Geodesy contributes to Atmospheric Science by providing some of the ECVs of the GCOS. Water Vapor is under-sampled in the current 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 met services. Advancements in NWP models to improve forecasting of extreme precipitation require GNSS troposphere products with a higher resolution in space and shorter delivery times than are currently in use. Homogeneously reprocessed GNSS data have high potential for monitoring WV 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 RT processing of GNSS observations can initialize PPP algorithms, 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-based and space-based geodetic data, use of those estimates in weather forecasting and climate monitoring
•Multi-GNSS and multi-instruments approaches to retrieve and inter-compare tropospheric parameters
•RT and reprocessed tropospheric products for now-casting, forecasting and climate
•Assimilation of GNSS tropospheric products in NWP and in climate reanalysis
•Production of SAR-based tropospheric parameters and use of them in NWP
•Methods for homogenization of long-term GNSS tropospheric products
•Studies of the delay properties of the GNSS signals for propagation experiments
•Usage of NWP data in GNSS data processing
•Techniques on retrieval of soil moisture from GNSS observations and of ground-atmosphere boundary interactions
•Estimates and methods using GNSS-R for the detection and characterization of sea level and sea ice changes
•Usage of satellite gravity observations for studying the atmospheric water cycle.
This session is also related to the activities of IAG Inter-Commission Committee on "Geodesy for Climate Research".
vPICO presentations: Thu, 29 Apr
The microwave signals passing through the troposphere are delayed by refraction. Its high variations, both in time and space, are caused mainly by water vapor. The tropospheric delay used to be considered only as a source of error that needed to be removed. Nowadays, these delays are also a source of interest. The tropospheric delays or integrated water vapor are being assimilated into nowcasting or numerical weather prediction (NWP) models. Moreover, long time series of tropospheric observations have become an important source of information for climate studies. On the other hand, the meteorological data support the space-geodetic community by providing models that can be used to reduce the troposphere impact on the signal propagation. Furthermore, the delays calculated by one microwave technique can be used to mitigate the errors in others.
There are several ways of observing the troposphere, especially considering water vapor. The classical meteorological are: in-situ measurements, radiosondes or radiometers, which allow to sense the amount of water vapor directly. Another, indirect way of observing the water vapor distribution is by using the Global Navigation Satellite Systems (GNSS). This method is called GNSS meteorology. Other microwave techniques such as Very Long Baseline Interferometry (VLBI) or Interferometric Synthetic Aperture Radar (InSAR) are also capable to retrieve the atmospheric information from their signals.
This contribution shows an overview of the troposphere sensing techniques and their applications. We present multi-comparisons of the tropospheric parameters, i.e. refractivity, tropospheric delays in zenith and slant directions and integrated water vapor. The integration of the different data sources often leads to an improved accuracy of the tropospheric products but requires a careful preparation of data. The combination of the data sources allows for using the techniques of complementary properties, for example InSAR with very high spatial resolution with GNSS observations of high temporal resolution. With the emergence of new technologies, some traditional ways of tropospheric measurements can be augmented with the new methods. For example, we have tested meteo-drones as an alternative to radiosondes. The comparisons with GNSS data shows a good agreement of the drone and microwave data, even better than with radiosonde. Moreover, we present the results of the GNSS data assimilation into NWP models and the developments towards multi-GNSS, real-time assimilation of advanced products such as slant delays and horizontal tropospheric gradients.
How to cite: Wilgan, K., Rohm, W., Bosy, J., Geiger, A., Siddique, M. A., Dick, G., and Wickert, J.: Tropospheric products as a signal of interest – overview of troposphere sensing techniques, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5929, https://doi.org/10.5194/egusphere-egu21-5929, 2021.
In climate research, the role of water vapour can hardly be overestimated. Water vapour is the most important natural greenhouse gas and is responsible for the largest known feedback mechanism for amplifying climate change. It also strongly influences atmospheric dynamics and the hydrologic cycle through surface evaporation, latent heat transport and diabatic heating, and is, in particular, a source of clouds and precipitation.
Atmospheric water vapour is highly variable, both in space and in time. Therefore, measuring it remains a demanding and challenging task. The Zenith Total Delay (ZTD) estimated from GNSS observations, provided at a temporal resolution of minutes and under all weather conditions, can be converted to Integrated Water Vapour (IWV), if additional meteorological variables are available. Inconsistencies introduced into long-term time series from improved GNSS processing algorithms, instrumental, and environmental changes at GNSS stations make climate trend analyses challenging. Ongoing re-processing efforts using state-of-the-art models aim at providing consistent time series of tropospheric data, using 24+ years of GNSS observations from global and regional networks. GNSS is reaching the “maturity age” of 30 years when climate normal of ZTD/IWV (and horizontal gradients) can be derived. Being not assimilated in numerical weather prediction model reanalyses, GNSS products can also be used as independent datasets to validate climate model outputs (ZTD/IWV). However, what is the actual use of GNSS ZTDs in climate monitoring? What are the advantages of using GNSS ZTDs for climate monitoring? In addition, what would be the best ZTD time series to serve the climate community?
The presentation will provide a review of the progress made in and the status of using GNSS tropospheric datasets for climate research, highlighting the challenges and pitfalls, and outlining the major remaining steps ahead. We will show examples demonstrating the benefits for climate monitoring brought by using GNSS ZTD and/or IWV datasets in complement to other observations.
This contribution is related to the activities of JWG C.2: Quality control methods for climate applications of geodetic tropospheric parameters, https://iccc.iag-aig.org/joint-work-groups/216, of the IAG Inter-Commission Committee on "Geodesy for Climate Research" (ICCC).
How to cite: Pacione, R., Santos, M., Dick, G., Jones, J., Pottiaux, E., Rinke, A., Van Malderen, R., and Elgered, G.: Ground-based GNSS for climate research: review and perspectives, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9087, https://doi.org/10.5194/egusphere-egu21-9087, 2021.
