The ionosphere, which strongly varies in time and space, is one of the largest error sources for microwave measurements, if not accounted for accurately. Especially for single‐frequency observations, precise ionospheric models are needed to correct for the ionospheric delays. This study explores the utility of Very Long Baseline Interferometry (VLBI) and the VLBI Global Observing System (VGOS) in determining ionospheric delays essential for precise microwave measurements. By comparing Vertical Total Electron Content (VTEC) derived from colocated S/X VLBI and VGOS stations during simultaneous sessions, our analysis reveals good agreement between these two systems and Global Ionosphere Maps (GIM). The results indicate that VGOS yields more stable VTEC estimates, with a smaller RMS increase in differences to the GIM, from 2.48 TECU during solar quiet to 4.19 TECU during solar active conditions, compared to S/X VLBI, which increases from 2.32 to 5.60 TECU. This suggests that VGOS, due to its broader frequency coverage and higher observation density, enables more accurate ionospheric monitoring during active phases. An analysis of long‐term VLBI data indicates good agreement with GIM and corresponds closely to the yearly and solar‐cycle periods. Based on the data used, the bias in the VTEC estimates as derived from S/X VLBI and GIM is −1.5 TECU during solar quiet periods and −4.3 TECU during solar active periods. In relation to VGOS, the bias relative to GIM is −2.2 TECU and −3.4 TECU for the same respective conditions. Overall, S/X VLBI and VGOS can contribute to enhancing our understanding of space weather effects on microwave techniques.

In recent decades, various studies have demonstrated that assimilating tropospheric parameters from ground-based GNSS receivers benefits numerical weather predictions (NWPs). However, the achieved performance is limited by the spatial resolution of GNSS, especially in the vertical direction. With the rapidly developing and growing market of unmanned aerial vehicles (UAVs) and the facilitates of integrating low-cost GNSS hardware into various autonomous systems over the last years, there is a potential to address this problem by utilizing UAVs to collect airborne GNSS data and generate zenith total delays (ZTDs). The airborne GNSS ZTDs can act as a potential complementary source to radiosonde data for obtaining vertical profiles of the troposphere, making it promising to investigate the impact of assimilating GNSS ZTDs of high spatio-temporal resolution in NWPs. In this study, we explored the use of GNSS data collected by a vertically ascending UAV, with ZTDs processed using the software CamaliotGNSS. Based on the airborne GNSS ZTDs, we conducted not only data assimilation but also weather predictions using the Weather Research and Forecasting Model (WRF). With the onboard meteorology observations as references, we found that assimilating airborne GNSS ZTDs positively impacted humidity and temperature forecasts, with their forecasting root-mean-square errors decreasing by about 19% and 29%, respectively. Moreover, by selecting and comparing different subsets of data, we found that better forecasts can be obtained with airborne GNSS ZTDs of higher spatio-temporal resolution. The positive results invite further exploration of applications of airborne platforms such as UAVs in the field of GNSS meteorology.

Global ionospheric mapping is essential for ionospheric research. However, conventional approaches often struggle to accurately capture small-scale ionospheric variations. This study proposes a deep ensemble method based on neural networks (NNs) that generates high-accuracy global vertical total electron content (VTEC) maps along with corresponding uncertainty estimates. To develop the machine learning model, we first determined the VTEC time series based on the carrier-to-code leveling method using multi-GNSS observations from global IGS stations. These VTEC time series were then used to train daily NNs, which subsequently generated global ionospheric maps (GIMs) for improved usability and accessibility. During our experiment covering the year 2023, the NNs achieved an average mean absolute error of 1.76 TEC Units (TECU) at 52 global test stations. Compared to IGS combined GIMs and two other representative GIMs from IGS analysis centers, the VTEC time series extracted from NN-GIMs showed better consistency with Jason-3 VTEC, achieving an average root mean squared error of 4.09 TECU after removing daily biases. Furthermore, NN-GIMs achieved the best baseline length repeatability in K-band Very Long Baseline Interferometry analysis. In single-frequency precise point positioning (SF-PPP) tests, NN-GIMs improved positioning accuracy by 11%, 9%, and 24% in the east, north, and up components compared to IGS combined GIMs. Additionally, the deep ensemble-based uncertainty quantification proved beneficial for weighting GNSS observations in SF-PPP, enhancing the positioning accuracy in low-latitude regions by approximately 14% compared to the elevation-based weighting scheme.

