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Signals used for Earth observation, when travelling through the atmosphere, are sensitive to refractivity; especially high spatio-temporal variations of water vapor are difficult to model and correct. Remaining unmodeled tropospheric delays deteriorate the positioning solution and therefore limit the accuracy of sensing and navigation applications. These delays are usually computed with empirical models based on ground meteorological parameters (pressure, temperature and water vapor partial pressure). However, existing models are not accurate enough for high-precision applications such as GNSS, where in consequence the so-called zenith total delay (ZTD) has to be estimated together with other unknown parameters (coordinates etc.). For decades, the Institute of Geodesy and Photogrammetry at ETH Zurich has been studying collocation methods for the modeling of tropospheric delays using meteorological parameters, successfully interpolating pointwise or integral atmospheric observations. Meanwhile, machine learning has become a widely used and valuable alternative, when big datasets are available for the training process. Indeed, we have already successfully predicted ZTDs based on meteorological parameters with an accuracy of 1–2 cm for locations (GNSS stations) already used in the training phase. However, difficulties arise to predict delays at new locations. In this work, we take a step forward in investigating the combination of machine learning algorithms and physical models used in a collocation approach to derive atmospheric delay fields at a very high resolution. Thus, without processing any GNSS data we can predict tropospheric delay fields everywhere in the area of investigation. In this paper, we firstly describe the designed architecture of the neural network, secondly, the combination of least-squares collocation and artificial neural networks for high-resolution prediction of tropospheric delays. We benefit from the complementary characteristics of these algorithms. While machine learning is capable of successfully predicting the variation of time series for given points, empirical models based on collocation are well suited for describing spatial variations within the area of investigation. Finally, we report the achieved performance for the entire territory of Switzerland (1–2 cm in terms of RMS), showing that the synergic combination of these algorithms can overcome the individual drawbacks of each method and provide more accurate delay estimates than either method individually. Datasets of 11 years, covering the territory of Switzerland, consisting of GNSS ZTDs from 72 permanent AGNES/COGEAR (swisstopo, ETHZ) stations and meteorological data from MeteoSwiss were used for this research., Acta Astronautica, 204, ISSN:0094-5765

With the introduction of the operating system Android 7 Nougat in the year 2016, it became possible to access Global Navigation Satellite System (GNSS) code and carrier phase observations. These observations can be processed with the state-of-the-art GNSS processing software packages, which allows an in-depth evaluation of the smartphone's GNSS performance. The availability of carrier phase observations enables sub-decimeter-level positioning. A few years ago, smartphones wearing dual-frequency GNSS chipsets hit the mass market. In this study, we investigate the capability of such a device for the estimation of tropospheric delays. Static measurements carried out over the period of two weeks are performed using a Google Pixel 4 XL smartphone. The measurements are processed using relative positioning methods with a baseline length of about 33 kilometers, where a continuously operating reference station (CORS) acts as a base. The estimated differential zenith tropospheric wet delay (dZWD), obtained for the smartphone are then combined with absolute values computed at the reference station, in order to obtain time series of Zenith Total Delay (ZTD). Using this method, we demonstrate that high-precision ZTDs can be successfully determined from smartphone GNSS observations. When comparing the estimated tropospheric delays with those determined at a nearby geodetic receiver to assess the accuracy of the acquired time series of ZTD, differences in the range of few millimeters to a centimeter are visible. We examine the impact of various error sources, such as antenna phase center variations and residual effects of the ionosphere. Given that the obtained accuracies are at the level of a centimeter and below, the suggested method shows the potential to resolve small-scale tropospheric structures in near real-time, and thus, could be an interesting data source for numerical weather prediction models or related GNSS crowdsourcing projects., Measurement Science and Technology, 34 (9), ISSN:0957-0233, ISSN:1361-6501

Global Navigation Satellite System (GNSS) is an essential tool for troposphere monitoring. Currently, GNSS meteorology depends mainly on the data from geodetic receivers of global or regional networks. However, these geodetic-grade GNSS stations are costly, and thus cannot be densely deployed, especially in less developed regions. Since the release of the Android 7 operating system in 2016, Android smartphones can be used to collect raw GNSS data. Considering that nowadays there are about 3 billion Android smartphones worldwide, a smartphone GNSS data crowdsourcing campaign was launched on March 17th 2022 as a part of the CAMALIOT project. About 5 TB of raw GNSS observations were collected around the world by more than 12 thousand users of the CAMALIOT Android application. In this contribution, we highlight the results related to the dedicated pipeline developed to process the crowdsourced smartphone GNSS data. Firstly, all the collected data were classified by a machine learning-based model to disregard observations of low quality. It was found that only about 2% of the collected data could potentially be used for troposphere delay estimation. The high-quality observations were then processed in the relative-positioning mode by forming baselines with the nearby geodetic stations. Several crowdsourced data sets were used to demonstrate that the zenith wet delays (ZWD) derived from smartphone data could achieve an accuracy of better than 10 mm. However, uncalibrated phase center variations of the smartphone antennas and multipath errors are still the main limitations to further improve the ZWD estimation. Overall, our study indicates that crowdsourced smartphone GNSS data is promising to densify the existing GNSS networks in terms of troposphere monitoring.

We demonstrate that the dynamic response of an engineered structure, including modeshape identification, can be obtained from just a single measurement at one position - if rotation is recorded in combination with translation. Such a single-station approach can save significant time, effort and cost when compared with traditional structural characterization using horizontal arrays. In our contribution we will focus on the monitoring of a high-rise building by tracking its dynamic properties and their variations due to environmental (e.g. temperature) and operational (e.g. wind) conditions (EOCs) over a 1-year period. We present a real-case structural identification procedure on the Prime Tower in Zurich. This is a 36-story tower of 126 m height, with a poured-in-place-concrete core and floors and precast-concrete columns; this concrete core structure, surrounded by a triple-glazed facade, is the third highest building in Switzerland. The building has been continuously monitored, over a 1-year period, by an accelerometer (EpiSensor), a co-located rotational sensor (BlueSeis) and a weather station located near the building center on the roof. Roof and vertical seismic arrays were deployed for short periods. The motion on the tower roof includes significant rotation as well as translation, which can be precisely captured by the monitoring station. More than 20 structural modes, including the first 6 fundamental modes, where translations are coupled with rotations, are tracked between 0.3 – 14 Hz. We will also show the variation of natural frequencies due to seasonal but also more short-term effects, in an effort to understand the effect of environmental and operational variability on structural deformation and response. Additionally, an amplification of the modes, not only during strong winds, but also during a couple of Mw 4.0 - 4.4 earthquakes at regional distance has been observed and analysed. The frequency band between 0.3 and 10 Hz is of key interest for earthquake excitation, making an investigation thereof essential. The work closes with a summary of the main benefits and potential in adopting collocated rotation and acceleration sensing for geo-infrastructure monitoring purposes.