Global Navigation Satellite System (GNSS) tropospheric path delays provide an important tool for studying Precipitable Water Vapor (PWV) variations. Here, we process and analyze PWV time series extracted from the Survey Of Israel Active Permanent Network (SOI-APN) GNSS ground receivers in the Eastern Mediterranean region. We derive the annual and seasonal PWV diurnal cycles along with the PWV long-term trends, annual and inter-annual variations. The data period spans from 5 to 21 years, ensuring its suitability for studying the PWV variations at different time scales. For the diurnal cycles, we focus on the summer months (JJA), where the Mediterranean Sea Breeze (MSB) plays a dominant role in transporting humidity inland. We find that for most stations, the diurnal amplitude in summer is the highest compared to the seasonal mean. Moreover, using the PWV peak hour in the coastal and highland stations, we detect a frontal MSB propagation from the coastline eastward inland combined with northern winds enhancement due to the Coriolis force. The peak hour is also correlated with the distance from the Mediterranean Sea shore, substantiating the MSB’s role as a key driver of the PWV diurnal variability during summer months. In addition, a strong correlation between the PWV diurnal cycle and the atmospheric Mixing Layer Height (MLH) diurnal variations is found using ceilometer data, suggesting that the MLH modulates the PWV. For the annual cycles, the PWV monthly mean values and variability are high in the summer months (JJA) however, Sep and Oct supersede the JJA values where Oct has the highest variability in all stations. We suggest that the Red-Sea Trough (RST) synoptical system plays a dominant factor in shifting the mean PWV annual peak values from the summer months to Oct. This is further substantiated by harmonic analysis which reveals a non-negligible semi-annual mode with peaks at Apr and Oct when the RST synoptical system is most frequent. The PWV inter-annual variations as represented by the monthly mean anomalies are consistent between all stations, thus suggesting a common regional driver. Moreover, a comparison between the PWV station average anomalies and the ERA5 (the European Centre for Medium-Range Weather Forecasts' latest global reanalysis) regional mean anomalies show a correlation of 0.95. Furthermore, a correlation of 0.72 was found between the regional mean moisture flux anomalies at 750 hPa taken from ERA5 and the station average PWV anomalies, implying that moisture flow accounts for most of the inter-annual variability, however the significance of the 750 hPa pressure level remains ambiguous. In the long term, we find an increasing regional mean trend of ~ 0.5 mm/decade for the whole data period (1998-2019) whereas for the last decade (2010-2019) we find a mean trend of ~ 1 mm/decade suggesting an accelerated moistening of the Eastern Mediterranean region.
How to cite: Ziskin Ziv, S., Alpert, P., Yair, Y., and Reuveni, Y.: Precipitable water vapor from GPS tropospheric path delays over the Eastern Mediterranean: trends, diurnal and long-term variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1804, https://doi.org/10.5194/egusphere-egu21-1804, 2021.
How far apart can two space geodetic sites be located to consider the integrated water vapor (hereinafter IWV) trends as equal, from a statistical viewpoint? How to do efficient feature selection with a given IWV time series? To address these questions, we utilize spatio-temporal variations of long-term IWV trends that were estimated employing very long baseline interferometry (VLBI), Global Navigation Satellite Systems (GNSS), and numerical weather prediction data (ERA5 reanalysis). We estimate coefficients for several spatial covariance functions; Hirvonen's model proves to be the most precise for our area of interest, Greater Europe. We find that the effective spatial resolution is around 56 km (for error level (p) < 0.05). Our investigations indicate that among else, altitude and proximity to the ocean are key factors affecting the IWV trend decorrelation lengths. We find good agreement between the spatially varying decorrelation lengths and established climate classification systems such as the latest Köppen-Geiger model. Moreover, the IWV trend variation as a function of data span and temporal resolution has been investigated. We find that varying the temporal resolution from one hour up to 30 days does not yield a statistically significant difference (p < 0.05) in the IWV trend and its uncertainty, provided the inherent autocorrelation is factored in and the data span remains. We also find that given the IWV time series length, the spread calculated from the estimated trends varying the start point of the time series, follows an exponential decrease σ(Δt) = 22Δt -1.7 + 0.008.
How to cite: Bamahry, F., Balidakis, K., Heinkelmann, R., and Schuh, H.: Estimating the effective spatio-temporal resolution of integrated water vapor trends based on VLBI, GNSS, and weather model data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4524, https://doi.org/10.5194/egusphere-egu21-4524, 2021.
Water vapour is a key variable of the water cycle and plays a special role in many atmospheric processes controlling the weather and climate. Nowadays, extreme weather events, such as storms, floods, landslides, heat waves and droughts are the main concerns of society. The Global Navigation Satellite System (GNSS) is one of the few tools that can be used as an atmospheric water vapour sensor and, simultaneously, provide continuous, unbiased, precise and robust atmosphere condition information. A significant impact on the tropospheric parameter determination in the processing of satellite observations has undoubtedly GNSS antenna phase centers model.
Therefore, the aim of our study is to investigate the impact of different GNSS antenna calibration models on the quality of the tropospheric parameter series applied for climate applications. We analyse the zenith total delays (ZTD) obtained from GNSS data processing and afterwards converted integrated water vapour (IWV). Three years of GNSS data collected at 40 European Reference Frame (EUREF) Permanent GNSS Network (EPN) stations were processed with the NAPEOS software. Precise Point Positioning (PPP) technique utilizing European Space Agency (ESA) precise satellite orbits and clocks was used to estimate the parameters. Several different processing variants were processed and inter-compared. The first group of solutions was obtained by applying the International GNSS Service (IGS) type-mean Phase Center Correction (PCC) models. In the second and third groups of solutions, PCC models from respectively individual field robot calibration and calibration in an anechoic chamber were used. All solutions were processed using GPS and Galileo observations. Moreover, in order to validate and assess the quality of the GNSS solutions, the tropospheric parameters obtained from ERA5 reanalysis were compared with GNSS estimates.
In general, the results of the study show that the NAPEOS software can provide high quality GNSS tropospheric delay time series. The initial results indicate that the impact of applying different PCC model calibrations is not negligible. ZTD estimates obtained from variants using ROBOT and IGS14 calibration are closer to ERA5 than estimates from variants that used calibrations in an anechoic chamber. In addition, multi-GNSS processing variants are closer to ERA5 than GPS only or Galileo only processing variants. The results also depend on the equipment (receiver and antenna) of the stations. Validation against the data from climate reanalysis confirms that all GNSS approaches provide high-quality ZTD estimates. Furthermore, there is a high agreement in the IWV distributions between GNSS and ERA5.
How to cite: Stępniak, K. and Krzan, G.: Analysis of tropospheric parameter time series obtained with various types of GNSS antenna phase center models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10694, https://doi.org/10.5194/egusphere-egu21-10694, 2021.
Tropospheric delay is one of the major error sources for space geodetic techniques such as Very Long Baseline Interferometry (VLBI) and Global Navigation Satellite System (GNSS). In this study, we compared the agreement of tropospheric zenith wet delay (ZWD) seasonal variations derived from VLBI and GNSS observations at 8 stations that are located at all around the globe. We have analysed time series of 8 years, starting in 2012 until end of 2019. Results show that VLBI_ZWD present clear seasonal variations which depend on the location of each station, in the tropics the variability is more pronounced than in mid-latitudes or polar regions. Furthermore, the VLBI_ZWD also shows a reasonably good agreement with seasonal fit model. When comparing zenith wet delays derived from co-located GNSS and VLBI stations at cut-off elevation angle, they agree quite well, which is proved by the high correlation coefficients, varying from 0.6 up to 0.95. The biases between the techniques are in mm level and standard errors of the whole time series are in few centimetres.
How to cite: Liu, V. J., Nordman, M., and Zubko, N.: Comparison of Tropospheric Zenith Wet Delay from VLBI and GNSS Estimations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2304, https://doi.org/10.5194/egusphere-egu21-2304, 2021.
