Piecewise trends and discontinuities in GNSS and SATTG time series
| Date | 2022 | ||||||||||||||||||||
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| Temporal extent | 1995-01-01 -2021-01-01 | ||||||||||||||||||||
| Author(s) | Oelsmann Julius 1, Passaro Marcello1, Sanchez Laura1, Dettmering Denise1, Schwatke Christian1, Seitz Florian1 |
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| Affiliation(s) | 1 : Deutsches Geodätisches Forschungsinstitut der Technischen Universität München (DGFI-TUM), Arcisstrasse 21, 80333 München, Germany | ||||||||||||||||||||
| DOI | 10.17882/90028 | ||||||||||||||||||||
| Publisher | SEANOE | ||||||||||||||||||||
| Keyword(s) | Vertical land motion, change points, discontinuities and trend changes, Bayesian Inference, GNSS, GPS, Satellite Altimetry, Tide Gauges, Relative Sea Level change, DiscoTimeS | ||||||||||||||||||||
| Abstract | This dataset contains estimates of piecewise trends and discontinuities in vertical land motion (VLM) time series. The time series are based on two techniques, the Global Navigation Satellite System (GNSS) and differences of satellite altimetry and tide gauge measurements (SATTG). SATTG data are based on monthly PSMSL tide gauge observations (Permanent Service for Mean Sea Level, Holgate et al. [2013]) and multi-mission satellite altimetry from DGFI-TUM. The coastal along-track altimetry SLA data feature latest corrections and adjustments, as well as coastal retracking (using the ALES retracker (Passaro et al., 2014)). The GNSS time series are obtained from the Nevada Geodetic Laboratory (NGL) of the University of Nevada (Blewitt et al. [2016], http://geodesy.unr.edu). This dataset contains information of piecewise trends, uncertainties, as well as discontinuity epochs and sizes in 606 SATTG and 381 GNSS time series. These parameters are estimated with a Bayesian change point detection method (DiscoTimeS), as described in ‘Bayesian modelling of piecewise trends and discontinuities to improve the estimation of coastal vertical land motion’. |
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| Licence |  |
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| Acknowledgements | We acknowledge previous developments, particularly the Facebook Prophet’s business time series forecasting solution (Taylor and Letham [2018]), which inspired the design of the model, as well as the extensive Python library of PyMC3 [Salvatier et al., 2016], which substantially eased the setup and implementation of DiscoTimeS. Different datasets were used to derive the provided estimates. The altimetry data, together with atmospheric as well as geophysical corrections are obtained from the Open Altimeter Database (OpenADB) operated by DGFI-TUM (https://openadb.dgfi.tum.de/en/, last access: 05 March 2021). AVISO, ESA, EUMETSAT, and PODAAC maintained the original altimeter datasets. The NGL-GNSS data are obtained from (http://geodesy.unr.edu, last access: 1 September 2020 - Blewitt et al. [2016]). Monthly tide gauge data from PSMSL are available at https://www.psmsl.org/data/obtaining/ (last access: 10 March 2021 - Holgate et al. [2013]). | ||||||||||||||||||||
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