Satellites in Low Earth Orbit (LEO) are essential for sounding the topside ionosphere. In this work, we present and validate a data set of Total Electron Content (TEC) and in situ electron density observations from the Gravity Recovery And Climate Experiment (GRACE) and GRACE-Follow-On missions as well as a TEC data set from the CHAllenging Minisatellite Payload mission. Concerning TEC, special emphasis is put to ensure optimal consistency to the already existing Swarm and Gravity field and steady-state ocean circulation explorer (GOCE) TEC data sets. The newly processed satellite missions allow covering two full solar cycles with LEO slant TEC. Furthermore, the twin satellite missions GRACE and GRACE-FO equipped with inter-satellite K-band ranging allows to derive the horizontal TEC and, due to the small inter-satellite distance of the satellite pairs, an approximation for local electron density. However, the derived value of electron density is relative and requires calibration using external information. In this work, the calibration is performed using the IRI-2016 model. Radar observations, as well as in situ electron density observations available from Swarm B Langmuir probes, are used for validation. Conjunctions between satellites are used to validate the TEC time series. The newly derived data set is shown to be highly consistent with the already existing data sets with standard deviations below 3 TECU for TEC (even 1 TECU was reached for low solar flux) and an offset below 7 × 10¹⁰ m⁻³ with a standard deviation near 1 × 10¹¹ m⁻³ for the electron density.

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Copernicus Sentinel–1 is a C-Band Synthetic Aperture Radar (SAR) satellite mission within the European Copernicus Programme. The two satellites Sentinel-1A and -1B were launched in April 2014 and 2016, respectively. The Copernicus POD (Precise Orbit Determination) Service is responsible for the determination of orbital and auxiliary products required by the Payload Data Ground Segment (PDGS). Precise orbits are determined based on the dual-frequency GPS (Global Positioning System) data delivered by dedicated geodetic-grade GPS receivers on-board the satellites. Several updates in the operational orbit determination were done during the years including an update of the GPS antenna reference point coordinates. The switch to GPS carrier phase ambiguity-fixing was a major improvement. A reprocessing of the entire mission span of both satellites became necessary to provide a consistent orbit time series for the mission based on state-of-the-art models and processing settings. Due to the lack of independent observation techniques, the Sentinel-1 orbit quality has been assessed by analysing processing metrics, orbit overlaps and orbit comparisons. For this purpose, members of the Copernicus POD Quality Working Group (QWG) provided reprocessed Sentinel-1 orbit time series based on their software packages and their orbit determination settings. A weighted average of all five delivered solutions - a combined orbit - serves as reference for the comparisons. The quality and reliability of this reference orbit depends among others on the number of available orbit solutions and whether a manoeuvre has been performed during the processed day or not. The mean orbit consistency between all orbit solutions is below 1 cm in 3D RMS for the entire mission time interval for both satellites. Only few days show inferior quality due to data gaps or orbit manoeuvres. Following this sophisticated validation process, the reprocessed Sentinel-1 orbits from the Copernicus POD Service have been made available to the user community.

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Polymer flooding offers the potential to recover more oil from reservoirs but requires significant investments, which necessitate a robust analysis of economic upsides and downsides. Key uncertainties in designing a polymer flood are often reservoir geology and polymer degradation. The objective of this study is to understand the impact of geological uncertainties and history matching techniques on designing the optimal strategy for, and quantifying the economic risks of, polymer flooding in a heterogeneous clastic reservoir. We applied two different history matching techniques (adjoint-based and a stochastic algorithm) to match data from a prolonged waterflood in the Watt Field, a semisynthetic reservoir that contains a wide range of geological and interpretational uncertainties. Next, sensitivity studies were carried out to identify first-order parameters that impact the net present value (NPV). These parameters were then deployed in an experimental design study using Latin hypercube sampling (LHS) to generate training runs from which a proxy model was created using polynomial regression. A particle swarm optimization (PSO) algorithm was employed to optimize the NPV for the polymer flood. The same approach was used to optimize a standard waterflood for comparison. Optimizations of the polymer flood and waterflood were performed for the history-matched model ensemble and the original ensemble. The optimal strategy to deploy the polymer flood and maximize NPV varies based on the history matching technique. The average NPV and the variance are predicted to be higher in the stochastic history matching compared to the adjoint technique. This difference is due to the ability of the stochastic algorithm to explore the parameter space more broadly, which created situations in which the oil in place is shifted upward, resulting in a higher NPV. Optimizing a history-matched ensemble leads to a narrow range in absolute NPV compared to optimizing the original ensemble. This difference is because the uncertainties associated with polymer degradation are not captured during history matching. The result of cross comparison, in which an optimal polymer design strategy for one ensemble member is deployed to the other ensemble members, predicted a decline in NPV but surprisingly still showed that the overall NPV is higher than for an optimized waterflood, even for suboptimal polymer injection strategies. This observation indicates that a polymer flood could be beneficial compared to a waterflood, even if geological uncertainties are not captured properly.

