Abstract
Vertical surface displacements from continuous Global Positioning System (GPS) stations often show strong seasonal signals, which in some cases may be associated with surface mass loading, including hydrological, and non-tidal oceanic and atmospheric loading. In Antarctica, many GPS stations show vertical motions in phase with seasonal snow accumulation changes, but these variations cannot be fully explained with snow load variations between seasons. Instead we show, for many sites in Antarctica, that a significant component of the annual cycle in vertical GPS coordinates time series may be related to snow/ice inside antennas causing as an artefact apparent seasonal variation, with amplitudes of up to 4 mm. We present a method based on the Empirical Mode Decomposition (EMD) algorithm to remove this artefact signal. The corrected GPS time series show an improvement in the agreement with displacements predicted by elastic modelling using GRACE-derived surface mass loads.
Highlights
Surface displacement time series, derived from the Global Positioning System (GPS), contain signatures of multiple geophysical phenomena, ranging from global tectonic plate motions (Feigl et al 1993) to local climatic effects (Davis et al 2004)
The resulting first intrinsic mode functions (IMFs) corresponds to the annual signal component related to snow effects, which we remove from the raw GPS time series
A core assumption of our method is that the “genuine” geophysical periodic surface loading signal can be restored from GRACE-derived elastic deformation
Summary
Surface displacement time series, derived from the Global Positioning System (GPS), contain signatures of multiple geophysical phenomena, ranging from global tectonic plate motions (Feigl et al 1993) to local climatic effects (Davis et al 2004). Vertical GPS time series often show seasonal variations dominated by yearly and half-yearly periods (van Dam et al 2007) These variations are mainly associated with the Earth’s response to surface hydrological and atmospheric loading caused by the water transfer between continents and oceans. The common practice to eliminate anomalous position estimates, within the geodetic community, is to flag snow-contaminated estimates based on the experience and knowledge of the local weather conditions of the GPS site, assuming a certain model of the time series. Larson (2013) developed an algorithm that uses SNR data to determine when the GPS signal has been impacted by snow or ice. The common practice to eliminate anomalous position estimates, within the geodetic community, is to flag snow-contaminated estimates based on the experience and knowledge of the local weather conditions of the GPS site, assuming a certain model of the time series. We assess and discuss the effect of snow intrusion on GPS time series and we introduce a method for removing this effect
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