Search

Multipath is the main unmodeled error source hindering high-precision Global Navigation Satellite System (GNSS) data processing. Conventional multipath mitigation methods, such as sidereal filtering (SF) and multipath hemispherical map (MHM), have certain disadvantages: they are either too complicated for implementation or not effective enough for multipath mitigation. In this study, we propose a machine learning (ML)-based multipath mitigation method. Multipath modelling was formulated as a regression task, and the multipath errors were fitted with respect to azimuth and elevation in the spatial-domain. We collected 30 days of 1 Hz GPS data to validate the proposed method. In total five short baselines were formed and multipath errors were extracted from the posfit residuals. ML-based multipath models, as well as observation-domain SF and MHM models, were constructed using 5 days of residuals before the target day and later applied for multipath correction. It was found that the XGBoost (XGB) method outperformed SF and MHM. It achieved the highest residual reduction rates, which were 24.9%, 36.2%, 25.5% and 20.4% for GPS P1, P2, L1 and L2 observations, respectively. After applying the XGB-based multipath corrections, kinematic positioning precisions of 1.6 mm, 1.9 mm and 4.5 mm could be achieved in east, north and up components, respectively, corresponding to 20.0%, 17.4% and 16.7% improvements compared to the original solutions. The effectiveness of the ML-based multipath model was further validated using 30 s sampling data. We conclude that the ML-based multipath mitigation method is effective, easy-to-use, and can be easily extended by adding auxiliary input features, such as signal-to-noise ratio (SNR), during model training.

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.

Multipath is the main unmodeled error source hindering high-precision GNSS (Global Navigation Satellite System) data processing. Classical multipath mitigation methods, such as sidereal filtering (SF) and multipath hemispherical map (MHM), have certain disadvantages: they are either too complicated for implementation or not effective enough for multipath mitigation. In this study, we demonstrate that machine learning (ML) based models, such as random forest, can overcome these drawbacks by spatial interpolation over sky map and thus mitigate multipath effectively. 30 days of 1 Hz geodetic grade GPS data as well as 6 days of low-cost data are used to train and test the ML models. Based on a series of test cases, the best number of days for model training and the validity period for the models are discussed in this contribution. For quantification, the multipath reduction rate and kinematic positioning precision are computed using different ML models and compared to those derived from SF and MHM. The statistical results show that the XGBoost ML model can achieve higher multipath reduction rates compared to SF and MHM, especially for pseudorange measurements, which is important for low-cost devices. It reduces the multipath by 48% and 55% for pseudorange and carrier phase measurements, respectively, and outperforms SF (40% and 52%) and MHM (37% and 49%). The positioning precision when using different multipath models is similar, with differences of less than 1 mm. We conclude that the ML based multipath mitigation method is effective and easy-to-use, which can be applied under real-time scenarios.

From Global Navigation Satellite Systems (GNSS) signals, accurate and high-frequency atmospheric parameters can be determined in all-weather conditions. GNSS tomography is a technique that takes advantage of these parameters, especially of slant troposphere observations between GNSS receivers and satellites, traces these signals through a 3-D grid of voxels, and estimates by an inversion process the refractivity of the water vapour content within each voxel. In the last years, the GNSS tomography development focused on numerical methods to stabilize the solution, which has been achieved to a great extent. Currently, we are facing new challenges and possibilities in the application of GNSS tomography in numerical weather forecasting, the main research objective of this paper. In the first instance, refractivity fields were estimated using two different GNSS tomography models (TUW, WUELS), which cover the area of central Europe during the period of 29 May–14 June 2013, when heavy-precipitation events were observed. For both models, slant wet delays (SWDs) were calculated based on estimates of zenith total delay (ZTD) and horizontal gradients, provided for 88 GNSS sites by Geodetic Observatory Pecny (GOP). In total, three sets of SWD observations were tested (set0 without compensation for hydrostatic anisotropic effects, set1 with compensation of this effect, set2 cleaned by wet delays outside the inner voxel model), in order to assess the impact of different factors on the tomographic solution. The GNSS tomography outputs have been assimilated into the nested (12 and 36 km horizontal resolution) Weather Research and Forecasting (WRF) model, using its three-dimensional variational data assimilation (WRFDA 3D-Var) system, in particular, its radio occultation observation operator (GPSREF). As only total refractivity is assimilated in GPSREF, it was calculated as the sum of the hydrostatic part derived from the ALADIN-CZ model and the wet part from the GNSS tomography. We compared the results of the GNSS tomography data assimilation to the radiosonde (RS) observations. The validation shows the improvement in the weather forecasting of relative humidity (bias, standard deviation) and temperature (standard deviation) during heavy-precipitation events. Future improvements to the assimilation method are also discussed.