The observations of geodetic Very Long Baseline Interferometry (VLBI) are affected by the troposphere, and this effect needs to be considered in the VLBI data analysis. The normal way of doing this is to estimate the zenith tropospheric delays and tropospheric gradients as additional parameter in the analysis. However, due to the poor geometric distributions of the observations in some VLBI sessions, like the Intensives, the tropospheric parameters cannot be estimated with a high accuracy. An alternative is to use external information on the tropospheric delay from Numerical Weather Prediction Models (NWM). Due to the increasing accuracy of the NWM, this alternative is becoming more and more interesting. In this work, we use tropospheric delays from the fifth ECMWF reanalysis, ERA5, in the analysis of VLBI data and evaluate the impacts on the results. We study the impact of different types of VLBI sessions, like Intensives, local networks, and global networks. The results of this study will show to what extent ERA5 data can be used to correct the tropospheric delays in geodetic VLBI. Furthermore, the results also give information on the accuracy of the tropospheric delays from NMW.
How to cite: Nilsson, T. and Balidakis, K.: Calibrating the Tropospheric Delays of VLBI Observations using Numerical Weather Prediction Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11956, https://doi.org/10.5194/egusphere-egu21-11956, 2021.
Atmospheric ties are induced by differences between the set-up of observing geodetic systems at co-location sites, are mainly attributed to frequency and position, and are usually quantified by zenith delay and gradient component offsets derived by weather models or in situ instuments.. Similar to local ties, they could be applied to combine datasets from several space geodetic techniques, thus contributing to the improvement of the realization of terrestrial reference frames (TRF). Theoretically, atmospheric ties are affected only by the height differences between antennas at the same site and meteorological conditions. Therefore, atmospheric ties could be determined analytically based on meteorological information from in situ measurements or weather models. However, there is often a discrepancy between the expected zenith delay differences and those estimated from geodetic analysis, potentially degrading a combined atmospheric ties solution should tight constraints be used. In this study, we set up a GNSS experiment campaign on the rooftop of a building in Telegrafernberg that offers unobscured data coverage for one month. We compared the estimated zenith delay and gradients from GNSS stations in this experiment, applying atmospheric ties from (1) meteorological data from the Global Pressure and Temperature model 3 (GPT3), (2) ERA5 reanalysis, and (3) in-situ measurements, as well as corrections derived from ray tracing (Potsdam Mapping Functions, PMF). The results show that atmospheric ties employing GPT3, ERA5, in-situ measurements, and ray tracing has an excellent and comparable performance in term of bias mitigation, but not in term of standard deviation, for zenith delay. Moreover, the unexpected bias in zenith delay was identified in the antenna with radome installation. A significantly large bias was identified in estimated gradients; the source of this discrepancy has been traced back to unmitigated multipath effects in this experiment.
How to cite: Kitpracha, C., Heinkelmann, R., Ramatschi, M., Balidakis, K., Männel, B., and Schuh, H.: Validation of tropospheric ties at the test setup GNSS co-location site in Potsdam, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15818, https://doi.org/10.5194/egusphere-egu21-15818, 2021.
The dynamics of water vapor distribution in the troposphere, measured with Global Navigation Satellite Systems (GNSS), is a subject of weather research and climate studies. With GNSS, remote sensing of the troposphere in Europe is performed continuously and operationally under the E-GVAP (http://egvap.dmi.dk/) program with more than 2000 permanent stations. These data are one of the assimilation system component of mesoscale weather prediction models (10 km scale) for many nations across Europe. However, advancing precise local forecasts for severe weather requires high resolution models and observing system. Further densification of the tracking network, e.g. in urban or mountain areas, will be costly when considering geodetic-grade equipment. However, the rapid development of GNSS-based applications results in a dynamic release of mass-market GNSS receivers. It has been demonstrated that post-processing of GPS-data from a dual-frequency low-cost receiver allows retrieving ZTD with high accuracy. Although low-cost receivers are a promising solution to the problem of densifying GNSS networks for water vapor monitoring, there are still some technological limitations and they require further development and calibration.
We have developed a low-cost GNSS station, dedicated to real-time GNSS meteorology, which provides GPS, GLONASS and Galileo dual-frequency observations either in RINEX v3.04 format or via RTCM v3.3 stream, with either Ethernet or GSM data transmission. The first two units are deployed in a close vicinity of permanent station WROC, which belongs to the International GNSS Service (IGS) network. Therefore, we compare results from real-time and near real-time processing of GNSS observations from a low-cost unit with IGS Final products. We also investigate the impact of replacing a standard patch antenna with an inexpensive survey-grade antenna. Finally, we deploy a local network of low-cost receivers in and around the city of Wroclaw, Poland, in order to analyze the dynamics of troposphere delay at a very high spatial resolution.
As a measure of accuracy, we use the standard deviation of ZTD differences between estimated ZTD and IGS Final product. For the near real-time mode, that accuracy is 5 mm and 6 mm, for single- (L1) and dual-frequency (L1/L5,E5b) solution, respectively. Lower accuracy of the dual-frequency relative solution we justify by the missing antenna phase center correction model for L5 and E5b frequencies. With the real-time Precise Point Positioning technique, we estimate ZTD with the accuracy of 7.5 – 8.6 mm. After antenna replacement, the accuracy is improved almost by a factor of 2 (to 4.1 mm), which is close to the 3.1 mm accuracy which we obtain in real-time using data from the WROC station.
How to cite: Hadas, T., Marut, G., Kapłon, J., and Rohm, W.: Real-time and near real-time ZTD from a local network of low-cost dual-frequency GNSS receivers., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5465, https://doi.org/10.5194/egusphere-egu21-5465, 2021.
Electromagnetic signals, as broadcasted by Global Navigation Satellite Systems (GNSS), are delayed when travelling through the Earth’s atmosphere due to the presence of water vapour. Parametrisations of this delay, most prominently the Zenith Total Delay (ZTD) parameter, have been studied extensively and proven to provide substantial benefits for atmospheric research and especially the Numerical Weather Prediction (NWP) model performance. Typically, regional/global networks of static reference stations are utilized to derive ZTD along with other parameters of interest in GNSS analysis (e.g. station coordinates). Results are typically used as a contributing data source for determining the initial conditions of NWP models, a process referred to as Data Assimilation (DA).
This contribution goes beyond the approach outlined above as it shows how reasonable tropospheric parameters can be derived from highly kinematic, single-frequency (SF) GNSS data. The utilized data was gathered at trains by the Austrian Federal Railways (ÖBB) and processed using the Precise Point Positioning (PPP) technique. Although the special nature of the observations yields several challenges concerning data processing, we show that reasonable results for ZTD estimates can be obtained for all four analysed test cases by using different PPP processing strategies. Comparison with ZTD calculated from ERA5 reanalysis data yields a very high correlation and an overall agreement from the low millimetre-range up to 5 cm, depending on solution and analysed travelling track. We also present the first tests of assimilation into a numerical weather prediction (NWP) model which show the reasonable quality of the results as between 30-100 % of the observations are accepted by the model. Furthermore, we investigate means to transfer the developed ideas to an operational stage in order to exploit the huge benefits (horizontal/temporal resolution) of this special dataset for operational weather forecasting.