The goal of the project “Co-location of Space Geodetic Techniques on Ground and in Space”, in the DFG funded research unit on reference systems and founded by the Swiss National Foundation (SNF), is the improvement of existing and the establishment of new ties between the space geodetic techniques, together with the assessment and reduction of technique-specific biases. To achieve this, the wealth of co-located instruments at the Geodetic Observatory Wettzell (Germany) are used, where systematic errors in the space geodetic techniques can be detected, assessed and removed on very short, well-known baselines. Within this paper we summarise results for three intra-technique co-location experiments in Wettzell. Firstly, an assessment of the GNSS to GNSS baselines in relation to the surveyed local ties shows discrepancies of up to 9 mm, for solutions based on the ionosphere-free linear combination. Secondly, an analysis of the short VLBI baseline shows that the use of a clock tie achieves a sub-mm agreement with respect to the local tie. And finally, initial results on the usage of differencing approaches on the short SLR baseline show that double-difference residuals are within ±4 mm. The results of this work show the potential of intra-technique studies on short baselines for the understanding of technique-specific biases and errors and for the monitoring of local ties., International Association of Geodesy Symposia, 152, ISSN:0939-9585, ISSN:2197-9359, Beyond 100: The Next Century in Geodesy, ISBN:978-3-031-09856-7, ISBN:978-3-031-09857-4

In this study we test whether principal components of the strain rate and stress tensors align within Switzerland. We find that 1) Helvetic Nappes line (HNL) is the relevant tectonic boundary to define different domains of crustal stress/surface strain rates orientations and 2) orientations of T- axes (of moment tensor solutions) and long-term asthenosphere cumulative finite strain (from SKS shear wave splitting) are consistent at the scale of the Alpine arc in Switzerland. At a more local scale, we find that seismic activity and surface deformation are in agreement but in three regions (Basel, Swiss Jura and Ticino); possibly because of the low levels of deformation and/or seismicity. In the Basel area, deep seismicity exists while surface deformation is absent. In the Ticino and the Swiss Jura, where seismic activity is close to absent, surface deformation is detected at a level of ~2 10E−8/yr (~6.3 10E−16/s)., Scientific Reports, 8, ISSN:2045-2322

Global Navigation Satellite Systems (GNSS) are very versatile sensors, which can be used for a variety of commercial and scientific applications. This holds especially true for different fields of remote sensing, such as atmospheric sounding or soil moisture monitoring. With the advent of low-cost dual-frequency GNSS equipment, certain applications are no longer restricted to the use of geodetic-grade instrumentation and can fully take leverage of the measurements in a second frequency band. In view of these emerging benefits, this study introduces the development and deployment of a multi-purpose GNSS station network in the Swiss Alps, called MPG-NET. We discuss the technical details of the station setup, in terms of GNSS hardware and technical design, as well as the quality of derived GNSS remote sensing products. In particular, our analyses focus on the quality of derived time series of zenith total delays (ZTD) and volumetric soil moisture content. Products are validated against benchmark data obtained from numerical weather models and in-situ sensors. For a prototype station, the results show a good agreement with the baseline, with errors of few millimeters for ZTD, and a remarkably high correlation for soil moisture content. Beside the documented value of low-cost GNSS for displacement monitoring (such as landslides or strong earthquakes), these findings are another step towards the establishment of a dense high-precision, multi-purpose GNSS network that comes at a very affordable price., Measurement, 216, ISSN:0263-2241, ISSN:1873-412X

Global Navigation Satellite System (GNSS) is a well-recognized observation technique in studies on the ionosphere due to its sensitivity to the total electron content (TEC). The era of modern smartphones, running on Android version 7.0 and higher, facilitates the acquisition of raw dual-frequency GNSS measurements, paving the way for the GNSS community data to be potentially exploited in geoscience applications. One can assume that the continuous progress in this domain may result in future in a performance of those smart devices reaching the level of GNSS receivers (and antennas) used for atmospheric monitoring. The prospective utilization of a very large number of GNSS-capable smartphones, as a dynamic crowdsourcing receiver network, could form thus an attractive source of complementary GNSS data, allowing to significantly increase the spatial resolution of observations available for the analysis and cover areas of the globe where GNSS receivers are not yet present. The enormous volume of prospective GNSS community data brings, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest, also in near-real time. The same applies to the analysis of such huge and heterogeneous data sets, requiring a dedicated approach in order to exploit the data in a thorough manner and fully benefit from such a concept. Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) is an ongoing ESA NAVISP project with activities covering acquisition of GNSS observations from modern smartphones and development of the dedicated infrastructure regarding GNSS processing and machine learning at scale. An Android application, developed within that project, is utilized to retrieve code and phase observations from the modern generation of smartphones. The acquired user-specific data is available to the user in the form of RINEX3-compliant files and can be uploaded by the user to the central server for subsequent processing. This contribution highlights the CAMALIOT project in relation to the ionosphere and provides information on the developed Android application, data ingestion and processing, complemented with methodology and initial results related to the TEC retrieval based on smartphone data collected in the vicinity of geodetic GNSS receivers, with the latter used for deriving reference time series. Concerning the smartphone data, the amount and quality of observations are much lower compared to the high-grade GNSS equipment and a dedicated pre-processing stage is needed in order to discard bad observations in a proper manner. An apparent correlation between the data quality, utilized frequency bands and satellite constellation involved is visible too. This area of GNSS still suffers from the limitations related mainly to the components comprising the smartphone, resulting in the lower quality of the acquired GNSS observations, compared to those obtained with the use of high-grade GNSS receivers and antennas. This translates to a greater susceptibility to multipath as well as a much more frequent occurrence of observation gaps and cycle slips, affecting the data availability and continuity of the carrier-phase measurements., EGUsphere

Radio signals transmitted by Global Navigation Satellite System (GNSS) satellites propagate through the atmosphere before being received on Earth. Thereby, the signal is delayed and tropospheric parameters can be estimated. The good global coverage of GNSS receivers, combined with the high temporal resolution and the high accuracy, make GNSS a suitable tool for studies on the atmosphere.Atmospheric delays are differentiated into a zenith hydrostatic (ZHD) and a non-hydrostatic, or zenith wet delay (ZWD). The hydrostatic part has a larger contribution (causing a delay of roughly 2.4 meters in the zenith direction) but can be modeled with sufficient accuracy using analytical methods. The ZWD has a smaller contribution (causing a delay between 0 to 40 centimeters) and depends mainly on the water vapour content in the atmosphere. However, due to the variable nature of water vapour, the ZWD is difficult to model and is therefore typically estimated. Its quantification is essential since it drives weather systems and climate change to a great extent. For many applications, such as weather forecasting or positioning using low-cost GNSS receivers such as smartphones, global real-time monitoring or even predictions of ZWD would be required and be beneficial.In the last decade, machine learning (ML) algorithms have gained a lot of interest and are successfully utilized in many different fields. Thereby, ML algorithms have proven to be able to efficiently process and combine large amounts of data and solve problems of various kinds.This motivated us to investigate the feasibility of ML algorithms for the prediction of tropospheric parameters, in particular ZWD, with the help of meteorological data such as the water vapour content. The work aims to develop a global model capable of predicting ZWD in space and time. Therefore, different ML algorithms are used to train a model based on meteorological features. The performance of the utilized algorithms is evaluated based on commonly used performance metrics, such as Root Mean Squared Error (RMSE) and R².Preliminary investigations are carried out utilizing 3000 GNSS stations distributed over Europe. The performance of various ML methods, such as Linear Regression methods, Random Forest, (Extreme) Gradient Boosting, and Multilayer Perceptron is compared. Furthermore, different feature combinations, as well as training sample sizes are investigated. It is revealed that linear methods are not able to properly reflect the observations. Instead, our Random Forest approach provides, so far, the highest model accuracy with an RMSE of 1.7 centimeters and an R² value of 0.88.

