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Strong typhoons hit the Japanese Islands repeatedly in 2019. Here we study one of these typhoons (2019 #19 Hagibis 915 hPa, 86 casualties) that landed central Japan on Oct.12 (local time) during the Rugby World Cup tournament, using two different space geodetic approaches, i.e. water vapor and crustal deformation. The first approach is the recovery of Precipitable Water Vapor (PWV) using the zenith wet delays (ZWD) estimated by the dense GNSS array in Japan GEONET. Because atmospheric water vapor concentrates in relatively low altitudes, high humidity is often difficult to recognize in ZWDs when the surface altitude is high. To overcome the difficulty, we reconstructed ZWDs, converted to sea-level values, by spatially integrating the tropospheric delay gradient (azimuthal asymmetry of water vapor) vectors. We also calculated convergence of such delay gradients, equivalent to water vapor convergence index (WVCI) proposed by Shoji (2013 Jour. Met. Soc. Japan). We found that very strong rainfall occurs in the region where both reconstructed ZWD and the delay gradient convergence index are high. Next, we studied vertical crustal movements associated with the water load brought by the typhoon, using the two solutions of the GEONET station coordinates, one from the official F3 solution and the other from the UNR data base. We confirmed subsidence down to ~2 cm in multiple regions where severe flood occurred. Such subsidence was observed to recover with a time constant of 1-2 days reflecting rapid drain of rain water to ocean due to large topographic slope and proximity to the sea. We could not identify, however, crustal uplift due to the low atmospheric pressure at the center of the typhoon.

How to cite: Heki, K., Arief, S., Yoshida, M., and Wei, Z.: Space geodetic study of the 2019 typhoon Hagibis: PWV and crustal subsidence , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3272, https://doi.org/10.5194/egusphere-egu2020-3272, 2020.

In a recent study we have shown how GNSS Zenith Wet Delay (ZWD) interpolation and therefore Integrated Water Vapor (IWV) maps can be improved by utilizing tropospheric gradients (Zus et al., 2019). For a station configuration with an average distance of 50 km in Germany and a period of two months in the summer 2013 we demonstrated an average improvement of 10% in interpolated ZWDs. We extended this work by a new study. It differs from the previous one in two respects: (1) we consider more than 1,200 stations with an average distance of 20 km in Japan and (2) ZWDs and tropospheric gradients are taken from the Nevada Geodetic Laboratory (NGL) (Blewitt et al., 2018). We present results and propose future directions. For example, we may consider a mixed approach where ZWDs and tropospheric gradients from a numerical weather prediction model are utilized as well.

Zus, F.; Douša, J.; Kačmařík, M.; Václavovic, P.; Balidakis, K.; Dick, G.; Wickert, J. Improving GNSS Zenith Wet Delay Interpolation by Utilizing Tropospheric Gradients: Experiments with a Dense Station Network in Central Europe in the Warm Season. Remote Sens. 2019, 11, 674. 

Blewitt, G., W. C. Hammond, and C. Kreemer (2018), Harnessing the GPS data explosion for interdisciplinary science, EOS, 99, https://doi.org/10.1029/2018EO104623.

How to cite: Zus, F., Balidakis, K., Pikridas, C., Dick, G., and Wickert, J.: Improving GNSS Zenith Wet Delay Interpolation by Utilizing Tropospheric Gradients: Results from the dense station network in Japan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3443, https://doi.org/10.5194/egusphere-egu2020-3443, 2020.

The motivation of this study is to assess the spatio-temporal patterns in the Zenith Total Delay (ZTD) time series estimated within the second re-processing campaign (1996-2014) of the EUREF Permanent GNSS Network (EPN, http://www.epncb.oma.be) for a set of European stations. In particular we used AS0 solution provided by the EPN analysis center ASI (Agenzia Spaziale Italiana Centro di Geodesia Spaziale, Italy), and GO1 and GO4 solutions provided by the EPN analysis center GOP (Geodetic Observatory Pecny, Czech Republic) along with the combined EPN Repro-2 products. Solutions differ by processing options and number of stations processed. We find that all individual ZTD solutions are characterized by pure autoregressive noise, which is reduced during the combination, meaning that some part of information is lost in the combination procedure. Combination procedure does not however affect spatial patterns of ZTD residuals (trend and seasonal signals are removed beforehand). They are almost the same for both individual and EPN Repro-2 combined solutions. This means that regional ZTD estimates reflect tropospheric dynamics even at very high-frequency signals of small variance. Therefore, we compute ZTD differences from the two GOP solutions GO1 and GO4, which only differ by unmodelled non-tidal atmospheric loading. We find 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 non-linear trends or large seasonal peaks. As these similarities are only observed for GO1 and GO4 differences, this indicates that unmodelled vertical loading effects contribute 50% of the ZTD noise, affecting errors of trends.

