-Estimates of the neutral atmosphere using ground- and space-based geodetic data
-Retrieval and comparison of tropospheric parameters from multi-GNSS, VLBI, DORIS and multi-sensor observations
-Nowcasting, forecasting, and climate research using RT and repro 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 in NWP
-Homogenization of long-term GNSS, VLBI tropospheric products
-Detection and characterization of sea level, snow depth, and sea ice changes, using GNSS-R
-Monitoring of soil moisture and ground-atmosphere boundary interactions using GNSS data
Global Navigation Satellite Systems (GNSS) Radio Occultation (RO) is one of the most promising remote sensing techniques for global atmospheric sounding. RO is a limb-sounding technique that uses GNSS signals, refracted during their propagation through the Earth’s atmosphere to a receiver on a low-Earth orbit (LEO) satellite. RO data have been proven to be of enormous value for data assimilation in numerical weather prediction (NWP) as well as in climate science over the two last decades. However, retrieving products such as temperature or humidity from RO observations is not straightforward and dedicated retrieval algorithms still have limitations, such as the need for external meteorological data. On the other hand, various new RO missions are now producing over 10,000 globally distributed profiles daily. This makes the technique interesting for the application of Artificial Intelligence (AI) models to different steps of the RO retrieval chain.
This study compares an existing retrieval method entitled AROMA (Advancing the GNSS-RO retrieval of atmospheric profiles using MAchine-learning), which is based on a multi-layer perceptron (MLP), with more sophisticated deep learning (DL) architectures such as Transformers and one-dimensional convolutional neural networks (1D-CNNs). All these models are trained on multiple years of data from different RO missions, using vertical profiles of bending angle and other RO parameters as input features and operational results from a standard retrieval algorithm as targets. Validation results using both a separate test data set as well as external data will be presented, aiming to give a recommendation on the most promising type of architecture to use for the RO wet retrieval problem.
How to cite: Aichinger-Rosenberger, M.: Comparison of different deep learning architectures for the retrieval of thermodynamic profiles from GNSS-RO , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19192, https://doi.org/10.5194/egusphere-egu26-19192, 2026.
Under global warming, high-precision and rapid monitoring of Arctic sea ice freeze-thaw cycles has become increasingly critical for understanding polar climate dynamics and predicting global climate impacts. Ground-based Global Navigation Satellite System-Reflectometry (GNSS-R) is emerging as a promising technique for such monitoring, yet prior research has primarily focused on distinguishing sea ice from open water, with limited validation of its ability to capture continuous freeze-thaw transitions. To address this gap, this study presents a novel multi-frequency combination strategy that integrates spectral area factors (SAF) derived from multi-frequency (L1, L2, L5) GNSS-R observations using a Bayesian classifier. The method enhances detection by leveraging both state-dependent differential signatures and inter-frequency correlations. Using five years of observations (2018–2022) from the coastal station TUKT in Tuktoyaktuk, Canada, we trained prior probability distributions with data from 2018–2020 and tested the approach on independent data from 2021–2022. The results demonstrate that the proposed method effectively captures the dynamic progression of freeze-thaw cycles. It achieves a sample-level classification accuracy of 92.72% and a daily accuracy of 98.49% during the test period. This performance meets practical application requirements, confirming the potential of ground-based GNSS-R as a reliable, cost-effective tool for the sustained monitoring of coastal Arctic sea ice freeze-thaw processes. This study thereby bridges the critical gap between theoretical research and operational environmental decision-making in polar regions.
How to cite: Yuan, X., He, S., and Wickert, J.: First Accuracy Assessment of Ground-Based GNSS-R for Coastal Arctic Sea Ice Freeze-Thaw Cycles Monitoring: A Five-Year Study (2018–2022) in Tuktoyaktuk, Canada, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4525, https://doi.org/10.5194/egusphere-egu26-4525, 2026.
Melt ponds play a critical role in regulating the surface albedo of Arctic sea ice and accelerating its melt through the ice–albedo feedback mechanism. However, their high spatial heterogeneity and rapid temporal evolution make large-scale, continuous monitoring extremely challenging. Spaceborne optical remote sensing remains the primary technique for retrieving melt pond fraction (MPF), but its effectiveness is severely limited under persistent cloud cover and polar night conditions. Although GNSS-R provides all-weather observations with high temporal resolution, its potential for melt pond monitoring has not yet been systematically evaluated, nor have practical monitoring strategies been established. This study evaluates the potential of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) for melt-pond monitoring and characterizes the mechanisms through which melt-pond surface properties influence the reflected GNSS-R signals. An electromagnetic forward scattering model was developed to simulate GNSS-R reflectivity as a function of MPF and open water fraction (OWF) in representative summer sea ice scenes. The model was validated using observations from the Tianmu-1 GNSS-R satellite and the optical melt pond data. We evaluated the model performance using pan-Arctic data on three distinct dates representing different stages of melt pond development: June 15, July 1, and August 15, 2023. The modeled reflectivity shows strong agreement with GNSS-R observations, yielding Pearson correlation coefficients of interval means values of 0.99, 0.97, and 0.93, and corresponding unbiased RMSE (ubRMSE) values of 0.76 dB, 1.91 dB, and 1.18 dB, respectively. The results demonstrate the potential of using GNSS-R for melt pond monitoring, supporting the development of GNSS-R–based MPF retrieval algorithms and fusion approaches that integrate traditional remote sensing data.
How to cite: Zhang, L., Ban, W., and Zhang, X.: Monitoring melt pond using Tianmu-1 GNSS-R Data: A Wind-concerned Model study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6330, https://doi.org/10.5194/egusphere-egu26-6330, 2026.
Global Satellite Navigation Systems Reflectometry (GNSS-R) is a passive bistatic radar technique that exploits the signals broadcasted by GNSS satellites as signals of opportunity. The scattering characteristics of surfaces such as oceans, ice, soil or vegetation are analyzed by comparing the signals received after a reflection off the Earth surface by a GNSS-R sensor to those received directly. Thanks to the global and continuous availability of multiple GNSS satellites signals, GNSS-R allows the simultaneous analysis of several reflections over different surface areas, with varied incidence angles and carrier frequencies.
Traditionally, GNSS-R is performed from ground stations, airborne platforms or Low Earth Orbit satellites. In this work, a GNSS receiving system was set onboard a sub-orbital sounding rocket, allowing for the collection of rare GNSS-R observations from altitudes varying between 310 and 80km in about 7 minutes of ballistic flight. Such configuration allows extending existing methodologies of surface water detection over wetland and sea-ice from airborne to spaceborne scenarios, notably with the specificity of the recording of direct and reflected signals piercing diversely through the ionospheric E- and F- layers along the flight, at grazing angles.
The flight was performed on November 11, 2024, at 7h38 UTC, as the MAPHEUS-15 (MAterials PHysics Experiment Under weightlessnesS) rocket was launched from the Esrange Space Center, in Sweden. A GNSS antenna, linked to a Syntony GNSS L1-L5 bit grabber, was attached at the bottom of the MAPHEUS-15 payload, aiming for the observation of grazing direct signals and of reflected signals at any elevation angle. The bit grabber digitized the raw RF signals at a 25MHz sampling rate for further software-defined processing.
While the receiving antenna suffered from radio interferences that limited the availability of the GPS L1 frequency, successive computations of GPS L5 Delay Doppler Maps (DDM) were successfully performed at a 1Hz rate, aided for 250ms non-coherent integration by a geometrical model of the direct and reflected signals paths. Reflection events were detected in the processed DDMs of 8 different GPS satellites, with elevations ranging from 0 to 70°, over Norway, Sweden, Finland, as well as over the Fram Strait area. The Fram Strait GNSS-R events were observed continuously for 150s, corresponding to a ground trace of about 300km, and further studied for sea and sea ice characterization. A second iteration of this experiment was performed during the MAPHEUS-16 flight on November 12, 2025, also displaying reflections over the Fram Strait area at grazing angles.
How to cite: Stienne, G., Semmling, M., Dreissigacker, C., Badia, P., Kallenbach, A., and Voigtmann, T.: Sea ice detection using GNSS-Reflectometry from sub-orbital rocket flight, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20644, https://doi.org/10.5194/egusphere-egu26-20644, 2026.
The diurnal variability of Integrated Water Vapor (IWV) plays an important role in land–atmosphere coupling, convection initiation, and the diurnal water cycle, yet its global observational characterization remains limited. Global Navigation Satellite Systems (GNSS) observations provide a unique capability for resolving IWV diurnal variability through continuous, all-weather, high–temporal-resolution measurements with long-term stability. In this study, we analyze a decade of GNSS-derived IWV observations from a global network of thousands of stations to characterize the climatological features of the IWV diurnal cycle. The analysis focuses on the spatial structure and harmonic characteristics of sub-daily IWV variability across different latitude bands and climate regimes. The results reveal a coherent global diurnal signal, with systematic variations in amplitude and phase that exhibit strong geographic dependence. In addition, we examine the representation of IWV diurnal variability in the ERA5 reanalysis by analyzing temporal features in ERA5 IWV time series and their potential influence on estimated diurnal harmonics. The comparison highlights the importance of accounting for reanalysis-related temporal artifacts when interpreting sub-daily variability. Based on the unique strengths of long-term, globally distributed GNSS observations, this work provides a robust observational framework for studying IWV diurnal variability and offers methodological insight for evaluating reanalysis and satellite-based water vapor products. The results are relevant for studies of atmospheric processes operating at sub-daily timescales and for the interpretation of water vapor observations from observing systems with limited temporal sampling.