The German polarimetric C-band weather radar Poldirad (Polarization Diversity Radar) was deployed for the international field campaign EUREC4A (ElUcidating the RolE of Cloud-Circulation Coupling in ClimAte) on the island of Barbados. Poldirad was operated on Barbados from February until August 2020. Focus of the installation was monitoring clouds and precipitation in the trade wind region east of Barbados. Different scanning modes were used with a temporal sequence of 5 minutes and a maximum range of 375 km. In addition to built-in quality control performed by the radar signal processor, it was found that the copoloar correlation coefficient ρHV can be used to remove contamination of radar products by sea clutter. Radar images were available in real-time for all campaign participants and onboard of research aircraft. Examples of mesoscale precipitation patterns, rain rate accumulation, diurnal cycle, and vertical distribution are given to show the potential of the radar measurements for further studies on the life cycle of precipitating shallow cumulus clouds and other related aspects. Poldirad data from the EUREC4A campaign are available on the EUREC4A AERIS database: https://doi.org/10.25326/218 (Hagen et al., 2021a) for raw data and https://doi.org/10.25326/217 (Hagen et al., 2021b) for gridded data.

The representation of microphysical processes in numerical weather prediction models remains a main source of uncertainty. To tackle this issue, we exploit the synergy of two polarimetric radars to provide novel observations of model microphysics parameterizations. In the framework of the IcePolCKa project (Investigation of the initiation of Convection and the Evolution of Precipitationusing simulatiOns and poLarimetric radar observations at C- and Ka-band) we use these observations to study the initiation of convection as well as the evolution of precipitation. At a distance of 23 km between the C-band PoldiRad radar of the German Aerospace Center (DLR) in Oberpfaffenhofen and the Ka-band Mira35 radar of the Ludwig-Maximilians-University of Munich (LMU), the two radar systems allow targeted observations and coordinated scan patterns. A second C-band radar located in Isen and operated by the German Weather Service (DWD) provides area coverage and larger spatial context. By tracking the precipitation movement, the dual-frequency and polarimetric radar observations allow us to characterize important microphysical parameters, such as predominant hydrometeor class or conversion rates between these classes over a significant fraction of the life time of a convective cell. A WRF (Weather Research and Forecasting Model) simulation setup has been established including a Europe-, a nested Germany- and a nested Munich- domain. The Munich domain covers the overlap area of our two radars Mira35 and Poldirad with a horizontal resolution of 400 m. For each of our measurement days we conduct a WRF hindcast simulation with differing microphysics schemes. To allow for a comparison between model world and observation space, we make use of the radar forward-simulator CR-SIM. The measurements so far include 240 coordinated scans of 36 different convective cells over 10 measurement days between end of April and mid July 2019 as well as 40 days of general dual-frequency volume scans between mid April and early October 2020. The performance of each microphysics scheme is analyzed through a comparison to our radar measurements on a statistical basis over all our measurements.

