Estimation of the Vertical Land Motion from GNSS Time Series and Application in Quantifying Sea-Level Rise
Sea-level rise observed at tide gauges must be corrected for vertical land motion, observed with GNSS, to obtain the absolute sea-level rise with respect to the centre of the Earth. Both the sea-level and vertical position time series contain temporal correlated noise that need to be taken into acco...
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ftcwashingtonuni:oai:digitalcommons.cwu.edu:geological_sciences-1125 2023-05-15T16:40:53+02:00 Estimation of the Vertical Land Motion from GNSS Time Series and Application in Quantifying Sea-Level Rise Montillet, Jean-Philippe Bos, Machiel S. Melbourne, Timothy I. Williams, Simon D.P. Fernandes, Rui M.S. Szeliga, Walter 2020-01-01T08:00:00Z https://digitalcommons.cwu.edu/geological_sciences/125 https://doi.org/10.1007/978-3-030-21718-1_11 unknown ScholarWorks@CWU https://digitalcommons.cwu.edu/geological_sciences/125 http://ezp.lib.cwu.edu/login?url=http://dx.doi.org/10.1007/978-3-030-21718-1_11 © Springer Nature Switzerland AG 2020 Geological Sciences Faculty Scholarship Information criterion Stochastic modeling Melting glacier Ice sheet ITRF2008 ITRF2014 Satellite altimetry Uplift rate Uncertainties Colored noise Power-law noise White noise Geomorphology Geophysics and Seismology Oceanography Tectonics and Structure text 2020 ftcwashingtonuni https://doi.org/10.1007/978-3-030-21718-1_11 2022-10-20T20:30:21Z Sea-level rise observed at tide gauges must be corrected for vertical land motion, observed with GNSS, to obtain the absolute sea-level rise with respect to the centre of the Earth. Both the sea-level and vertical position time series contain temporal correlated noise that need to be taken into account to obtain the most accurate rate estimates and to ensure realistic uncertainties. Satellite altimetry directly observes absolute sea-level rise but these time series also exhibit colored noise. In this chapter we present noise models for these geodetic time series such as the commonly used first order Auto Regressive (AR), the General Gauss Markov (GGM) and the ARFIMA model. The theory is applied to GNSS and tide gauge data from the Pacific Northwest coast. Text Ice Sheet Central Washington University: ScholarWorks Pacific 317 344 |
institution |
Open Polar |
collection |
Central Washington University: ScholarWorks |
op_collection_id |
ftcwashingtonuni |
language |
unknown |
topic |
Information criterion Stochastic modeling Melting glacier Ice sheet ITRF2008 ITRF2014 Satellite altimetry Uplift rate Uncertainties Colored noise Power-law noise White noise Geomorphology Geophysics and Seismology Oceanography Tectonics and Structure |
spellingShingle |
Information criterion Stochastic modeling Melting glacier Ice sheet ITRF2008 ITRF2014 Satellite altimetry Uplift rate Uncertainties Colored noise Power-law noise White noise Geomorphology Geophysics and Seismology Oceanography Tectonics and Structure Montillet, Jean-Philippe Bos, Machiel S. Melbourne, Timothy I. Williams, Simon D.P. Fernandes, Rui M.S. Szeliga, Walter Estimation of the Vertical Land Motion from GNSS Time Series and Application in Quantifying Sea-Level Rise |
topic_facet |
Information criterion Stochastic modeling Melting glacier Ice sheet ITRF2008 ITRF2014 Satellite altimetry Uplift rate Uncertainties Colored noise Power-law noise White noise Geomorphology Geophysics and Seismology Oceanography Tectonics and Structure |
description |
Sea-level rise observed at tide gauges must be corrected for vertical land motion, observed with GNSS, to obtain the absolute sea-level rise with respect to the centre of the Earth. Both the sea-level and vertical position time series contain temporal correlated noise that need to be taken into account to obtain the most accurate rate estimates and to ensure realistic uncertainties. Satellite altimetry directly observes absolute sea-level rise but these time series also exhibit colored noise. In this chapter we present noise models for these geodetic time series such as the commonly used first order Auto Regressive (AR), the General Gauss Markov (GGM) and the ARFIMA model. The theory is applied to GNSS and tide gauge data from the Pacific Northwest coast. |
format |
Text |
author |
Montillet, Jean-Philippe Bos, Machiel S. Melbourne, Timothy I. Williams, Simon D.P. Fernandes, Rui M.S. Szeliga, Walter |
author_facet |
Montillet, Jean-Philippe Bos, Machiel S. Melbourne, Timothy I. Williams, Simon D.P. Fernandes, Rui M.S. Szeliga, Walter |
author_sort |
Montillet, Jean-Philippe |
title |
Estimation of the Vertical Land Motion from GNSS Time Series and Application in Quantifying Sea-Level Rise |
title_short |
Estimation of the Vertical Land Motion from GNSS Time Series and Application in Quantifying Sea-Level Rise |
title_full |
Estimation of the Vertical Land Motion from GNSS Time Series and Application in Quantifying Sea-Level Rise |
title_fullStr |
Estimation of the Vertical Land Motion from GNSS Time Series and Application in Quantifying Sea-Level Rise |
title_full_unstemmed |
Estimation of the Vertical Land Motion from GNSS Time Series and Application in Quantifying Sea-Level Rise |
title_sort |
estimation of the vertical land motion from gnss time series and application in quantifying sea-level rise |
publisher |
ScholarWorks@CWU |
publishDate |
2020 |
url |
https://digitalcommons.cwu.edu/geological_sciences/125 https://doi.org/10.1007/978-3-030-21718-1_11 |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
Ice Sheet |
genre_facet |
Ice Sheet |
op_source |
Geological Sciences Faculty Scholarship |
op_relation |
https://digitalcommons.cwu.edu/geological_sciences/125 http://ezp.lib.cwu.edu/login?url=http://dx.doi.org/10.1007/978-3-030-21718-1_11 |
op_rights |
© Springer Nature Switzerland AG 2020 |
op_doi |
https://doi.org/10.1007/978-3-030-21718-1_11 |
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344 |
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1766031310376140800 |