How to cite: Yuan, P., Blewitt, G., Kreemer, C., Li, Z., Lu, R., Xia, P., Jiang, W., Schuh, H., Wickert, J., and Deng, Z.: Global Characterization of IWV Diurnal Variability from GNSS and Its Relevance to ERA5 Reanalysis Products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2451, https://doi.org/10.5194/egusphere-egu26-2451, 2026.
Tropospheric zenith wet delay (ZWD) is one of the major error sources for space geodetic techniques and plays a vital role in meteorological research. Accurate prior estimates for ZWD can significantly improve the performance of geodetic applications, such as precise kinematic positioning. Current single machine learning ZWD models have limitations in modeling the high spatiotemporal variations of moisture in the lower atmosphere and in their generalization capabilities. To mitigate these limitations, this work introduces a hybrid learning framework that combines multiple machine learning models. The proposed model offers stronger generalization capabilities, improving the ZWD modeling and forecasting accuracy.
When comparing the RMSE, the proposed model outperforms existing machine and deep learning-based ZWD models, the empirical GPT-3 model, and the traditional models such as the Saastamoinen and Askne & Nordius models. In the blind case, when surface meteorological data are not used, the RMSE is reduced by 25.76% compared to the GPT-3 model. When using surface meteorological parameters, the proposed model achieves improvements of 47.05% and 34.24% compared to Saastamoinen and Askne & Nordius, respectively.
The generalization capabilities of the models were evaluated at non-modeled sites. The proposed model demonstrates improvements in overall external performance, with a particularly significant increase of 26.14% in the blind case compared to GPT-3. When sites access meteorological data, the model shows improvements of 45.23% and 34.31% compared to Saastamoinen and Askne & Nordius, respectively.
The spatiotemporal analysis shows the improved stability and precision of the proposed model over the other models evaluated in this work, indicating promising prospects for it in real-time and rapid geodetic applications.
How to cite: H. Sharouda, M., Zhang, W., Mo, Z., M. Elisy, M., Sun, H., Hosny, M., and Lou, Y.: A Hybrid Machine Learning Approach for Modeling Tropospheric Zenith Wet Delay with Enhanced Generalization Performance. , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2493, https://doi.org/10.5194/egusphere-egu26-2493, 2026.
Accurate estimation of the Zenith Wet Delay (ZWD) is essential for GNSS meteorology and atmospheric water vapor monitoring, with important applications in weather forecast and climate monitoring. With the growing availability of reanalysis data sets such as ERA5 and dense GNSS networks, machine learning (ML) offers a powerful means to integrate these data sources and learn the statistical relationships between atmospheric variables and tropospheric delays.
This study presents a machine-learning framework for predicting ZWD using ERA5 atmospheric profiles and a multi-year data set of GNSS observations across Europe. We applied the GNSS ZTD observations from 2018 to 2023, from which ZWD is obtained using Zenith Hydrostatic Delay (ZHD) computed from ERA5. An XGBoost model is trained using GNSS stations from 2018–2022 and evaluated on independent stations excluded from training to ensure that the results reflect true spatial generalization. Under this station-based cross-validation strategy, the model reaches an RMSE of approximately 9 mm on the validation stations and about 9.5 mm on entirely independent test stations in 2023. These results demonstrate that our method can effectively capture ZWD variability and generalize across heterogeneous environments.
By learning a data-driven mapping between ERA5 atmospheric fields and GNSS-derived delays, the proposed approach enables rapid, spatially continuous estimation of ZWD, supporting applications in GNSS meteorology, numerical weather prediction, and climate monitoring.
How to cite: Zhang, L., Peng, Y., Zus, F., Deng, Z., and Wickert, J.: GNSS Zenith Wet Delay prediction from ERA5 using Machine Learning with cross-station generalization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5181, https://doi.org/10.5194/egusphere-egu26-5181, 2026.
Large weather foundation models have recently emerged as a powerful paradigm for global weather forecasting, leveraging transformer-based architectures pretrained on vast and heterogeneous Earth system datasets. Despite their success, accurately predicting moisture-related processes - particularly those associated with atmospheric water vapor and precipitation - remains a key challenge. Global Navigation Satellite System (GNSS) observations provide an independent and physically meaningful source of information on atmospheric water vapor through signal delays induced along the signal path, offering an opportunity to enhance data-driven weather models.
In this work, we investigate the integration of GNSS-derived Zenith Wet Delays (ZWDs) into Aurora, a state-of-the-art large weather foundation model based on a hierarchical vision transformer architecture. Building on Aurora’s pretrained representations, we perform full fine-tuning using ten years of ERA5 reanalysis data augmented with surface-level ZWD fields generated by the ZWDX global forecasting model. To rigorously assess the contribution of GNSS information, we conduct controlled experiments in which identical model configurations are fine-tuned both with and without the inclusion of ZWDs. Experiments are performed on two model scales, comprising approximately 250 million and 1.3 billion parameters.
To enable stable learning when introducing the additional GNSS-derived variable, we propose an adaptive loss-weight scheduling strategy that gradually increases the contribution of the ZWD loss during training. This approach allows the model to successfully learn the new variable while maintaining performance on the original atmospheric fields. The learned ZWD representations reach an accuracy comparable to that of the other variables included during pretraining.
Beyond the direct prediction of ZWDs, we analyze the influence of GNSS information on moisture-related atmospheric variables, including specific humidity from the original pretraining set and precipitation, which is added during fine-tuning alongside ZWDs. The inclusion of ZWDs leads to measurable changes in the prediction skill of these variables at the surface and, for specific humidity, throughout the atmospheric column. While the magnitude and physical interpretation of these effects are still under investigation, the results indicate that GNSS-derived information is effectively utilized by the model and influences its internal representation of atmospheric moisture.
A central objective of this research is to assess whether GNSS-informed foundation models can improve the prediction of precipitation and nowcasting of extreme weather events, where accurate moisture representation is critical. Ongoing work extends the evaluation to shorter lead times and event-based analyses. Future developments include incorporating direct GNSS station measurements instead of interpolated products and developing regional high-resolution forecasting setups to better exploit the spatial density of GNSS networks, with the ultimate goal of enhancing forecasts of localized, high-impact extreme events.
How to cite: Trentini, L., Lehmann, F., Crocetti, L., and Soja, B.: Integrating GNSS-Derived Atmospheric Delays into Large Weather Foundation Models , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6706, https://doi.org/10.5194/egusphere-egu26-6706, 2026.
Global Navigation Satellite System (GNSS) is an established atmospheric monitoring technique delivering water vapour data in near-real time. The advancement of GNSS processing made the quality of real-time GNSS tropospheric products comparable to near-real-time solutions. In addition, they can be provided with a temporal resolution of 5 min and latency of 10 min, suitable for severe weather nowcasting. This presentation exploits the added value of sub-hourly real-time GNSS tropospheric products for the nowcasting of convective storms in Bulgaria. A convective Storm Demonstrator (Storm Demo) is build using real-time GNSS tropospheric products and Instability Indices to derive site-specific threshold values in support of public weather and hail suppression services. The Storm Demo targets the development of service featuring GNSS products for two regions with hail suppression operations in Bulgaria, where thunderstorms and hail events occur between May and September, with a peak in July. The Storm Demo real-time Precise Point Positioning processing is conducted with the G-Nut software with a temporal resolution of 5 min for 12 ground-based GNSS stations in Bulgaria. Real-time data evaluation is done using reprocessed products and the achieved precision is below 9 mm, which is within the nowcasting requirements of the World Meteorologic Organisation. For the period May–September 2021, the seasonal classification function for thunderstorm nowcasting is computed and evaluated. The added value of the high temporal resolution of the GNSS tropospheric gradients is investigated for a several storm case. Evaluation of real-time tropospheric products from Galileo will be presneted in addition.
How to cite: Guerova, G., Dousa, J., Dimitrova, T., Stoycheva, A., Václavovic, P., and Penov, N.: GNSS storm nowcasting demonstrator for Bulgaria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1688, https://doi.org/10.5194/egusphere-egu26-1688, 2026.