How to cite: Bogusz, J., Klos, A., Pacione, R., Humprey, V., and Dobslaw, H.: Improved estimates of non-tidal environmental loading contribution into Zenith Total Delay series over Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4898, https://doi.org/10.5194/egusphere-egu2020-4898, 2020.

Convective processes in the tropical atmosphere and their diurnal cycles have important repercussions for the circulations in the tropical regions and beyond. Monitoring of the water vapour content in the tropical atmosphere remains a challenge due to its high temporal and spatial variability. Global models tend to fail to correctly capture the diurnal convection, limiting forecasting accuracy. In this work, we investigated precipitable water vapour (PWV) diurnal cycle, precipitation and infrared  brightness temperature (TB) data over the tropical area. We used in-situ observations from 44 IGS (International GNSS Service) stations covering time span of 18 years, together with satellite-based precipitation and cloudiness data, taken from the Tropical Rainfall Measurement Mission gridded dataset (TRMM 3B42 v7) and the global, merged infrared (IR) dataset, respectively. The data provided an opportunity to examine the characteristics of a diurnal cycle of PWV, precipitation and TB over the study area in greater detail than before.

In particular, our results show that the diurnal cycle of PWV and TB were almost entirely dominated by mono-modal distributions. The diurnal cycle of precipitation onshore (continental areas or big islands; continental regime) had a single late afternoon peak, and that offshore (small islands; oceanic regime) had both a midday and a nocturnal peak. Daily amplitude phase shift of PWV and precipitation at onshore stations with a continental regime consistently occurred at the same time, while TB maximum peaked about five hours later. Furthermore, results show that the daily mean and the amplitude of the diurnal cycle of PWV, precipitation and TB appeared smaller on offshore stations, exhibited to an oceanic regime, than on onshore, continental stations. Additional analysis of seasonal variations of GNSS-derived PWV shows the usefulness of such measurements for tracking propagation of longer-scale phenomena, such as Inter Tropical Convergence Zone (ITCZ), Southeast Asian monsoon or East Asian summer monsoon.

How to cite: Bałdysz, Z., Nykiel, G., Baranowski, D., Latos, B., and Figurski, M.: Diurnal cycle of GNSS-derived precipitable water vapour in tropical regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10138, https://doi.org/10.5194/egusphere-egu2020-10138, 2020.

For the documentation of time and space variations of water vapor in atmosphere during the Nawdex campaign (North Atlantic, Autumn 2016), a ground network of more than 1200 coastal continuously operation reference GNSS stations has been analyzed. This network spreads from Caribbeans to Morocco through Greenland. Retrieved IWV have been used to evaluate ERAI and ERA5 reanalysis and highlight improvements made by ERA5 (-0.2 +/- 1.6 kg/m2 vs -0.3 +/- 2.1 kg/m2 overall). They are also used to describe high impact weather events that took place during the experiment.

The analysis of this ground GNSS network has been completed with the IWV retrieved from GPS data acquired by the French RV Atalante which cruises in the area during the experiment. IWV from shipborne receiver are consistent with both ERAI and ERA5 reanalysis (1.0 +/- 3.2 kg/m2 and 1.3 +/- 2.0 kg/m2 respectively) ; shipborne IWV also agree with IWV from nearby ground GNSS stations (-0.4 +/- 0.9 kg/m2). These results confirm the quality of shipborne IWV retrievals and opens up prospects for use in climatology and meteorology.

How to cite: Bosser, P., Olivier, B., and Nicolas, L.: IWV retrieval from ground and shipborne GPS receivers during NAWDEX, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6956, https://doi.org/10.5194/egusphere-egu2020-6956, 2020.

Atmospheric ties are affected by the differences of atmospheric parameters of space geodetic techniques at co-location sites. Similar to local ties, they could be applied along with local ties for a combination of space geodetic techniques to improve the realization of terrestrial reference frames (TRF). Theoretically, atmospheric ties are affected by the height differences between antennas at the same site and meteorological conditions. Therefore, atmospheric ties could be determined by analytical equation based on meteorological information from in situ measurements or weather model. However, there is often a discrepancy between the expected zenith delay differences and those estimated from geodetic analysis, thus potentially degrading a combined atmospheric ties solution. In this study, we analyse the time series of zenith delays from co-located GNSS antennas at Wettzell (height differences below 3 meters), for 11 years (2008–2018). GNSS observations were analyzed with Bernese GNSS software version 5.2 with double-differencing technique and relative tropospheric delay and gradients were estimated with L1, L2, and the ionosphere-free (L3) linear combination thereof. Atmospheric ties were derived analytically employing meteorological data from Global Pressure and Temperature model 3 (GPT3) and ERA5 reanalysis, as well as corrections derived from ray tracing (Potsdam Mapping Functions, PMF). The comparison shows that zenith delay differences are dominated by equipment changes. The discrepancies between atmospheric ties and estimated zenith delay differences are frequency dependent, with the L1 solutions being the least biased. For these small vertical differences, seasonal signals are not significant for all frequencies.

