The common mode error (CME) and optimal noise model are the two most important factors affecting the accuracy of time series in regional Global Navigation Satellite System (GNSS) networks. Removing the CME and selecting the optimal noise model can effectively improve the accuracy of GNSS coordinate time series. The CME, a major source of error, is related to the spatiotemporal distribution; hence, its detrimental effects on time series can be effectively reduced through spatial filtering. Independent component analysis (ICA) is used to filter the time series recorded by 79 GPS stations in Antarctica from 2010 to 2018. After removing stations exhibiting strong local effects using their spatial responses, the filtering results of residual time series derived from principal component analysis (PCA) and ICA are compared and analyzed. The Akaike information criterion (AIC) is then used to determine the optimal noise model of the GPS time series before and after ICA/PCA filtering. The results show that ICA is superior to PCA regarding both the filter results and the consistency of the optimal noise model. In terms of the filtering results, ICA can extract multisource error signals. After ICA filtering, the root mean square (RMS) values of the residual time series are reduced by 14.45%, 8.97%, and 13.27% in the east (E), north (N), and vertical (U) components, respectively, and the associated speed uncertainties are reduced by 13.50%, 8.06% and 11.82%, respectively. Furthermore, different GNSS time series in Antarctica have different optimal noise models with different noise characteristics in different components. The main noise models are the white noise plus flicker noise (WN+FN) and white noise plus power law noise (WN+PN) models. Additionally, the spectrum index of most PN is close to that of FN. Finally, there are more stations with consistent optimal noise models after ICA filtering than there are after PCA filtering.

Global Positioning System (GPS) provides critical geodetic constraints on Earth's elastic response induced by ice and other mass loadings in Greenland. Previous studies focused on long‐term ice mass changes, with relatively fewer studies on transient signals and had used fewer GPS stations. Here we reconstructed 44 Greenland‐wide coastal decadal GPS time series using multichannel singular spectral analysis and quantified the origins of spatiotemporal patterns of transient to seasonal signals. We used an exhaustive list of geophysical processes, including surface mass balance (SMB), atmospheric pressure, continental hydrology, nontidal ocean loading, bedrock thermal expansion, precipitation, runoff, and ice discharge, and identified that most of Greenland's GPS sites exhibit SMB‐induced transient signals during 2012–2015, with maximum displacement reaching 20.45 mm. Analyses of differenced time series between adjacent GPS stations near peripheral glaciers reveal that locally varying seasonal signals are primarily attributable to SMB, followed by atmosphere.

Due to the coverage limitation of T/P-series altimeters, the lack of bathymetric data under large ice shelves, and the inaccurate definitions of coastlines and grounding lines, the accuracy of ocean tide models around Antarctica is poorer than those in deep oceans. Using tidal measurements from tide gauges, gravimetric data and GPS records, the accuracy of seven state-of-the-art global ocean tide models (DTU10, EOT11a, GOT4.8, FES2012, FES2014, HAMTIDE12, TPXO8) is assessed, as well as the most widely-used conventional model FES2004. Four regions (Antarctic Peninsula region, Amery ice shelf region, Filchner-Ronne ice shelf region and Ross ice shelf region) are separately reported. The standard deviations of eight main constituents between the selected models are large in polar regions, especially under the big ice shelves, suggesting that the uncertainty in these regions remain large. Comparisons with in situ tidal measurements show that the most accurate model is TPXO8, and all models show worst performance in Weddell sea and Filchner-Ronne ice shelf regions. The accuracy of tidal predictions around Antarctica is gradually improving.

