Session G5.2

G5.2 | Atmospheric and Environmental Monitoring with Space-Geodetic Techniques

Atmospheric and Environmental Monitoring with Space-Geodetic Techniques

Co-organized by AS5

Convener: Rosa Pacione | Co-conveners: Maximilian Semmling, Mayra Oyola MercedECSECS, Kyriakos BalidakisECSECS, Henrik Vedel

Orals

| Tue, 25 Apr, 08:30–09:40 (CEST)

Room -2.91

Posters on site

| Attendance Tue, 25 Apr, 16:15–18:00 (CEST)

Hall X2

Posters virtual

| Attendance Tue, 25 Apr, 16:15–18:00 (CEST)

vHall GMPV/G/GD/SM

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.

Orals: Tue, 25 Apr | Room -2.91

Chairpersons: Rosa Pacione, Kyriakos Balidakis, Maximilian Semmling

08:30–08:40

EGU23-1442

G5.2

On-site presentation

Validation of the geostrophic approximation and the potential of long-term radio occultation data for wind field monitoring

Julia Danzer, Irena Nimac, and Gottfried Kirchengast

Global long-term stable wind fields are valuable information for climate analyses of atmospheric dynamics. Given shortcomings of available observations their monitoring remains a challenging task. One promising option for progress are radio occultation (RO) satellite data, where the winds are estimated using the geostrophic approximation. Hence, in this study we focus on two goals, explored through European Re-Analysis ERA5 and RO datasets, using monthly-mean January and July data over 2007-2020 with 2.5° × 2.5° resolution. First, we compare actual and geostrophic ERA5 wind speeds to evaluate the validity of the geostrophic approximation. Second, we test how well ERA5 and RO geostrophic winds agree. We find the geostrophic approximation to work well within 2 m/s accuracy almost globally (5°-85° latitude), especially over the summer hemisphere; larger differences (up to about 5 m/s) may occur in the winter stratosphere. We noticed the effect of large mountain ranges on the wind flow as a wave-like pattern, also in the difference between RO and ERA5 geostrophic winds, pointing to effects of different geopotential height estimations. Generally, RO and ERA5 geostrophic winds showed very good agreement. In the long-term, systematic differences in decadal trends of higher than 0.5 m/s per decade were found at subtropical latitudes, mainly related to observing system changes in the year 2016 that influenced ERA5. Together with the validity of the geostrophic approximation, this indicates that the long-term stability of RO-derived wind field monitoring can provide added value to reanalysis winds, for the benefit of climate monitoring and analyses.

How to cite: Danzer, J., Nimac, I., and Kirchengast, G.: Validation of the geostrophic approximation and the potential of long-term radio occultation data for wind field monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1442, https://doi.org/10.5194/egusphere-egu23-1442, 2023.

08:40–08:50

EGU23-4465

G5.2

On-site presentation

Near realtime tomographic reconstruction of atmospheric water vapour using multi-GNSS observations in Central Europe

Szabolcs Rozsa, Bence Turak, and Abir Khaldi

Continuously operating reference stations (CORS) are widely used to provide near realtime estimates of zenith tropospheric delays and tropospheric gradients to be assimilated in numerical weather models. Due to the latest evolution of global satellite navigation systems and ground based augmentation services, it became feasible to estimate the 3D distribution of wet refractivities in near realtime.

This paper presents a near realtime tomographic reconstuction algorithm utilizing the zenith tropospheric delays and tropospheric gradients obtained from the processing of several GNSS networks in Hungary (HU), Slovakia (SK), Romania (RO) and Ukraine (UK). The estimated zenith tropospheric delays (ZTDs) and tropospheric gradients are used to restore the slant wet delays (SWD) affecting the observed satellite-receiver range. The SWDs are used as input for a tomographic reconstruction algorithm based on the multiplicative algebraic reconstruction technique. The developed software tool includes a stepwise outlier detection module to select the most reliable slant wet delays for the tomographic reconstruction. It provides the wet refractivities in a pre-defined voxel model on an hourly basis over the HU-SK-RO-UA cross-border region.

The derived refractivity profiles have been validated with radiosonde observations. The results show that our GNSS tomography approach could reconstruct the refractivities with the standard deviation of 5 ppm below 3 km of altitude, while the standard deviation decreased to the level of 0.3 ppm at the altitude of 10 km.

The estimated tropospheric delays as well as the refractivity profiles are made available online to the meteorologists community in Little-R format and can be directly assimilated in the Weather Research & Forecast numerical model.

