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Quality of Virtual Data

Quality of Virtual Data. by Pasi Häkli Finnish Geodetic Institute. Use of real-time GNSS applications, especially network RTK, increased rapidly Network RTK: VRS approch – no information about quality due to limitations in data transfer

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Quality of Virtual Data

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  1. Quality of Virtual Data by Pasi Häkli Finnish Geodetic Institute

  2. Use of real-time GNSS applications, especially network RTK, increased rapidly Network RTK: VRS approch – no information about quality due to limitations in data transfer Virtual data backbone (all processing at the user end done with respect to that) Computed zero baselines in order to get the quality and performance of the system without unnecessary biases e.g. site effects Background and motivation

  3. FinnRef + virtual data Data • FinnRef and GNSSnet.fi networks used • zero baselines: • virtual data generated to the locations of the FinnRef stations • computed baseline between FinnRef and virtual data • FinnRef serves the best possible national ETRS89 coordinates (also independent from VRS network) • ETRS89: EUREF-FIN (=ETRF96), epoch: • plate tectonics 1989.0 • intraplate 1997.0

  4. Computation and analysis • Temporal quality • daily solutions – systematic errors and homogeneity • hourly solutions – more details for post-processing applications • Kinematic solutions – for real-time applications • Spatial quality • National • local

  5. Temporal quality – daily solutions DEGE

  6. Temporal quality – daily solutions JOEN

  7. Temporal quality – daily solutions KEVO

  8. Temporal quality – daily solutions KIVE

  9. Temporal quality – daily solutions KUUS

  10. Temporal quality – daily solutions METS

  11. Temporal quality – daily solutions OLKI

  12. Temporal quality – daily solutions OULU

  13. Temporal quality – daily solutions ROMU

  14. Temporal quality – daily solutions SODA

  15. Temporal quality – daily solutions TUOR

  16. Temporal quality – daily solutions VAAS

  17. Temporal quality – daily solutions VIRO

  18. Temporal quality – daily solutions • Long-term daily solutions: • good repeatability (small deviation) • some systematics • some site-dependent effects • Influencing factors: • Reference coordinates • Environmental effects • Instrumentation • … Table. Standard deviation and rms of the 10-month time series of daily solutions (gross errors excluded)

  19. What effects on quality? • reference coordinates • Change at DOY 90/2006 METS

  20. What effects on quality? • reference coordinates • Change at DOY 90/2006 OULU

  21. What effects on quality? • Environmental effects • Snow, … SODA

  22. What effects on quality? • Environmental effects • Snow, … ROMU

  23. What effects on quality? • Instrumentation • Antenna/receiver change VIRO

  24. What effects on quality? • Data • Gaps, bad data, … <20h data KEVO

  25. Spatial quality • results show some systematics in up component – are they spatially correlated somehow? • national level: probably land uplift? • local: modelling of biases in the network or inaccuracies in interpolation? Spatial quality studied from the data between 3-5/2006 so far

  26. VIRO excluded Spatial quality – nationwide • land uplift? computed from FinnRef computed from virtual data average of timeseries converted to annual ”error” and relative to METS

  27. Spatial quality – nationwide • land uplift? • at least a good correlation (VIRO excluded)

  28. satellite • 1) Observations at reference stations of GNSSnet.fi at the epoch tc ref. stn Spatial quality – nationwide • land uplift? • EUREF-FIN epochs: • rigid plate motion: 1989.0 • intra-plate: 1997.0 tc

  29. satellite • 1) Observations at reference stations of GNSSnet.fi at the epoch tc 1997.0 ref. stn Spatial quality – nationwide • land uplift? • EUREF-FIN epochs: • rigid plate motion: 1989.0 • intra-plate: 1997.0 • 2) Forcing coordinates to epoch 1997.0 – generating “a land uplift bias” to model tc

  30. satellite • 1) Observations at reference stations of GNSSnet.fi at the epoch tc ref. stn Spatial quality – nationwide • land uplift? • EUREF-FIN epochs: • rigid plate motion: 1989.0 • intra-plate: 1997.0 • 2) Forcing coordinates to epoch 1997.0 – generating “a land uplift bias” to model • 3) Geometrical displacement of reference station data tc 1997.0 vrs stn

  31. satellite • 1) Observations at reference stations of GNSSnet.fi at the epoch tc ref. stn Spatial quality – nationwide • land uplift? • EUREF-FIN epochs: • rigid plate motion: 1989.0 • intra-plate: 1997.0 • 2) Forcing coordinates to epoch 1997.0 – generating “a land uplift bias” to model • 3) Geometrical displacement of reference station data + interpolated biases tc 1997.0 vrs stn

  32. satellite • 1) Observations at reference stations of GNSSnet.fi at the epoch tc ref. stn Spatial quality – nationwide • land uplift? • EUREF-FIN epochs: • rigid plate motion: 1989.0 • intra-plate: 1997.0 • 2) Forcing coordinates to epoch 1997.0 – generating “a land uplift bias” to model • 3) Geometrical displacement of reference station data + interpolated biases = virtual data at 1997.0 tc 1997.0 vrs stn

  33. Spatial quality – nationwide satellite • land uplift? • EUREF-FIN epochs: • rigid plate motion: 1989.0 • intra-plate: 1997.0 • 1) Observations at reference stations of GNSSnet.fi at the epoch tc • 2) Forcing coordinates to epoch 1997.0 – generating “a land uplift bias” to model • 3) Geometrical displacement of reference station data + interpolated biases = virtual data at 1997.0 FinnRef stn tc • 4) FinnRef data at tc 1997.0 vrs stn ref. stn

  34. 5) Zero baseline: = land uplift (1997.0- tc) + some additional biases Spatial quality – nationwide satellite • land uplift? • EUREF-FIN epochs: • rigid plate motion: 1989.0 • intra-plate: 1997.0 • 1) Observations at reference stations of GNSSnet.fi at the epoch tc • 2) Forcing coordinates to epoch 1997.0 – generating “a land uplift bias” to model • 3) Geometrical displacement of reference station data + interpolated biases = virtual data at 1997.0 FinnRef stn tc • 4) FinnRef data at tc 1997.0 vrs stn ref. stn

  35. Spatial quality – local • modelling of biases in the network or inaccuracies in interpolation?

  36. Spatial quality – local • modelling of biases in the network or inaccuracies in interpolation? • to find out: • standard deviations • distance to master station  Not so obvious but some correlation

  37. Temporal quality – hourly and kinematic solutions 1 week of data – GPS week 1373 (120-126/2006)

  38. Temporal quality – hourly and kinematic solutions KIVE Hourly

  39. Temporal quality – hourly and kinematic solutions KIVE Kinematic

  40. Summary • Virtual data precise (=small standard deviation) but some systematic errors remain • Biases caused by: • Reference coordinates • Environmental effects like snow • Intrumentation • Modelling of errors and/or interpolation errors • … • Land uplift seen in virtual data – how to fix this? Introducing geodynamical model in VRS generation process? • Key issue if cm-level accuracies are wanted in national reference frames!

  41. Thanks! Part of these results were published in: Häkli, P. (2006): Quality of Virtual Data Generated from the GNSS Reference Station Network. Shaping the Change, XXIII FIG Congress, Munich, Germany, October 8-13, 2006. (http://www.fig.net/pub/fig2006/papers/ps05_01/ps05_01_04_hakli_0520.pdf)

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