Time-variable gravity field recovery from kinematic positions of Low Earth Orbiting satellites

<p>For the monitoring of mass transport and mass distribution in the Earth’s system, the gravity field and its temporal variations provide an important source of information. Dedicated satellite missions like GRACE and GRACE-FO allow to resolve the Earth’s time-variable gravity field based on ultra-precise inter-satellite ranging. In addition, any (non-dedicated) Low Earth Orbiting (LEO) satellite equipped with an on-board GNSS receiver may also serve as a gravity field sensor. For this purpose, the collected GNSS data is used to derive kinematic LEO orbit positions that can subsequently be utilized as pseudo-observations for gravity field recovery. Although this technique is less sensitive and restricted to the long wavelength part of the gravity field, it provides valuable information, particularly for those months where no inter-satellite ranging measurements are available from GRACE or GRACE-FO. Furthermore, the increasing number of operational LEO satellites makes it attractive to produce combined Multi-LEO gravity field solutions that will take advantage of the variety of complementary orbital configurations and can offer additional sensitivities to selected coefficients of solutions based on inter-satellite ranging.</p><p>At the Astronomical Institute of the University of Bern (AIUB) GPS-based kinematic orbits are routinely processed for various LEO satellites like missions dedicated to gravity (GOCE, GRACE/-FO), altimetry (Jason, Sentinel), or further constellations of Earth-observing satellites like SWARM. Beside conventional ambiguity-float orbits, also ambiguity-fixed orbits are recently being computed based on new phase bias and clock products of the Center for Orbit Determination in Europe (CODE). The kinematic orbit positions offer the opportunity to derive time series of monthly gravity field solutions for the different LEO satellites that are eventually combined on the level of normal equations.</p><p>In this contribution, we will present first results of our effort to generate a combined time series of monthly gravity field solutions based on the kinematic orbits of multiple LEO satellites. In a first step, the focus is laid on the GRACE/-FO missions that provide the longest time series in terms of collected GNSS data and that will therefore serve as a backbone for future combinations. We analyze the impact of accelerometer data on the recovery of time-variable mass variations. This will be particularly important for the handling of non-dedicated gravity missions, for which accelerometer measurements are usually not available. Furthermore, we study and compare the performance of gravity field recoveries based on ambiguity-float and ambiguity-fixed kinematic orbit solutions. We assess our results with respect to superior gravity field models based on inter-satellite ranging for selected areas with strong mass change signals like in Greenland, West-Antarctica or the Amazon river basin.</p>

Current status of project SWEETS: Estimating thermospheric neutral mass densities from satellite data at various altitudes

<p>Within the project SWEETS (funded by the FFG Austria) it is intended to develop a forecasting model, to predict the expected impact of solar events, like coronal mass ejections (CMEs), on satellites at different altitudes between 300-800 km. For the realization, scientific data, such as kinematic orbit information and accelerometer measurements, from a wide variety of satellites are incorporated. Based on the evaluation of the impact of several hundred solar events on the thermosphere the forecasting will be realized through a joint analysis and evaluation of solar wind plasma and magnetic field data observed at the Lagrange point L1.<br>In this contribution we show first preliminary results of thermospheric densities estimates based on kinematic orbit information for different satellite missions (e.g., TerraSAR-X, TanDEM-X, Swarm A-C, GRACE, GRACE-FO, CHAMP). To validate the outcome, we compare the results with state-of-the-art thermospheric models as well as with densities estimated from accelerometer measurements if available. Finally, for some specific CME events we will perform a comparison between the post-processed density estimates and results from our preliminary forecasting tool.</p>

Stochastic noise modelling of kinematic orbit positions in the Celestial Mechanics Approach

Gravity field models may be derived from kinematic orbit positions of Low Earth Orbiting satellites equipped with onboard GPS (Global Positioning System) receivers. An accurate description of the stochastic behaviour of the kinematic positions plays a key role to calculate high quality gravity field solutions. In the Celestial Mechanics Approach (CMA) kinematic positions are used as pseudo-observations to estimate orbit parameters and gravity field coefficients simultaneously. So far, a simplified stochastic model based on epoch-wise covariance information, which may be efficiently derived in the kinematic point positioning process, has been applied. We extend this model by using the fully populated covariance matrix, covering correlations over 50 min. As white noise is generally assumed for the original GPS carrier phase observations, this purely formal variance propagation cannot describe the full noise characteristics introduced by the original observations. Therefore, we sophisticate our model by deriving empirical covariances from the residuals of an orbit fit of the kinematic positions. We process GRACE (Gravity Recovery And Climate Experiment) GPS data of April 2007 to derive gravity field solutions up to degree and order 70. Two different orbit parametrisations, a purely dynamic orbit and a reduced-dynamic orbit with constrained piecewise constant accelerations, are adopted. The resulting gravity fields are solved on a monthly basis using daily orbital arcs. Extending the stochastic model from utilising epoch-wise covariance information to an empirical model, leads to a – expressed in terms of formal errors – more realistic gravity field solution.