We demonstrate that the extended dynamic response of an engineered structure can be obtained from just a single measurement at one position if rotation is recorded in combination with translation. Such a single station approach could save significant time, effort and cost when compared with traditional structural characterization using arrays. In our contribution we will focus on the monitoring of a high-rise building by tracking its dynamic properties, e.g., natural frequencies, mode shapes and damping. We present the results of the system identification for the Prime Tower in Zurich – with a height of 126 m, this concrete frame structure is the third highest building in Switzerland. It has been continuously monitored by an accelerometer (EpiSensor) and a co-located rotational sensor (BlueSeis) located near the building center on the roof for the past year. The motion on the tower roof includes significant rotations as well as translation, which can be precisely captured by the monitoring station. More than 9 natural frequencies, including the first 3 fundamental modes, as well as the next two overtones, where translations are coupled with rotations, are observed between 0.3 – 10 Hz, a frequency band of key interest for earthquake excitation, making an investigation essential. Using temporary arrays of accelerometers located across the roof and along the length of the building to perform a traditional dynamic characterisation, we can compare the array solution with the new single location solution in terms of system identification for the Prime Tower., EGUsphere

In recent years, dual-frequency GNSS chipsets became available on the mass market. The ongoing developments in sensor and processing technologies steadily improve the positioning performance so that nowadays sub-decimeter accuracies can be achieved with such devices, even in real-time. Thus these sensors become a powerful, inexpensive choice for equipping or densifying existing GNSS monitoring networks. Station densification can be of significant added value for earthquake early warning systems, assimilation of GNSS water vapor estimates into numerical weather prediction models and the detection of severe weather events. Even if somewhat noisier, smartphone data can be used for GNSS-based remote sensing purposes as well.This contribution is twofold, and focusses on both, the current capabilities and the perspectives of these GNSS low-cost technologies for such remote sensing applications. In the first part we highlight the accuracy of PPP-enabled seismic and tropospheric monitoring using low-cost loggers and stations developed in-house. We show that differential smartphone GNSS observations on short- and medium-length baselines can be used to sense the state of the regional troposphere. In the second part, we present first results on the performance of the u-blox D9S application board, which enables highest precision by PPP-RTK with ambiguity resolution, and the feasibility of high-precision positioning is assessed for long baselines involving smartphone data as well. Finally, we briefly discuss the potential of data-driven methods for mitigating multipath, which is still one of the main error sources when using equipment of low quality. Concerning the GNSS processing, we rely on further-developed versions of open-source and commercial GNSS software packages. Regarding sensor technology, u-blox chips -- which are currently deployed in our self-sufficient GNSS stations -- are used together with different low-cost and medium-grade GNSS antennas (both, patch and recent helical-type low-cost antennas).We conclude that low-cost GNSS sensor technology is on the way to satisfy the same demands in accuracy as geodetic-grade equipment -- centimeter-level accuracy can be obtained, even in real-time. New possibilities for station densifications arise by employing low-cost, autonomous stations or by crowdsourcing of GNSS data with smartphones. These observations can aid in resolving small-scale structures in the atmosphere, or for a quick detection and localization of geohazards.

With the introduction of the operating system Android 7 Nougat in the year 2016, it became possible to access the GNSS code and carrier phase observations on Android smartphones. These observations can now be processed with state-of-the-art GNSS processing software, which allows an in-depth evaluation of the smartphone`s GNSS performance. The availability of the carrier phase observations is an important step towards sub-decimeter-level positioning. Since a few years, there are also smartphones on the market that are equipped with dual-frequency GNSS chipsets.In this presentation, the capability of dual-frequency GNSS smartphones for the estimation of tropospheric delays is investigated. Static measurements over several weeks are performed using a Google Pixel 4 XL smartphone. The measurements are processed using relative positioning methods in a real-time mode, where a Continuously Operating Reference Station (CORS) acts as a base. The estimated differential tropospheric parameters – derived for short and medium baseline lengths – are then added to the absolute values computed at the reference station by Precise Point Positioning (PPP). Using this method, we demonstrate that the tropospheric zenith total delays can be successfully determined from smartphone observations. When comparing the estimated tropospheric delays with those determined at a nearby geodetic receiver, differences in the range of a few millimeters to centimeters are visible. In view of these accuracies, the suggested method shows the potential to resolve small-scale tropospheric structures and thus, can be an interesting data source for numerical weather prediction models or related GNSS crowdsourcing projects.

In this contribution we highlight the challenges of the combination of space geodetic techniques through local ties, during the rigorous estimation of geodetic parameters, for the realisation of a global Terrestrial Reference Frame (TRF). Local ties at geodetic fundamental sites play an important role in the determination of a TRF, providing the necessary links to connect the different geodetic techniques. Moreover, the growing demands on the accuracy and stability of the ITRF, have turned the analysis of the quality of these ties into a crucial element to achieve a highly consistent frame, providing additionally the opportunity to identify technique-specific systematic biases when comparing the space geodetic results with local measurements.To study the impact of the local ties at fundamental sites, we use the Very Long Baseline Interferometry (VLBI) observations collected during the CONT17 campaign, together with simultaneous Global Navigation Satellite System (GNSS) observations of a subset of the International GNSS Service (IGS) global network. To this end, our approach performs the rigorous estimation of all parameter types common to the two techniques, namely station coordinates, troposphere zenith delays and gradients, and the full set of five Earth Orientation Parameters (EOPs) and their rates, and we include their full variance-covariance information during the combination process. In the central step of this processing scheme, we realise the GNSS-VLBI combination via the "official" ITRF coordinate ties, using and evaluating different weighting schemes, to obtain a unique set of consistent parameters. Moreover, we study the impact of tropospheric ties between the collocated VLBI and GNSS stations, which are essential for the height estimates. Thus, based on the analysis of the station coordinate repeatabilities and the characteristics and behaviour of the EOPs, we discuss the impact of the accuracy and weighting of the local coordinate and troposphere ties on the estimation of the different geodetic parameters.

