G5.1 | Atmospheric and Environmental Monitoring with Space-Geodetic Techniques and Contributions to Extreme Weather Studies
EDI
Atmospheric and Environmental Monitoring with Space-Geodetic Techniques and Contributions to Extreme Weather Studies
Co-organized by AS5
Convener: Rosa Pacione | Co-conveners: Maximilian Semmling, Kyriakos Balidakis, Henrik Vedel
Orals
| Wed, 17 Apr, 08:30–10:15 (CEST)
 
Room -2.91
Posters on site
| Attendance Wed, 17 Apr, 16:15–18:00 (CEST) | Display Wed, 17 Apr, 14:00–18:00
 
Hall X2
Posters virtual
| Attendance Wed, 17 Apr, 14:00–15:45 (CEST) | Display Wed, 17 Apr, 08:30–18:00
 
vHall X2
Orals |
Wed, 08:30
Wed, 16:15
Wed, 14:00
Geodesy contributes to atmospheric science by providing some of the essential climate variables of the Global Climate Observing System. Water Vapor (WV) is currently under-sampled in meteorological and climate observing systems. Obtaining more high-quality humidity observations is essential to weather forecasting and climate monitoring. The production, exploitation and evaluation of operational GNSS-Met for weather forecasting is well established in Europe due to 20+ years of cooperation between the geodetic community and the national meteorological services. Improving the skill of numerical weather prediction (NWP) models to forecast extreme precipitation requires GNSS products with a higher spatio-temporal resolution and shorter turnaround. Homogeneously reprocessed GNSS data (e.g., IGS repro3) have high potential for monitoring water vapor climatic trends and variability. With shortening orbit repeat periods, SAR measurements are a new source of information to improve NWP models. Using NWP data within real-time (RT) GNSS data analysis can initialize PPP algorithms, thus shortening convergence times and improving positioning. GNSS signals can be used for L-band remote sensing when Earth-surface reflected signals are considered. GNSS-R contributes to environmental monitoring with estimates of soil moisture, snow depth, ocean wind speed, sea ice concentration and has the potential to be used to retrieve near-surface WV.
We welcome, but not limit, contributions on:
•Estimates of the neutral atmosphere using ground- and space-based geodetic data and the use thereof 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 numerical weather prediction 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
•Studying the atmospheric water cycle employing satellite gravimetry.

Session assets

Orals: Wed, 17 Apr | Room -2.91

Chairpersons: Rosa Pacione, Maximilian Semmling, Kyriakos Balidakis
08:30–08:35
08:35–08:45
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EGU24-20132
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On-site presentation
Giovanni Nico, Pedro Mateus, and Joao Catalao

In this work, we discuss the potential and perspective use of InSAR meteorology within the Destination Earth (DestineE) initiative. The joined use of high-resolution Numerical Weather Models (NWM),such as the Weather Research and Forecasting (WRF) model, and the next large availability and redundancy of C- and L-band interferometric SAR missions (besides the current Sentinel-1 A&B and SAOCOM missions and the next Sentinel-1 C&D, N.G., ROSE-L, ALOS-4, NISAR), provides an example of the digital model of Earth that could support the complex task of anticipating extreme weather events.

There are two main approaches of applied mathematics to digitalization: Physics-Based and Data Driven. Physics-based models (PBMs) can give useful information on the processes to be described without the need for huge datasets, a first idea of what variables shall be monitored and provide a means for generalization.

Data-driven approaches imply the use of methods from Machine Learning or even Deep Learning to "learn from data collected by sensors". Artificial Intelligence (AI) tools need very high amounts and can be used to find hidden patterns in the data. Such a pattern can be refined whenever new data are collected. NWMs are an example of a physics-based Digital Twin.

We focus on using WRF and InSAR meteorology to continuously update the Digital Twins of the atmosphere. The data lake consists of Sentinel-1 data (high-resolution PWV maps), the output variables of the ERA5 model. The digital twin engine consists of the 3D-Var assimilation of Sentinel-1 PWV maps, which provide a numerical tool to generate replicas of the NWM (e.g., WRF, AROME, COSMO). We want to demonstrate that it is possible to get: 1) Hints to change/modify the assumptions of NWMs; 2) Hints to reduce the extension of approximations; 3) Extend the limits of applications of WRF to better predict extreme weather events.

How to cite: Nico, G., Mateus, P., and Catalao, J.: On the contribution of InSAR Meteorology to a Digital Twin Of The Atmosphere, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20132, https://doi.org/10.5194/egusphere-egu24-20132, 2024.

08:45–08:55
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EGU24-1091
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ECS
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On-site presentation
shivika saxena, chandrakanta ojha, and ramji dwivedi

Precipitable water vapor (PWV), an essential climate variable enlisted by the Global Climate Observing System (GCOS), can be efficiently mapped using popular Earth Observation techniques like Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). When used in a combination, these techniques complement each other in terms of temporal and spatial resolution for producing high-resolution PWV. Tropospheric water vapor (measured as wet delay) can be perceived in two components: non-turbulent (ZWD_NT) as well as turbulent information (ZWD_T). Wet delays from InSAR acquisitions are able to capture only the turbulent information, hence need to be enhanced. The non-turbulent component further can be spatially classified into shortwave and longwave components. In this study, GNSS observations from a dense network of twelve GNSS CORS for 2021, from the newly established CORS network by Survey of India with a homogeneous spread over Uttarakhand (UK) state, is used to establish the ZWD_NT model. We develop an exponential elevation-dependent model for shortwave components, incorporating seasonal variations and a location-dependent model for long wave components. Model assessment shows the performance of the developed model when a satisfactory mean RMSE of 8.32 mm is obtained through internal checks, which shows the efficacy of the developed model in capturing elevation dependency and seasonal variations. Further, a geodetic framework is conceptualized wherein the values derived from developed ZWD_NT model are appended to non-differential ZWD_T estimated after Small BAseline Subset InSAR (SBAS-InSAR) processing at measurements points density of about 50 million points from 30 ascending pass Sentinel 1A acquisitions, to arrive at full atmospheric information (ZWD_total). A previously developed weighted mean temperature (Tm) model for the highly undulating himalayan foothills region in the UK, is incorporated in the conversion of ZWD_total to PWV, for better accuracy and further assessment. A high resolution combination of PWV derived from complementary GNSS and InSAR techniques can be efficiently utilized in improving the numerical weather prediction (NWP) skill as well as monitoring extreme weather event since the spatial variations in local tropospheric conditions of a hilly terrain are quite frequent. When validated against PWV from ERA5 reanalysis data, a mean RMSE of 9.5 mm is obtained, except for the monsoon period, when RMSE falls in the range of 10-20mm. This may be due to the fact that InSAR partial non differential PWV captures the spatially correlated artifacts especially in the temporal vicinity of a rainy event. The results show that the proposed approach can effectively enhance the InSAR derived non-differential PWV and provide useful information at a high spatial resolution in a varied topography in lesser Himalayan.These high-resolution PWV maps hold great promise for enhancing meteorological understanding and quantitative analysis, specially during heavy rainfall in a complex terrain like UK. With the upcoming NISAR mission for the Indian subcontinent, spatio-temporal analysis of tropospheric parameters can be further enhanced for weather forecasting.

