The Influence of Geomagnetic Storm of 7‐8 September 2017 on the Swarm Precise Orbit Determination

Zhang, Keke; Li, Xingxing; Xiong, Chao; Meng, Xiangli; Yuan, Yongqiang; Zhang, Xiaohong

doi:10.1029/2018ja026316

Cited by 16 publications

(10 citation statements)

References 33 publications

(55 reference statements)

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“…Savitzky‐Golay filter fitted the background with a linear basis function set based on least squares in a series of frames of a given length determined by the cut off period. This method has been widely used in processing dTEC data to show TID, and could extract the wave structure effectively (K. Zhang et al., 2019; S. R. Zhang et al., 2019).…”

Section: Resultsmentioning

confidence: 99%

Dynamics of Thermospheric Traveling Atmospheric Disturbance During a Geomagnetic Storm

Liu

et al. 2023

JGR Space Physics

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Traveling atmospheric disturbance (TAD) plays an important role in the energy and momentum transfer from the lower atmosphere to the upper atmosphere, and from high‐ to low‐latitudes. It is common to observe TADs propagating toward low latitudes because of enhanced Joule heating and/or the Lorentz force at the high‐latitude ionosphere during storm time. However, energy or momentum variation associated with their equatorward propagation remains unclear. Two geomagnetic storms occurred on 7–8 September 2017 and upper atmospheric disturbances are observed by the Swarm satellites and the Global Navigation Satellite System Total Electron Content network. We conduct a model simulation and term analysis of the energy equation to investigate the dominant terms of TAD. Adiabatic heating, conduction heating, and advection heating dominate the energy budget of TAD. Adiabatic heating plays an important role in the energy budget by transferring the most energy of TAD. An anti‐phase relationship between adiabatic and conduction heating is found in the propagation of these TADs. An in‐phase relationship between adiabatic and advection heating is also found. Physical processes behind these anti‐phase and in‐phase relationships are illustrated with a schematic. Finally, based on the dominant terms and the relationships between them, the whole process of generation, propagation, and dissipation of TAD is given.

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Section: Resultsmentioning

confidence: 99%

Dynamics of Thermospheric Traveling Atmospheric Disturbance During a Geomagnetic Storm

Liu

et al. 2023

JGR Space Physics

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“…A geomagnetic storm, which was triggered by a coronal mass ejection that broke out on 7-8 September 2017, was analyzed to investigate the impact of spaceborne observational errors on the BDS POD [35]. Studies show that the quality of the LEO constellations, i.e., the Swarm constellation, are severally degraded during the main phases of storms and that the maximum degradation of the orbit accuracy can be over 10 cm [18]. The main reason for the Swarm orbit accuracy degradation is the enhanced thermospheric mass density at the Swarm altitude, which increased the atmospheric drag for the LEO satellites.…”

Section: Gnss/leo Combined Pod During a Solar Stormmentioning

confidence: 99%

“…Degraded GNSS signals can reduce the availability and reliability of the GNSS-based POD. In addition, with increasing ionospheric density, the accuracy of a conventional orbit determination is seriously reduced [18].…”

Section: Introductionmentioning

confidence: 99%

LEO-Constellation-Augmented BDS Precise Orbit Determination Considering Spaceborne Observational Errors

