We welcome, but not limit, contributions on:
• Estimates of the neutral atmosphere using ground- and space-based geodetic data and their use in weather forecasting and climate monitoring
• Retrieval and comparison of tropospheric parameters from multi-GNSS, VLBI, DORIS and multi-sensor observations
• Now-casting, forecasting, and climate research using RT and reprocessed tropospheric products, employing NWP and machine learning
• Assimilation of GNSS tropospheric products in NWP and in climate reanalysis
• Production of SAR tropospheric parameters and assimilation thereof in NWP
• Homogenization of long-term GNSS and VLBI tropospheric products
• Delay properties of GNSS signals for propagation experiments
• Exploitation of NWP data in GNSS data processing
• Techniques for soil moisture retrieval from GNSS data and for ground-atmosphere boundary interactions
• Detection and characterization of sea level, snow depth and sea ice changes, using GNSS-R
• Investigating the atmospheric water cycle using satellite gravimetry
The Global Navigation Satellite System radio occultation (GNSS-RO) technique has been demonstrated to significantly enhance our understanding of the free atmosphere, with particular emphasis on the Upper Troposphere and Lower Stratosphere. We present improved estimations of global three-dimensional wind fields derived from low-pass filtered monthly mean geopotential height data. Geostrophic, gradient, and equatorial balance winds were estimated to provide foundational wind estimates, consistent with methodologies employed in previous studies. To advance beyond the traditional reliance on geostrophic and gradient wind, higher-order terms in the horizontal momentum equations, specifically advection and curvature, were considered. Newly derived wind fields were systematically evaluated against original winds from the European Center for Medium-Range Weather Forecast (ECMWF) Reanalysis 5 (ERA5), using a best-estimate algorithm. Building upon this methodology, the application of the best-estimate algorithm revealed that geostrophic winds incorporating advection were most applicable in the troposphere, while gradient winds with advection showed superior estimates in the stratosphere. Thus, the tropopause acts as a physical boundary delineating the domains of applicability for geostrophic and gradient-based wind approximations. Compared to their fundamental formulations, advection significantly improved both geostrophic and gradient wind estimates relative to the original ERA5 winds. In tropical regions, equatorial balance winds considering curvature provided the most accurate estimates across the entire vertical extent. Our findings emphasize the significant potential of GNSS-RO geopotential height data to move beyond the limitations of traditional geostrophic and gradient wind approximations. The results pave the way for creating a comprehensive global three-dimensional wind field climatology by leveraging the unique advantages of GNSS-RO, such as long-term consistency, high vertical resolution, and global coverage. This dataset will be a valuable resource for the scientific community, supporting climate monitoring and enhancing the understanding of atmospheric dynamics, particularly in the stratosphere, where data assimilation in reanalyses remains limited.
How to cite: Unegg, J.: Beyond Geostrophic and Gradient Wind: Enhancing Radio Occultation Wind Field Estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-869, https://doi.org/10.5194/egusphere-egu25-869, 2025.
Monitoring the Earth's atmosphere is a fundamental activity aimed at understanding its structure and the processes occurring within it. These efforts contribute significantly to improving the quality of numerical weather models and forecasts.
In recent years, GNSS (Global Navigation Satellite System) observations have emerged as one of the primary sources of information about the troposphere, the lowest layer of the atmosphere. The use of electromagnetic wave properties, which respond to changing atmospheric conditions, is gaining recognition due to its stability, availability in all weather conditions, and the density of observations. GNSS observations are increasingly considered a viable alternative to traditional weather stations, radiosondes, and microwave satellites. However, their application is limited by challenges in deploying receivers in aquatic or mountainous regions. Furthermore, the effectiveness of ground-based GNSS observations is hindered by poor vertical resolution.
An alternative to ground-based GNSS observations is their space-based counterpart—radio occultations (RO). These observations, which are independent of ground infrastructure, serve as an ideal complement to traditional ground-based methods. However, their horizontal and temporal resolution is very limited. Their exclusive use in experiments, such as ROMEX, may not demonstrate their full potential, which can be better realized through integration with other tools, particularly GNSS tomography.
Recent studies have shown that integrating ground-based and space-based GNSS observations in a tomographic solution improves solution quality by approximately 10% on average and reduces total solution errors by about 5%. In regions without GNSS ground stations, the error reduction can reach as much as 30%.
Therefore, in this study, we extend this research by testing the feasibility of using a modified INTOMO (INtegrated TOMOgraphy) software with space-based observations only. The program employs 3D ray tracing to simulate RO ray paths between Low Earth Orbit (LEO) and Global Positioning System (GPS) satellites, along with a Kalman filter to calculate the variability of the system of equations. The observation errors are assessed using a pre-defined formula based on RO geometries.
The results presented in this study are derived from the initial phase of research conducted over five days in sea and water-land areas using RO observations from publicly available UCAR services as well as ROMEX data. Each day represents different atmospheric conditions, ranging from sunny weather to tropical cyclones. Additionally, we estimate the errors in the tomographic solution and validate our results using the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 and Weather Research and Forecasting (WRF) models and RO processing package (ROPP), with the GPT2 model serving as the a priori data input for tomography.
How to cite: Cegła, A., Makuch, S., Rohm, W., Moeller, G., Trzcina, E., Hordyniec, P., Gurmani, S., and Hanna, N.: From ground- to space-based GNSS tomography - initial results and concepts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12037, https://doi.org/10.5194/egusphere-egu25-12037, 2025.
On one hand, current processing of GNSS signals has the ability to provide the amount of
water vapor between the satellite and the ground. This information is of major interest
because these observations have good temporal resolution and are insensitive to weather
conditions. Furthermore, when the station network is dense enough, it is possible to retrieve
the 3-D water vapor field through GNSS tomography.
Although this method has already proven effective, this presentation will first detail new
approaches development to adapt to the Reunion Island context and particularly over the
Piton de la Fournaise. Indeed, this area has a dense distribution of stations while the rest of
the island shows a weaker distribution. Hence, a classic grid mesh is therefore not suited for
this situation and we developed a Voronoï adaptive mesh scheme to better account for the
irregular network geometry. Likewise, the inversion scheme used is being upgraded to a
Single Value Decomposition (SVD) approach shown to be more effective in the literature.
On the other hand, InSAR technique consists of measuring ground deformation by difference
between two radar measurements of satellite/ground distance. However, these two
measurements are not carried out at the same time and, thus, the water vapor field which
impact such observations is different for each measurement, notably in tropical regions
where water vapor variability is very important.
Hence, a second aspect of our work will be to apply the improved 3-D water vapor retrieval
obtained with the new approaches defined above in order to propose a better correction
scheme for InSAR retrieval of slow ground deformation signals at Piton de la Fournaise, a
precursor sign of possible volcanic activity.
How to cite: Gerville, H., Van Baelen, J., Morel, L., Albino, F., Durand, F., Peltier, A., and Boissier, P.: 3-D water vapor field retrieval by GNSS tomography for InSAR observation correctionapplied to deformations of Piton de la Fournaise in Réunion Island, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-926, https://doi.org/10.5194/egusphere-egu25-926, 2025.
A three-dimensional water vapor density field has advantages in monitoring atmospheric water vapor, especially for reflecting the vertical motion. The existing multi-source tomography models are around a fusion of single-source remote-sensing signal and Global Navigation Satellite System (GNSS) data. However, different remote-sensing data have advantages and disadvantages regarding spatiotemporal resolution and accuracy. When only single-source remote-sensing data is integrated for tomography, the model's available scenarios are severely limited by weather conditions. Therefore, we construct a tomography model by fusing multi-band spaceborne remote-sensing data and high-precision ground GNSS data, the former includes near-infrared MODIS image, long-wave infrared FengYun-4A image, and morphed integrated microwave image MIMIC. The equations system of the tomographic model is solved based on different strategies of weight determination using the weighted least square algorithm. In addition, to consider the dynamic variations of tropopause height in the research area, the tropopause detection products of Fengyun-4B with high spatial coverage are used to determine the boundary of the tomographic region, and the constraints of model is built by historical GNSS occultation observations. To verify our method, the proposed model is validated by water vapor density from reanalysis and radiosonde data, respectively. The results show that the reasonable prior weights are essential when using multi-source data to perform tightly coupled tomography, the RMSEs of water vapor density are less than 2 g/m3 in most epochs. Compared to the tomographic model based on only GNSS data, the accuracy improvement of the tomographic model fusing multi-band remote sensing data is higher than that of any tomographic model using single-source remote-sensing data. Also, the proposed tomography model can better compensate for the shortcomings of poor time continuity of integrated individual remote-sensing data to expand the application scenarios of the fusion tomographic model.