Kinematic orbit positioning applying the raw observation approach

<p>The kinematic strategy for precise orbit determination (POD) of low earth orbit (LEO) satellites uses only geometric observations to estimate the satellite orbit and does not take any forces into account. This strategy requires a large amount of observation data for one epoch to determine the three-dimensional satellite position. One possibility to get these data is the usage of the spaceborne global navigation satellite system (GNSS) technology, which provides a high number of accurate observations. Following Zehentner (2016) the kinematic orbit positioning applying the raw observation approach by using a least-squares adjustment has shown promising results with a high accuracy.</p><p>By applying this approach the kinematic orbits for several LEO satellite missions are estimated and subsequently validated by a comparison with state of the art gravity field solutions. Furthermore due to the fact that solar events causes an orbit decay, these precise determined orbit data are used to analyze solar event impacts on LEO satellites.</p>

PPP-based Swarm kinematic orbit determination

The Swarm mission of the European Space Agency (ESA) offers excellent opportunities to study the ionosphere and to provide temporal gravity field information for the gap between the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO). In order to contribute to these studies, at the Institut für Erdmessung (IfE) Hannover, a software based on precise point positioning (PPP) batch least-squares adjustment is developed for kinematic orbit determination. In this paper, the main achievements are presented. The approach for the detection and repair of cycle slips caused by ionospheric scintillation is introduced, which is based on the Melbourne–Wübbena and ionosphere-free linear combination. The results show that around 95 % of cycle slips can be repaired and the majority of the cycle slips occur on L2. After the analysis and careful preprocessing of the observations, 1-year kinematic orbits of Swarm satellites from September 2015 to August 2016 are computed with the PPP approach. The kinematic orbits are validated with the reduced-dynamic orbits published by the ESA in the Swarm Level 2 products and SLR measurements. The differences between IfE kinematic orbits and ESA reduced-dynamic orbits are at the 1.5, 1.5 and 2.5 cm level in the along-track, cross-track and radial directions, respectively. Remaining systematics are characterized by spectral analyses, showing once-per-revolution period. The external validation with SLR measurements shows RMSEs at the 4 cm level. Finally, fully populated covariance matrices of the kinematic orbits obtained from the least-squares adjustment with 30, 10 and 1 s data rate are discussed. It is shown that for data rates larger than 10 s, the correlation between satellite positions should be taken into account, for example, for the recovery of gravity field from kinematic orbits.

PPP-based Swarm kinematic orbit determination

ESA's Swarm mission offers excellent opportunities to study the ionosphere and to bridge the gap in gravity field recovery between GRACE and GRACE-FO. In order to contribute to these studies, at IfE Hannover, a software based on Precise Point Positioning (PPP) batch least-squares adjustment is developed for kinematic orbit determination. In this paper, the main achievements are presented. The approach for the detection and repair of cycle slips caused by ionospheric scintillation is introduced, which is based on the Melbourne-Wübbena and ionosphere-free linear combination. The results show that around 95 % cycle slips can be repaired and the majority of the cycle slips occur on L2. After the analysis and careful preprocessing of the observations, one year kinematic orbits of Swarm satellites from Sept., 2015 to Aug., 2016 are computed with the PPP approach. The kinematic orbits are validated with the reduced-dynamic orbits published by ESA in Swarm Level 2 products and the SLR measurements. The differences between our kinematic orbits and ESA reduced-dynamic orbits are at the 1.5 cm, 1.5 cm and 2.5 cm level in the along, cross and radial track, respectively. Remaining systematics are characterised by spectral analyses. The external validation with SLR measurements shows rms errors at the 4 cm level. Finally, fully populated covariance matrices of the kinematic orbits obtained from 30 s, 10 s and 1 s data rate are discussed. It is shown that for data rates larger than 10 s, the correlation should be taken into account when using POD coordinates as input for the gravity field recovery.