The ULR-repro3 GPS data reanalysis and its estimates of vertical land motion at tide gauges for sea level science

Abstract. A new reanalysis of Global Navigation Satellite System (GNSS) data at or near tide gauges worldwide was produced by the University of La Rochelle (ULR) group within the third International GNSS Service (IGS) reprocessing campaign (repro3). The new solution, called ULR-repro3, complies with the IGS standards adopted for repro3, implementing advances in data modelling and corrections since the previous reanalysis campaign and extending the average record length by about 7 years. The results presented here focus on the main products of interest for sea level science: the station position time series and associated velocities on the vertical component at tide gauges. These products are useful to estimate accurate vertical land motion at the coast and supplement data from satellite altimetry or tide gauges for an improved understanding of sea level changes and their impacts along coastal areas. To provide realistic velocity uncertainty estimates, the noise content in the position time series was investigated considering the impact of non-tidal atmospheric loading. Overall, the ULR-repro3 position time series show reduced white noise and power-law amplitudes and lower station velocity uncertainties compared with the previous reanalysis. The products are available via SONEL (https://doi.org/10.26166/sonel_ulr7a; Gravelle et al., 2022).

[1]  A. Demoulin,et al.  Impact of offsets on assessing the low-frequency stochastic properties of geodetic time series , 2022, Journal of Geodesy.

[2]  Z. Altamimi,et al.  Analysis of IGS repro3 Station Position Time Series , 2021 .

[3]  A. Demoulin,et al.  Influence of Aperiodic Non‐Tidal Atmospheric and Oceanic Loading Deformations on the Stochastic Properties of Global GNSS Vertical Land Motion Time Series , 2021, Journal of Geophysical Research: Solid Earth.

[4]  G. Blewitt,et al.  GPS Imaging of Global Vertical Land Motion for Studies of Sea Level Rise , 2021, Journal of Geophysical Research: Solid Earth.

[5]  Damien J. Allain,et al.  FES2014 global ocean tide atlas: design and performance , 2021 .

[6]  J. M. Torta,et al.  International Geomagnetic Reference Field: the thirteenth generation , 2021, Earth, Planets and Space.

[7]  E. Schipper,et al.  Frontiers in Climate Change Adaptation Science: Advancing Guidelines to Design Adaptation Pathways , 2020, Current Climate Change Reports.

[8]  Jim R Ray,et al.  Chameleonic Noise in GPS Position Time Series , 2020, Journal of Geophysical Research: Solid Earth.

[9]  F. Landerer,et al.  The causes of sea-level rise since 1900 , 2020, Nature.

[10]  G. Wöppelmann,et al.  Vertical land motion in the Southwest and Central Pacific from available GNSS solutions and implications for relative sea levels , 2019, Geophysical Journal International.

[11]  P. Woodworth,et al.  Forcing Factors Affecting Sea Level Changes at the Coast , 2019, Surveys in Geophysics.

[12]  D. Raucoules,et al.  Vertical land motion and relative sea level changes along the coastline of Brest (France) from combined space-borne geodetic methods , 2019, Remote Sensing of Environment.

[13]  D. Chambers,et al.  Coastal Sea Level and Related Fields from Existing Observing Systems , 2019, Surveys in Geophysics.

[14]  G. Blewitt,et al.  Harnessing the GPS Data Explosion for Interdisciplinary Science , 2018, Eos.

[15]  M. Marcos,et al.  Uncertainty of the 20th century sea-level rise due to vertical land motion errors , 2017 .

[16]  Z. Altamimi,et al.  ITRF2014 plate motion model , 2017 .

[17]  Shailen D. Desai,et al.  Evaluating predicted diurnal and semidiurnal tidal variations in polar motion with GPS‐based observations , 2016 .

[18]  J. Ray,et al.  The IGS contribution to ITRF2014 , 2016, Journal of Geodesy.

[19]  M. Marcos,et al.  Vertical land motion as a key to understanding sea level change and variability , 2016 .

[20]  G. Blewitt,et al.  MIDAS robust trend estimator for accurate GPS station velocities without step detection , 2016, Journal of geophysical research. Solid earth.

[21]  R. Dach,et al.  CODE’s new solar radiation pressure model for GNSS orbit determination , 2015, Journal of Geodesy.

[22]  Z. Altamimi,et al.  Global coseismic deformations, GNSS time series analysis, and earthquake scaling laws , 2014 .

[23]  J. Ray,et al.  Impacts of GNSS position offsets on global frame stability , 2014 .

[24]  R. Dill,et al.  Numerical simulations of global‐scale high‐resolution hydrological crustal deformations , 2013 .

[25]  Alireza Amiri-Simkooei,et al.  On the nature of GPS draconitic year periodic pattern in multivariate position time series , 2013 .

[26]  Matt A. King,et al.  Detecting offsets in GPS time series: First results from the detection of offsets in GPS experiment , 2013 .

[27]  T. Nilsson,et al.  GPT2: Empirical slant delay model for radio space geodetic techniques , 2013, Geophysical research letters.

[28]  M. Bouin,et al.  Rates of sea‐level change over the past century in a geocentric reference frame , 2009 .

[29]  Tilo Schöne,et al.  IGS Tide Gauge Benchmark Monitoring Pilot Project (TIGA): scientific benefits , 2009 .

[30]  M. Bouin,et al.  Geocentric sea-level trend estimates from GPS analyses at relevant tide gauges world-wide , 2007 .

[31]  Peter Steigenberger,et al.  Reprocessing of a global GPS network , 2006 .

[32]  H. Schuh,et al.  Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium‐Range Weather Forecasts operational analysis data , 2006 .

[33]  Yehuda Bock,et al.  Error analysis of continuous GPS position time series , 2004 .

[34]  Mike P. Stewart,et al.  Aliased tidal signatures in continuous GPS height time series , 2003 .

[35]  Claude Boucher,et al.  New trends for the realization of the international terrestrial reference system , 2002 .

[36]  D. U. Sanli,et al.  Geocentric sea level trend using GPS and >100‐year tide gauge record on a postglacial rebound nodal line , 2001 .

[37]  Thomas A. Herring,et al.  Effects of atmospheric azimuthal asymmetry on the analysis of space geodetic data , 1997 .

[38]  Michael B. Heflin,et al.  Global geodesy using GPS without fiducial sites , 1992 .

[39]  Karl-Rudolf Koch,et al.  Maximum likelihood estimate of variance components , 1986 .

[40]  H. D. Patterson,et al.  Recovery of inter-block information when block sizes are unequal , 1971 .

[41]  N. K. Pavlis,et al.  The development and evaluation of the Earth Gravitational Model 2008 ( EGM 2008 ) , 2012 .

[42]  M. Bouin,et al.  Correlated errors in GPS position time series: Implications for velocity estimates , 2011 .

[43]  Jan Kouba,et al.  A simplified yaw-attitude model for eclipsing GPS satellites , 2009 .

[44]  J. Ray,et al.  Anomalous harmonics in the spectra of GPS position estimates , 2008 .

[45]  W. I. Bertiger,et al.  Effects of antenna orientation on GPS carrier phase , 1992 .