For the first time in the International Terrestrial Reference Frame (ITRF) history, the ITRF2014 is generated with an enhanced modeling of nonlinear station motions, including seasonal (annual and semiannual) signals of station positions and postseismic deformation for sites that were subject to major earthquakes. Using the full observation history of the four space geodetic techniques (very long baseline interferometry (VLBI), satellite laser ranging (SLR), Global Navigation Satellite Systems (GNSS), and Doppler orbitography and radiopositioning integrated by satellite (DORIS)), the corresponding international services provided reprocessed time series (weekly from SLR and DORIS, daily from GNSS, and 24 h session-wise from VLBI) of station positions and daily Earth Orientation Parameters. ITRF2014 is demonstrated to be superior to past ITRF releases, as it precisely models the actual station trajectories leading to a more robust secular frame and site velocities. The ITRF2014 long-term origin coincides with the Earth system center of mass as sensed by SLR observations collected on the two LAGEOS satellites over the time span between 1993.0 and 2015.0. The estimated accuracy of the ITRF2014 origin, as reflected by the level of agreement with the ITRF2008 (both origins are defined by SLR), is at the level of less than 3 mm at epoch 2010.0 and less than 0.2 mm/yr in time evolution. The ITRF2014 scale is defined by the arithmetic average of the implicit scales of SLR and VLBI solutions as obtained by the stacking of their respective time series. The resulting scale and scale rate differences between the two solutions are 1.37 (±0.10) ppb at epoch 2010.0 and 0.02 (±0.02) ppb/yr. While the postseismic deformation models were estimated using GNSS/GPS data, the resulting parametric models at earthquake colocation sites were applied to the station position time series of the three other techniques, showing a very high level of consistency which enforces more the link between techniques within the ITRF2014 frame. The users should be aware that the postseismic deformation models are part of the ITRF2014 products, unlike the annual and semiannual signals, which were estimated internally with the only purpose of enhancing the velocity field estimation of the secular frame.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2016JB013098

ITRF2014: A new release of the International Terrestrial Reference Frame modeling nonlinear station motions

/pdf/the-antarctica-component-of-postglacial-rebound-model-ice-6g-b104vz7w4u.pdf

The Antarctica component of postglacial rebound model ICE-6G_C (VM5a) based on GPS positioning, exposure age dating of ice thicknesses, and relative sea level histories

Vertical land motions are a key element in understanding how sea levels have changed over the
past century and how future sea levels may impact coastal areas. Ideally, to be useful in long-term sea
level studies, vertical land motion should be determined with standard errors that are 1 order of magnitude
lower than the contemporary climate signals of 1 to 3 mm/yr observed on average in sea level records, either
using tide gauges or satellites. This metrological requirement constitutes a challenge in geodesy. Here we
review the most successful instrumental methods that have been used to determine vertical displacements
at the Earth’s surface, so that the objectives of understanding and anticipating sea levels can be addressed
adequately in terms of accuracy. In this respect, the required level of uncertainty is examined in two case
studies (global and local). A special focus is given to the use of the Global Positioning System (GPS) and to the
combination of satellite radar altimetry with tide gauge data. We update previous data analyses and assess
the quality of global satellite altimetry products available to the users for coastal applications. Despite recent
advances, a near-plateau level of accuracy has been reached. The major limitation is the realization of the
terrestrial reference frame, whose physical parameters, the origin and the scale factor, are beyond the scope
of a unique technique such as the GPS. Additional practical but nonetheless important issues are associated
with the installation of GPS antennas, such as ensuring that there is no unknown differential vertical motion
with the tide gauge.

/pdf/vertical-land-motion-as-a-key-to-understanding-sea-level-472mdp7ion.pdf

Vertical land motion as a key to understanding sea level change and variability

Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil-Sen median trend estimator, for which the ordinary version is the median of slopes vij  = (xj-xi )/(tj-ti ) computed between all data pairs i > j. For normally distributed data, Theil-Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil-Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one-sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root-mean-square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences.

/pdf/midas-robust-trend-estimator-for-accurate-gps-station-4nqsni3ooe.pdf

MIDAS robust trend estimator for accurate GPS station velocities without step detection.

Uncertainty of the 20th century sea-level rise due to vertical land motion errors

[1] The accuracy of Global Positioning System (GPS) time series is degraded by the presence of offsets. To assess the effectiveness of methods that detect and remove these offsets, we designed and managed the Detection of Offsets in GPS Experiment. We simulated time series that mimicked realistic GPS data consisting of a velocity component, offsets, white and flicker noises (1/f spectrum noises) composed in an additive model. The data set was made available to the GPS analysis community without revealing the offsets, and several groups conducted blind tests with a range of detection approaches. The results show that, at present, manual methods (where offsets are hand picked) almost always give better results than automated or semi‒automated methods (two automated methods give quite similar velocity bias as the best manual solutions). For instance, the fifth percentile range (5% to 95%) in velocity bias for automated approaches is equal to 4.2 mm/year (most commonly ±0.4 mm/yr from the truth), whereas it is equal to 1.8 mm/yr for the manual solutions (most commonly 0.2 mm/yr from the truth). The magnitude of offsets detectable by manual solutions is smaller than for automated solutions, with the smallest detectable offset for the best manual and automatic solutions equal to 5 mm and 8 mm, respectively. Assuming the simulated time series noise levels are representative of real GPS time series, robust geophysical interpretation of individual site velocities lower than 0.2–0.4 mm/yr is therefore certainly not robust, although a limit of nearer 1 mm/yr would be a more conservative choice. Further work to improve offset detection in GPS coordinates time series is required before we can routinely interpret sub‒mm/yr velocities for single GPS stations.