How to cite: saxena, S., ojha, C., and dwivedi, R.: Establishing Zenith Wet Delay model (ZWD) and developing a framework for generating high resolution PWV for extreme weather monitoring using MT-InSAR and GNSS for Indian Himalayan region , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1091, https://doi.org/10.5194/egusphere-egu24-1091, 2024.

08:55–09:05
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EGU24-15000
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ECS
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On-site presentation
Sanam Motlaghzadeh, Maksym Vasiuta, Marja Bister, Angel Navarro Trastoy, Lauri Tuppi, Torsten Mayer-gürr, and Heikki Järvinen

Satellites in Earth orbit are exposed to Earth radiation, consisting of reflected solar and emitted thermal radiation, thereby exerting a radiation pressure force that causes acceleration and affects the orbits. Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission aiming to retrieve the Earth gravity potential is critically dependent on accounting for all non-gravitational forces, including the Earth radiation. Although weather-of-the-day; e.g., clouds and their properties, has a major role in Earth radiation pressure, only climatology has been used so far to represent this force. Using climatological data doesn’t account for orbit perturbations owing to weather-related transient changes in the Earth radiation pressure. We show here that the top-of-atmosphere radiation fluxes computed with a numerical weather prediction model explain most of the measured variations in the radial acceleration of the GRACE-FO satellite. Our physics-based modelling corrects a hitherto unexplained lack of power spectral density in the measured accelerations. For example, we can accurately model the accelerations associated with a tropical storm in Indian Ocean in December 2020, which would not be possible when using climatological data. Our results demonstrate that using a global numerical weather prediction model significantly improves the simulation of non-gravitational effects in the satellites’ orbit. This advancement will allow more precise gravity retrieval and its applications in Earth sciences. 

How to cite: Motlaghzadeh, S., Vasiuta, M., Bister, M., Navarro Trastoy, A., Tuppi, L., Mayer-gürr, T., and Järvinen, H.: Estimating GRACE-FO orbit perturbations with numerical weather prediction models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15000, https://doi.org/10.5194/egusphere-egu24-15000, 2024.

09:05–09:15
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EGU24-15444
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On-site presentation
Andreas Dielacher, Michael Moser-Moritsch, Walter Hoermanseder, Maximilian Semmling, Weiqiang Li, Florian Zus, Mario Moreno, Jens Wickert, Estel Cardellach, Hossein Nahavandchi, and Camille Pirat

The PRETTY CubeSat In-Orbit Demonstrator (IOD) Mission has been finally launched on 9th October 2023 into a Sun-Synchrounous Orbit (SSO) in 560km height. The Launch and Early Orbit Phase (LEOP) was successful, meaning that communication with the CubeSat was possible, solar panels and VHF antennas are deployed. The commissioning phase is started. The 3U CubeSat hosts two scientific payloads, a radiation dosimeter and a novel GNSS-Reflectometry payload. The GNSS-reflectometer will be measuring earth surface under grazing elevation angles at the L5 frequency, in order to obtain altimetric altitude under various surface conditions (e.g., ocean waters or sea ice). The measurements will be done by correlating the direct and reflected signal (the so called interferometric approach), exploiting the full bandwidth of the GNSS signal.

An Algorithm Theoretical Baseline Document (ATBD) has been created within the scientific consortium and first simulation results have been conducted (and the results are analyzed within the consortium). For this presentation we will focus on the status of the satellite and present the first results obtained from space.

How to cite: Dielacher, A., Moser-Moritsch, M., Hoermanseder, W., Semmling, M., Li, W., Zus, F., Moreno, M., Wickert, J., Cardellach, E., Nahavandchi, H., and Pirat, C.: PRETTY – First experience from a 3U CubeSat In-Orbit Demonstrator for GNSS-Reflectometry under grazing angle geometry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15444, https://doi.org/10.5194/egusphere-egu24-15444, 2024.

09:15–09:25
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EGU24-5628
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ECS
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On-site presentation
Mina Rahmani, Jamal Asgari, and Jens Wickert

    Soil moisture (SM) is a crucial factor influencing the exchange of energy between the soil and the atmosphere, playing a key role in hydrological processes. GNSS Reflectometry (GNSS-R) has recently become an innovative method for remotely monitoring various geophysical and hydrological parameters, including soil moisture. GNSS-R operates by utilizing signals from Global Navigation Satellite Systems (GNSS) that are reflected off the Earth's surface. In addition to the quantity of soil moisture content, the features of vegetation, such as vegetation water content, and the soil surface roughness influence GNSS-R observations. Consequently, accurately parameterizing these effects is essential for achieving precise and high-quality estimates of soil moisture. Nevertheless, separating the influences of surface roughness and vegetation on reflected signals is often challenging.

     In this context, we employed a methodology aimed at assessing and mapping the sensitivity of GNSS-R observations to soil roughness effects. This analysis is based on observations collected by NASA's GNSS-R mission, CYGNSS, on a global scale in 2021. Initially, we endeavored to explore the responsiveness of CYGNSS observations to soil effects across a regular 0.2-degree global grid. The results revealed that CYGNSS observations exhibit sensitivity to soil effects over around 90% of the Earth's land surface covered by CYGNSS, spanning latitudes from 37° in the Northern Hemisphere to 37° in the Southern Hemisphere for all longitude values. Nevertheless, they show low sensitivity in the remaining 10% of land areas, primarily attributed to the impact of dense vegetation covers, particularly in the Amazon and Congo forests. In the second step, over regions where CYGNSS observations are sensitive to soil effects, we attempted to compute a map of the roughness parameter (Hr). To achieve this goal, we suggested integrating the effects of both vegetation and roughness into a single parameter, referred to as VR in this study. Initially, VR values were retrieved on a global scale from CYGNSS by inverting the L-MEB model. The L-MEB (Land Microwave Emission Model with Briggs approach) is a radiative transfer model used to simulate microwave emissions from land surfaces for remote sensing applications. Then, the effects of vegetation and soil roughness included in the VR parameter were decoupled by assuming a linear relationship between VR and Leaf Area Index (LAI) (~0.5 in this research) for the purpose of mapping the roughness parameter, Hr.

    In this study, the obtained Hr values range from 3.2 to 4.6. The spatial distribution of Hr values is observed to be influenced by predominant vegetation types, where forests demonstrate higher roughness values (Hr = 4-4.6), whereas deserts, shrubs, crops, and bare soils exhibit lower values (Hr = 3.2-3.4). We also inferred vegetation optical depth (VOD) using CYGNSS observations in conjunction with estimated Hr values as an ancillary dataset. The evaluation of the obtained VOD in comparison with Vegetation Water Content (VWC) and LAI produced correlation coefficients of 0.57 and 0.71, confirming the effectiveness of the recently introduced Hr dataset in our research and highlighting its promising potential for future applications in GNSS-R.