How to cite: Kitpracha, C., Balidakis, K., Heinkelmann, R., and Schuh, H.: Assessment on atmospheric parameters at co-location sites, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6517, https://doi.org/10.5194/egusphere-egu2020-6517, 2020.

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, for example, tropospheric delays or integrated water vapor information 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 is supporting the space-geodetic community by providing models that can be used to reduce the troposphere impact on the signal propagation.

There are several ways of observing the troposphere, especially considering water vapor.  First one are the classical meteorological: in-situ measurements, radiosondes or radiometers, from which we can sense directly the amount of water vapor. 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), Interferometric Synthetic Aperture Radar (InSAR) or space-based Radio Occultations (RO) can also be used in a similar way to GNSS.

This contribution presents an overview of the troposphere sensing techniques with examples of their applications. We present a multi-comparison of the tropospheric products, 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 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 radiosondes.

How to cite: Wilgan, K., Rohm, W., Bosy, J., Geiger, A., Siddique, M. A., Wickert, J., and Dick, G.: Tropospheric products as a signal of interest – overview of troposphere sensing techniques, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9418, https://doi.org/10.5194/egusphere-egu2020-9418, 2020.

The signal-to-noise ratio (SNR) data is widely used in GNSS reflectometry to derive water or snow surface heights and surface characteristics like roughness or soil moisture. In a marine environment the attenuation of the SNR oscillation is related to the roughness of the sea surface. It was shown that the significant wave height (SWH) of the water surface can be calculated from the analysis of the attenuation.

The attenuation depends strongly on the relation between the coherent and the incoherent part of the scattered power. The correlation length of the sea surface governs the incoherent part and varies with respect to the direction of the line of sight relative to the wave direction. The resulting anisotropic characteristic of the attenuation yields a directional pattern of the cutoff angle at which the coherence is lost. The cutoff angle can be deduced from the attenuation of the SNR data, from which the wave direction can be derived. The contribution will recapitulate the relation between the sea state and the cutoff angle based on sea surface simulations and present the analysis of experimental data from a GNSS station in the North Sea.

A sea state observation would be incomplete without an information about the wave period. The wave period does not influence the SWH but the correlation length of the sea surface. Hence, for a particular SWH, different peak wave periods should yield different correlation length for a line of sight in the wave direction. Analysis based on sea surface simulations show that it should be possible to derive the peak wave period as a function of the SWH and the maximum cutoff angle of the SNR attenuation. The results of this analysis will be presented here, too.

How to cite: Reinking, J., Roggenbuck, O., and Even-Tzur, G.: Sea state observation using ground-based GNSS-SNR data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2709, https://doi.org/10.5194/egusphere-egu2020-2709, 2020.

The PRETTY mission is a 3U CubeSat mission, hosting two different payloads, a radiation dosimeter and an interferometric GNSS reflectometer. The intended launch is planned in 2022.

The reflectometer payload has been built, using flight representative hardware and mounted inside a portable setup. Two campaigns have been carried out, a first one to verify the setup in real world condition and the second one to record reflectometry data over the Danube river. The reflections over the river are analyzed and compared to a reference data set obtained from basemap.at (which is released under Open Government Data Österreich Lizenz CC-BY 4.0).

The hardware is capable of generating complex and power waveforms at the same time, and the reflection events are visible in both. Since PRETTY is aiming for phase altimetry, only coherent measurements are conducted with an integration time of 20ms .

The re-tracking algorithm for the specular point and height estimation are based on [1]. Due to the low elevation angle and receiver height, the effects from the ionosphere is not considered , however effects from the atmosphere have to be included in the data re-tracking process. The reflection peaks, and the signal to noise ratio of the peaks, are large enough detect the peak and to calculate the height of the reflection point. The height retrieval is shown in the paper.

The results are promising w.r.t. the performance of the overall structure of the PRETTY GNSS-R payload  in order to deliver altimetry results on a low-cost CubeSat platform.