How to cite: Aichinger-Rosenberger, M., Hanna, N., and Weber, R.: Tropospheric delay parameters derived from GNSS-tracking data of a fast-moving fleet of trains, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14662, https://doi.org/10.5194/egusphere-egu21-14662, 2021.
The Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign was conducted from September 2019 to October 2020. It aimed to observe the Arctic region's environmental parameters, considered to be the epicenter of the effects of climate change. On this occasion, a multi-GNSS antenna was deployed on the R/V Polarstern. This installation aims mainly at estimating tropospheric delays, a proxy for the determination of atmospheric water vapor content. The number of observations of this type in the marine - and moreover polar - domain remains extremely limited so far. This experiment is also a good opportunity to carry out a comparative study of the tropospheric delay solutions that can be provided by different geodetic processing software. The underlying idea is to evaluate the repeatability of the different products and the overall state-of-the-art accuracy. We propose here to process the GNSS data acquired during the polar campaign with several packages (namely Bernese GNSS Software, GINS, TRACK, and CSRS-PPP) and compare the results and their agreement level. The data are also validated from observations made on land by GNSS stations at Bremerhaven (Germany), Tromsø (Norway) & Ny Ålesund (Svalbard), the VLBI station of Ny Ålesund, and the ECMWF ERA5 numerical model.
How to cite: Sakic, P., Männel, B., Semmeling, M., and Wickert, J.: A comparative study of different tropospheric delay solutions applied to GNSS observations of the MOSAiC expedition., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15396, https://doi.org/10.5194/egusphere-egu21-15396, 2021.
The mean temperature weighted with water vapor pressure (Tm) is an important parameter to obtain precipitable water vapor (PWV) from the Global Navigation Satellite Systems (GNSS) observations. This study investigates the possible impacts of equatorial troposphere on Tm estimates and its relation with surface temperature Ts. We calculated Tm in Indonesia from a Numerical Weather Model at nine InaCORS sites. We used 3-hourly ERA5 pressure, temperature, and humidity profiles for the year 2019. We found that Tm and surface temperature Ts in Indonesia have low correlation, less than 0.4. Seasonal and site-specific Tm-Ts relationships have slightly higher correlation, although the values can vary significantly. The highest correlation of around 0.7 is found at site CPUT in Kalimantan. We calculated Tm at nine additional stations in Kalimantan and found that stations located farther from the coast tend to have higher correlation, independent of the seasons. This suggests that Tm is also influenced by the vicinity to the coast. Based on our findings, the use of a general Tm-Ts relationship in Indonesia may not be appropriate. Further studies are necessary to develop an improved Tm over Indonesian region.
How to cite: Putri, N., Boehm, J., Wijaya, D. D., Kuntjoro, W., Tanuwijaya, Z., and Pradipta, D.: On the determination of weighted mean temperature in Indonesia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10531, https://doi.org/10.5194/egusphere-egu21-10531, 2021.
As one of the most important components of the global hydrologic cycle, atmospheric water vapor shows significant variability in both space and time over a large range of scales. This variability results from the interactions of many different factors, including topography and the presence of specific atmospheric processes. One of the key regions for affecting global climatic variations lies in the sub-Antarctic zone over the Southern Ocean with its Antarctic Circumpolar Current and the associated Antarctic Convergence. There, in this cold and maritime region, lies South Georgia Island with its weather and climate being largely affected by both the dominating ocean currents and the westerly winds in this zone. While the island forms an important outpost for various surface observations in this largely under sampled and extremely remote region, it also forms a barrier for these winds due to its high topography. This, in turn, leads to various local meteorological phenomena, such as warm Foehn winds, which have a significant impact on the near-surface meteorology and contribute to the accelerated glacier retreat observed for the northeast of the island.
Surface meteorological data have been available for several stations near King Edward Point (KEP) in South Georgia for much of the 20th century. Since 2013 and 2014, Global Navigation Satellite System (GNSS) data have been available at five locations around the periphery of the island. In this study, we investigate the consistency between the different surface meteorological data sets and along with GNSS Precipitable Water Vapour we use these to analyse historic Foehn events.
How to cite: Erkihune, E., Hunegnaw, A., and Teferle, F. N.: Investigation of Foehn Events on South Georgia Island using Meteorological Surface Data and GNSS Precipitable Water Vapour, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16200, https://doi.org/10.5194/egusphere-egu21-16200, 2021.
Signals from the Global Navigation Satellite System (GNSS) travel through the whole atmosphere and encounter fluctuations of the index of refraction. The long-term variations of the tropospheric refractive index delay the signals, whereas its random variations correlate with the phase measurements. The power spectral density of microwave phase difference can be derived from physical considerations by combining results from the Kolmogorov theory and electromagnetic wave propagation. Four different dominant noise regimes are expected. Their cutoff frequencies can be estimated with the unbiased Whittle Maximum Likelihood estimator; They provide information about the scale lengths of turbulence which are directly linked with the size of the eddies or swirling motion present in the free atmosphere. Dependencies of these parameters with the satellite geometry or the time of the day pave the way for a better comprehension of how tropospheric turbulence acts as correlating GNSS phase observations. The result is less empirical modeling of GNSS phase correlations to improve the positioning results and avoid an overestimation of their precision. We use GPS single differences from 290 m distant antenna positions recorded during two days in 2013 in a common clock experiment at the Physikalisch Technische Bundesanstalt in Braunschweig Germany to explain our methodology, based on adequate filtering of the residuals to mitigate multipath effects.
How to cite: Kermarrec, G. and Schön, S.: Estimation of the scale lengths of turbulence from GPS single difference phase observations , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-58, https://doi.org/10.5194/egusphere-egu21-58, 2021.
