Study of the effects on GPS coordinate time series caused by higher-order ionospheric corrections calculated using the DIPOLE model

Deng, Liansheng; Jiang, Weiping; Chen, Hua; Zhu, Zhaohan; Wen, Zhao

doi:10.1016/j.geog.2017.01.004

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Many studies indicate that not only white noise, but also colored noise exists in GNSS position time series [29,30] and that the combination of white noise and power law noise is quite common [7][8][9]. Therefore, the combination of white and power law noise was employed in noise simulation.…”

Section: Case 1: White Noise and Power Law Noise Combinationmentioning

confidence: 99%

The Consideration of Formal Errors in Spatiotemporal Filtering Using Principal Component Analysis for Regional GNSS Position Time Series

Shen

2018

Remote Sensing

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Abstract:In the daily operation of regional GNSS (Global Navigation Satellite System) networks, the formal errors of all stations' coordinate components are calculated. However, spatiotemporal filtering based on traditional Principal Component Analysis (PCA) for regional GNSS position time series does not take these formal errors into account. This paper developed a PCA-based approach to extract Common Mode Error (CME) from the position time series of a regional GNSS station network, where formal errors were applied to construct a weight factor. Because coordinate components with larger errors have smaller weight factors in extracting CME, the performance of our proposed approach was anticipated to be better than the traditional PCA approach. The position time series of 25 stations in the Yunnan Province, China, were analyzed using our approach, as well as the traditional PCA approach. The average errors of the residual time series after removing the CMEs with our approach were 1.30 mm, 1.72 mm, and 4.62 mm for North, East and Up components, and the reductions with respect to those of the original time series were 18.23%, 15.42%, and 18.25%, respectively. If CMEs were removed from the traditional PCA approach, the corresponding average errors were 1.34 mm, 1.81 mm, and 4.84 mm, with reductions of 15.84%, 10.86%, and 14.32%, respectively. Compared to the traditional PCA approach, the average errors of our approach were reduced by about 2.39%, 4.56%, and 3.93% in the North, East and Up components, respectively. Analysis of CME indicated that it mainly contained white and flicker noise. In the synthetic position time series with 500 repeated simulations, the CME extracted by our approach was closer to the true simulated values than those extracted by the traditional PCA approach, regardless of whether local effects were considered or not. Specifically, the mean root mean square (RMS) reduction of our approach, relative to PCA, ranged from 1.35% to 3.93%. Our simulations illustrated that the RMS reductions depended not only on the magnitude, but also the variation of the formal error series, which further highlights the necessity of considering formal errors in spatiotemporal filtering.

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Section: Case 1: White Noise and Power Law Noise Combinationmentioning

confidence: 99%

The Consideration of Formal Errors in Spatiotemporal Filtering Using Principal Component Analysis for Regional GNSS Position Time Series

Shen

2018

Remote Sensing

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“…Deng et al () showed the convenience of incorporating the second‐ and third‐order ionospheric corrections, especially in areas with higher ionospheric delay variability, after processing the Crustal Movement Observation Network of China during more than one solar cycle. Deng et al () showed in detail the impact of using the simplified dipole geomagnetic model, against the more realistic International Geomagnetic Reference Field (IGRF). Banville et al () studied the estimation and application of I2+ corrections in the context of PPP.…”

Section: Introductionmentioning

confidence: 99%

Impact and Implementation of Higher‐Order Ionospheric Effects on Precise GNSS Applications

et al. 2017

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High precision Global Navigation Satellite Systems (GNSS) positioning and time transfer require correcting signal delays, in particular higher‐order ionospheric (I2+) terms. We present a consolidated model to correct second‐ and third‐order terms, geometric bending and differential STEC bending effects in GNSS data. The model has been implemented in an online service correcting observations from submitted RINEX files for I2+ effects. We performed GNSS data processing with and without including I2+ corrections, in order to investigate the impact of I2+ corrections on GNSS products. We selected three time periods representing different ionospheric conditions. We used GPS and GLONASS observations from a global network and two regional networks in Poland and Brazil. We estimated satellite orbits, satellite clock corrections, Earth rotation parameters, troposphere delays, horizontal gradients, and receiver positions using global GNSS solution, Real‐Time Kinematic (RTK), and Precise Point Positioning (PPP) techniques. The satellite‐related products captured most of the impact of I2+ corrections, with the magnitude up to 2 cm for clock corrections, 1 cm for the along‐ and cross‐track orbit components, and below 5 mm for the radial component. The impact of I2+ on troposphere products turned out to be insignificant in general. I2+ corrections had limited influence on the performance of ambiguity resolution and the reliability of RTK positioning. Finally, we found that I2+ corrections caused a systematic shift in the coordinate domain that was time‐ and region‐dependent and reached up to −11 mm for the north component of the Brazilian stations during the most active ionospheric conditions.

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Comparative Analysis of Higher‐Order Ionospheric Delay on PPP Long‐Term Coordinate Time Series and Residual Modeling Using Horizontal Gradients and RINEX Data

Yang,

Jiang,

et al. 2024

Space Weather

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Higher‐order ionospheric corrections, including second‐ and third‐order effects and signal bending, are essential for improving Global Navigation Satellite System (GNSS) time series accuracy. However, their influence on long‐term Precise Point Positioning (PPP) time series, particularly signal bending, remains underexplored. Most studies rely on Global Ionosphere Maps (GIMs) for second‐order delay corrections due to the complexity of RINEX‐based inversions. Analyzing data from 37 International GNSS Service stations (2009–2020), we found that higher‐order ionospheric effects predominantly impact the North component of mid‐ and low‐latitude stations. Second‐ and third‐order delays contribute 50% and 20% to annual and semi‐annual amplitudes, respectively, while signal bending accounts for 5% and 2%. Additionally, signals traveling along the magnetic field direction induce phase delays, with equatorial regions experiencing more frequent and intense ionospheric activity. This leads to maximum horizontal velocity impacts of 0.45 mm/yr at the equator and a distinct southern trend at mid‐ and low‐latitudes. GIM‐based corrections increase seasonal signal amplitudes and root mean square error (RMSE) at some stations due to discrepancies with RINEX‐based corrections, which generate higher‐order ionospheric residuals. These residuals contribute 48.6% of the RMSE in RINEX‐based corrections, surpassing the 39.8% from second‐ and third‐order delays. To address this, we propose a GIM‐based residual model incorporating horizontal gradients, reducing RMSE by 48%, 26%, and 52% for the East, North, and Up components, respectively. Monthly or quarterly gradient updates during quiet ionospheric periods ensure precise corrections, enhancing GNSS time series and coordinate system accuracy.

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Study of the effects on GPS coordinate time series caused by higher-order ionospheric corrections calculated using the DIPOLE model

Cited by 3 publications

References 16 publications

The Consideration of Formal Errors in Spatiotemporal Filtering Using Principal Component Analysis for Regional GNSS Position Time Series

The Consideration of Formal Errors in Spatiotemporal Filtering Using Principal Component Analysis for Regional GNSS Position Time Series

Impact and Implementation of Higher‐Order Ionospheric Effects on Precise GNSS Applications

Comparative Analysis of Higher‐Order Ionospheric Delay on PPP Long‐Term Coordinate Time Series and Residual Modeling Using Horizontal Gradients and RINEX Data

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