The number of low Earth orbit (LEO) satellites equipped with Global Navigation Satellite System (GNSS) receivers is rapidly increasing. GNSS observations in space are no longer limited to a small number of Earth observation satellites, but the rapid development of large nanosatellite constellations enables a dense network of GNSS observations around the Earth. An example of this is the Astrocast CubeSat constellation, to which we contribute with our low-cost multi-GNSS payload board. The first 10 satellites of the Astrocast constellation have successfully been launched on 24 January 2021 (5 satellites) and 30 June 2021 (5 satellites). Further Astrocast CubeSats equipped with dual-frequency GNSS receivers will be launched in the coming years, completing a constellation of 100 satellites by 2024.The formation of a homogeneous and highly dynamic GNSS network in space holds great potential for geodetic Earth observation, as it has some advantages over a ground-based GNSS network and GNSS observations on board single or formation-flying satellites: A space-based GNSS network can be autonomously processed in a double-difference mode without the need for ground observations, thus, GNSS signals are not affected by tropospheric refraction, and it provides a better observation geometry improving the sensitivity to certain geodetic parameters. In this study, we investigate the feasibility of forming such a space-based GNSS network for estimating geodetic parameters, namely the orbit parameters of the LEO and GNSS satellites, the antenna phase center corrections of the GNSS satellites, and the low-degree coefficients of the Earth’s gravity field including the geocenter coordinates.We consider 3 different constellation scenarios: (1) A LEO constellation of 36 satellites uniformly distributed over 6 orbital planes with an inclination of 55° and (2) the expected configuration of the complete Astrocast constellation, with sun-synchronous polar orbits and equatorial orbits. In both cases (1) and (2), the GNSS observations are simulated with the Bernese GNSS software based on the given orbit specifications. (3) In a third scenario, we use real GNSS observations from various existing Earth observation missions, including GRACE, OSTM/Jason-2 and Swarm, which are combined to a pseudo-constellation.For each scenario, the number of possible GNSS single- and double-differences and the corresponding baseline lengths will be computed. Based on these observations, we will examine, how well carrier-phase ambiguities can be resolved and how this depends on the constellation configuration. With a network processing of GNSS double-difference observations, we will estimate concrete parameters related to the LEO orbits, the GNSS antenna phase center corrections and the Earth’s gravity field. To estimate the expected accuracy for these parameters, we examine their sensitivity to small errors in the observation data resulting from, e.g., the force model, once-per-revolution parameters, stochastic pulses or small accelerations like ocean tide or Earth albedo effects. Based on this research, we will draw conclusions about the potential of large satellite constellations to complement or replace the existing geodetic Earth observation missions in the future.

Global Navigation Satellite System (GNSS) receivers are very versatile sensors, which have not only revolutionized positioning and navigation applications, but also provide numerous opportunities for environmental monitoring and remote sensing. Beside the monitoring of long-term ground movements and geodynamics, typical applications include the provision of water vapor estimates for numerical weather prediction (NWP) and climate studies as well as real-time applications such as seismic and geohazard monitoring. The rising number and quality of low-cost GNSS equipment, coupled with innovative telecommunication approaches (Internet of Things), allow for an increased and more cost-effective usage of such devices for those monitoring purposes, and thus foster a fast decision-making process.An especially beneficial approach is the collocation of GNSS sensors at already existing meteorological or seismic stations. By using available infrastructure for power supply and communication, it can provide a sustainable and energy-effective extension of existing monitoring capabilities. The different parameters collected on-site can be used for cross-validation or provision of corrections for GNSS positioning. Furthermore, through (close to) real-time availability of observations, such collocated stations can aid early-warning systems for many different types of natural hazards (from extreme weather events to landslides and earthquakes). At the Institute of Geodesy and Photogrammetry at ETH Zürich we develop GNSS instrumentation to equip meteorological stations from the SwissMetNet (SMN). The work is carried out in the course of a pilot study in cooperation with MeteoSwiss.This contribution introduces initial results of the SMN station Zürich-Affoltern, where the first prototype GNSS payload has been installed. We highlight key capabilities of the low-cost GNSS equipment used for these high-precision geomonitoring purposes. Moreover, we discuss concrete ideas for the build-up of a dedicated collocation network within the DACH (Germany-Austria-Switzerland) border area and the opportunities arising from it. These opportunities include the sustainable enhancement of infrastructure for climate change monitoring in the Alpine region as well as the build-up of early-warning systems for multiple types of geohazards. The latter might be achieved through the combination of parameters collected on-site with complementary data (e.g. satellite observations or NWP output) using innovative, data-driven approaches. Finally, we showcase examples and the potential of recent and ongoing works using these data-driven approaches.

Atmospheric interaction with microwaves causes refraction as well as delays of electromagnetic signals. Although the effects are the same for all microwave signals including GNSS and radar, different spatio-temporal sampling of the tropospheric delays, different frequencies resulting in different phase sensitivity to path delays changes, as well as different processing strategies, assumptions and algorithms may lead to differences in the quantified tropospheric estimates. In case of GNSS, the most typical troposphere-related product is the zenith total delay - quantified after the mapping of all GNSS slant delays in the zenith direction. On the other side, double-difference tropospheric slant delays in persistent scatterer interferometry can be obtained in an iterative manner by isolating and subsequently adding the unwrapped low spatial frequency components of the phase residuals to update the estimate of the tropospheric phase. The updated tropospheric phase is then subtracted before the next iteration of point-wise regression-based estimations of topographic corrections and surface displacements. This iterative process is repeated for all scatterers with acceptable standard deviation of the phase residuals until convergence is reached. For comparison of the different tropospheric delays, the spatio-temporal characteristics of GNSS and InSAR observations must be considered. In this work, we use statistical interpolation methods to collocate the GNSS ZTDs with the InSAR measurements. Moreover, as another external (independent) observation we consider 3D fields of numerical weather models, which are integrated in the slant direction to produce (relative) tropospheric delay maps.As a case study, an alpine region in the Valais area, Switzerland has been selected, which is an interesting scenario due to the high variability of the refractive index over complex terrain. A relatively dense GNSS network, as well as an interferometric time series of Synthetic Aperture Radar (SAR) images are available for the time span of 2008-2013. After introducing the available observations into the collocation approach, we perform the comparison and evaluation of the different tropospheric delays. In addition, we address the following two questions: How should the correct signal part be considered when modeling tropospheric delays using collocation? What is the effect of the GNSS network in terms of size and resolution? This work is an effort in understanding the different estimated/modeled delays, and it aims to set a baseline and a framework for the fusion of GNSS and InSAR tropospheric delays for the monitoring of the atmospheric state over complex terrain.