The study investigates Swarm data including in-situ electron density (ED) measured by Langmuir Probes (LP) and total electron content (TEC) from precise orbit determination (POD) GNSS receivers in time of Chile-Illapel earthquake (EQ) and tsunami in 2015. The research is based on the symbiosis of Swarm data, ground GNSS data and seismic records combined with the information on EQs and tsunamis. The FFT-based filtering and short-term Fourier transform (STFT) analysis are used in detection of seismic ionospheric disturbances (SID) in ED from LP and POD TEC data. The classification of the spectral characteristics of disturbing along-track signals is supported by their simultaneous search in ground GNSS observations, which gives an opportunity for the validation of the spectral recognition. Ground GNSS data, due to several tens of satellites and thousands of stations, provide the only full spatiotemporal view on SIDs and enable the inspection of their spatial shapes, spatial relations and speeds. The location of dense ground GNSS networks is however limited to selected places. Swarm and other LEO satellite data, in turn, are globally distributed, but they are dense only along the orbital tracks. Therefore, 1D nature of Swarm along-track observations, fast satellite movement and limited chance for spatiotemporal correlation due to the non-repeating orbits, strongly require spectral analysis for better recognition of the signals. The detection of SIDs from along-track Swarm data is also complicated due to the variety of disturbing signals occurring in the ionosphere, and the spectral analysis is also crucial there. STFT spectral approach to along-track Swarm data gives an opportunity for distinguishing the signals of different origin. The analyses of Swarm data provide interesting observations of ionospheric disturbances not only directly related with the largest EQ events and tsunami, but also occurring during entire periods of enhanced seismic activity and at larger distances from EQ epicenter. The disturbing signals triggered by the largest EQs and tsunami were also observed. However their amplitude in the ionosphere is not always such dominating as the amplitude of some other, associated disturbances on the neighboring days. This difference in scale can suggest that the electron disturbances in the ionosphere are rather more generally related to the crustal motion and seismic activity, than solely correlated with large EQs.
How to cite: Jarmolowski, W., Wielgosz, P., Krypiak-Gregorczyk, A., Milanowska, B., and Haagmans, R.: Seismic ionospheric disturbances related to Chile-Illapel 2015 earthquake and tsunami observed by Swarm and ground GNSS stations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8218, https://doi.org/10.5194/egusphere-egu21-8218, 2021.
The H2020 TWIGA - Transforming Weather Water data into value-added Information services for sustainable Growth in Africa - project aims to establish various services in sub-Saharan Africa for a better management of water resources by linking satellite, in-situ and modelled information. The delivery of timely and accurate weather forecasts is one of the envisaged services. GNSS (Global Navigation Satellite Systems) and SAR (Synthetic Aperture Radar) data provide information on the atmospheric water vapor content, which can be assimilated into Numerical Weather Prediction (NWP) models. The assimilation enables these models to exploit observations for a better simulation of the atmospheric dynamics and the subsequent improvement of the forecasts. The activities related to GNSS, SAR and NWP integration are presented in what follows.
As for GNSS, the modeling of ionospheric errors was investigated for the recently deployed single-frequency low-cost sensors in Uganda. A quality assessment of three different algorithms (ANGBAS, SEID, goSEID) for synthetic L2 observations reconstruction, evaluating the impact on the Zenith Total Delay (ZTD) estimation, was carried out. The three methods show good performances with an overall accuracy ranging between 0.1 and 1 cm when the corrections are computed from geodetic stations at distances up to 65 km from the target receiver. Additionally, an operational system for the retrieval of near real-time GNSS ZTD was implemented. It shows a precision lower than 1 cm, compatible with the target requirements for the assimilation into NWP models.
GNSS is also used to perform the orbital corrections of the SAR products, reducing the large-scale errors like phase trends and biases. The merging of multiple Sentinel-1 frames to cover extended areas requires large computational resources. Work is ongoing to deal with the computationally intensive unwrapping of large interferograms. Moreover, the removal of ionospheric delays, which are not related to the water vapor content, is under development.
Concerning NWP, the Weather Research and Forecasting (WRF) model has been used, at cloud-resolving scales, to test the sensitivity of the simulations of three heavy rainfall events (in Uganda and in South Africa) to the Planetary Boundary Layer (PBL) and the microphysical numerical schemes. Non-local PBL schemes are found to outperform the local PBL scheme considered in the study, because they better describe the vertical atmospheric mixing. In parallel, by exploiting a multiphysics set of numerical simulations in West Africa, it was found that the spatial variability of the surface heat fluxes significantly affects the lower atmospheric dynamics. This happens through a differential heating of the atmosphere across soil moisture gradients. Experiments on the assimilation of water vapor data are ongoing.
How to cite: Meroni, A. N., Mascitelli, A., Barindelli, S., Petrushevsky, N., Manzoni, M., Molinari, M. E., Gatti, A., Tagliaferro, G., Lagasio, M., Parodi, A., Realini, E., Monti-Guarnieri, A. V., and Venuti, G.: Towards the integration of GNSS, SAR and NWP for heavy rainfall forecast in sub-Saharan Africa within the TWIGA project, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5704, https://doi.org/10.5194/egusphere-egu21-5704, 2021.
GNSS Tomography is a promising tool to reconstruct the wet refractivity field (Nw) related to water vapor due to the continuous pass of GNSS rays through the atmosphere. To improve observation geometry compared to a sole GPS/ Glonass system scenario, applying further multi-GNSS observations in GNSS Tomography has become an essential research point in the recent decade. Therefore, the aim of this presentation is to investigate the impact of different constellations to solve the ill-posed inverse problem to retrieve a wet refractivity field by focusing on Galileo's effect on the accuracy of the estimated refractivity. Regarding this, the designed models loosely constrained due to provide an optimum situation for assessing the influence of Galileo constellation in the tomography solution. Test computations are based on data from a regional RTK-GNSS network close to Vienna operated by the Austrian power-supply company EVN (EnergieVersorgung Niederösterreich) and mostly located in the west part of Austria. The span DoYs 233-246 in August 2019 was chosen as a period of interest due to the high precipitation during that time. Consequently, we have considered the following processing schemes: 1- GPS+ Glonass (GR), 2- GPS+ Galileo (GE), and 3- GPS + Glonass + Galileo (GRE) to generate the reconstructed Nw field by means of the in-house Tomography software TOMTRP. Furthermore, as the Slant Tropospheric Delays (SWDs) and corresponding residuals are used as input data for GNSS tomography, so the impact of the mentioned schemes to estimate SWDs has been investigated here. Finally, in order to analyze the efficiency of the three schemes, the reconstructed refractivity profiles are compared to radiosonde profiles available in that area.
How to cite: Adavi, Z. and Weber, R.: Impact of the Galileo constellation on GNSS Tropospheric Tomography , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-41, https://doi.org/10.5194/egusphere-egu21-41, 2021.
The numerical weather model starts from the initial state of the Earth's atmosphere in a given place and time. The initial state is created by blending the previous forecast runs (first-guess), together with observations from different platforms. The better the initial state, the better the forecast; hence, it is worthy to combine new observation types. The GNSS tomography technique, developed in recent years, provides a 3-D field of humidity in the troposphere. This technique shows positive results in the monitoring of severe weather events. However, to assimilate the tomographic outputs to the numerical weather model, the proper observation operator needs to be built.