How to cite: Rozsa, S., Turak, 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, 23–28 Apr 2023, EGU23-4465, https://doi.org/10.5194/egusphere-egu23-4465, 2023.

08:50–09:00

EGU23-8800

G5.2

ECS

Virtual presentation

Sensing the tropospheric water vapor from grazing-angle Spaceborne GNSS-R

Yang Wang and Jade Morton

In this work, we investigate a novel method to estimate the tropospheric wet delay, and further the vertically integrated water vapor (VIWV) and its horizontal gradients, using the coherent-reflection GNSS signals received by a CubeSat in the low-Earth orbit (LEO). It can complement existing observation approaches over some polar and ocean regions where GNSS signals can be coherently reflected.

The precise altimetry using coherent-reflection GNSS signal carrier phase measurements has gained popularity over the past couple of decades for the observation of ocean, sea ice, lake, and river surfaces. The troposphere delay error is found to be a major error source for GNSS-R phase altimetry, especially at a low elevation angle. However, if we can model the reflection surface elevation relatively well, then the estimated residual phase from GNSS-R signal can be dominantly contributed by the mis-modeled tropospheric wet delay, and GNSS-R signal can become a new data source for tropospheric water vapor sensing. The GNSS-R approach can observe the horizontal gradients of VIWV along the specular point (SP) track, as the SP moves at high speeds of ~5 km/s, and the spatial resolution is 10s of km by ~1 km.

In the presentation, we will provide examples using Spire Global’s grazing-angle GNSS-R data and comparisons with the ECMWF reanalysis VIWV data. We will also discuss the applicable regions, performance, and error mitigations of the proposed method in estimating tropospheric wet delay and issues to be addressed in the further retrieval of VIWV.

How to cite: Wang, Y. and Morton, J.: Sensing the tropospheric water vapor from grazing-angle Spaceborne GNSS-R, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8800, https://doi.org/10.5194/egusphere-egu23-8800, 2023.

09:00–09:10

EGU23-2615

G5.2

Virtual presentation

Fast observation operator for GNSS tropospheric gradients

Florian Zus, Rohith Thundathil, Galina Dick, and Jens Wickert

Raw data collected at a single Global Navigation Satellite System (GNSS) station allow the estimation of the Zenith Total Delay (ZTD) and the tropospheric gradient. In order to make use of such data in Numerical Weather Prediction (NWP) the observation operators must be developed. Our current observation operator for tropospheric gradients is based on a linear combination of ray-traced tropospheric delays (Zus et al., 2022). Although this observation operator is tuned for high speed and precision it remains difficult to be implemented into NWP Data Assimilation (DA) systems. In this contribution we introduce a simple and fast observation operator which is based on the closed-form expression depending on the north–south and east–west horizontal gradients of radio refractivity (Davis et al., 1993). We run the Weather Research and Forecasting (WRF) model (horizontal resolution of 10km) and find that for the considered geographical region (central Europe) and time period (summer season) the root-mean-square deviation between the tropospheric gradients calculated by the fast and original approach is about 0.15 mm. In essence, the observation operator error is non negligible but acceptable for assimilation. In a first step we implemented the developed operator in our experimental DA system (Zus et al., 2019) and run a series of experiments to check the usefulness of the new approach. We present results from this assimilation experiments where we utilize both simulated and real observations. In the next step we will implement the fast observation operator in the WRF DA system in support of the research project EGMAP (Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction).

Davis, J., Elgered, G., Niell, A., and Kuehn, K.: Ground-based measurement of gradients in the “wet” radio refractivity of air, Radio Sci., 28, 1003–1018, 1993.

Zus, F.; Douša, J.; Kačmařík, M.; Václavovic, P.; Dick, G.; Wickert, J. Estimating the Impact of Global Navigation Satellite System Horizontal Delay Gradients in Variational Data Assimilation. Remote Sens. 2019, 11, 41.

Zus, F., Galina, D., and Wickert, J.: Development of a cost efficient observation operator for GNSS tropospheric gradients, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1079

How to cite: Zus, F., Thundathil, R., Dick, G., and Wickert, J.: Fast observation operator for GNSS tropospheric gradients, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2615, https://doi.org/10.5194/egusphere-egu23-2615, 2023.

09:10–09:20

EGU23-14626

G5.2

ECS

On-site presentation

A generic approach to parameterize the scaling properties of atmospheric delays in InSAR time-series

Gert Mulder, Freek van Leijen, and Ramon Hanssen

The observed phase in time-series of Interferometric Synthetic Aperture Radar (InSAR) products is a combination of primarily differential topography, line-of-sight displacement and atmospheric delay contributions. These components need to be disentangled to derive accurate atmospherical products from InSAR. However, isolating the atmospheric component from InSAR has been proven difficult as it is spatiotemporally highly dynamic and a superposition of two atmospheric states.

