ITRF2008 plate motion model Altamimi, Z.; Métivier, L.; Collilieux, X.
Journal of Geophysical Research: Solid Earth,
July 2012, Letnik:
117, Številka:
B7
Journal Article
Recenzirano
Odprti dostop
The ITRF2008 velocity field is demonstrated to be of higher quality and more precise than past ITRF solutions. We estimated an absolute tectonic plate motion model made up of 14 major plates, using ...velocities of 206 sites of high geodetic quality (far from plate boundaries, deformation zones and Glacial Isostatic Adjustment (GIA) regions), derived from and consistent with ITRF2008. The precision of the estimated model is evaluated to be at the level of 0.3 mm/a WRMS. No GIA corrections were applied to site velocities prior to estimating plate rotation poles, as our selected sites are outside the Fennoscandia regions where the GIA models we tested are performing reasonably well, and far from GIA areas where the models would degrade the fit (Antarctica and North America). Our selected velocity field has small origin rate bias components following the three axis (X, Y, Z), respectively 0.41 ± 0.54, 0.22 ± 0.64 and 0.41 ± 0.60 (95 per cent confidence limits). Comparing our model to NNR‐NUVEL‐1A and the newly available NNR‐MORVEL56, we found better agreement with NNR‐MORVEL56 than with NNR‐NUVEL‐1A for all plates, except for Australia where we observe an average residual rotation rate of 4 mm/a. Using our selection of sites, we found large global X‐rotation rates between the two models (0.016°/Ma) and between our model and NNR‐MORVEL56 of 0.023°/Ma, equivalent to 2.5 mm/a at the Earth surface.
Key Points
We estimated a global motion model of 14 major plates consistent with ITRF2008
We found a small frame origin rate bias, at the level of 0.4 +/‐0.6 mm/yr
The precision of the estimated model is at the level of 0.3 mm/a WRMS
Unlike the past International Terrestrial Reference Frame (ITRF) versions where global long‐term solutions were combined, the ITRF2005 uses as input data time series (weekly from satellite techniques ...and 24‐h session‐wise from Very Long Baseline Interferometry) of station positions and daily Earth Orientation Parameters (EOPs). The advantage of using time series of station positions is that it allows to monitor station non‐linear motion and discontinuities and to examine the temporal behavior of the frame physical parameters, namely the origin and the scale. The ITRF2005 origin is defined in such a way that it has zero translations and translation rates with respect to the Earth center of mass, averaged by the Satellite Laser Ranging (SLR) time series spanning 13 years of observations. Its scale is defined by nullifying the scale and its rate with respect to the Very Long Baseline Interferometry (VLBI) time series spanning 26 years of observations. The ITRF2005 orientation (at epoch 2000.0) and its rate are aligned to the ITRF2000 using 70 stations of high geodetic quality. The estimated level of consistency of the ITRF2005 origin (at epoch 2000.0) and its rate with respect to the ITRF2000 is respectively 0.1, 0.8, 5.8 mm and 0.2, 0.1, 1.8 mm/yr along the X, Y and Z‐axis. We estimate the formal errors on these components to be 0.3 mm and 0.3 mm/yr. We believe that this low level of agreement between the two frame origins is most probably due to the poor SLR network geometry and its degradation over time. The ITRF2005 combination involving 84 co‐location sites revealed a scale inconsistency of 1 ppb (6.3 mm at the equator), at epoch 2000.0, and 0.08 ppb/yr between the SLR and VLBI long‐term solutions as obtained by the stacking of their respective time series. Possible causes of this inconsistency may include the poor SLR and VLBI networks and their co‐locations, local tie uncertainties, systematic effects and possible inconsistent model corrections used in the data analysis of both techniques. For the first time of the ITRF history, the ITRF2005 rigorous combination provides self‐consistent series of EOPs, including Polar Motion from VLBI and satellite techniques and Universal Time and Length of Day from VLBI only. A velocity field of 152 sites with an error less than 1.5 mm/yr is used to estimate absolute rotation poles of 15 tectonic plates that are consistent with the ITRF2005 frame. This new absolute plate motion model supersedes and significantly improves that of the ITRF2000 which involved six major tectonic plates.
ITRF2020 Plate Motion Model Altamimi, Z.; Métivier, L.; Rebischung, P. ...