The 2023–2025 world tour of the Italian Navy’s Amerigo Vespucci ship offers a unique and remarkable laboratory for multidisciplinary environmental observations over the global oceans, where direct measurements remain extremely limited. Among the various research activities conducted onboard by the Sea Study Center of Genoa University, Precipitable Water Vapor (PWV) evaluations contribute to advancing the understanding of marine atmospheric processes. PWV plays a central role in regulating atmospheric moisture, influencing convection, and shaping the development of extreme precipitation events; yet its variability over the open sea remains poorly constrained due to the limited availability of continuous measurement platforms. As the ship circumnavigates the globe, it continuously records data through an onboard Global Navigation Satellite System (GNSS) and weather station system, transforming the ship into a moving atmospheric observatory. As known, the GNSS observations are influenced by the presence of troposphere, which influence is parametrized through the Zenith Total Delay (ZTD). In the present work, ZTD is estimated with Precise Point Positioning (PPP) strategy. PWV is then obtained using well-established relations, combining ZTD estimates with onboard pressure and temperature measurements. A key innovation of this work is the creation of a global, georeferenced PWV database derived exclusively from ship-based observations, considering the complexities introduced by ship motion, sensor integration, and highly variable marine environments. This dataset is expected to represent a useful contribution to study the meteorological models at sea. The present work represents a first approach for comparing GNSS and Numerical Weather Prediction (NWP) model-derived PWV values, to assess their consistency, quantify uncertainties, and evaluate the potential of assimilating ship-based PWV observations into operational forecasting systems.
How to cite: Javed, N.: Precipitable Water Vapor tracking in the oceans with GNSS and meteorological observations: the Amerigo Vespucci ship World Tour (2023-2025), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8085, https://doi.org/10.5194/egusphere-egu26-8085, 2026.
Global Navigation Satellite System (GNSS) tropospheric gradients provide critical insights into atmospheric moisture distribution, whereas zenith total delays (ZTD) quantify the integrated moisture content along the zenith direction. Integrating both observation types enables more effective adjustment of moisture fields and correction of their dynamics within numerical models. Clearly, in areas with limited station coverage, assimilating tropospheric gradients alongside ZTD observations enhances model performance. This study investigates improvements to the lower-tropospheric water vapor correction, with particular attention to increasing station density in the GNSS network. A two-month regional simulation is conducted to support this analysis.
Our research will transition from the regional Weather Research and Forecasting model to a global-scale assimilating advanced GNSS observations using the Model for Prediction Across Scales (MPAS), which includes both ground- and satellite-based GNSS observations. This effort is undertaken through the new DFG (German Research Foundation) funded project titled “Assimilation of advanced GNSS atmospheric remote sensing observations into the MPAS system.”
Reference:
Thundathil, R., Zus, F., Dick, G. and Wickert, J., 2025. Assimilation of global navigation satellite system (GNSS) zenith delays and tropospheric gradients: a sensitivity study utilizing sparse and dense station networks. Atmospheric Measurement Techniques, 18(19), pp.4907-4922. https://doi.org/10.5194/amt-18-4907-2025
How to cite: Thundathil, R., Zus, F., Dick, G., and Wickert, J.: Vertical adjustment of water vapor in the lower troposphere by assimilating GNSS tropospheric gradients , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10747, https://doi.org/10.5194/egusphere-egu26-10747, 2026.
Tropospheric water vapor is a key component of Earth’s climate system and plays a central role in atmospheric processes such as cloud formation, precipitation, and the transport of heat through evaporation and condensation. Its behavior is closely tied to atmospheric temperature via the Clausius-Clapeyron relation, which states that the amount of water vapor (in saturated air) increases exponentially with rising temperature. For real water vapor changes from multi-year to decadal time periods, several studies have revealed deviations from this theoretical scaling at regional spatial scales, highlighting the need for robust observational data to better understand these variations.
In this contribution, we estimate total-column tropospheric water vapor trends over a five-year period for a comparative performance evaluation, using multiple observational techniques, including ground-based radiometers operating in the microwave and thermal infrared bands, multi-Global Navigation Satellite System (GNSS) solutions, and reanalysis data. Each technique exhibits unique advantages and limitations, and comparing their outputs provides valuable insights into the consistency of total column water vapor retrievals and their potential for sensor fusion and synergistic retrievals.
We conducted an intercomparison of the total column water vapor trends, to assess biases, identify potential sensor drifts, and evaluate the overall accuracy of the individual trend estimates. Basis of this analysis are water vapor retrievals over 2021 to 2026 from measurements of co-located radiometers and a six-station GNSS station network, which are part of the WegenerNet Open-Air Laboratory for Climate Change Research in southeastern Austria. To obtain total column water vapor estimates from infrared radiometers, we simulate clear-sky brightness temperatures in the respective frequency bands from reanalysis data and use gradient-boosted regression trees with additional predictors to approximate the relation between total column water vapor and brightness temperature. A similar approach is used for the microwave radiometer. Our multi-GNSS water vapor estimates are based on precise-point-positioning solutions for each of the six stations.
We find that processing choices and hyperparameters play a crucial role for the estimated short-term trends for both the radiometer retrievals and the GNSS estimates. While we see an overall agreement between the observational techniques in trend direction, significant differences remain. We discuss the possible causes of the differences and related options for improvement learned from this intercomparison.
How to cite: Kvas, A., Fuchsberger, J., Haas, S. J., Rabensteiner, S., and Kirchengast, G.: Intercomparison of total column water vapor trends from ground-based radiometry and multi-GNSS solutions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12531, https://doi.org/10.5194/egusphere-egu26-12531, 2026.
Since mid 2023, the Onsala Space Observatory is operating a new modern microwave radiometer, Greta, which is a commercial product of type HATPRO-G5. It is co-located with the other microwave radiometer, Konrad, which has been developed and built at Onsala. Konrad has been in operation since 2000 and is usually operated in so-called sky-mapping mode. The data of complete sky-scanning sequence are then analyzed together, providing zenith wet delay and wet horizontal gradient results with a temporal resolution of 5 minutes. This type of data are available for this study from the beginning of 2023 to July 2024. In addition to operating in a similar sky-mapping mode, the new radiometer Greta has been operated synchronised with VGOS observations during several VGOS 24 h sessions from the year 2023 to 2024. This means that Greta was performing measurements of the local atmosphere in the same direction as the VGOS telescopes at Onsala, thus providing slant wet delay measurements for each individual VGOS observation. Together with the slant hydrostatic delays, calculated from ground pressure measurements, the possibility to avoid estimating the delays due to the neutral atmosphere exists and are evaluated. We present an update of using these slant delays as external a priori information in the VGOS data analysis.
How to cite: Haas, R., Feng, P., and Elgered, G.: Microwave radiometer observations for VGOS data processing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17681, https://doi.org/10.5194/egusphere-egu26-17681, 2026.
The tropospheric wet delay is an important error source in precise GNSS positioning and is routinely modeled through the estimation of zenith wet delay (ZWD) and horizontal tropospheric delay gradients. While GNSS ZWD has been successfully used in climate studies and operational numerical weather prediction (NWP), the meteorological exploitation of tropospheric gradients remains limited, partly due to challenges in their interpretation, consistency, and sensitivity to processing strategies. The gradients reflect horizontal asymmetries in the neutral atmosphere and can, in principle, be inferred from ZWD differences between nearby GNSS stations, assuming a suitable vertical scaling of refractivity gradients. In this study, we investigate the consistency and variability of single-station GNSS-estimated tropospheric gradients using dense station pairs in southern Sweden from the SWEPOS GNSS network. We compare single-station gradients estimated directly from GNSS processing with inter-station horizontal gradients derived from ZWD differences. The two types of gradients are linked using water vapor scale heights derived from ERA5 atmospheric profiles, together with the assumption that the refractivity gradient scales with the amount of water vapor. Using one year of data, we assess the impact of different processing configurations and evaluate the temporal and spatial variability of GNSS tropospheric gradients. Our results show that, on a per-station basis, ZWD estimates are generally stable under commonly adopted processing options, whereas gradient estimates are, as expected, significantly more sensitive to processing settings, such as elevation cut-off angles and temporal constraints. Furthermore, a high degree of correlation between single-station gradients and inter-station horizontal gradients is found for station pairs with separations of less than about 25 km. We therefore propose that inter-station gradients can be used as a reference for tuning GNSS gradient estimation strategies, ensuring consistency in gradient magnitude. These findings highlight both the potential and the challenges of GNSS-estimated gradient products and provide guidance for their application in atmospheric monitoring.
How to cite: Feng, P., Haas, R., and Elgered, G.: On the consistency and variability of GNSS-estimated tropospheric gradients, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19400, https://doi.org/10.5194/egusphere-egu26-19400, 2026.
Wet Path Delay (WPD) to correct sea level measurements from satellite altimetry is estimated by on-board microwave radiometers (MWR) observations. However, in cases where on-board MWR retrievals are invalid or absent, WPD must be derived from external sources, such as scanning imaging MWR or atmospheric models. Instead of WPD, these alternative sources provide total column water vapor (TCWV) values, introducing the need for converting TCWV into WPD. In its state-of-the-art, this conversion can be performed solely from TCWV or from a combination of TCWV and near surface air temperature. The first approach, which is the focus of this study, is particularly relevant when the external products only provide TCWV. In this context, this paper presents, first, a comprehensive intercomparison of the methods available in the literature and, second, an improved TCWV-WPD conversion. Results show that one of the existing functions underestimates WPD by up to 1.2 cm in regions of high water vapor content, while the other provides accurate WPD values only under specific conditions. This study proposes an updated methodology that yields accurate WPD across the entire TCWV range, highlighting the importance of a reliable TCWV-WPD conversion for accurate sea level estimation when valid MWR observations are unavailable.