Here we propose an approach to parameterize the stochastic properties of the single-epoch atmospheric delay field as a way to represent the atmospheric signal in InSAR.

We found that the atmospheric signal of a time-series of interferograms can be characterized by structure functions, which can be used to isolate the single-epoch structure functions. Using two isotropic and three anisotropic scaling parameters it is then possible to construct a structure function characterizing the atmospheric signal per SAR acquisition. Especially, the isotropic parameters for the small scale and large scale atmospheric delay variations, can be used to characterize the atmospheric signal. For a test set of 150 Sentinel-1 acquisitions, this results in a difference in signal strength of the InSAR atmospheric signal with a factor of about 10 for small scale and 50 for large scale variations.

Our parametrization demonstrates that the scaling properties of the InSAR atmospheric signal for different SAR acquisitions are very similar and can be described using only five parameters. After parameter estimation we can then provide time-series of the expected atmospheric signal using distance and direction only for any combination of points within the InSAR image.

How to cite: Mulder, G., van Leijen, F., and Hanssen, R.: A generic approach to parameterize the scaling properties of atmospheric delays in InSAR time-series, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14626, https://doi.org/10.5194/egusphere-egu23-14626, 2023.

09:20–09:30

EGU23-16054

G5.2

On-site presentation

Assessment of parameters describing the signal delay in the neutral atmosphere derived from VGOS sessions

Rüdiger Haas, Periklis-Konstantinos Diamantidis, Gunnar Elgered, Jan Johansson, Tobias Nilsson, and Tong Ning

The VLBI Global Observing System (VGOS) is the next generation VLBI system for geodetic and astrometric VLBI. It has been designed by the International VLBI Service for Geodesy and Astrometry (IVS) to improve the accuracy and precision of the estimated geodetic parameters by one order of magnitude compared to the so-called legacy S/X VLBI system. During the VGOS design phase, small-scale and rapid variations in the signal propagation delay caused by the neutral atmosphere were identified as one of the major limiting error sources in terms of accuracy of geodetic VLBI. Performing as many observations as possible per time unit to cover the local sky at the stations as uniformly as possible, has been developed as a strategy to address this topic. The VGOS idea is to achieve this goal by employing fast-slewing radio telescopes ,of typically 12–13 m diameter, that are equipped with broad-band receiving devices of reasonably high sensitivity and digital backends with high sampling capability. Compared to standard S/X legacy VLBI sessions, at least a factor of two in the number of observations per station is currently achieved within operational VGOS sessions (VO). Dedicated VGOS Research and Development (R&D) sessions (VR) achieve an even larger number of observations through minimizing the scan lengths.

VGOS is still in its build-up phase and by 2022 the VGOS operational network has reached 10 internationally distributed stations. Among those is the Onsala Space Observatory which is operationally active with its VGOS twin telescopes since 2019. We analyse VGOS sessions of both VO- and VR-series and assess the current ability of VGOS to sense small-scale, rapid variations in the signal propagation delay caused by the neutral atmosphere. We compare the VGOS-derived results to corresponding results from simultaneous observation with co-located instrumentation at VGOS sites, i.e. receiving equipment for Global Navigation Satellite System (GNSS) observations. For the Onsala station we compare also to the results derived from the ground-based microwave radiometer.

How to cite: Haas, R., Diamantidis, P.-K., Elgered, G., Johansson, J., Nilsson, T., and Ning, T.: Assessment of parameters describing the signal delay in the neutral atmosphere derived from VGOS sessions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16054, https://doi.org/10.5194/egusphere-egu23-16054, 2023.