Precise orbit determination is vital for the increasingly vast number of space objects around the Earth. Moreover, accurate orbit prediction of GNSS satellites is essential for many real-time geodetic applications, including real-time navigation. The typical way to obtain accurate orbit predictions is using physics-based orbit propagators. However, the prediction errors accumulate with time because of insufficient modeling of the changing perturbing forces. Motivated by the rapid expansion of computing power and the considerable data volume of satellite orbits available in recent years, we can apply machine learning (ML) and deep learning (DL) algorithms to assess if they can be used to further reduce orbit errors. In this study, we focus on the orbit prediction of GNSS constellations. We investigate the potential of using different ML and DL algorithms for improving the accuracy of the ultra-rapid products from IGS. As ground truth we consider the IGS final products, and the differences between the ultra-rapid and final products are computed and serve as targets for the ML/DL methods. In this context, we combine the advantages of physics-based and data-driven ML/DL methods. Since the major errors of GNSS orbits are expected to be caused by the deficiency of solar radiation pressure models, we consider different related parameters as additional features to implicitly model the solar impact, such as the C0,0 terms of global ionosphere maps. In order to accurately model the effect of solar radiation pressure on the radial, along-track and cross-track components of the satellite orbit system, the geometric relation between the Sun, the satellite and the Earth are also considered. Furthermore, the performances of different ML/DL algorithms are compared and discussed. Due to the temporal characteristics of the problem, certain sequential modeling algorithms, such as Long Short-Term Memory and Gated Recurrent Unit, show superiority. Our approach shows promising results with average improvements of over 40% in 3D RMS within the 24-hours prediction interval of the ultra-rapid products., EGUsphere

The low-cost GNSS market is still dominated by single-frequency devices. Since a few years, some manufacturers also produce low-cost dual-frequency receivers. Typically, these devices and instruments are much smaller and less expensive than geodetic dual-frequency equipment. However, this comes at the cost of a poorer usability, and a potentially poorer hardware and internal signal processing, resulting in observables with lower quality. Furthermore, GNSS low-cost antennas have worse performance than their geodetic-grade counterparts, especially in terms of multipath mitigation. In this contribution, the capability of such low-cost GNSS devices for real-time and high-rate geomonitoring purposes is investigated. For this purpose, we assembled the low-cost dual-frequency receiver u-blox ZED-F9P in a small and light-weight data logger with very low power consumption, which is thus very well suited for field campaigns in remote areas. Static measurements are performed to test the measurement accuracy and stability. To determine the influence of the antenna type on the measurement result, different antennas covering different price segments are connected to the low-cost receiver. Beside the static tests in kinematic mode, strong-motion dynamic ground movements are simulated with a robot, as they occur for large earthquakes. All experiments also involve measurements with a JAVAD SIGMA geodetic receiver, that have been carried out in parallel -- these are used for comparison purposes. For the processing of the measurements real-time precise point positioning (PPP) is used, since this is the primary method of choice for GNSS seismology. We focus on ambiguity float solutions which are determined under real-time conditions using the Centre National d’Etudes Spatiales (CNES) open-source PPP software and the CNES real-time products. This work demonstrates, that even with low-cost GNSS devices it is possible to obtain a precision of few centimeters (and even millimeters over short time intervals). Thus, these devices can be used to monitor dynamic ground movements at the centimeter level, which is a requirement for strong-motion seismology. However, it must be considered that the quality of the antenna used can have a significant influence on the results. In addition, ambiguity resolution has proven to be challenging when using low-cost antennas. Therefore, a more in-depth investigation is necessary to exploit the full potential of the ambiguity resolution, also with other software packages. However, we conclude that these devices provide an excellent basis for the densification of existing GNSS monitoring networks, as needed for strong-motion seismology, or atmospheric tomography methods.

With test satellites already in space, the Swiss company Astrocast is currently in the process of establishing a constellation of about 80 nanosatellites for commercial purposes that are operating in a low Earth orbit (LEO). As a result of the collaboration with ETH Zürich, such satellites will be equipped with both low-cost multi-GNSS dual-frequency receivers and a small array of laser retroreflectors for satellite laser ranging (SLR). In the future, this set of geodetic instruments could be also extended with a simple, compact and low-power transmitter compliant with the next-generation very long baseline interferometry (VLBI) system, known as the VLBI Global Observing System (VGOS). Therefore, apart from scientific studies based on such state-of-the-art multi-GNSS receivers in space, the Astrocast nanosatellite network could also be examined in terms of satellite co-locations. In this case, the new geometrical connections in space could be realized together with all ground-based instruments that can observe the co-location satellites. Assuming sufficient precision of such observations and good knowledge of the spacecraft environment, this approach could result in an enhanced quantity of tie measurements at a high spatio-temporal resolution, potentially leading also to an enhanced quality of common geodetic parameters. However, accurate orbit determination is of high importance, whenever considering potential co-location in space or, in general, estimating various global parameters of geophysical interest. In this contribution, we focus on precise orbit determination (POD) of LEO Astrocast-type nanosatellites based on global SLR-only, VGOS-only as well as combined SLR-VGOS observations. The impact of this concept on various geodetic parameters and the derived orbits is studied on the basis of Monte-Carlo simulations carried out with the c5++ analysis software. All simulated data are combined on the observation level and used to derive satellite orbits and to estimate both, station-based and global geodetic parameters. Our study is based on VGOS-type schedules created in VieSched++ and consisting of both quasar and satellite observations. In addition to the simulated laser measurements to Astrocast satellites, the SLR-related solutions include also global observations to LAGEOS-1/2 satellites. Our considerations involve solutions with different time intervals, satellite observation precision levels and quantity of the considered cube satellites, providing thus initial insights concerning prospective utilization of LEO cube satellite technology for space geodesy in the era of the Global Geodetic Observing System., EGUsphere

GNSS antennas are a key factor in precise GNSS positioning. With the increasing availability of low-cost dual-frequency GNSS receivers also the demands on low-cost GNSS antennas increases. Unfortunately, the electronic center of most GNSS antennas is not located in the mechanical Antenna Reference Point (ARP). As a consequence, Phase Center Corrections (PCC) have to be introduced to correct for frequency-dependent signal delays within the antenna system. The PCCs are typically in the range of several millimeters to centimeters. Thus, uncorrected phase center variations can be a significant error source in precise positioning.For the purpose of antenna calibration, the Institute of Geodesy and Photogrammetry at ETH Zürich acquired a six-axis industrial robot of type KUKA AGILUS KR 6 R900 sixx. In an initial study, the absolute accuracy of the robot has been determined to be better than 1.5 mm (standard deviation). By introducing a set of extended Denavit-Hartenberg parameters, the absolute position accuracy of the robot is further increased to 0.3 mm over the entire workspace and 0.1 mm for a predefined sequence of robot poses, respectively. Therefore, the robot operates well below the phase noise of the GNSS measurements (typically around 1 mm) and is therefore seen as suitable for the calibration of GNSS antennas with sub-millimeter accuracy.Besides the numerous benefits of absolute field calibration with an industrial robot, several challenges remain if it comes to low-cost GNSS antennas. The main challenges are that for each antenna a specific mounting system has to be built and that low-cost antennas are in general less shielded against multipath (compared to geodetic antennas). Besides, only little information exists about the stability of the electronic reference point and how much the electronic properties change when the antenna is mounted on different platforms (cars, drones, cubesats, etc).To address the critical issues in low-cost GNSS antenna calibration and study the impact of the PCCs on the positioning solution, a calibration campaign has been initiated at ETH Zürich in autumn 2020. In this campaign, a set of low-cost multi-GNSS dual-frequency patch and loop antennas - suited for centimeter-positioning - has been calibrated and tested. Therefore, in the vicinity of the GNSS reference station (ETH2) the robot has been installed and a sequence of randomized robot poses has been executed in which the ARP of each antenna was defined as rotation point. The GNSS signals recorded during this sequence were processed together with the robot attitude information using the time-differencing approach defined by D. Willi (2019) using a spherical harmonics parameterization.The PCCs obtained from the calibration campaign were stored in ANTEX files for a subsequent validation. In this presentation, we will highlight the developed calibration procedures for low-cost GNSS antennas, summarize the main results of the calibration and validation campaign, and will give the framework in which a calibration of low-cost GNSS antennas is considered beneficial.Willi D., GNSS receiver synchronization and antenna calibration, PhD Thesis, ETH Zürich, 2019, https://www.research-collection.ethz.ch/handle/20.500.11850/308750

