Journal of Geodesy最新文献

As its contribution to the latest release of the International Terrestrial Reference Frame, ITRF2020, the International GNSS Service (IGS) provided a 27-year-long series of daily “repro3” terrestrial frame solutions obtained by combining reprocessed solutions from ten Analysis Centers. This contribution represents an improvement over the previous contribution to ITRF2014, not only by the inclusion of more stations with longer and more complete position time series, but also by a general reduction in random and systematic errors. The IGS contribution to ITRF2020 also provided, for the first time, an independent estimate of the terrestrial scale based on the calibration of the Galileo satellite antennas. Despite the various observed improvements, the repro3 station position time series remain affected by a variety of random and systematic errors. This includes spurious periodic variations in several frequency bands, originating mostly from orbit and tide modeling errors, on top of a combination of white and flicker noise, whose origins remain to be precisely understood. These various components should carefully be accounted for when modeling GNSS station position time series and interpreting them in terms of Earth’s surface deformation. The Galileo-based scale of the repro3 solutions is found to be significantly offset (by (+)4.3 mm at epoch 2015.0) and drifting (by (+)0.11 mm/year) from the SLR/VLBI-based scale of ITRF2020. The reasons for this offset and drift remain to be uncovered.

Satellite product combination has been a major effort for the International GNSS Service Analysis Center Coordinator to improve the robustness of orbits, clocks and biases over original AC-specific contributions. While the orbit and clock combinations have been well documented, combining phase biases is more of a challenge since they have to be aligned with the clocks precisely to preserve the exactitude of integer ambiguities in precise point positioning (PPP). In the case of dual-frequency signals, frequency-specific phase biases are first translated into an ionosphere-free form to agree with the IGS satellite clocks, and they can then be integrated as integer clocks to facilitate a joint combination. However, regarding multi-frequency phase biases, forming their ionosphere-free counterparts would be cumbersome as they are linearly dependent. We therefore propose a concept of “frequency-specific integer clock” where all third-frequency phase biases are integrated individually with satellite clocks to enable an efficient frequency-wise combination. The resultant combined product will ensure all-frequency PPP ambiguity resolution over any frequency choices and observable combinations. Our combination test based on the GPS/Galileo satellite products from four IGS-ACs in 2020 showed that the mean phase clock/bias consistencies among ACs for all third-frequency signals (i.e., GPS L5, Galileo E6 and E5b) were as high as 10 ps, and the ambiguity fixing rates were all around 95%. Both quantities reached the same levels as those for the baseline frequencies (i.e., GPS L1/L2 and Galileo E1/E5a). The combined products outperformed AC-specific products since outlier contributions were excluded in the combination.

Monitoring the time-variable geopotential identifies the mass redistribution across the Earth and reveals, e.g., climate change and availability of water resources. The features of interest are characterized by spatial and temporal scales accessible only through space missions. Among the most important gravity missions are GRACE (2002–2017), its successor GRACE-FO (since 2018), and GOCE (2009–2013), which all sense the Earth’s gravity field via the geopotential derivatives. We investigate the geopotential estimation through frequency comparisons between orbiting clocks by means of the Doppler-canceling technique, describing the clocks’ behavior in the Earth’s gravitational field via Einstein’s general relativity. The novelty of this approach lies in measuring gravity by sensing the geopotential itself. The proof of principle for the measurement is achieved through an innovative mission scenario: for the first time, the observations are collected by a probing clock in LEO. We show gravity solutions obtained by simulating an estimation problem via our proposed architecture. The results suggest that we can conceivably retrieve the geopotential coefficients with accuracy comparable to the GRACE measurement concept by employing clocks with stabilities of order ({10}^{-18}). Presently, terrestrial clocks can routinely attain fractional frequency stabilities of ({10}^{-18}), whereas spaceborne clocks are still at the ({10}^{-15}) level. While our findings are promising, further analysis is needed to obtain more realistic indications on the feasibility of an actual mission, whose realization will be possible when clock technology reaches the required performance. The goal is for the technique investigated in this study to become a future staple for gravity field estimation.

This paper introduces a novel method for solving the resection problem in two and three dimensions based on conformal geometric algebra (CGA). Advantage is taken because of the characteristics of CGA, which enables the representation of points, lines, planes, and volumes in a unified mathematical framework and offers a more intuitive and geometric understanding of the problem, in contrast to existing purely algebraic methods. Several numerical examples are presented to demonstrate the efficacy of the proposed method and to compare its validity with established techniques in the field. Numerical simulations indicate that our vector geometric algebra implementation is faster than the best-known algorithms to date, suggesting that the proposed GA-based methods can provide a more efficient and comprehensible solution to the two- and three-dimensional resection problem, paving the way for further applications and advances in geodesy research. Furthermore, the method’s emphasis on graphical and geometric representation makes it particularly suitable for educational purposes, allowing the reader to grasp the concepts and principles of resection more effectively. The proposed method has potential applications in a wide range of other fields, including surveying, robotics, computer vision, or navigation.