This study demonstrates the TOMOREF operator dedicated to the assimilation of the GNSS tomography‐derived 3‐D fields of wet refractivity in a Weather Research and Forecasting (WRF) Data Assimilation (DA) system. The new tool has been tested based on wet refractivity fields derived during a very intense precipitation event. The results were validated using radiosonde observations, synoptic data, ERA5 reanalysis, and radar data. In the presented experiment, a positive impact of the GNSS tomography data assimilation on the forecast of relative humidity (RH) was noticed (an improvement of root‐mean‐square error up to 0.5%). Moreover, within 1 hour after assimilation, the GNSS data reduced the bias of precipitation up to 0.1 mm. Additionally, the assimilation of GNSS tomography data had more influence on the WRF model than the Zenith Total Delay (ZTD) observations, which confirms the potential of the GNSS tomography data for weather forecasting.
How to cite: Hanna, N., Trzcina, E., Kryza, M., and Rohm, W.: TOMOREF operator as a tool to improve weather forecasts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7150, https://doi.org/10.5194/egusphere-egu21-7150, 2021.
Global Navigation Satellite Systems (GNSS) sense the atmosphere remotely and provide low-cost, high-quality information about its state. Nowadays, radio occultation (RO) profiles from space platforms and tropospheric delays from ground-based stations are routinely assimilated in Numerical Weather Models (NWM).
In spite of provision of valuable information for weather forecasting, both space- and ground-based data have significant limitations. The RO technique has low horizontal resolution and does not provide reliable profiles in the first 3-5km of the troposphere. Whereas, the station-specific integrated value of troposphere are sparse and pose a problem to NWM adjoint operator for correcting model fields at different heights. These deficiencies could be resolved by the GNSS tomography technique that utilizes an inverse Radon transform to derive the 3D refractivity distribution over certain troposphere space. The combination of space-based and ground-based observations in the tomographic model will enable us to increase the number of intersections of GNSS signals and improve the refractivity solution within individual model locations.
The aim of this research is to harness the full potential of Space 4.0 era, rapidly growing numbers of RO and GNSS satellite constellations as well as low-cost GNSS ground-based networks worldwide. We will not only use current infrastructure but also examine impact of future constellations on model performance. 3D model of refractivity from dense observations should be an excellent tool in weather prediction. Our previous research proves that the assimilation of the GNSS tomography outputs into the NWM improves relative humidity and the short-term weather forecasts. Therefore, the research goal of this project is to assess the benefit of integrated tomography model on the severe weather prediction and urban scale weather models.
How to cite: Rohm, W., Hordyniec, P., Möller, G., Kryza, M., Trzcina, E., and Taszarek, M.: Combination of ground-based and space-based GNSS tomography (2021-2025), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13464, https://doi.org/10.5194/egusphere-egu21-13464, 2021.
With its high temporal resolution, unique mesoscale sampling scale and full weather capability, GNSS is now contributing as an important tool for monitoring the global atmospheric environment. The GNSS tropospheric zenith delay and the corresponding precipitable water vapor data (PW) are already widely applied in many weather models. High precision GNSS processing also estimates tropospheric delay gradients, which contain azimuthal isotropic information about the state of the atmosphere. However, the application of GNSS tropospheric delay gradients is not yet fully explored because of several obstacles. First, it suffers from the satellite constellations geometry and multipaths, and the gradients estimations are noisier than the zenithal delays. Second, the delay gradients were first developed for positioning purposes. GNSS tomography takes advantage of the delay gradients but requires a dense GNSS network. Here we introduce a new method to obtain the line-of-sight wet refractivity from a stand-alone GNSS receiver. We assume that the wet refractivity is mainly governed by a scale height (exponential law) and that the departures from the decaying exponential can be mapped as a set of low degree 3D Zernike functions and Chebyshev polynomials. We show up examples of inversion with data acquired at the IGS station in Tahiti, French Polynesia. We will also discuss the possibility of joint inversions with other measurements, using radiosonde data as an example.
How to cite: Peng, F., Fei, L., Jianguo, Y., and Barriot, J.-P.: Line of Sight Refractivity from a standalone GNSS Receiver and Collocated Radiosonde data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6615, https://doi.org/10.5194/egusphere-egu21-6615, 2021.
The conventional Global Navigation Satellite System (GNSS) processing is typically contaminated with errors due to atmospheric variabilities, such as those associated with the mesoscale phenomena. These errors are manifested in the parameter estimates, including station coordinates and atmospheric products. To enhance the accuracy of these GNSS products further, a better understanding of the local-scale atmospheric variability is necessary. As part of multi-GNSS processing, station coordinates, carrier phase ambiguities, orbits, zenith total delay (ZTD) and horizontal gradients are the main parameters of interest. Here, ZTD is estimated as the average zenith delay along the line-of-sight to every observed GNSS satellite mapped to the vertical while the horizontal gradients are estimated in NS and EW directions and provide a means to partly account for the azimuthally inhomogeneous atmosphere. However, a better atmospheric description is possible by evaluating the slant path delay (SPD) or slant wet delay (SWD) along GNSS ray paths, which are not resolved by ordinary ZTD and gradient analysis. SWD is expected to provide better information about the inhomogeneous distribution of water vapour that is disregarded when retrieving ZTD and horizontal gradients. Usually, SWD cannot be estimated directly from GNSS processing as the number of unknown parameters exceeds the number of observations. Thus, SWD is generally calculated from ZTD for each satellite and may be dominated by un-modelled atmospheric delays, clock errors, unresolved carrier-phase ambiguities and near-surface multipath scattering.
In this work, we have computed multipath maps by stacking individual post-fit carrier residuals incorporating the signals from four GNSS constellations, i.e. BeiDou, Galileo, Glonass and GPS. We have selected a subset of global International GNSS Service (IGS) stations capable of multi-GNSS observables located in different climatic zones. The multipath effects are reduced by subtracting the stacked multipath maps from the raw post-fit carrier phase residuals. We demonstrate that the multipath stacking technique results in significantly reduced variations in the one-way post-fit carrier phase residuals. This is particularly evident for lower elevation angles, thus, producing a retrieval method for SWD that is less affected by site-specific multipath effects. We show a positive impact on SWD estimation using our multipath maps during increased atmospheric inhomogeneity as induced by severe weather events.
How to cite: Hunegnaw, A., Ejigu, Y. G., Teferle, F. N., and Elgered, G.: Multi-GNSS Slant Wet Delay Retrieval Using Multipath Mitigation Maps, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16243, https://doi.org/10.5194/egusphere-egu21-16243, 2021.
