Vertical land movements

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ALTIMETRY

Analyses of Sea Level Differences (Satellite altimetry minus tide gauge data)

Solution : ULR6

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  • Credits
  • sonel.png
  • Data
  • Vertical velocities
  • Network
showlayers hidelayers
  • Legend
  • (mm/year)
  • p6 > +6
  • p4 > +4
  • p2 > +2
  • p1 > +1
  • pdemi > +0.5
  • pm0 -0.5/+0.5
  • mdemi < -0.5
  • m1 < -1
  • m2 < -2
  • m4 < -4
  • m4 < -6
  • p6 GPS@TG
  • p4 RF & GPS@TG
  • p2 RF
  • p1 OTHER

The ULR6a GPS solution is a preliminary version of the reanalysis of 19 years of GPS data from 1995 to 2014 that has been undertaken within the framework of the 2nd data reprocessing campaign of the International GNSS Service (IGS). Its associated vertical velocity field is expressed in ITRF2008.

Double-differenced ionosphere-free GPS carrier phase observations from a global network of 754 stations were reanalyzed using GAMIT/GLOBK software version 10.5. The data set covered the period January 1995 to December 2013. Station coordinates, satellite orbits, Earth orientation parameters (EOPs), and zenith tropospheric delay parameters every hour, were estimated. The data analysis strategy (models, corrections...) was compliant with the specifications adopted by the IGS for this reanalysis (more information here). Details can also be found in the IGS-like ULR analysis centre form associated with this solution.

The GPS velocities were obtained in the final step of the analysis which combined the daily GAMIT/GLOBK global network solutions (station coordinates with full co-variances) from the entire data time span into a long-term solution using CATREF software. This long-term solution (ULR6a) was aligned to ITRF2008 using minimal constraints over a selected set of IGS Reference Frame stations. A detailed description can be found in the reference below.

General

Analysis center: ULR Number of stations: 674
Solution: ULR6a CGPS@TG 482
Date of publication: 2016 Reference Frame 194
Time span: 1995.0 - 2013.9 Others 77
Reference Frame: ITRF 2008

Vertical velocity field

Estimated (robust) velocities: 493
CGPS@TG 349
Average of formal errors: 0.54 mm/yr
Median of formal errors: 0.36 mm/yr

To assign more realistic uncertainties on the GPS velocities, the noise content in the position time series was examined by the maximum likelihood estimation (MLE) technique using CATS software (Williams, 2008). Time series were detrended at the CATREF stage. Details on the GPS velocity uncertainty assessment are given in the reference below.

- Vertical velocities table

The ULR6a_Vertical-Velocities_Table provides the vertical GPS velocities and uncertainties for the 497 stations fulfilling the criteria of 3 years of minimum length without discontinuities and with data gaps not exceeding 30%.

- Daily residual time series

The ULR6_detrend.zip file contains individual station data files of daily residual position time series in ITRF2008 with respect to the linear long-term combined solution at the reference epoch. These residuals (detrended) are expressed in meters in the local frame (North, East, and Up). The reference position and the 3D velocity in the local coordinate system (East, North, Up) are provided in the header of each file.

- Position discontinuities

The ULR6a_discontinuities_Table.txt file provides the position offsets that were estimated.

- Solution file

The long-term solution ULR6a in SINEX format (567 Mo) can be downloaded too. However, we recommend its use only to those familiar with SINEX files, and having a good geodetic background and experience.

The ULR5 GPS solution is the result of a reanalysis of 16 years of GPS data from 1995 to 2010. Its vertical velocity field is published in Global and Planetary Change. It is expressed in ITRF2008.

Double-differenced ionosphere-free GPS carrier phase observations from a global network of 420 stations were reanalyzed using GAMIT/GLOBK software version 10.4. The data set covered the period January 1995 to December 2010 and 282 stations out of 420 were co-located with a tide gauge. Station coordinates, satellite orbits, Earth orientation parameters (EOPs), and zenith tropospheric delay parameters every hour, were estimated. Details on the GPS data processing strategy (models, corrections,...) can be found in the IGS-like ULR analysis centre form associated with this solution.

The GPS velocities were obtained in the final step of the analysis which combined the weekly GAMIT/GLOBK global network solutions (station coordinates with full co-variances) from the entire data time span into a long-term solution using CATREF software. This long-term solution (ULR5) was aligned to ITRF2008 using minimal constraints over a selected set of IGS Reference Frame stations. A detailed description can be found in the reference below.

General

Analysis center: ULR Number of stations: 420
Solution: ULR5 CGPS@TG 282
Date of publication: 2012 Reference Frame 191
Time span: 1994.0 - 2010.9 Others 6
Reference Frame: ITRF 2008

Vertical velocity field

Estimated (robust) velocities: 326
CGPS@TG 232
Average of formal errors: 0.36 mm/yr
Median of formal errors: 0.28 mm/yr

Stacking

WRMS


TRANSORMATION PARAMETERS

To assign more realistic uncertainties on the GPS velocities, the noise content in the position time series was examined by the maximum likelihood estimation (MLE) technique using CATS software. Time series were detrended and deseasoned (annual cycles) at the CATREF stage. Details on the GPS velocity uncertainty assessment will be given in the below-mentioned paper.

