G5.2

G5 EDI

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.

Co-organized by AS5
Convener: Rosa Pacione | Co-conveners: Maximilian Semmling, Henrik Vedel, Kyriakos BalidakisECSECS, Norman Teferle, Gregor MoellerECSECS, Karina WilganECSECS, Witold Rohm
Presentations
| Mon, 23 May, 15:10–18:30 (CEST)
 
Room -2.91

Presentations: Mon, 23 May | Room -2.91

Chairpersons: Maximilian Semmling, Karina Wilgan, Norman Teferle
GNSS Reflectometry
15:10–15:20
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EGU22-974
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ECS
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solicited
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Highlight
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Virtual presentation
Clara Chew and Eric Small

Even the youngest child knows that fresh water is crucial for life, and it’s easy to see and appreciate our reservoirs, lakes, and rivers for the numerous services they provide, not only for drinking water but also for transportation and the health of our ecosystems. But inland surface water is both a friend and a foe. Too much of it can be devastating for communities—floods are one of the costliest natural disasters, and they often disproportionally impact the most vulnerable members of those communities. Too little of it, though, can be just as destructive. Years-long droughts empty reservoirs, increase wildfire risk, and can lead to conflict over remaining water resources. Quantifying the amount and extent of inland surface water is thus important for knowing where we lie in this delicate balance between abundance and scarcity.

A variety of approaches to map flood and inundation dynamics already exist, be they stream gage data, hydrologic models, or remote sensing observations from satellites. All of them have advantages and challenges, and none alone provide a complete picture of the extent of surface water at any one particular moment. This presentation will describe a new approach to mapping flooding and inundation dynamics, which can provide complementary information to that which already exists via other sensors, models, or networks. This approach uses spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) observations to infer surface water extent. Currently, the vast majority of spaceborne GNSS-R data come from the Cyclone GNSS (CYGNSS) constellation, a NASA mission comprised of eight small satellites orbiting the tropics.  Here, we will present flood inundation maps derived from CYGNSS data for the full period of record (2017 – present), which are gridded to three km and have a temporal revisit rate of three days. We will discuss the retrieval algorithm, its validation, limitations of our approach, and plans to disseminate the data to the public. Finally, we will comment on the potential of GNSS-R data beyond CYGNSS to provide hydrologic information to the broader research community and other end users.

How to cite: Chew, C. and Small, E.: Mapping Flooding and Inundation Dynamics Using Spaceborne GNSS-R Observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-974, https://doi.org/10.5194/egusphere-egu22-974, 2022.

15:20–15:26
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EGU22-8226
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On-site presentation
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Tianqi Xiao, Milad Asgarimehr, Caroline Arnold, Daixin Zhao, Tobias Weigel, Lichao Mou, and Jens Wickert

 

The capability of Deep Learning (DL) for operational wind speed retrieval from the measured Delay-Doppler Maps (DDMs) is recently characterized. It is shown that such techniques can lead to a significant improvement in the derived atmospheric data products. A global ocean dataset is developed processing the measurements of NASA Cyclone GNSS (CYGNSS). The model is based on convolutional layers for direct feature extraction from bistatic radar cross-section (BRCS) DDMs and fully connected layers for processing ancillary technical and higher-level input parameters. This model leads to an RMSE of 1.36 m/s and a significant improvement of 28% in comparison to the officially operational retrieval algorithm.

From the theoretical knowledge, several error sources are known, the modeling and correction of which is not easy due to their highly nonlinear interaction with other and the dependent parameters. DL is potentially able to learn such trends and correct the associated biases. For instance, rain splash on the ocean surface and swell waves alter the surface roughness, and consequently, the GNSS scattering patterns, which appear as a considerable bias in GNSS-R wind products. The magnitude of such biases is nonlinearly dependent on several technical and environmental parameters including the reflection geometry, and ocean surface state. After a brief introduction to the known physical mechanisms, we discuss how a DL-based fusion with data on bias-causing parameters, can improve the wind speed predictions.

How to cite: Xiao, T., Asgarimehr, M., Arnold, C., Zhao, D., Weigel, T., Mou, L., and Wickert, J.: Deep learning in spaceborne GNSS-R: Recent methodologies and atmospheric products, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8226, https://doi.org/10.5194/egusphere-egu22-8226, 2022.

15:26–15:32
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EGU22-10832
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ECS
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On-site presentation
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Mario Moreno, Maximilian Semmling, Georges Stienne, Wafa Dalil, Mainul Hoque, Jens Wickert, and Serge Reboul

Sea level rise and sea state variability due to climate change and global warming are major research topics in the scientific community. Wind speed (WS) and significant wave height (SWH) are usable parameters to monitor the sea state threats and the impact of the ocean weather conditions in coastal areas. GNSS reflectometry (GNSS-R) has shown considerable promise as a remote sensing technique for ocean parameters estimation. Multiple studies have been successfully conducted in the recent two decades by using GNSS-R ground-based, airborne and spaceborne data to retrieve geophysical properties of the sea surface.

The focus of this study is to investigate the Doppler shift of the reflected signal as observable to estimate the Doppler spread (DS) and determine its correlation with sea state changes, making use of GNSS-R airborne data in coastal areas. An additional aim is to study the possibility of using the Doppler spread as a metric for coherent GNSS reflectometry for applications such as precise altimetry and precise total electron content (TEC) estimates. An experiment was conducted from the 12th to the 19th of July 2019 along Opal Coast, between the cities of Calais and Boulogne-sur-Mer, France. The experiment consisted of multiple flights at an altitude of ~780m (a.m.s.l). The direct and reflected signals were received by dual-polarized (Right-Handed and Left-Handed Circular Polarizations) antenna mounted on a gyrocopter.