GNSS reflectometry (GNSS-R) ability to remote sense the Earth’s surface is affected by an atmospheric bias, as pointed out by several recent studies. In particular, sea level altimetry retrievals are biased in proportion to the reflector height, while by-products, such as tidal amplitudes, are underestimated. Previously, we developed an atmospheric ray-tracing procedure to solve rigorously the three-point boundary value problem of ground-based GNSS-R observations. We defined the reflection-minus-direct or interferometric delay in terms of vacuum distance and radio length. We clarified the roles of linear and angular refraction in splitting the total delay in two components, along-path and geometric. We introduced for the first time two subcomponents of the atmospheric geometric delay, the geometry shift and geometric excess. Finally, we defined atmospheric altimetry corrections necessary for unbiased altimetry retrievals based on half of the rate of change of the atmospheric delays with respect to sine of elevation angle. Later, for users without access to ray-tracing software, we developed closed-form expressions for the atmospheric delay and altimetry correction. The first expression accounts for the angular component of refraction (bending), leading to a displaced specular reflection point. The second one accounts for the linear component (speed retardation) in a homogeneous atmosphere. The expressions are parametrized in terms of refractivity and elevation bending, which can be obtained from empirical models, such as the GPT2 or Bennet’s, or fine-tuned based on in situ pressure and temperature. We also provide a correction for the satellite elevation angle such that the refraction effect is nullified. We validated these expressions against rigorous ray-tracing results and showed that the discrepancy is caused by assumptions in the derivation of the closed formulas. We found the corrections to be beneficial even for small reflector heights, as approximated half of the atmospheric effect originates above the receiving antenna at low satellite elevation angles.
How to cite: Nikolaidou, T., Santos, M., Williams, S. D. P., and Geremia-Nievinski, F.: Validation of closed-form expressions for the atmospheric altimetry correction in ground-based GNSS reflectometry based on rigorous ray-tracing, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13118, https://doi.org/10.5194/egusphere-egu21-13118, 2021.
The dielectric properties of sea ice differ significantly from the open-water surface when we consider the L-band frequency range of GNSS signals. In contrast to water, the signal’s penetration into sea ice can reach several decimeters depending on properties like salinity, temperature and thickness. Exploiting these different dielectric properties is a key to use GNSS for sea-ice remote sensing. For this purpose, GNSS reflectometry measurements have been conducted over the Arctic Ocean during the MOSAiC expedition (Multidisciplinary drifting Observatory for the Study of Arctic Climate). A combined receiver setup was used that allows the here described reflectometry study and another study for atmosphere sounding. The setup was mounted, in close cooperation with the Alfred-Wegener-Institute (AWI), on the German research icebreaker Polarstern that drifted during nine months of the expedition with the Arctic sea ice.
Here, an initial study is presented that focuses on the expedition’s first leg in autumn 2019 when the ship started drifting at about 85°N to 87°N in the Siberian Sector of the Arctic. Profiles of sea-ice reflectivity are derived with daily resolution considering reflection data recorded at left-handed (LH) and right-handed (RH) circular polarization. Respective model predictions of reflectivity are assuming a sea-ice bulk medium or a sea-ice slab. The later allows to include the effect of signal penetration down to the underlying water. Results of comparison between LH profiles and bulk model confirm the reflectivity contrast (about 10 dB) between sea ice and water. The particularly low level of LH reflectivity in the late observation period (December 2019) indicates the presence of low-saline multiyear (MY) ice. A bias due to snow accumulating on the ice surface may occur. A snow-extended reflection model, driven by additional snow data, can help in future for clarification.
Anomalies of observed reflectivity with respect to bulk model predictions are especially obvious at lowest elevation angles. According to the model, the slope of profiles at low elevations is about 1.0 to 1.2 dB/°. The observation shows significantly lower values (< 0.5 dB/°) including negative slopes. A comparison of LH results with the ice slab model provides clarification. The anomalies are induced by signal penetration leading to interference pattern of reflections from the ice’s surface and bottom. Slope retrievals quantify the anomaly and allow a coarse estimation of the mean sea-ice temperature (about -10°C in December 2019) based on the slab model predictions. Further investigations are needed to better understand sea-ice reflectivity at RH polarization. RH profiles show a response to sea ice and features at low elevation angles that cannot be explained by current reflection models.
As a conclusion, GNSS reflectometry is sensitive to dielectric sea-ice properties. Estimates of ice type/salinity and temperature are reported based on LH observation data. These findings will be exploited to further strengthen the application of GNSS signals for sea-ice remote sensing. Future studies on GNSS observations from ships and satellites are anticipated.
How to cite: Semmling, M., Wickert, J., Kreß, F., Hoque, M., Divine, D., and Gerland, S.: Dielectric sea-ice properties examined by GNSS reflectometry: Findings of the MOSAiC expedition, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13513, https://doi.org/10.5194/egusphere-egu21-13513, 2021.
The radiation balance of our planet affect climate system that showing signs of breaking down due to the rising temperatures, melting of ice and water flows to the oceans from glaciers. In the last decade, GNSS Meteorology and Reflectometry methods are increasingly used for global climate change studies that provides important parameters such as water vapor in the troposphere and ice/sea level measured based on reflected signals. The main purpose of the study is retrieving meteorological and physical parameters of the Earth's surface in the Antarctica to contribute monitoring climate change. For this purpose, dual antenna and single antenna GNSS stations were specially designed within the scope of TUBITAK research project 118Y322 to produce output by combining an ultrasonic sensor to detect real-time ice/sea level. These two GNSS stations including meteorological station were installed on Horseshoe Island during 4th National Antarctic Science Expedition of Turkey (TAE-4). It is believed that these stations will contribute to monitor global climate change by providing important information about troposphere and physical characteristics of Earth surface. In this study, the processes and objectives from the design works of the stations to their installation in Antarctica are explained.
How to cite: Selbesoglu, M. O., Yavasoglu, H. H., Karabulut, M. F., Gulal, V. E., Karaman, H., Kamasak, M. E., Oktar, O., and Ozsoy, B.: Ground-Based GNSS Meteorology and Reflectometry Studies on Horseshoe Island during the 4th National Antarctic Science Expedition of Turkey: Installation and configuration of sea/ice level and water vapor monitoring stations., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15952, https://doi.org/10.5194/egusphere-egu21-15952, 2021.
NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission has continued to provide measurements of land surface specular scattering since its launch in December 2016. CYGNSS’s operates in a GNSS-R configuration in which CYGNSS satellites together with GPS satellites form a bistatic radar geometry with GPS satellites acting as transmitters and CYGNSS satellites acting as receivers. The fundamental GNSS-R measurement obtained using the CYGNSS observatories is the delay-Doppler map (DDM), from which normalized radar cross section (NRCS) estimates are derived. The sensitivity of CYGNSS measurements to a wide range of surface properties has motivated their use for soil moisture retrievals.
This presentation reports an updated analysis of soil moisture retrieval errors using a previously reported time series soil moisture retrieval algorithm that considers a multi-year CYGNSS dataset. The presentation also reports recent progress in which further simplifications to the proposed algorithm are introduced that limit its need for ancillary soil moisture data and promote use in an operational capacity. This is accomplished, in part, through the incorporation of a recently developed global Level-1 coherence detection methodology and the use of a soil moisture climatology.