The challenge of the ice microphysical processes representation in numerical weather models is a well-known phenomenon as it can lead to high uncertainty due to the variety of ice microphysics. As ice microphysical properties can strongly affect the initiation of precipitation as well as the type and amount of it, we need to better understand the complexity of ice processes. To accomplish this, better microphysics information through ice retrievals from measurements is needed. The multi-wavelength radar method is nowadays becoming more and more popular in such microphysics retrievals. Taking advantage of different scattering regimes (Rayleigh or Mie), information about the size of atmospheric hydrometeors can be inferred using different radar bands. For this study, dual-wavelength reflectivity ratio measurements were combined with polarimetric measurements to estimate the size of ice hydrometeors. The measurements were obtained by using the synergy of the C-band POLDIRAD weather radar from the German Aerospace Center, located in Oberpfaffenhofen, and the Ka-band MIRA-35 cloud radar from the Ludwig Maximilian University of Munich. Along with the dual-wavelength dataset, the Differential Reflectivity (ZDR) from POLDIRAD was used as a polarimetric contribution for the shape estimation of the detected ice particles. The radar observations were compared with T-matrix scattering simulations for the development of a retrieval scheme of ice microphysics. In the course of these studies, different assumptions were considered in the simulations. To capture the size variability, a Gamma particle size distribution (PSD) with different values of median volume diameter (MVD) was used. The soft spheroid approximation was used to approximate the ice particle shapes and to simplify the calculation and variation of their aspect ratios and effective densities. The selection of the most representative mass-size relation was the most crucial for the scattering simulations. In this study, we explored the modified Brown and Francis as well as the aggregates mass-size relation. After comparing the simulations to radar observations, we selected the better fitting one, i.e. aggregates, excluding the Brown and Francis as the simulated particles appeared to be too fluffy. Using the aggregates formulas, Look-Up tables (LUTs) for MVD, aspect ratio, and IWC were developed and used in the ice microphysics retrieval scheme. Here, we present preliminary microphysics retrievals of the median size, shape, and IWC of the detected hydrometeors combining the simulations in LUTs with the radar observations from different precipitation events over the Munich area.

The science guiding the EUREC4A campaign and its measurements is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC4A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC4A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC4A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement.

The atmospheric delay experienced by a signal of the Global Navigation Satellite System (GNSS) is proportional to the water vapour content along the signal path. This fact is typically exploited in GNSS Meteorology by introducing GNSS derived atmospheric parameters like the Zenith Wet Delay (ZWD) in data assimilation schemes. In numerous studies, the positive impact on the (especially precipitation) forecast has been demonstrated. However, while mostly precipitation-related studies represent the current focus of research, other meteorological phenomena might also be investigated by means of GNSS. The present study represents an initial investigation on the detection of another important meteorological phenomena using GNSS time series: Foehn winds. Foehn denotes a gusty, warm fall wind occurring in mountainous regions worldwide, leading to a relatively mild climate in affected areas. On the other hand, Foehn can also be characterized as severe weather leading to disasters, due to the high wind speeds frequently encountered. The proposed detection method of Foehn in ZWD time series is based on the significant drying/wetting effects on the lee/luv side of an affected mountain range associated with Foehn. The comparison of ZWD from stations on both sides of the main Alpine ridge reveals characteristic features like distinctive ZWD minima/maxima and significant decrease in correlation between the stations. In this study we investigate a number of well-documented Foehn events in the Swiss Alps (therefore called Alpine Foehn) using ZWD time series from the Automated GNSS Network Switzerland (AGNES) station network, operated by the Swiss Federal Office of Topography (swisstopo). Based on these case studies, an assessment of the usability of GNSS-ZWD for Foehn detection is presented and possible strengths and weaknesses will be analysed. Finally, an outlook on possible improvements and innovative extensions to the presented approach is given. These range from embedment of ZWD data in operational Foehn classification and the application of Machine-Learning techniques for detection, to the establishment of collocated GNSS/weather stations, which come with a number of scientific benefits - not only for Foehn investigations but GNSS Meteorology in general., EMS Annual Meeting Abstracts, 18, ISSN:1812-7053