How to cite: Vieira, T., Aguiar, P., Lázaro, C., and Fernandes, M. J.: Wet Path Delay for Satellite Altimetry computed from External Water Vapor Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20327, https://doi.org/10.5194/egusphere-egu26-20327, 2026.
Hourly near-real-time (NRT) GNSS zenith total delay (ZTD) observations provide continuous information on tropospheric variability and are increasingly used for tropospheric monitoring. Within E‑GVAP, many analysis centres (ACs) deliver hourly NRT ZTD estimates over Europe. While this multi‑centre setup provides redundancy, analysis-to-analysis differences in processing strategies and varying data availability/latency introduce time and site-dependent inconsistencies that complicate downstream use.
We present a machine‑learning (ML) fusion framework that combines hourly NRT ZTD from E-GVAP AC streams into a single, quality-controlled consensus ZTD with an associated uncertainty estimate. The ML component is formulated as a lightweight supervised “ensemble/meta‑learner”, where each AC is treated as an expert and the model learns adaptive, station, and time-dependent weights from features derived only from the NRT streams and station metadata. Predictors include Inter AC consistency metrics (spread/robust dispersion), recent ZTD tendencies, station coordinates, and completeness (latency indicators). The ML fusion is benchmarked against robust non‑ML baselines (mean, median, and best single‑AC selection).
To avoid dependency on post‑processed tropospheric final products (e.g., IGS/CODE final ZTD), performance is assessed against ERA5 reanalysis by deriving station‑specific hourly tropospheric delays at each GNSS site, accounting for model and station height differences. Station surface pressure is used to compute the hydrostatic delay and isolate the wet delay component, enabling targeted evaluation of humidity‑driven variability. We quantify bias, dispersion, and temporal variability for individual AC solutions and for the fused product, and examine how learned weights and uncertainty respond to changing meteorological regimes and data availability. The resulting hourly and uncertainty/QC information support more reliable NRT tropospheric products for monitoring and assimilation‑oriented workflows.
How to cite: Hunegnaw, A., Teferle, R., and Jones, J.: Machine‑learning fusion of hourly E-GVAP near‑real‑time GNSS ZTD: ERA5-referenced evaluation and uncertainty estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20913, https://doi.org/10.5194/egusphere-egu26-20913, 2026.
It is well known that GPS signals are affected by the amount of water vapor contained in the troposphere. This phenomenon creates delays, which can be converted into a corresponding integrated water vapor content along the receiver–satellite path (Slant Integrated Water Vapor, SIWV). Moreover, when a dense network of GPS stations is available, we obtain an ensemble of such SIWV paths that crisscross over the network area. Hence, by defining a three-dimensional regular grid composed of different boxes, called voxels, over our area of interest, and using a tomographic inversion method, we can retrieve the water vapor density in each voxel of the grid. Thus, this allows us to obtain a 3-D field of water vapor density above our area of interest.
Here, we implement this approach on Reunion Island (a South West Indian Ocean Volcanic tropical island about 2500km²), which counts approximately 40 GPS stations. We had take into account for some local specificities: 1°/ the orography of this volcanic island is extremely sharp with high altitude gradients between neighboring stations, and 2°/ the spatial distribution of the GPS stations is very heterogeneous with a high density (about half of the stations) distributed around the active volcano of Piton de la Fournaise. Therefore, two developments were carried out. First, regarding the tomographic geometry, we use Voronoï diagram to implement a grid adapted to the spatial distribution of the GPS stations. Second, the tomographic inversion method itself was improved using the more robust truncated singular value decomposition (TSVD) approach using the L-curve technique to define the analysis threshold (Moeller, 2017).
To validate these developments, the results obtained from the tomographic inversion was compared to 30 water vapor profiles obtained during a radio sounding campaign conducted in Saint-Philippe (SE of the island, close to the Piton de la Fournaise) between May 2025 and July 2025.
How to cite: Gerville, H., Van Baelen, J., Durand, F., Morel, L., and Albino, F.: Development and Validation of an Enhanced GPS Tomography Algorithm for Reunion Island, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8966, https://doi.org/10.5194/egusphere-egu26-8966, 2026.
The neutral mass density of the upper thermosphere can be determined by orbit and accelerometer data from Low Earth Orbit (LEO) satellites. Especially the accelerometers of geodetic satellites, measuring the non-gravitational accelerations acting on these satellites, are a very useful observation for precise density estimation also on very short time scales.
In this contribution we present our density and wind estimation approach with focus on the wind estimation. In the accelerometer data differences to modelled non-gravitational accelerations persist, which are only attributable to aerodynamic accelerations due to an additional wind, especially for high solar activity. Utilizing a thermospheric wind model like HWM14 reduces the differences slightly, but by far not sufficiently. Hence, for a long time (e.g. by TU Delft) efforts have been made to estimate not only density but also winds. We show the potential and problems of the wind estimation with different approaches, and the influence on the alongside estimated neutral density. We use the GRACE mission, which, gives the opportunity to compare results from both GRACE satellites, being on the same orbit with a distance of only about 200 km, by time-shifting the data from the position of the one to the other satellite. Furthermore, we compare our results with data from TU Delft.
Our density datasets and lots of auxiliary data for GRACE/-FO are available on our data server: www.zarm.uni-bremen.de/zarm_daten
How to cite: Wöske, F., Rievers, B., and Huckfeldt, M.: On thermospheric neutral density and wind estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14603, https://doi.org/10.5194/egusphere-egu26-14603, 2026.
Global Navigation Satellite System (GNSS), based tropospheric sensing provides valuable, high-temporal-resolution observations for numerical weather modeling, but its application in dense urban environments remains challenging due to severe multipath interference and non-line-of-sight (NLOS) signal reception. These effects introduce geometry-dependent biases that destabilize Precise Point Positioning (PPP) and significantly degrade Zenith Tropospheric Delay (ZTD) estimation, limiting the usability of crowdsourced and low-cost GNSS data in cities. This study presents a ray-tracing-assisted method for urban GNSS multipath mitigation that combines ray-tracing with PPP processing. Using (Level-Of-Detail) LOD1 3D city models and raytracing, GNSS signal propagation is explicitly simulated to classify satellite observations into line-of-sight (LOS), Echo, reflected, diffracted, mixed multipath, and NLOS components.
First, a simulation is performed to develop a city-scale “healthy zone” identification strategy by mapping LOS satellite availability across dense urban areas. Locations exhibiting sufficient unobstructed LOS visibility are identified as favorable sites for crowdsourced data collection for ZTD estimation. This strategy enables systematic and reliable collection of GNSS observations while mitigating multipath effects, thereby improving the spatial coverage and quality of urban ZTD.
Second, a ray-tracing–assisted PPP framework is developed, in which multipath contaminated observations are adaptively excluded or down-weighted based on their physically modeled propagation characteristics derived using raytracing. This raytracing-assisted PPP approach is evaluated using real urban GNSS data collected at a stationary location in Hong Kong. The results demonstrate that conventional, unmitigated PPP suffers from large code residuals (50–100 m), meter-level positioning errors, and strongly biased ZTD estimates. In contrast, the proposed method reduces code and phase residuals to approximately 2 m and 0.02 m, respectively, achieving sub-meter positioning accuracy, and improves ZTD precision by more than two orders of magnitude.
The results indicate that geometry-aware, physics-based multipath modeling is a critical enabler for reliable urban ZTD estimation. By jointly leveraging ray tracing and adaptive filtering in PPP and extending the framework toward potential mobile GNSS deployment, this work lays the foundation for ZTD retrieval in dense urban environments. Such an approach facilitates the future assimilation of crowdsourced GNSS observations into next-generation numerical weather prediction systems, supporting enhanced atmospheric monitoring in cities.
How to cite: Mehdi, S., Rohm, W., Wareyka-Glaner, M. F., and Zhang, G.: Geometry-Aware PPP for Reliable GNSS Tropospheric Sensing in Dense Urban Environment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18365, https://doi.org/10.5194/egusphere-egu26-18365, 2026.
Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a new technique for ocean wind speed retrieval, offering unprecedented temporal resolution and all-weather capacity. However, the track-wise sampling of current GNSS-R missions leads to substantial spatial and temporal gaps in gridded wind fields. In this study, we apply a physics-informed 4DVarNet scheme to reconstruct gap-free ocean surface wind fields from Cyclone GNSS (CYGNSS) observations. This end-to-end scheme operates by following the four-dimensional variational (4DVar) data assimilation principle, where a dynamic prior model provides state forecasts, and a gradient solver minimizes the 4DVar loss function. Both parts are implemented through physics-informed neural networks, i.e., a bilinear autoencoder, and a convolutional Long-Short-Term Memory (LSTM) network, respectively, which are trained using European Center for Medium-Range Weather (ECMWF) ERA5 hourly 10-meter ocean wind products as reference. NOAA CYGNSS Version 1.2 level 2 (L2) wind speed retrieval products from 2018-2022 were gridded at 0.25° spatial resolution and 1-hour, 3-hour, and 6-hour temporal resolutions over the western North Pacific (0-37°N, 100°-160°E). Validation using independent 2021 data shows that the reconstructed wind fields achieve RMSEs of 1.13 m/s, 1.16 m/s, and 1.24 m/s relative to ERA5 winds, and 1.40 m/s, 1.41 m/s, and 1.48 m/s relative to Advanced Microwave Scanning Radiometer-2 all-weather winds, for the 1-hour, 3-hour, and 6-hour gridded products, respectively. Furthermore, 3-hour results show a better performance for wind speeds larger than 20 m/s, indicating a better tradeoff between the number of grids with available GNSS-R observables in each map (coverage rate) and a enough data frequency to capture the temporal variations. The interpolation error of the developed 4DVarNet model shows a strong dependence on coverage rate, with a correlation coefficient of -0.849 after applying a 7-day rolling average. Error discrepancies between GNSS-R and ERA5 reconstructed winds could contribute to recalibrating GNSS-R observables or improving the ECMWF forecasting model. Case studies demonstrate the capability of the reconstructed fields to capture tropical cyclone coverage and evolution. For Super Typhoon Surigae in 2021, the peak intensity derived from GNSS-R reconstructions is temporally consistent with International Best Track Archive for Climate Stewardship (IBTrACS) records, while ERA5 data exhibit a two-day delay. For Tropical Storm Kompasu in 2021, pronounced wind asymmetries and a well-defined eye structure were detected. In the storm-centric coordinate, the maximum wind occurs in the northeast quadrant with a radius of 587.5 km, approximately 38% larger than that in the northwest quadrant on 2021-10-09 06:00 UTC. Despite these encouraging results, the reconstructed products still exhibit track-wise artifacts, high-wind underestimation, and limited uncertainty characterization. However, these results demonstrate the great potential of 4DVarNet in gap filling and data assimilation. Future work will integrate additional GNSS-R missions, including Fengyun-3, Tianmu-1, and recently launched ESA HydroGNSS, and develop tropical cyclone specific models using complementary high-wind reference datasets to further improve coverage and accuracy.
How to cite: Du, H., Fablet, R., Nguyen, N., Li, W., Cardellach, E., and Chapron, B.: Gap-free GNSS-R Wind Field Reconstruction Using a Physics-Informed 4DVarNet Scheme, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5618, https://doi.org/10.5194/egusphere-egu26-5618, 2026.
Due to the climate change crisis and a growing global population, natural resources and ecosystem stability face significant stress. To assess and manage these challenges, continuous monitoring of vegetation conditions at fine spatial resolution is essential. Leaf Area Index (LAI) is a key biophysical parameter for determining vegetation status. The Sentinel-2 (S2) optical satellites offer a great source for LAI retrieval, with five-day revisit time and fine spatial resolution of 10 meters. However, optical observations are frequently hindered by clouds which limit continuous global coverage. To overcome this limitation, spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) technology offers an all-weather complementary source as an alternative. GNSS-R is an emerging remote sensing technique involving a bistatic radar configuration that continuously collects surface-reflected signals regardless of weather conditions. The objective of this study is to explore the synergy between Cyclone Global Navigation Satellite System (CYGNSS) science data and S2 to retrieve a continuous LAI product within a machine learning framework. We utilized CYGNSS L1 v3.2 science data from low-Earth orbits, covering a latitudinal range of ±38° over the two-year period of 2022–2023, with 18 months allocated for model training and 6 months for independent testing. After masking the impact of open water, a machine learning model was developed to integrate CYGNSS-derived observables with auxiliary data to retrieve LAI. This approach leverages the high temporal density and all-weather capabilities of CYGNSS to fill gaps in S2-derived LAI, leading to improved spatiotemporal continuity in vegetation monitoring.
Keywords: GNSS-R, Sentinel-2, LAI, Vegetation, Monitoring, Machine Learning
How to cite: Adavi, Z., Ghassemi, B., Moeller, G., and Vuolo, F.: Preliminary Result of Synergy between Optical Satellite and GNSS-R Technique to Retrieve Vegetation Parameters, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8338, https://doi.org/10.5194/egusphere-egu26-8338, 2026.
Sub-daily soil moisture dynamics are critical for understanding land-atmosphere coupling, however GNSS Interferometric Reflectometry (GNSS-IR) for soil moisture estimation has traditionally been limited to daily temporal resolution. We improve the resolution of GNSS-IR soil moisture estimates using a rolling average (boxcar filter) aggregated at hourly time steps with window lengths of up to 12 hours. This approach produces an apparent diurnal soil moisture signal, however further investigation reveals the dominating presence of a systematic error we term the "sidereal drift artifact."
This artifact arises from the mismatch between the solar day (24 h) and the GPS orbital repeat period, the sidereal day (~23 h 56 m). Each satellite track drifts approximately 4 minutes earlier in local solar time per day, completing a full cycle through all 24 hours in just under a year. Each track samples a distinct spatial footprint characterised by different vegetation density, soil properties, and topography, resulting in systematic inter-track measurement biases. As the subset of tracks contributing to any given time window rotates throughout the year, these spatial biases become aliased into the temporal domain. This behaviour can be observed when processing existing stations worldwide and is additionally shown through the simulation of a synthetic GPS measurement constellation with track specific biases.
We evaluate the performance of our initial methods for mitigating inter-track biases, including pairwise track comparisons and an existing vegetation correction. These approaches show partial success in removing or attenuating the artifact, particularly at Plate Boundary Observatory (PBO) site Marshall (MFLE) in the western United States, where the corrected signal has peak timing estimates consistent with in-situ sensors. We conclude with a discussion of the requirements of sub-daily GNSS-IR soil moisture retrievals and site characteristics that determine vulnerability to sidereal aliasing.
How to cite: Townsend, G., Han, S.-C., Larson, K., and Yeo, I.-Y.: Towards Sub-daily GNSS-IR Soil Moisture Estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20491, https://doi.org/10.5194/egusphere-egu26-20491, 2026.
FORMOSAT-7/COSMIC-2 radio occultation (RO) measurements have great potential in monitoring the deep troposphere and offering crucial insights into the Earth’s planetary boundary layer. However, the RO data retrieved from the deep troposphere can have severe bias under specific thermodynamic conditions. This bias originates from the limitations of the retrieval technique, the assumptions used in the algorithm and atmospheric influences. This study examines the characteristics of the RO bending angle bias (BAB). Based on those characteristics, this study proposes a machine learning algorithm based on a multi-layer perceptron neural network, which is trained with different input proxies to assess region-dependent BAB. The results show that the BAB model is adequate to accurately predict the BAB in the deep troposphere in different regions. This research highlights the promise of advanced methodologies in improving RO retrieval and promotes data applications in the lower atmosphere.
How to cite: Pham, G.-H. and Yang, S.-C.: Bias characteristics and estimation of the FORMOSAT-7/COSMIC-2 radio occultation bending angle in the deep troposphere with a machine learning algorithm, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21633, https://doi.org/10.5194/egusphere-egu26-21633, 2026.
GNSS radio occultation (RO) provides high-quality atmospheric profiles of variables such as temperature and pressure. Recent efforts have succeeded in propagating the related systematic and random error effects from the raw measurements to the resulting profiles, attaching a measure of observational uncertainty to each one. In this work we build upon these profile-level uncertainty estimates and propagate them to aggregated mean fields for climate applications. In this context, sampling uncertainties also need to be considered. This approach is applied to the GNSS RO time series of refractivity, dry temperature, and physical temperature. The results show that random and residual sampling uncertainties decrease with increasing aggregation size and are comparable in magnitude. They dominate refractivity uncertainty at small aggregation scales and contribute substantially to temperature uncertainty. Systematic uncertainty is the main source of uncertainty for refractivity at larger aggregation scales, as well as for pressure and dry temperature at commonly used aggregation sizes. Uncertainties exhibit strong spatial variability, with the largest values occurring in polar regions. There are also substantial, mission-dependent variations within the time series.
How to cite: Ladstädter, F., Scher, S., Schwärz, M., Innerkofler, J., and Kirchengast, G.: Propagated random, systematic, and sampling uncertainties in GNSS radio occultation climate time series, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21731, https://doi.org/10.5194/egusphere-egu26-21731, 2026.