Acknowledgments: This work was supported by Natural Science Foundation of China (42192534 and 42388102).
How to cite: Xu, T. and Li, S.: Atmospheric Water Vapor Tomography based on GNSS and Multi-band Remote Sensing Measurements Combination, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2480, https://doi.org/10.5194/egusphere-egu25-2480, 2025.
Chile's diverse climate spans a remarkable range, from the hyper-arid desert in the north to a Mediterranean climate in the center, temperate humid conditions in the south, and polar tundra in Patagonia. This climatic gradient provides a unique opportunity to study the synchronized variability of tropospheric water vapor (TWV) and precipitation processes. In recent years, GNSS has emerged as a powerful satellite-based tool capable of capturing not only tectonic deformation but also meteorological processes. One of the key parameters derived from GNSS processing is Zenith Total Delay (ZTD), which represents the delay in GNSS signal propagation caused by the troposphere. ZTD is composed of the hydrostatic and wet delays, with the wet delay closely linked to TWV, making it an essential metric for studying atmospheric water vapor dynamics. We use GNSS ZTD observations, spanning between 15 and 28 years, to analyze the intra-seasonal and interannual probability density functions (PDFs) of TWV. Additionally, we examine the co-variability between daily average TWV and accumulated precipitation to establish links between TWV peaks and precipitation events.
Our results reveal significant differences in TWP PDFs across Chile, ranging from log-normal to normal and inverse log-normal distributions. Notably, the relationship between TWV and precipitation is stronger in central, southern, and Patagonian regions, suggesting regional variability in underlying atmospheric processes. Potential mechanisms driving these differences, such as climatic controls and local meteorological dynamics, are discussed in detail.
These findings provide a benchmark for evaluating the representativeness of general circulation models (GCMs) by comparing observed and modeled TWV distributions. Furthermore, they lay the groundwork for future research into the TWV-precipitation relationship at daily and sub-daily timescales, critical for improving weather forecasts and understanding hydrological processes.
How to cite: Valenzuela, R. and Jara, J.: GNSS-derived tropospheric water vapor and precipitation co-variability along continental Chile, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7362, https://doi.org/10.5194/egusphere-egu25-7362, 2025.
Global Navigation Satellite System (GNSS) meteorology has proven to be a useful tool for the study of weather phenomena and climate change. The sensitivity of GNSS signals to the distribution of water vapor and liquid water in the atmosphere has led to numerous applications of tropospheric delay data products, ranging from the evaluation of numerical weather prediction (NWP) models via data assimilation into NWP models, to the observation-driven analysis of rainfall events. In this study, we investigate the behavior of non-hydrostatic zenith delay (ZWD), integrated water vapor (IWV), and tropospheric gradients before, during, and after heavy short-duration local-scale convective precipitation events. The study area located in the WegenerNet 3D Open-Air Laboratory for Climate Change Research Feldbach Region (WEGN3D Open-Air Lab) is situated in the Alpine forelands of southeastern Austria and covers an extent of about 22 km by 16 km. The WEGN3D Open-Air Lab consists of a six-station GNSS network with baselines between 5 km and 10 km, 156 closely spaced meteorological stations, an X-band precipitation radar, and a microwave and broadband infrared radiometer for tropospheric profiling and cloud structure observations, respectively. We generate non-hydrostatic zenith delay maps for the study region with a temporal resolution of 150 seconds by combining estimated ZWD and tropospheric gradients. These high-resolution ZWD maps are then used to derive IWV maps using surface meteorological measurements and tropospheric profile statistics. We further exploit the approximate relationship between the spatial derivatives of ZWD with tropospheric gradients to compute gradient fields for the entire ZWD map domain. We compare the spatial patterns of these high-resolution datasets with X-band radar-derived precipitation during heavy convective precipitation events with small spatial extent and high spatial variability. In line with previous studies, we find that the location of precipitation cells is well reflected in the ZWD, IWV, and gradient maps before, during, and after the event, even for very localized, short-lived precipitation events with an extent of only a few kilometers. This shows that GNSS meteorology can provide insights into heavy precipitation events approaching the microscale.
How to cite: Kvas, A., Haas, S., Fuchsberger, J., and Kirchengast, G.: High-resolution zenith delay and tropospheric gradient fields track precipitation during heavy local-scale rainfall events , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10048, https://doi.org/10.5194/egusphere-egu25-10048, 2025.
The Global Navigation Satellite System (GNSS) tropospheric gradients offer valuable information about how moisture is distributed in the atmosphere. These gradients are determined by studying variations in how the atmosphere refracts signals, which are measured based on delays from satellites positioned at different angles. Zus et al. (2023) developed a tropospheric gradient operator that has been added to the Weather Research and Forecasting (WRF) model. Thundathil et al. (2024) conducted several impact experiments showing promising improvements using this operator.
We are currently integrating data from MPG-NET, a multi-purpose GNSS station network in the Swiss Alps (Aichinger-Rosenberger, Matthias, et al., 2023), and data from the Swabian MOSES (Modular Observation Solutions for Earth Systems) field campaign of 2023, which focused on extreme hydro-meteorological events in southwestern Germany. As part of this work, we are simulating the occurrence of hailstorm activity in July 2023. We plan to present initial results from the assimilation of ZTD and gradients for this event.
References:
Zus, F., Thundathil, R., Dick, G., & Wickert, J. (2023). Fast Observation Operator for Global Navigation Satellite System Tropospheric Gradients. Remote Sensing, 15(21), 5114.
Thundathil, R., Zus, F., Dick, G., & Wickert, J. (2024). Assimilation of GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model version 4.4. 1. Geoscientific Model Development, 17(9), 3599-3616.
Aichinger-Rosenberger, M., Wolf, A., Senn, C., Hohensinn, R., Glaner, M. F., Moeller, G., ... & Rothacher, M. (2023). MPG-NET: A low-cost, multi-purpose GNSS co-location station network for environmental monitoring. Measurement, 216, 112981.
How to cite: Thundathil, R. M., Zus, F., Schwitalla, T., Aichinger-Rosenberger, M., Dick, G., and Wickert, J.: Impact of assimilating GNSS Tropospheric Gradients along with Zenith Total Delays for Severe Weather Prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10658, https://doi.org/10.5194/egusphere-egu25-10658, 2025.
Subcarrier modulated signals, such as BDS-3 B2 and Galileo E5, are widely used to improve the spectral compatibility and ranging accuracy of Global Navigation Satellite Systems (GNSS). However, designing signal processing and observation extraction techniques for subcarrier modulated signals is still challenging for the navigation community. In GNSS-R application, GNSS-R phase altimetry is proposed to provide sea surface height information as an economical and accurate technology to solve the resolution problem. However, phase unwrapping and phase integer ambiguity pose significant challenges for the application of GNSS-R phase altimetry. To overcome these obstacles, the utilization of subcarrier modulated signals is noticed for the first time, presenting a novel opportunity for GNSS-R phase altimetry due to the substantial subcarrier wavelength of 19.5 meters. In the work, we developed a subcarrier modulated signal processing strategy that can track both the upper and lower band signals. By combining the two signals, we construct virtual signals whose phase matches the subcarrier phase. These virtual signals undergo processing using long-time coherent integration and sliding filtering to enhance the signal-to-noise ratio and minimize errors. Subsequently, subcarrier phase measurements are extracted from these virtual signals. To validate the effectiveness, subcarrier phase measurements are applied to a GNSS-R altimetry station and drone platform. When compared with accurate in-situ SSH results, it is evident that the phase is much easier to unwrap and the phase integer ambiguity is easier to fix. Furthermore, the accuracy can achieve centimeter-level precision.