Geophysical research letters,
28 December 2023, Letnik:
50, Številka:
24
Journal Article
Recenzirano
Odprti dostop
A tectonic Plate Motion Model (PMM) is essential for geodetic applications, while contributing to the understanding of geodynamic processes affecting the Earth's surface. We introduce a PMM derived ...from the horizontal velocities of 518 sites extracted from the ITRF2020 solution. These sites were chosen away from plate boundaries, Glacial Isostatic Adjustment regions, and other deforming zones. Unlike the ITRF2014‐PMM, which showed no significant Origin Rate Bias (ORB), velocities used to determine the ITRF2020‐PMM exhibit a statistically significant ORB (0.74 ± 0.09 mm/yr along the Z‐component). Users are advised to add the estimated ORB to the horizontal velocities predicted by the ITRF2020‐PMM rotation poles for full consistency with the ITRF2020. However, the predicted vertical velocities resulting from the addition of the ORB should be discarded. The overall precision with which the ITRF2020 velocity field is represented by the rigid ITRF2020‐PMM is at the level of 0.25 mm/yr WRMS.
Plain Language Summary
The Earth's surface is divided in large and small tectonic plates, which evolve and move slowly over time, resulting in lateral displacements of the ground surface typically of the order of a few cm/yr. Because of the relative motion between tectonic plates, plate boundaries can be either divergent (when two plates move away from each other), convergent (when two plates collide) or transform (when two plates slide past each other). Plate motion models are used to quantify the relative motions of the plates with respect to each other, and are determined using geological data or observations collected by space geodesy instruments distributed over different plates at the Earth's surface. In the latter case, space geodesy observations from the four space geodetic techniques covering more than 40 years of data are analyzed to estimate the long‐term displacements (or velocities) of each instrument in a well defined and self‐consistent global reference frame. The derived velocity field is then used to estimate a comprehensive plate motion model (PMM). This article presents a PMM for 13 tectonic plates based on a subset of the velocity field from the recently released International Terrestrial Reference Frame 2020 (ITRF2020); see https://itrf.ign.fr/en/solutions/ITRF2020.
Key Points
We derive a plate motion model for 13 tectonic plates from the ITRF2020 horizontal velocity field
Built under the rigid‐plate motion hypothesis, the model represents the ITRF2020 velocity field with a precision of 0.25 mm/yr WRMS
The residual velocities would show a global northward motion if a translation rate was not included in the inversion model
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.
On 17 April 2011, all analysis centers (ACs) of the International GNSS Service (IGS) adopted the reference frame realization IGS08 and the corresponding absolute antenna phase center model igs08.atx ...for their routine analyses. The latter consists of an updated set of receiver and satellite antenna phase center offsets and variations (PCOs and PCVs). An update of the model was necessary due to the difference of about 1 ppb in the terrestrial scale between two consecutive realizations of the International Terrestrial Reference Frame (ITRF2008 vs. ITRF2005), as that parameter is highly correlated with the GNSS satellite antenna PCO components in the radial direction.
For the receiver antennas, more individual calibrations could be considered and GLONASS-specific correction values were added. For the satellite antennas, all correction values except for the GPS PCVs were newly estimated considering more data than for the former model. Satellite-specific PCOs for all GPS satellites active since 1994 could be derived from reprocessed solutions of five ACs generated within the scope of the first IGS reprocessing campaign. Two ACs separately derived a full set of corrections for all GLONASS satellites active since 2003.
Ignoring scale-related biases, the accuracy of the satellite antenna PCOs is on the level of a few cm. With the new phase center model, orbit discontinuities at day boundaries can be reduced, and the consistency between GPS and GLONASS results is improved. To support the analysis of low Earth orbiter (LEO) data, igs08.atx was extended with LEO-derived PCV estimates for big nadir angles in June 2013.
On April 17, 2011, the International GNSS Service (IGS) stopped using the IGS05 reference frame and adopted a new one, called IGS08, as the basis of its products. The latter was derived from the ...latest release of the International Terrestrial Reference Frame (ITRF2008). However, the simultaneous adoption of a new set of antenna phase center calibrations by the IGS required slight adaptations of ITRF2008 positions for 65 of the 232 IGS08 stations. The impact of the switch from IGS05 to IGS08 on GNSS station coordinates was twofold: in addition to a global transformation due to the frame change from ITRF2005 to ITRF2008, many station coordinates underwent small shifts due to antenna calibration updates, which need to be accounted for in any comparison or alignment of an IGS05-consistent solution to IGS08. Because the heterogeneous distribution of the IGS08 network makes it sub-optimal for the alignment of global frames, a smaller well-distributed sub-network was additionally designed and designated as the IGS08 core network. Only 2 months after their implementation, both the full IGS08 network and the IGS08 core network already strongly suffer from the loss of many reference stations. To avoid a future crisis situation, updates of IGS08 will certainly have to be considered before the next ITRF release.