A software receiver is used to process the direct and reflected signals from the right-hand channel. The resulting in-phase (I) and quadrature (Q) components (at 50 Hz rate) of the reflected signals are analyzed in the spectral domain every ten seconds to obtain the relative Doppler shift and power estimates. The coherence is established by analyzing the phase observations obtained from I and Q. The sensitivity of the reflected signal estimates and the sea state is determined by the correlation between the Doppler Spread with wind speed and significant wave height. The latter two were obtained from the atmospheric, land and oceanic climate model, ERA5, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF).

Initial results have shown promising performance at a calm sea (WS: 2.9 m/s and SWH: 0.26 m) and grazing angles. Satellites with low elevations (E < 10°) present a Doppler Spread of 0.3 Hz and its Pearson correlations with respect to WS and SHW are 0.89 and 0.75, respectively. The performance is relatively poor for high elevation events (E > 30°). The DS increases up to 2.1 Hz and the correlation decrease to 0.55 and 0.42 respectively. Coherence conditions are still under study; however, preliminary phase analysis reveals coherent observations at events with elevations below 15° and sea state with a significant wave height of 0.26 m.

How to cite: Moreno, M., Semmling, M., Stienne, G., Dalil, W., Hoque, M., Wickert, J., and Reboul, S.: Sea state dependent Doppler spread as a limit of coherent GNSS reflectometry from an airborne platform, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10832, https://doi.org/10.5194/egusphere-egu22-10832, 2022.

Advancements in Tropospheric Parameter Estimation and Assimilation
15:32–15:38
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EGU22-2772
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On-site presentation
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Olivier Bock

Integrated Water Vapour (IWV) measurements from similar or different techniques are often inter-compared for calibration and validation purposes. Results are traditionally interpreted is terms of bias (difference of the means), standard deviation of the differences, and slope and offset parameters of a linear regression between the IWV measurements of the tested instrument with respect to the reference instrument. When the two instruments are located at different elevations, a correction must be applied to account for the contribution of atmosphere between the sites. Therefore, empirical formulations are often used. In this work it is shown that the widely-used model based on a standard, exponential, profile for water vapour density cannot properly correct the contribution of the atmospheric layer on the bias, slope, and offset parameters simultaneously. For example, correcting the bias degrades the slope and offset parameters, and vice-versa, with this model. An alternative method is proposed to derive an empirical model from real profiles observed by radiosondes. The method is developed for the special case of a tropical mountainous area with high IWV contents and strong diurnal and seasonal variations. Its application is illustrated with two examples, i) GPS to GPS comparisons and ii) GPS to satellite microwave radiometer comparisons.

How to cite: Bock, O.: An improved vertical correction method for the inter-comparison of Integrated Water Vapour measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2772, https://doi.org/10.5194/egusphere-egu22-2772, 2022.

15:38–15:44
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EGU22-13374
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Presentation form not yet defined
Julio A. Castro-Almazán, Begoña García-Lorenzo, Casiana Muñoz-Tuñón, and Ignacio Romero

Since proposed by Bevis et al. in 1992, GNSS Meteorology has become a very competitive field, mainly because it provides a relatively cheap, extensive network of stations, with global and 365d/24h coverage. Therefore, the method has been widely validated by comparison with other reference techniques. A majority of the comparisons show both dry or wet bias for the PWV obtained from GNSS, depending on the particular station, as a consequence of such an heterogeneous network working on a vast set of atmospheric scenarios. Thus, most of the effort has been focused on getting the best possible global information, guarantying homogeneity. Instead, the goal of a PWV monitor for Astronomy is not homogeneity, but getting the most out of a particular station. In this work we aimed a high precision and high accuracy real-time monitor supporting IR and μW astronomical observations. We have reviewed the technique and identified the external factors impacting in the final error, including local barometric pressure and temperature measurements, weighted mean temperature based on local vertical profiles, and an independent calibration through a detailed comparison with high vertical resolution radiosondes. We discuss the final error and accuracy achieved, the lower detection limit of the technique, and possible new improvements.

How to cite: Castro-Almazán, J. A., García-Lorenzo, B., Muñoz-Tuñón, C., and Romero, I.: The Precipitable Water Vapour GNSS monitors at the Canary Islands Astronomical Observatories: towards an optimization of the final accuracy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13374, https://doi.org/10.5194/egusphere-egu22-13374, 2022.

15:44–15:50
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EGU22-3450
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ECS
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Virtual presentation
Weixing Zhang, Yidong Lou, Yaozong Zhou, Xianjie Li, Jingna Bai, and Zhenyi Zhang

Wuhan University has joined the E-GVAP program as one of the analysis centers (ACs) since 2019. Data at about 300 global GNSS stations are routinely processed in near-real-time at Wuhan University for tropospheric delay product. Both the single GPS solution and the recent multi-GNSS (GPS/GLONASS/Galileo/BDS) solution have been delivered to E-GVAP. Besides the near-real-time processing, we also work on the long-term historical data reprocessing for climate research and on the real-time processing for atmospheric monitoring. In the reprocessing work, by taking the recent ERA5 and homogenized radiosonde data as reference, we have made comprehensive investigations on the impacts of different models (e.g., the mapping function) and settings (e.g., the cut-off elevation angle) using global data of more than two decades. The long-term water vapor product was used for climate analysis and for inter-comparisons among different techniques (GNSS, radiosonde, reanalysis, MODIS, etc.). In the real-time processing work, we have carried out a four-month real-time water vapor retrieval campaign in three cities of China in 2021 by including the latest BeiDou-3 satellites and by using the high-accuracy satellite product transmitted by BeiDou-3 GEO satellites on the B2b signal. The data processing work in reprocessing, near-real-time and real-time modes at Wuhan University will be systematically introduced and some applications in climate research and atmospheric monitoring will be presented.

How to cite: Zhang, W., Lou, Y., Zhou, Y., Li, X., Bai, J., and Zhang, Z.: Ground-based GNSS data processing for atmospheric water vapor retrieval at Wuhan University and some applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3450, https://doi.org/10.5194/egusphere-egu22-3450, 2022.