Global Navigation Satellite Systems (GNSS) sense the atmosphere remotely and provide low-cost, high-quality information about its state. Nowadays, radio occultation (RO) profiles from space platforms and tropospheric delays from ground-based stations are routinely assimilated in Numerical Weather Models (NWM).In spite of provision of valuable information for weather forecasting, both space- and ground-based data have significant limitations. The RO technique has low horizontal resolution and does not provide reliable profiles in the first 3-5km of the troposphere. Whereas, the station-specific integrated value of troposphere are sparse and pose a problem to NWM adjoint operator for correcting model fields at different heights. These deficiencies could be resolved by the GNSS tomography technique that utilizes an inverse Radon transform to derive the 3D refractivity distribution over certain troposphere space. The combination of space-based and ground-based observations in the tomographic model will enable us to increase the number of intersections of GNSS signals and improve the refractivity solution within individual model locations. The aim of this research is to harness the full potential of Space 4.0 era, rapidly growing numbers of RO and GNSS satellite constellations as well as low-cost GNSS ground-based networks worldwide. We will not only use current infrastructure but also examine impact of future constellations on model performance. 3D model of refractivity from dense observations should be an excellent tool in weather prediction. Our previous research proves that the assimilation of the GNSS tomography outputs into the NWM improves relative humidity and the short-term weather forecasts. Therefore, the research goal of this project is to assess the benefit of integrated tomography model on the severe weather prediction and urban scale weather models.

Many studies have shown that multi-wavelength radar measurements can be valuable in inferring information about the size of observed hydrometeors in the atmosphere. Dual-wavelength radar method is widely known in such retrievals as it takes advantage of the different scattering behavior of hydrometeors in Rayleigh and MIE regime. Hydrometeors with sizes much smaller than the radar wavelength, act like Rayleigh scatterers and their radar reflectivity Z is proportional to the sixth power of their size. While these particles become larger due to riming or aggregation processes, with sizes comparable or larger than the radar wavelength, MIE effects can occur and thus, Z is proportional to the second power of their size. In the framework of IcePolCKa (Investigation of the initiation of Convection and the Evolution of Precipitation using simulatiOns and poLarimetric radar observations at C- and Ka-band) project, the evolution of ice in the precipitation formation will be studied exploiting these differences in both scattering regimes. Except for the logarithmic radar reflectivity difference, known as Dual-Wavelength Ratio (DWR) or Dual-Frequency Ratio (DFR), between C-band POLDIRAD weather radar from German Aerospace Center (DLR) in Oberpfaffenhofen and the Ka-band MIRA-35 cloud radar from Ludwig Maximilian University of Munich (LMU), other measured polarimetric variables from both radars, i.e. Differential Reflectivity (ZDR), Reflectivity Difference (ZDP), Linear Depolarization Ratio (LDR) will be also used. In addition to observations, scattering algorithms, i.e. T-matrix, will provide scattering simulations for a variety of ice particles shapes, sizes and mass-size relations. Combining DWR, polarimetric measurements and simulations the shape and/or the density of the observed ice particles will be retrieved. In this presentation, we will describe the instrumentation setup as well as the measuring methods in detail. Furthermore, we will present preliminary results of the retrieval approach using T-matrix calculations and measurements. Our first dataset consist of observations during snow events over Munich in January 2019 in order to avoid strong attenuation effects in the Ka-band.

GPS tomography has been investigated since 2000 as an attractive tool for retrieving the 3D field of water vapour and wet refractivity. However, this observational technique still remains a challenging task that requires improvement of its methodology. This was the purpose of this study, and for this, GPS data from the Australian Continuously Operating Research Station (CORS) network during a severe weather event were used. Sensitivity tests and statistical cross-comparisons of tomography retrievals with independent observations from radiosonde and radio-occultation profiles showed improved results using the presented methodology. The initial conditions, which were associated with different time-convergence of tomography inversion, play a critical role in GPS tomography. The best strategy can reduce the normalised root mean square (RMS) of the tomography solution by more than 3 with respect to radiosonde estimates. Data stacking and pseudo-slant observations can also significantly improve tomography retrievals with respect to non-stacked solutions. A normalised RMS improvement up to 17% in the 0-8 km layer was found by using 30 min data stacking, and RMS values were divided by 5 for all the layers by using pseudo-observations. This result was due to a better geometrical distribution of mid- and low-tropospheric parts (a 30% coverage improvement). Our study of the impact of the uncertainty of GPS observations shows that there is an interest in evaluating tomography retrievals in comparison to independent external measurements and in estimating simultaneously the quality of weather forecasts. Finally, a comparison of multi-model tomography with numerical weather prediction shows the relevant use of tomography retrievals to improving the understanding of such severe weather conditions. Web of Science 2 1 art. no. 30

