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Despite the growing number of GNSS sites, GNSS meteorology still suffers from insufficient observational data. This deficiency is particularly evident in GNSS tomography processing in the lowermost part of the troposphere, where the highest variability of weather conditions is observed. To address this challenge, we propose an integrated tomography solution that uses both ground-based GNSS observations and radio occultation (RO) excess phase in the same tomography model. Our integrated approach benefits from improved geometry and increased number of observations. In order to verify presented methodology, we use 41 GNSS stations and 78 RO profiles gathered during four different high precipitation events over the region of southern Poland. The results are validated against radiosonde observations and WRF reanalysis. Our study indicates an enhancement in the representation of wet refractivity on the order of 20 to 30% RMSE decrease when RO are integrated into the tomography solution with the mean error of final integrated product equal 3.5 ppm. [ABSTRACT FROM AUTHOR]

Radio signals transmitted by Global Navigation Satellite System (GNSS) satellites experience tropospheric delays. While the hydrostatic part, referred to as zenith hydrostatic delay (ZHD) when mapped to the zenith direction, can be analytically modelled with sufficient accuracy, the wet part, referred to as zenith wet delay (ZWD), is much more difficult to determine and needs to be estimated. Thus, there exist several ZWD models which are used for various applications such as positioning and climate research. In this study, we present a data-driven, global model of the spatial ZWD field, based on the Extreme Gradient Boosting (XGBoost). The model takes the geographical location, the time, and a number of meteorological variables (in particular, specific humidity at several pressure levels) as input, and can predict ZWD anywhere on Earth as long as the input features are available. It was trained on ZWDs at 10718 GNSS stations and tested on ZWDs at 2684 GNSS stations for the year 2019. Across all test stations and all observations, the trained model achieved a mean absolute error of 6.1 mm, respectively, a root mean squared error of 8.1 mm. Comparisons of the XGBoost-based ZWD predictions with independently computed ZWDs and baseline models underline the good performance of the proposed model. Moreover, we analysed regional and monthly models, as well as the seasonal behaviour of the ZWD predictions in different climate zones, and found that the global model exhibits a high predictive skill in all regions and across all months of the year. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) radio occultation (RO) is a space-based remote sensing technique that measures the bending angle of GNSS signals as they traverse the Earth's atmosphere. Profiles of the microwave index of refraction can be calculated from the bending angles. High accuracy, long-term stability, and all-weather capability make this technique attractive to meteorologists and climatologists. Meteorologists routinely assimilate RO observations into numerical weather models. RO-based climatologies, however, are complicated to construct as their sampling density is highly non-uniform and too sparse to resolve synoptic variability in the atmosphere. In this work, we investigate the potential of machine learning (ML) to construct RO climatologies and compare the results of a ML construction with Bayesian interpolation (BI), a state-of-the-art method to generate maps of RO products. We develop a feed-forward neural network applied to COSMIC-2 RO observations and simulate data taken from the atmospheric analyses produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). Atmospheric temperature, pressure and water vapor are used to calculate microwave refractivity at 2, 3, 5, 8, 15, and 20 km geopotential height, with each level representing a different dynamical regime of the atmosphere. The simulated data are the values of microwave refractivity produced by ECMWF at the geolocations of the COSMIC-2 RO constellation, which fall equatorward of 46? latitude. The maps of refractivity produced using the neural networks better match the true maps produced by ECMWF than maps using BI. The best results are obtained when fusing BI and ML, specifically when applying ML to the post-fit residuals of BI. At the six iso-heights, we obtain post-fit residuals of 10.9, 9.1, 5.3, 1.6, 0.6 and 0.3 N-units for BI and 8.7, 6.6, 3.6, 1.1, 0.3 and 0.2 N-units for the fused BI&ML, respectively. These results are independent of season. The BI&ML method improves the effective horizontal resolution of the posterior longitude-latitude refractivity maps. By projecting the original and the inferred maps at 2 km iso-height onto spherical harmonics, we find that the BI-only technique can resolve refractivity in the horizontal up to spherical harmonic degree 8 while BI&ML can resolve maps of refractivity using the same input data up to spherical harmonic degree 14. [ABSTRACT FROM AUTHOR]

