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| Titel |
Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry |
| VerfasserIn |
S. M. Barbosa, M. E. Silva, M. J. Fernandes |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 13, no. 2 ; Nr. 13, no. 2 (2006-06-20), S.177-184 |
| Datensatznummer |
250011735
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| Publikation (Nr.) |
 copernicus.org/npg-13-177-2006.pdf |
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| Zusammenfassung |
| 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
autoregressive 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. |
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