09:30–09:40

EGU23-17311

G5.2

On-site presentation

Assessing long-term ZTD trends for climate

Marcelo C. Santos, Jordan Rees, Kyriakos Balidakis,, Anna Klos, and Rosa Pacione

In this presentation we showcase results of an ongoing effort to assess long-term ZTD trends for eventual use in climate models, either for assimilation or validation. We have been analyzing the ZTD time series estimated from six REPRO3 IGS Analysis Centers (ACs), namely, COD, ESA, GFZ, GRG, JPL, TUG, to compare their long-term trends. Long-term here means 20 years or longer. About thirty stations have been selected globally for this purpose. The estimated ZTD time series have gone through a process of homogenization using ERA-5 derived ZTDs as reference. The homogenized data is then averaged to daily values to minimize potential influences coming from different estimation strategies used by individual ACs. As mentioned, our interest is with the long-term signal. Similar averaging is applied to the ERA-5 ZTDs. Two combinations, using weighted mean and (a robust) least median of squares, are being generated from the six homogenized ACs. The combinations serve as quality control to each ACs. Analysis of the trends generated from each one of the seven ZTD times series is performed looking at their similarities in both time and frequency domains. Results obviously vary depending on the geographical location. For example, for station ALBH, in Canada, inter-AC scatter is 0.47 mm/decade for the trends, 0.11 mm for the annual amplitudes, and 0.29 degrees for the annual phase.

How to cite: C. Santos, M., Rees, J., Balidakis,, K., Klos, A., and Pacione, R.: Assessing long-term ZTD trends for climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17311, https://doi.org/10.5194/egusphere-egu23-17311, 2023.

Posters on site: Tue, 25 Apr, 16:15–18:00 | Hall X2

Chairpersons: Henrik Vedel, Maximilian Semmling, Kyriakos Balidakis

X2.89

EGU23-1405

G5.2

Variability of GNSS PWV in global tropics over different time scales

Zofia Bałdysz, Grzegorz Nykiel, Dariusz B. Baranowski, and Beata Latos

The variability of water vapour in the global tropics has impacts on global circulation patterns through atmospheric teleconnections. Various timescales of its fluctuations strongly affect weather, including interannual to daily changes. Although traditional techniques of water vapour measurements have always been vital in understanding complex thermodynamic processes, they still face some limitations related to e.g. temporal sampling, non-automatic data collection, or cost efficiency. In light of this, global navigation satellite systems (GNSS) can state for a valuable source of precipitable water vapour (PWV) data. Taking advantage of a long time series of collected by International GNSS Service (IGS) observations, we performed an analysis of the variability of 18-year GNSS PWV over the tropics, from interannual to daily time scales. Results have proved, that the GNSS technique is capable of capturing long-term changes in PWV resulting from climate modes, such as El Nino Southern Oscillations or Indian Ocean Dipole. Additionally, analysis of diurnal GNSS variability, together with satellite-based precipitation and cloudiness data, taken from the Tropical Rainfall Measurement Mission gridded dataset (TRMM 3B42 v7) and the global, merged infrared (IR) dataset, have enabled reliable estimation of changes that are in line with various regimes of tropical deep convection. In particular, our results show that the diurnal cycle of PWV and TB were almost entirely dominated by mono-modal distributions. The diurnal cycle of precipitation onshore (continental areas or big islands; continental regime) had a single late afternoon peak, and that offshore (small islands; oceanic regime) had both a midday and a nocturnal peak. The daily amplitude phase shift of PWV and precipitation at onshore stations with a continental regime consistently occurred at the same time, while TB maximum peaked about five hours later. Furthermore, results show that the daily mean and the amplitude of the diurnal cycle of PWV, precipitation, and TB appeared smaller on offshore stations, exhibited to an oceanic regime, than on onshore, continental stations.

How to cite: Bałdysz, Z., Nykiel, G., Baranowski, D. B., and Latos, B.: Variability of GNSS PWV in global tropics over different time scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1405, https://doi.org/10.5194/egusphere-egu23-1405, 2023.

X2.90

EGU23-5596

G5.2

ECS

Troposphere Monitoring Based on Crowdsourced Smartphone GNSS Data

Yuanxin Pan, Grzegorz Kłopotek, Markus Rothacher, Linda See, Rudi Weinacker, Tobias Sturn, Ian McCallum, Vicente Navarro, and Benedikt Soja

Global Navigation Satellite System (GNSS) is an essential tool for troposphere monitoring. Currently, GNSS meteorology depends mainly on the data from geodetic receivers of global or regional networks. However, these geodetic-grade GNSS stations are costly, and thus cannot be densely deployed, especially in less developed regions. Since the release of the Android 7 operating system in 2016, Android smartphones can be used to collect raw GNSS data. Considering that nowadays there are about 3 billion Android smartphones worldwide, a smartphone GNSS data crowdsourcing campaign was launched on March 17^th 2022 as a part of the CAMALIOT project. About 5 TB of raw GNSS observations were collected around the world by more than 12 thousand users of the CAMALIOT Android application. In this contribution, we highlight the results related to the dedicated pipeline developed to process the crowdsourced smartphone GNSS data. Firstly, all the collected data were classified by a machine learning-based model to disregard observations of low quality. It was found that only about 2% of the collected data could potentially be used for troposphere delay estimation. The high-quality observations were then processed in the relative-positioning mode by forming baselines with the nearby geodetic stations. Several crowdsourced data sets were used to demonstrate that the zenith wet delays (ZWD) derived from smartphone data could achieve an accuracy of better than 10 mm. However, uncalibrated phase center variations of the smartphone antennas and multipath errors are still the main limitations to further improve the ZWD estimation. Overall, our study indicates that crowdsourced smartphone GNSS data is promising to densify the existing GNSS networks in terms of troposphere monitoring.