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Gravity fields derived from GPS tracking of the three Swarm satellites have shown artifacts near the geomagnetic equator, where the carrier phase tracking on the L2 frequency is unable to follow rapid ionospheric path delay changes due to a limited tracking loop bandwidth of only 0.25 Hz in the early years of the mission. Based on the knowledge of the loop filter design, an analytical approach is developed to recover the original L2 signal from the observed carrier phase through inversion of the loop transfer function. Precise orbit determination and gravity field solutions are used to assess the quality of the correction. We show that the a posteriori RMS of the ionosphere-free GPS phase observations for a reduced-dynamic orbit determination can be reduced from 3 to 2 mm while keeping up to 7% more data in the outlier screening compared to uncorrected observations. We also show that artifacts in the kinematic orbit and gravity field solution near the geomagnetic equator can be substantially reduced. The analytical correction is able to mitigate the equatorial artifacts. However, the analytical correction is not as successful compared to the down-weighting of problematic GPS data used in earlier studies. In contrast to the weighting approaches, up to 9–10% more kinematic positions can be retained for the heavily disturbed month March 2015 and also stronger signals for gravity field estimation in the equatorial regions are obtained, as can be seen in the reduced error degree variances of the gravity field estimation. The presented approach may also be applied to other low earth orbit missions, provided that the GPS receivers offer a sufficiently high data rate compared to the tracking loop bandwidth, and provided that the basic loop-filter parameters are known.

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Although the knowledge of the gravity of the Earth has improved considerably with CHAMP, GRACE, and GOCE (see appendices for a list of abbreviations) satellite missions, the geophysical community has identified the need for the continued monitoring of the time-variable component with the purpose of estimating the hydrological and glaciological yearly cycles and long-term trends. Currently, the GRACE-FO satellites are the sole dedicated provider of these data, while previously the GRACE mission fulfilled that role for 15 years. There is a data gap spanning from July 2017 to May 2018 between the end of the GRACE mission and start the of GRACE-FO, while the Swarm satellites have collected gravimetric data with their GPS receivers since December 2013. We present high-quality gravity field models (GFMs) from Swarm data that constitute an alternative and independent source of gravimetric data, which could help alleviate the consequences of the 10-month gap between GRACE and GRACE-FO, as well as the short gaps in the existing GRACE and GRACE-FO monthly time series. The geodetic community has realized that the combination of different gravity field solutions is superior to any individual model and set up the Combination Service of Time-variable Gravity Fields (COST-G) under the umbrella of the International Gravity Field Service (IGFS), part of the International Association of Geodesy (IAG). We exploit this fact and deliver the highest-quality monthly GFMs, resulting from the combination of four different gravity field estimation approaches. All solutions are unconstrained and estimated independently from month to month. We tested the added value of including kinematic baselines (KBs) in our estimation of GFMs and conclude that there is no significant improvement. The non-gravitational accelerations measured by the accelerometer on board Swarm C were also included in our processing to determine if this would improve the quality of the GFMs, but we observed that is only the case when the amplitude of the non-gravitational accelerations is higher than during the current quiet period in solar activity Using GRACE data for comparison, we demonstrate that the geophysical signal in the Swarm GFMs is largely restricted to spherical harmonic degrees below 12. A 750 km smoothing radius is suitable to retrieve the temporal variations in Earth's gravity field over land areas since mid-2015 with roughly 4 cm equivalent water height (EWH) agreement with respect to GRACE. Over ocean areas, we illustrate that a more intense smoothing with 3000 km radius is necessary to resolve large-scale gravity variations, which agree with GRACE roughly at the level of 1 cm EWH, while at these spatial scales the GRACE observes variations with amplitudes between 0.3 and 1 cm EWH. The agreement with GRACE and GRACE-FO over nine selected large basins under analysis is 0.91 cm, 0.76 cm yr-1, and 0.79 in terms of temporal mean, trend, and correlation coefficient, respectively. The Swarm monthly models are distributed on a quarterly basis at ESA's Earth Swarm Data Access (at https://swarm-diss.eo.esa.int/, last access: 5 June 2020, follow Level2longterm and then EGF) and at the International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/series/02_COST-G/Swarm, last access: 5 June 2020), as well as identified with the DOI https://doi.org/10.5880/ICGEM.2019.006 (Encarnacao et al., 2019).