15:50–15:56
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EGU22-13491
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Presentation form not yet defined
Assessment of parameters describing the signal delay in the neutral atmosphere derived fromVGOS observations
(withdrawn)
Rüdiger Haas, Jan Johansson, Gunnar Elgered, Periklis-Konstantinos Diamantidis, and Tobias Nilsson
15:56–16:02
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EGU22-1789
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ECS
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On-site presentation
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Kyriakos Balidakis, Florian Zus, and Henryk Dobslaw

The reduction of geophysically induced high-frequency harmonic variations inherent in space geodetic measurements is carried out on the basis of physically-driven empirical models that may vary spatially, under the implicit assumption that they do not vary in time. Motivated by the fact that the parameters driving processes such as the atmospheric tides (largely induced by the absorbtion of ultraviolet and infrared radiation by Ozone and water vapor) that in turn partly excite the oceanic tides are affected by climate change, in this contribution we put the time-invariance modelling assumption to the test with a focus on harmonic deformation and atmospheric delay. To study temporal variations in atmospheric tides, we have analyzed hourly series from ECMWF’s latest reanalysis, the ERA5, including its back-extension. To validate the harmonic estimates and the temporal evolution thereof, we have resorted to two largely independent reanalyses: the MERRA2 and the JRA55. Recovering the evolution of harmonic coefficients has been carried out employing a square-root information filter (SRIF) and a Dyer-McReynolds smoother. Subsequently, the pressure harmonic fields are convolved with load Green’s kernels to simulate crustal displacements and harmonic pressure, temperature, and humidity fields are used to evaluate the refractivity integrals via ray-tracing. We found that the most important high-frequency waves, that is, the solar diurnal and semi-diurnal exhibit marked secular and seasonal variations. We find that harmonic S2 estimates for sites in the tropics from an hourly five-year long time series segment vary by up to 25% depending on the temporal boundaries thereof and the annual amplitudes of the S2 amplitude series from the SRIF can exceed 10 Pa.

How to cite: Balidakis, K., Zus, F., and Dobslaw, H.: On the Temporal Variability of Tidal Atmospheric Signals, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1789, https://doi.org/10.5194/egusphere-egu22-1789, 2022.

16:02–16:08
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EGU22-1493
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ECS
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On-site presentation
Anna Klos, Janusz Bogusz, Rosa Pacione, Vincent Humphrey, and Henryk Dobslaw

We assess the impact of the environmental loading on the Zenith Total Delay (ZTD) time series estimated within the second re-processing campaign of the EUREF Permanent GNSS Network (EPN). In particular, we used one solution provided by the ASI (Agenzia Spaziale Italiana Centro di Geodesia Spaziale, Italy), and two solutions provided by the GOP (Geodetic Observatory Pecny, Czech Republic) EPN analysis centers, along with the combined products. We find that ZTD time series derived within individual solutions are characterized by pure autoregressive noise, which is reduced during the combination in favor of white noise. This reduces the error of ZTD trends and is of great importance for numerous applications, as climate analyses, where trend is taken into account. Combination procedure does not however affect spatio-temporal patterns of ZTD residuals (linear trend and seasonal signals removed beforehand). We observe no impact of non-tidal oceanic loading on the ZTD residuals. Then, we compute ZTD differences from the two GOP solutions, which only differ by unmodelled non-tidal atmospheric loading. We prove that there is a similarity between the ZTD differences and non-tidal atmospheric loading which is strongly demonstrated in terms of unusual loading events, as significant inter-annual signals or large seasonal peaks. As these similarities account for 54% of ZTD differences, this indicates that unmodelled non-tidal atmospheric loading effect contributes to the ZTD residuals interpreted as a noise, affecting errors of trends. Therefore, we recommend that the non-tidal atmospheric loading is included at the observation level, once high-significance of ZTD parameters is required.

How to cite: Klos, A., Bogusz, J., Pacione, R., Humphrey, V., and Dobslaw, H.: Quantifying the impact of environmental loading on Zenith Tropospheric Delays in Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1493, https://doi.org/10.5194/egusphere-egu22-1493, 2022.

16:08–16:14
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EGU22-1079
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Virtual presentation
Florian Zus, Dick Galina, and Jens Wickert

GNSS data collected at a single station allow the estimation of the Zenith Total Delay (ZTD) and tropospheric gradients. In order to make use of such data in numerical weather prediction the observation operators must be developed. The development of a cost efficient observation operator for ZTDs is a straightforward task. On the other hand the development of a cost efficient observation operator for tropospheric gradients is not an easy task. It is also important to bear in mind that for variational data assimilation the corresponding tangent-linear and adjoint operators must be coded.

Our current observation operator for tropospheric gradients is based on dozens of tropospheric delays (Zus et al., 2019). Thereby each tropospheric delay is computed with high precision utilizing a technique called ray-tracing. Clearly, this makes the current observation operator for tropospheric gradients for practical applications too expensive. In this contribution we show how to reduce the computational cost. For example, as expected the high precision with which the tropospheric delays are computed is not too crucial. In addition, the number of tropospheric delays that are involved in the computation of the tropospheric gradients can be reduced. The tropospheric gradients can be understood as a specific linear combination of tropospheric delays. Hence, the difficulty in the derivation of the tangent-linear (adjoint) code for tropospheric gradients lies in the difficulty in the derivation of the tangent-linear (adjoint) code for tropospheric delays. However, this does actually not pose a problem as these codes are available from our previous work.

The output of this study is a cost efficient observation operator (a piece of Fortran code), which, together with its tangent-linear and adjoint operator, is ready to be implemented into existing assimilation systems. One of them is our experimental assimilation system (Zus et al., 2019). Another one will be the assimilation system of the Weather Research and Forecasting (WRF) model in support of the research project EGMAP (Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction) funded by the German Research Foundation (DFG).

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. https://doi.org/10.3390/rs11010041

How to cite: 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, https://doi.org/10.5194/egusphere-egu22-1079, 2022.