/pdf/detecting-offsets-in-gps-time-series-first-results-from-the-379wlalhab.pdf

Detecting offsets in GPS time series: First results from the detection of offsets in GPS experiment

The precise point positioning (PPP) is a popular positioning technique that is dependent on the use of precise orbits and clock corrections. One serious problem for real-time PPP applications such as natural hazard early warning systems and hydrographic surveying is when a sudden communication break takes place resulting in a discontinuity in receiving these orbit and clock corrections for a period that may extend from a few minutes to hours. A method is presented to maintain real-time PPP with 3D accuracy less than a decimeter when such a break takes place. We focus on the open-access International GNSS Service (IGS) real-time service (RTS) products and propose predicting the precise orbit and clock corrections as time series. For a short corrections outage of a few minutes, we predict the IGS-RTS orbits using a high-order polynomial, and for longer outages up to 3 h, the most recent IGS ultra-rapid orbits are used. The IGS-RTS clock corrections are predicted using a second-order polynomial and sinusoidal terms. The model parameters are estimated sequentially using a sliding time window such that they are available when needed. The prediction model of the clock correction is built based on the analysis of their properties, including their temporal behavior and stability. Evaluation of the proposed method in static and kinematic testing shows that positioning precision of less than 10 cm can be maintained for up to 2 h after the break. When PPP re-initialization is needed during the break, the solution convergence time increases; however, positioning precision remains less than a decimeter after convergence.

/pdf/maintaining-real-time-precise-point-positioning-during-sltv00w8xt.pdf

Maintaining real-time precise point positioning during outages of orbit and clock corrections

Various types of biases in Global Navigation Satellite System (GNSS) data preclude integer ambiguity fixing and degrade solution accuracy when not being corrected during precise point positioning (PPP). In this contribution, these biases are first reviewed, including satellite and receiver hardware biases, differential code biases, differential phase biases, initial fractional phase biases, inter-system receiver time biases, and system time scale offset. PPP models that take account of these biases are presented for two cases using ionosphere-free observations. The first case is when using primary signals that are used to generate precise orbits and clock corrections. The second case applies when using additional signals to the primary ones. In both cases, measurements from single and multiple constellations are addressed. It is suggested that the satellite-related code biases be handled as calibrated quantities that are obtained from multi-GNSS experiment products and the fractional phase cycle biases obtained from a network to allow for integer ambiguity fixing. Some receiver-related biases are removed using between-satellite single differencing, whereas other receiver biases such as inter-system biases are lumped with differential code and phase biases and need to be estimated. The testing results show that the treatment of biases significantly improves solution convergence in the float ambiguity PPP mode, and leads to ambiguity-fixed PPP within a few minutes with a small improvement in solution precision.

/pdf/on-biases-in-precise-point-positioning-with-multi-1apvvr27we.pdf

On biases in precise point positioning with multi-constellation and multi-frequency GNSS data

This contribution proposes two new precise point positioning (PPP) models that use triple-frequency data, designed to accelerate convergence of carrier-phase float ambiguities. The first model uses a triple-frequency ionosphere-free linear combination that has minimum noise propagation and geometry-preserving properties. The second model uses a mixed code and carrier-phase linear combination with the same properties. A third model was also implemented, which uses individual uncombined triple-frequency measurements. The three models were validated using triple-frequency GPS data and their performance was compared to the traditional dual-frequency model in terms of the convergence time taken to achieve and maintain a uniform three-dimensional accuracy of 5 cm. Testing includes PPP processing of 1-h measurement blocks using 1–8 days of data from three locations in Australia. It was shown that all the three triple-frequency models had improved solution convergence time compared to the traditional PPP dual-fre...

/pdf/triple-frequency-gnss-models-for-ppp-with-float-ambiguity-3j3gdu8xbr.pdf

Triple-frequency GNSS models for PPP with float ambiguity estimation: performance comparison using GPS

Detecting and repairing cycle slips and clock jumps are crucial data preprocessing steps when performing Precise Point Positioning (PPP). If left unrepaired, cycle slips and clock jumps can adversely affect PPP convergence time, accuracy and precision. This paper proposes algorithms for detecting and repairing cycle slips and clock jumps using multiconstellation and multi-frequency (MCMF) GNSS data. It is shown that availability of a third frequency enables reliable validation of detected cycle slips. This is because triple frequency analysis can identify the frequency on which the cycle slip occurred as part of the detection process. A clock jump detection and repair procedure is also proposed for a receiver with both carrier phase and code measurements showing jumps. The proposed method uses the average code and phase linear combination and applies to static data. A spline function is used to approximate the data for a pre-defined time window prior to each measuring epoch and a test is performed for detecting presence of a clock jump by comparing the interpolated value to measured value. The algorithm can effectively determine clock jumps for single frequency data from a single constellation as well as MCMF GNSS data. However, MCMF GNSS data adds redundancy, hence improves the reliability of the clock jump detection algorithm. It is recommended to detect and repair clock jumps when using PPP to allow improved modelling of the receiver clock offset in the dynamic model.

Manoj Deo

Papers

Detecting offsets in GPS time series: First results from the detection of offsets in GPS experiment

Maintaining real-time precise point positioning during outages of orbit and clock corrections

On biases in precise point positioning with multi-constellation and multi-frequency GNSS data

Triple-frequency GNSS models for PPP with float ambiguity estimation: performance comparison using GPS

Cycle Slip and Clock Jump Repair with Multi-Frequency Multi-Constellation GNSS data for Precise Point Positioning