For more than a decade, GNSS-meteorology has been increasingly used operationally in Europe particularly for data assimilation in Numerical Weather Prediction (NWP) models, mainly thanks to the EIG EUMETNET GNSS Water Vapour Program (E-GVAP, 2005-today). As such, GNSS has become a well-established, mature observing technique for data assimilation applications. Over this period however, scientists and specialists in GNSS-meteorology noted the clear potential for enhancements and novelties in the domain. The work carried out by the COST Action ES1206 Working Group 2 members addressed these potential enhancements and novelties from the meteorological point of view, in collaboration with WG1. This included the establishment of discussion channels with forecasters in order to determine which GNSS products would be best suited for their day-to-day operational requirements. Particular areas of interest include engaging more operational forecasters (e.g. use of meteorological case studies), especially for non-numerical nowcasting of severe weather, and getting more meteorological agencies to assimilate GNSS products in regions of Europe where they were not yet/well exploited. It also included the development of the techniques and tools necessary to benefit from the brand new products developed by the Action WG1 and WG2 members, namely real-time GNSS tropospheric products for rapid-cycle NWP and non-numerical nowcasting, data assimilation of horizontal tropospheric gradients and tropospheric slant delays as well as tomographic products. Finally, the work carried out by the WG2 members brought operational improvements through dialog, transfer of knowledge, and standardisation (e.g. the new standardized tropo-SINEX format or the development of assimilation operators). The major WG2 outcomes are discussed in this Chapter.

Over the last decade, near real-time analysis of GPS data has become a well-established atmospheric observing tool, primarily coordinated by the EIG EUMETNET GPS Water Vapour Programme (E-GVAP) in Europe. In the near future, four operational GNSS will be available for commercial and scientific applications with atmospheric science benefiting from new signals from up to 60 satellites observed at any one place and time, however, many challenges remain regarding their optimal combined utilization. Besides raw data streaming, recent availability of precise real-time orbit and clock corrections enable wide utilization of autonomous Precise Point Positioning (PPP), which is particularly efficient for high-rate, real-time and multi-GNSS analyses.

In this section a summary of the national progress reports is given. GNSS4SWEC Management Committee (MC) members provided outline of the work conducted in their countries combining input from different partners involved. In the COST Action paticipated member from 32 COST countries, 1 Near Neighbour Country and 8 Intrantional Partners from Australia, Canada, Hong Kong and USA. The text reflects the state as of 1 January 2018.

From Global Navigation Satellite Systems (GNSS) signals, accurate and high-frequency atmospheric parameters can be determined in all-weather conditions. GNSS tomography is a novel technique that takes advantage of these parameters, especially of slant troposphere observations between GNSS receivers and satellites, traces these signals through a 3D grid of voxels and estimates by an inversion process the refractivity of the water vapour content within each voxel. In the last years, the GNSS tomography development focused on numerical methods to stabilize the solution, which has been achieved to a great extent. Currently, we are facing new challenges and possibilities in the application of GNSS tomography in numerical weather forecasting – the main research objective of this paper. In the first instance, refractivity fields were estimated using two different GNSS tomography models (TUW, WUELS), which cover the area of Central Europe during the period of 29 May–14 June 2013, when heavy precipitation events were observed. For both models, Slant Wet Delays (SWD) were calculated based on estimates of Zenith Total Delay (ZTD) and horizontal gradients, provided for 72 GNSS sites by Geodetic Observatory Pecny (GOP). In total, three sets of SWD observations were tested (set0 without compensation for hydrostatic anisotropic effects, set1 with compensation of this effect, set2 cleaned by wet delays outside the inner voxel model). The GNSS tomography outputs have been assimilated into the nested (12- and 36-km horizontal resolution) Weather Research and Forecasting (WRF) model, using its three-dimensional variational data assimilation (WRFDA 3DVar) system, in particular its radio occultation observations operator (GPSREF). As only total refractivity is assimilated in GPSREF, it was calculated as the sum of the hydrostatic part derived from the ALADIN-CZ model and the wet part from the GNSS tomography. We compared the results of the GNSS tomography data assimilation to the radiosonde (RS) observations. The validation shows the improvement in the weather forecasting of relative humidity (bias, standard deviation) and temperature (standard deviation) during heavy precipitation events. Future improvements to the assimilation method are also discussed.