How to cite: He, Y., Gao, F., Meng, X., and Xu, T.: Extraction and Application of Subcarrier Phase Measurements in GNSS-R Altimetry, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5428, https://doi.org/10.5194/egusphere-egu25-5428, 2025.
Monitoring sea ice thickness (SIT) is essential for understanding the role of polar sea ice in the Earth system and addressing the challenges posed by its rapid changes. Arctic sea ice regulates global temperatures, supports ecosystems, and drives ocean circulation. Its decline disrupts ecosystems, intensifies coastal hazards, and impacts indigenous communities. Similarly, Antarctic Sea ice influences ocean stratification, buffers ice shelves from disintegration, sustains marine food webs, and affects human activities such as shipping and fisheries. Comprehensive SIT monitoring in both polar regions is vital for advancing climate science and assessing polar ecosystem health.
This study develops novel algorithms for SIT retrieval using dual-polarimetric and multi-incidence angle GNSS-R data from the Spire Global Inc. constellation. Spire’s GNSS-R receivers collect forward-scattered reflections over ice surfaces in grazing-angle configurations with right-hand circular polarization (RHCP) antennas and near-nadir configurations with left-hand circular polarization (LHCP) antennas. These datasets are utilized in a multi-parameter optimization framework based on a sea ice coherent reflection model to characterize SIT in the critical range of 0.5 m to 1.5 m, where existing remote sensing techniques show significant relative errors. The proposed two-layer model leverages the sensitivity of GNSS-R reflectivity to variations in sea ice dielectric properties, salinity, and density and was validated with data from the Soil Moisture Active Passive (SMAP) mission in its reflectometer configuration (SMAP-R). The algorithms address the measurement gap between thin and thick ice while enhancing spatial and temporal resolution, enabling weekly coverage of the Arctic and Antarctic Oceans. These advancements provide critical insights into SIT dynamics across both regions, addressing gaps in existing techniques.
This research highlights the potential of GNSS-R technology in sea ice monitoring, contributing to the development of robust SIT retrieval algorithms. By advancing SIT retrieval capabilities, this work lays the foundation for improving climate models, informing navigation and resource management, and aiding in the conservation of fragile polar ecosystems facing environmental changes.
© 2025 California Institute of Technology. Government sponsorship acknowledged.
How to cite: Kim, S., Rodriguez-Alvarez, N., Bosch-Lluis, X., and Oudrhiri, K.: Advancing Sea Ice Thickness Retrieval with Spire GNSS-R Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14987, https://doi.org/10.5194/egusphere-egu25-14987, 2025.
GNSS Reflectometry (GNSS-R) satellite constellations offer unprecedentedly large datasets. This creates a unique opportunity to harness the power of AI for Earth system monitoring using GNSS-R. By using these vast datasets, AI models can “learn” effectively and adaptively. The AI for GNSS-R (AI4GNSS-R) project unlocks the potential of deep learning to produce high-quality and innovative data products.
Previously, we introduced CyGNSSnet, a deep learning model based on a CNN-LSTM architecture, which achieved an RMSE of 1.36 m/s—representing a substantial 28% improvement over the officially operational retrieval algorithm. Building on this success, we now present an advanced version of the model that incorporates external precipitation data through data fusion. This enhanced approach achieves an RMSE of 1.57 m/s for rain-affected data, significantly improving wind speed predictions under extreme weather conditions. For land monitoring, we demonstrate the retrieval of Vegetation Water Content (VWC) from GNSS-R data. Using architectures such as LeNet, our models achieve RMSEs below 0.6 kg/m² compared to SMAP VWC data, validating GNSS-R's capability for global vegetation moisture monitoring. A highlight of our research is the development of a GNSS-R general foundation model using self-supervised learning. This model facilitates the fusion of multimodal data and enables scalable and accurate retrieval of variety of parameters such as wind speed, soil moisture, and VWC with limited labeled data. Our findings emphasize the scalability, adaptability, and applicability of next-generation AI models for GNSS-R. These advancements establish a new benchmark for the achievable quality and extends application spectrum of spaceborne GNSS-R.
How to cite: Asgarimehr, M., Zhao, D., Xiao, T., Izadgoshasb, H., and Wickert, J.: AI for GNSS Reflectometry: Setting a New Benchmark for Earth Surface and Atmospheric Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19045, https://doi.org/10.5194/egusphere-egu25-19045, 2025.
Posters on site: Tue, 29 Apr, 16:15–18:00 | Hall X1
Global warming phenomena lead to melting glaciers, rising sea levels, droughts, and irregular seasonal patterns, especially in polar regions. Besides, water vapor plays a significant role in these processes, contributing to about 60% of the natural greenhouse effect. Increasing temperature raises the atmosphere's capacity for water vapor, creating a positive feedback loop that aggravates global warming and extreme weather events. In polar regions, global warming is causing increased annual rainfall. Despite low overall precipitation, this phenomenon accelerates the melting of snow and ice, impacting local ecosystems. Future projections indicate that precipitation along Antarctica's coastline is expected to increase over the next 80 years. This increase may enhance surface melting through various processes. Consequently, monitoring atmospheric water vapor is crucial for understanding global climate dynamics and weather patterns. However, due to the harsh conditions in the polar regions, there is a shortage of conventional measurements, which makes global atmospheric reanalysis models crucial. The specific humidity and air pressure from the reanalysis models can be used to calculate Precipitable Water Vapor (PWV) (measured in meters), which is one of the most commonly used parameters for measuring atmospheric water vapor. Nevertheless, biases and discrepancies in the models may influence the data, particularly in polar regions where observations are scarce. In addition, the estimation of meteorological parameters can be acquired not only based on meteorological station data but also with the help of geodetic satellite data. Global Navigation Satellite Systems (GNSS) signals are subject to tropospheric refraction as they pass through the Earth's atmosphere, and the resulting zenith delays are divided into two components: hydrostatic (ZHD) and wet delay (ZWD). Moreover, the ZWD can be utilized to compute the PWV by multiplying a conversion factor. PWV can also be obtained by using air temperature and dew point temperature data from radiosonde observations at specific pressure levels. In this study, it is aimed to investigate and compare PWV values produced from GNSS-based, radiosonde-based, and global meteorological reanalysis models. Within the scope of the study, International GNSS Service (IGS) stations, which are located in the Antarctica continent, were used to calculate GNSS-based PWV. Besides, the radiosonde dataset retrieved from the Integrated Global Radiosonde Archive version 2.2 (IGRA 2.2) was used to obtain radiosonde-based PWV. As reanalysis datasets, the most recent reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF), and the National Aeronautics and Space Administration (NASA) were used. The fifth-generation reanalysis product from the ECMWF called ERA-5 and the second-generation version of NASA’s Modern-Era Retrospective analysis for Research and Applications called MERRA-2 data were used to obtain reanalysis based PWV. As a result of the study, root mean square errors (RMSE) and correlation values of GNSS-based PWV compared to radiosonde-based and reanalysis-based PWV were investigated for each IGS station. Besides, it was evaluated whether the GNSS technique could be used as an alternative to other methods in studies related to the troposphere and meteorology in the Antarctic continent.
How to cite: Mutlu, B. and Erol, S.: Assessment of GNSS-based PWV against radiosonde observation and reanalysis datasets in Antarctica, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-965, https://doi.org/10.5194/egusphere-egu25-965, 2025.
Tropospheric delay is one of the main sources of error affecting high-precision positioning and navigation and is a key parameter for detecting water vapour in the Global Navigation Satellite System (GNSS).
This delay is typically divided into wet and hydrostatic components. ZTD is described as the sum of the Zenith Hydrostatic Delay (ZHD) and the Zenith Wet Delay (ZWD) and can be combined with surface pressure and temperature to estimate the integrated content of water vapour above GNSS station.
In climate change context, the precipitable water vapour (PW) is key parameter of atmospheric process and dynamics and its variation is very high in space and time. Its accuracy is paramount for any geodetic or climatic study.
In recent years, data from atmospheric reanalysis products such as ERA-Interim, ERA5 (the fifth generation of reanalysis from the European Centre for Medium-Range Weather Forecasts) have been widely used to obtain information on tropospheric delay and water vapour (Li et al., 2015; Zhou et al., 2020...)