How to cite: Pan, Y., Kłopotek, G., Rothacher, M., See, L., Weinacker, R., Sturn, T., McCallum, I., Navarro, V., and Soja, B.: Troposphere Monitoring Based on Crowdsourced Smartphone GNSS Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5596, https://doi.org/10.5194/egusphere-egu23-5596, 2023.

X2.91

EGU23-8052

G5.2

Optimized Use of Engineered Features from GNSS Reflectometry for Prediction of Significant Wave Heights

Jan Becker and Ole Roggenbuck

GNSS interferometric reflectometry is a well-established technique in Ocean Remote Sensing that can be used for the retrieval of sea surface characteristics. In particular, the evaluation of interference patterns in GNSS signal-to-noise ratio (SNR) observations allows for an estimation of the Significant Wave Height (SWH), e.g. by relating the SWH to the attenuation that is typically present in the oscillating interference pattern for increasing elevation angle of the signal-emitting GNSS satellite.

Recently, we developed new machine learning methods for the analysis of GNSS SNR observation data obtained from the research platform FINO 2 in the Baltic Sea. The core element thereof is the extraction of various engineered features from SNR interference patterns by means of kernel regression and clustering techniques. The various engineered features were used as input for the prediction of the SWH with supervised machine learning models (artificial neural networks, bagged regression trees, linear models). In a case study, these predictions provided a remarkable improvement in accuracy compared to predictions which solely use a common feature stemming from the aforementioned attenuation in the SNR interference pattern.

However, an optimized extraction of information from the various and partially redundant engineered features for the prediction task is desirable, aiming at the reduction of model complexity without reducing predictive performance. This goal is successfully addressed in the present work by applying a forward selection scheme and a principal component analysis for the set of available engineered features. The usage of the engineered features can also be optimized by tuning the hyperparameters of complex supervised machine learning models used for the SWH prediction. Such a tuning is performd by means of a grid search for a random forest model applied to the engineered features. This optimization represents an advancement of the application of the bagged regression trees with an improvement in accuracy of the respective SWH predictions.

The improved methods for SWH prediction at FINO2 are outlined and the impact of the involved optimizations concerning the use of the engineered features is evaluated and discussed in detail in a case study.

How to cite: Becker, J. and Roggenbuck, O.: Optimized Use of Engineered Features from GNSS Reflectometry for Prediction of Significant Wave Heights , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8052, https://doi.org/10.5194/egusphere-egu23-8052, 2023.

X2.92

EGU23-9910

G5.2

ECS

Tropospheric tomography – integration of ground- and space-based GNSS observations

Natalia Hanna and Robert Weber

On the way transmitter-receiver, the Global Navigation Satellite Systems (GNSS) signal is attenuated and delayed by the presence of water vapor. This information serves as the input to the GNSS tomography - a robust technique in water vapor estimation concerning its amount and distribution in the troposphere.

GNSS rays pass through the tomographic grid built over a dense network of ground-based GNSS stations. Due to the constant movement of the GNSS satellites influencing their elevation angle and visibility, the measurement geometry varies in time. The model elements are either over or under-determined during the chosen time span within the area of interest; hence, the system of observation equations is mixed-determined.

However, to enhance the tomographic solution, the model can be supplied with additional data, e.g., from the radio occultation (RO). The RO technique provides the space-based signal delay between the low Earth orbit (LEO) and GNSS satellites. Products obtained from the RO measurements consist of bending angles and vertical dry- and wet-atmosphere data.

In this study, we analyze the COSMIC-1 radio occultation events in the tomographic domain located in the Netherlands in February 2018. The observation system in the ATom GNSS software was extended with the space-based wet refractivity profiles (level 2 data). We compare the obtained 3D wet refractivity field to the GNSS ground-based-only tomographic solution and the radiosonde measurements. The drawbacks and potential development of the applied solution are also discussed.

How to cite: Hanna, N. and Weber, R.: Tropospheric tomography – integration of ground- and space-based GNSS observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9910, https://doi.org/10.5194/egusphere-egu23-9910, 2023.