Using data from the Continuously Operating Reference Stations (CORS), recorded in March 2010 during severe weather in the Victoria State, in southern Australia, sensitivity and statistical results of GPS tomography retrievals (water vapour density and wet refractivity) from 5 models have been tested and verified – considering independent observations from radiosonde and radio occultation profiles. The impact of initial conditions, associated with different time-convergence of tomography inversion, can reduce the normalised RMS of the tomography solution with respect to radiosonde estimates by a multiple (up to more than 3). Thereby it is illustrated that the quality of the apriori data in combination with iterative processing is critical, independently of the choice of the tomography model. However, the use of data stacking and pseudo-slant observations can significantly improve the quality of the retrievals, due to a better geometrical distribution and a better coverage of mid- and low-tropospheric parts. Besides, the impact of the uncertainty of GPS observations has been investigated, showing the interest of using several sets of data input to evaluate tomography retrievals in comparison to independent external measurements, and to estimate simultaneously the quality of NWP outputs. Finally, a comparison of our multi-model tomography with numerical weather prediction from ACCESS-A model shows the relevant use of tomography retrieval to improve the understanding of such severe weather conditions, especially about the initiation of the deep convection.

An extensive validation of line-of-sight tropospheric slant total delays (STD) from Global Navigation Satellite Systems (GNSS), ray tracing in numerical weather prediction model (NWM) fields and microwave water vapour radiometer (WVR) is presented. Ten GNSS reference stations, including collocated sites, and almost 2 months of data from 2013, including severe weather events were used for comparison. Seven institutions delivered their STDs based on GNSS observations processed using 5 software programs and 11 strategies enabling to compare rather different solutions and to assess the impact of several aspects of the processing strategy. STDs from NWM ray tracing came from three institutions using three different NWMs and ray-tracing software. Inter-techniques evaluations demonstrated a good mutual agreement of various GNSS STD solutions compared to NWM and WVR STDs. The mean bias among GNSS solutions not considering post-fit residuals in STDs was -0.6 mm for STDs scaled in the zenith direction and the mean standard deviation was 3.7 mm. Standard deviations of comparisons between GNSS and NWM ray-tracing solutions were typically 10 mm +/- 2 mm (scaled in the zenith direction), depending on the NWM model and the GNSS station. Comparing GNSS versus WVR STDs reached standard deviations of 12 mm +/- 2 mm also scaled in the zenith direction. Impacts of raw GNSS post-fit residuals and cleaned residuals on optimal reconstructing of GNSS STDs were evaluated at intertechnique comparison and for GNSS at collocated sites. The use of raw post-fit residuals is not generally recommended as they might contain strong systematic effects, as demonstrated in the case of station LDB0. Simplified STDs reconstructed only from estimated GNSS tropospheric parameters, i.e. without applying post-fit residuals, performed the best in all the comparisons; however, it obviously missed part of tropospheric signals due to non-linear temporal and spatial variations in the troposphere. Although the post-fit residuals cleaned of visible systematic errors generally showed a slightly worse performance, they contained significant tropospheric signal on top of the simplified model. They are thus recommended for the reconstruction of STDs, particularly during high variability in the troposphere. Cleaned residuals also showed a stable performance during ordinary days while containing promising information about the troposphere at low-elevation angles. Web of Science 10 6 2208 2183