In December 2018 and April 2019, two 3-unit cube satellites of the company Astrocast were launched into orbit. Both satellites are equipped with our low-cost single-frequency multi-GNSS payload board, which provides almost continuous on-board receiver solutions containing the position from GNSS code observations and the velocity from Doppler measurements. We make use of these independent observation types (positions and velocities) to identify and analyse systematic biases in the receiver solution. Therefore, we estimate the parameters of a dynamic orbit model using three different approaches: fitting the orbit model (1) to the positions only, (2) to the velocities only and (3) to both, positions and velocities.After removing outliers, the position residuals from the position-only approach are at a level of about 5 m, the velocity residuals from the velocity-only approach at about 15 cm/s. When computing the positions with the velocity-only approach, however, the residuals are much larger and show a once-per revolution periodicity with amplitudes of up to 40 m. Besides that, we identify two offsets in the residuals which are independent of the observation type: a radial position bias of -3 m and an along-track velocity bias of -1.2 cm/s. Additionally, we observe two offsets which are dependent on the observation type: an along-track offset of 13 m in the position residuals when using the velocity-only approach and a radial offset of 1.3 cm/s in radial velocities when using the position-only approach.The periodicity in radial and along-track direction is related to the orbit eccentricity and may be due to a general deficiency, when using velocities to estimate geometric orbit parameters. When comparing the orbits from the position-only and the velocity-only approach, we find an offset in the right ascension of the ascending node, which corresponds to a maximum cross-track position difference of 40 m at the equator. We show that this effect is caused by a periodic bias in the velocity solutions with a maximum at the poles. A possible cause for such a periodicity in the velocity solutions may be dynamic effects in the receiver tracking loops related to the LEO satellite velocity relative to the GNSS constellation, which can vary strongly within one revolution.Our results show that both, the radial position offset and the along-track velocity offset are dependent on the altitude of the satellite and are likely to be caused by ionospheric refraction. The explanation for the along-track position offset and the along-track velocity offset, however, is not that obvious. We found that these two offsets are geometrically related and, thus, must have the same physical cause. Based on the combined position-and-velocity approach we demonstrate that they originate from a velocity bias rather than from a position bias. To explain the physical cause of such a radial velocity offset, we will study the ionospheric effects on GNSS code and Doppler measurements in more detail, where we use a 3D-ionosphere model and take also the altitude of the two satellites into account.

High-precision GNSS (Global Navigation Satellite System) positioning can reach an accuracy at the millimeter level, and by collecting these data over years, very small ground movements can be resolved. These data can be used to study geodynamics, like continental drifts, tidal- and non-tidal loading effects, or earthquakes, for example. Here we focus on the sensitivity of GNSS for resolving these different types of movements. We derive minimal detectable displacements for linear drift rates, annual- and semiannual periodic motions, and offsets – these are main parameters of the so-called standard linear trajectory model for GNSS station motions. For our analysis, our data comes from several hundreds of permanent GNSS stations across Europe– the GNSS stations’ coordinates are obtained at a daily sampling rate, with almost 25 years of data available for some stations. Based on cleaned residual GNSS time series we calibrate a “power-law plus white noise” stochastic model for each station. Together with the functional trajectory model we compute the formal errors of the movement parameters based on a least-squares adjustment. Based on these errors we then introduce the statistics to derive minimum detectable displacements for the movement parameters. Our analysis shows that the minimum detectable trends can be as low as few tenth of millimeters per year. The minimum detectable amplitudes at the annual and semiannual periods are at the millimeter level or lower, and the detectable offset is few millimeters on average, too. As expected, the minimum detectable displacements depend strongly on the length of the datasets and on the noise characteristics. Another important parameter is the number of discontinuities and offsets for each station – it impacts the minimum detectable trend. We conclude that such an analysis can be very useful for sensitivity studies in climate change monitoring. Furthermore, the methodology cannot only be applied in the field of GNSS time series analysis, but also to any other time series data in geosciences., EGUsphere

High spatia-temporal variability of atmospheric water vapor is directly reflected in the tropospheric pathdelays that microwave satellite signals experience. The so-called zenith total delays (ZTDs) need to be estimated in case of Global Navigation Satellite Systems (GNSS). Usually, models describe the ZTD with three meteorological parameters measured on ground: pressure, temperature and partial water vapor pressure. However, these models are determined empirically and it is especially a struggle to accurately determine the delay caused by the water vapor (wet delay) from meteorological data. In this work, we provide an alternative approach of estimating the tropospheric path delay using machine learning (ML) algorithms. During the last two decades machine learning algorithms have become widely used in many fields of science and engineering. Therefore, a large amount of time series of ZTDs and meteorological data and the successful applicability of machine learning to various applications are the main motivation behind this work. Besides, we also investigated another approach to compute ZTDs, based on the well-known Saastamoinen model [Saastamoinen, 1973], after interpolating the meteorological parameters at GNSS sites. The idea behind this work is to genarate GNSS zenith pathdelays without nrocessing any GNSS data, but only using meteorological parameters. Therefore, GNSS zenith pathdelays from 72 permanent stations in Switzerland and meteorological data from the permanent SwissMetNet network (with over 120 stations) have been used for training and validation for a period of 11 years. The distribution of the sites all over Switzerland allows the network to be trained and validated with stations at different altitudes and with various meteorological conditions. The ML approach showed an overall accuracy of 1.6 cm in terms of standard deviation, with almost no bias. Moreover, results show that stations at higher altitudes can benefit more from this approach. Compared to the Saastamoinen model, it had an overall improvement of about 20%, with a much better estimation in summer periods, when the amount of water vapor is higher. This work is a contribution to using ML algorithms to compensate for atmospheric errors in GNSS signals, and to compare its capabilities with empirically derived models.

