Search

Global navigation satellite system reflectometry(GNSS-R)has shown great potential in surface and marine environment parameters monitoring, with the advantages of extensive data sources, low cost, high spatial and temporal resolution, and has become an important approach for sea surface height(SSH)inversion. The existing research mainly focuses on the short-term GPS sea surface inversion within 3-6 months, which is difficult to reflect the seasonal variation and interannual characteristics of SSH. Meanwhile, only the influence of vertical velocity is considered in the dynamic sea surface correction, and the vertical acceleration of sea surface fluctuation is ignored, resulting in the poor estimation accuracy of low and high tide levels. For the above issues, a shore-based tracking station BRST in France was taken as an example, and the reflected signals of 3 consecutive years from BDS/GPS/GLONASS/Galileo system were used to estimate SSH. Based on Lomb-Scargle spectrum analysis and second-order dynamic tide level correction model, a robust regression strategy was adopted. The estimation results were compared with tide gauge records to analyze the trend of sea level change. The results show that the inversion results of GNSS-R technology are in good agreement with tide gauge records, and the inversion accuracy has a tendency to improve year by year, with a root mean square error(RMSE)of 7.57 cm and a correlation coefficient of 0.935 for the three-year daily average; the seasonal variation of SSH is obvious, the average SSH is high in autumn and winter, and low in summer, and the seasonal variation of SSH is opposite to that of temperature; the differences of amplitude for nine tidal constituents including M2, S2, K1, O1, N2, K2, P1, Q1 and M4, range from 0.06 cm to 6.76 cm with the mean absolute error(MAE)of 1.60 cm, and the differences of phase range from 0.03° to 6.96° with the MAE of 2.45°. It is further verified that the reliability of GNSS-R technology in monitoring SSH change in the frequency domain. [ABSTRACT FROM AUTHOR]

Objectives: There are obvious seasonal variations in the GPS height time series, which affect the improvement of precision and can be corrected by both mathematical modelling and geophysical mechanisms. Compared to least square fitting, singular spectrum analysis (SSA) can extract random seasonal signals effectively through signal reconstruction, which is unaffected by the assumed sinusoidal waves. According to the elastic loading theory, the gravity recovery and climate experiment (GRACE) can be used to calculate the vertical surface displacement caused by changes in terrestrial water storage. Methods: This paper mainly studies the feasibility of correcting the seasonal variations in GPS heights using SSA and GRACE inversion results. The height time series of 27 GPS stations in Australia with a time span of from 5 to 10 years were chosen and combined with GRACE simultaneous inversions. Results: Because the spatial resolutions of GRACE are coarse and the loading displacement is much more sensitive to near‑field mass changes than far‑field ones, the amplitudes of GRACE ‑inferred hydrological loading deformations are significantly smaller than GPS. The weighted root mean square (WRMS) are reduced at 22 stations after GRACE ‑inferred displacement corrections, and the correlation coefficients between deformations estimated by GPS and GRACE range from 0.12 to 0.78 with a mean value of 0.43, indicating that GPS and GRACE results have good consistency and correlation. SSA is used to extract the annual signals of vertical displacements derived from GPS and GRACE, and contribution rates of singular spectral variance of annual signals are 21.60% and 34.48%, respectively, expressing that annual signals are the main components of GRACE inferred results. Geographical climatic conditions have a significant impact on the consistency of annual signals derived from GPS and GRACE. Compared with the arid areas in central and western Australia, the amplitude and phase of annual signals derived from GPS and GRACE are more consistent in the northern region with seasonal rainfall. Furthermore, cross wavelet transform (XWT) finds that the vertical displacement series derived from GPS and GRACE of each station have a significant resonance period of one year. The circular average phase angles of GPS/GRACE at the period closet to 1 cycle per year (cpy) outside the cone of influence range from − 74.03° to 67.23° . The mean XWT ‑ based semblances range from 0.28 to 0.99 with an average value of 0.79, showing that there is a significant positive correlation between the annual variations derived from GPS and GRACE. Conclusions: Overall, GRACE‑inferred deformations can explain the annual variations of GPS‑derived displacements, particularly in areas with high hydrological loading. It is possible to correct the annual signals of GPS heights by GRACE inversions, but the effect is not as good as the SSA‑filtered annual signals. [ABSTRACT FROM AUTHOR]

In order to effectively extract useful information from time series of the global naviga- tion satellite system(GNSS) sites, and improve the modeling accuracy of coordinate time series, this paper proposes a nonlinear motion modeling method combining wavelet decomposition and singular spectrum analysis. Experiments were carried out using global positioning system(GPS) vertical coordinate time series of 11 stations around the world from 1999 to 2018. Methods: Firstly, the coordinate time series is decom-posed into different scales by wavelet decomposition.And then the singular spectrum analysis(SSA) is per- formed on the high-frequency part and the low -frequency part of each layer. Finally, combine the fitting value that is the fitting coordinate time series, and the fitting effect of the new method was evaluated. Results: The results show that, compared with the simple singular spectrum analysis method, the new method can extract useful information such as trend and period more accurately from the limited scale of the time series with noise. And reduce the partial period items in the singular spectrum analysis method to some extent, for example, the seasonal period item and the monthly period item are regarded as the probability of noise rejec- tion, and the modeling accuracy is improved by 26%. Conclusions: The purpose is to propose a nonlinear motion modeling method based on wavelet decomposition and singular spectrum analysis, so as to improve the modeling accuracy of coordinate time series. [ABSTRACT FROM AUTHOR]