- Vertical velocities table

The ULR5_Vertical-Velocities_Table provides the vertical GPS velocities and uncertainties for the 326 stations fulfilling the criterias of 3 years of minimum length without discontinuities and with data gaps not exceeding 30%.

- Weekly residual time series

The ULR5_detrend.zip file contains individual station data files of weekly residual position time series in ITRF2008 with respect to the linear long-term combined solution at the reference epoch. These residuals (detrended and deseasoned) are expressed in meters in the local frame (North, East, and Up). The reference position and the 3D velocity in the local coordinate system (East, North, Up) are provided in the header of each file. The ulr5.kmz file shows a Google Earth presentation of the above weekly data files by cliking on a station symbol. The associated IGS-like sitelog can be accessed too, as well as a plot of the residual position time series in the vertical.

- Position discontinuities

The ULR5_discontinuities_Table.txt file provides the position offsets that were estimated.

JPEG - 191.2 kb
ULR5 on Google Earth


- Solution file

The long-term solution ULR5 in SINEX format (250 Mo) can be downloaded too. However, we recommend its use only to those familiar with SINEX files, and having a good geodetic background and experience.

- ULR5 on Google Earth

The ULR5_Vertical_Velocites.kmz file shows a Google Earth presentation of the ULR5 vertical velocities. For each GPS station a vertical cylinder whose height is proportionnal to the estimated velocity is drawn ( yellow for subsidence, red for uplift).

The ULR4 GPS solution is the result of a reanalysis of 13 years of GPS data from 1996 to 2008. Its vertical velocity field is published in Journal of Geophysical Research. It is expressed in ITRF2005.

Double-differenced ionosphere-free GPS carrier phase observations from a global network of 316 stations were reanalyzed using GAMIT/GLOBK software version 10.34. The data set covered the period January 1996 to December 2008 and 216 stations out of 316 were co-located with a tide gauge. Station coordinates, satellite orbits, Earth orientation parameters (EOPs), and zenith tropospheric delay parameters every 2 hours, were estimated. Details on the GPS data processing strategy (models, corrections,...) can be found in the IGS-like ULR analysis centre form associated with this solution.

The GPS velocities were obtained in the final step of the analysis which combined the weekly GAMIT/GLOBK global network solutions (station coordinates with full co-variances) from the entire data time span into a long-term solution using CATREF software. This long-term solution (ULR4) was aligned to ITRF2005 using minimal constraints over a selected set of IGS Reference Frame stations. A detailed description can be found in the reference below.

Under construction...

To assign more realistic uncertainties on the GPS velocities, the noise content in the position time series was examined by the maximum likelihood estimation (MLE) technique using CATS software. Time series were detrended and deseasoned (annual cycles) at the CATREF stage. Details on the GPS velocity uncertainty assessment will be given in the below-mentioned paper.

- Vertical velocities table

The ulr4_vertical-velocities_table provides the vertical GPS velocities and uncertainties for the 275 stations fulfilling the criterias of 2.5 years of minimum length without discontinuities and with data gaps not exceeding 30%.

- Weekly residual time series

The ulr4_detrend.zip file contains individual station data files of weekly residual position time series in ITRF2005 with respect to the linear long-term combined solution at the reference epoch. These residuals (detrended and deseasoned) are expressed in meters in the local frame (North, East, and Up). The reference position and the 3D velocity in the local coordinate system (East, North, Up) are provided in the header of each file. Theulr4.kmz file shows a Google Earth presentation of the above weekly data files by cliking on a station symbol. The associated IGS-like sitelog can be accessed too, as well as a plot of the residual position time series in the vertical.

- Position discontinuities

The ulr4_discontinuities_table.txt file provides the position offsets that were estimated.

- Solution file

The long-term solution ULR4 in SINEX format (47 Mo) can be downloaded too. However, we recommend its use only to those familiar with SINEX files, and having a good geodetic background and experience.

Citation

Please cite this paper if you find the results useful:

A. Santamaria-Gomez, M. Gravelle, X. Collilieux, M. Guichard, B. Martin Miguez, P. Tiphaneau, G. Wöppelmann (2012) : Mitigating the effects of vertical land motion in tide gauge records using a state-of-the-art GPS velocity field. Global and Planetary Change, Vol. 98-99, pp. 6-17.