The Global Navigation Satellite System (GNSS) is a key asset for tropospheric monitoring. Currently, GNSS meteorology relies primarily on geodetic-grade stations. However, such stations are too costly to be densely deployed, which limits the contribution of GNSS to tropospheric monitoring. In 2016, Google released the raw GNSS measurement application programming interface for smartphones running on Android version 7.0 and higher. Given that nowadays there are billions of Android smartphones worldwide, utilizing those devices for atmospheric monitoring represents a remarkable scientific opportunity. In this study, smartphone GNSS data collected in Germany as part of the Application of Machine Learning Technology for GNSS IoT Data Fusion (CAMALIOT) crowdsourcing campaign in 2022 were utilized to investigate this idea. Approximately 20 000 raw GNSS observation files were collected there during the campaign. First, a dedicated data processing pipeline was established that consists of two major parts: machine learning (ML)-based data selection and ionosphere-free precise point positioning (PPP)-based zenith total delay (ZTD) estimation. The proposed method was validated with a dedicated smartphone data collection experiment conducted on the rooftop of the ETH campus. The results confirmed that ZTD estimates of millimeter-level precision could be achieved with smartphone data collected in an open-sky environment. The impacts of observation time span and utilization of multi-GNSS observations on ZTD estimation were also investigated. Subsequently, the crowdsourced data from Germany were processed by PPP with the ionospheric delays interpolated using observations from surrounding satellite positioning service of the German National Survey (SAPOS) GNSS stations. The ZTDs derived from ERA5 and an ML-based ZTD product served as benchmarks. The results revealed that an accuracy of better than 10 mm can be achieved by utilizing selected high-quality crowdsourced smartphone data. This study demonstrates high-precision ZTD determination with crowdsourced smartphone GNSS data and reveals success factors and current limitations.

The era of modern smartphones, running on Android version 7.0 and higher, facilitates nowadays acquisition of raw dual-frequency multi-constellation GNSS observations. This paves the way for GNSS community data to be potentially exploited for precise positioning, GNSS reflectometry or geoscience applications at large. The continuously expanding global GNSS infrastructure along with the enormous volume of prospective GNSS community data bring, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest. In addition, such large datasets cannot be managed manually anymore, leading thus to the need for fully automated and sophisticated data processing pipelines. Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) was an ESA NAVISP Element 1 project (NAVISP-EL1-038.2) with activities aiming to address the aforementioned points related to GNSS community data and their exploitation for scientific applications with the use of Machine Learning (ML). This contribution provides an overview of the CAMALIOT project with information on the designed and implemented cloud-native software for GNSS processing and ML at scale, developed Android application for retrieving GNSS observations from the modern generation of smartphones through dedicated crowdsourcing campaigns, related data ingestion and processing, and GNSS analysis concerning both conventional and smartphone GNSS observations. With the use of the developed GNSS engine employing an Extended Kalman Filter, example processing results related to the Zenith Total Delay (ZTD) and Slant Total Electron Content (STEC) are provided based on the analysis of observations collected with geodetic-grade GNSS receivers and from local measurement sessions involving Xiaomi Mi 8 that collected GNSS observations using the developed Android application. For smartphone observations, ZTD is derived in a differential manner based on a single-frequency double-difference approach employing GPS and Galileo observations, whereas satellite-specific STEC time series are obtained through carrier-to-code leveling based on the geometry-free linear combination of GPS and Galileo observations. Although the ZTD and STEC time series from smartphones were derived on a demonstration basis, a rather good level of consistency of such estimates with respect to the reference time series was found. For the considered periods, the RMS of differences between the derived smartphone-based time series of differential zenith wet delay and reference values were below 3.1 mm. In terms of satellite-specific STEC time series expressed with respect to the reference STEC time series, RMS of the offset-reduced differences below 1.2 TECU was found. Smartphone-based observations require special attention including additional processing steps and a dedicated parameterization in order to be able to acquire reliable atmospheric estimates. Although with lower measurement quality compared to traditional sources of GNSS data, an augmentation of ground-based networks of fixed high-end GNSS receivers with GNSS-capable smartphones would however, form an interesting source of complementary information for various studies relying on GNSS observations.

The ionosphere is a significant error source in space-geodetic techniques, such as the Global Navigation Satellite System (GNSS) and satellite radar altimetry. This is especially pronounced for single-frequency receivers, as ionospheric delays cannot be mitigated by ionosphere-free combinations. Therefore, for high-precision space applications, we need an accurate ionospheric model to provide ionospheric corrections at desired times and locations, such as global ionospheric maps (GIMs) that depict the global distribution of vertical total electron content (VTEC). In this study, we propose a neural network (NN)-based global ionospheric model to predict global VTEC with higher accuracy compared with conventional GIMs. We first determined VTEC based on the carrier-to-code leveling method using multi-GNSS observations from global IGS stations. The derived VTEC time series for all training station-satellite pairs were then used to train the NN-based model. During our experiment in April 2022, a period of high solar activity, the average mean absolute error of VTEC predictions at 47 global test stations was 1.7 TECU. The performance of the NN-based models were also evaluated by single-frequency precise point positioning and compared with GIMs provided by the Chinese Academy of Sciences since the same differential code bias products were used. The NN-based models demonstrated a noteworthy enhancement in the positioning precision at 47 test stations, achieving improvements of 12%, 20%, and 8% for the east, north, and up components, respectively.

