© Author(s) 2006. This work is licensed under a Creative Commons License. Nonlinear Processes in Geophysics Multivariate autoregressive modelling of sea level time series from
Abstract. 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 a...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.110.8040 2023-05-15T17:31:03+02:00 © Author(s) 2006. This work is licensed under a Creative Commons License. Nonlinear Processes in Geophysics Multivariate autoregressive modelling of sea level time series from S. M. Barbosa M. E. Silva M. J. Fern The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.8040 http://www.nonlin-processes-geophys.net/13/177/2006/npg-13-177-2006.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.8040 http://www.nonlin-processes-geophys.net/13/177/2006/npg-13-177-2006.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.nonlin-processes-geophys.net/13/177/2006/npg-13-177-2006.pdf text ftciteseerx 2016-01-07T13:41:24Z Abstract. 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. 1 Text North Atlantic Unknown |
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Abstract. 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. 1 |
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The Pennsylvania State University CiteSeerX Archives |
format |
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author |
S. M. Barbosa M. E. Silva M. J. Fern |
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S. M. Barbosa M. E. Silva M. J. Fern © Author(s) 2006. This work is licensed under a Creative Commons License. Nonlinear Processes in Geophysics Multivariate autoregressive modelling of sea level time series from |
author_facet |
S. M. Barbosa M. E. Silva M. J. Fern |
author_sort |
S. M. Barbosa |
title |
© Author(s) 2006. This work is licensed under a Creative Commons License. Nonlinear Processes in Geophysics Multivariate autoregressive modelling of sea level time series from |
title_short |
© Author(s) 2006. This work is licensed under a Creative Commons License. Nonlinear Processes in Geophysics Multivariate autoregressive modelling of sea level time series from |
title_full |
© Author(s) 2006. This work is licensed under a Creative Commons License. Nonlinear Processes in Geophysics Multivariate autoregressive modelling of sea level time series from |
title_fullStr |
© Author(s) 2006. This work is licensed under a Creative Commons License. Nonlinear Processes in Geophysics Multivariate autoregressive modelling of sea level time series from |
title_full_unstemmed |
© Author(s) 2006. This work is licensed under a Creative Commons License. Nonlinear Processes in Geophysics Multivariate autoregressive modelling of sea level time series from |
title_sort |
© author(s) 2006. this work is licensed under a creative commons license. nonlinear processes in geophysics multivariate autoregressive modelling of sea level time series from |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.8040 http://www.nonlin-processes-geophys.net/13/177/2006/npg-13-177-2006.pdf |
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North Atlantic |
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North Atlantic |
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http://www.nonlin-processes-geophys.net/13/177/2006/npg-13-177-2006.pdf |
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.8040 http://www.nonlin-processes-geophys.net/13/177/2006/npg-13-177-2006.pdf |
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