X2.93

EGU23-10506

G5.2

ECS

High-precision tropospheric product processing and meteorological application based on GNSS observations from aerial-based highly dynamic platforms

Zhenyi Zhang, Weixing Zhang, and Yidong Lou

The Global Navigation Satellite System (GNSS) is an all-weather, high-precision space geodetic technique that can solve positioning results while providing high-precision tropospheric products. Abundant studies have demonstrated the positive impact of traditional ground-based GNSS on numerical weather prediction using data assimilation tools. As tropospheric profile information is promisingly to satisfy the increasing data demand for improving short-range forecast accuracy, it is very important to obtain and assimilate high accuracy, high spatial and temporal resolution, and three-dimensional GNSS tropospheric products. Fortunately, the aerial vehicle market is booming and represented by unmanned aerial vehicle, providing convenient platforms for obtaining richer GNSS tropospheric products at different position and heights. However, there are still some key challenges in processing and assimilating GNSS tropospheric products from such highly dynamic platforms. In our study, we firstly improve the accuracy of aerial-based GNSS tropospheric products up to millimeter level by applying a new data processing method which optimizes the stochastic constraint for GNSS zenith wet delay. Thereafter, we evaluate the impact of those high spatial and temporal tropospheric products on meteorological application by assimilating them into numerical weather models. Experiments are conducted to prove the feasibility of aerial-based GNSS meteorology and point out some topics for further investigation.

How to cite: Zhang, Z., Zhang, W., and Lou, Y.: High-precision tropospheric product processing and meteorological application based on GNSS observations from aerial-based highly dynamic platforms, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10506, https://doi.org/10.5194/egusphere-egu23-10506, 2023.

X2.94

EGU23-13749

G5.2

ECS

3D ground and space-based ray tracing tomography model – methodology and implementation

Adam Cegla, Witold Rohm, Paweł Hordyniec, Gregor Moeller, Estera Trzcina, and Natalia Hanna

One of the indispensable elements of high-resolution weather forecast systems is the provision of reliable initial conditions using observations. Among the methods for collecting meteorological data, besides the quality of measurements, their time and space variability play a crucial role. Hence, GNSS observations stand out as stable, bias-free alternatives for weather stations, radiosondes, or microwave satellites.

Current studies of GNSS observations in weather forecasting give promising results. However, the observations themselves are subject to errors due to their geometry, mainly caused by insufficient vertical and horizontal resolution. Therefore, applying them in an operational forecasting model is challenging. A possible way to solve this is to integrate space and ground-based observations into one tomography model.

The solution should be able to detect local, extreme weather phenomena with repeatable uncertainty and high numerical stability. Hence, we propose a precise 3D ray tracing solution for effective simulations of the ray path between the GNSS satellite and the GNSS receiver (Low Earth Orbiting LEO satellite), along with the ground receiver. Although, the combination of these results in one computationally efficient and stable model is a complex task.

The following step is the 3D ray tracing simulation integration into a modified TOMO2 operator dedicated to the tomography of 3D wet refractivity fields. The ray tracing module collects information on ray points’ refractivity and distance traversed in models’ voxels along the ray path. Then delivers it to mutual observational matrices for ground- and space-based simulations.

This study focuses on the methodology of integrated tomography modeling. Results are compared to the ground-based only GNSS tomography solution and validated with radiosondes profiles. The case studies are based on severe weather events in Poland with RO data delivered by SPIRE company and GNSS ground-based observations produced by UPWr. Numerical Weather Model input comes from European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5.

How to cite: Cegla, A., Rohm, W., Hordyniec, P., Moeller, G., Trzcina, E., and Hanna, N.: 3D ground and space-based ray tracing tomography model – methodology and implementation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13749, https://doi.org/10.5194/egusphere-egu23-13749, 2023.

X2.95

EGU23-14654

G5.2

ECS

Ionospheric Impact on GNSS Reflectometry in the Tropical Region: A Simulation Study with NEDM model

Mario Moreno, Maximilian Semmling, Mainul Hoque, and Jens Wickert

The ionosphere is a layer of Earth's upper atmosphere that is ionized by solar radiation. It plays a crucial role in the propagation of Global Navigation Satellite System (GNSS) signals, as these signals pass through the ionosphere on their way from the GNSS satellite to the receiver. The irregularities in the ionospheric electron density may have a significant impact on the GNSS signals, causing delays and phase and amplitude scintillations.

