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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

One of the indispensable elements of high-resolution weather forecast systems is the provision of reliable initial conditions using observations. Among the methods for collecting meteorological data, besides the quality of measurements, their time and space variability play a crucial role. Hence, GNSS observations stand out as stable, bias-free alternatives for weather stations, radiosondes, or microwave satellites.Current studies of GNSS observations in weather forecasting give promising results. However, the observations themselves are subject to errors due to their geometry, mainly caused by insufficient vertical and horizontal resolution. Therefore, applying them in an operational forecasting model is challenging. A possible way to solve this is to integrate space and ground-based observations into one tomography model.The solution should be able to detect local, extreme weather phenomena with repeatable uncertainty and high numerical stability. Hence, we propose a precise 3D ray tracing solution for effective simulations of the ray path between the GNSS satellite and the GNSS receiver (Low Earth Orbiting LEO satellite), along with the ground receiver. Although, the combination of these results in one computationally efficient and stable model is a complex task.The following step is the 3D ray tracing simulation integration into a modified TOMO2 operator dedicated to the tomography of 3D wet refractivity fields. The ray tracing module collects information on ray points’ refractivity and distance traversed in models’ voxels along the ray path. Then delivers it to mutual observational matrices for ground- and space-based simulations. This study focuses on the methodology of integrated tomography modeling. Results are compared to the ground-based only GNSS tomography solution and validated with radiosondes profiles. The case studies are based on severe weather events in Poland with RO data delivered by SPIRE company and GNSS ground-based observations produced by UPWr. Numerical Weather Model input comes from European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5.

The Constellation of High-performance Exosphere Science Satellites (CHESS) mission is a dedicated CubeSat mission designed to analyse the upper atmosphere of Earth in situ. The status of this dynamic region is driven by external and internal forces, causing variations in temperature, the chemical composition, total number density, and their altitude profiles. Although such measurements are key for our understanding of the origin and evolution of the habitable atmosphere of Earth, the community lacks updated, local, detailed measurements. Recent technological advancements in instrumentation enable sensitive in situ measurements of the exosphere–thermosphere–ionosphere region with small satellites. On one hand, the miniaturisation of a high-performance mass spectrometer enables measurements of the chemical composition and density. On the other hand, a new generation of dual-frequency Global Navigation Satellite System (GNSS) receivers enable precise orbit determination. From the knowledge of the evolution of the orbits, the atmospheric drag and hence, the density can be derived complementary. Additionally, the GNSS receivers provide the dispersive line-of-sight total electron content from the linear combination of dual-frequency carrier phase measurements that can be converted into total column density between the spacecraft in low Earth orbit and the satellites of the GNSS in higher orbits. Thanks to the elliptic orbit of the spacecraft, the altitude profiles of these number density measurements of species can be converted into exospheric temperatures. Measuring with a constellation will allow for overcoming the space-time degeneracy to analyse the drivers and mechanisms including basic physics in detail. The CHESS mission is scheduled for launch in early 2026. Once available, the collected data will also be compared with atmospheric escape data on, for example, Venus and Mars to provide insights into the different evolution of these rocky, initially comparable planets.

Nanosatellite technology opens up new possibilities for earth observation. In the next decade, large satellite constellations will arise with hundreds, up to thousand of satellites in low earth orbit. A number of satellites will be equipped with rather low-cost sensors, such as GNSS receivers, suited for atmospheric monitoring. However, the future evolution in atmospheric science leans not only on densified observing systems but also on new, more complex analysis methods. In this regard, tomographic principles provide a unique opportunity for sensor fusion. The difficulty in performing the conversion of integral measurements into 3D images is that the signal ray path is not a straight line and the number of radio sources and detectors is limited with respect to the size of the object of interest. Therefore, the inverse problem is either solved linearly or iterative nonlinear. In this chapter, an overview about the individual solving techniques for the tomographic problem is presented, including strategies for removing deficiencies of the ill-posed problem by using truncated singular value decomposition and the L-curve technique. Applied to dense nanosatellite formations, a new quality in the reconstruction of the 3D water vapor distribution is obtained, which has the potential for leading to further advances in atmospheric science.

