Assessment of vertical TEC mapping functions for space-based GNSS observations

Zhong, Jiahao; Lei, Jiuhou; Dou, Xiankang; Yue, Xinan

doi:10.1007/s10291-015-0444-6

Cited by 74 publications

(71 citation statements)

References 32 publications

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“…This indicates that potential errors from CODE GIM and JPL GIM in our study will have a negligible impact on final PEC extraction. In addition, the single layer height (or called the ionospheric effective height, IEH) is a very key parameter in TEC under the widely used single layer model (SLM) assumption [44,45], but actually the optimal plasmaspheric effective height is considerably greater than the commonly adopted value (usually 450 km) in GIM vTEC determination [46,47]. That means the single layer height (450 km) used in GIM calculation is not suitable for estimating the PEC (included in vTEC from the ground-based receiver to the GNSS satellite) above T/J satellite orbit altitude any more, and it leads to smaller PEC than the actual one, so the PEC error may exist in the systematic bias and the extracted PEC.…”

Section: Discussionmentioning

confidence: 99%

Plasmaspheric Electron Content Inferred from Residuals between GNSS-Derived and TOPEX/JASON Vertical TEC Data

et al. 2018

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Abstract:The plasmasphere, which is located above the ionosphere, is a significant component of Earth's atmosphere, and the plasmasphere electron content (PEC) distribution is determined by different physical mechanisms to those of the ionosphere electron content (IEC). However, the observation for the PEC is very limited. In this study, we introduced a methodology (called zero assumption method, which is based on the assumption that PEC can reach zero) to extract the PEC over TOPEX/JASON (T/J) and global navigation satellite system (GNSS) overlapping areas. Results show that the daily systematic bias (T/J vertical TEC > GNSS-derived vertical TEC) for both low (2009) and high (2011) solar activity condition is consistent, and the systematic bias for JASON2 and JASON1 is different. We suggest that systematic biases predominantly arise from the sea state bias (SSB), especially the tracker bias. After removing the systematic bias, we extracted reliable PEC inferred from differences between GNSS-derived vertical TEC and T/J vertical TEC data. Finally, the characteristics of the plasmaspheric component distribution for different local times, latitudes, and seasons were investigated.

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Section: Discussionmentioning

confidence: 99%

Plasmaspheric Electron Content Inferred from Residuals between GNSS-Derived and TOPEX/JASON Vertical TEC Data

et al. 2018

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“…The only unknown parameter in Equation (8) is h shell . Different mapping functions and IEH calculation methods are evaluated for space-borne data processing in [25], and it is found that the F&K mapping functions along with IEH from centroid method is more suitable for LEO-based DCB estimation and TEC conversion than other models mentioned in their study, which shows a relative error generally below 10% at 800 km altitude during different solar activities. In our study, the F&K mapping function with centroid-derived IEH is adopted.…”

Section: Dcb Estimation Methodsmentioning

confidence: 92%

“…Since FY3C satellite orbits above the Earth surface at about 836 km, which is actually at the altitude of plasmasphere, the mapping functions used for ground stations such as in [9,24] are not applicable. As indicated in some studies [18,20,25], the geometric mapping function (F&K) proposed in [19] can be used if spherical symmetry is assumed. The F&K geometric mapping function can be expressed as:…”

Section: Dcb Estimation Methodsmentioning

confidence: 99%

“…In our study, the F&K mapping function with centroid-derived IEH is adopted. The IEH is computed using below equation proposed in [25]: h shell = (0.0027F 107 + 1.79)·h leo − 5.52F 107 + 1350 (9) where F 107 is solar radio flux at 10.7 cm, which is often used for representation of solar activity. Based on above description, the leveled carrier-to-code ionospheric observables can be expressed in a closed form where only VTEC, GNSS satellite DCBs and receiver DCBs remain unknowns.…”

Section: Dcb Estimation Methodsmentioning

confidence: 99%

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GPS and BeiDou Differential Code Bias Estimation Using Fengyun-3C Satellite Onboard GNSS Observations