GNSS reflectometry (GNSS-R) is a promising technique for atmospheric sounding. Multiple studies have been successfully conducted in the recent decade by using GNSS-R ground-based, airborne and spaceborne data e.g., to estimate ionospheric disturbances from the reflected signals. However, further investigations are needed to precisely characterize ionospheric effects for GNSS-R altimetric applications.

This study presents simulation results of ionospheric delay for reflection events in tropical regions. The first-order ionospheric effects are estimated along the ray paths by deriving the slant total electron content from the Neustrelitz Electron Density Model (NEDM). The geometry of the simulated events refers to reflectometry records of the SPIRE satellite constellation and the satellite navigation system GPS on 2021/03/01.

Initial analysis has shown promising results. As solar activity increases (indicated by solar radio flux F10.7 index), an increase in the total ionospheric phase delay is evident. Between 0h and 8h local time, there is a delay of 2 to 10 meters. For the time interval from 8h to 16h, the delay is from 14 up to 22 meters, with the maximum at noon. In the sunset period from 16h to 24h, the ionospheric delay reduces from 9 to 3 meters, respectively. The height above the Earth’s surface at which the highest amount of electron content is found along the ray path is ~290 km. This altitude corresponds to the F-region which has the highest concentration of free electrons. The analyzed events correspond to elevation angles from 5 to 30 degrees. The highest ionospheric delay is found at elevation angles between 10 and 20 degrees also depending on the local time.

How to cite: Moreno, M., Semmling, M., Hoque, M., and Wickert, J.: Ionospheric Impact on GNSS Reflectometry in the Tropical Region: A Simulation Study with NEDM model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14654, https://doi.org/10.5194/egusphere-egu23-14654, 2023.

Posters virtual: Tue, 25 Apr, 16:15–18:00 | vHall GMPV/G/GD/SM

Chairpersons: Mayra Oyola Merced, Maximilian Semmling, Kyriakos Balidakis

vGGGS.1

EGU23-10991

G5.2

Enabling subfield scale soil moisture mapping in near real-time by recycling L-band GNSS signals from drones

Mehmet Kurum, Mehedi Farhad, Volkan Senyurek, and Ali Gurbuz

Accurate measurement of soil moisture (SM) at high spatiotemporal resolutions is one of the critical challenges of site-specific precision agriculture. Traditionally SM is measured manually or using in-situ SM probes scattered in the field. Although these observations are generally accurate and reliable up to the sensitivity level of the SM probe, it is very time-consuming, costly, and inefficient for large heterogeneous fields to acquire high-resolution SM measurements. Over the last several decades, microwave remote sensing approaches have become popular for measuring spatially distributed SM. Several space-borne missions, such as SMAP and SMOS, have been launched to provide surface SM measurements globally. Although all current satellite missions and their SM products are critical for many large-scale research and studies, their coarse spatial resolution (about 40km) makes it impractical for precision agriculture applications.

To enable subfield scale soil moisture mapping in near real-time, our team has recently developed an unmanned aircraft systems ( UAS) -based multi-sensory system with Global Navigation Satellite System (GNSS) reflectometry (GNSS-R), a multispectral camera, and a LIDAR. A down-facing GNSS antenna with a ground plane blocks the direct GNSS signals, and it collects reflected carrier-to-noise density ratio (C/N0) measurements from multiple specular points on the ground for each visible GNSS satellite. The multispectral camera provides spectral images in blue, green, red, red edge, and near-infrared (NIR) bands. The LIDAR offers a 3D representation of the surface and vegetation. Such a comprehensive dataset has been collected in a field under different management practices for the last three years. The study field was organized with a split-plot arrangement and was planted with corn and cotton as the main crops. We have performed 581 flights over the study field and collected more than 4 TB of data, including visual and multispectral images and LIDAR point clouds. More than 2.5 million L-band reflection samples have been collected over the field. In addition, in-situ SM and intense manual SM observations over the field have been collected as ground truth information.

Observed GNSS-R data is dependent not only on the SM but also on the vegetation, surface roughness, topography, soil texture, GNSS satellites' positions, transmitter characteristics, receiver orientation, and flight parameters through a combination of linear and nonlinear relations. To learn such a relationship, we developed a machine learning (ML) model using multiple sensory input features for high-resolution, low-cost, and easily accessible SM mapping for precision agriculture. In this study, we will present the multi-year field campaigns and the development of the ML framework with convolutional and fully connected neural network layers for SM mapping that can utilize multiple imageries jointly with other physical and microwave data and calculate features relevant to SM. During the model development, site and time-independent cross-validation methods are used for better model generalization and performance evaluation.

