Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry
International audience 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 t...
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ftinsu:oai:HAL:hal-00302723v1 2023-11-12T04:22:06+01:00 Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry Barbosa, S. M. Silva, M. E. Fernandes, M. J. Department of Applied Mathematics 2006-06-20 https://hal.science/hal-00302723 https://hal.science/hal-00302723/document https://hal.science/hal-00302723/file/npg-13-177-2006.pdf en eng HAL CCSD European Geosciences Union (EGU) hal-00302723 https://hal.science/hal-00302723 https://hal.science/hal-00302723/document https://hal.science/hal-00302723/file/npg-13-177-2006.pdf info:eu-repo/semantics/OpenAccess ISSN: 1023-5809 EISSN: 1607-7946 Nonlinear Processes in Geophysics https://hal.science/hal-00302723 Nonlinear Processes in Geophysics, 2006, 13 (2), pp.177-184 [PHYS.ASTR.CO]Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO] [SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph] [SDU.STU]Sciences of the Universe [physics]/Earth Sciences info:eu-repo/semantics/article Journal articles 2006 ftinsu 2023-10-25T16:26:31Z International audience 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 Institut national des sciences de l'Univers: HAL-INSU |
institution |
Open Polar |
collection |
Institut national des sciences de l'Univers: HAL-INSU |
op_collection_id |
ftinsu |
language |
English |
topic |
[PHYS.ASTR.CO]Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO] [SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph] [SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
spellingShingle |
[PHYS.ASTR.CO]Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO] [SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph] [SDU.STU]Sciences of the Universe [physics]/Earth Sciences Barbosa, S. M. Silva, M. E. Fernandes, M. J. Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry |
topic_facet |
[PHYS.ASTR.CO]Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO] [SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph] [SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
description |
International audience 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. |
author2 |
Department of Applied Mathematics |
format |
Article in Journal/Newspaper |
author |
Barbosa, S. M. Silva, M. E. Fernandes, M. J. |
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 |
publisher |
HAL CCSD |
publishDate |
2006 |
url |
https://hal.science/hal-00302723 https://hal.science/hal-00302723/document https://hal.science/hal-00302723/file/npg-13-177-2006.pdf |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
ISSN: 1023-5809 EISSN: 1607-7946 Nonlinear Processes in Geophysics https://hal.science/hal-00302723 Nonlinear Processes in Geophysics, 2006, 13 (2), pp.177-184 |
op_relation |
hal-00302723 https://hal.science/hal-00302723 https://hal.science/hal-00302723/document https://hal.science/hal-00302723/file/npg-13-177-2006.pdf |
op_rights |
info:eu-repo/semantics/OpenAccess |
_version_ |
1782337253744312320 |