Time-variable gravity field from satellite constellations using the energy integral

Abstract

SUMMARY The magnetic field mission Swarm, expected to be launched in 2012, comprises a constellation of three satellites. As all of them are equipped with GPS receivers and accelerometers, they can be used for gravity field recovery. We study the capability of a Swarm‐like constellation for (time‐variable) gravity field recovery and compare it with a gravity field tandem mission of GRACE‐type. Due to the lower accuracy of the GPS measurements compared with GRACE low–low satellite‐to‐satellite tracking (SST), the accuracy of a Swarm derived gravity field cannot compete with the state‐of‐the‐art GRACE models. However, unlike the GRACE mission, Swarm allows for the derivation of GPS‐baselines between the satellites in directions other than purely along‐track. This makes Swarm an interesting case for studying the error structure of gravity field models derived from various constellation geometries. Therefore, one focus of this study is the general analysis of different baseline constellations independent of the observation accuracy, that is, we do not restrict ourselves to just the actual Swarm case of pure GPS‐baselines. To make the results comparable to GRACE, we explicitly study the error behaviour of the different Swarm baseline geometries assuming GRACE‐type K‐band ranging (KBR) links. This gives an indication of candidate constellations for future missions; at least in regards to the general error structure. To study the potential of Swarm for recovering time‐variable components of the gravity field, we have set up a 2‐yr simulation to recover annual and semi‐annual components of continental hydrology. We show that Swarm has the potential to recover the annual signal up to spherical harmonic degree 6. This is of interest should there be a gap between the end of the GRACE mission and the launch of a follow‐on mission. All our simulations make use of the energy integral method. This method is usually (implicitly) formulated for static potential fields. Therefore, it is necessary for our study to investigate the properties of this method when applied to the analysis of time‐variable fields.

Greenland mass variation from time‐variable gravity in the absence of GRACE

“…This gap has been proposed to be bridged with gravity field information obtained from GNSS (Global Navigation Satellite Systems) tracking of low-Earth orbiting satellites [cf. Gunter et al, 2011;Lin et al, 2012;Wang et al, 2012;Weigelt et al, 2013], albeit with lower accuracy and resolution as compared to GRACE.…”

HUST-GOGRA2018s: A new gravity field model derived from the combination of GRACE and GOCE data

“…In contrast, the distance between two high precision accelerometers located in the same axis of satellite gravity gradiometer (SGG) in GOCE is only 50 cm, which can detect the gravity signal at small scale. Due to the special north-south tracking pattern in GRACE mission, the sectorial and near-sectorial spherical harmonic coefficients are determined with poor quality in GRACE-only gravity field models (Wang et al 2012;Zhou et al 2016). Fortunately, these errors can be reduced by GOCE mission.…”

Can GPS-Derived Surface Loading Bridge a GRACE Mission Gap?

“…GRACE-derived empirical orthogonal functions, Pilinski and Nerem 2011). using absolute and relative GNSS tracking to and between low-flying orbiters equipped with accelerometers, like the SWARM satellite constellation, in order to derive the low-degree time variable field up to degree 6 or so (Wang et al 2012); using GNSS tracking on GRACE A and/or B once the K-band ranging system would no longer being operational is along the same line of reasoning. using the technique known as loading inversion, where GNSS-measured, global network deformations are inverted into low-degree load distribution, possibly together with complementary information (Blewitt et al 2001).…”