The main objective of this study is to compute precipitable water vapour from ERA5 reanalysis for four stations in Algeria, which have different types of climate. We opt for using integration method for different level of pressure with ERA5.
The values of water vapour are also compared with radiosondes profiles and GNSS data. The results of this work shows good agreement with a correlation that is not less than not 0.95 and 0.70 compared as radiosondes profiles (Namaoui et al., 2022). The first results are encouraging, in particular for meteorological applications with good hope to introduce another dataset as GNSS to more understand the variation and behaviour of water vapour over a long period of observation.
How to cite: Houaria, N.: Evaluation of atmospheric water vapour based on ERA5 Reanalysis Products and GNSS Observations in Algeria., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8874, https://doi.org/10.5194/egusphere-egu25-8874, 2025.
In 2019-2020, the International GNSS Service (IGS) coordinated its third reanalysis of the complete history of GNSS data collected by the IGS global network since 1994. Ten analysis centers (ACs) participated in this so-called “Repro3” effort, using the latest models and methodologies. Several of them provided, among other products, zenith tropospheric delay (ZTD) estimates.
In this study, we analyze the quality of ZTD estimates from four ACs (COD, GFZ, GRG, and TUG) along three different aspects: 1) the number and size of ZTD outliers, 2) the mean ZTD differences (or biases), and the standard deviation of ZTD differences between ACs as well as with respect to the ERA5 reanalysis, and 3) the homogeneity of the ZTD time series at individual sites. Overall statistics and case studies are presented for 200 sites.
We find an overall agreement between ACs at the sub-millimeter (bias) and sub-centimeter (standard deviation) level. However, a notable number of outliers and inhomogeneities are observed at individual sites. These are attributed to differences in metadata, particularly antenna information, and, to a lesser extent, to differences in processing strategies and software-specific features (e.g., outlier editing). Controlling the quality of metadata and optimizing the processing strategy are two major pathways for improving the quality of GNSS ZTD estimates for use in climate analysis.
This work is part of the activities of a Joint Working Group of the IAG Inter-Commission Committee on “Geodesy for Climate Research” (ICCC), in cooperation with the IGS ACs.
How to cite: Breton, H., Bock, O., and Nahmani, S.: Consistency and Homogeneity of ZTD Estimates from IGS Repro3, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9979, https://doi.org/10.5194/egusphere-egu25-9979, 2025.
Interferometric Synthetic Aperture Radar (InSAR) data stacks offer a means to derive integrated water vapor (IWV) from phase delay observations along the satellite line of sight with high spatial resolution. Since interferometric observations are differential in both space and time, they capture relative IWV changes but lack absolute values, which is the variable integration constant. Existing approaches to obtain absolute IWV from InSAR are typically validated against GNSS observations, weather models, or other remote sensing datasets. However, these validations primarily assess the integration methods rather than the observed interferometric phase delay representing partial wet delay.
In this study we investigate the reverse approach by comparing native differential partial wet delay observations from Persistent Scatterer InSAR (PSI), mapped to zenith wet delay (ZWD), with forward-modeled double differential ZWD (DsDt ZWD) derived from GNSS and ERA-5 data. The analysis focuses on a region in Central Europe spanning the France-Germany-Switzerland border from March 2015 to July 2019.
The methodology incorporates data from 4.2 million persistent scatterer (PS) points, ERA-5 ZWD interpolated to these locations, and hourly tropospheric wet delay data from 16 GNSS stations. Temporal and spatial differences were computed to generate synthetic DsDt ZWD data stacks, enabling direct comparison of GNSS and ERA-5 ZWD. Analyses were conducted in single differential (temporal) and double differential (temporal and spatial) domains, with evaluations performed at GNSS stations using statistical metrics such as the coefficient of determination (R²) and the Kling-Gupta Efficiency (KGE) index. Seasonal variability was also assessed. Additionally, the study examines how local distances around GNSS stations affect the correlation (R² and KGE) between GNSS-InSAR and GNSS-ERA data, evaluating their impact on measurement consistency.
Results reveal valuable insights into the performance of InSAR, GNSS, and ERA-5 ZWD. In the single differential domain, the variance of Dt ZPWD from InSAR aligns closely with GNSS and ERA-5 data. While scatter plots confirm a linear relationship between GNSS and ERA-5, GNSS vs. InSAR trends appear nonlinear. Applying double differences significantly enhances the correlation between GNSS and InSAR, surpassing that of GNSS and ERA-5. The KGE index highlights improved GNSS-InSAR performance, particularly in correlation (R) and variability ratio (Alpha). Seasonal analyses show that GNSS-InSAR excels during summer, with mean R² values twice those of GNSS-ERA, whereas GNSS-ERA performs better in winter. Regional variability is observed, with higher differences in R² and KGE values at stations in the Rhine Valley.
In conclusion, this study demonstrates the capability of PS-InSAR to provide high-resolution, accurate differential ZWD estimates, particularly during summer. PS-InSAR shows a stronger correlation with GNSS data in the double differential domain compared to ERA-5, underscoring the value of high-resolution ZWD data. Systematic variations in GNSS-InSAR correlation, identified as potential quality indicators for GNSS ZWD products, further highlight the importance of integrating multi-source geodetic data to enhance ZWD monitoring.
How to cite: Zárate, A., Schenk, A., Kamm, B., and Mayer, M.: Investigation on systematic deviations of absolute and double differential partial wet delay between GNSS, PS-InSAR, and ERA-5 model observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15774, https://doi.org/10.5194/egusphere-egu25-15774, 2025.
The variometric approach leverages the use of dual-frequency combinations of time-single differences of GNSS phase observations. Successfully applied in GNSS seismology (VADASE, [1,2]) and GNSS ionospheric seismology (VARION, [3]), this methodology is explored in this study for its potential in near real-time tropospheric delay tracking. Its application to weather forecasting could significantly improve current tools, allowing for a timely detection of severe weather events through real-time tropospheric delay monitoring. This research investigates the retrieval of absolute ZTD trends from variometric zenith tropospheric delay (VZTD) estimates, derived through two different approaches. The first strategy employs a two-unknown weighted least-squares-based functional model to estimate VZTD and receiver clock offset (VCLKR). Reseach shows that this approach may lead to potential ill-conditioning of the design matrix in the least-squares process, possibly due to the correlation between the estimated parameters. To address this issue, a second approach is introduced that utilizes a dedicated functional model to separate the estimation of the two parameters. The comparative analysis of VZTD estimates on permanent station data highlights significant discrepancies between the two approaches, with pronounced differences in the magnitude of the retrieved absolute ZTD trends. This study provides evidence of the sensitivity of the two-variable estimation approach to the correlation between VZTD and VCLKR. Separate variable estimation offers improved results, with the ZTD trend being more consistent with the reference Precise Point Positioning (PPP) estimates. This analysis demonstrates that separating the estimation of VZTD and VCLKR parameters enhances the reliability of absolute ZTD estimates through the variometric approach.
[1] Benedetti, Elisa, et al. ”Global Navigation Satellite Systems seismology for the 2012 M w 6.1 Emilia earthquake: Exploiting the VADASE algorithm.” Seismological Research Letters 85.3 (2014):649-656
[2] Colosimo, Gabriele, et al. ”Realˆatime GPS seismology with a stand−alone receiver: A preliminary feasibility demonstration” Journal of Geophysical Research: Solid Earth 116.B11 (2011).
[3] Savastano, Giorgio, et al. ”Real−time detection of tsunami ionospheric disturbances with a stand-alone GNSS receiver: A preliminary feasibility demonstration.” Scientific reports 7.1 (2017): 46607
How to cite: Fratini, R., De Pace, A. M., Mazzoni, A., and Crespi, M.: Near Real-Time Tropospheric Delay Assessment Through theVariometric Approach: A Comparative Study of Two-Variable and Separate-Variable Functional Models for ZTD Estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15927, https://doi.org/10.5194/egusphere-egu25-15927, 2025.