[1] W. Li, E. Cardellach, F. Fabra, S. Ribó and A. Rius, "Assessment of Spaceborne GNSS-R Ocean Altimetry Performance Using CYGNSS Mission Raw Data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 1, pp. 238-250, Jan. 2020. doi: 10.1109/TGRS.2019.2936108

How to cite: Dielacher, A., Fragner, H., Moritsch, M., Wickert, J., Koudelka, O., Hoeg, P., Cardellach, E., Martin-Neira, M., Semmling, M., Walker, R., Hörmer, A., and Wenger, M.: First Field-Test results of iGNSS-R instrument of the PRETTY payload, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12986, https://doi.org/10.5194/egusphere-egu2020-12986, 2020.

We investigate a long-term ground-based GNSS-R dataset to evaluate the effect of sea state on the polarization of the reflected signals. The dataset consists of one-year polarimetric observations recorded at Onsala space observatory in Sweden in 2016 using right- and left-handed circular polarization (RHCP and LHCP) antennas. One up-looking antenna to receive direct signal and two side-looking antennas to collect reflections are installed at about 3 meters above sea level. The data is collocated with the measurements from a nearby tide-gauge and meteorological station.

We focus on precise power estimation using a polarimetric processor based on Lomb–Scargle periodogram at precisely observed sea levels. The processor converts 0.1 Hz coherent in-phase and quadrature correlation sums provided by a reflectometry receiver to power estimates of the direct and reflected signals. The power estimates are reduced to three power ratios, i.e. cross-, co-, and cross to co-polarization. A model, describing the elevation dependent power loss due to sea surface roughness, is then utilized to invert the calculated power ratios to the standard deviation of sea surface height.

Analysis of about 14000 events found in the dataset (~40 continuous tracks per day) shows a fair agreement with the wind speeds as an indicator of the sea state. Although an increasing sensitivity to sea state is observed for all the power ratios at elevation angles above 10 degrees, the measurements from the co-polar link seem to be less affected by the surface roughness. The results reveal that the existing model cannot predict the effect of sea surface roughness in a comprehensive way. The different response of RHCP and LHCP observations to roughness is evident, however, the polarization dependence is not covered by the model. The deviations from the model are particularly clear at lowest elevations (<5 deg) where the roughness effect is expected to vanish. The results indicate that roughness also affect observations at lowest elevation angles. In this elevation range the expected dominance of the RHCP component above the LHCP component is not observed.  A different approach is required to model the influence of sea state in GNSS-R. The increasing amount of reflectometry data may allow to retrieve an empirical relation between coherent reflection power and sea state in future investigation.

How to cite: Hoseini, M., Semmling, M., Rennspiess, E., Ramatschi, M., Haas, R., Strandberg, J., Nahavandchi, H., and Wickert, J.: On the Impact of Sea State on GNSS-R Polarimetric Observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21823, https://doi.org/10.5194/egusphere-egu2020-21823, 2020.

How to cite: Annane, B., Leidner, M., Hoffman, R., Huang, F., and Garrisson, J.: Influence of Assimilating CYGNSS Ocean Surface Wind Data on Tropical Cyclone Analyses and Predictions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20553, https://doi.org/10.5194/egusphere-egu2020-20553, 2020.

GNSS tomography is an all-weather condition remote sensing technique in the field of meteorology that is gaining considerable attention in recent years. The water vapor distribution and related parameters like wet refractivity in the troposphere can be reconstructed with reasonable Spatio-temporal resolution in this method. To achieve this goal, the troposphere is divided into a number of 3D elements (voxels). Then, the system of the observation equations is defined by a relation between the wet refractivity field and the distance traveled by GNSS rays through voxels. However, propagated signals do not pass through some of the model elements. Thereby, the reconstructed wet refractivity field suffers in terms of solution uniqueness. Consequently, additional data sources and horizontal and/or vertical constraints should be applied to avoid the singularity of the estimated field. In this presentation, the combination of wet refractivity maps computed from Geostationary Operational Environmental Satellite (GOES) sounder and refractivity fields obtained by GNSS tomography is demonstrated to achieve a unique solution. The GOES-R sounder products are provided hourly with a 10 km spatial resolution. Therefore, GOES-R wet refractivity maps are used to constrain the system of equations and consequently, the tomographic solution leads to an improved reconstructed wet refractivity field. To analyze the efficiency of the proposed data, a 3D tomographic model is defined over a regional area covered by the Continuously Operating Reference Station (CORS) Network in the United States. Moreover, radiosonde measurements in the area of interest are used to achieve the feasibility and correctness of the estimated 3D wet refractivity images.

How to cite: Adavi, Z. and Weber, R.: Analysis of GOES-R as a Constraint in GNSS Tropospheric Tomography , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14965, https://doi.org/10.5194/egusphere-egu2020-14965, 2020.