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Precise science orbits for the first 4 years of the Swarm mission have been generated from onboard GPS measurements in a systematic reprocessing using refined models and processing techniques. Key enhancements relate to the introduction of macro-models for a more elaborate non-gravitational force modeling (solar radiation pressure, atmospheric drag and lift, earth albedo), as well as carrier phase ambiguity fixing. Validation using satellite laser ranging demonstrates a 30% improvement in the precision of the reduced dynamic orbits with resulting errors at the 0.5–1 cm level (1D RMS). A notable performance improvement is likewise achieved for the kinematic orbits, which benefit most from the ambiguity fixing and show a 50% error reduction in terms of SLR residuals while differences with respect to reduced dynamic ephemerides amount to only 1.7 cm (median of daily 3D RMS). Compared to the past kinematic science orbits based on float-ambiguity estimates, the new kinematic position solutions exhibit a factor of reduction of two to three in Allan deviation at time scales of 1000s and higher, and promise an improved recovery of low-degree and -order gravity field coefficients in Swarm gravity field analyses.

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Sentinel-1A is the first satellite of the European Copernicus programme. Equipped with a Synthetic Aperture Radar (SAR) instrument the satellite was launched on April 3, 2014. Operational since October 2014 the satellite delivers valuable data for more than two years. The orbit accuracy requirements are given as 5. cm in 3D. In order to fulfill this stringent requirement the precise orbit determination (POD) is based on the dual-frequency GPS observations delivered by an eight-channel GPS receiver.The Copernicus POD (CPOD) Service is in charge of providing the orbital and auxiliary products required by the PDGS (Payload Data Ground Segment). External orbit validation is regularly performed by comparing the CPOD Service orbits to orbit solutions provided by POD expert members of the Copernicus POD Quality Working Group (QWG). The orbit comparisons revealed systematic orbit offsets mainly in radial direction (approx. 3. cm). Although no independent observation technique (e.g. DORIS, SLR) is available to validate the GPS-derived orbit solutions, comparisons between the different antenna phase center variations and different reduced-dynamic orbit determination approaches used in the various software packages helped to detect the cause of the systematic offset. An error in the given geometry information about the satellite has been found. After correction of the geometry the orbit validation shows a significant reduction of the radial offset to below 5. mm. The 5. cm orbit accuracy requirement in 3D is fulfilled according to the results of the orbit comparisons between the different orbit solutions from the QWG.

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The Swarm satellite mission provides important information regarding the temporal changes of Earth’s gravity field. Several European institutes routinely process Swarm GPS data to produce kinematic orbits, which forms the basis for the estimation of monthly gravity fields. Each institute follows a different gravity field estimation approach and all together they provide complementary advantages. As a result, the combined gravity field model is superior to any individual contribution, improving the accuracy of the measurement of mass transport processes. These models are an integral part of the European Gravity Service for Improved Emergency Management project (http://egsiem.eu), thus providing independent input, in additional to dedicated geodetic data. We illustrate the agreement of the Swarm models with the much more accurate GRACE solutions, at 1666km wavelength and over most of the Swarm mission lifetime. We additionally highlight large surface mass transport processes represented by the Swarm GPS data. @en

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 short-arc 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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The Swarm mission consists of three satellites orbiting the Earth at low orbital altitudes. The onboard GPS receivers, star cameras, and laser retro-reflectors make the Swarm mission an interesting candidate to explore the contribution of Swarm GPS data to the recovery of both the static and time-variable gravity fields. We use 1.5 years of Swarm GPS and attitude data to generate kinematic positions of high quality to perform gravity field determination using the Celestial Mechanics Approach. The generated gravity fields reveal severe systematic errors along the geomagnetic equator. Their size is correlated with the ionospheric density and thus strongly varying over the analyzed time period. Similar to the findings of the GOCE mission, the systematic errors are related to the Swarm GPS carrier phase data and may be reduced by rejecting GPS data affected by large ionospheric changes. Such a measure yields a strong reduction of the systematic errors along the geomagnetic equator in the gravity field recovery. Long wavelength signatures of the gravity field may then be recovered with a similar quality as achieved with GRACE GPS data, which makes the Swarm mission well suited to bridge a potential gap between the current GRACE and the future GRACE Follow-On mission.

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Electronic power inverters are capable of quickly delivering reactive power to maintain customer voltages within operating tolerances and to reduce system losses in distribution grids. This paper proposes a systematic and data-driven approach to determine reactive power inverter output as a function of local measurements in a manner that obtains near optimal results. First, we use a network model and historic load and generation data and do optimal power flow to compute globally optimal reactive power injections for all controllable inverters in the network. Subsequently, we use regression to find a function for each inverter that maps its local historical data to an approximation of its optimal reactive power injection.
The resulting functions then serve as decentralized controllers in the participating inverters to predict the optimal injection based on a new local measurements. The method achieves near-optimal results when performing voltage- and capacityconstrained loss minimization and voltage flattening, and allows for an efficient volt-VAR optimization (VVO) scheme in which legacy control equipment collaborates with existing inverters to facilitate safe operation of distribution networks with higher levels of distributed generation.@en