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...
Published in: | Nonlinear Processes in Geophysics |
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ftcopernicus:oai:publications.copernicus.org:npg33319 2023-05-15T17:31:03+02:00 Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry Barbosa, S. M. Silva, M. E. Fernandes, M. J. 2018-01-15 application/pdf https://doi.org/10.5194/npg-13-177-2006 https://npg.copernicus.org/articles/13/177/2006/ eng eng doi:10.5194/npg-13-177-2006 https://npg.copernicus.org/articles/13/177/2006/ eISSN: 1607-7946 Text 2018 ftcopernicus https://doi.org/10.5194/npg-13-177-2006 2020-07-20T16:27:15Z 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. Text North Atlantic Copernicus Publications: E-Journals Nonlinear Processes in Geophysics 13 2 177 184 |
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Open Polar |
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Copernicus Publications: E-Journals |
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English |
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 |
Text |
author |
Barbosa, S. M. Silva, M. E. Fernandes, M. J. |
spellingShingle |
Barbosa, S. M. Silva, M. E. Fernandes, M. J. Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry |
author_facet |
Barbosa, S. M. Silva, M. E. Fernandes, M. J. |
author_sort |
Barbosa, S. M. |
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 |
publishDate |
2018 |
url |
https://doi.org/10.5194/npg-13-177-2006 https://npg.copernicus.org/articles/13/177/2006/ |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
eISSN: 1607-7946 |
op_relation |
doi:10.5194/npg-13-177-2006 https://npg.copernicus.org/articles/13/177/2006/ |
op_doi |
https://doi.org/10.5194/npg-13-177-2006 |
container_title |
Nonlinear Processes in Geophysics |
container_volume |
13 |
container_issue |
2 |
container_start_page |
177 |
op_container_end_page |
184 |
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1766128362010443776 |