We present status and atmospheric retrieval results for the FORMOSAT-7/COSMIC-2 (COSMIC-2) mission. COSMIC-2 mission jointly managed by NOAA and Taiwan's National Space Organization (NSPO) and consists of six satellites launched on June 25, 2019 into a 24-degree inclination orbit. The primary payload is the JPL developed Tri-GNSS Radio-occultation System (TGRS). Tracking data from two upward looking precise orbit determination antennas are used for orbit and clock determination as well as ionospheric total electron content retrieval. Two limb-viewing radio occultation antennas provide more than 4000 daily profiles of the neutral atmosphere (e.g. bending angle, refractivity and temperature) from typically 60 km to 1 km above the Earth's surface. The secondary payloads are the Ion Velocity Meter (IVM) and tri-band Radio Frequency Beacon (RFB). The UCAR data processing center receives level-0 data from a set of downlink stations and processes them into higher level weather and space weather products in near real-time and post-processing modes. Products are transferred in near real-time to NOAA, NSPO, and operational weather centers worldwide. In this presentation we summarize mission/instrument status and summarize science results from the cal/val and initial operating phases of the mission. Results presented will include geographic coverage, neutral atmosphere profile quality and impacts on numerical weather prediction, as well as space weather product evaluation. We conclude with future activities and timelines.

How to cite: Weiss, J.-P. and Xia-Serafino, W.: COSMIC-2 Status and GNSS Radio Occultation Results, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12556, https://doi.org/10.5194/egusphere-egu2020-12556, 2020.

InSAR derived Atmospheric Phase Screens (APSs) contain the difference between the atmospheric delay along the SAR sensor line-of-sight of two acquisition epochs: the slave and the master epochs. Using estimates of the atmospheric state at the master epoch, coming from independent sources, the APSs can be transformed into maps of tropospheric Zenith Total Delay (ZTD), that is related to the columnar atmospheric water vapor content. Assimilation experiments of such products into numerical weather prediction (NWP) models have shown a positive impact in the prediction of convective storms.

In this work, a systematical comparison between various APS and ZTD products aims at determining the optimal procedure to go from APSs to InSAR-derived absolute ZTD maps, i.e. to estimate the master delay map. Two different approaches are compared.

The first is based on a stack of ZTD maps produced with the assimilation of GNSS ZTD observations into an NWP model. This acts as a physically based interpolator of the GNSS values, which have a spatial resolution much coarser than the InSAR APS one.

The second is based on a stack of ZTD maps derived by an Iterative Tropospheric Decomposition (ITD) model, as implemented in the GACOS service. In this case, the high-resolution ZTD maps are obtained by an iterative interpolation of a global atmospheric circulation model values and GNSS values where available.

The results of the comparisons and sensitivity tests on the number of ZTD maps needed to derive the unknown master delay map are shown.

How to cite: Barindelli, S., Gatti, A., Lagasio, M., Manzoni, M., Mascitelli, A., Monti Guarnieri, A., Montrasio, M., Realini, E., Tagliaferro, G., and Venuti, G.: From InSAR derived relative tropospheric Slant Total Delay maps to absolute Zenith Total Delay maps: comparisons between tropospheric delay products to define a strategy for meteorological applications., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17920, https://doi.org/10.5194/egusphere-egu2020-17920, 2020.

How to cite: Bernet, L., Brockmann, E., von Clarmann, T., Kämpfer, N., Mahieu, E., Mätzler, C., Stober, G., and Hocke, K.: Water vapour trends in Switzerland from radiometry, FTIR and GNSS ground stations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3646, https://doi.org/10.5194/egusphere-egu2020-3646, 2020.

Water vapor is an important medium for the transmission moisture and latent heat in the atmosphere. It is one of the most abundant and dominant greenhouse gases in the atmosphere, which is crucial for global warming. With higher temperatures, the specific humidity will also increase as predicted by the nonlinear Clausius-Clapeyron relationship, indicating a positive feedback loop. Hence, estimation of the trend of Integrated Water Vapor (IWV) in the atmosphere is of great importance for global warming research. However, previous studies have shown that the trends of IWV are usually rather small. Therefore, it is important to estimate the IWV trend and its associated uncertainty with a reasonable mathematical model for the homogenized time series from homogenously reprocessed GPS data sets. Since the 1990s, the Global Positioning System (GPS) has successfully been employed to retrieve IWV with a high temporal resolution, all-weather condition and with global coverage. In this work, we used the hourly GPS Zenith Total Delay (ZTD) time series for 1995.0-2017.0 at 21 European GPS stations derived from a homogeneous data reprocessing. For the conversion of ZTD to IWV, we employed the meteorological variables from ERA5, a state-of-the-art atmosphere reanalysis product newly released by the European Centre for Medium-Range Weather Forecasts (ECMWF). Then, we investigated the influence of noise model assumptions within the mathematical model on the uncertainties of IWV trend estimates. As expected, the results confirmed that the assumption of a white noise only model tends to underestimate the trend uncertainty. A first-order autoregressive process is the preferred mathematical model for a more realistic estimation of the IWV trend uncertainty.