Alternative platforms and Sensor Fusion
16:14–16:20
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EGU22-1811
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Virtual presentation
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Pierre Bosser, Victor Bennini, Mohammed Bouasria, Yanis Grit, and Aurélie Panetier

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. The accuracy of these receivers in terms of positioning has been proved. Various studies have also highlighted the ability of these receivers to precisely monitor the atmospheric water vapour. The low cost of such receivers enables large deployment, thus presenting an advantage for many geoscience applications.

In this context, we have developed a hydrographic buoy prototype, equipped with low-cost GNSS receiver and antenna. This buoy was first aimed to be used for the monitoring in delayed time of offshore tides and currents, by a precise point positioning analysis of the GNSS raw data. In addition, the ability of this low-cost GNSS buoy for the water vapour monitoring was also investigated through the assessment of Zenith Tropospheric Delay (ZTD) estimates from the post-processing of the raw data. The comparisons with ZTD estimates from a nearby ground-based GNSS geodetic antenna and the ECMWF fifth ReAnalysis (ERA5), provide pretty good results with RMS differences lower than 10 mm. 

These conclusive results highlight the opportunities for the use of such low-cost systems for meteorology and climatology applications over the Oceans.

How to cite: Bosser, P., Bennini, V., Bouasria, M., Grit, Y., and Panetier, A.: A low-cost GNSS buoy for water vapour monitoring over the Oceans, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1811, https://doi.org/10.5194/egusphere-egu22-1811, 2022.

16:20–16:26
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EGU22-5237
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ECS
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On-site presentation
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Aurélie Panetier, Pierre Bosser, and Ali Khenchaf

The aim of this work is to study the impact of the processing parameterization on the estimation of the zenith total delay (ZTD) from a shipborne GNSS antenna measurement.

For this purpose, we used a simplified observation model, and simulated a realistic configuration of measurements (ephemerids, troposphere, motion of the shipborne antenna). Different sources of error that could affect the measurement were also simulated. The impact of these errors was then evaluated on the estimation by Kalman filtering, using different parameterizations (multi-constellation, solution sampling, random walk process noise for the ZTD estimates, observation weighting, cut-off angle).

As it could have been expected, low cut-off angle (in the range of 3 to 7 degrees) and multi-constellation provide more accurate results. The choice of the data weighting is shown to significantly impact the difference on the estimates, and the use of a square-root of sine function, or uniform weighting of elevation gives the most conclusive results. High value of random walk process noise for ZTD estimates should also be avoided. Globally, the accuracy of the ZTD estimation can be improved up to more than 90% according to the configuration.

The results of this work will be helpful to set up an optimal parameterization for the processing of massive dataset of GNSS measurements acquired from shipborne antennas.

How to cite: Panetier, A., Bosser, P., and Khenchaf, A.: Investigation of shipborne GNSS ZTD retrieval processing parameters by simulation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5237, https://doi.org/10.5194/egusphere-egu22-5237, 2022.

16:26–16:32
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EGU22-6873
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ECS
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Virtual presentation
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Matthias Aichinger-Rosenberger, Gregor Moeller, Roland Hohensinn, and Markus Rothacher

Global Navigation Satellite System (GNSS) receivers are very versatile sensors, which have not only revolutionized positioning and navigation applications, but also provide numerous opportunities for environmental monitoring and remote sensing. Beside the monitoring of long-term ground movements and geodynamics, typical applications include the provision of water vapor estimates for numerical weather prediction (NWP) and climate studies as well as real-time applications such as seismic and geohazard monitoring. The rising number and quality of low-cost GNSS equipment, coupled with innovative telecommunication approaches (Internet of Things), allow for an increased and more cost-effective usage of such devices for those monitoring purposes, and thus foster a fast decision-making process.

An especially beneficial approach is the collocation of GNSS sensors at already existing meteorological or seismic stations. By using available infrastructure for power supply and communication, it can provide a sustainable and energy-effective extension of existing monitoring capabilities. The different parameters collected on-site can be used for cross-validation or provision of corrections for GNSS positioning. Furthermore, through (close to) real-time availability of observations, such collocated stations can aid early-warning systems for many different types of natural hazards (from extreme weather events to landslides and earthquakes). At the Institute of Geodesy and Photogrammetry at ETH Zürich we develop GNSS instrumentation to equip meteorological stations from the SwissMetNet (SMN). The work is carried out in the course of a pilot study in cooperation with MeteoSwiss.

This contribution introduces initial results of the SMN station Zürich-Affoltern, where the first prototype GNSS payload has been installed. We highlight key capabilities of the low-cost GNSS equipment used for these high-precision geomonitoring purposes. Moreover, we discuss concrete ideas for the build-up of a dedicated collocation network within the DACH (Germany-Austria-Switzerland) border area and the opportunities arising from it. These opportunities include the sustainable enhancement of infrastructure for climate change monitoring in the Alpine region as well as the build-up of early-warning systems for multiple types of geohazards. The latter might be achieved through the combination of parameters collected on-site with complementary data (e.g. satellite observations or NWP output) using innovative, data-driven approaches. Finally, we showcase examples and the potential of recent and ongoing works using these data-driven approaches.

How to cite: Aichinger-Rosenberger, M., Moeller, G., Hohensinn, R., and Rothacher, M.: MPG-S-NET: A multi-purpose low-cost GNSS collocation station network , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6873, https://doi.org/10.5194/egusphere-egu22-6873, 2022.

