Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry

• Type: Article in International Scientific Journal
• Date: 2006

Authors

Barbosa, SM (Author) Other; Silva, ME (Author) FCUP; Fernandes, MJ (Author) FCUP

Journal

Title: Nonlinear Processes in Geophysics

Vol. 13

Pages: 177-184

ISSN: 1023-5809

Publisher: Copernicus

Indexing

ISI Web of Knowledge - 14 Citations

Scopus - 16 Citations

Scientific classification

FOS: Natural sciences > Earth and related Environmental sciences

Other information

Authenticus ID: P-004-QE8

DOI: 10.5194/npg-13-177-2006

Abstract (EN): This work addresses the autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry mission. Datasets from remote sensing applications are typically very large and correlated both in time and space. Multivariate analysis methods are useful tools to summarise and extract information from such large space-time datasets. Multivariate autoregressive analysis is a generalisation of Principal Oscillation Pattern (POP) analysis, widely used in the geosciences for the extraction of dynamical modes by eigen-decomposition of a first order autoregressive model fitted to the multivariate dataset of observations. The extension of the POP methodology to autoregressions of higher order, although increasing the difficulties in estimation, allows one to model a larger class of complex systems. Here, sea level variability in the North Atlantic is modelled by a third order multivariate autoreerressive model estimated by stepwise least squares. Eigen-decomposition of the fitted model yields physically-interpretable seasonal modes. The leading autoregressive mode is an annual oscillation and exhibits a very homogeneous spatial structure in terms of amplitude reflecting the large scale coherent behaviour of the annual pattern in the Northern hemisphere. The phase structure reflects the seesaw pattern between the western and eastern regions in the tropical North Atlantic associated with the trade winds regime. The second mode is close to a semi-annual oscillation. Multivariate autoregressive models provide a useful framework for the description of time-varying fields while enclosing a predictive potential.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8