Soil moisture is sensed using a time-series retrieval in which NRCS ratios derived from CYGNSS measurements are used to form a system of equations that can be solved for a times series of surface reflectivities. While the NRCS exhibits a dependence on a wide range of properties such as soil moisture, soil composition, vegetation cover, and surface roughness, NRCS ratios in consecutive acquisitions, at sufficiently low latency, exhibit a direct proportionality to reflectivity ratios that are a function of soil permittivity and therefore soil moisture. The dependence of NRCS ratios on reflectivity facilitates a location dependent inversion of reflectivity to soil moisture through a dielectric mixing model. The use of NRCS ratios however results in N-1 equations for the N soil moistures in the time series, thereby necessitating the incorporation of additional information typically expressed in terms of maximum and/or minimum soil moisture (or reflectivity) values over the time series when solving the system. These values can be obtained either from ancillary data from other systems or from a soil moisture climatology as incorporated in this presentation.
Retrieved moisture values from the updated algorithm are compared against observed values reported by the Soil Moisture Active Passive (SMAP) mission. The findings suggest that there exists potential for using GNSS-R systems for global soil moisture retrievals with an RMS error on the order of 0.06 cm3/cm3 over varied terrain. The dependence of the algorithm’s retrieval error on land cover class, soil texture, and moisture variability trends will be reported in detail in this presentation.
How to cite: Al-Khaldi, M., Johnson, J., and Gleason, S.: Progress in Time Series Soil Moisture Retrievals Using the Cyclone Global Navigation Satellite System Mission, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8572, https://doi.org/10.5194/egusphere-egu21-8572, 2021.
Climate change has been a major worldwide concern over the last decades. One of its consequences is an acceleration of the coastal erosion in littoral areas such as the Opal Coast, North of France. In this regard, one of the topics of the multidisciplinary, state funded, project MARCO (Recherche marine et littorale en Côte d’Opale) is the highly space and time resolved study of sea state in the English Channel.
As part of MARCO, this study has been focused on Global Satellite Navigation Systems Reflectometry (GNSS-R), a bistatic radar technique that uses the signals broadcasted by GNSS satellites as signals of opportunity. A GNSS-R receiver analyzes the signals reaching the receiver directly as well as the signals previously reflected off the Earth surface. Remote sensing application of GNSS-R considers today properties of water bodies, land and ice surfaces. In this work, the objective is to retrieve sea state and related wind speed information from the analysis of direct and reflected GNSS signals.
Several sets of GNSS front-end data have been recorded along the Opal Coast, between the cities of Calais and Boulogne-sur-Mer, between the 12th and 19th of July 2019. The signals were sensed by a dual polarization (Right-Handed and Left-Handed Circular Polarizations) antenna mounted on a gyrocopter. Four datasets of ~18min obtained at an altitude of ~780m above sea level at a speed of ~95 km/h are analyzed by studying the RHCP signals received from 9 GPS satellites for each flight. Considering the altitude of the copter, the major axis of the observed first Fresnel zone is of 25m, 70m and 950m for respective satellite elevation angles of 85° (maximum observed), 30° (regular) and 5° (minimum observed). The raw data is sampled at a frequency of 16.368MHz. The in-phase and quadrature components, for both the direct and reflected signals, are obtained at a rate of 50 Hz. The sea state dependent surface reflectivity is estimated every minute.
The signals are processed using a software receiver by means of Delay, Phase and Frequency Locked tracking Loops (DLL, PLL, FLL), aided by a modeling of the difference between the direct and reflected paths for the DLL of the reflected signal. The phasors of the resulting in-phase and quadrature components of the reflected signal are analyzed in the spectral domain in order to determine their coherency and subsequently retrieve the sea state. A rough sea yields reflections from a large surface area, resulting in a non-coherent mixture of phasors and a spread peak in the reflected signal spectrum. A calm sea yields specular reflection from small surface area resulting in a spectrum with a sharp peak. Preliminary results show Pearson correlation coefficients between the spectral spread of the peak and ERA5 wind speeds of 0.61 (high elevations) to 0.94 (low elevations).
An important contribution of the airborne GNSS-R system applied in this work is the high spatial resolution of the data. The main perspective of this work is to further improve its time resolution, up to 50Hz.
How to cite: Stienne, G., Moreno, M., Semmling, M., Reboul, S., and Wickert, J.: Airborne system for coastal sea state estimation using GNSS-Reflectometry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11341, https://doi.org/10.5194/egusphere-egu21-11341, 2021.
Soil moisture remote sensing on a global scale has been an active area of research over the past few decades due to its essential role in agriculture and in the prediction of some natural disasters. In this regard, GNSS-Reflectometry (GNSS-R) is proven as an efficient tool for the measurement of soil moisture content using remote sensing techniques. GNSS-R is a bi-static radar technique that uses the L-band GNSS signals as sources of opportunity to characterize Earth's surface, due to the fact that the reflected signals are often affected by the properties of the reflecting surface. In the context of this work, it is important to detect and fastly reach the area of interest (reflecting surface) for which the soil moisture content shall be monitored. A GNSS-R setup onboard a gyrocopter meets all the requirements of our application. This paper is dedicated to the study of airborne GNSS-R techniques for soil moisture monitoring using a low-altitude airborne carrier with a high rate (1ms for GPS C/A) carrier-to-noise ratio (C/N0) observations.
To cope with the rapid displacement of the satellites footprints along the receiver trajectory, high rate (1000 Hz rate) C/N0 observations are processed. For this purpose, real flight experimentation has taken place on October 19, 2020 for 45 min. During the flight, the gyrocopter maintained a low-altitude of approximately 315m above the ground with an average speed of 95 km/h. Based on that, the size of the major axis of the first Fresnel zones that constitute the detected footprints ranged between 1,316m for a minimum elevation angle of 3° and 15m for a maximum elevation angle of 75°. Concerning the temporal resolution of the application, the raw data were sampled at a frequency of 25MHz and the C/N0 estimates were realized at a rate of 1000Hz.
During the flight, an average of 9 GPS satellites have been detected of which 4 GPS satellite signals were extensively analyzed to observe the reflectivity corresponding to land, beach, and sea reflections. After analyzing the Delay Doppler Maps which provides an image of the scattering cross-section in terms of time and frequency and consequently tracking the corresponding signals, the 1ms C/N0 estimations were derived using the in-phase components of the signals as observations. The reflected signals are then linked to the footprints of the satellites and thus to the reflecting surfaces from which each processed signal has reflected using the GPS time, attitude, and position provided by onboard sensors and the GPS time extracted from the digitized GNSS signals. The ultimate aim of this study is to obtain reflectivity measurements from high rate C/N0 observations in order to provide a soil moisture mapping of the studied area, where we notice that the signals reflected from the beach had the best reflectivity followed by sea then land reflections.
How to cite: Issa, H., Stienne, G., Reboul, S., Semmling, M., Raad, M., Faour, G., and Wickert, J.: High-rate GNSS Reflectometry Estimates for Airborne Soil-moisture Detection, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11577, https://doi.org/10.5194/egusphere-egu21-11577, 2021.
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