Compared to alluvial floods, flash or pluvial floods are difficult to predict because they result from intense and brief periods of extreme precipitation. Luxembourg has a history of being impacted by floods, with notable occurrences in January 2011, May 2016, December 2017, January 2018, February 2019, and February 2020. However, July 2021 stands out as the most severe flood year on record in the region. In this study we aim to develop, a high-resolution numerical weather predicRon (NWP) model for effective local heavy rainfall prediction in a nowcasting scenario and provide real-time for flood simulation. The modeling relies on the Weather Research and ForecasRng (WRF) model, which incorporates local Global Navigation Satellite System (GNSS) zenith total delays and precipitation observations to simulate small-scale, high-intensity convective precipitation. As part of this, we will also test run the LISFlood flood model in an operational inundation forecast mode, meaning that the flood model will be run with the WRF precipitation forecasts as inputs. The WRF model was configured for the Greater Region, utilizing a horizontal grid resolution of 12 km and incorporating high-resolution static datasets. Meteorological data, i.e., June 20 -July 20, 2021, from the Global Forecast System (GFS) were employed in the model setup as initial boundary condition. Zenith Total Delay (ZTD) data collected from various GNSS stations (245) across Germany and Luxembourg were assimilated into the model. Additionally, observational datasets, including Surface Synoptic Observations (SYNOP), Upper Air Data, Radiosonde measurements (TEMP), and Tropospheric Airborne Meteorological Data Reporting (TAMDAR) were assimilated. Following this integration, an sensitivity analysis of various meteorological parameters such as precipitation, surface temperature (T2), and relative humidity was performed.
How to cite: Rehman, H. U., Teferle, F. N., Hunegnaw, A., Schumann, G., Wickert, J., Zus, F., and Muraleedharan Thundathil, R.: Evaluating the Performance of Numerical Weather Prediction Models for Precipitation and Temperature in Luxembourg and the Greater Region: Insights from WRF and WRFDA 3D-Var, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20963, https://doi.org/10.5194/egusphere-egu25-20963, 2025.
GNSS Zenith Total Delay (ZTD) estimates are quantities of great interest by climate modellers since atmospheric water vapour is the major greenhouse gas. Therefore, the importance of its accurate, long-term monitoring and evaluation of trends and variability, potentially serving as independent benchmarks to climatological models, both on longer trends derived from GNSS, but also shorter trends, which could be used for assimilation and validation of climate models. ZTD estimates are determined on a regular basis by several processing centers as well as by demand. It has also been demonstrated that series of ZTD estimates can be used for quality control purposes. At the same time, GNSS reached the “maturity age” of 30 years when climate normals of ZTD and gradients can be derived. But what would be the best ZTD series to serve the climate community? What series would offer the most realistic trends? This poster discusses an on-going investigation under the auspices of the International Association of Geodesy, through a joint working group nested within the Inter-Commission Committee on Geodesy for Climate Research. In a previous study, we made use of the ZTD series derived by the third reprocessing campaign (REPRO3), based on a variety of processing modes and models. But this study was partial as the ZTD times series estimated by the Analysis Centers were not covering the same periods. This time, ZTD time series are generated using dedicated PPP scientific software suites. The generated trends are to be compared and analysed.
How to cite: C. Santos, M., Pacione, R., Balidakis, K., Byrant, S., Dick, G., Hughes, R., Jones, J., Keernik, H., Klos, A., Lou, Y., Marques, H., Nahmani, S., Nikolaidou, T., Rannat, K., Valenzuela, R., Weixing, Z., Yao, Y., and Yuan, P.: Ground-GNSS ZTD trends for climate models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11277, https://doi.org/10.5194/egusphere-egu25-11277, 2025.
Global Navigation Satellite System (GNSS) is an established atmospheric observing system for atmospheric water vapour with high spatiotemporal resolution. Water vapour is under-sampled in the current meteorological and climate-observing systems, and thus obtaining and exploiting more high-quality humidity observations is essential.
The operational GNSS data processing at GFZ provides all tropospheric products, zenith total delays (ZTD), precipitable water vapour (PWV), slant total delays (STD) and tropospheric gradients in the framework of different meteorological projects like e.g. E-GVAP ("The EUMETNET EIG GNSS Water Vapour Programme", http://egvap.dmi). E-GVAP is in charge of the collection and quality control of operational GNSS tropospheric products for numerical weather prediction. GFZ is one of the E-GVAP Analysis Centres and processes about 600 GNSS stations in near real-time. The tropospheric products provided by GFZ are used by European weather services for operational forecasts.
GFZ contributes to climate research within the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN). Established in 2006, GRUAN, is an international reference observing network of sites measuring essential climate variables above the Earth's surface. Currently, this network comprises 33 reference sites worldwide, designed to detect long-term trends of key climate variables such as temperature and humidity. GFZ hosts a central processing facility for the GNSS data and is responsible for the installation of GNSS hardware, data transfer, processing and archiving, as well as derivation of GNSS-PWV products according to the GRUAN requirements. A complementary small scale regional climate station network is the Austrian WegenerNet, which provides since 2007 measurements of hydrometeorological variables with very high spatial and temporal resolution. GNSS-PWV measurements have been included as a priority one measurement of the essential climate variable water vapour to both GRUAN and WegenerNet climate station networks.
GNSS-derived tropospheric products and results of selected validation studies will be presented.
How to cite: Dick, G., Zus, F., Thundathil, R., Panetier, A., and Wickert, J.: GNSS for Weather Forecast and Climate Research at GFZ, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12098, https://doi.org/10.5194/egusphere-egu25-12098, 2025.
This study proposes a stacking machine learning (SML) model for the vertical interpolation of precipitable water vapor (PWV), addressing the issue of missing near-surface water vapor information in radio occultation (RO) profiles, where measurements do not reach the surface. The model integrates data from over 1500 ground-based Global Navigation Satellite System (GNSS) stations and more than 300,000 profiles from the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), and is trained and validated in two regions of the Northern Hemisphere. Results show that in the North American region, the SML model reduces the root-mean-square error (RMSE) of PWV estimates by over 33% compared to conventional models. In China and Southeast Asia, the RMSE reduction is about 42%, demonstrating notable improvements over conventional model approaches. External validation with radiosondes shows close agreement between the SML-interpolated RO-PWV and radiosonde-PWV. Additionally, the SML-interpolated RO-PWV exhibits high consistency with PWV estimates from RO profiles of the Meteorological Operational satellites (Metop by ROMSAF), which contain complete (background) near-surface information. The SML model performs reliably across various terrains and climatic conditions. This study also preliminarily explores the model performance for extreme weather conditions, providing insights for future enhancements. The new SML model provides a high-accuracy and effective solution for PWV estimation and contributes to advancements in water vapor monitoring, weather forecasting, and atmospheric science research.
How to cite: Shi, J., Li, M., Li, W., Zhang, K., and Steiner, A.: Stacking machine learning model for vertical interpolation of precipitable water vapor using GNSS networks and radio occultation data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8993, https://doi.org/10.5194/egusphere-egu25-8993, 2025.
We present a collaborative research project between Polish and Chinese scientists, supported by national research funding agencies, to advance GNSS remote sensing (RS) for atmospheric studies. Recent advancements in space technologies, artificial intelligence (AI), and information and communication technologies (ICT) have significantly enhanced our ability to observe, model, and predict atmospheric processes. AI-powered GNSS RS offers robust capabilities for acquiring essential atmospheric parameters, such as water vapor content and profiles, rain rates, wind speeds, and cloud composition.
This project focuses on bridging mathematical models, physical processes, and space- and ground-based observations to achieve the following objectives:
- Data Fusion: Standardize and integrate GNSS RS measurements from ground- and space-based platforms.
- Innovative Methods: Exploit advanced observation techniques, including signal polarimetry and reflectometry.
- Network Integration: Harness the potential of multi-constellation satellite networks, including GNSS, LEO satellites, and Starlink-like constellations, for atmospheric studies.
- AI-Driven Modeling: Develop seamless connections between GNSS observations and weather and climate models using AI and Digital Twin technologies to investigate interactive atmospheric mechanisms.
This research is supported by NCN project UMO-2023/48/Q/ST10/00278, fostering Polish-Chinese scientific collaboration.
