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Seasonal low-degree changes in terrestrial water mass load from global GNSS measurements Meyrath, Thierry ; van Dam, Tonie ; et al in Journal of Geodesy (2017), 91(11), 1329-1350 Large-scale mass redistribution in the terrestrial water storage (TWS) leads to changes in the low-degree spherical harmonic coefficients of the Earth's surface mass density field. Studying these low ... [more ▼] Large-scale mass redistribution in the terrestrial water storage (TWS) leads to changes in the low-degree spherical harmonic coefficients of the Earth's surface mass density field. Studying these low-degree fluctuations is an important task that contributes to our understanding of continental hydrology. In this study, we use global GNSS measurements of vertical and horizontal crustal displacements that we correct for atmospheric and oceanic effects, and use a set of modified basis functions similar to Clarke et al. (2007) to perform an inversion of the corrected measurements in order to recover changes in the coefficients of degree-0 (hydrological mass change), degree-1 (center of mass shift) and degree-2 (flattening of the Earth) caused by variations in the TWS over the period January 2003 - January 2015. We infer from the GNSS-derived degree-0 estimate an annual variation in total continental water mass with an amplitude of $(3.49 \pm 0.19) \times 10^{3}$ Gt and a phase of $70 \pm 3^{\circ}$ (implying a peak in early March), in excellent agreement with corresponding values derived from the Global Land Data Assimilation System (GLDAS) water storage model that amount to $(3.39 \pm 0.10) \times 10^{3}$ Gt and $71 \pm 2^{\circ}$, respectively. The degree-1 coefficients we recover from GNSS predict annual geocentre motion (i.e. the offset change between the center of common mass and the center of figure) caused by changes in TWS with amplitudes of $0.69 \pm 0.07$ mm for GX, $1.31 \pm 0.08$ mm for GY and $2.60 \pm 0.13$ mm for GZ. These values agree with GLDAS and estimates obtained from the combination of GRACE and the output of an ocean model using the approach of Swenson et al. (2008) at the level of about 0.5, 0.3 and 0.9 mm for GX, GY and GZ, respectively. Corresponding degree-1 coefficients from SLR, however, generally show higher variability and predict larger amplitudes for GX and GZ. The results we obtain for the degree-2 coefficients from GNSS are slightly mixed, and the level of agreement with the other sources heavily depends on the individual coefficient being investigated. The best agreement is observed for $T_{20}^C$ and $T_{22}^S$, which contain the most prominent annual signals among the degree-2 coefficients, with amplitudes amounting to $(5.47 \pm 0.44) \times 10^{-3}$ and $(4.52 \pm 0.31) \times 10^{-3}$ m of equivalent water height (EWH), respectively, as inferred from GNSS. Corresponding agreement with values from SLR and GRACE is at the level of or better than $0.4 \times 10^{-3}$ and $0.9 \times 10^{-3}$ m of EWH for $T_{20}^C$ and $T_{22}^S$, respectively, while for both coefficients, GLDAS predicts smaller amplitudes. Somewhat lower agreement is obtained for the order-1 coefficients, $T_{21}^C$ and $T_{21}^S$, while our GNSS inversion seems unable to reliably recover $T_{22}^C$. For all the coefficients we consider, the GNSS-derived estimates from the modified inversion approach are more consistent with the solutions from the other sources than corresponding estimates obtained from an unconstrained standard inversion. [less ▲] Detailed reference viewed: 247 (15 UL)Quality Evaluation of the Weekly Vertical Loading Effects Induced from Continental Water Storage Models Li, Zhao ; van Dam, Tonie ; et al in Willis, Pascal (Ed.) Proceedings of the 2013 IAG Scientific Assembly, Potsdam, Germany, 1-6 September, 2013 (2015) To remove continental water storage (CWS) signals from the GPS data, CWS mass models are needed to obtain predicted surface displacements. We compared weekly GPS height time series with five CWS models ... [more ▼] To remove continental water storage (CWS) signals from the GPS data, CWS mass models are needed to obtain predicted surface displacements. We compared weekly GPS height time series with five CWS models: (1) the monthly and (2) three-hourly Global Land Data Assimilation System (GLDAS); (3) the monthly and (4) one-hourly Modern- Era Retrospective Analysis for Research and Applications (MERRA); (5) the six-hourly National Centers for Environmental Prediction-Department of Energy (NCEP-DOE) global reanalysis products (NCEP-R-2). We find that of the 344 selected global IGS stations, more than 77% of stations have their weighted root mean square (WRMS) reduced in the weekly GPS height by using both the GLDAS and MERRA CWS products to model the surface displacement, and the best improvement concentrate mainly in North America and Eurasia.We find that the one-hourly MERRA-Land dataset is the most appropriate product for modeling weekly vertical surface displacement caused by CWS variations. The threehourly GLDAS data ranks the second, while the GLDAS and MERRA monthly products rank the third. The higher spatial resolution MERRA product improves the performance of the CWS model in reducing the scatter of the GPS height by about 2–6% compared with the GLDAS. Under the same spatial resolution, the higher temporal resolution could also improve the performance by almost the same magnitude. We also confirm that removing the ATML and NTOL effects from the weekly GPS height would remarkably improve the performance of CWS model in correcting the GPS height by at least 10%, especially for coastal and island stations. Since the GLDAS product has a much greater latency than the MERRA product, MERRA would be a better choice to model surface displacements from CWS. Finally, we find that the NCEP-R-2 data is not sufficiently precise to be used for this application. Further work is still required to determine the reason. [less ▲] Detailed reference viewed: 151 (11 UL)Singular spectrum analysis for modeling seasonal signals from GPS time series ; van Dam, Tonie ; et al in Journal of Geodynamics (2013), 72 Seasonal signals in GPS time series are of great importance for understanding the evolution of regional mass fluctuations, i.e., ice, hydrology, and ocean mass. Conventionally these signals quasi-annual ... [more ▼] Seasonal signals in GPS time series are of great importance for understanding the evolution of regional mass fluctuations, i.e., ice, hydrology, and ocean mass. Conventionally these signals quasi-annual and semi-annual signals are modeled by least-squares fitting harmonic terms with a constant amplitude and phase. In reality, however, such seasonal signals are modulated, i.e., they will have a time-variable amplitude and phase. Recently, Davis et al.