Quantifying discrepancies in the three-dimensional seasonal variations between IGS station positions and load models

Niu, Yujiao; Wei, Na; Li, Min; Rebischung, Paul; Shi, Chuang; Chen, Guo

doi:10.1007/s00190-022-01618-9

Cited by 9 publications

(10 citation statements)

References 81 publications

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“…Therefore, this study reflects the uplift in Sichuan region through the overall change. Seasonal variations, load model errors and component shares of thermoelastic variability in horizontal and vertical deformations have been explained [31]. If a cleaner time series is desired, noise models need to be considered in addition to filtered CME [15].…”

Section: Discussionmentioning

confidence: 99%

A Novel Method for Analyzing the Spatiotemporal Characteristics of GNSS Time Series: A Case Study in Sichuan Province, China

Chen,

Zhang,

Wang

et al. 2024

Applied Sciences

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The motion of a continuously operating reference station is usually dominated by the long-term crustal motions of the tectonic block on which the station is located. Monitoring changes in the coordinates of reference stations located at tectonic plate boundaries allows for the calculation of velocity fields that reflect the spatial and temporal characteristics of the region. This study analyzes the spatiotemporal relationships of regional reference frame points with GNSS data from 25 reference stations in Sichuan, China, from 2015 to 2021. The common mode errors are extracted and eliminated by principal component analysis. A time series function model is developed for the reference stations and their constituent baselines for calculating the velocity field. Subsequently, the spatiotemporal characteristics of the regional reference frame in Sichuan is analyzed by a stochastic model. The results show that the influences of the common mode error on the horizontal and vertical directions of the reference stations is 2.5 mm and 4.3 mm, respectively. Generally, the horizontal motion of the reference stations in the Sichuan region tends to be in the southeast direction and the vertical motion trend is mainly uplifting. The east–west and vertical components of the baseline tend to be shortened, and the random influence among the reference stations is larger in the north–south and east–west directions—0.39 mm and 0.54 mm, respectively. Polynomial functions are more appropriate for constructing the fitted random influence covariance model.

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Section: Discussionmentioning

confidence: 99%

A Novel Method for Analyzing the Spatiotemporal Characteristics of GNSS Time Series: A Case Study in Sichuan Province, China

Chen,

Zhang,

Wang

et al. 2024

Applied Sciences

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“…It is generated mainly by the varying environmental loading throughout the year, but can also be contributed by draconitic period, thermal expansion of ground and monuments, systematic errors or by spurious effects (e.g., Dong et al., 2002; Penna et al., 2007; Ray et al., 2008). On the short‐term temporal‐scale, periodic variations occur as the overtones of seasonal changes, but they are coupled with a plethora of effects due to tidal constituents unmodelled or mismodeled during the processing, GNSS‐specific signals, such as imperfect modeling of orbits, unexpected movement of the GNSS monument, errors in clocks, mismodeling of the large‐scale effects, changes in GNSS processing and errors in the assumptions or background models whose predictions are used during the processing; all these effects will show up in the time series of station displacement (Amiri‐Simkooei et al., 2017; Bos et al., 2015; Dong et al., 2006; Gruszczynski et al., 2018; Langbein & Svarc, 2019; Matviichuk et al., 2020; Niu et al., 2022; Ray et al., 2008; Saji et al., 2020; White et al., 2022; X. Xu et al., 2017; P. Xu et al., 2019). Superposition of the above effects means that the standard deviation of the series may change over the years, and the individual displacements may be correlated with each other.…”

Section: Quality Of Gps Displacementsmentioning

confidence: 99%

“…However, it is not the best option in the context of Earth system monitoring based on GNSS data. Recent analyses have shown that GNSS station displacement time series provided by IGS have the smallest standard deviation, are spatially consistent, and have good correlation with environmental loading models, although contributions from individual analysis centers differ (Niu et al., 2022). The above makes the IGS solution considered the “gold standard.” Unfortunately, the number of GNSS stations processed by the IGS does not provide a sufficiently dense distribution of stations needed for local or regional analysis.…”

Section: Introductionmentioning

confidence: 99%

Introducing the Idea of Classifying Sets of Permanent GNSS Stations as Benchmarks for Hydrogeodesy

