Updated GNSS velocity solution in the Nordic and Baltic countries with a semi-automatic offset detection method

Sonja Lahtinen*, Lotti Jivall, Pasi Häkli, Maaria Nordman

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
92 Downloads (Pure)

Abstract

In Fennoscandia, the Glacial Isostatic Adjustment (GIA) causes intraplate deformations that affect the national static reference frames. The GNSS-determined velocities are important data for constraining the GIA models, which are necessary for maintaining the national reference frames. The Nordic Geodetic Commission (NKG) has published a dense and consistent GNSS station velocity solution in 2019, and we present now an update of the solution covering additional 3.5 years of data. Undetected positional offsets are the main factor decreasing the accuracy of the velocity estimates. We developed a method for the semi-automatic offset detection to improve the quality of our solution. The results show that we could correctly detect 74% of the manually determined offsets, and the undetected offsets would have caused a median 0.1 mm/y bias in trend. The method pointed out some otherwise unnoticed offsets and will decrease the need for manual analysis in the future. The updated velocity solution especially improves the velocity estimates of the newly established stations and the quality of the velocity estimates in Baltic countries. The formal uncertainties estimated using the power-law plus white noise model were at a median of 0.06 and 0.15 mm/y for horizontal and vertical velocities, respectively. However, we concluded that the systematic velocity uncertainties due to the reference frame alignment were approximately at the same level.

Original languageEnglish
Article number9
Number of pages12
JournalGPS Solutions
Volume26
Issue number1
DOIs
Publication statusPublished - Jan 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • GNSS velocities
  • Offset detection
  • Reference frames
  • Time series analysis
  • Uncertainties

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