Remote sensing of water vapour using the Global Navigation Satellite System (GNSS) is a well-established technique and reliable data source for numerical weather prediction (NWP). However, one of the phenomena rarely studied using GNSS are foehn winds. Since foehn winds are associated with significant humidity gradients between two sides of a mountain range, tropospheric estimates from GNSS are also affected by their occurrence. Time series reveal characteristic features like distinctive minima and maxima as well as a significant decrease in the correlation between the stations. However, detecting such signals becomes increasingly difficult for large datasets. Therefore, we suggest the application of machine learning algorithms for the detection and prediction of foehn events by means of GNSS troposphere products. This initial study develops a new, machine learning-based method for detection and prediction of foehn events at the Swiss station Altdorf by utilising long-term time series of high-quality GNSS troposphere products. Data from the Automated GNSS Network Switzerland (AGNES) and various GNSS sites from neighbouring countries as well as records of an operational foehn index are used to investigate the performance of several different classification algorithms based on appropriate statistical metrics. The two best-performing algorithms are fine tuned and tested in four dedicated experiments using different feature setups. The results are promising, especially when reprocessed GNSS products are utilised and the most dense station setup is used. Detection- and alarm-based measures reach levels between 66 %–80 % for both tested algorithms and thus are comparable to those from studies using data from meteorological stations and NWP. For operational prediction, limitations due to the availability and quality of GNSS products in near-real time (NRT) exist. However, they might be mitigated to a significant extent by provision of additional NRT products and improved data processing in the future. Results also outline benefits for the results when including geographically relevant stations (e.g. high-altitude stations) in the utilised datasets. [ABSTRACT FROM AUTHOR]

Tropospheric water vapor is one of 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 nonlinear 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. With their complementary high temporal and spatial resolutions, Global Navigation Satellite Systems (GNSS) meteorology and Interferometric Synthetic Aperture Radar (InSAR) satellite remote sensing represent a significant alternative to generally sparsely distributed radio sounding observations. In addition, data fusion with collocation and tomographical methods enables the construction of detailed maps in either two or three dimensions. Finally, by assimilation of these observation-derived datasets with dynamical regional atmospheric models, tropospheric water vapor fields can be determined with high spatial and continuous temporal resolution. 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 for the extent of the GNSS Upper Rhine Graben Network (GURN) region. The dataset contains hourly 2D fields of integrated water vapor (IWV) and 3D fields of water vapor density (WVD) for four multi-week, variable season periods between April 2016 and October 2018 at a spatial resolution of (2.1 km) 2. Zenith total delay (ZTD) from GNSS and collocation and refractivities are provided as intermediate products. InSAR (Sentinel-1A/B)-derived double differential slant total delay phases (ddSTDPs) and GNSS-based ZTDs are available for March 2015 to July 2019. The validation of data assimilation with five independent GNSS stations for IWV shows improving Kling–Gupta efficiency (KGE) scores for all seasons, most notably for summer, with collocation data assimilation (KGE = 0.92) versus the open-cycle simulation (KGE = 0.69). The full dataset can be obtained from 10.1594/PANGAEA.936447. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. • Machine learning to predict time series of GNSS tropospheric delays. • The prediction of tropospheric delays at new locations is difficult. • Least-squares collocation can successfully interpolate in space tropospheric delays. • Combination of machine learning and collocation (physical models) methods. • High-resolutions spatio-temporal tropospheric fields. [ABSTRACT FROM AUTHOR]

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. • Design and implementation of a low-cost GNSS station setup • GNSS co-location at meteorological observation sites • Tropospheric delay and soil moisture products from a prototype station [ABSTRACT FROM AUTHOR]