Klos,

Kusche,

Leszczuk

et al. 2023

JGR Solid Earth

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We propose a novel approach to classify sets of GNSS (Global Navigation Satellite System) permanent stations as benchmarks for hydrogeodesy. Benchmarks are trusted sets of GNSS stations whose displacements are classified as significantly and positively correlated with hydrospheric changes and identified in a three temporal‐scales: short‐term, seasonal and long‐term. We use 63 vertical displacement time series processed at the Nevada Geodetic Laboratory (NGL) for the period 1998‐2021 from stations located within Amazon basin and show that estimates of trends and annual signals, including the annual phase maximum, are very coherent with water surface levels provided by altimetry missions. We compute vertical displacements from GRACE (Gravity Recovery and Climate Experiment) and GRACE Follow‐On gravity missions and predict those also from Global Land Water Storage (GLWS) v2.0 dataset which values are produced by assimilation of GRACE into WaterGAP Global Hydrological Model (WGHM). We divide vertical displacements from the three datasets into the pre‐defined temporal‐scales of short‐term, seasonal and long‐term, using non‐parametric wavelet analysis. For each temporal‐scale, correlation coefficients are computed between GNSS‐measured and GRACE‐derived / GLWS‐predicted displacements. We present the benefits of applying high‐resolution GRACE‐assimilating hydrology model to benchmark GNSS stations, which are particularly evident when using spherical harmonic coefficients higher than 120. Their increase causes the number of stations included in the benchmarks to rise by up to 15% for short‐term. Benchmarking allows hydrogeodesy to take advantage of a broader set of GNSS stations that were previously omitted, such as earthquake‐affected sites and those where a possible poroelastic response is observed.

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“…Niu et al. (2022) found that modeled SML displacements based on MGFs can only explain about 40% and 20% of GNSS‐derived annual deformation for vertical and horizontal components, respectively. Since GNSS technology, with its dense spatial resolution, can infer finely varied upper mantle and crust structure on a local/regional scale, which are commonly ignored so‐far in global SML modeling.…”

Section: Introductionmentioning

confidence: 99%

“…Niu et al (2022) considers that loading effects in the European region are complicated and loads deformation vectors are disorderly. Moreover, PREMCRU model gets abnormal worst correction at the European station MORP (1.686°W, 55.213°N) for North and Up components.…”

mentioning

confidence: 99%

Impacts of Local Green's Functions on Modeling Atmospheric Loading Effects for GNSS Reference Stations

Fan,

Jiang,

et al. 2024

Earth and Space Science

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The Green's function approach is well‐established and widely used for modeling the surface mass loading displacements. Global mean Green's functions (MGFs) are commonly applied without considering local variations of the crustal structure. Derived from the modified layered Earth structure, the local Green's functions (LGFs) are theoretically beneficial to generate more accurate deformation, since they consider interior information of the local crust. This paper analyzed the differences among MGFs from Gutenberg–Bullen A model and two sets of LGFs derived from modified PREM Earth models consolidating with two crust models TEA12 and CRUST1.0, hereafter called PREMTEA and PREMCRU, respectively. Utilizing MGFs and two sets of LGFs, we modeled the corresponding 3D atmospheric loading displacements for 984 ITRF2014 stations and compared them with the ITRF2014 residuals. The results show that LGFs from PREMTEA and PREMCRU perform well in further promoting scatter reduction for ∼72%, ∼56%, and ∼85% of stations for the Up, East and North components, respectively. The improvements for the North components are significant (up to 3.6%). In particular, stations in the east coastal areas of North America and the west edge of Greenland exhibit further promoting scatter reduction for the East components (up to ∼2.5%), while those located in west coastline of North America show better performance for the North components. Nevertheless, there are significant anomalies in northern Europe for PREMCRU, the mutation margin of which should be carefully considered when using a resolution higher than 1°. In the area around station MORP (358.31°W, 55.21°N, sited at coastline of Britain), we suggest using PREMTEA model.

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Quantifying discrepancies in the three-dimensional seasonal variations between IGS station positions and load models

Cited by 9 publications

References 81 publications

A Novel Method for Analyzing the Spatiotemporal Characteristics of GNSS Time Series: A Case Study in Sichuan Province, China

A Novel Method for Analyzing the Spatiotemporal Characteristics of GNSS Time Series: A Case Study in Sichuan Province, China

Introducing the Idea of Classifying Sets of Permanent GNSS Stations as Benchmarks for Hydrogeodesy

Impacts of Local Green's Functions on Modeling Atmospheric Loading Effects for GNSS Reference Stations

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