In Global Navigation Satellite System (GNSS) tomography, precise information about the tropospheric water vapor distribution is derived from integral measurements like ground-based GNSS slant wet delays (SWDs). Therefore, the functional relation between observations and unknowns, i.e., the signal paths through the atmosphere, have to be accurately known for each station–satellite pair involved. For GNSS signals observed above a 15∘ elevation angle, the signal path is well approximated by a straight line. However, since electromagnetic waves are prone to atmospheric bending effects, this assumption is not sufficient anymore for lower elevation angles. Thus, in the following, a mixed 2-D piecewise linear ray-tracing approach is introduced and possible error sources in the reconstruction of the bended signal paths are analyzed in more detail. Especially if low elevation observations are considered, unmodeled bending effects can introduce a systematic error of up to 10–20 ppm, on average 1–2 ppm, into the tomography solution. Thereby, not only the ray-tracing method but also the quality of the a priori field can have a significant impact on the reconstructed signal paths, if not reduced by iterative processing. In order to keep the processing time within acceptable limits, a bending model is applied for the upper part of the neutral atmosphere. It helps to reduce the number of processing steps by up to 85 % without significant degradation in accuracy. Therefore, the developed mixed ray-tracing approach allows not only for the correct treatment of low elevation observations but is also fast and applicable for near-real-time applications.

The troposphere blind model RTCA MOPS is the minimum operational performance standard for global positioning systems. With a standard deviation of 2.3% of the ZTD, it enables us to mitigate the main part of the tropospheric effect on GNSS signals. Nevertheless, the comparison of RTCA MOPS with modern troposphere models like the ESA model or GPT2 shows the limitation of RTCA MOPS and points out the potential of modern troposphere blind models based on climatological series derived from numerical weather data. The ESA model profits from a more advanced wet delay model and a higher spatial resolution. GPT2 shows the smallest mean bias on surface level in comparison to ray-tracing and IGS data and profits from additional mapping function coefficients – especially if the user is interested in tropospheric delay at low elevation angles. A revision of GPT2 - called GPT2w - combines the benefits of both aforementioned models. Copyright © 2014 Institute of Navigation.

In this paper, a concept is presented which makes use of inter-satellite ranging (ISR) techniques for monitoring the atmospheric state. Thereby, for this study it is assumed that future GNSS satellites are equipped with an ISR payload and that each satellite is supposed to connect to other satellites in different orbital planes sequentially. For further analysis a Galileo constellation of 24 (27 and 30) MEO, four IGSO and one MEO spare satellite was simulated over a period of one year to reconstruct the paths of the ranging signals and to identify events when the signal is travelling through the atmosphere. Based on the detailed analysis of the transit events we identify inter-satellite ranging not only as complementing technique to improve the satellite orbit and clock accuracy of the GNSS constellation but also its large potential for monitoring the state of the neutral atmosphere and ionosphere. Thereby, due to the unique observation geometry between satellites in MEO, IGSO or in transfer orbit, range measurements can be obtained for specific observation arcs. Some of them show similarities to radio occultation profiles on long baselines, others allow e.g. to permanently scan down to specific layers in the atmosphere. Latter is possible if e.g. a spare satellite is placed in orbit so that a permanent link is installed to another MEO satellite in the same orbital plane. In addition to the geometric analysis, also a propagation analysis was carried out to identify atmospheric losses, which may harm signal reception. So far studies dealing with inter satellite ranging do not consider signal paths through the atmosphere. Thus, ISR link budgets were recalculated for various frequencies in the microwave band in order to get a first guess about the atmospheric losses and whether signal reception is possible after the inter-satellite link has passed the atmosphere. Considering frequencies at three bands (K, S and C), while for K-band signal reception is guaranteed only under specific atmospheric conditions, S- or C-band seems to be most promising when the entire atmosphere (ionosphere and neutral atmosphere) is monitored.