de Fısica
2 Departamento de Geofísica [Santiago]
3 ECOLA - Echanges Côte-Large

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 Nino-Southern Oscillation phenomena (ENSO)) an optimal set of embedding parameters exists, that allows constructing the main characteristics of the El Nino 1982-1983 and 1997-1998 events, directly from measurements up to 3 to 4 years in advance.

Mots clés: LYAPUNOV EXPONENT; PREDICTION MODEL; PREDICTABILITY; STATE; MONSOON; SYSTEMS; NOISE; ENSO

Domaines: Océan, Atmosphère

H. F. Astudillo, F. A. Borotto, R. Abarca-Del-Rio. Embedding reconstruction methodology for short time series - application to large El Nino events. Nonlinear Processes in Geophysics, 2010, 17 (6), pp.753-764. ⟨10.5194/npg-17-753-2010⟩. ⟨hal-00994368⟩