et al. 2021

Remote Sensing

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The precise orbit determination (POD) accuracy of the Chinese BeiDou Navigation Satellite System (BDS) is still not comparable to that of the Global Positioning System because of the unfavorable geometry of the BDS and the uneven distribution of BDS ground monitoring stations. Fortunately, low Earth orbit (LEO) satellites, serving as fast moving stations, can efficiently improve BDS geometry. Nearly all studies on Global Navigation Satellite System POD enhancement using large LEO constellations are based on simulations and their results are usually overly optimistic. The receivers mounted on a spacecraft or an LEO satellite are usually different from geodetic receivers and the observation conditions in space are more challenging than those on the ground. The noise level of spaceborne observations needs to be carefully calibrated. Moreover, spaceborne observational errors caused by space weather events, i.e., solar geomagnetic storms, are usually ignored. Accordingly, in this study, the actual spaceborne observation noises are first analyzed and then used in subsequent observation simulations. Then, the observation residuals from the actual-processed LEO POD during a solar storm on 8 September 2017 are extracted and added to the simulated spaceborne observations. The effect of the observational errors on the BDS POD augmented with different LEO constellation configurations is analyzed. The results indicate that the noise levels from the Swarm-A, GRACE-A, and Sentinel-3A satellites are different and that the carrier-phase measurement noise ranges from 2 mm to 6 mm. Such different noise levels for LEO spaceborne observations cause considerable differences in the BDS POD solutions. Experiments calculating the augmented BDS POD for different LEO constellations considering spaceborne observational errors extracted from the solar storm indicate that these errors have a significant influence on the accuracy of the BDS POD. The 3D root mean squares of the BDS GEO, IGSO, and MEO satellite orbits are 1.30 m, 1.16 m, and 1.02 m, respectively, with a Walker 2/1/0 LEO constellation, and increase to 1.57 m, 1.72 m, and 1.32 m, respectively, with a Walker 12/3/1 constellation. When the number of LEO satellites increases to 60, the precision of the BDS POD improves significantly to 0.89 m, 0.77 m, and 0.69 m for the GEO, IGSO, and MEO satellites, respectively. While 12 satellites are sufficient to enhance the BDS POD to the sub-decimeter level, up to 60 satellites can effectively reduce the influence of large spaceborne observational errors, i.e., from solar storms.

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“…Here, the high-order ionospheric (HOI) terms are neglected since it was reported that they were not the main cause of LEO satellite systematic errors near the magnetic equatorial area. In addition, the orbit accuracy improvement with HOI terms corrected could be very limited [35][36][37]. The IF observation equations are formulated as follows:…”

Section: Observation Modementioning

confidence: 99%

Real-Time Kinematic Precise Orbit Determination for LEO Satellites Using Zero-Differenced Ambiguity Resolution

Zhang

et al. 2019

Remote Sensing

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The rapid growing number of earth observation missions and commercial low-earth-orbit (LEO) constellation plans have provided a strong motivation to get accurate LEO satellite position and velocity information in real time. This paper is devoted to improve the real-time kinematic LEO orbits through fixing the zero-differenced (ZD) ambiguities of onboard Global Navigation Satellite System (GNSS) phase observations. In the proposed method, the real-time uncalibrated phase delays (UPDs) are estimated epoch-by-epoch via a global-distributed network to support the ZD ambiguity resolution (AR) for LEO satellites. By separating the UPDs, the ambiguities of onboard ZD GPS phase measurements recover their integer nature. Then, wide-lane (WL) and narrow-lane (NL) AR are performed epoch-by-epoch and the real-time ambiguity–fixed orbits are thus obtained. To validate the proposed method, a real-time kinematic precise orbit determination (POD), for both Sentinel-3A and Swarm-A satellites, was carried out with ambiguity–fixed and ambiguity–float solutions, respectively. The ambiguity fixing results indicate that, for both Sentinel-3A and Swarm-A, over 90% ZD ambiguities could be properly fixed with the time to first fix (TTFF) around 25–30 min. For the assessment of LEO orbits, the differences with post-processed reduced dynamic orbits and satellite laser ranging (SLR) residuals are investigated. Compared with the ambiguity–float solution, the 3D orbit difference root mean square (RMS) values reduce from 7.15 to 5.23 cm for Sentinel-3A, and from 5.29 to 4.01 cm for Swarm-A with the help of ZD AR. The SLR residuals also show notable improvements for an ambiguity–fixed solution; the standard deviation values of Sentinel-3A and Swarm-A are 4.01 and 2.78 cm, with improvements of over 20% compared with the ambiguity–float solution. In addition, the phase residuals of ambiguity–fixed solution are 0.5–1.0 mm larger than those of the ambiguity–float solution; the possible reason is that the ambiguity fixing separate integer ambiguities from unmodeled errors used to be absorbed in float ambiguities.

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The Influence of Geomagnetic Storm of 7‐8 September 2017 on the Swarm Precise Orbit Determination

Cited by 16 publications

References 33 publications

Dynamics of Thermospheric Traveling Atmospheric Disturbance During a Geomagnetic Storm

Dynamics of Thermospheric Traveling Atmospheric Disturbance During a Geomagnetic Storm

LEO-Constellation-Augmented BDS Precise Orbit Determination Considering Spaceborne Observational Errors

Real-Time Kinematic Precise Orbit Determination for LEO Satellites Using Zero-Differenced Ambiguity Resolution

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