Chemical composition and density measurements of the upper atmosphere provide key insights into the origin and evolution of celestial objects. The density and the chemical composition of planet’s atmosphere may evolve from a hostile, dense primary atmosphere to a life‐harbouring tertiary atmosphere as present on Earth by losing atmospheric species to space upon formation of the object. The present-day atmospheric escape rates can be measured or inferred from exospheric temperatures and density profiles, for many species of the upper atmosphere. By modelling, these escape rates can be adapted to conditions in the past to study the evolution of the atmospheric composition. The terrestrial exosphere and upper ionosphere are complex dynamic regions that adapt to several endogenous and exogenous drivers. The Sun as such a driver forces the upper atmosphere to respond to the solar UV/EUV flux and solar energetic particles. These external influences cause a variation of both the density, extent, and the chemical composition of the upper atmosphere on several time scales ranging from minutes to the age of the Solar System leading to various phenomena including atmospheric evolution and night-side transport of species. Although these interactions have been extensively studied over the past decades, the scientific community still lacks some basic measurements in the terrestrial upper atmosphere to derive the exospheric temperatures and its variation as a function of the corresponding drivers. Figure 1: Computer model of the 3U CHESS satellite. Thus, we designed the CHESS mission consisting of two 3U CubeSat-type satellites on different orbits. The science payload of each spacecraft consists of a new generation of dual-frequency GNSS receivers for total air density measurements from drag and total electron content above the satellites and a compact time-of-flight mass spectrometer for in situ measurements of both the chemical composition and density in the upper atmosphere. Enabled by these two novel scientific instruments, the primary goal of the CHESS mission is to record an inventory of the neutral species and ions present at various heights in the exosphere. From these measurements, we will derive numerous atmospheric properties including exospheric temperature profiles, compositional variations of the exosphere, atmospheric escape rates, as function of the external drivers.

The ITRF is realized through the combination of the individual solutions (station coordinates and velocities) of the four space geodetic techniques. This combination features two main aspects: 1) precise local ties, to establish the link among techniques, and 2) full variance-covariance information of each solution, to account for the accuracy of each technique. Although this approach strives to obtain accurate geodetic products, this combination is not entirely "rigorous'', especially when it comes to the Earth Orientation Parameters (EOPs). In the current realization of the ITRF only polar motion (x-pole and y-pole) are combined, whereas UT1-UTC and nutation offsets and rates are taken from the solution of a single technique (VLBI). Furthermore, parameters such as troposphere delays and clock offsets are not part of the combination strategy. Thus, a rigorous estimation of the ITRF, with consistent EOPs and with appropriate tropospheric and clock ties, is still a challenge to be met.To achieve a rigorous combination, all parameter types common to more than one space geodetic observation technique should be combined including their full variance-covariance information as well as the corresponding ties. In particular, as both, GNSS and geodetic VLBI, are based on microwave frequencies, their physical models and their parameter types are closely related. These common parameters are the site coordinates and velocities, troposphere estimates, EOPs and (possibly) clock estimates. Thus, using the data from the CONT17 campaign as a case study, we discuss the processing scheme, the challenges and initial results of a rigorous combination of VLBI and GNSS observations, in order to estimate and densify EOPs, particularly diurnal and sub-diurnal variations for polar motion and UT1-UTC, and to realize an inter-technique tropospheric tie, thus remedying the deficiencies mentioned above. The results obtained constitute an important step towards the realization of a rigorous ITRF solution that is able to improve the accuracy and consistency of all geodetic products.

Current best practice in monitoring earthquake strong motion are dense networks comprising strong motion accelerometers that measure acceleration over a broad frequency and amplitude range. These instruments are capable of measuring translational motions of large earthquakes, but lack sensitivity to very low frequencies or permanent displacements. However, it is widely accepted that during earthquakes the rotational component of the ground motion, both static and dynamic, is large enough to contaminate the derived displacements from these sensors. Modern rotational sensors, are also very broadband, have a large dynamic range, and are not sensitive to translational motions. We explore the value of complementing accelerometers with these rotational sensors at seismic strong motion monitoring stations.The assessment of the errors introduced into accelerometer records from rotational ground motions is only possible with co-located rotational instruments sensitive enough to record the small rotation rates accompanying the translational motion. Operating accelerometers alongside gyros and additionally GNSS instrumentation should allow us to record the full 6 components (6C) of near-field earthquake motions, with increasing fidelity across a very broad frequency band for the strongest motions.We aim to demonstrate how, using a combination of the three sensor types, we can recover the full 6C ground motion, and hence also more reliable displacement records, using a versatile industrial six-axis robot that can produce controlled and repeatable 6C motion across a broad frequency band. Through the precise feedback loop used by the robot to stabilize its precise trajectory, we get a 6C recording of the driven motion represented by Euler rotations and displacements, which we use as ground truth. By simulating a combination of translational and rotational motions on the robot, we show that the 6C Kalman filter can accurately reproduce the clean simulated translational motion. By using a Kalman filter, we attempt to combine the different data sets using prediction and weighting of the observation data for an optimal solution. Our methodology tries to take into account the strengths and weaknesses of the individual instruments that are providing partly redundant and partly complementary ground motion information.

In this paper we highlight the potential of geodetic high-precision and high-rate GNSS (Global Navigation Satellite System) sampling (1 to 100 Hz) for resolving seismic ground motions, of both the near and the far field of an earthquake. The analysis of the budget and characteristics of the error of high-rate GNSS displacement time series yields results, discussion, and conclusions on the sensitivity and waveform resolvability as well as on the derivation of a minimum detectable displacement (in the statistical sense).Based on these analyses, we show how GNSS can contribute to optimal broadband displacement and velocity waveform products by means of data fusion by combining measurements taken from co-located sensors – e.g. accelerometers or gyroscopes – in real-time, near real-time and postprocessing mode. Concerning the inclusion of GNSS for such an analysis, we also briefly explore the ability of GNSS to record signals from different earthquake magnitudes and epicentral distances. We show that high-rate GNSS is sensitive to displacements down to the level of a few millimeters, and even below – an example also comes from the detection of very small vibrations from 100 Hz GNSS data.We analyze measurements of synthetized signals obtained from experiments with a shake table, as well as from real data from strong earthquakes, namely the 6.5 Mw event of 2016 near the city of Norcia (Italy) and the 7.0 Mw Kumamoto earthquake of 2016 (Japan). Based on these data and our main findings, we finally discuss the role of GNSS in Earthquake Early Warning in terms of a fast hypocenter localization and reliable magnitude estimation.

As one of the major contributors to the realisation of the International Terrestrial Reference System (ITRS), the Global Navigation Satellite Systems (GNSS) are prone to suffer from irregularities and discontinuities in time series. While often associated with hardware/software changes and the influence of the local environment, these discrepancies constitute a major threat for ITRS realisations. Co-located GNSS at fundamental sites, with two or more available instruments, provide the opportunity to mitigate their influence while improving the accuracy of estimated positions by examining data breaks, local biases, deformations, time-dependent variations and the comparison of GNSS baselines with existing local tie measurements. With the use of co-located GNSS data from a subset sites of the International GNSS Service network, this paper discusses a global multi-year analysis with the aim of delivering homogeneous time series of coordinates to analyse system-specific error sources in the local baselines. Results based on the comparison of different GNSS-based solutions with the local survey ties show discrepancies of up to 10 mm despite GNSS coordinate repeatabilities at the sub-mm level. The discrepancies are especially large for the solutions using the ionosphere-free linear combination and estimating tropospheric zenith delays, thus corresponding to the processing strategy used for global solutions. Snow on the antennas causes further problems and seasonal variations of the station coordinates. These demonstrate the need for a permanent high-quality monitoring of the effects present in the short GNSS baselines at fundamental sites.