The Global Navigation Satellite System (GNSS) is a well-recognized tool to probe the Earth’s atmosphere. This contribution highlights how GNSS data collected from smartphones of voluntary contributors can be used to determine parameters of the troposphere and ionosphere. In this regard, the application of machine learning (ML) to characterize the quality of the crowd-sourced data and model atmospheric parameters is discussed. We demonstrate that in certain cases, GNSS data from smartphones can reach a precision that would allow such data to densify observations from existing geodetic infrastructures.

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.

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.

Raw observations (carrier-phase and code observations) from the Global Navigation Satellite System (GNSS) can now be accessed from Android mobile phones (Version 7.0 onwards). This paves the way for GNSS data to be utilized for low-cost precise positioning or in ionospheric or tropospheric applications. This paper presents results from data collection campaigns using the CAMALIOT mobile app. In the first campaign, 116.3 billion measurements from 11,828 mobile devices were collected from all continents. Although participation decreased during the second campaign, data are still being collected globally. In this contribution, we demonstrate the potential of volunteered geographic information (VGI) from mobile phones to fill data gaps in geodetic station networks that collect GNSS data, e.g. in Brazil, but also how the data can provide a denser set of observations than current networks in countries across Europe. We also show that mobile phones capable of dual-frequency reception, which is an emerging technology that can provide a richer source of GNSS data, are contributing in a substantial way. Finally, we present the results from a survey of participants to indicate that participation is diverse in terms of backgrounds and geography, where the dominant motivation for participation is to contribute to scientific research.

High-precision global ionospheric modeling is important for radio communication, navigation, or studies on space weather. Traditional spatial ionospheric modeling approaches include spherical harmonics and trigonometric B-splines. The Ionospheric Associated Analysis Centers (IAAC) of the International GNSS Service (IGS) use these methods to model vertical total electron content (VTEC) globally, and generate Global Ionospheric Maps (GIMs). Due to the limitations of spatial modeling approaches, conventional GIMs cannot comprehensively describe the spatial feature of the ionosphere. With the capability of capturing complex and non-linear relationships of diverse data, machine learning (ML) has been increasingly applied to ionospheric modeling. Currently, most of the existing ML-related studies focused on temporal prediction of ionospheric states and rarely considered the aspect of the spatial modeling of VTEC. Although some studies predicted global ionosphere maps using machine learning, they used conventional GIMs as inputs, implying that the precision of the ML-based spatial modeling could be limited by traditional methods and quantity of input GNSS observations utilized to generate GIMs.The goal of this study is the spatial interpolation of VTEC using ML methods for the generation of ML-based GIMs. We first determine VTEC using carrier-to-code levelling through Kalman filter and based on geometry-free multi-GNSS observations from GNSS stations of the IGS network. The derived satellite-specific VTEC time series are then used to train the ML models. Several algorithms, such as extreme gradient boosting and random forest, are applied and their performance is evaluated. Moreover, VTEC from satellite altimetry is used as an additional means to assess the quality of the generated ML models. Finally, we compare the acquired ML-based GIMs with conventional GIMs to investigate the advantage of using the proposed approach for global VTEC modeling.

Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing the latest version by default [v1]Atmospheric monitoring with GNSS IoT data fusion based on machine learningAuthorsBenediktSojaiDVicenteNavarroiDGrzegorzKłopotekiDMarkusRothacheriDLindaSeeiDTobiasSturnRudiWeinackerIanMcCallumSee all authors Benedikt SojaiDCorresponding Author• Submitting AuthorETH ZurichiDhttps://orcid.org/0000-0002-7010-2147view email addressThe email was not providedcopy email addressVicente NavarroiDEuropean Space AgencyiDhttps://orcid.org/0000-0002-7381-3470view email addressThe email was not providedcopy email addressGrzegorz KłopotekiDETH ZurichiDhttps://orcid.org/0000-0001-7854-7055view email addressThe email was not providedcopy email addressMarkus RothacheriDETH ZurichiDhttps://orcid.org/0000-0002-7993-8573view email addressThe email was not providedcopy email addressLinda SeeiDIIASAiDhttps://orcid.org/0000-0002-2665-7065view email addressThe email was not providedcopy email addressTobias SturnIIASAview email addressThe email was not providedcopy email addressRudi WeinackerIIASAview email addressThe email was not providedcopy email addressIan McCallumIIASAview email addressThe email was not providedcopy email address

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.