How to cite: Rohm, W., Hordyniec, P., Kapłon, J., Trzcina, E., Haji-Aghajany, S., Sun, P., Li, L., and Zhang, K.: New horizon of tropospheric studies using the next generation GNSS, Network of Satellite Constellations and AI, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15408, https://doi.org/10.5194/egusphere-egu25-15408, 2025.
Global Navigation Satellite Systems (GNSS) tomography is a rapidly developing method in meteorology that provides 3D grid-based information about water vapour distribution in the lower troposphere. The standard tomographic solutions are derived by processing signal delays between satellites and ground-based GNSS receiver networks. As the technique has advanced, additional observational data sources have been integrated into the process, enhancing its accuracy and applicability.
Low Earth orbit (LEO) satellites can provide signal delays similar to those from ground-based networks by tracking GNSS signals. This technique is known as GNSS radio occultation (RO) and relies on radio transmissions from GNSS satellites, where signals pass through the atmosphere and undergo refraction. The degree of refraction is influenced by atmospheric temperature and water vapor concentration. With the exponential increase in the number of LEOs satellites over the past 30 years, this technique has been a cornerstone for atmospheric measurements. It is widely used in meteorological offices as a tool for weather forecasting and shows strong potential for improving tomographic applications.
The Weather Research and Forecasting (WRF) Model, equipped with its tomographic operator tomoref, facilitates the integration of tomographic products into meteorological fields. In recent years, several studies have explored available practices for tomographic data assimilation. In this work, we present two variants for assimilating combined RO and tomographic solutions.
In the first approach, radio occultation-derived wet refractivity profiles from the UCAR COSMIC program were incorporated into the tomographic solution using the ATom tomographic software, enhanced with its RO extension. The 3DVar assimilation of tomographic wet refractivity fields into the WRF Data Assimilation system was performed for both combined and ground-based solutions at selected epochs when radio occultation events occurred within the defined domain. The model’s performance was further validated by comparing it to a solution that assimilated conventional GNSS observations. For ground-based stations, GNSS signal delays, expressed as Zenith Total Delays (ZTDs), were assimilated using the gpsztd operator, while space-derived total refractivity profiles were incorporated using the gpsref operator. The resulting meteorological parameters were then compared to external data sources, including radiosondes, meteorological sites, and ERA5 data.
As part of the ongoing OPUS NCN project, an alternative approach to observation integration is being developed. This integrated tomographic solution combines ground-based GNSS observations with RO excess phase data from SPIRE Global within a unified tomography model on the phase observation level. Since RO events are often unevenly distributed across space and time, the combined tomographic observations address these limitations by filling data gaps with ground-based observations. The resulting wet refractivity fields are then assimilated using a variational approach, incorporating the tomographic data into the model over a broader assimilation window. With further fine-tuning, the presented methodology for assimilating tomographic products demonstrates significant potential for future testing in meteorological centres.
How to cite: Hanna, N., Gurmani, S., Trzcina, E., Rohm, W., Cegła, A., Hordyniec, P., Makuch, S., Möller, G., Kryza, M., and Weber, R.: Synergy of GNSS Tomography and Radio Occultation: Methods for Assimilating Refined Water Vapor Fields, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15298, https://doi.org/10.5194/egusphere-egu25-15298, 2025.
Global Navigation Satellite Systems (GNSS) are a powerful tool for high-resolution tropospheric sensing, offering valuable data for weather forecasting and climate monitoring. One of the key techniques for estimating three-dimensional fields of humidity-related parameters in the troposphere using GNSS data is tomography. Recent studies revealed its potential for application in data assimilation into numerical weather prediction models. However, the accuracy of the tomographic models depends heavily on the approach to the distribution of the tomographic nodes. Traditionally, the nodes are placed on a regular grid, without accounting for the uneven distribution of GNSS signal information in the troposphere, which often leads to suboptimal results.
In this study, we propose a novel approach to optimize the spatial arrangement of tomographic nodes, based on the geometry of the GNSS signals and the non-uniformity of the information they provide. The proposed algorithm is based on four steps to define the optimal arrangement of the tomographic nodes: 1) selecting the most valuable GNSS signals in terms of geometry (those that cross with other signals), 2) identification of the intersection spots, 3) cluster analysis of the defined intersections using DBSCAN algorithm, 4) introducing regularly-distributed nodes in the locations with lower-density of the GNSS signals to increase stability of the model. The final solution is performed using a node-based parameterization approach with interpolation of wet refractivity based on natural cubic splines. To evaluate the effectiveness of the optimized node distribution, we compare the performance of our approach with that of three conventional parameterization methods (both voxel-based and node-based with trilinear and spline-based interpolation), using cross-validation based on outputs from the Weather Research and Forecasting (WRF) model and radiosonde observations. The methodology was tested in an urban area, using zenith tropospheric delay estimates from a dense network of 16 low-cost multi-GNSS receivers located in Wrocław, Poland, with an average inter-receiver distance of 3 km.
The results show that the optimized node placement improves the accuracy of wet refractivity estimation, with a reduction in RMSE of 0.5–2 ppm, especially in the altitude range of 0.5–2.0 km. The proposed solution gave the best results compared to the other parameterization approaches (both voxel-based and node-based). The largest impact was noticed in the areas where the highest number of the GNSS signals’ intersections occurred. This study highlights the importance of considering the geometry of GNSS signals when designing tomographic networks and suggests that optimizing node distribution is a promising avenue for future research in GNSS-based tropospheric sensing.
How to cite: Trzcina, E., Rohm, W., and Smolak, K.: Optimizing GNSS Tomographic Node Distribution Using Signal Geometry for Enhanced Tropospheric Sensing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16138, https://doi.org/10.5194/egusphere-egu25-16138, 2025.
Neutral atmospheric bending angles derived from GNSS Radio Occultation (GNSS-RO) data are essential for estimating atmospheric properties such as temperature, humidity, and pressure. The region of interest for atmospheric properties extends up to 80 km, where ionospheric effects remain and require ionospheric corrections for accurate RO bending angle retrievals. First-order ionospheric terms are typically removed using a linear combination of L1 and L2 bending angles. However, this approach leaves behind higher-order terms, known as residual ionospheric errors (RIEs), which introduce systematic biases into the RO data.
Healy and Culverwell (2015) demonstrated that RIEs are theoretically proportional to the square of the difference between L1 and L2 bending angles, scaled by a coefficient, kappa, which varies with ionospheric conditions. Kappa correction is a convenient method to estimate RIEs directly from bending angle data without relying on external ionospheric data such as electron density profiles. Angling et al. (2018) proposed a simple linear model to estimate kappa as a function of altitude, F10.7, and solar zenith angle. They used the NeQuick model to generate electron density profiles and derived the linear model for kappa estimation. However, since NeQuick is a monthly median ionospheric electron density model, it has limitations in representing real-world ionospheric variability, leading to discrepancies between the kappa values from the NeQuick-based model and those estimated from actual data. Therefore, a more realistic derivation of kappa using actual RO data is needed to develop an improved kappa model.
This study aims to enhance kappa correction by using real electron density profiles derived from GNSS-RO data. A double Chapman layer is fitted to electron density profiles from COSMIC-II data, incorporating the characteristics of the E and F layers to provide continuous representations of the real electron density profiles. Ray-tracing simulations are conducted to obtain L1 and L2 ionospheric bending angles, which are then used to derive kappa values. These kappa values are analyzed under various ionospheric conditions, characterized by user-end parameters such as F10.7, local time, geomagnetic latitude, and altitude.
To examine more accurately the numerical relationship between kappa and these parameters, kappa data is classified by F10.7 to represent different solar activity conditions (e.g., solar minimum and maximum), and is also divided by local time (e.g., noon, midnight, and transition periods). Kappa values for each class are then fitted to the remaining parameters. The findings suggest that kappa values from the model proposed by Angling et al. (2018) differ from those estimated using observational data in this study. By directly deriving kappa values from real data and applying separate fits for different classes of solar activity and local time periods, the modeling accuracy can be enhanced. This study shows the necessity of tailored kappa corrections for different ionospheric conditions, improving techniques for correction of RIEs in GNSS-RO data.