Ground-based GNSS (Global Navigation Satellite System) receivers have been used to estimate precipitable water vapor (PWV) with high temporal resolution. The quality in terms of precision and confidence has given the opportunity to explore this feature to predict the occurrence of thunderstorms. A sharp increase in the GNSS-PWV time series before the intense precipitation events has been found, which indicates the occurrence of this phenomenon and consequently demonstrates a good potential for application in nowcasting activities. This increasing pattern in the PWV-GNSS time series before strong precipitation has been termed GPS-PWV-jumps and occurs because of the water vapor convergence and the continued formation of cloud condensate and precipitation particles. This study presents an overview of the development of this technique in Brazil, presenting a summary of the latest results using the data collected in different campaigns in the last years over different regions of Brazilian territory. GNSS receivers and several instruments to observe the precipitation, such as disdrometers and X-band radar, were used. The long database has been explored, and extensive analyses of results were carried out using wavelet cross-correlation analysis, lag correlation method, and contingency table after defining a method to predict the precipitation using GNSS-PWV jump information. This approach is innovative because it uses only GNSS data and, consequently, the infrastructure used by geodesic applications, such as GNSS receiver networks present in big cities, can be explored for this purpose without additional investments. However, there are some challenges that need to be addressed yet, such as the PWV-GNSS-jump production in near real time, which involves the data reception and data processing in a suitable time to be evaluated and applied to the issuance of disaster warnings. Another challenge, just as important as the first, is ensuring that the performance of the GNNS-PWV jump is maintained when using near-real-time estimates. These challenges are treated in this work as an opportunity for researchers exploring artificial intelligence methods, which are discussed, and some possible strategies are presented. The future perspective of the GNSS receiver application as a humidity information data source used in the evaluation and data assimilation process in the community development of the MONAN (Model for Ocean-laNd-Atmosphere predictioN) model is also discussed.
How to cite: Sapucci, L., Almeida, S., Machado, W., Anochi, J., and Lemos, G.: PWV-GNSS JUMP as a tool for nowcasting in Brazil: an overview, the challenges, and opportunities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1762, https://doi.org/10.5194/egusphere-egu26-1762, 2026.
The assimilation of GNSS tropospheric gradients into Numerical Weather Prediction models requires the development of observation operators, a process constrained by a trade-off between accuracy and computational cost. As an initial step, a computationally efficient operator, which we refer to as the fast tropospheric gradient operator, was implemented and tested within the WRF data assimilation system (Thundathil et al., 2024). This presentation details the implementation and testing of a rigorous tropospheric gradient operator. Based on a linear combination of ray-traced tropospheric delays, this operator demands greater computational resources but minimizes errors by replicating the method used in the GNSS data analysis. With both operators now implemented and freely available to WRF users, a significant obstacle has been removed for research studies and operational applications. The other major challenge, namely the provision of high-quality tropospheric gradients in (near) real-time, remains a task for GNSS data analysis.
Reference:
Thundathil, R., Zus, F., Dick, G., and Wickert, J.: Assimilation of GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model version 4.4.1, Geosci. Model Dev., 17, 3599–3616, https://doi.org/10.5194/gmd-17-3599-2024, 2024.
How to cite: Zus, F., Thundathil, R., Dick, G., and Wickert, J.: Implementing and testing the rigorous GNSS tropospheric gradient operator in the WRF data assimilation system, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1945, https://doi.org/10.5194/egusphere-egu26-1945, 2026.
Water vapor (WV) plays a fundamental role in tropospheric processes, as most atmospheric moisture is confined to this layer. However, homogeneous and globally distributed observations of the lower troposphere—up to about 5–6 km altitude—remain limited. Filling this observational gap would significantly improve short-term climate analyses and the performance of numerical weather prediction (NWP) models.
Within theoretical activities supported by ESA, a novel retrieval concept called Normalized Differential Spectral Attenuation (NDSA) was developed to estimate integrated water vapor (IWV) from microwave attenuation measurements in the 17–21 GHz frequency range along tropospheric propagation paths. The method is based on the estimation of a spectral sensitivity coefficient (S), defined as the differential attenuation between two closely spaced carrier frequencies with a relative separation smaller than 2%. We demonstrated a linear relationship between S and IWV, enabling a simple and robust retrieval scheme. These investigations also highlighted the suitability of NDSA for spaceborne applications, including co- and counter-rotating Low Earth Orbit (LEO) satellite geometries. The Italian Space Agency funded the SATCROSS project to assess the technological feasibility of a dedicated satellite mission and to develop a ground-based prototype capable of performing NDSA measurements on terrestrial microwave links at 19 GHz.
A critical step toward an operational space-based system is the quantitative assessment of the accuracy and reliability of IWV estimates derived from the prototype through validation against independent observing techniques. A first validation campaign was in 2024, comparing IWV retrieved by the NDSA prototype with measurements from a MAX-DOAS instrument observing the same atmospheric volume along a 91 km link between the meteorological station “Giorgio Fea” (San Pietro Capofiume, 10 m a.s.l.) and the climate observatory at Mount Cimone (2165 m a.s.l.). Additional reference data were provided by radiosondes, hygrometers, and GNSS. While the results were encouraging, significant signal scintillation affected the NDSA measurements due to a large fraction of the link remaining within the terrain boundary layer.
The present work focuses on a second campaign carried out in 2025 along a 160 km high-altitude microwave link connecting Mount Cimone to Mount Amiata (1738 m a.s.l.). For the first time, the NDSA prototype was tested on a link with nearly constant height and limited ground influence, closely approximating the geometry of a LEO-to-LEO satellite link with a tangent height of about 2000 m. This setup enabled verification of the theoretical relationship between the spectral sensitivity parameter S and IWV, with particular attention to the linear model coefficients reported by the authors in previous papers. ERA5 reanalysis data (25-km linear res.), integrated along the full link, were also compared with in situ hygrometer measurements and GNSS-derived IWV. Overall, IWV estimates from the different techniques show good agreement in capturing daily and seasonal variability, while ERA5 systematically underestimates IWV due to its coarser resolution. At shorter timescales, discrepancies increase during periods of enhanced tropospheric turbulence, induced by air mass movements. Criteria for real-time identification of high-scintillation conditions were defined, demonstrating the capability of NDSA to detect precipitation while preserving WV information.
How to cite: Facheris, L., Argenti, F., Cuccoli, F., Cortesi, U., del Bianco, S., Montomoli, F., Gai, M., Baldi, M., Barbara, F., Donati, A., Melani, S., Ortolani, A., Viti, M., Antonini, A., Rovai, L., Castelli, E., Papandrea, E., Achilli, A., Busetto, M., and Calzolari, F.: Path-Integrated tropospheric water vapor from a mountain-to-mountain microwave link: a summer/autumn NDSA campaign compared with ERA5 and instrumental data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5444, https://doi.org/10.5194/egusphere-egu26-5444, 2026.
Precise Point Positioning (PPP) provides zenith wet delay (ZWD) and horizontal tropospheric gradients as key tropospheric parameters. The availability of real-time satellite orbit and clock products enables real-time tropospheric monitoring, which is currently mainly based on IGS Real-Time Service (IGS-RTS) products. In this study, we evaluate real-time tropospheric parameters derived from the newly released GFZ real-time orbit and clock streams. The assessment is performed using both the GFZ global station network and the regional GEONET network operated by the Geospatial Information Authority of Japan (GSI), focusing on ZWD and horizontal gradients. An analysis of one week of data in June 2025 shows that under calm meteorological conditions, real-time ZWD and gradients achieve an accuracy better than 3 mm with respect to the solution derived from GFZ final products, with a data completeness of 99.8%. A case study focusing on strong convective conditions, exemplified by typhoon events over the Pacific Ocean east of Japan in August 2025, indicates no noticeable degradation in the precision and latency of real-time ZWD and tropospheric gradients. The comparison with ultra-rapid products, which include predicted orbit and clock components, shows that real-time ZWD and gradients consistently outperform ultra-rapid solutions. Furthermore, comparisons among multiple analysis centers (ACs) show that tropospheric solutions generated using GFZ real-time streams exhibit competitive accuracy, stability, and completeness.
How to cite: He, S., Brack, A., and Wickert, J.: Evaluation of Real-Time ZWD and Tropospheric Gradients Derived from GFZ Real-Time Orbit and Clock Products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7012, https://doi.org/10.5194/egusphere-egu26-7012, 2026.
SPOTGINS provides global GNSS station position and zenith tropospheric delay (ZTD) time series for nearly 5,000 stations, covering the period from May 2000 to the present. SPOTGINS is a consortium of research institutions — initially French, now expanding internationally — that processes a global station network using CNES’s GINS software in precise point positioning (PPP) mode with integer ambiguity resolution. The initiative leverages the expertise and advanced satellite products of GRG, the French IGS Analysis Center operated by CNES and CLS. By adopting a standardized processing strategy, auxiliary products, and consistent metadata, the consortium distributes computational workload among partners while maintaining sub-millimeter-level consistency in positions and ZTDs.
This paper presents results from the first large-scale quality assessment of SPOTGINS ZTD time series. Evaluation metrics include outlier detection statistics, bias and random noise estimation against independent references, and tests of temporal homogeneity.
How to cite: Bock, O., Boy, J.-P., Gravelle, M., Loyer, S., Nahmani, S., Nicolas Duroy, J., Pollet, A., Sakic, P., Santamaria, A., and Wautelet, G.: SPOTGINS: a new global GNSS tropospheric delay data set derived using GINS software, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7942, https://doi.org/10.5194/egusphere-egu26-7942, 2026.
