Swarm kinematic orbits and gravity fields from 18 months of GPS data

Jäggi, Adrian; Dahle, Christoph; Arnold, Daniel; Bock, Heike; Meyer, Ulrich; Beutler, Gerhard; IJssel, José van den

doi:10.1016/j.asr.2015.10.035

Cited by 69 publications

(102 citation statements)

References 30 publications

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“…Three latitude bands are highlighted for these events from a global view: one is at low latitudes between ±5 and ±20 • magnetic latitude (MLAT), forming two bands along the magnetic equator (white line) and most prominent at longitudes between −135 and 45 • E; the other two regions are at high latitudes above 50 • |MLAT| in both hemispheres, also following the magnetic latitude lines and most prominent at longitudes close to the magnetic poles. Compared to the northern hemisphere, more events are observed in the southern high latitudes, which is similar to the result shown in Fig. 14 of Jäggi et al (2016) that used 1-month GPS data of Swarm. Additionally, very few events are also observed at middle latitudes around east Asian longitudes.…”

Section: Gps Signal Loss Event Detectionsupporting

confidence: 77%

“…For example, the GPS observation data were first delivered with a time resolution of 10 s before 15 July 2014, and then increased to 1 s resolution afterwards; the field of view (FOV) and phase-locked loop (PLL) bandwidth have also been changed, but carried out at different dates for the three satellites. The details of the Swarm GPS receiver updates can be found in Table 1 of Van den Ijssel et al (2016).…”

Section: Swarm Mission and Onboard Gps Receiversmentioning

confidence: 99%

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Climatology of GPS signal loss observed by Swarm satellites

2018

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Abstract. By using 3-year global positioning system (GPS) measurements from December 2013 to November 2016, we provide in this study a detailed survey on the climatology of the GPS signal loss of Swarm onboard receivers. Our results show that the GPS signal losses prefer to occur at both low latitudes between ±5 and ±20 • magnetic latitude (MLAT) and high latitudes above 60 • MLAT in both hemispheres. These events at all latitudes are observed mainly during equinoxes and December solstice months, while totally absent during June solstice months. At low latitudes the GPS signal losses are caused by the equatorial plasma irregularities shortly after sunset, and at high latitude they are also highly related to the large density gradients associated with ionospheric irregularities. Additionally, the highlatitude events are more often observed in the Southern Hemisphere, occurring mainly at the cusp region and along nightside auroral latitudes. The signal losses mainly happen for those GPS rays with elevation angles less than 20 • , and more commonly occur when the line of sight between GPS and Swarm satellites is aligned with the shell structure of plasma irregularities. Our results also confirm that the capability of the Swarm receiver has been improved after the bandwidth of the phase-locked loop (PLL) widened, but the updates cannot radically avoid the interruption in tracking GPS satellites caused by the ionospheric plasma irregularities. Additionally, after the PLL bandwidth increased larger than 0.5 Hz, some unexpected signal losses are observed even at middle latitudes, which are not related to the ionospheric plasma irregularities. Our results suggest that rather than 1.0 Hz, a PLL bandwidth of 0.5 Hz is a more suitable value for the Swarm receiver.

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Section: Gps Signal Loss Event Detectionsupporting

confidence: 77%

Section: Swarm Mission and Onboard Gps Receiversmentioning

confidence: 99%

Climatology of GPS signal loss observed by Swarm satellites

2018

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“…Therefore, a dedicated processing strategy has been developed to convert the accelerometer observations to thermospheric neutral densities, which relies on nongravitational accelerations that are derived from the precise Swarm GPS tracking data to determine the low-frequency information (Siemes et al 2015). Swarm GPS high-low satellite-to-satellite tracking observations can also be used to determine the Earth's gravity field (Jäggi et al 2015). Using Swarm GPS data for the continued monitoring of the Earth's time-variable gravity field could become especially relevant during a potential gap between the current GRACE mission and the planned GRACE Follow-On mission, which is scheduled for launch in 2017 (Flechtner et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Impact of Swarm GPS receiver updates on POD performance