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

Nanosatellite technology opens up new possibilities for Earth observation. We expect that in the next decade large satellite constellations will arise with hundred, up to thousand satellites in low Earth orbit. While most constellations will be dedicated to internet of things and global communication, a larger number of satellites will be also equipped with rather low-cost sensors, such as GNSS receivers, suited for monitoring of the Earth’s atmosphere. However, future evolution of atmospheric science leans not only on densified observing systems but also on new, more complex analysis methods. In this respect, tomographic principles provide a unique opportunity for sensor fusion and therefore, contribute to a better understanding of the specific sensor characteristics and to exploit their full potential for sensing the three-dimensional atmospheric state. One difficulty in performing the conversion of integral measurements into three-dimensional images is that the signal ray path is not a straight line but rather dependent on the object properties along the signal path. Another challenge is related to the limited number of radio sources and detectors with respect to the size of the object of interest. Therefore, the inverse problem is either solved linearly or iterative non-linear. In this chapter, the individual solving techniques for the tomographic problem are presented, including strategies for removing deficiencies of the ill-posed problem by using truncated singular value decomposition and L-curve technique. Applied to dense nanosatellite formations, a new quality in the reconstruction of the 3D water vapor distribution is obtained, which has the potential for leading to further advances in atmospheric science.

Tropospheric water vapor is among the most important trace gases of the Earth’s climate system and its temporal and spatial distribution is critical for the genesis of clouds and precipitation. Due to the pronounced dynamics of the atmosphere and the non-linear relation of air temperature and saturated vapor pressure, it is highly variable which hampers the development of high resolution and three-dimensional maps of regional extent. As a complement to the sparsely distributed radio sounding observation network, GNSS meteorology and interferometric radar satellite remote sensing can assist with their complementary high temporal or spatial resolution. In addition, data fusion with collocation and tomography methods enables the construction of detailed maps in either two or three dimensions. By assimilation of these observation based datasets into regional dynamic atmospheric models the optimal state of the tropospheric water vapor conditions can be guessed. In the following, a collection of basic and processed datasets, obtained with the above listed methods, is presented that describes the state and course of atmospheric water vapor within the range of the GNSS Upper Rhine Graben Network (GURN) region.

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.

The number of tropospheric observations that are assimilated in current numerical weather forecasting systems, is large. From automatic weather stations, to geostationary satellites, from radiosondes to polar orbiting microwave satellites. Amid the currently available data sources, GNSS stands out as a bias free, self calibrating, high fidelity temperature and water vapour measurements. Until recently GNSS was used weather forecasting only in two ways: as a ground-based point, high-frequency observation of integrated water vapour (IWV) or zenithal integrated observation of temperature and water vapour, or as a sparse space-based profile observation of temperature and water vapour content (provided as a refractivity or bending angle profile). In the last couple of years GNSS tomography, a 3D imaging technique, is gaining attention as a weather model data source. However, low space resolution combined with large uncertainty of the tomography reconstruction makes this technique difficult to apply in operational forecasting. Therefore this technique, to be considered as a valuable data source in weather models, has to be numerically stable with known repeatable uncertainty. We believe that a way forward is to combine space-based and ground-based observations using the tomography principle. A way forward is to effectively simulate the signal trajectory between the GNSS transmitter and GNSS receiver (Low Earth Orbiting LEO satellite). 3D ray-tracing modelling of the radio occultation (RO) event based on Numerical Weather Model is performed. The challenge here is to make these ray-tracing results comparable with excess phase observations at the LEO satellite. Modelling by 3D ray-tracing is performed by the modified Atmospheric TOMography (ATOM) software with the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 model. This module uses the position of the GNSS satellites as starting point and iteratively propagates the signal path by collecting information on refractive parameters along its path based on nodal points. This study is based on the ten selected RO events from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) report “Optimising tracking strategies for Radio Occultation. Task 1 - the profile dataset.”. Modelling was performed by varying the grid resolution of the ERA5 model and the length of a propagator step size segment to obtain total excess phase delay values. Additionally, Radio Occultation Processing Package (ROPP) 2D ray-tracing multiple phase screen simulation was run to confront obtained from ATOM phase delays. The COSMIC Data Analysis and Archive Center (CDAAC) observed excess phase was used as a reference data source.

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.