References
Healy,S.B., & Culverwell,I.D. (2015). A modification to the standard ionospheric correction method used in GPS radio occultation. Atmospheric Measurement Techniques, 8(8), 3385–3393.https://doi.org/10.5194/amt-8-3385-2015
Angling,M.J., Elvidge S., & Healy,S.B. (2018). Improved model for correcting the ionospheric impact on bending angle in radio occultation measurements. Atmospheric Measurement Techniques, 11(4), 2213–2224.https://doi.org/10.5194/amt-11-2213-2018
How to cite: Park, J., Chang, J., Sun, K., and Lee, J.: Residual Ionospheric Error Correction in GNSS Radio Occultation Bending Angles: Parametric Analysis using Electron Density Profiles Derived from COSMIC-II Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18658, https://doi.org/10.5194/egusphere-egu25-18658, 2025.
Global navigation satellite system-reflectometry (GNSS-R) is considered a promising technology for monitoring marine environments. However, there is still a lack of GNSS-R stations that provide comprehensive data. For this reason, a stationary marine information dynamic monitoring platform using GNSS-R method was constructed in Weihai City, Shandong Province, China. This station consists of two antennas, an intermediate frequency (IF) data collector, a wind sensor and a laptop computer with GPUs. The up-looking antenna is used for direct signal receiving and the down-looking antenna serves for reflected signals. The wind sensor records wind speed and wind direction simultaneously at 1Hz. The collector can export IF data with 62MHz sampling rate which covers signals including GPS L1/L5, BDS B1I/B1C/B2a and Galileo E1/ E5a. The laptop with self-developed software defined receiver (SDR) is employed for processing large amounts of IF data and outputting sea surface height observations based on dual-antennas method in real-time. In the preliminary study, the results based on code-delay method show that the accuracy of BDS B2a can realize centimeter altimetry after post-processing while the accuracy of GPS L5 is approximately in the decimeter range due to the limited number of satellites within the visible range. Additionally, these observations can be also used to retrieve wind speed. We look forward to more research on GNSS-R in the future, for which we can provide data collected by this GNSS-R station.
How to cite: Meng, X., Gao, F., Xu, T., and Wang, N.: A Ground-based GNSS-R Marine Environment Dynamic Monitoring Station, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8951, https://doi.org/10.5194/egusphere-egu25-8951, 2025.
Global Navigation Satellite System Reflectometry (GNSS-R) has long been explored for retrieving sea ice properties, but in-situ validation in the central Arctic during the freezing season is rare, limiting its application. The primary objective of this study is to advance the current understanding of multi-polarization GNSS-R remote sensing for sea ice. This paper presents observations from the full-polarization GNSS-R(FpolGNSSR) prototype during the MOSAiC expedition. FpolGNSSR, with four polarization channels and high antenna gain (11.3 dB), aims to assess the impact of sea-ice thickness and permittivity on GNSS-R data, with observations from October 2019 to January 2020, the onset period of ice growth. A four-layer model simulates reflectivity, and the sensitivity of multi-polarization GNSS-R to sea ice is qualitatively analyzed. Subsequently, a simplified model reveals a linear relationship between reflectivity and ice thickness, with regression showing a correlation of 0.74 (P<0.01). The optimal RMSE of sea ice thickness retrieval is 0.13 m for first-year ice in freezing season (0.3–1.0 m thick).
How to cite: Liu, B., Lei, R., Wan, W., Xia, J., Semmling, M., Zhang, J., Xu, Y., Sun, Y., Spreen, G., and Spreen, G.: Polarimetric GNSS-Reflectometry data over sea ice during the MOSAiC expedition, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10034, https://doi.org/10.5194/egusphere-egu25-10034, 2025.
GNSS-R is an emerging technology for remote sensing of soil moisture with the advantages of high tempo spatial resolutions at low cost. Most of the current research has been carried out on the basis of space borne observations, and a large number of results have been obtained. Due to the high altitude of satellites, the Fresnel reflection zone is usually a few kilometers in diameter, which does not meet the requirements of fine agriculture. Ground-based and UAV-borne GNSS-R, due to their low altitude, can provide effective observations to address above problem. We have built a ground-based GNSS-R observatory at the Experimental Farm of Weihai Academy of Agricultural Sciences, Shandong Province China. The site is equipped with mainly high gain a downward facing left-handed circularly polarized antenna, an upward facing right-handed circularly polarized antenna, an IF signal collector and a computer platform for running the SDR. Currently the main observables that can be output include: SNR, Power Ratio, DDM, etc. The retrievals were evaluated by the in-suit measurements from TDR and the results show that the accuracy of the solutions can reach 3%. In addition, a UAV-based GNSS-R setups are also built and effective measurements were obtained.
How to cite: Gao, F., Qian, C., Li, X., Wei, J., Mao, J., Meng, X., Wang, N., Zhu, Y., and He, Y.: A ground-based GNSS-R station for soil moisture monitoring , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14650, https://doi.org/10.5194/egusphere-egu25-14650, 2025.
Posters virtual: Thu, 1 May, 14:00–15:45 | vPoster spot 1
EGU25-18280 | ECS | Posters virtual | VPS23
Real-Time ZTD correction grid based on augmented GNSS network for navigation servicesThu, 01 May, 14:00–15:45 (CEST) | vP1.23
Tropospheric refraction is one of the major error sources in satellite-based positioning. The delay of radio signals caused by the troposphere ranges from 2m at the zenith to 20m at low elevation angles, depending on pressure, temperature and humidity along the path of the signal transmission. If the delay is not properly modeled, positioning accuracy can degrade significantly. Empirical tropospheric models, with or without meteorological observations, are used to correct these delays but they are limited in accuracy and spatial resolution resulting in up to a few decimeters error in positioning solutions. The present availability of ground-based GNSS networks and the state of the art of GNSS processing techniques enable precise estimation of Zenith Tropospheric Delays (ZTD) with different latency ranging from real time to post-processing.
We present a method for computing ZTD residual fields interpolating, through Ordinary Kriging, the residuals between GNSS-derived and model-computed ZTD at continuously operating GNSS stations. GNSS ZTD estimates, obtained in real time and in PPP mode, are augmented by a multi-prediction model based on a Graph Neural Network model trained using one year of Near Real Time ZTD observations and a model using a polynomial plus harmonic interpolation. A combination strategy is defined to merge GNSS ZTD estimates at sites with the predicted values, where predicted ZTD values act as hole fillers for stations missing from the GNSS network at the current epoch. The residual ZTD field, obtained from PPP/prediction model and ZTD empirical model, is modelled as a random process and for each epoch a variogram is estimated and fitted to characterize the spatial correlation of the process. At a known user location, ZTD value is obtained as the sum of site interpolated ZTD residual and modeled-ZTD value. The algorithm is validated with respect to GNSS ZTD estimates provided by an external provider at a selection of sites not included in the network used to fed the computation. Details about validation and possible improvements will be provided.
How to cite: Basoni, A., Pacione, R., Bagaglini, L., and Lanotte, R.: Real-Time ZTD correction grid based on augmented GNSS network for navigation services, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18280, https://doi.org/10.5194/egusphere-egu25-18280, 2025.
EGU25-13415 | ECS | Posters virtual | VPS23
Decoding the signal of extreme weather events in the Azores archipelago using GNSS and atmospheric reanalysis productsThu, 01 May, 14:00–15:45 (CEST) | vP1.24
The rapidly changing climate is amplifying both the frequency and severity of extreme weather events in the Azores archipelago, Portugal. Understanding the underlying dynamics of these events is essential for effective mitigation. Atmospheric water vapor data derived from the Global Navigation Satellite System (GNSS) data and reanalysis outputs from an atmospheric general circulation model offer valuable tools for studying the behavior of weather fronts around the Atlantic Ocean environment of the Azores. This research aims to conduct a detailed comparison between GNSS-based measurements and atmospheric reanalysis data, such as those available from ERA/MERRA2, focusing on the detection of small-scale atmospheric structures with high temporal resolution. We utilize atmospheric reanalysis products to decode long-term trends in the frequency and severity of extreme weather events in the Azores. We then apply statistical methods to identify consistencies and differences between these two approaches in capturing atmospheric water vapor patterns. By combining water-vapor estimates from both GNSS data and atmospheric reanalysis, we are able to characterize the dynamics of atmospheric turbulence from small (few meters) to large (few tens of kilometers) scales.