How to cite: Kurum, M., Farhad, M., Senyurek, V., and Gurbuz, A.: Enabling subfield scale soil moisture mapping in near real-time by recycling L-band GNSS signals from drones, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10991, https://doi.org/10.5194/egusphere-egu23-10991, 2023.

vGGGS.2

EGU23-2992

G5.2

Multi-source water vapor tomography based on ray-tracing technique

Ming Shangguan, Meng Dang, and Xu Cheng

vGGGS.3

EGU23-6426

G5.2

ECS

Seasonal-scale atmospheric water cycle study using ground-based GPS receivers, Aeronet, and NWP models over the central High Atlas Mountains (Morocco)

Khalid Lakhouidsi and Abdelali Fadil

Precipitable water vapor PWV is one of the major greenhouse gases. Monitoring Long-term variations in water vapor concentrations, which have a direct effect as a greenhouse gas and involve amplifying other feedbacks such as albedo and clouds. Monitoring of Precipitable water vapor on a continuous and steady basis has become possible over the recent decades due to the increased ground-based GPS receivers at global scale. However, the High Atlas Mountains remained a data-void zone until recently, when some ground-based GPS receiving stations were deployed to monitor geodynamic movements in the High Atlas belt. This paper investigates seasonal changes in precipitable water vapor, moisture transport, and precipitation over the central High Atlas using data from three ground-based GPS (FSSM, OUCA, and TIOU), Aeronet (AErosol RObotic NETwork) (Saada), GPCP, and NCEP reanalysis II over seven years (2016 to 2022). The ZTD (Zenith Tropospheric Delay) and the PWV results using the GipsyX/RTGx software show significantly better results than the Aeronet PWV measurements based on the acquired data. The reanalysis and observations are in good correlation regarding moisture transport and precipitation. Precipitable water increases in late summer, but precipitation peaks in winter and spring over the central high atlas.

Keywords: Precipitable water vapor, GPS, Zenith total delay, Aeronet, NCEP.

How to cite: Lakhouidsi, K. and Fadil, A.: Seasonal-scale atmospheric water cycle study using ground-based GPS receivers, Aeronet, and NWP models over the central High Atlas Mountains (Morocco), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6426, https://doi.org/10.5194/egusphere-egu23-6426, 2023.

vGGGS.4

EGU23-9059

G5.2

Water vapour monitoring over France using the low-cost GNSS collaborative network Centipede

Pierre Bosser, Julien Ancelin, Marianne Métois, Lucie Rolland, and Maurin Vidal

In recent years, the significant growth of positioning applications has come with the development of low-cost dual frequency Global Navigation Satellite Systems (GNSS) receivers. Such receivers allow high precision positioning to be achieved. They are becoming increasingly popular for geosciences as they allow for large numbers of equipment at affordable prices.

Founded in 2019, the Centipede network is a collaborative permanent GNSS network that aims to offer free real-time centimetric positioning to any user. The network is currently made up of more than 330 low-cost reference stations located mainly in France and has nearly 500 regular users. Since July 2022, the GNSS raw data acquired by the stations forming this network have been archived by the Réseau National GNSS (RENAG) scientific network data centre in order to evaluate their suitability for geosciences applications such as tectonic, sea-level monitoring, troposphere and ionosphere studies.

In this study, we present a first evaluation of the use of this dataset for the retrieval of integrated water vapour content (IWV) over five months in 2022. Troposphere delays are estimated from a PPP analysis and are converted to IWV using a standard procedure. Then, the IWV are compared with those retrieved for GNSS stations from the “conventional” French permanent GNSS networks, the Réseau GNSS Permanent (RGP) and RENAG. The results of these comparisons are conclusive, with root mean square differences in the range of 0.5-2 kg/m2 and high correlations of the time series registered by nearby low-cost and geodetic-class receivers.

These results confirm the high potential of low-cost GNSS networks. The development of such network is a real opportunity for geoscience applications, particularly in poorly instrumented areas. In such areas, their contribution could be especially significant for meteorological or climatological applications for which the monitoring of water vapour by GNSS is widely used.

How to cite: Bosser, P., Ancelin, J., Métois, M., Rolland, L., and Vidal, M.: Water vapour monitoring over France using the low-cost GNSS collaborative network Centipede, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9059, https://doi.org/10.5194/egusphere-egu23-9059, 2023.

vGGGS.5

EGU23-10661

G5.2

Sensitivity of water vapor vertical shape in GNSS atmospheric observation

Mikiko Fujita