Global Navigation Satellite System (GNSS) station coordinate errors over seasonal and longer time scales are known to be spatially and temporally correlated with flicker noise spectra. Overlaying ...this are strong annual and semiannual variations that cannot be explained by any single phenomenon. Next most prominent are harmonics of the GPS draconitic year with periods of (351.4/N) days. One explanation is that errors in the standard model for Earth orientation parameter (EOP) tidal variations near 12 and 24 h periods are absorbed into the resonant GPS orbit and daily EOP estimates, resulting mainly in draconitic and fortnightly alias signatures for 24 h product sampling. With the change in International GNSS Service (IGS) station coordinates from weekly to daily resolution in August 2012, it is now possible to study subseasonal performance. All IGS Analysis Centers (ACs) show fortnightly signals, but the resolution will not be sufficient to distinguish direct from aliased subdaily tidal error sources till two more years of data are available. Nevertheless, aliased errors from the subdaily EOP tide model are expected. All but one of the ACs that includes GLONASS data have signals at ~8 day periods, the ground repeat period for GLONASS orbits. This most likely arises from larger geographically correlated orbit errors for GLONASS. Two ACs possess unique short‐period features that appear to be caused by peculiarities of their analysis strategies.
Key Points
Subseasonal GNSS positioning errors can only recently be well studied
Fortnightly spectral peaks are common and signify tide model errors
Some analysis groups have spurious features related to their processing options
The results from a carefully implemented GPS analysis, using a strategy adapted to determine accurate vertical station velocities, are presented. The stochastic properties of our globally distributed ...GPS position time series were inferred, allowing the computation of reliable velocity uncertainties. Most uncertainties were several times smaller than the 1–3 mm/yr global sea level change, and hence the vertical velocities could be applied to correct the long tide gauge records for land motion. The sea level trends obtained in the ITRF2005 reference frame are more consistent than in the ITRF2000 or corrected for Glacial‐Isostatic Adjustment (GIA) model predictions, both on the global and the regional scale, leading to a reconciled global rate of geocentric sea level rise of 1.61 ± 0.19mm/yr over the past century in good agreement with the most recent estimates.
Prior studies of the power spectra of GPS position time series have found pervasive seasonal signals against a power-law background of flicker noise plus white noise. Dong et al. (
2002
) estimated ...that less than half the observed GPS seasonal power can be explained by redistributions of geophysical fluid mass loads. Much of the residual variation is probably caused by unidentified GPS technique errors and analysis artifacts. Among possible mechanisms, Penna and Stewart (
2003
) have shown how unmodeled analysis errors at tidal frequencies (near 12- and 24-hour periods) can be aliased to longer periods very efficiently. Signals near fortnightly, semiannual, and annual periods are expected to be most seriously affected. We have examined spectra for the 167 sites of the International GNSS (Global Navigation Satellite Systems) Service (IGS) network having more than 200 weekly measurements during 1996.0–2006.0. The non-linear residuals of the weekly IGS solutions that were included in ITRF2005, the latest version of the International Terrestrial Reference Frame (ITRF), have been used. To improve the detection of common-mode signals, the normalized spectra of all sites have been stacked, then boxcar smoothed for each local north (N), east (E), and height (H) component. The stacked, smoothed spectra are very similar for all three components. Peaks are evident at harmonics of about 1 cycle per year (cpy) up to at least 6 cpy, but the peaks are not all at strictly 1.0 cpy intervals. Based on the 6th harmonic of the N spectrum, which is among the sharpest and largest, and assuming a linear overtone model, then a common fundamental of 1.040 ± 0.008 cpy can explain all peaks well, together with the expected annual and semiannual signals. A flicker noise power-law continuum describes the background spectrum down to periods of a few months, after which the residuals become whiter. Similar sub-seasonal tones are not apparent in the residuals of available satellite laser ranging (SLR) and very long baseline interferometry (VLBI) sites, which are both an order of magnitude less numerous and dominated by white noise. There is weak evidence for a few isolated peaks near 1 cpy harmonics in the spectra of geophysical loadings, but these are much noisier than for GPS positions. Alternative explanations related to the GPS technique are suggested by the close coincidence of the period of the 1.040 cpy frequency, about 351.2 days, to the “GPS year”; i.e., the interval required for the constellation to repeat its inertial orientation with respect to the sun. This could indicate that the harmonics are a type of systematic error related to the satellite orbits. Mechanisms could involve orbit modeling defects or aliasing of site-dependent positioning biases modulated by the varying satellite geometry.
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