Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry
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...
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ftdoajarticles:oai:doaj.org/article:6d2a43522ad24f3d916082546a21e082 2023-05-15T17:31:08+02:00 Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry S. M. Barbosa M. E. Silva M. J. Fernandes 2006-01-01T00:00:00Z https://doaj.org/article/6d2a43522ad24f3d916082546a21e082 EN eng Copernicus Publications http://www.nonlin-processes-geophys.net/13/177/2006/npg-13-177-2006.pdf https://doaj.org/toc/1023-5809 https://doaj.org/toc/1607-7946 1023-5809 1607-7946 https://doaj.org/article/6d2a43522ad24f3d916082546a21e082 Nonlinear Processes in Geophysics, Vol 13, Iss 2, Pp 177-184 (2006) Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 article 2006 ftdoajarticles 2022-12-31T14:30:31Z 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. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 |
spellingShingle |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 S. M. Barbosa M. E. Silva M. J. Fernandes Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry |
topic_facet |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
S. M. Barbosa M. E. Silva M. J. Fernandes |
author_facet |
S. M. Barbosa M. E. Silva M. J. Fernandes |
author_sort |
S. M. Barbosa |
title |
Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry |
title_short |
Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry |
title_full |
Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry |
title_fullStr |
Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry |
title_full_unstemmed |
Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry |
title_sort |
multivariate autoregressive modelling of sea level time series from topex/poseidon satellite altimetry |
publisher |
Copernicus Publications |
publishDate |
2006 |
url |
https://doaj.org/article/6d2a43522ad24f3d916082546a21e082 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Nonlinear Processes in Geophysics, Vol 13, Iss 2, Pp 177-184 (2006) |
op_relation |
http://www.nonlin-processes-geophys.net/13/177/2006/npg-13-177-2006.pdf https://doaj.org/toc/1023-5809 https://doaj.org/toc/1607-7946 1023-5809 1607-7946 https://doaj.org/article/6d2a43522ad24f3d916082546a21e082 |
_version_ |
1766128470379724800 |