16:32–16:38
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EGU22-4444
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ECS
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Virtual presentation
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Zhenyi Zhang, Weixing Zhang, Yidong Lou, Yaozong Zhou, Jingna Bai, and Zhixuan Zhang

Water vapor is of great importance to the atmosphere and weather research. Airborne GNSS-based tropospheric delay estimation can reveal the atmosphere profile information, which is of more importance than site-based products and acts as an independent observation for meteorological application. On the other hand, progress in the meteorological community such as numerical weather models (NWMs) and forecast operations have the potential to augment GNSS. Many studies have investigated methods for applying NWMs in GNSS, mainly considering NWMs as a priori information. However, most methods implemented are merely suitable for static ground stations. They may not be optimal for dynamic platforms like unmanned aerial vehicles (UAVs) since the troposphere condition changes dramatically with vertical velocity and height. Under this background, we propose an NWM/GNSS tightly coupled method to take the best advantage of NWMs into GNSS data processing. The technique utilizes NWMs as a priori and considers the vertical distribution of the atmosphere to adaptively adjust the stochastic model for tropospheric delay estimation depending on the actual circumstance. The proposed method has been evaluated by an experiment using UAV and Global Forecast System (GFS) and found an improvement of precision and stability of tropospheric delay estimates.

How to cite: Zhang, Z., Zhang, W., Lou, Y., Zhou, Y., Bai, J., and Zhang, Z.: NWM/GNSS tightly coupled tropospheric delay estimation and application on an unmanned aerial vehicle (UAV) platform, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4444, https://doi.org/10.5194/egusphere-egu22-4444, 2022.

Coffee break
Chairpersons: Rosa Pacione, Kyriakos Balidakis, Witold Rohm
17:00–17:06
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EGU22-2732
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ECS
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On-site presentation
Adam Cegła, Paweł Hordyniec, Gregor Moeller, Estera Trzcina, Natalia Hanna, and Witold Rohm

The number of tropospheric observations that are assimilated in current numerical weather forecasting systems, is large. From automatic weather stations, to geostationary satellites, from radiosondes to polar orbiting  microwave satellites. Amid the currently available data sources, GNSS stands out as a bias free, self calibrating, high fidelity temperature and water vapour measurements. 
Until recently GNSS was used weather forecasting only in two ways: as a ground-based point, high-frequency observation of integrated water vapour (IWV) or zenithal integrated observation of temperature and water vapour, or as a sparse space-based profile observation of temperature and water vapour content (provided as a refractivity or bending angle profile). In the last couple of years GNSS tomography, a 3D imaging technique, is gaining attention as a weather model data source. However, low space resolution combined with large uncertainty of the tomography reconstruction makes this technique difficult to apply in operational forecasting. 
Therefore this technique, to be considered as a valuable data source in weather models, has to be numerically stable with known repeatable uncertainty. We believe that a way forward is to combine space-based and ground-based observations using the tomography principle. A way forward is to effectively simulate the signal trajectory between the GNSS transmitter and GNSS receiver (Low Earth Orbiting LEO satellite). 3D ray-tracing modelling of the radio occultation (RO) event based on Numerical Weather Model is performed. The challenge here is to make these ray-tracing results comparable with excess phase observations at the LEO satellite.  
Modelling by 3D ray-tracing is performed by the modified Atmospheric TOMography (ATOM) software with the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 model. This module uses the position of the GNSS satellites as starting point and iteratively propagates the signal path by collecting information on refractive parameters along its path based on nodal points. This study is based on the ten selected RO events from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) report “Optimising tracking strategies for Radio Occultation. Task 1 - the profile dataset.”. Modelling was performed by varying the grid resolution of the ERA5 model and the length of a propagator step size segment to obtain total excess phase delay values. Additionally, Radio Occultation Processing Package (ROPP) 2D ray-tracing multiple phase screen simulation was run to confront obtained from ATOM phase delays. The COSMIC Data Analysis and Archive Center (CDAAC) observed excess phase was used as a reference data source. 

How to cite: Cegła, A., Hordyniec, P., Moeller, G., Trzcina, E., Hanna, N., and Rohm, W.: INTOMO -  Modeling of satellite to satellite excess phase (GNSS to LEO), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2732, https://doi.org/10.5194/egusphere-egu22-2732, 2022.

17:06–17:12
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EGU22-4471
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ECS
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Virtual presentation
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Endrit Shehaj, Othmar Frey, Gregor Moeller, Matthias Aichinger-Rosenberger, Alain Geiger, and Markus Rothacher

Atmospheric interaction with microwaves causes refraction as well as delays of electromagnetic signals. Although the effects are the same for all microwave signals including GNSS and radar, different spatio-temporal sampling of the tropospheric delays, different frequencies resulting in different phase sensitivity to path delays changes, as well as different processing strategies, assumptions and algorithms may lead to differences in the quantified tropospheric estimates. In case of GNSS, the most typical troposphere-related product is the zenith total delay - quantified after the mapping of all GNSS slant delays in the zenith direction. On the other side, double-difference tropospheric slant delays in persistent scatterer interferometry can be obtained in an iterative manner by isolating and subsequently adding the unwrapped low spatial frequency components of the phase residuals to update the estimate of the tropospheric phase. The updated tropospheric phase is then subtracted before the next iteration of point-wise regression-based estimations of topographic corrections and surface displacements. This iterative process is repeated for all scatterers with acceptable standard deviation of the phase residuals until convergence is reached. For comparison of the different tropospheric delays, the spatio-temporal characteristics of GNSS and InSAR observations must be considered. In this work, we use statistical interpolation methods to collocate the GNSS ZTDs with the InSAR measurements. Moreover, as another external (independent) observation we consider 3D fields of numerical weather models, which are integrated in the slant direction to produce (relative) tropospheric delay maps.

As a case study, an alpine region in the Valais area, Switzerland has been selected, which is an interesting scenario due to the high variability of the refractive index over complex terrain. A relatively dense GNSS network, as well as an interferometric time series of Synthetic Aperture Radar (SAR) images are available for the time span of 2008-2013. After introducing the available observations into the collocation approach, we perform the comparison and evaluation of the different tropospheric delays. In addition, we address the following two questions: How should the correct signal part be considered when modeling tropospheric delays using collocation? What is the effect of the GNSS network in terms of size and resolution? This work is an effort in understanding the different estimated/modeled delays, and it aims to set a baseline and a framework for the fusion of GNSS and InSAR tropospheric delays for the monitoring of the atmospheric state over complex terrain.

