[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (1)

Search Parameters:
Keywords = TRF stacking

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
16 pages, 3790 KiB  
Technical Note
Assessment of the Improvement in Observation Precision of GNSS, SLR, VLBI, and DORIS Inputs from ITRF2014 to ITRF2020 Using TRF Stacking Methods
by Jin Zhang, Chengli Huang, Lizhen Lian and Simeng Zhang
Remote Sens. 2024, 16(7), 1240; https://doi.org/10.3390/rs16071240 - 31 Mar 2024
Viewed by 1442
Abstract
International terrestrial reference frame (ITRF) input data, generated by Global Navigation Satellite Systems (GNSS), Satellite Laser Ranging (SLR), Very Long Baseline Interferometry (VLBI), and Doppler Orbitography and Radiopositioning integrated by satellite (DORIS) combination centers (CCs), are considered to be relatively high-quality and accurate [...] Read more.
International terrestrial reference frame (ITRF) input data, generated by Global Navigation Satellite Systems (GNSS), Satellite Laser Ranging (SLR), Very Long Baseline Interferometry (VLBI), and Doppler Orbitography and Radiopositioning integrated by satellite (DORIS) combination centers (CCs), are considered to be relatively high-quality and accurate solutions. Every few years, these input data are submitted to the three ITRS combination centers, namely Institut Géographique National (IGN), Deutsches Geodätisches Forschungsinstitut at the Technische Universität München (DGFI-TUM), and Jet Propulsion Laboratory (JPL), to establish a multi-technique combined terrestrial reference frame (TRF). Generally, these solutions have undergone three rounds of outlier removal: the first at the technique analysis centers during solution generations and the second during the technique-specific combination by the CCs; ITRS CCs then perform a third round of outlier removal and preprocessing during the multi-technique combination of TRFs. However, since the primary objective of CCs is to release the final TRF product, they do not emphasize the publication of analytical preprocessing results, such as the outlier rejection rate. In this paper, our specific focus is on assessing the precision improvement of ITRF input data from 2014 to 2020, which includes evaluating the accuracy of coordinates, the datum accuracy, and the precision of the polar motions, for all four techniques. To achieve the above-mentioned objectives, we independently propose a TRF stacking approach to establish single technical reference frameworks, using software developed by us that is different from the ITRF generation. As a result, roughly 0.5% or less of the SLR observations are identified as outliers, while the ratio of DORIS, GNSS, and VLBI observations are below 1%, around 2%, and ranging from 1% to 1.2%, respectively. It is shown that the consistency between the SLR scale and ITRF has improved, increasing from around −5 mm in ITRF2014 datasets to approximately −1 mm in ITRF2020 datasets. The scale velocity derived from fitting the VLBI scale parameter series with all epochs in ITRF2020 datasets differs by approximately 0.21 mm/year from the velocity obtained by fitting the data up to 2013.75 because of the scale drift of VLBI around 2013. The decreasing standard deviations of the polar motion parameter (XPO, YPO) offsets between Stacking TRFs and 14C04 (20C04) indicate an improvement in the precision of polar motion observations for all four techniques. From the perspective of the weighted root mean square (WRMS) in station coordinates, since the inception of the technique, the station coordinate WRMS of DORIS decreased from 30 mm to 5 mm for X and Y components, and 25 mm to 5 mm for the Z component; SLR WRMS decreased from 20 mm to better than 10 mm (X, Y and Z); GNSS WRMS decreased from 4 mm to 1.5 mm (X and Y) and 5 mm to 2 mm (Z); while VLBI showed no significant change. Full article
Show Figures

Figure 1

Figure 1
<p>Station and core station distributions of Space Geodesy input data. “Common” and “Common Core” refers to stations and core stations used in both 2014 and 2020. “2020”, “2014”, “2020 Core” and “2014 Core” means stations and core stations used only in 2014 or 2020. The numbers of stations are given in parentheses. Subfigures (<b>a</b>), (<b>b</b>), (<b>c</b>) and (<b>d</b>) are station distribution maps of GNSS, SLR, VLBI and DORIS respectively.</p>
Full article ">Figure 2
<p>Translation and scale parameter series between the observations and the Stacking Space Geodesy TRFs under two types of constraints: internal (inner) constraints and minimum constraints. The linear fit lines along with their slopes for the results of minimum constraints are also illustrated.</p>
Full article ">Figure 2 Cont.
<p>Translation and scale parameter series between the observations and the Stacking Space Geodesy TRFs under two types of constraints: internal (inner) constraints and minimum constraints. The linear fit lines along with their slopes for the results of minimum constraints are also illustrated.</p>
Full article ">Figure 3
<p>WRMS of posterior station coordinate residuals between observations and stacked Space Geodesy TRFs. To better identify variations in station accuracy, quadratic polynomial fits were applied to the WRMS series for GNSS (yellow line), SLR (red line), and DORIS (purple line).</p>
Full article ">Figure 4
<p>Residuals of XPO and YPO from Stacking Space Geodesy TRFs compared to IERS14C04, IERS20C04 (red) and ITRF2014 EOP, ITRF2020 EOP (blue). The mean and standard deviation (std) statistics for the residual sequences are presented below the respective graphs.</p>
Full article ">
Back to TopTop