(2012) proposed a Kalman filter based approach to capture the stochastic seasonal behavior of geodetic time series. Singular Spectrum Analysis (SSA) is a non-parametric method, which uses time domain data to extract information from short and noisy time series without a priori knowledge of the dynamics affecting the time series. A prominent benefit is that trends obtained in this way are not necessarily linear. Further, true oscillations can be amplitude and phase modulated. In this work, we will assess the value of SSA for extracting time-variable seasonal signals from GPS time series. We compare our SSA-based results to those obtained using 1) least-squares analysis and 2) Kalman filtering. Our results demonstrate that SSA is a viable and complementary tool for extracting modulated oscillations from GPS time series. [less ▲] Detailed reference viewed: 548 (28 UL)Strategies to mitigate aliasing of loading signals while estimating GPS frame parameters ; van Dam, Tonie ; et al in Journal of Geodesy (2012), 86(1), 1-14 Although GNSS techniques are theoretically sensitive to the Earth center of mass, it is often preferable to remove intrinsic origin and scale information from the estimated station positions since they ... [more ▼] Although GNSS techniques are theoretically sensitive to the Earth center of mass, it is often preferable to remove intrinsic origin and scale information from the estimated station positions since they are known to be affected by systematic errors. This is usually done by estimating the parameters of a linearized similarity transformation which relates the quasi-instantaneous frames to a long-term frame such as the International Terrestrial Reference Frame (ITRF). It is well known that non-linear station motions can partially alias into these parameters. We discuss in this paper some procedures that may allow reducing these aliasing effects in the case of the GPS techniques. The options include the use of well-distributed sub-networks for the frame transformation estimation, the use of site loading corrections, a modification of the stochastic model by downweighting heights, or the joint estimation of the low degrees of the deformation field. We confirm that the standard approach consisting of estimating the transformation over the whole network is particularly harmful for the loading signals if the network is not well distributed. Downweighting the height component, using a uniform sub-network, or estimating the deformation field perform similarly in drastically reducing the amplitude of the aliasing effect. The application of these methods to reprocessed GPS terrestrial frames permits an assessment of the level of agreement between GPS and our loading model, which is found to be about 1.5 mm WRMS in height and 0.8 mm WRMS in the horizontal at the annual frequency. Aliased loading signals are not the main source of discrepancies between loading displacement models and GPS position time series. [less ▲] Detailed reference viewed: 142 (2 UL)Quality assessment of GPS reprocessed terrestrial reference frame ; ; et al in GPS Solutions (2011), 15(3), 219--231 The International GNSS Service (IGS) contributes to the construction of the International Terrestrial Reference Frame (ITRF) by submitting time series of station positions and Earth Rotation Parameters ... [more ▼] The International GNSS Service (IGS) contributes to the construction of the International Terrestrial Reference Frame (ITRF) by submitting time series of station positions and Earth Rotation Parameters (ERP). For the first time, its submission to the ITRF2008 construction is based on a combination of entirely reprocessed GPS solutions delivered by 11 Analysis Centers (ACs). We analyze the IGS submission and four of the individual AC contributions in terms of the GNSS frame origin and scale, station position repeatability and time series seasonal variations. We show here that the GPS Terrestrial Reference Frame (TRF) origin is consistent with Satellite laser Ranging (SLR) at the centimeter level with a drift lower than 1 mm/year. Although the scale drift compared to Very Long baseline Interferometry (VLBI) and SLR mean scale is smaller than 0.4 mm/year, we think that it would be premature to use that information in the ITRF scale definition due to its strong dependence on the GPS satellite and ground antenna phase center variations. The new position time series also show a better repeatability compared to past IGS combined products and their annual variations are shown to be more consistent with loading models. The comparison of GPS station positions and velocities to those of VLBI via local ties in co-located sites demonstrates that the IGS reprocessed solution submitted to the ITRF2008 is more reliable and precise than any of the past submissions. However, we show that some of the remaining inconsistencies between GPS and VLBI positioning may be caused by uncalibrated GNSS radomes. [less ▲] Detailed reference viewed: 190 (3 UL) |
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