2016

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The Swarm satellites are equipped with state-of-the-art Global Positioning System (GPS) receivers, which are used for the precise geolocation of the magnetic and electric field instruments, as well as for the determination of the Earth's gravity field, the total electron content and low-frequency thermospheric neutral densities. The onboard GPS receivers deliver high-quality data with an almost continuous data rate. However, the receivers show a slightly degraded performance when flying over the geomagnetic poles and the geomagnetic equator, due to ionospheric scintillation. Furthermore, with only eight channels available for dual-frequency tracking, the amount of collected GPS tracking data is relatively low compared with various other missions. Therefore, several modifications have been implemented to the Swarm GPS receivers. To optimise the amount of collected GPS data, the GPS antenna elevation mask has slowly been reduced from 10° to 2°. To improve the robustness against ionospheric scintillation, the bandwidths of the GPS receiver tracking loops have been widened. Because these modifications were first implemented on Swarm-C, their impact can be assessed by a comparison with the close flying Swarm-A satellite. This shows that both modifications have a positive impact on the GPS receiver performance. The reduced elevation mask increases the amount of GPS tracking data by more than 3 %, while the updated tracking loops lead to around 1.3 % more observations and a significant reduction in tracking losses due to severe equatorial scintillation. The additional observations at low elevation angles increase the average noise of the carrier phase observations, but nonetheless slightly improve the resulting reduced-dynamic and kinematic orbit accuracy as shown by independent satellite laser ranging (SLR) validation. The more robust tracking loops significantly reduce the large carrier phase observation errors at the geomagnetic poles and along the geomagnetic equator and do not degrade the observations at midlatitudes. SLR validation indicates that the updated tracking loops also improve the reduced-dynamic and kinematic orbit accuracy. It is expected that the Swarm gravity field recovery will benefit from the improved kinematic orbit quality and potentially also from the expected improvement of the kinematic baseline determination and the anticipated reduction in the systematic gravity field errors along the geomagnetic equator. Finally, other satellites that carry GPS receivers that encounter similar disturbances might also benefit from this analysis.

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“…9 illustrates the effect of applying a Gaussian smoothing with 500 km radius, which is unsuitable to suppress the noise in the Swarm models. The noise suppression is deficient enough to allow the characteristic signature of the geomagnetic equator to be seen on the top left figure; this feature is further described by Jäggi et al (2016). As a consequence of the higher noise, the spatial correlation further decreases to 0.58 (compared to 0.66 when applying a smoothing radius of 833 km) and the RMS difference increases to 5.2 mm geoid height (from 3.8 mm for 833 km smoothing radius).…”

Section: Detailed Comparison With the Grace Modelsmentioning

confidence: 68%

“…To keep the time series of gravity field models uninterrupted in the gap between the two gravimetric missions, alternative data must be used. Using the hl-SST data, such as from the Swarm satellites, is a good option as demonstrated by Weigelt et al (2013), Baur (2013), Sośnica et al (2014) and Weigelt et al (2014) on the basis of simulated data, as well as by Jäggi et al (2016) and Bezděk et al (2016) using Swarm hl-SST data.…”

Section: Introductionmentioning

confidence: 99%

Gravity field models derived from Swarm GPS data

et al. 2016

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It is of great interest to numerous geophysical studies that the time series of global gravity field models derived from Gravity Recovery and Climate Experiment (GRACE) data remains uninterrupted after the end of this mission. With this in mind, some institutes have been spending efforts to estimate gravity field models from alternative sources of gravimetric data. This study focuses on the gravity field solutions estimated from Swarm global positioning system (GPS) data, produced by the Astronomical Institute of the University of Bern, the Astronomical Institute (ASU, Czech Academy of Sciences) and Institute of Geodesy (IfG, Graz University of Technology). The three sets of solutions are based on different approaches, namely the celestial mechanics approach, the acceleration approach and the shortarc approach, respectively. We derive the maximum spatial resolution of the time-varying gravity signal in the Swarm gravity field models to be degree 12, in comparison with the more accurate models obtained from K-band ranging data of GRACE. We demonstrate that the combination of the GPS-driven models produced with the three different approaches improves the accuracy in all analysed monthly solutions, with respect to any of them. In other words, the combined gravity field model consistently benefits from the individual strengths of each separate solution. The improved accuracy of the combined model is expected to bring benefits to the geophysical studies during the period when no dedicated gravimetric mission is operational.

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Swarm kinematic orbits and gravity fields from 18 months of GPS data

Cited by 69 publications

References 30 publications

Climatology of GPS signal loss observed by Swarm satellites

Climatology of GPS signal loss observed by Swarm satellites

Impact of Swarm GPS receiver updates on POD performance

Gravity field models derived from Swarm GPS data

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