How to cite: Shehaj, E., Frey, O., Moeller, G., Aichinger-Rosenberger, M., Geiger, A., and Rothacher, M.: Relative tropospheric delay fields by GNSS, InSAR and NWP models in an Alpine Region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4471, https://doi.org/10.5194/egusphere-egu22-4471, 2022.

17:12–17:18
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EGU22-480
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ECS
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Virtual presentation
Csilla Szárnya, István Bozsó, Eszter Szűcs, and Viktor Wesztergom

In the last decades, the development of space geodesy methods has allowed much more accurate observations of planetary surface dynamics than before. The various SAR satellites, like global navigation systems, make their observations in different microwave frequency ranges (1-10 GHz). The Earth's atmosphere is transparent to the microwave signal, but the factors affecting wave propagation (propagation direction and velocity) in the medium are time-dependent, the medium is anisotropic, inhomogeneous and, in the case of the ionosphere, dispersive. Without the correction of such atmospheric artifacts the resulting signal delay is evaluated as a displacement during processing, which can be in the order of tens of meters.

In order to get information about the actual geophysical processes from the displacement values derived from satellite data, the effects on wave propagation must also be taken into account. Radar interferometric methods are particularly suitable for detecting processes with velocities in the order of a few mm/year, but are limited by the lack of quantitative knowledge of the signal delay in the wave propagation, which is of particular importance for the study of processes on a regional scale.

Wave propagation in the neutral atmosphere is mostly distorted by refraction due to water vapour, and the correction is complicated by the dynamic variation of the water vapour content and the inaccurate knowledge of the atmospheric water vapour. In the ionosphere, in addition to Faraday-rotation and electron density dependent refraction, the dispersive nature of the medium is another source of error.

Transient atmospheric phenomena (frontal and thunderstorm systems, ionospheric disturbances, sporadic E layers, etc.), which are predominantly inhomogeneous in nature, further complicate the correction of their effects, but also provide an excellent opportunity to study them. The Sentinel-1 satellite images cover an area  of 250 km x 250 km with  a resolution of 5 m x 20 m. This resolution may prove useful for studying atmospheric inhomogeneities.

In radar interferometric processing, virtual displacements generated by atmospheric phenomena can be investigated in areas that are assumed to be geologically stable and contain well-identified objects that provide strong signal reflection. For the latter, corner reflectors  points specifically designed for this purpose have already been developed.

In the area of Sopron (Hungary), there are 4 such installed permanent artificial reflectors. By including these points and by comparing measurements from the local ionosonde and meteorological station, we have studied the influence of atmospheric phenomena on radar interferometric processing and the applicability of radar interferometry for the study of atmospheric phenomena.

How to cite: Szárnya, C., Bozsó, I., Szűcs, E., and Wesztergom, V.: Impact of transient atmospheric phenomena on radar interferometric processing of Sentinel-1 SAR satellite images, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-480, https://doi.org/10.5194/egusphere-egu22-480, 2022.

17:18–17:24
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EGU22-2067
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On-site presentation
Wenyuan Zhang, Gregor Moeller, Shubi Zhang, Nanshan Zheng, and Nan Ding

Atmospheric water vapor is an important greenhouse gas in the Earth’s atmosphere and significantly impact the thermodynamics of the atmosphere. Due to its dramatic spatio-temporal variability, knowing the three-dimensional (3D) distribution of it is a key goal of atmospheric observation that has been very difficult to attain. However, Global Navigation Satellite System (GNSS) tomography is a promising technique that retrieves the 3D observation of atmospheric water vapor using data from all satellite constellations with a dense station network. In the last decades, various tomography algorithms were developed based on the fixed-scale tomography (FST) system with an unchangeable tomographic domain and voxel. Here we demonstrate the development of a new adaptive variable-scale tomography (AVST) system to determine the optimal dynamic boundary of tomography area and the adaptive resolution of tomography voxel in different atmospheric layers. First, the optimal regular tomography region of each layer is constructed by the boundary optimal approach based on the convex hull algorithm. Subsequently, we define a water vapor index (WVI) and introduce a WVI invariance discretized principle to obtain the variable-scale voxels in different layers.

The proposed method is applied to reconstruct the 3D adaptive water vapor fields over Hong Kong region using the GNSS data in August 2017. For validations, we compared the tomographic water vapor profiles with the reference profiles from radiosonde, and assessed the tomographic overall distributions using independent ERA5 data. The results show that AVST approach is superior to the FST method in both water vapor profiles and 3D distributions, with the mean root-mean-square-error (RMSE) improved by 30% and 23%, respectively. Such improvements highlight the significant potential of the proposed principle for reconstructing the 3D adaptive atmospheric water vapor fields to advance rainfall forecast and meteorological research.

How to cite: Zhang, W., Moeller, G., Zhang, S., Zheng, N., and Ding, N.: Dynamic Tomography Principle: An Adaptive Variable-Scale Approach to GNSS Atmospheric Water Vapor Tomography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2067, https://doi.org/10.5194/egusphere-egu22-2067, 2022.

Extreme weather events and climate
17:24–17:30
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EGU22-2454
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On-site presentation
Michel Van Camp, Olivier de Viron, Alain Dassargues, Laurent Delobbe, and Kristel Chanard

The use of seismometer and gravimeter captures complementary data and brings a new understanding of the July 2021 catastrophic floods in Belgium. A sudden increase in seismic noise coincides with the testimony reporting on a “tsunami” downstream of the Membach geophysical station, along the Vesdre valley. Concurrently, the gravimeter evidenced a rising saturation of the weathered zone, thus showing less and less water accumulation. When rain re-intensified after a 3-hour break, the saturated state of the subsoil induced an accelerated increase of the runoff, as revealed by the Vesdre River flow, in a much stronger way than during the rainy episodes just before. We show that a gravimeter can detect in real-time the saturation of the catchment subsoil and soil. This saturation resulted, when the rain re-intensified, in a sudden, devastating and deadly flood. This opens perspectives to use real-time gravity for early warnings of such events

How to cite: Van Camp, M., de Viron, O., Dassargues, A., Delobbe, L., and Chanard, K.: Extreme hydrometeorological events, a challenge for geodesy and seismology networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2454, https://doi.org/10.5194/egusphere-egu22-2454, 2022.