How to cite: Yuan, P., Hunegnaw, A., Teferle, F. N., and Kutterer, H.: Statistical significance of trend estimations for integrated water vapor time series obtained from GPS technique: a case study in Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11728, https://doi.org/10.5194/egusphere-egu2020-11728, 2020.

The World Meteorological Organization (WMO) recommends the use of climate normals for dealing with the analysis of variations and trends of the meteorological parameters or be used as input to predictive climate models. The suggested period is 30 years, but shorter periods can also be employed. We computed zenith total delay (ZTD) and zenith wet delay (ZWD) series for each node of NCEP1 numerical weather model, starting in 1948. We computed climate normals of those two parameters using periods of 1, 5, 10, 15, 20 and 30 years, with and without the annual signature. To assess window size impact, we looked at variations and correlation of trends derived from the various solutions. Results shows the obvious better smoothing using larger windows and the decrease of the impact of annual signature. Regions with positive trends appear to be concentrated in continental masses and the equator line, and the most significant negative trends are in the oceans. ZTD increase is caused primarily by an increase in ZWD and is an indication of variations in ZWD variables. In the case of water vapor, such an increase in ZWD shows us a probable increase in the amount of water vapor in the atmosphere. Comparisons with trends computed from GNSS-derived ZTD and ZWD series are included with the caveat that time period for such comparisons must be shorter.

How to cite: C. Santos, M., Moura, M., Nikolaidou, T., and Balidakis, K.: Long-term ZTD and ZWD series and climate normals using NCEP1, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20989, https://doi.org/10.5194/egusphere-egu2020-20989, 2020.

This work aims to provide a quick review of different experiments conducted in the past for the estimation of integrated water vapor content from shipborne GNSS receiver. This state of the art will be confronted with results obtained using GPS data acquired by the French Hydrographic Ship Borda on a cruise over Atlantic Ocean and Mediterranean Sea, from Brest to Toulon in August 2015; the estimated IWV are compared with satellite observations (MODIS) and outputs from numerical weather prediction models (ERAI, ERA5, Arpege, Arome); while differences between GPS and MODIS retrievals reach almost 4 kg/m2 in terms of RMS, agreement is generally much better with numerical models (2 up to 3 kg/m2 in terms of RMS). Use of real-time orbit and clocks product is also investigated in order to assess the performance of near real-time GPS-IWV estimation for NWP purposes. We will draw out the prospects in terms of possibilities and opportunities for the use of shipborne GNSS IWV for meteorology and climatology.

How to cite: Bock, O., Bosser, P., Caumont, O., Legouge, R., and Laurain, N.: IWV retrieval from shipborne GPS receiver on hydrographic ship Borda, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7518, https://doi.org/10.5194/egusphere-egu2020-7518, 2020.

Atmospheric events are driven by surface sea physical parameters, including the exchanges of water vapor with the overlying atmosphere. Oceans cover around 70 percent of the Earth's surface and influence the atmospheric circulation, causing some of the main weather events. The lack of surface observations over the vast ocean areas is a critical problem to be addressed for improving the performance of weather forecasting.

Even if weather observations over sea from ships have been collected for over 200 years and used for meteorological research and climate applications, only recently the availability of different telecommunication solutions make real time access to measurements possible, even from remote areas. This is consequently opening new opportunities to use data from marine areas in operational weather applications.

Ground based GNSS receivers has been used for many years to determine a quantity that is of major interest for meteorologists and climatologists, the water vapor content, derived from the Zenith Path Delay. GNSS meteorology has been also tested over ships during some measurement campaigns in the past.

This work presents the implementation of the first GNSS meteo infrastructure on ships operating on the northwestern Mediterranean Sea, involving 9 commercial vessels, real-time collecting a list of GNSS meteo parameters: the signals from Galileo, GPS, GLONASS and Beidou constellations, measurements of pressure, temperature, humidity, wind and precipitation. These 9 moving platforms are complemented by a number of fixed ground platforms, used as a reference.

The difficulties in ship based GNSS meteorology, with respect to the classical approaches from fixed stations, lie both in the exposure of the hardware instruments to challenging environmental conditions as in the open sea and in the computation algorithms, which must be applied to kinematic conditions and continuously solve the receiver position with very high accuracy.