How to cite: Rahmani, M., Asgari, J., and Wickert, J.: Worldwide Small-Scale Land Surface Roughness Retrieval at L-band Using Space-born GNSS-R Observations , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5628, https://doi.org/10.5194/egusphere-egu24-5628, 2024.

09:25–09:35
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EGU24-16080
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ECS
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On-site presentation
Zhixiang Mo, Yidong Lou, Weixing Zhang, Yaozong Zhou, Peida Wu, and Zhenyi Zhang

Global Navigation Satellite System (GNSS) radio occultation (RO) is one of the most crucial observations in atmospheric and climate science. GNSS RO globally produces accurate and long-term stable vertical profiles for essential climate variables such as refractivity and temperature at high vertical resolution in all weather conditions. Currently, various RO satellite constellation programs have been developed by nations and companies, and the growing quantity of RO observations can contribute not only globally but also has the potential to benefit specific regions, such as China. To investigate the potential of RO observation in China, the performance of five operational RO measurements from COSMIC-2, MetOp-B/C, FY-3D/E, Spire and PlanetiQ on data coverage capabilities and quality are assessed by comparing with ERA5 and radiosonde over China. The results of data coverage showed that all RO missions can acquire extensive coverage over China with effective low-altitude penetration capability, whereas MetOp-B/C exhibits some gaps in local time coverage. The results of data quality confirmed that commercial Spire and PlanetiQ are comparable to those of national-led COSMIC-2, MetOp-B/C and FY3D/E, even though Spire exhibited a lower signal-to-noise ratio (SNR). The mean bending angle and refractivity relative differences of all RO measurements are within ±2.9% and ±1.5/0.9% (with respect to ERA5/radiosonde) in the altitude range of 5 to 35 km, respectively, and the corresponding relative standard deviations (SD) are less than 6% and 1.8/2.2%, respectively. Mean temperature and specific humidity differences of all RO measurements are within ±0.8/1.0 K and ± 0.7/1.0 g/kg, respectively, from the near surface to 15 km, with SD of less than 2.1/2.0 K and 1.8/1.7 g/kg. These results can help users further understand the strengths and weaknesses of these RO observations and indicate the significant application potential of numerous high-quality RO profiles from various RO measurements, which is anticipated to enhance numerical weather predictions for China.

How to cite: Mo, Z., Lou, Y., Zhang, W., Zhou, Y., Wu, P., and Zhang, Z.: Performance assessment of GNSS radio occultation measurements from five missions over China , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16080, https://doi.org/10.5194/egusphere-egu24-16080, 2024.

09:35–09:45
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EGU24-10463
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ECS
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On-site presentation
Angel Navarro Trastoy, Ghodsiyeh Motlagh Zadeh, Maksym Vasiuta, Patrick Dumitraschkewitz, Torsten Mayer-Gürr, and Heikki Jarvinen

Representing the tropospheric slant delays in geodesy can get complicated due to the inhomogeneity and fast variations of the weather. Mapping functions are the most common used tool for this task, but due to the lack of information when calculating the parameters of the mapping functions, relevant errors could appear. The errors in the zenithal direction come from the limitations of the mapping functions, and in the azimuthal direction come from the asymmetries in the sight-field of the receiver. New representations, as the full skyviews representation made by University of Helsinki, have proven to lead to better results in the computation of GNSS products using orbit processing softwares, but these are expensive, both computationally and in size. In this study, we apply the mapping functions approach using the Least Travel Time ray-tracer with larger amounts of mapping functions per receiver, and a 1-hour update of all the parameters. We believe that a more precise use of the slant delays would lead to a better computaiton of GNSS products, along with a important data assimilation to the weather forecast from the residuals obtained in the Least Squares Adjustment used in the processing. The results show that the error induced when using mapping functions converges quickly to a minimum when we increase the amount of mapping functions used per receiver. The most efficient number of mapping functions is 10, being equidistant (one mapping functions every 36 degrees in azimuth).

How to cite: Navarro Trastoy, A., Motlagh Zadeh, G., Vasiuta, M., Dumitraschkewitz, P., Mayer-Gürr, T., and Jarvinen, H.: Tropospheric slant delays interpolating multiple mapping functions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10463, https://doi.org/10.5194/egusphere-egu24-10463, 2024.

09:45–09:55
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EGU24-10250
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ECS
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On-site presentation
Zhenyi Zhang, Mengjie Liu, Valeria Huber, Gregor Möller, Jan Henneberger, Philipp Kryenbühl, Lukas Hammerschmidt, Grzegorz Kłopotek, and Benedikt Soja

In recent decades, various studies have demonstrated that assimilating tropospheric parameters from ground-based GNSS receivers benefits numerical weather predictions (NWPs). However, the achieved performance is limited by the spatial resolution of GNSS, especially in the vertical direction. With the rapidly developing and growing market of unmanned aerial vehicles (UAVs) and the facilitates of integrating low-cost GNSS hardware into various autonomous systems over the last years, there is a potential to address this problem by utilizing UAVs to collect airborne GNSS data and generate zenith total delays (ZTDs). The airborne GNSS ZTDs can act as a potential complementary source to radiosonde data for obtaining vertical profiles of the troposphere, making it promising to investigate the impact of assimilating GNSS ZTDs of high spatio-temporal resolution in NWPs.

In this study, we explored the use of GNSS data collected by a vertically ascending UAV, with ZTDs processed using the software CamaliotGNSS. Based on the airborne GNSS ZTDs, we conducted not only data assimilation but also weather predictions using the Weather Research and Forecasting Model (WRF). With the onboard meteorology observations as references, we found that assimilating airborne GNSS ZTDs positively impacted humidity and temperature forecasts, with their forecasting root-mean-square errors decreasing by about 19% and 29%, respectively. Moreover, by selecting and comparing different subsets of data, we found that better forecasts can be obtained with airborne GNSS ZTDs of higher spatio-temporal resolution. The positive results invite further exploration of applications of airborne platforms such as UAVs in the field of GNSS meteorology. 

How to cite: Zhang, Z., Liu, M., Huber, V., Möller, G., Henneberger, J., Kryenbühl, P., Hammerschmidt, L., Kłopotek, G., and Soja, B.: Assimilating UAV-based GNSS ZTDs for Numerical Weather Predictions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10250, https://doi.org/10.5194/egusphere-egu24-10250, 2024.

09:55–10:05
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EGU24-2068
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ECS
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On-site presentation
Lingke Wang and Hansjörg Kutterer

Integrated Water Vapor (IWV) stands as a pivotal parameter in contemporary environmental research, offering crucial insights into atmospheric dynamics. Due to the inherent challenges in direct measurement, IWV necessitates estimation through various methods. Current approaches, including Global Navigation Satellite System (GNSS), radiosondes, radiometers, and satellite remote sensing, have inherent limitations, resulting in a scarcity of high spatial resolution data. While GNSS technology, radiosondes, and radiometers provide precision, they are confined to specific locations, imposing spatial coverage constraints. On the other hand, satellite remote sensing offers expansive, high spatial resolution IWV data, yet its accuracy is hindered under cloudy conditions and limited by satellite ground tracks.