Shi

et al. 2017

Remote Sensing

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Differential code biases (DCBs) are important parameters in GNSS (Global Navigation Satellite System) applications such as positioning as well as ionosphere remote sensing. In comparison to the conventional approach, which utilizes ground-based observations and parameterizes global ionosphere maps together with DCBs, a method is presented for GPS and BeiDou system (BDS) satellite DCB estimation using onboard observations from the Chinese Fengyun-3C (FY3C) satellite. One month worth of GPS and BDS data during March 2015 was exploited and the GPS C1C-C2W and BDS C2I-C7I DCBs were explored. To improve DCB estimation precision, the dual frequency carrier phase measurements leveled by code measurements were used to form basic observation equation. Code multipath errors of the FY3C onboard GPS/BDS observations were assessed and modeled as grid maps, and their impact on DCB estimation was analyzed. By correcting code multipath errors, the stability of DCB estimates was improved by 5.0%, 3.1%, 16.2% and 13.6% for GPS, and BDS geosynchronous orbit satellites (GEOs), inclined geosynchronous satellite orbit satellites (IGSOs) and medium Earth orbit satellites (MEOs), respectively. The monthly stability of FY3C-based DCBs was at the order of 0.1 ns for GPS satellites, 0.2 ns for BDS GEOs and 0.1 ns for BDS IGSOs and MEOs. By comparison to the ground-based DCB products issued by other institutions, FY3C-based DCBs showed stability degradation for BDS C02 and C05 satellites, while, for other satellites, the stability reached a similar or even superior level. The estimated FY3C receiver DCB stability was at the order of 0.2 ns for both GPS and BDS. In addition to the DCB estimates, the obtained vertical total electron content above the FY3C satellite orbit was also investigated and its realism was examined in physical and numerical aspects.

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“…It is interesting to investigate whether this long-term variation of the LEO DCBs is also associated with the GPS satellite replacement. Zhong et al [27] have assessed the longterm variation of the GPS satellite DCBs. Since only the combined satellite-receiver DCB can be actually determined, if the satellite and receiver DCBs need to be further separated from the combined satellite-receiver DCB, an additional constraint condition is required.…”

Section: Dcb Variations Of Multiple Leo Satellitesmentioning

confidence: 99%

Determination of Differential Code Bias of GNSS Receiver Onboard Low Earth Orbit Satellite

Zhong

Lei

Yue

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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The uncertainty of differential code bias (DCB) is one of the main error sources in the low Earth orbit (LEO) based total electron content (TEC) retrieval, whereas the derivation of the LEO DCB is not systematically studied. In this paper, we propose an improved DCB estimation method (ZERO method) based on the assumption that the LEO-based TEC can reach zero and also optimize the parameter configuration in the commonly used least square method (LSQ method). In the improved ZERO method, the combination of the lower quartile minimum relative TEC during each orbital revolution with the daily minimum relative TEC gives a stable and reliable DCB estimation. For the LSQ method, the 3-TECU cutoff vertical TEC with 10 • cutoff elevation is considered to offer a reasonable DCB estimation. Subsequently, Global Positioning System (GPS) observations from multiple LEO satellites at different altitudes are used to study the variability of the LEO DCBs. Our results revealed that the LEO DCBs underwent obvious long-term variation and periodic oscillations of months. Moreover, the CHAMP data illustrated that the long-term variation of LEO DCBs is partly associated with the GPS satellite replacement, and the periodic variation can be attributed to the variation of the hardware thermal status, represented by the receiver CPU temperature in this study.

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Assessment of vertical TEC mapping functions for space-based GNSS observations

Cited by 74 publications

References 32 publications

Plasmaspheric Electron Content Inferred from Residuals between GNSS-Derived and TOPEX/JASON Vertical TEC Data

Plasmaspheric Electron Content Inferred from Residuals between GNSS-Derived and TOPEX/JASON Vertical TEC Data

GPS and BeiDou Differential Code Bias Estimation Using Fengyun-3C Satellite Onboard GNSS Observations

Determination of Differential Code Bias of GNSS Receiver Onboard Low Earth Orbit Satellite

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