Previous studies of GNSS-IR mainly focused on the legacy L1C signal; the potential of modernized signals (L2C and L5Q) has not yet been fully exploited. In this paper, we applied the Multichannel Singular Spectrum Analysis (M-SSA) method to extract common interference patterns from different frequencies simultaneously. The three-frequency (L1C, L2C, and L5Q) signal-to-noise ratio (SNR) measurements from a total of 840 satellite rising and setting arcs, occurring between day of year 250 to 279 in year 2020 and 2021, were used. By comparing GNSS-IR reflector heights obtained from the original and M-SSA-reconstructed SNR time series, we found that M-SSA significantly improves the between-frequency consistency, as shown by an increase in the values of R-squared of linear regression from (0.69, 0.67, 0.89) to (0.95, 0.96, 0.98), and a decrease in RMSE from (0.10 m, 0.10 m, 0.06 m) to (0.04 m, 0.04 m, 0.02 m) for S1C-S2C, S1C-S5Q, and S2C-S5Q pair, respectively. Our results validate (1) the effectiveness of the M-SSA method in extracting common interference patterns from multi-frequency SNR time series, and (2) the superiority of modernized civil signals L2C and L5Q over the legacy L1C signal in GNSS-IR studies. We also emphasize the important role that the L5 signal will play in future GNSS-IR research because of its compatibility and interoperability among different satellite navigation systems.

Due to limit of coverage in TOPEX/Poseidon (T/P) satellite and sparseness of in-situ tide gauges around Antarctica, the accuracy of global ocean tide models in Antarctic seas is relatively poorer than in low- and mid-latitude regions. To better understand ocean tides in Prydz Bay, east Antarctica, a GPS receiver was deployed on floating sea ice to measure tide-induced ice motion in multiple campaigns. Four online Precise Point Positioning (PPP) services are used to process the GPS data in the kinematic PPP mode, and UTide software is used to separate the major tidal constituents. Comparison between results from different processing methods (relative processing solutions from Track, kinematic PPP solutions from online services) and with bottom pressure gauge (BPG) shows that, high-accuracy tidal information can be obtained from GPS observations on floating sea ice, the root-sum-square (RSS) for the eight major constituents (O1, K1, P1, Q1, M2, S2, N2, K2) is below 4 cm. We have also studied the impacts of data span and filter edge effects at daily boundaries on the accuracy of tide estimates, and found that to obtain reliable tide estimates and neglect the filter edge effects, continuous observation longer than 30 days is necessary. Our study suggests that GPS provides an independent method to estimate tides in Prydz Bay, and can be an alternative to tidal gauges, which are costly and hard to maintain in Antarctica.

The Great Wall bay has become an important investigation area due to the establishment of the Chinese Antarctic Great Wall scientific research station. Establishing a seamless chart datum and its transformation model with other vertical data is the key to unifying the sea–land measurement in vertical references. In view of this, two transformation models are constructed using two different realizations of the geodetic height of the mean sea surface (MSS), coupled with a seamless chart datum model of Lowest Astronomical Tide (LAT). One transformation model (Solution 1) forms MSS height by summing the modeled values of the Earth Gravitational Model 2008 (EGM2008) and averaged absolute dynamic topography based on gridded satellite altimetry products from January 2017 to April 2018. The other transformation model (Solution 2) utilizes the gridded MSS geodetic height product of DTU15, which was derived from multimission satellite altimetry from 1993 to 2015. The LAT seamless chart datum model was calculated based on the tidal constituents derived from the Atlantic Ocean 2008 model. The experiment area is located $-62.350°$ to $-62.100°\mathrm{S}$ and $-59.050°$ to $-58.600°\mathrm{W}$ , which includes the entire Great Wall bay, and the accuracy of Solution 1 and Solution 2 have been assessed to be 12.0 and 11.2 cm according to the error propagation law. Then, compared with the field observation result by the pressure tidal gauge in the Great Wall bay, there exists in Solution 1 and Solution 2 differences of 127.6 and 42.2 cm, respectively. This shows that Solution 2 is much better than Solution 1, although it still far exceeds the accuracy evaluation of the solutions. The reasons for this has to do with the lengths of time of realized MSSs and the poor accuracy of satellite altimetry in the coastal waters.