This study addresses these challenges by introducing a regional IWV prediction model based on Machine Learning. Leveraging IWV data from diverse GNSS stations within a specified region, the study establishes a regional IWV prediction model utilizing an adaptive least squares support vector machine (ALSSVM). This predictive model enables accurate IWV estimation at any designated location within the region, incorporating inputs such as latitude, longitude, height, and temperature. Significantly, the model attains remarkable predictive accuracy, with an overall average root mean square error (RMSE) of 2 millimeters.

The model's performance exhibits variability across different seasons and terrains, illustrating its adaptability to diverse environmental conditions. The study further evaluates the reliability of the conventional ERA5 IWV calculation method in the specified region by comparing it against the predicted results from the proposed IWV prediction model. In conclusion, the developed model is applied to conduct a climate analysis, demonstrating its practical utility in environmental research for the transnational Upper Rhine Graben region.

Keywords:

Global Navigation Satellite System (GNSS), Integrated Water Vapor (IWV), least squares support vector machine (LSSVM), Climate Analysis

How to cite: Wang, L. and Kutterer, H.: Advancing Integrated Water Vapor Estimation: Introducing an Enhanced Regional Prediction Model Utilizing Improved Least Squares Support Vector Machine for the Upper Rhine Graben Region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2068, https://doi.org/10.5194/egusphere-egu24-2068, 2024.

10:05–10:15
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EGU24-10874
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ECS
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On-site presentation
Peng Yuan, Kyriakos Balidakis, Jungang Wang, and Zhiguo Deng

Tropospheric delay is one of the most important error sources for space geodetic techniques, such as the Global Navigation Satellite Systems (GNSS). A priori tropospheric Zenith Hydrostatic and Wet Delays (ZHD and ZWD) should be obtained properly in advance to the GNSS data processing. Numerical Weather Model (NWM) is capable to provide accurate tropospheric zenith delays at any specific location with sophisticated calculation. As a more convenient alternative, the tropospheric zenith delays can be first modeled with NWM as a 2-D grid on the Earth surface and then corrected to the height of the specific location. In this case, accurate vertical correction algorithm is crucial. However, though empirical analytical models have been developed for the vertical correction of tropospheric zenith delays, their accuracies are limited due to the large spatiotemporal variability of the delays. In this work, we propose a Machine Learning (ML) model based on neural network for the vertical corrections of both ZHD and ZWD. The training data is obtained from the state-of-the-art NWM, the fifth-generation global reanalysis of European Centre for Medium-Range Weather Forecasts (ERA5). The proposed ML model is capable to reconstruct the tropospheric delays at any height from the Earth surface to up to 14 km. The precision of the ML model is superior to the analytical models with global average RMS values less than 2 and 3 mm for ZHD and ZWD, respectively. Therefore, it provides a convenient alternative to the sophisticated vertical integration of NWM for ordinary users with slight precision loss.

How to cite: Yuan, P., Balidakis, K., Wang, J., and Deng, Z.: A machine learning model for the vertical correction of tropospheric zenith delays, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10874, https://doi.org/10.5194/egusphere-egu24-10874, 2024.

Posters on site: Wed, 17 Apr, 16:15–18:00 | Hall X2

Display time: Wed, 17 Apr, 14:00–Wed, 17 Apr, 18:00
Chairpersons: Henrik Vedel, Maximilian Semmling, Kyriakos Balidakis
X2.20
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EGU24-17013
Rüdiger Haas and Gunnar Elgered

Observations performed with ground-based space geodetic and remote sensing techniques are sensitive to the amount of water vapour in the neutral atmosphere. Corresponding parameters that describe the signal delay in the troposphere can be derived for example from the analysis of data collected from geodetic Very Long Baseline Interferometry (VLBI), Global Navigation Satellite System (GNSS), as well as microwave radiometers. The latter instruments are often referred to as water vapour radiometers (WVR).

The Onsala Space Observatory (OSO) operates a number of such instruments for VLBI, GNSS and WVR measurements, all co-located within about 600 m. Among these are the Onsala twin telescopes (OTT), two modern 13.2~m diameter radio telescopes performing observations in the VLBI Global Observing System (VGOS) of the International VLBI Service for Geodesy and Astrometry (IVS). The OTT are the first operational VGOS twin telescopes worldwide and are contributing with observations to the IVS since 2019. OSO also operates eight permanently installed GNSS stations, of which two are official stations in the International GNSS Service (IGS) network. Furthermore, OSO operates ground-based microwave radiometers, which are used for atmospheric research and perform continuous observations of the water vapour content in the neutral atmosphere. Data analysis of all three techniques, VLBI, GNSS and WVR, allows to derive information on the temporal and spatial variations of water vapour in the neutral atmosphere. Using co-located instrumentation within a few hundred metres distance thus offers a perfect opportunity for comparisons and assessments of the results.

We focus on data recorded at OSO during 2022 and compare the parameters describing the signal delay in the neutral atmosphere, i.e. the so-called equivalent zenith total delays and the linear horizontal delay gradients. The temporal resolution of the derived parameters is 15 min or less. Out of more than 40 VGOS experiments in 2022, each of a duration of 24 h, we have WVR data covering at least half of the session in all except one.  We have examples where the equivalent zenith wet delay only varies by 2--3 cm over an experiment during rather stable atmospheres.  When the atmosphere is more variable the zenith wet delay can vary by more than 10 cm over 24 h.

How to cite: Haas, R. and Elgered, G.: Tropospheric Parameters Derived From Co-located Instrumentation at the Onsala Space Observatory, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17013, https://doi.org/10.5194/egusphere-egu24-17013, 2024.

X2.21
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EGU24-14710
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ECS
Maksym Vasiuta, Angel Navarro Trastoy, Lauri Tuppi, Sanam Motlaghzadeh, and Heikki Järvinen

Modelling of the microwave signal delay in the neutral atmosphere (i.e., the tropospheric delay) is a crucial part of GNSS observations processing. The design of observation-modelling algorithms is based on signal ray tracing. Considering advancements in modern Numerical Weather Prediction (NWP) models and high standards of GNSS product quality, it is necessary to revise the existing ray-tracing algorithms. We developed an improved least-travel time (LTT) ray-tracer with robust physics assumptions, based on the original LTT algorithm. Both new and original LTT implementations, along with the state-of-the-art VieVS Ray-tracer (RADIATE), are supplied with numerical weather data by the Open Integrated Forecasting System model (OpenIFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). These three ray tracers are justly compared in the setup of modelling the skyview delays for 256 GNSS stations during one month (December 2016). The skill of these delay products is assessed as the quality of GNSS precise orbit determination (POD) products of the GPS constellation made by the orbit solver GROOPS (Gravity Recovery Object Oriented Programming System) software toolkit of the Graz University of Technology. The GNSS POD metrics which have been analysed are orbit midnight discontinuities (MD) and precise point positioning (PPP) error. In the context of these metrics, the usage of the new LTT algorithm leads to better orbit products, compared to the original LTT and the RADIATE ray tracers.