How to cite: Ramrajvel, N., Mondal, D., Elosegui, P., Paine, S., Mateus, P., and Mendes, V.: Decoding the signal of extreme weather events in the Azores archipelago using GNSS and atmospheric reanalysis products, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13415, https://doi.org/10.5194/egusphere-egu25-13415, 2025.
EGU25-17506 | ECS | Posters virtual | VPS23
First results about evaluation of forecasted numerical weather model data set in view of neutral atmosphere delayThu, 01 May, 14:00–15:45 (CEST) | vP1.25
The neutral atmosphere that extending from the surface of earth to about 80 km overhead is the electrically neutral part (within a certain frequency band which GNSS signals fall) of the atmosphere. There is no doubt that neutral atmosphere has a delaying effect on transmitted radio waves. Spilker (1996) noted that the more precise term of this delaying effect is neutral atmosphere delay, even though this delaying effect has been traditionally referred to as just troposphere delay. At all events, the delaying effect has propagated into satellite observations, and we must deal with it appropriately in order to achieve precise satellite positioning results. There are many geodesists have been making their contributions to treatment of neutral atmosphere delay, and how to get satisfactory supports from numerical weather model data set is one of the efforts making to calibrate this delaying effect more precisely up-to-date. Currently, both Earth observation network and technology have great improvement, which results in wonderful increase of Earth observational data as well as the subsequent numerical weather model data set. Briefly speaking, numerical weather model data set which generally provided by different organizations and/or institutions is a global and/or regional gridded meteorological data set with specific temporal-spatial resolution. Generally, reanalysis data set and forecast data set are usually considered to be the two main data set representations, and they both provide two types of data level, i.e., three-dimensional pressure levels and two-dimensional surface level. The data set contains some usually used meteorological parameters, such as height, temperature, pressure, humidity. With these meteorological parameters, some main terms related to neutral atmosphere delay, such as hydrostatic/wet delay, gradient factors and mapping factors can all be calculated without any difficulty by using computing techniques like raytracing and interpolation. Undoubtedly, the performance of different types of data set that mentioned above in representing neutral atmosphere delay are not all the same. Definitely, some interesting and meaningful comparison results have found and widely propagated by many scholars. In this work, we put more emphasis on evaluation of the forecast data set from neutral atmosphere delay point of view, considering there is an objective fact that satellite positioning industry especially the (near) real-time positioning has vigorous development, in which the calibration of neutral atmosphere delay is required more and more accurate and timely-supported. Besides time-delayed reanalysis data set and time-advanced forecast data set, microwave radiometer data set and radiosonde data set are also employed. The first results show that empirical model such as UNB3 can only state the normal level of delaying effect and the obtained delay values are either larger or smaller; the pressure levels data set performs better than the surface level data set with very high proportion in time domain; even though reanalysis data set generally has good performance, forecast data set can work for the neutral atmosphere delay calibration with relatively satisfactory support in term of accuracy.
This work is supported by the National Natural Science Foundation of China (42304010), the Youth Foundation of Changzhou Institute of Technology (E3-6207-21-060, 31020222007).
How to cite: Wang, M.: First results about evaluation of forecasted numerical weather model data set in view of neutral atmosphere delay, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17506, https://doi.org/10.5194/egusphere-egu25-17506, 2025.
EGU25-18910 | Posters virtual | VPS23
Extending Global and Regional Near Real-Time GNSS ZTD Solutions Using BSW5.4 at the University of Luxembourg: Contributions to E-GVAPThu, 01 May, 14:00–15:45 (CEST) | vP1.26
The University of Luxembourg (UL), in collaboration with the United Kingdom Met Office, continues to advance the provision of global and regional near real-time (NRT) Zenith Total Delays (ZTDs) from GNSS ground networks to support operational meteorological products within the EUMETNET EIG GNSS Water Vapour Programme (E-GVAP). E-GVAP facilitates coordination and uptake of NRT GNSS-based atmospheric monitoring, which is indispensable for assimilation in Numerical Weather Prediction (NWP) models across Europe, including at the Met Office, where high-temporal-resolution data enhance mesoscale weather forecasting. This study highlights the collaborative efforts of the Met Office and UL in delivering accurate, timely meteorological data from GNSS. The partnership has resulted in the development and enhancement of NRT processing systems using the state-of-the-art Bernese GNSS software version 5.4 (BSW5.4), generating ZTD products at both UL and the Met Office at 1-hour intervals globally and regionally, and at sub-hourly intervals regionally. Over the past year, UL has focused on developing hourly NRT ZTD solutions for global and regional networks, and more recently extending them to sub-hourly intervals (down to 15 minutes) for regional coverage, thereby refining the temporal resolution for E-GVAP users. In particular, we are now prepared to provide NRT products in the form of a global hourly product (ULGH), a regional hourly product (ULRH), and a regional sub-hourly product (ULRS) to E-GVAP. As part of the system's development, we validate our latest global, regional, and sub-hourly ZTD solutions against established NRT outputs from E-GVAP and benchmark post-processed Double-Difference Network (DDN) products, while also verifying Integrated Water Vapour (IWV) estimates against ECMWF Reanalysis v5 (ERA5). Finally, we highlight how higher-frequency updates can positively influence NWP assimilation in rapidly evolving weather situations, detailing data flow and latency management that ensure reliable NRT ZTD delivery to E-GVAP participants and the Met Office. By extending temporal coverage from hourly to sub-hourly in regional networks and continuing our global solutions, we advance the utility of GNSS-based atmospheric sensing for short-term weather forecasting, providing consistent, high-quality NRT GNSS products for meteorological operations in Europe and beyond.
How to cite: Hunegnaw, A., Teferle, F., and Jones, J.: Extending Global and Regional Near Real-Time GNSS ZTD Solutions Using BSW5.4 at the University of Luxembourg: Contributions to E-GVAP , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18910, https://doi.org/10.5194/egusphere-egu25-18910, 2025.
EGU25-10677 | ECS | Posters virtual | VPS23
Comparisons between GAMIT-derived Zenith Tropospheric Delay (ZTD) values from AWS and GNSS met sensor valuesThu, 01 May, 14:00–15:45 (CEST) | vP1.31
Precise estimation of Zenith Tropospheric Delay (ZTD) is crucial for improving the accuracy of data from Continuously Operating Reference Stations (CORS), particularly in applications requiring high-precision GNSS positioning. This study focuses on evaluating various ZTD models to identify the most accurate approach for mitigating atmospheric delays in CORS data. The research compares ZTD values derived from Automatic Weather Stations (AWS), GNSS meteorological sensors, and temperature-pressure-humidity-based models calculated using the GAMIT software with reference values obtained from co-located weather stations and global atmospheric models.
The methodology involves processing GNSS observations from selected CORS sites using multiple ZTD estimation models, including empirical approaches. The accuracy of these models is assessed using key performance metrics such as root mean square error (RMSE), mean bias, and correlation with actual weather conditions.
Preliminary results indicate that empirical models show better consistency in stable atmospheric conditions. Additionally, comparisons between GAMIT-derived ZTD values and those from AWS and GNSS met sensors reveal insights into the reliability and precision of each data source under different atmospheric conditions.
The study highlights that precise ZTD estimation is essential for reducing atmospheric errors in CORS data, thereby enhancing GNSS-based applications such as geodesy, surveying, and real-time positioning. The research concludes that combining inputs from various meteorological data sources offers the best accuracy across diverse CORS networks, particularly in regions with varying climatic conditions and atmospheric dynamics.
How to cite: Agarwal, D., Mahato, S., Gandugade, P. B., Nagarajan, B., and Dikshit, O.: Comparisons between GAMIT-derived Zenith Tropospheric Delay (ZTD) values from AWS and GNSS met sensor values, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10677, https://doi.org/10.5194/egusphere-egu25-10677, 2025.