Two different processing schemes have been applied to the dataset (i.e. few months): the first one is based on differential GNSS using the TRACK suite of GAMIT software, and the second one is based on precise point positioning using the GLAB software. As it is well known, if network solutions are adopted (as in the first case), the satellites and receivers clock errors can be eliminated with very high accuracy, while PPP-based methods (as in the second case) require ultrafast precise satellite ephemeris products, but they give the possibility to implement standalone instruments, so not to send large amounts of full RINEX files to a ground processing centre.

The ZPD quantities retrieved from the first period of observations aboard ships are shown, using both the techniques. The comparison shows some discrepancies both in the absolute quantity and in the short-term trends. Even if preliminary, the comprehension of the quality of such an unprecedent source of information is of great interest, because the perspectives of this infrastructure are both scientific and operational, thinking for example to the data assimilation into numerical weather prediction models.

How to cite: Antonini, A., Ortolani, A., Sonnini, A., Viti, M., Fibbi, L., Cristofori, S., and Montagnani, S.: A ship-based network for GNSS-meteorology over the northwestern Mediterranean Sea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19217, https://doi.org/10.5194/egusphere-egu2020-19217, 2020.

China has built  national BDS reference stations (≥210 ) covering the entire territory and has been operating continuously for more than 3 years. In 2020, BDS  satellite navigation and positioning system will be fully built and provide global services, providing a good source of data for the use of ground-based BDS observations for water vapor detection and analysis. The author used national BDS reference stations observation data which covered China area  in 2019, combined with sounding observation data, to detect and analyze the temporal and spatial changes of water vapor , and given preliminary analysis results of the  water vapor detection performance and accuracy based on BDS observation . The results show that the detection results of atmospheric precipitation between BDS and sounding system are more consistent, which can reflect the change of atmospheric precipitation.  The system errors and standard deviations of the calculation results which based on  the BDS  observations and the sounding observations are relatively large, which may be related to orbit model and  the system stability of BDS needs to be improved.

How to cite: Zhang, Q.: Detection of Spatiotemporal Changes of Water Vapor based on Large-scale BDS Reference Stations in China Area, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6837, https://doi.org/10.5194/egusphere-egu2020-6837, 2020.

During the last decade the stability of GNSS clocks has increased dramatically. New generation GNSS satellites are equipped with highly precise and stable clocks and the clock parameters can be predicted with even picoseconds accuracy for several hours. In this work we determined and predicted 90 days precise orbits and clocks of up to 115 satellites from GPS, GLO, GAL, BDS2/3 and QZSS. Based on the calculated and predicted orbit and clock products (SP3) we processed data from about 140 globally distributed stations using PPP in 24 hours static mode. The first 22 hours part uses the calculated satellite products and the last two hours part uses the predicted satellite products. The estimated parameters are daily station coordinates and 30 min tropospheric parameters (ZTD). To validate the last 2-hours of ZTD we generate a reference solution based on 24-hour calculated SP3 products. We also performed a statistical comparison with ECMWF weather model data which yields a root mean square deviation of about 12 mm. This initial comparison indicates that the ZTD estimated from predicted satellite orbit and clocks are sufficiently accurate for time critical meteorological applications.

How to cite: Deng, Z., Zus, F., Balidakis, K., Jens, W., and Schuh, H.: Retrieving tropospheric parameters using predicted multi-GNSS orbit and clock, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17964, https://doi.org/10.5194/egusphere-egu2020-17964, 2020.

Precipitable water vapor (PWV) is one of the key variables in the water and energy cycles, whereas current PWV products are subject to spatiotemporal discontinuity, low accuracy, and/or coarse resolution. Based on two widely used global PWV products, i.e., satellite-based MODIS and reanalysis-based ERA5 products, here we propose a data fusion approach to generate PWV maps of spatiotemporal continuity and high resolution (0.01°, daily) for the Upper Brahmaputra River (UBR, referred to as the Yarlung Zangbo River in China) basin in the Tibetan Plateau (TP) during the monsoon period (May‒September) from 2007‒2013. Results show that the fused PWV estimates have good agreement with ground-based PWV measurements from eight GPS stations (correlation coefficient = 0.87‒0.97, overall bias = -0.35‒1.78 mm, and root mean square error = 1.17‒2.04 mm), which greatly improve the accuracy of the MODIS PWV product. The high-resolution fused PWV maps provide detailed spatial variations which are generally consistent with those from the MODIS estimates under confident clear conditions and ERA5. During the monsoon period from 2007‒2013, monthly average PWV estimates across the UBR basin vary from ~6 to ~12 mm, and for each month high PWV values are found mainly along the UBR valley and at the basin outlet. The developed data fusion approach maximizes the potential of satellite and reanalysis-based PWV products for monitoring PWV and can be extended to other data available sources and study regions. The generated PWV estimates are highly valuable in understanding the water and energy cycles and retrieving atmospheric and surface variables for the south TP and its downstream areas.