How to cite: Vasiuta, M., Navarro Trastoy, A., Tuppi, L., Motlaghzadeh, S., and Järvinen, H.: Improved Least Travel Time ray-tracing operator for GNSS tropospheric delays, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14710, https://doi.org/10.5194/egusphere-egu24-14710, 2024.

X2.22
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EGU24-2184
Analysis  of Classical Tropospheric Models Based on Measured Meteorological Elements
(withdrawn after no-show)
Qinglan Zhang, Junli Wu, Xiaoqing Wang, Peng ZHang, Zhanyi Sun, Yongshang Wang, and Shenghao Liang
X2.23
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EGU24-6211
Ryuichi Ichikawa, Yusaku Ohta, Kentaro Araki, Takuya Tajiri, Mikiko Fujita, Hideki Ujihara, Takayoshi Yamada, Takaaki Jike, Hiroshi Imai, Masahiro Minowa, and Yuya Takashima

We have initiated a new research project to analyze the behaviors of precipitable water vapor with high spatial and temporal resolutions using a dense global navigation satellite system (GNSS) network and next-generation microwave radiometers. Recently, line-shaped rainbands with extreme and hazardous characteristics have been occurring frequently in Japan, leading to disasters such as severe flooding and landslides. However, there is insufficient knowledge regarding the generation mechanism of cumulonimbus clouds within these rainbands. Our project has four research subobjectives: (1) to develop a novel microwave radiometer for use in millimeter-wave spectroscopy, enabling high-resolution and high-precision monitoring of water vapor behavior, and conduct field measurements using this radiometer for proof of concept; (2) to conduct high-resolution water vapor measurements using a dense network of low-cost GNSS receivers; (3) to conduct GNSS water vapor tomography for estimating precise temporal and spatial variations; and (4) to numerically predict weather precisely using dense-measurement water vapor datasets and fine GNSS tomography results. Our project is aimed at not only the advancement of mesoscale meteorology but also application to space geodetic techniques such as very long baseline interferometry (VLBI) and GNSS. Regarding the first subobjective, significant progress has been achieved in the development of a next-generation microwave radiometer utilizing millimeter-wave spectroscopy since 2018. To date, we have successfully engineered a new front-end module equipped with an orthomode transducer (OMT) and a wideband feed. The prototype of the complete receiver system has a wide bandwidth feed spanning from 16 to 58 GHz, facilitating the measurement of two frequency bands: 16-28 GHz (H2O) and 50-58 GHz (O2). We plan to integrate this system into a 40-meter-class dish telescope to assess its performance in detecting water vapor variability this summer. For the observation of GNSS precipitable water vapor, we first installed a low-cost GNSS receiver and a commercial-based microwave radiometer at Kagoshima University in early November 2023 as a preliminary observation to understand the variability of water vapor in the southern Kyushu area. In addition to the precipitable water vapor information obtained from this observation, we plan to investigate the variability of water vapor in this area based on the information obtained from the GNSS Earth observation network (GEONET) system of the Geospatial Information Authority of Japan (GSI) and a commercial-based GNSS observation network. Our presentation will include preliminary results and an outlook on future developments. This work received support through JSPS KAKENHI Grant Numbers JP21H04524 and 23H00221.

How to cite: Ichikawa, R., Ohta, Y., Araki, K., Tajiri, T., Fujita, M., Ujihara, H., Yamada, T., Jike, T., Imai, H., Minowa, M., and Takashima, Y.: Novel Real-time Observation of High-resolution Water Vapor Behavior for Detection of Precursors of Cumulonimbus Clouds and Investigation of Their Evolution: - Preliminary Results -, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6211, https://doi.org/10.5194/egusphere-egu24-6211, 2024.

X2.24
|
EGU24-19978
Alessandra Maria De Pace, Rachele Fratini, Augusto Mazzoni, and Mattia Crespi

The variometric approach has been demonstrated effective in GNSS seismology and GNSS ionospheric seismology to estimate ground shaking (VADASE) and earthquake/tsunami induced ionospheric disturbances (VARION) in real time for years. In this study the same variometric approach has been utilized to appraise the potential for real-time tracking of tropospheric delay (VARTROPO): this investigation holds significance for timely enhancements in weather forecasting, by incorporating this data into numerical weather models.
In contrast to the prevailing method of tracking tropospheric delay, which relies on employing a mapping function and estimating a singular zenith tropospheric delay (ZTD) for all satellites within a specific time interval, the proposed approach is based on the estimation of single-epoch variation of the slant tropospheric delay (VSTD) for individual satellite. The low-pass filtering process and the integration of this variation over time, starting from a known initial value of the STD, allows to estimate the STD in near (due to low-pass filtering) real time for each satellite. It is noteworthy that the proposed approach allows to highlight the azimuthal anisotropy of the troposphere, valuable during periods of intense weather fronts.
The preliminary research focuses on evaluating how the estimates derived from the proposed approach, in near real-time scenario, match with both the official ZTD estimates provided by CDDIS and those obtained through Precise Point Positioning (PPP) technique. In this respect, it has to be underlined that the assessment hereafter illustrated has been developed: (i) using 1-second rate GNSS data; (ii) in both terms of ZTDs and STDs, using a standard 1/sin(elevation) mapping function for conversion; (iii) with fixed position of the GNSS permanent station (only the receiver clock variation has been estimated in the variometric approach); (iv) without multipath mapping and removal. The first presented comparison is between VARTROPO and PPP (MATE station; satellite G03; 1st October 2023) (Figure 1).

Fig. 1

VARTROPO derived VSTD and VZTD exhibits higher noise level; therefore, to mitigate the highfrequency noise, a simple low-pass filter (moving median) with different moving windows (from 5 seconds to 2 minutes) has been applied. Then, the different low-pass filtered VZTDs have been integrated over time, starting from the ZTD at the initial epoch as derived from PPP, and compared to the ZTDs estimated by PPP (Figure 2).

 Fig2

The differences between the reconstructed VARTROPO ZTDs trends and the PPP ZTDs have been represented (Figure 3).

  Fig.3

The second comparison is between VARTROPO and CDDIS, to substantiate the aforementioned findings (Figures 4, 5).

     Fig4

 Fig5

In conclusion, it has been understood that simple moving medians are able to effectively low-pass filter the VARTROPO ZTDs: with 2-minute moving window the agreement with PPP ZTDs and CDDIS ZTDs are at within 1-2 millimeters, what preliminarily demonstrate that near real-time track of the tropospheric delay is feasible. Next research steps will involve: (i) enhancing the VSTDs estimates (e.g. with multipath mitigation); (ii) investigating the possibility to estimate troposphere azimuthal anisotropy in presence of weather fronts.