17:30–17:36
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EGU22-9247
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ECS
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On-site presentation
Zohreh Adavi, Elżbieta Lasota, Witold Rohm, and Robert Weber

Nowadays, weather forecast is an important factor of everyday life that we should be well prepared for. Especially the amount of rainfall can positively or negatively influence our lifestyle. While a moderate rainfall is supportive for agriculture or provision of potable water, too much rainfall can  cause disasters like floods. Therefore, accessing the rain information in near-real-time is beneficial in all aspects. In recent years, GNSS meteorology has been widely utilized as a valuable tool to better interact with the weather conditions in the now-casting and forecasting applications. Nevertheless, rainfall cannot be estimated directly from the GNSS measurements, and therefore some other methods like Artificial Intelligence (AI) are employed to do so. One of the well-known methods in AI is Machine Learning (ML) which focuses on data in order to model or classify various cases such as anomaly detection, earthquake prediction, and rainfall classification. The main objective of this research is to develop a predictive model for accumulated rain every 3 hours for an area populated with 21 GNSS stations of the EUREF Permanent GNSS Network (EPN). For this purpose, we applied different ML methods. The period of interest ranges from 2017 January to 2021 October. The years 2017 to 2020 are used for training, and 2021 is utilized to evaluate the rain model. The temperature, atmospheric pressure, wind speed, wind direction, relative humidity, Zenith Wet Delay (ZWD), Gradients (GN-S, GE-W), Total Electron Contents (TEC) are selected as input parameters in ML. Besides, the rain product from Global Satellite Mapping of Precipitation (GSMaP) is considered as the reference of the model. Finally, the accumulated rain prediction models are derived every 3 hours over the area of interest.

How to cite: Adavi, Z., Lasota, E., Rohm, W., and Weber, R.: Applying Machine Learning Methods to predict rain using GNSS products and meteorological parameters, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9247, https://doi.org/10.5194/egusphere-egu22-9247, 2022.

17:36–17:42
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EGU22-5189
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ECS
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On-site presentation
Karina Wilgan, Galina Dick, Florian Zus, and Jens Wickert

The year 2021 abounded in many severe weather events. The Ahr Valley flood in July, where almost 200 people have lost their lives, was the deadliest natural disaster in Germany since 1962. This shows that heavy precipitation is still one of the most dangerous weather phenomena in Europe. Improving its prediction will lead to better warning systems e.g., against flash floods, debris falls or landslides. One way of improving the forecasts is the assimilation of external data. Several weather services operationally assimilate the data from Global Navigation Satellite Systems (GNSS), mostly the GPS-only zenith total delay (ZTD) or integrated water vapor (IWV) into their Numerical Weather Models (NWMs).  

The current research project of the German Research Foundation DFG (Advanced MUlti-GNSS Array for Monitoring Severe Weather Events, AMUSE), performed in a cooperation of TUB, GFZ and the German Weather Service (DWD), focuses on the assimilation of the advanced multi-GNSS products, especially slant total delays (STDs), into NWMs. In this study, we present the derivation of the multi-GNSS (at the moment GPS/GLONASS/Galileo) tropospheric products at GFZ, i.e. the ZTDs, STDs and tropospheric gradients, for the severe floods in July 2021 in Germany. The obtained parameters are compared with the global NWMs: ERA5 reanalysis of ECMWF and two forecast models: ICON run by the DWD and GFS run by the US Weather Service. The results show that all considered GNSS solutions have a similar level of agreement with the NWMs. However, for the flood regions in the western Germany, the biases from the multi-GNSS solutions are smaller compared to the GPS-only solutions. The NWM parameters are compared also with each other. There are differences between the particular models, however, the differences are smaller than between the NWM and GNSS.

How to cite: Wilgan, K., Dick, G., Zus, F., and Wickert, J.: Multi-GNSS Meteorology at GFZ Potsdam: Severe flood events in Germany in July 2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5189, https://doi.org/10.5194/egusphere-egu22-5189, 2022.

17:42–17:48
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EGU22-1872
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ECS
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Virtual presentation
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Miaomiao Wang

Abstract: Usually, one can use the line-of-sight observations of GNSS (Global Navigation Satellite System) in not only positioning and navigation, but also in describing the medium that they pass through, such as ionosphere and troposphere. Particularly, for a specific station, one can further use obtained tropospheric wet delay in zenith direction to calculate integrated water vapor (IWV) or precipitable water vapor (PWV) with a transfer coefficient related to in-situ meteorological elements. Further, with IWVs or PWVs at a number of stations in a region, one can additionally discover the spatio-temporal distribution of water vapor by using the so-called tomography technology, noted as TWV hereafter. In this work, both retrieved PWVs and TWVs are analyzed and used in monitoring a rainfall process in Hong Kong ranging from 113.86°E to 114.36°E and 22.05°N to 22.40°N. A self-generated water vapor tomography package named GWATOS (GNSS Water vapor Tomography Software) is employed, and the Kalman filtering is used in the package to try to include more information that is valuable. The in-situ GNSS observations with interval of 5s and meteorological observations with interval of 60s at 18 stations in SatRef (Satellite positioning Reference station network) from 1st to 31st of May, 2016 are processed with BERNESE software by using the precise point positioning mode to retrieve the tropospheric delays. The temporal resolution of resulted PWVs is 30 minutes, and spatial resolution of TWVs is 0.05° in longitude, 0.07° in latitude and 15 unequal layers in height. In the experiment, the radiosonde profile data sets with temporal resolution of 12 hours at a station named Kings Park provided by University of Wyoming are used to externally assess GNSS retrieved water vapor. The retrieved PWVs show good consistency with results from radiosonde. The PWVs indicate obvious periods of high values, e.g., at 10th and 21st of May, and frequent variations, e.g., at 27th and 28th of May. As an example, the regional PWVs itself and its variation both in time and in space are analyzed to monitor the earlier-start, ongoing and end stage of rainfall process in 10th of May, where both Red and Amber rainstorm warning are given. The results depict that abnormal period of PWVs are in good agreement with recorded rainfall period by Hong Kong Observatory. The results of retrieved TWVs show good internal and external agreements, the statistics are about 2.0mm and 1.7mm, respectively. The water vapor and its spatio-temporal variations in layers lower than 400 m and between 400m and 800m are investigated emphatically. The results show that both retrieved PWVs and TWVs with high spatio-temporal resolution could reflect rainfall process to some extent, especially the earlier-start and end stage of rainfall. That is to say, GNSS retrieved water vapor could be used to monitor the rainfall process in an auxiliary way. More importantly, no matter PWVs or TWVs could be used to trace the movement of water vapor or its structure both in time and in space, which can be further used in meteorology study for more detail information.

