Key points are not available for this paper at this time.
Abstract The GNSS time series is affected by hydrological loading (HYDL) associated with water storage, non-tidal atmospheric loading (NTAL), and non-tidal oceanic loading (NTOL) in the Earth’s surface, potentially leading to time lag phenomena and nonlinear deformation exhibiting annual and semi-annual variations. Studying the influence of load on GNSS time series is essential for acquiring high-precision velocity fields and uncertainties. In this study, we developed a quantitative metric, DTW-N, to investigate the impact of environmental load on the GNSS time series by employing the dynamic temporal warping (DTW) algorithm and normalizing (N) the cumulative error. The DTW-N method was utilized on the coordinate time series of 236 GNSS stations located on the West Coast region of the United States from January 2008 to August 2024. The DTW-N algorithm dynamically adjusts the time series to achieve optimal alignment between the load data and GNSS time series, thereby calculating a more accurate cumulative error. Considering the accuracy in quantifying the impact of environmental loads on GNSS velocity fields, DTW-N can effectively capture nonlinear changes and local errors between time series and accurately measure the correction effect of environmental loads on GNSS data. The Pearson correlation coefficient (PCC) was adopted to assess the effectiveness of DTW-N, and the results showed that the DTW-N can effectively analyse the impacts of various environmental loads. The DTW-N metric, used to quantify the relative contributions of environmental loading effects, indicated the following order of impact: NTOL > NTAL > HYDL > COM-COR. Meanwhile, DTW-N is performed to explain the impact of environmental loads on the GNSS velocity field, and the U-test is used for velocity uncertainty evaluation. The velocity uncertainty converged within the range of 0.100 mm/a to 0.150 mm/a.
Sun et al. (Mon,) studied this question.