Validation of Modelled Displacements due to Atmospheric Loading Using the European CGPS Network
Trond A. Håkonsen(1), Hans-Peter Plag(2,3) and Halfdan P. Kierulf(3)
(1) Norwegian Technical University, Institute for Geomatics
(2) Nevada Bureau of Mines and Geology and Seismological Laboratory
University of Nevada, Reno, Mailstop 178, Reno, NV 89557, USA
(3) Norwegian Mapping Authority
3507 Honefoss. Norway
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Monitoring secular vertical land motion for applications such as sea level variations requires a long-term stable reference frame and a station motion model describing the motion of the station from sub-daily time scales to interannual time scales sufficiently accurate. On these time scales, station motion is mainly caused by earth tide, ocean tidal loading, polar motion, and surface loading due to atmophere, ocean and terrestrial hydrosphere. In tectonically active regions or regions with large man-made subsidence, these tectonic and man-made processes may contribute significantly to the station motion, too. The IERS Conventions 2000 (Mc Carthy and Petit, 2003) specify the models for most of these processes. However, for surface loading, the model predictions are still too uncertain to give rigorous recommendations for their treatment in space-geodetic analyses.
Precise Point Positioning (PPP) is a methodology for CGPS analysis that results in time series referred to a globally homogeneous reference frame and is thus well suited for the purpose of sea level studies. However, PPP is prone to be affected by so-called Common Mode Errors (CME), which are mainly due to unaccounted station motion leading to errors in satellite orbits and clocks as well as distortions of the global reference frame. One contribution to CMEs comes from unaccounted displacements due to surface loading.
Here we use time series of displacement determined with PPP for selected stations of the European CGPS network to identify the atmospheric loading signal in vertical land motion. We intercompare five different model predictions using two different surface pressure data sets (NCEP and ECMWF), different reference frames and different computational approaches (available at http://www.sbl.statkart.no). The intercomparison on the one hand is based on direct regression of the time series of observed and predicted displacements, and, on the other hand, uses the main common modes found in an Emphirical Orthogonal Functions (EOF) analysis of both the observed vertical land motion and the predicted displacements. The results show that