How to cite: De Pace, A. M., Fratini, R., Mazzoni, A., and Crespi, M.: Near real-time GNSS meteorology: a preliminary feasibility demonstration based on the variometric approach , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19978, https://doi.org/10.5194/egusphere-egu24-19978, 2024.

X2.25
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EGU24-20834
Performance Analysis of Near Realtime Tropospheric Delay Estimation using Low-cost GNSS Receivers
(withdrawn after no-show)
Szabolcs Rozsa, Bence Ambrus, and Bence Turák
X2.26
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EGU24-8726
Pierre Bosser and Pierre Tulet

MAP-IO (Marion Dufresne Atmospheric Program Indian Ocean) is a research project that aims to collect long-term atmospheric, biological and marine observations to document the under-instrumented areas of the Indian and Southern Oceans. To achieve this, the French Research Vessel (R/V) Marion-Dufresne, based in Réunion Island in the Indian Ocean, has been equipped with around twenty autonomous instruments to take measurements along its route.

Among the instruments deployed on this R/V, a GNSS antenna has been in operation since autumn 2020, providing unique and continuous measurements of atmospheric water vapour in this part of the world. The data were initially transmitted daily to shore for ultra-rapid (day +1) and rapid (day +3) routine analysis. The quality of the retrieved tropospheric delays and integrated water vapour contents was highlighted in a previous study. With more reliable transmission facilities, raw GNSS data are now transmitted hourly and can be analysed with a latency of less than 30 minutes.

In this study, we provide an initial assessment of the near real-time (h+20min) processing performed as part of this project. This evaluation is based on GNSS raw data collected in August 2023 during a rotation of the R/V Marion-Dufresne in the French Austral Islands (Crozet, Kerguelen, Saint-Paul and Amsterdam Islands). Processing is performed every hour over a 24-hour window, using the raw hourly data transmitted by the R/V and the cumulative real-time ephemerides and clocks provided by JPL / GDGPS. Only the last hour of each analysis is then considered. Over this period, the estimated tropospheric delays are in good agreement with the rapid routine solution, with differences of less than 1 cm RMS. The comparison with the analysis and the 6h forecast of the Météo-France's global numerical weather prediction model Arpège highlight the benefits of these shipborne GNSS measurements for numerical weather prediction.

The medium-term objective of this work is to establish an operational procedure for the assimilation of these near real-time GNSS tropopheric solutions into numerical weather prediction models.

How to cite: Bosser, P. and Tulet, P.: Near real-time water vapour monitoring with shipborne GNSS for numerical weather prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8726, https://doi.org/10.5194/egusphere-egu24-8726, 2024.

X2.27
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EGU24-21040
Addisu Hunegnaw, Norma Teferle, and Jonathan Jones

Recently, the University of Luxembourg (UL), in collaboration with the United Kingdom Met Office, has started providing accurate near real-time (NRT) Zenith Total Delays (ZTDs) from networks of GNSS ground stations. This initiative is in alignment with the operational meteorological products from various analysis centers available at the EUMETNET EIG GNSS Water Vapour Programme (E-GVAP) and the team in Luxembourg envisages to re-start its contributions in the near future. Active in Europe, E-GVAP coordinates NRT GNSS-based atmospheric content monitoring to support Numerical Weather Prediction (NWP) modelling with products that are crucial for mesoscale models throughout Europe, for example, for the Met Office. GNSS technology is essential for accurately measuring atmospheric parameters such as ZTD and Integrated Water Vapor (IWV) at high frequencies, regardless of weather conditions. In addition, GNSS data are low-cost when compared to conventional meteorological systems. Ensuring the NRT availability of these data for NWP assimilation systems requires numerous methods in GNSS data handling and processing, quality assurance, and distribution.  The study details the collaborative work between the UK Met Office and the University of Luxembourg in providing accurate and rapidly available meteorological data through GNSS technology. This collaboration has led to the development and update of various systems for the processing of GNSS observations to produce advanced NRT ZTD products at UL and the Met Office. These products are generated at 1-hour intervals on both global and regional scales, and at sub-hourly intervals regionally. This study primarily aims to provide a thorough review and accuracy assessment of NRT ZTD products from the UL, comparing their precision with benchmark data from both post-processed and NRT ZTD estimates from various EGVAP analysis centres. The NRT GNSS processing systems at UL use the Bernese GNSS Software (BSW) versions 5.2 and 5.4 with a double-differencing (DD) approach, and similarly, the post-processed benchmark ZTD estimates employs the DD positioning strategy using the same software packages.

How to cite: Hunegnaw, A., Teferle, N., and Jones, J.: New Developments in Near Real-Time GNSS Zenith Total Delay Estimates at the University of Luxembourg, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21040, https://doi.org/10.5194/egusphere-egu24-21040, 2024.

X2.28
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EGU24-22538
Marcelo C. Santos, Kyriakos Balidakis, Anna Kloss, Rosa Pacione, and Jordan Rees

We present findings from an ongoing investigation into the evaluation of long-term trends in ground-based GNSS-derived Zenith Total Delay (ZTD) for potential integration into climate models, either for assimilation or validation purposes. Our analysis focuses on ZTD time series obtained from six REPRO3 IGS Analysis Centers (ACs) – COD, ESA, GFZ, GRG, JPL, and TUG – spanning 20 years or more. Thirty stations from the IGS global network were selected for this study. The ZTD time series underwent a homogenization process, utilizing ERA-5 derived ZTDs as a reference, followed by daily value averaging to minimize potential discrepancies arising from diverse estimation strategies employed by individual ACs. Similar averaging procedures were applied to ERA-5 ZTDs and the IGS tropo-product if already reprocessed in REPRO3. Two combinations, employing weighted mean and a robust least median of squares, were generated from the six homogenized ACs, serving as quality control measures for each AC. Analysis of trends in each of the nine ZTD time series was conducted in both time and frequency domains, revealing geographical variations in results. For instance, at station ALBH in Canada, the inter-AC scatter was 0.47 mm/decade for trends, 0.11 mm for annual amplitudes, and 0.29 degrees for annual phases.

How to cite: C. Santos, M., Balidakis, K., Kloss, A., Pacione, R., and Rees, J.: Climate trends derived from long-term ground-based GNSS-derived Zenith Total Delay (ZTD), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22538, https://doi.org/10.5194/egusphere-egu24-22538, 2024.

