|2.||Authors||Médéric Gravelle , Kévin Gobron , Guy Wöppelmann |
|Affiliations|| : UMR 7266 LIENSs, CNRS/LRU, La Rochelle, France.  : Royal Observatory of Belgium (ORB), Brussels, Belgium.|
|3.||Title||The ULR-repro3 GPS data reanalysis solution (aka ULR7)|
|4.||Publisher||SONEL Data Assembly Centre|
|6.||Subject||vertical land motion, sea level, GNSS, tide gauge|
|7.||Contributor||UMR 7266 LIENSs, IGN, CNRS, La Rochelle Université|
|8.||Temporal coverage||2000-01-01 / 2020-12-31|
|12.||Licence||Creative Commons License 4.0 CC:BY-NC-ND|
|13.||Supplement to||Gravelle, M., Gobron, K., Wöppelmann, G., et al. (2022) : The ULR-repro3 GPS data reanalysis and its estimates of vertical land motion at tide gauges for sea level science, Earth System Science Data, submitted June 2022.|
|14.||Description||The ULR analysis center has participated in the third reprocessing campaign (repro3) of the International GNSS Service (IGS). Its ULR-repro3 solution (aka ULR7) included 601 stations for which the GNSS data available between 2000.0 and 2021.0 was reprocessed using the models and corrections adopted by the IGS for repro3. |
The main steps and features of ULR-repro3 reanalysis are as follows. First, daily GNSS solutions were computed using a free-network weighted least squares adjustment strategy. That is, station positions, Earth Orientation parameters, satellite orbits and zenith tropospheric delays were adjusted simultaneously using GAMIT Software version 10.71. The station networks were regional, but one global. This step yielded a number of daily solutions (subnets) of less than 50 stations each that were expressed in their own (daily) terrestrial frame.
Secondly, the regional and global subnets were combined into daily global solutions (all stations included and expressed in the same but undetermined frame). These daily global solutions were then aligned to the ITRF2014 frame using a time-dependent functional model that included station positions at a reference epoch, velocities, seasonal signals (annual and semi-annual) and transformation parameters (translation, rotation, scale) between the daily undetermined frames and the ITRF2014 for a subset of IGS core stations. Station position offset discontinuities (mostly due to equipment changes or earthquakes), velocity changes and post-seismic displacement signals were added, as appropriate (based on metadata information and visual inspection of the series). This step included manual editing to identify (and remove) outliers as well as additional non-documented position offset discontinuities. It was iterated until convergence (analyst subjective criteria). From this step, daily position time series in the ITRF2014 frame for all (601) stations were retained. A minimum of three continuous years without an offset in the time series was required for the next step to estimate vertical velocities. This selection criteria yielded 554 daily station position time series, among which 457 are nearby a tide gauge (less than 15 km).
The last step was concerned with the estimation of the parameters of interest and their uncertainties, in which both a functional and a stochastic model were adjusted to each of the station position time series from the previous step. To limit biases in the parameter estimation, non-tidal atmospheric loading displacements were subtracted from the position time series prior to this adjustment using the products provided by the Earth System Modelling team of the German research center for geosciences. The stochastic model accounted for a linear combination of white noise and power law process, whose parameters were estimated using the Restricted Maximum Likelihood Estimation method. The functional model included long-term linear trends, position offset discontinuities, and periodic signals (annual, semiannual, and terannual signals, GPS draconitics of 1.04 year up to the 8th harmonics and three fortnightly signals). The parameters of this functional model and their uncertainties were estimated using the weighted least squares estimator and taking the inverse of the estimated observation covariance matrix as weight matrix. Further details in the companion paper submitted to ESSD.