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  • AVISO CCI CSIRO

Analyses of Sea Level Differences (Satellite altimetry minus tide gauge data)

  • Credits
  • sonel_60w.png
  • imedea_csic.jpg
showlayers hidelayers
  • Legend
  • (mm/year)
  • p6 > +6
  • p4 > +4
  • p2 > +2
  • p1 > +1
  • pdemi > +0.5
  • pm0 -0.5/+0.5
  • mdemi < -0.5
  • m1 < -1
  • m2 < -2
  • m4 < -4
  • m4 < -6
  • p6 GPS@TG
  • p4 RF & GPS@TG
  • p2 RF
  • p1 OTHER
PNG - 449.4 kb

In briefest terms, the approach consists in differencing the sea level time series from a tide gauge with an equivalent time series from satellite altimetry. To the extent that both instruments measure identical ocean signals, their difference is a proxy for the vertical position of the tide gauge. Assuming that the instrumental drifts are negligible, the time series of the sea level differences will then be dominated by vertical land motion at the tide gauge. This is illustrated in the figure on the right (click on the image to enlarge).

It is essential that the processing of both data types be as consistent as possible too. For instance, since an atmospheric correction is typically applied to satellite altimetry data, we applied the identical correction to the tide-gauge data. The details of the atmospheric corrections and their accuracies are not relevant here, since common errors will cancel in the differentiation. It is the consistency of models and corrections applied to both datasets that must be ensured.

- Tide gauge data:
All tide gauge data used in our calculations are from the Revised Local Reference (or ’RLR’) dataset of the PSMSL. We selected tide gauges with more than 15 years and a minimum of 70% of valid monthly mean sea levels over the satellite altimetry period, starting in 1993.0.

- Satellite altimetry data:
We used time series of satellite altimetry mean sea level anomalies (SLA), i.e., sea surface heights with respect to an arbitrary temporal mean (it can be different from one provider to the other), whose primary raison d’être is to avoid large numerical values (decametric ellipsoidal heights with regard to the expected centimeter level changes in mean sea levels). Note that we considered satellite altimetry data from several data suppliers.

Here, we are dealing with the data set identified as Global MSLA heights in delayed time ("all sat merged") from the Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO; http://www.aviso.altimetry.fr/en/data.html). The temporal sampling was homogenized to time series of monthly data.

- Sea level differences:
Time series of sea level differences (SLD) between monthly tide gauge and satellite altimetry data were built using three variants of satellite SLA time series, namely the closest grid point, the most correlated grid point, and the average within a 1° radius from the tide gauge location. However, since the differences between these variants were negligible, we adopted the SLA averaged within a radius of 1° around the tide gauge location.

Once computed, the time series of sea level differences (SLD) were checked visually to discard those that displayed clear non linear behaviour, i.e., for which vertical land motion would not be adequately modelled by a linear trend. We then estimated the linear trend of the de-seasoned and de-trended SLD time series. All quoted uncertainties represent 1-sigma standard errors.

- Future updates:
We plan to update the calculations of the sea level differences on a yearly basis, because as the time series lengthen and the satellite providers improve their products (new models, corrections, algorithms...), some level of improved precision is expected. However, there can be real differences between sea levels measured by a tide gauge at the coast and that measured off-shore by a satellite altimeter, due to the ocean processes which occur between the two points. This could be interesting to investigate and monitor. By contrast, for most open ocean locations, there is usually a high degree of correlation between variability in the two types of sea level. In this case, the vertical land motion can be estimated, provided the instrumental drifts are negligible. If there are (drifts), it enables tide gauge data to be used as a check on the satellite altimeter information and its stability and, conversely, it enables gross errors in tide gauge datum to be identified.

The web map enables the users to navigate from one site (station) to another, and click on a specific site in the map to see its attributes. By clicking further, an individual web page for the site can be accessed, where details on the data and meta-data are displayed for that site. The user can then get some additional statistics and download files with the associated time series (sea level differences...). Here, we provide a more comprehensive means to download the data from all the sites.

- Table of linear trends of sea level differences

The vertical_velocities_ALTG_AVISO.txt provides the vertical velocities and uncertainties for the 478 stations for which we computed the monthly sea level differences (satellite altimetry minus tide gauge data) and derived a linear trend (see About).

- Time series of monthly sea level differences

The useries_aviso.zip archive (.zip file) contains the 478 individual files of monthly sea level differences (satellite altimetry minus tide gauge data) time series. The format of the individual files is rather straightforward. The values are expressed in millimetres with respect to an arbitrary datum. The choice of the datum was arbitrary to avoid negative values (a constant of about 7000 mm was added to the differences).

Citation

Please cite this paper if you find the results useful:

G. Wöppelmannn & M. Marcos (2016) : Vertical land motion as a key to understanding sea level change and variability. Reviews of Geophysics, 54, pp. 64-92.

Analyses of Sea Level Differences (Satellite altimetry minus tide gauge data)

Work in progress...

Analyses of Sea Level Differences (Satellite altimetry minus tide gauge data)

Work in progress...