A new algorithm for global navigation satellite system attitude determination onboard a spacecraft was developed. A distinct feature of the algorithm is the extrapolation of the measurements to a common epoch within a Kalman filter. The necessity for the extrapolation arises from the usage of non-synchronized low-cost receivers. The extrapolation terms typically range from $$- 6$$ to + $$6$$ m for u-blox receivers. Thus, no solution can be obtained without a proper extrapolation. A validation was carried out with synthetic data as well as with signal simulator data. The algorithm delivers an attitude estimation with an accuracy below 1° for three orthogonal baselines of 10 cm length. In conclusion, the algorithm is proven to work, offering a very efficient method of attitude determination onboard a spacecraft.

Global Navigation Satellite Systems (GNSS) have been used as a key technology for satellite orbit determination for about 30 years. With the increasing popularity of miniaturized satellites (e.g., CubeSats that are nanosatellites based on standardized 10 cm-sized units) the need for an adapted payload for orbit determination arises. We developed a small-size versatile GNSS payload board using commercial off-the-shelf single-frequency GNSS receivers with extremely small weight (1.6 g), size (12.2 x 16.0 x 2.4 mm3 ) and power consumption (100 mW). The board features two separate antenna connectors and four GNSS receivers – two connected to each antenna. This redundancy lowers the risk of a total payload failure in case one receiver is malfunctioning. Two prototypes of the GNSS positioning board have been successfully launched onboard the Astrocast-01 and -02 3-unit cube satellites with altitudes of 575 and 505 km, respectively. The multiGNSS receivers are capable of tracking the GNSS satellites of the four major systems, i.e., GPS, GLONASS, BeiDou and Galileo. In addition, both satellites are equipped with a small array of three laser retroreflectors enabling orbit validation with Satellite Laser Ranging (SLR). After the two precursor missions, a constellation of 80 satellites is planned, allowing the formation and computation of a highly uniform polyhedron in space with cm-accuracy, relevant for geocenter, reference frame, and GNSS orbit determination. At present, we have continuous receiver PVT solutions available. The real-time onboard orbit determination results indicate that the receivers perform very well on both satellites. The RMS of a daily orbit fitting is, after removing one or the other outlier, at the level of 2-5 meters despite errors caused by the ionosphere and the orbit model. For a few satellite arcs, the recording of GNSS raw phase and code data was enabled, allowing orbit determination in a post-processing mode. This allows a better assessment of the achievable orbit quality and an overall performance estimation. The tests performed so far include the improvement of the orbit quality by eliminating the ionospheric refraction based on a linear combination of phase and code observations, the comparison of various single-system solutions and advances in combining the different tracking systems for orbit determination. In collaboration with the Zimmerwald Observatory in Switzerland a first SLR campaign was conducted that successfully tracked both nanosatellites. The SLR measurements with their high accuracy were then analyzed to validate the orbits of the Astrocast satellites derived from GNSS measurements. We will present details on the payload board, on the results of the orbit determination in real-time and in post-processing mode based on the low-cost single-frequency multi-GNSS receivers onboard the satellites and on the SLR orbit validation., EGUsphere

In this study, a multiple algorithm approach to the analysis of GNSS coordinate time series for detecting geohazards and anomalies is proposed. This multiple algorithm approach includes the novel use of spatial and temporal analyses. In the spatial analysis algorithm, the spatial autoregressive model was used, assuming that the GNSS coordinate time series from a network of stations are spatially dependent. Whereas in the temporal analysis algorithm, it is assumed that the GNSS coordinate time series of a single station is temporally dependent and an artificial neural network is used to extract this dependency as a nonparametric model. This multiple algorithm approach was examined using (i) the BIGF network of GNSS stations in the British Isles and (ii) the GNSS stations of the GEONET network in Japan for the Tohoku‐Oki 2011 Mw9.0 earthquake. It was demonstrated in these case studies that this multiple algorithm approach can be used to detect the effect of a geohazard such as an earthquake on the GNSS network coordinate time series and to detect regional anomalies in the GNSS coordinate time series of a network. The spatial analysis algorithm seemed to be more suitable to detect coordinate offsets in the low‐frequency component and/or variations in the long‐term trends of the GNSS coordinate time series, while it is less reliable in detecting sudden large magnitude coordinate offsets due to earthquakes, as the effects at one station propagate to nearby stations. In contrast, the temporal analysis algorithm detects coordinate offsets in the high‐frequency component which makes it effective in detecting sudden large coordinate offsets in the GNSS coordinate time series such as those due to earthquakes. Thus, it was shown the complementary of the temporal and spatial analysis algorithms and their successful application for the magnitude and frequency content of the anomalies in the two case studies., Journal of Geophysical Research: Solid Earth, ISSN:2169-9313, ISSN:0148-0227, ISSN:2169-9356

Battelier, B., et al., We present the scientific motivation for future space tests of the equivalence principle, and in particular the universality of free fall, at the 10 level or better. Two possible mission scenarios, one based on quantum technologies, the other on electrostatic accelerometers, that could reach that goal are briefly discussed. This publication is a White Paper written in the context of the Voyage 2050 ESA Call for White Papers., Open Access funding enabled and organized by Projekt DEAL.

GNSS observations provided by the global tracking network of the International GNSS Service (IGS, Dow et al. in J Geod 83(3):191–198, 2009) play an important role in the realization of a unique terrestrial reference frame that is accurate enough to allow a detailed monitoring of the Earth’s system. Combining these ground-based data with GPS observations tracked by high-quality dual-frequency receivers on-board low earth orbiters (LEOs) is a promising way to further improve the realization of the terrestrial reference frame and the estimation of geocenter coordinates, GPS satellite orbits and Earth rotation parameters. To assess the scope of the improvement on the geocenter coordinates, we processed a network of 53 globally distributed and stable IGS stations together with four LEOs (GRACE-A, GRACE-B, OSTM/Jason-2 and GOCE) over a time interval of 3 years (2010–2012). To ensure fully consistent solutions, the zero-difference phase observations of the ground stations and LEOs were processed in a common least-squares adjustment, estimating all the relevant parameters such as GPS and LEO orbits, station coordinates, Earth rotation parameters and geocenter motion. We present the significant impact of the individual LEO and a combination of all four LEOs on the geocenter coordinates. The formal errors are reduced by around 20% due to the inclusion of one LEO into the ground-only solution, while in a solution with four LEOs LEO-specific characteristics are significantly reduced. We compare the derived geocenter coordinates w.r.t. LAGEOS results and external solutions based on GPS and SLR data. We found good agreement in the amplitudes of all components; however, the phases in x- and z-direction do not agree well.