X2.29
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EGU24-15082
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ECS
Jingna Bai, Yidong Lou, Weixing Zhang, Yaozong Zhou, Zhenyi Zhang, Chuang Shi, and Jingnan Liu

Homogenized atmospheric water vapour data is an important prerequisite for climate analysis. Compared with other techniques, GPS has inherent homogeneity advantage, but it still requires reprocessing and homogenization to eliminate impacts of applied strategy and observation environmental changes where a selection of proper processing strategies is critical. Here, we reprocess GPS observations at 44 IGS stations during 1995 to 2014. We focus on the influence of the mapping function, the elevation cut-off angle and homogenization on long-term reprocessing results, in particular for Zenith Tropospheric Delays (ZTD) products. Moreover, for the first time, we include the mapping function (VMF3) and exploit homogenized radiosonde data as a reference for ZTD trend evaluations. Our analysis shows that both site position and ZTD solutions achieved the best accuracy when using VMF3 and 3° elevation cut-off angle. Regarding the long-term ZTD trends, we find that homogenization can reduce the trend inconsistency among different elevation cut-off angles. ZTD trend results show that the impact of mapping functions is very small. On the other hand, the discrepancy can reach 0.60 mm/year by using different elevation cut-off angles. We suggest the low elevation cut-off angles (3° or 7°) for the best estimates of ZTD reprocessing time series when compared to homogenized radiosonde data or ERA5 reference time series.

How to cite: Bai, J., Lou, Y., Zhang, W., Zhou, Y., Zhang, Z., Shi, C., and Liu, J.: Impact analysis of processing strategies for long-term GPS zenith tropospheric delay (ZTD), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15082, https://doi.org/10.5194/egusphere-egu24-15082, 2024.

X2.30
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EGU24-15330
|
ECS
Analysis of Monsoon Characteristics in China based on GNSS Precipitable Water Vapor (PWV)
(withdrawn)
Zhixuan Zhang, Yidong Lou, Weixing Zhang, Yunchang Cao, and Hong Liang
X2.31
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EGU24-8792
Florian Zus, Annika Oertel, Rohith Muraleedharan Thundathil, Galina Dick, Peter Knippertz, and Jens Wickert

The Swabian MOSES (Modular Observation Solutions for Earth Systems) field campaign was conducted between June and September 2023 in the southeastern Black Forest, the Neckar Valley and the Swabian Alb in southwestern Germany. It focused on hydro-meteorological extreme events, including the initiation and intensification of convective events which are accompanied by heavy rain and can lead to local flooding. As a part of the observing system the GFZ installed eight additional GNSS stations in the region of interest and operated them in near real time during the measurement campaign. The precise point positioning technique was utilized to provide Integrated Water Vapor (IWV) estimates with a temporal resolution of 15 min. In this contribution we provide a first comparison of these IWV estimates with those derived from atmospheric (re-) analysis datasets. We utilize the atmospheric reanalysis ERA5 (horizontal resolution 31 km) and the operational analysis ICON-D2 (horizontal resolution 2 km) provided by the German Weather Service. Ground-based GNSS data are not assimilated into ERA5 and ICON-D2. In general, we find good agreement between GNSS and (re-)analysis estimates: the root mean square error is 1-2 kg/m2. Our goal is to better understand the remaining station specific systematic and random deviations. For example, for all stations, the random deviations are smaller for the high compared to the low resolution model data. We attribute this to smaller representative errors and smaller forward model (interpolation) errors. However, for the systematic deviations the result is not too obvious. Comparisons with measurements from instruments which are collocated with the GNSS stations are envisaged to better understand the issue.  

How to cite: Zus, F., Oertel, A., Muraleedharan Thundathil, R., Dick, G., Knippertz, P., and Wickert, J.: Validation of GNSS-based Integrated Water Vapor for the Swabian MOSES 2023 field campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8792, https://doi.org/10.5194/egusphere-egu24-8792, 2024.

X2.32
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EGU24-13449
Umberto Tammaro, Vincenzo Carbone, Vincenzo Capparelli, Fabio Lepreti, and Claudio Martino

The INGV operates a network of about 60 permanent GNSS stations to monitor the Neapolitan volcanic area (southern Italy), which includes three active volcanoes: Somma Vesuvio, Campi Flegrei and the island of Ischia. In this study we consider only the GPS constellation, whose signals are transmitted in the microwave band. Therefore, they suffer a delay while propagating in the troposphere. Bearing in mind that the refractive index in the atmosphere is a function of the water vapour content, pressure and temperature, tropospheric delay can be assimilated into short-term weather forecast models and used in long-term climate studies. We analyse a data set ranged about 14 years (2006-2019) of continuous GPS data, to evaluate the tropospheric delay to be used as a probe tool to quantify precipitable water and track its spatial-temporal evolution. We limit the analysis to the area of Somma Vesuvio, a strato-vulcano that covers an area of 165 km2 and is about 1200 meters high, to study also the effect of the steep topography on the spatial distribution of the precipitable water content. The data are analysed in terms of empirical functions (IMF), organised in ascending order with a parameter ranging from 0 to 15, plus the trend. The trend found is not a linear growth, but grows to a maximum that is in the middle of the time range of about 11 years and then decreases. It is very interesting that the correlation with the solar cycle is high. Therefore, the next developments will be to analyze other data sets to verify the generality of this result.

How to cite: Tammaro, U., Carbone, V., Capparelli, V., Lepreti, F., and Martino, C.: The tropospheric delay of the GPS signal and its correlation with the solar cycle, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13449, https://doi.org/10.5194/egusphere-egu24-13449, 2024.

X2.33
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EGU24-14947
|
ECS
Aurélien Pira, Alvaro Santamaría-Gómez, and Guy Woppelmann

Coastal water monitoring is of increasing importance for applications such as sea level monitoring and urban planning. Currently, traditional tide gauge by radar measurement remains the most widely used method, but it involves placing a sensor close to the water surface, which can lead to its destruction, particularly in hostile maritime environments.

Sea level measurement by GNSS-R offers a promising alternative to traditional tide gauge methods by enabling continuous and global sea level measurements (e.g., Larson et al., 2013). It has the significant advantage of limiting the constraints linked to the installation of sensors physically close to the water surface, as a GNSS antenna can be placed away from the coast or on a high structure. Furthermore, this technique takes advantage of the high availability of existing GNSS installations around the globe, which would make it possible to considerably extend the scope of tide gauge measurements on a global scale.

Most of the methods used in GNSS-R are based on the analysis of the signal/noise ratio (SNR). They generally use a spectral analysis based on a Lomb-Scargle periodogram and are effective for monitoring mean sea level at the centimeter level (e.g., Larson et al., 2013; Santamaría-Gómez and Watson, 2017; Peng et al., 2021). However, they require a relatively long portion of the SNR series to obtain a precise estimate of the oscillation frequency of the SNR signal. This has the effect of limiting the sampling rate of the measurement series and limits spectral methods to the observation of slow variations in sea level such as the tide. Other approaches use Kalman filtering and show that it is possible to achieve an accuracy of less than 5cm in near real time (e.g., Strandberg, Hobiger and Haas, 2019; Liu et al., 2023). Furthermore, these methods show that it is possible to considerably increase the data sampling rate and thus monitor rapid variations in sea level. This extends the scope of GNSS-R techniques to all applications requiring real-time sea level measurement.

We present a novel approach for measuring sea level by analyzing SNR signals with Kalman filtering. This approach relies on the estimation of the oscillation frequency and amplitude of SNR signals using an extended Kalman filter. It has the advantage of providing sea level height estimates at a sampling rate as high as the SNR measurements. The major constraint linked to the method lies essentially in the estimation of the initial phase of the SNR signals, which particularly affects the fit of the SNR signals from setting satellites.