Remote sensing of water vapor using the Global Navigation Satellite System (GNSS) is a well-established technique and reliable data source for Numerical Weather Prediction (NWP). One of the phenomena rarely studied using GNSS are foehn winds. Since foehn winds are associated with significant humidity gradients between lee/luv side of a mountain range, tropospheric estimates from GNSS are also affected by their occurrence. Time series reveal characteristic features like distinctive minima/maxima and significant decrease in correlation between the stations. However, detecting such signals becomes increasingly difficult for large data sets. Therefore, we suggest the application of machine learning algorithms for detection and prediction of foehn events from GNSS troposphere products. The present study uses long-term time series of high-quality GNSS troposphere products from the Automated GNSS Network Switzerland (AGNES) as well as records of operational foehn index to investigate the performance of several different classification algorithms based on appropriate statistical metrics. The two best-performing algorithms are fine-tuned and employed on two years of test data. The results show very promising results, especially when reprocessed GNSS products are utilized. Detection- and alarm-based measures reach levels of 70–85 % for both tested algorithms and thus are comparable to those from studies using data from meteorological stations and NWP. For operational prediction, some limitations due to the availability and quality of GNSS products in near-real time (NRT) exist. However, they might be mitigated to a significant extend by provision of additional NRT products and improved data processing in the future.

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

The atmospheric measurements made by the eight Mars orbiters in operation (as of April 2021) significantly improved our understanding of the Martian weather and climate. However, while some of the existing Mars orbiters will reach their lifetime, innovative and cost-effective observation concepts are requested - not only to guarantee continued observation but also to address potential gaps in the existing observing network. Inspired by the success of the two MarsCube One (MarCO) satellites we have established an observation concept, which is based on a series of cubesats, carried to Mars and injected into a low Mars orbit in a so-called string-of-pearls formation. Over the lifetime of the cubesats of about one Martian year, a series of radio occultation (RO) experiments will be carried out with up to 180 partially overlapping RO measurements per day. For processing of the unique set of observations, tomographic principles are applied to the RO measurements for the reconstruction of high-resolution 2D temperature and pressure fields of the lower Martian atmosphere. Unlike the standard Abel transform approach, the derived products are not dependent on spherical symmetry assumptions. Thus, an insight into various unresolved atmospheric phenomena is obtained - especially of those which are characterized by distinct horizontal gradients in pressure and temperature, e.g. as observed at the day-night terminator, during dust storms, or over complex terrain., Radio Science, 56 (7), ISSN:0048-6604, ISSN:1944-799X

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.

The atmospheric measurements made by the six Mars orbiters in operation (as of July 2020) significantly improved our understanding of the Martian weather and climate. However, while some of these o...

Within the International Association of Geodesy (IAG), a new working group was formed with the intention to bring together researchers and professionals working on tomography-based concepts for sensing the neutral atmosphere with space-geodetic techniques. Hereby the focus lies on Global Navigation Satellite Systems (GNSS) but also on complementary observation techniques, like Interferometric Synthetic Aperature Radar (InSAR) or microwave radiometers, sensitive to the water vapor distribution in the lower atmosphere. In the next four years (2019-2023), we will address current challenges in tropospheric tomography with focus on ground-based and space-based measurements, the combination of measurement techniques and the design of new observation concepts using tomographic principles. While geodetic GNSS networks are nowadays the backbone for troposphere tomography studies, further local densifications, e.g. at airports, cities or fundamental stations are necessary to achieve very fine spatial and temporal resolution. Besides, the combination of ground-based GNSS with other microwave techniques like radio occultation or InSAR seems to be beneficial due their complementary nature. Therefore, several further developments in the field of tropospheric tomography are required. This includes more dynamical tomography models - adaptable to varying input data, advanced ray-tracing algorithms for the reconstruction of space-based observations and the coordination of a benchmark campaign. In this presentation, we will give an overview about the current challenges in tropospheric tomography and the objectives of working group. The latter will also include standards for data exchange and therefore, make tomographic products available for the assimilation into numerical weather prediction models but also for various other disciplines, which rely on accurate wet refractivities or derived products like tropospheric signal delays., EGUsphere

Refractivity of the Earth's troposphere is directly reflected in the observations of space geodesy such as Global Navigation Satellite Systems (GNSS) and satellite radars. Therefore, they can provide valuable information about the quantity and spatio-temporal distribution of atmospheric gases. With improvement of GNSS modeling strategies and increasing network density tomographic approaches were developed. In this paper, we present an alternative tomographic approach based on collocation method to estimate the refractivity field from GNSS observables. Collocation adjustment is used to estimate parameters that relate GNSS observables (such as zenith or slant delays) with refractivities. Therefore, the parameters are used to compute the refractivity field. With this approach, for end-to-end simulations, the refractivity field can be reconstructed with an accuracy of 2.5 ppm and 4.5 ppm in terms of mean and standard deviation respectively; these results are comparable to typical tomography results.