The goal for the next generation of terrestrial reference frames (TRF) is to achieve a 1-mm- and 0.1-mm/yr-accurate frame realization through the combination of reference station solutions by multi-technique geodetic observatories. A potentially significant source of error in TRF realizations is the inter-system ties between the instruments at multi-technique stations, usually independently determined through ground-based local surveying. The quality of local tie surveys is varied and inconsistent, largely due to differences in measurement techniques, surveying instruments, site conditions/geometries, and processing methods. The Global Geodetic Observing System (GGOS) has tried to address these problems by issuing guidelines for the construction and layout of future multi-technique observatories, promoting uniformity and quality while minimizing existing problems with local surveying that are exacerbated over longer baseline distances. However, not every observatory is going to be able to completely satisfy these guidelines, and in this work, a successful endeavor to satisfy the accuracy goals while exceeding the GGOS baseline guideline is detailed for the McDonald Geodetic Observatory (MGO) in the Davis Mountains of Texas, USA. MGO consists of a VLBI Geodetic Observing System (VGOS), infrastructure in place for a Space Geodesy Satellite Laser Ranging (SGSLR) telescope, and several Global Navigation Satellite Systems (GNSS) stations spanning a 900 m baseline and a 120 m elevation change. The results of the local ties between the GNSS stations across the near-kilometer baseline, as measured from their antenna reference points, show sub-mm precision and 1 mm accuracy validated through repeatability across several surveys conducted in 2021as well as 1 mm consistency with the monthly averaged daily solutions of the GNSS-based positioning. In this paper, we report these results as well as the framework of the surveys with sufficient detail and rigor in order to give confidence to the quality claims and to present the novel design and techniques employed in the procedure, processing, and error-budget analysis, which were determined through iterative research methods across repeated survey campaigns.

The Jet Propulsion Laboratory (JPL) develops JPL-GIM, a software for generating global ionospheric maps (GIMs) of total electron content (TEC) using measurements from multiple Global Navigation Satellite System (GNSS) constellations. Within this overview paper, we delve into the current state and the most recent enhancements of JPL-GIM. An adaptable Kalman filter provides maps with user-defined temporal and spatial resolutions, while concurrently delivering essential covariance data for uncertainty assessment. Furthermore, multiple shell models offer a versatile framework to balance accuracy and computational efficiency. We present the five operational JPL GIM products (JPLG, JPRG, JPLI, JPLD, JPRT), highlighting JPLG and JPRG, our products routinely delivered to the International GNSS Service (IGS), and introduce a new near-real-time product (JPRT). As an added demonstration of JPL-GIM’s capabilities, we present a very-high-resolution (2-minute, multi-GNSS, 1000-station) configuration to showcase JPL-GIM’s ability to resolve long-lasting effects of the 2022 Hunga Tonga-Hunga Ha’apai eruption. Validations using independent datasets confirm the accurate reproduction of ionospheric variations across all latitudinal bands.

In this contribution, we introduce, in analogy to penalized ambiguity resolution, the concept of penalized misclosure space partitioning, with the goal of directing the performance of the DIA-estimator towards its application-dependent tolerable risk objectives. We assign penalty functions to each of the decision regions in misclosure space and use the distribution of the misclosure vector to determine the optimal partitioning by minimizing the mean penalty. As each minimum mean penalty partitioning depends on the given penalty functions, different choices can be made, in dependence of the application. For the DIA-estimator, we introduce a special set of penalty functions that penalize its unwanted outcomes. It is shown how this set allows one to construct the optimal DIA-estimator, being the estimator that within its class has the largest probability of lying inside a user specified tolerance region. Further elaboration shows how these penalty functions are driven by the influential biases of the different hypotheses and how they can be used operationally. Hereby the option is included of extending the misclosure partitioning with an additional undecided region to accommodate situations when it will be hard to discriminate between some of the hypotheses or when identification is unconvincing. By extending the analogy with integer ambiguity resolution to that of integer-equivariant ambiguity resolution, we also introduce the maximum probability estimator within the similar larger class.

Traditionally, GNSS space-based and ground-based estimates of tropospheric conditions are performed separately. It leads to limitations in the horizontal (e.g., a single space-based radio occultation profile covers a 300 km slice of the troposphere) and vertical resolution (e.g., ground-based estimates of troposphere conditions have spacing equal to stations’ distribution) of the tropospheric products. The first stage to achieve an integrated model is to create an effective 3D ray-tracing algorithm for the satellite-to-satellite (radio occultation) path reconstruction. We verify the consistency of the simulated data with the RO observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-1) Data Analysis and Archive Center (CDAAC) in terms of excess phase and bending angle. The results show that our solution provides an effective RO excess phase, with a relative error varying from 35% at the height of 25–30 km (1.0–1.5 m) to 0.5% at heights 5–10 km (0.1–1 m) and 14 to 2% at heights below 5 km (2–14 m). The bending angle retrieval on simulated data attained for high-resolution ray-tracing, bias lower than 2% with respect to the observed bending angle. The optimal solution takes about 1 s for one transmitter–receiver pair with a tangent point below 5 km altitude. The high-resolution processing solution takes 3 times longer.

In this study, a novel two-scale spherical radial basis function (SRBF) modeling method is proposed for regional gravity field determination. First, satellite-only global gravity field models (GGMs) are combined with airborne gravity data at medium-frequency bands, and a series of combined gravity field models based on band-limited SRBFs are established for the mountainous areas of California and Oregon. The combined gravity field models are then compared with the airborne-only gravity field models. The results show that the combined models exhibit standard deviation (STD) values of 0.106–0.120 m in terms of geoid height differences w.r.t. the global positioning system (GPS)/leveling data, while the corresponding airborne-only models yield STD values of 0.126–0.131 m. The STD values of the combined models are reduced by 0.9–2.0 cm, which implies a potential benefit for the medium-frequency gravity field modeling by combining GGM and airborne gravity data. Second, after removing the low-frequency and medium-frequency gravity field signals as well as the residual terrain model signals from gravity data, a second SRBF modeling process is implemented using multisource residual gravity data. Subsequently, a high-resolution two-scale SRBF gravity field model is constructed for the mountainous areas of California and Oregon. The results indicate that the STD of geoid height differences for the two-scale SRBF model w.r.t. the GPS/leveling data is 0.098 m, with reductions of 3.0–6.2 cm compared to the models based on the single-scale SRBF modeling method. These findings indicate the effectiveness of the two-scale SRBF modeling method for refining the regional gravity field model in complex areas.