Comparative Analysis of Higher‐Order Ionospheric Delay on PPP Long‐Term Coordinate Time Series and Residual Modeling Using Horizontal Gradients and RINEX Data
Abstract 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 sig...
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Wiley
2024-12-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2024SW004178 |
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| author | Kaichun Yang Weiping Jiang Zhao Li Xin Ding Ran Lu |
| author_facet | Kaichun Yang Weiping Jiang Zhao Li Xin Ding Ran Lu |
| author_sort | Kaichun Yang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-fb9ca0a8b7734d26bb363efed62530a6 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
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| series | Space Weather |
| spelling | doaj-art-fb9ca0a8b7734d26bb363efed62530a62025-02-01T08:10:33ZengWileySpace Weather1542-73902024-12-012212n/an/a10.1029/2024SW004178Comparative Analysis of Higher‐Order Ionospheric Delay on PPP Long‐Term Coordinate Time Series and Residual Modeling Using Horizontal Gradients and RINEX DataKaichun Yang0Weiping Jiang1Zhao Li2Xin Ding3Ran Lu4GNSS Research Center Hubei Luojia Laboratory Wuhan University Wuhan ChinaGNSS Research Center Hubei Luojia Laboratory Wuhan University Wuhan ChinaGNSS Research Center Hubei Luojia Laboratory Wuhan University Wuhan ChinaGNSS Research Center Hubei Luojia Laboratory Wuhan University Wuhan ChinaGNSS Research Center Hubei Luojia Laboratory Wuhan University Wuhan ChinaAbstract 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.https://doi.org/10.1029/2024SW004178higher‐order ionospheric delayGNSS coordinate time seriessignal bendinghorizontal gradientsglobal ionosphere mapshigher‐order ionospheric residuals |
| spellingShingle | Kaichun Yang Weiping Jiang Zhao Li Xin Ding Ran Lu Comparative Analysis of Higher‐Order Ionospheric Delay on PPP Long‐Term Coordinate Time Series and Residual Modeling Using Horizontal Gradients and RINEX Data Space Weather higher‐order ionospheric delay GNSS coordinate time series signal bending horizontal gradients global ionosphere maps higher‐order ionospheric residuals |
| title | Comparative Analysis of Higher‐Order Ionospheric Delay on PPP Long‐Term Coordinate Time Series and Residual Modeling Using Horizontal Gradients and RINEX Data |
| title_full | Comparative Analysis of Higher‐Order Ionospheric Delay on PPP Long‐Term Coordinate Time Series and Residual Modeling Using Horizontal Gradients and RINEX Data |
| title_fullStr | Comparative Analysis of Higher‐Order Ionospheric Delay on PPP Long‐Term Coordinate Time Series and Residual Modeling Using Horizontal Gradients and RINEX Data |
| title_full_unstemmed | Comparative Analysis of Higher‐Order Ionospheric Delay on PPP Long‐Term Coordinate Time Series and Residual Modeling Using Horizontal Gradients and RINEX Data |
| title_short | Comparative Analysis of Higher‐Order Ionospheric Delay on PPP Long‐Term Coordinate Time Series and Residual Modeling Using Horizontal Gradients and RINEX Data |
| title_sort | comparative analysis of higher order ionospheric delay on ppp long term coordinate time series and residual modeling using horizontal gradients and rinex data |
| topic | higher‐order ionospheric delay GNSS coordinate time series signal bending horizontal gradients global ionosphere maps higher‐order ionospheric residuals |
| url | https://doi.org/10.1029/2024SW004178 |
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