Atmospheric water vapour drives weather processes and climate variability, yet its strong spatiotemporal heterogeneity makes accurate three-dimensional (3D) monitoring challenging. GNSS atmospheric tomography enables reconstruction of 3D wet refractivity fields from slant tropospheric delays, however reconstruction accuracy is highly sensitive to the design of the tomographic voxel grid, particularly in the vertical dimension, which has received comparatively limited attention.
We develop a GNSS tomography framework to investigate the impact of vertical grid design on wet refractivity reconstruction accuracy. Horizontal discretization (latitude–longitude) is kept fixed, while multiple vertical grid configurations are tested, including a reference vertical grid adopted from previous work [1], homogeneous layer thicknesses (100, 500, and 1000 m). Furthermore, two adaptive, station-specific vertical grid layouts are derived from radiosonde profiles. The adaptive approach tailors the vertical resolution of the voxel grid to the local moisture gradients obtained from the latest radiosonde observations. This model adapts the vertical resolution of the grids to the closest radiosonde observation both spatially as well as temporarily.
The methodology is applied over the Carpathian Basin using dense GNSS observations, precise satellite orbits (SP3), VMF1 tropospheric mapping functions, and radiosonde soundings over a period of 10 days with twice-daily epochs. Three-dimensional wet refractivity fields are reconstructed using the Multiplicative Algebraic Reconstruction Technique (MART), with radiosonde profiles used as a priori information and independent profiles for validation.
The results demonstrate a clear dependence of performance on altitude based on RMS zenith wet delay (ZWD) errors. In the lower troposphere (0–4 km), adaptive vertical grids yield markedly improved reconstruction accuracy, with RMS values of 0.009–0.034 m, whereas the reference and coarse homogeneous grids exhibit substantially larger RMS errors. In the mid-troposphere (4–8 km), errors decrease to the order of 10⁻³ m, with comparable performance between adaptive grids and fine homogeneous discretizations. In the upper troposphere (>8 km), all grid configurations perform similarly, with RMS values generally below 2×10⁻³ m, indicating that adaptive discretization is not necessary in moisture-poor layers. These findings highlight the critical role of adaptive vertical grid design for accurate GNSS wet refractivity tomography in the lower troposphere.
[1] Rózsa, S., Turák, B., and Khaldi, A.: Near Realtime tomographic reconstruction of atmospheric water vapour using multi-GNSS observations in Central Europe, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4465, https://doi.org/10.5194/egusphere-egu23-4465, 2023.
How to cite: Khaldi, A. and Rózsa, S.: Impact of Vertical Grid Design on GNSS Tomographic Reconstruction of Tropospheric Wet Refractivity , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10828, https://doi.org/10.5194/egusphere-egu26-10828, 2026.
Global Navigation Satellite System (GNSS) tomography is a robust technique used to estimate the amount and three-dimensional distribution of water vapour in the troposphere. This information is critical for numerical weather prediction (NWP), as water vapour is a highly variable atmospheric constituent that strongly influences weather processes. The technique relies on observations of GNSS signal delays, which are attenuated and slowed by atmospheric moisture as signals travel from satellites to ground-based receivers. However, the effectiveness of ground-based GNSS tomography is frequently hindered by ill-conditioned or mixed-determined systems, in which model elements become over- or under-determined due to continuously changing satellite geometry. As a result, significant data gaps arise, particularly in regions with sparse ground receiver coverage, such as oceans, deserts, or mountainous areas.
To address these limitations, recent research has focused on integrating space-based GNSS Radio Occultation (RO) observations into tomographic models. The RO technique involves Low Earth Orbit (LEO) satellites receiving GNSS signals that propagate nearly horizontally through the atmosphere, providing high-vertical-resolution profiles of refractivity, temperature, and water vapour. The growing importance of RO data is reflected in international efforts to increase occultation density, with recommendations calling for tens of thousands of daily observations to support NWP applications. In contrast to ground-based observations, which predominantly sample the atmosphere along near-vertical paths, RO measurements supply complementary horizontal information. This complementary geometry improves voxel filling within the tomographic grid and helps resolve the ill-posedness of the inversion problem.
Various tomographic grid parametrisation strategies have been developed to integrate ground- and space-based GNSS observations into a unified tomographic framework. In ground-based GNSS tomography, wet refractivity is estimated by relating it to the lengths of slant wet delay (SWD) ray-path segments within individual voxels. Ray-point coordinates and segment lengths are obtained by reconstructing signal paths using known transmitter and receiver positions through three-dimensional ray-tracing techniques. When combining different types of GNSS observations, the signal reconstruction strategy is observation-type dependent: three-dimensional ray tracing is applied to RO excess phase observations (Level 1a), whereas occultation point coordinates are directly provided for RO wet refractivity profiles (Level 2). Observation-specific uncertainty schemes can further be applied to improve solution robustness.
This study provides a generic assessment of key factors governing tomographic wet refractivity estimation, including ground network density, voxel filling rate, RO event availability, and uncertainty treatment. Results from integrated tomography approaches demonstrate that even a limited number of RO observations can substantially improve wet refractivity estimates, reduce reconstruction errors, and increase the number of filled voxels, particularly for sparse ground networks. Ultimately, the combined ground- and space-based GNSS products are well suited for assimilation into NWP models, enabling a more complete and reliable three-dimensional representation of atmospheric humidity.
How to cite: Hanna, N., Moeller, G., and Weber, R.: Combining ground- and space-based GNSS observations to mitigate data gaps in numerical weather prediction , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11819, https://doi.org/10.5194/egusphere-egu26-11819, 2026.
Zenith Total Delay (ZTD) estimates derived from GNSS observations are essential for atmospheric and geodetic applications. When processed using Precise Point Positioning (PPP), ZTD time series exhibit enhanced stability compared to network-based approaches. However, occasional outliers - ranging from a few centimetres to several meters - still occur, potentially degrading product quality and impacting downstream applications. The mechanisms driving these anomalies remain poorly understood, and their characterisation is critical for improving PPP-based ZTD products. This study examines the nature, origins, and possible mitigation strategies for such outliers in order to enhance the reliability of GNSS-derived tropospheric parameters.
We perform sensitivity tests using the CNES’s GINS software in PPP mode with integer ambiguity resolution, complemented by simplified PPP-like simulations, to identify the mechanisms underlying ZTD outliers. Particular attention is given to pre-processing procedures, which are critical for detecting and handling problematic observations and significantly impact ZTD accuracy. Building on this diagnostic phase, we explore parameter regularisation strategies aimed at mitigating the occurrence of ZTD outliers while preserving high processing quality. These analyses provide insights into both the origin of anomalies and practical approaches for improving the robustness of PPP-based tropospheric products.
In addition, we investigate complementary post-processing screening methods based either on purely statistical approaches or on the comparison with independent atmospheric reanalysis ZTD data. Combined with the strategies described above, these methods aim to reduce ZTD outliers while preserving geophysical variability. This integrated approach enhances GNSS positioning performance and improves the reliability of long-term GNSS-derived tropospheric time series, supporting climate monitoring and other atmospheric applications.
How to cite: Breton, H., Bock, O., Nahmani, S., Bosser, P., Santamaría-Gómez, A., Pollet, A., and Loyer, S.: Understanding and reducing ZTD outliers in GNSS PPP-derived products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11916, https://doi.org/10.5194/egusphere-egu26-11916, 2026.
Atmospheric water vapour plays a critical role in climate change and in the occurrence of hydro-climatic extreme weather events; however, its long-term monitoring is subject to considerable uncertainties. GNSS-derived tropospheric products represent an independent, high-temporal-resolution observational data source capable of addressing this gap. Nevertheless, the reliable use of these data in climate analyses requires the identification and removal of potential inhomogeneities related to instrumentation and processing changes.
In this study, atmospheric water vapour variability, long-term trends, and extreme moisture conditions over Türkiye are investigated using GNSS Zenith Total Delay (ZTD) time series. The analyses primarily employ GNSS tropospheric products reprocessed by the University of Nevada, Reno (UNR). Station-based homogenization is applied to all time series to eliminate artificial discontinuities and to ensure their suitability for climate analysis. Integrated Water Vapour (IWV) is derived using consistent meteorological inputs, and trend behaviour is assessed using robust non-parametric methods. Hydro-climatic extremes are defined based on percentile-based thresholds (P10 and P90).
Selected long-term GNSS stations are further examined to assess the sensitivity of the results to different processing strategies using IGS Repro3 solutions. Radiosonde observations are used to evaluate the physical consistency of GNSS-derived IWV, while ERA5 reanalysis data provide a reference for comparison and contextual interpretation. The results indicate that consistent long-term trends and changes in extreme moisture conditions can be robustly identified in homogenized GNSS IWV series, including shifts in the frequency of extreme weather conditions. Furthermore, GNSS observations are shown to capture rapid moisture variations more clearly than reanalysis products, in which such signals are often smoothed.
This study highlights the contribution of homogenized GNSS tropospheric observations to monitoring atmospheric water vapour variability and hydro-climatic extremes over Türkiye.
How to cite: Zengin Kazancı, S. and Çelik, B.: Extreme Weather Events and Atmospheric Water Vapor Trends from Homogenized GNSS Tropospheric Observations over Türkiye, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17177, https://doi.org/10.5194/egusphere-egu26-17177, 2026.