GNSS-R measurements were carried out with a sampling frequency of 1 second and compared to those of a tide gauge colocated on the Aix Island ILDX site (France). By combining data from different existing GNSS systems (GPS, GLONASS, Galileo, BDS) and considering all available carriers, we estimate that it is possible to obtain an RMS error of less than 5cm on sites with high tidal ranges (± 6m).

How to cite: Pira, A., Santamaría-Gómez, A., and Woppelmann, G.: Extended Kalman filtering applied to high-rate GNSS-R sea level measurements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14947, https://doi.org/10.5194/egusphere-egu24-14947, 2024.

X2.34
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EGU24-18019
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ECS
|
Mario Moreno, Maximilian Semmling, Georges Stienne, Mainul Hoque, and Jens Wickert

The ionosphere, spanning 60 to 2000 km above the Earth’s surface, plays a crucial role in Global Navigation Satellite System (GNSS) signal propagation, as signals traverse this layer on their path from the GNSS satellite to the receiver. In GNSS Reflectometry (GNSS-R), coherent observations are prominent in regions with smooth reflecting surfaces and grazing elevation angles (5° - 30°). However, within this elevation range, higher ionospheric effects (e.g., delay biases) are expected due to the longer path signals travel through the atmosphere.

Dual-frequency receivers can mitigate first-order ionospheric effects by using an ionosphere-free linear combination of code or carrier measurements. Single-frequency receivers, on the other hand, rely on a model to compensate for ionospheric refraction. In this study, the Neustrelitz Electron Density Model (NEDM2020) has been employed to estimate the slant total electron content (slant TEC) along the direct, incident, and reflected ray paths. The reflection events have been simulated using the orbit data from the Spire Global CubeSat constellation.

In preparation for the single-frequency GNSS-R ESA “PRETTY” mission data, this study conducts a comprehensive characterization of relative ionospheric delay, Doppler shift, and variations in the heights at which the maximum electron density is found along the ray paths. The investigation spans different elevation angle ranges, latitude-dependent regions, diurnal changes, and solar activity conditions. The results span a wide range of slant TEC values from 10 TECU between the reflection point and receiving satellite at moderate elevations (15°) to 300 TECU between transmitter and receiver (direct path) at very low elevations (5°). These results correspond to periods of low solar activity (March 2021). The ongoing study focuses on identifying and correcting the ionosphere impact in satellite data of the CyGNSS and PRETTY missions based on the developed simulation scheme.

How to cite: Moreno, M., Semmling, M., Stienne, G., Hoque, M., and Wickert, J.: Ionospheric Impact on GNSS Reflectometry: A correction approach for the PRETTY satellite data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18019, https://doi.org/10.5194/egusphere-egu24-18019, 2024.

Posters virtual: Wed, 17 Apr, 14:00–15:45 | vHall X2

Display time: Wed, 17 Apr, 08:30–Wed, 17 Apr, 18:00
Chairpersons: Henrik Vedel, Maximilian Semmling, Kyriakos Balidakis
vX2.1
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EGU24-2522
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ECS
Miaomiao Wang, Ping Li, Ruyu Lu, Shuheng Wang, and Fangyu Zhang

One of the dominant error sources for Global Navigation Satellite System (GNSS) measurements is the correction for delay of an electromagnetic wave as it traverses the neutral atmosphere, which is usually shorted as tropospheric delay. Generally, tropospheric delay must be calculated or estimated since refractivity along ray path is not easily or economically measured. Empirically, the line of sight delay for either hydrostatic or wet component is modeled as product of zenith delay and a mapping function. The accuracy of estimated geodetic parameters for GNSS could be limited due to the indeterminacy of mapping function, when observations are typically made to low elevation angles. Nowadays, there are many different empirical tropospheric delay mapping functions are generated and used in GNSS applications, the sensitivity of mapping functions to height above the geoid of point of observations are mostly corrected with method and formulas proposed in Niell (1996), in which the height corrections are only concerned in hydrostatic delay mapping function. In Niell (1996), the adopted form of height correction for hydrostatic delay mapping function is linearly dependent on height, and the linear coefficient is empirically chosen for fitting precision purpose. In this work, similar to many current works about modelling mapping factors to operational mapping functions, with the help of ERA-Interim reanalysis data set from the European Centre for Medium-range Weather Forecasts (ECMWF), the sensitivity of both hydrostatic and wet delay mapping factors to height are calculated and preliminary analyzed. As an important part of the work, the needed data set sources, i.e., the tropospheric delays at some ground-based stations along some previously set ray paths with different elevation angles, azimuth angles, heights and time epochs, are generated with a self-generated tropospheric delay ray-tracing package named GTRATS (Gnss Tropospheric delay RAy Tracing Software). The first results show that for a specific not low elevation angle, the variations of both hydrostatic and wet mapping factors to the height are not too obvious; the hydrostatic mapping factors change linearly to height with different performances, while wet mapping factors are not with linearly change, especially for low elevation angles; height corrections with method in Niell (1996) for hydrostatic mapping factors actually perform well in some cases, while maybe correct too much or too less in some stations at some time epochs, thus maybe new correction strategy can be accounted; the height correction should also be concerned for wet delay mapping functions, while there is no obvious, regular and reliable relation or appearance can be observed according to our first results, and more efforts should be made for the examination and investigation of this problem.

How to cite: Wang, M., Li, P., Lu, R., Wang, S., and Zhang, F.: First results about height correction of tropospheric delay mapping function in GNSS applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2522, https://doi.org/10.5194/egusphere-egu24-2522, 2024.

vX2.2
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EGU24-5929
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ECS
Dhiman R. Mondal, Pedro Elosegui, Lucy Brock, Scott Paine, Pedro Mateus, and Virgilio Mendes

The rapidly changing climate is escalating the frequency and intensity of extreme weather events in the Azores, Portugal. It is crucial to comprehend the dynamics of these events to mitigate them. Atmospheric water vapor data from the Global Navigation Satellite System (GNSS) and reanalysis products from an atmospheric general circulation model can be utilized to investigate the dynamics of weather fronts in the Azores Islands. A primary goal of our study is to conduct a comprehensive comparison between GNSS and MERRA2-based atmospheric reanalysis data and derive small-scale atmospheric structures with high-temporal resolution. Using statistical analysis, we will unveil the similarities and discrepancies between the two approaches in capturing atmospheric water vapor patterns. Emphasizing an exploratory methodology, we will showcase our findings using a restricted dataset that centers on specific instances of extreme precipitation witnessed in the Azores Islands.

How to cite: Mondal, D. R., Elosegui, P., Brock, L., Paine, S., Mateus, P., and Mendes, V.: Probing the Dynamics of Extreme Weather Events in the Azores, Portugal, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5929, https://doi.org/10.5194/egusphere-egu24-5929, 2024.