Geodetic measurements allow the determination of a wide variety of parameters describing the Earth system, including its shape, gravity field, and orientation in space. The importance of such parameters to science and society is manifested through geodetic contributions to the examination of geodynamic phenomena, climate change monitoring and navigation both on the Earth's surface and in space. In a recent effort led by the Global Geodetic Observing System (GGOS), a set of Essential Geodetic Variables (EGVs) has been defined, which are key quantities characterizing geodetic properties of the Earth. Certain requirements have been assigned to EGVs, including accuracy, spatio-temporal resolution, and latency.For many real-time applications, the latency of geodetic products has become increasingly critical. Forecasts of certain EGVs at various time horizons are needed to accommodate the user's needs for many applications. In addition, spatial prediction of geodetic quantities on standardized grids on global and regional scales are of great benefit to certain scientific disciplines. The Space Geodesy group at ETH Zurich has thus established a new Geodetic Prediction Center (GPC), which aims to produce spatio-temporal predictions of various EGVs by employing state-of-the-art methods and providing them freely to the scientific community and other interested parties.In the field of time series forecasting and spatial prediction, machine learning (ML) has become increasingly powerful in recent years due to its high accuracy, efficiency in coping with large amounts of heterogeneous data sets, and capability of capturing complex relationships between various data sources. For instance, ML allows to include auxiliary data in geodetic predictions, also in the cases when no mathematical or physical relation is known. The application of ML has demonstrated promising results in terms of geodetic time series prediction and is thus the tool of choice for many of the parameters provided by the GPC. ML methods applied in this framework include tree-based methods such as random forest as well as variants of convolutional and recurrent neural networks. Such a ML-based approach allows to assimilate geodetic measurements, environmental models, and auxiliary data sets with the aim to provide predictions of utmost accuracy.Currently, ETH Zurich is invested in the prediction of Earth orientation parameters, Earth angular momentum functions, station coordinates, tropospheric zenith wet delays, ionospheric total electron content, and satellite orbits. In this contribution, an overview of these efforts in the framework of the Geodetic Prediction Center will be provided, highlighting the most recent scientific results.

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.

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.

The volume of data produced worldwide is growing rapidly, from 33 zettabytes in 2018 to an expected 175 zettabytes in 2025 Furthermore, the way in which data are stored and processed will change dramatically over the coming 5 years. Today 80% of the processing and analysis of data takes place in centralised computing facilities, and 20% in smart connected objects, such as cars, home appliance, manufacturing robots and computing facilities close to the user ('edge computing'). By 2025 these proportions are likely to be inverted. In the GNSS space segment, according to current development plans, over 120 GNSS satellites (including European Galileo satellites) will provide, already this decade, continuous data, in several frequencies, without interruption and on a permanent basis. This unique opportunity for science has been recognised by the European Space Agency (ESA) with the creation of the Navigation Science Office, which leverages on GNSS infrastructure to deliver innovative solutions across Earth Science, Space Science, Metrology and Fundamental Physics domains. At the core of this initiative, the GNSS Science Support Centre (GSSC) combines Big Data and Machine learning (ML) technologies to extract knowledge and discover patterns between GNSS-related inputs and outputs given the sheer volume of data. In this work, we introduce key GNSS Science Use Cases, providing a detailed view of GSSC on-going initiatives regarding troposphere and ionosphere characterisation through ML science pipelines to exploit a unique, publicly available repository of multifaceted GNSS data and products.

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.

The emergence of BeiDou and Galileo as operational Global Navigation Satellite Systems (GNSS), in addition to Global Positioning System (GPS) and GLONASS which are already in use, opens up possibilities in delivering geodetic products with higher precision. Apart from ensuring the homogeneity of the derived products, multi-GNSS analysis takes the advantage of new frequencies and an improved sky coverage. This should lead to better phase ambiguity resolution and an improved estimation of target parameters such as zenith wet delays (ZWD), troposphere gradients (GRD) and station positions. The International GNSS Service (IGS) has realised this potential by initiating the Multi-GNSS Experiment (MGEX) which provides orbit, clock and observation data for all operational GNSS. Correspondingly, the multi-technique space geodetic analysis software c5++ has been augmented with a MGEX-compliant GNSS module. Based on this new module and the Precise Point Positioning (PPP) approach using six-month of data, an assessment of the derived geodetic products is carried out for several GNSS receivers located at the Onsala core site. More specifically, we perform both single- and multi-GNSS data analysis using Kalman filter and least-squares methods and assess the quality of the derived station positions, ZWD and GRD. A combined solution using all GNSS together is carried out and the improvement with respect to station position repeatabilities is assessed for each station. Inter-system biases, which homogenise the different time scale that each GNSS operates in and are necessary for the multi-GNSS combination, are estimated and presented. Finally, the applied inter-system weighting is discussed as well as its impact on the derived geodetic products.