Systematic Dry Bias and Geographic Dependencies in a High-Resolution NWM's Zenith Total Delay Revealed by GNSS and Radiosonde Validation
1Tsebeje, S. Y., 1,2Wang, J., 3Dodo, J. D. and 1,2Schuh, H.
1) Technische Universität Berlin, Berlin, Germany
2) GFZ, Helmholtz Centre for Geosciences, Potsdam, Germany.
3) Centre for Geodesy and Geodynamics (CGG) National Space Research and Development
Agency (NASRDA), Toro. Nigeria.
Abstract
This study reveals a systematic dry bias and distinct geographic dependencies in high-resolution Numerical Weather Model ERA5 (NWM) Zenith Total Delay (ZTD) estimates, as comprehensively validated against GNSS and Radiosonde (RS) observations for 2022. We analyzed data from 13 African stations, including four collocated sites with RS and GNSS reference points. While the NWM shows excellent agreement with RS data (mean RMSE: 0.0009 m, R > 0.996), a consistent dry bias is evident when compared with the GNSS-derived ZTD, averaging –0.0042 m at the collocated sites. The bias is moderately correlated with station elevation (R = –0.731), indicating a poorer model performance at higher altitudes. Importantly, spatial interpolation from the NWM grid to non-collocated GNSS sites did not introduce a statistically significant additional bias (p-value: 0.7719), indicating that the error was intrinsic to the model rather than its post-processing. Furthermore, a significant temporal error autocorrelation and large dry bias in the Integrated Water Vapour were identified. The findings highlight the model's water vapour parameterization, especially over complex terrain, as the primary source of error rather than spatial representativeness, with clear evidence for prioritizing improvements in the physical formulation of the model over adjustments to interpolation strategies.
How to cite: Tsebeje, S. Y.: Systematic Dry Bias and Geographic Dependencies in a High-Resolution NWM's Zenith Total Delay Revealed by GNSS and Radiosonde Validation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17674, https://doi.org/10.5194/egusphere-egu26-17674, 2026.
The present study is framed within the research cooperation between University of Genoa (UniGe) and Italian Space Agency (ASI) for the exploitation of the Global Navigation Satellite System (GNSS) data acquired through the “New National GNSS Fiducial Network”, implemented by ASI. The established collaborative research aims to operationally deploy the GNSS for Meteorology (G4M) procedure, developed by UniGe’s Geomatics Laboratory, to generate Precipitable Water Vapor (PWV) maps at Italian territorial extent. In this context, the focus of the contribution is on assessing the correlation length of Zenith Total Delay (ZTD), the key parameter to evaluate PWV, as a function of the distribution of GNSS stations belonging to the ASI’s National GNSS Fiducial Network. The evaluation of correlation length serves as a preliminary step toward the assessment of the geographical extent and achievable spatial resolution of the PWV maps derived from G4M procedure. Suitable areas for experimentation are subsequently identified, accounting for different weather conditions at national level. Therefore, the PWV maps derived in this study can serve as a preliminary assessment of nationwide meteorological conditions, highlighting potentially critical areas that warrant further investigation at a higher detail.
How to cite: Ferrando, I., Bertazzini, E., Federici, B., Gachpaz, S., Khalid Ahmed Albashir, A., Pinnizzotto, G., Benedetto, C., Vespe, F., and Sguerso, D.: Assessment of correlation length and spatial resolution for GNSS-based Precipitable Water Vapor maps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21020, https://doi.org/10.5194/egusphere-egu26-21020, 2026.
Posters virtual: Thu, 7 May, 14:00–18:00 | vPoster spot 3
EGU26-4327 | ECS | Posters virtual | VPS25
The initial results about optimum the random walk process noise rate for GNSS tropospheric delay estimationThu, 07 May, 14:06–14:09 (CEST) vPoster spot 3
Abstract: Unlike ionosphere, troposphere is nondispersive and delays cannot be determined from observations of signals at different radio frequencies. In GNSS data processing, station height, receiver clock error and tropospheric delay are highly correlated to each other, especially in kinematic situations. Although zenith hydrostatic delay can be provided with sufficient accuracy, zenith wet delay, which is more spatially and temporally varying than hydrostatic component, has to be carefully processed. Usually, temporal dependence of tropospheric delays at zenith is modeled as a random walk process with a solely given process noise rate σrw in GNSS processing. The usually used σrw is a constant throughout whole process session and is in range of 3~10 mm per sqrt hour. This setting is generally appropriate for desirable GNSS positioning estimation in normal conditions. However, modeling zenith tropospheric delay by using a constant σrw in whole session will be unsatisfactory in cases of special weather conditions, e.g., the shower case. The σrw is a measure of magnitude of typical variation of zenith path delay or its residual after calibration in a given time. Values of σrw that are too large could weaken strength for geodetic estimation, while values that are too small may introduce systematic errors, since a strong constraint for tropospheric unknowns is imposed to stabilize the system. The random walk model for wet delay must be constrained approximately to "correct" value to obtain optimum parameters estimates. Assuming temporal change of tropospheric delay at an arbitrary station can be described by random walk model, the process noise levels were calculated by some scholars. They employed water vapor radiometric, surface meteorological measurements and numerical weather model data set for optimum selection of σrw. In general, although a lot of efforts have made to optimize post-processing and/or real-time GNSS tropospheric delay estimation, stochastic modeling of zenith wet delay remains insufficiently investigated, especially for kinematic applications. Since temporal variation of zenith wet delay depends on water vapor content in atmosphere, it seems to be reasonable that constraints should be geographically and/or time dependent. In this work, we first investigate sensitivity of both station coordinates and zenith wet delay estimators on different σrw values, and then try to propose to take benefit from post-processed static or kinematic estimated tropospheric delay to obtain the optimum σrw. The general objective is that if zenith tropospheric delays are of different variation characteristic, e.g., relatively stable or rapid changing, then a varying σrw, e.g., small or large value, could be employed, which should be more theoretically feasible compared with a invariant σrw. The initial results show that the new method can efficiently obtain epoch-wise σrw values at different stations. Compared to results from conventional constant σrw value, time-varying noise rate can improve precision of PPP solutions. We note that this first results represent performance view at several selected stations, more works should be done to draw global or even long-term conclusions.
This work is supported by National Natural Science Foundation of China (42304010), Youth Foundation of Changzhou Institute of Technology (YN21046).
How to cite: Wang, M., Lu, B., and Zhong, Q.: The initial results about optimum the random walk process noise rate for GNSS tropospheric delay estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4327, https://doi.org/10.5194/egusphere-egu26-4327, 2026.
EGU26-14641 | ECS | Posters virtual | VPS25
Improving GNSS Water Vapor Monitoring in Cyprus climate change hotspot Using MWR-Derived TmThu, 07 May, 15:12–15:15 (CEST) vPoster spot 3
The Eastern Mediterranean is a recognized climate-change hotspot, characterized by strong summertime subsidence, sharp land–sea moisture gradients, and frequent thermodynamic extremes. Although Global Navigation Satellite System (GNSS) observations provide continuous and all-weather monitoring of precipitable water vapor (PWV), their accuracy critically depend on the weighted mean atmospheric temperature (Tm) used to convert zenith total delay (ZTD) into water vapor content. This study presents the first comprehensive analysis of radiometric data acquired under the Cyprus GNSS Meteorology (CYGMEN) strategic infrastructure project, established to monitor the thermodynamic state of the Eastern Mediterranean atmosphere. This study quantifies the impact of Tm uncertainty on GNSS-PWV retrievals and assesses the benefit of ground-based microwave radiometer (MWR) observations under extreme thermodynamic conditions.
MWR- and GNSS-derived products are evaluated against Vaisala RS41 radiosonde observations at Nicosia, Cyprus, for the period March–October 2025. Baseline validation demonstrates that the MWR provides highly accurate temperature profiling in the boundary layer (correlation coefficient r > 0.98) and reliable integrated water vapor estimates, with an RMSE of 1.72 kg m⁻² relative to radiosondes. However, the MWR exhibits limited skill in resolving vertical humidity structure, as indicated by a negative coefficient of determination (R² = −2.87) for moisture scale-height comparisons. This highlights that the primary strength of the MWR lies in constraining the column-integrated thermodynamic state rather than detailed vertical moisture profiling.
Incorporation of MWR-derived Tm into the GNSS processing chain substantially improves PWV retrievals during periods of strong thermodynamic variability, particularly under high-PWV and subsidence-dominated conditions typical of the Eastern Mediterranean summer. The proposed GNSS–MWR synergistic framework provides a physically consistent pathway to reduce Tm-related uncertainties and enhance GNSS-PWV reliability in climate-sensitive regions.
How to cite: Oikonomou, C., Parde, A. N., and Haralambous, H.: Improving GNSS Water Vapor Monitoring in Cyprus climate change hotspot Using MWR-Derived Tm, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14641, https://doi.org/10.5194/egusphere-egu26-14641, 2026.