How to cite: Wang, M.: Spatio-temporal distributed water vapor retrieved from GNSS observations and its usage in monitoring a rainfall process in Hong Kong, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1872, https://doi.org/10.5194/egusphere-egu22-1872, 2022.

17:48–17:54
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EGU22-6891
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Virtual presentation
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Junsheng Ding, Junping Chen, and Wenjie Tang

Polar precipitable water vapor (PWV) is expected to increase under a warming climate. However, the conventional approach cannot provide sufficient long-term PWV records due to the high maintenance costs. Fortunately, the exponential explosion in the number of geodetic-quality Global Navigation Satellite System (GNSS) stations has broken this deadlock. Utilizing 20 radiosonde (RS) and 105 GNSS station data over two decades (1994-2020), we analyzed and evaluated the spatial and temporal variability characteristics of PWV in Antarctica and Greenland. The multi-year mean PWV values for Antarctica and Greenland were 5.63 ± 1.67 mms and 7.63 ± 1.35 mms, respectively, with annual standard deviations (STD) of PWV of 1.60 ± 0.77 mms and 3.44 ± 0.92 mms, respectively. In both Antarctica and Greenland, the PWV annual STD shows a gradual increase from the land center to the edge; while the PWV mean decreases with increasing latitude in Greenland, there is no significant latitudinal correlation in Antarctica. There is no significant regional difference in PWV trends, and from the statistical results, both Antarctica and Greenland show an increasing trend from year to year. The PWV trends in Antarctica and Greenland were 0.29 ± 0.77 mm/decade and 0.27 ± 0.64 mm/decade, respectively, with relative PWV trends of 5.98 ± 12.93%/decade and 3.87 ± 8.45%/decade, respectively.

How to cite: Ding, J., Chen, J., and Tang, W.: Increasing trend of Precipitable Water Vapor in Antarctica and Greenland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6891, https://doi.org/10.5194/egusphere-egu22-6891, 2022.

17:54–18:00
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EGU22-13470
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Virtual presentation
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Monia Negusini, Boyan Petkov, Vincenza Tornatore, Stefano Barindelli, Leonardo Martelli, Pierguido Sarti, and Claudio Tomasi

The atmospheric humidity in the Polar Regions is an important factor for the global budget of water vapour, which is a significant indicator of Earth’s climate state and evolution. The Global Navigation Satellite System (GNSS) can make a valuable contribution in the calculation of the amount of Precipitable Water Vapour (PW). We focus on Polar Regions, especially Antarctica. 20-year GPS observations, acquired by more than 40 GNSS geodetic stations, were processed with the purpose of ensuring the utmost accuracy of the PW retrieval, adopting homogeneous, consistent, and up-to-date processing strategies. We also estimated PW from radio-sounding stations (RS), which operate Vaisala radiosondes, co-located with GNSS stations. The PW values from global atmospheric reanalysis model were used for validation and comparison, very high correlation coefficients between times series, have been highlighted both in the Arctic and Antarctica. A small dry bias of RS vs. GPS values was found in the Arctic, while no clear behaviour is present in Antarctica. The PWGPS and PWRS seasonal variations are consistent, as also confirmed by scatter plots.

After validation, long-term trends, both for Arctic and Antarctic regions, were estimated using Hector scientific software, which allows the estimation of trends from time series with temporal correlated noise. We applied a function to estimate the linear trend plus the annual/semiannual signals, and autoregressive noise model AR(1) which best fits the residuals of all investigated PW time series. We investigated also on the choice of the most suitable noise model, this study was useful in determining the residuals of the time series, once the trend and seasonal signals were subtracted. Positive PWGPS trends dominate at Arctic sites near the borders of the Atlantic Ocean. Sites located at higher latitudes show no significant values. Negative PWGPS trends were observed in the Arctic region of Greenland and North America. A similar behaviour was found in the Arctic for PWRS trends. The stations in the West Antarctic sector show a general positive PWGPS trend, while the sites on the coastal area of East Antarctica exhibit some significant negative PWGPS trends, while in most cases, no significant PWRS trends were found. The present work confirms also that GPS is also able to provide reliable estimates of water vapour content in regions where data are sparse and not easy to collect as the Arctic and Antarctic regions are.

How to cite: Negusini, M., Petkov, B., Tornatore, V., Barindelli, S., Martelli, L., Sarti, P., and Tomasi, C.: Water Vapour assessment using GNSS and Radiosondes and long-term trends estimation over Polar Regions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13470, https://doi.org/10.5194/egusphere-egu22-13470, 2022.

18:00–18:06
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EGU22-5923
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Presentation form not yet defined
Fifteen-years of Tropopause Altitude monitoring by GNSS Radio Occultation Measurements
(withdrawn)
Francesco Vespe and Andrea Andrisani
18:06–18:30