How to cite: Li, X. and Long, D.: An improvement in accuracy and spatiotemporal continuity of the MODIS precipitable water vapor product based on a data fusion approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6282, https://doi.org/10.5194/egusphere-egu2020-6282, 2020.

Precipitable water vapor (PWV) is an important parameter reflecting the amount of solid water in the atmosphere, which is widely utilized in the studies of numerical weather prediction (NWP) and climate change. The microwave radiance measurements made by the space-based remote sensing satellites give us the opportunity to make the climate studies on a global scale. So far, PWV retrieval over the ocean has a long data record and the technology is very mature, but in the case of PWV retrieval over land, it is more challenging to isolate the atmospheric signals from the varied surface signals. In this study, we will apply a new retrieval method over land based on the dual-polarized difference (vertical and horizontal) at 19 GHz and 23 GHz using the brightness temperatures from the Global Change Observation Mission-Water (GCOM-W)/Advanced Microwave Scanning Radiometer 2 (AMSR2). We found polarization difference in brightness temperatures has an exponential relation on the amount of PWV. The validation results of the PWV retrieval from the ground-based GNSS stations show that the proposed method has a mean accuracy of 3.9 mm. Thus, the proposed method can give a possibility to improve the accuracy of data assimilation in the NWP applications and is useful for the studies of global climate change with the long-term data records.

How to cite: Jiang, N., Xu, Y., and Xu, T.: A new method of PWV retrieval over land with remote sensing data: a case of AMSR2, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17756, https://doi.org/10.5194/egusphere-egu2020-17756, 2020.

The recent release of mass-marked dual-frequency receivers opens up the opportunity to facilitate the cost-efficient estimation of Zenith Tropospheric Delays (ZTDs) from Global Navigation Satellite System (GNSS) observations. We present results of ZTD estimations from a low-cost dual-frequency GNSS receiver (U-blox ZED-F9) equipped with a range of different quality and priced antennas. It is demonstrated that the receiver itself is able to produce high quality ZTD estimations with higher grade antennas. However, the noise introduced by applying the ionosphere-free linear combination in Precise Point Positioning (PPP), makes the low-cost antenna performance initially a major challenge. With Root Mean Square Errors (RMSE) between 15 mm and 24 mm for low-cost antennas the results were at first not adequate for meteorological purposes. We demonstrate an easy-to-apply relative antenna calibration that increased the ZTD accuracy significantly for the tested low-cost antennas. After applying antenna corrections the error is reduced to a level that is adequate for meteorological applications (RMSE ~4 mm).

How to cite: Krietemeyer, A., van der Marel, H., ten Veldhuis, M., and van de Giesen, N.: Improving the antenna performance for Zenith Tropospheric Delay estimations with consumer-grade antennas and a low-cost dual-frequency receiver, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9663, https://doi.org/10.5194/egusphere-egu2020-9663, 2020.

Water vapor (WV) is the most variable greenhouse gas in the atmosphere, which acts as a key feature in climate change studies and plays a crucial role in global warming. Its spatiotemporal distribution is necessary for understanding the hydrological cycle, and consequently can be used as an input factor in climatological studies at global, regional, and local scales. Integrated water vapor (IWV), which is defined as the amount of vertically integrated water vapor, can also augment atmospheric modeling at local and regional scales because it is frequently used in energy budget and evapotranspiration assessments. Currently, there are numerous existing atmospheric models which are able to estimate IWV amount, nevertheless, they fail to obtain extremely accurate results compared with in-situ measurements such as radiosondes. Here, we present a new methodology for improving Weather Research and Forecast (WRF) model predictions accuracy, by using data assimilation technique, which combines estimated 2D IWV regional maps, derived from GPS tropospheric path delays, along with the WRF numerical model output to generate an optimal approximation of the evolving sate of the system. This is done as opposed to pervious works, which assimilated single point measurements, either from radiosondes or GPS zenith wet delay (ZTD) estimation, demonstrating some extant of improvement in the WRF prediction accuracy compare to the standalone WRF numerical runs. Using the suggested technique, our results shows a decrease of up to 30% in the root mean square difference relative to the radiosonde data for WRF predictions assimilated with 2D GPS-IWV regional maps compare to the standalone WRF numerical runs.

How to cite: Reuveni, Y. and Leontiev, A.: Using 2D integrated water vapor (IWV) maps derived from GPS tropospheric path delays for augmenting Weather Research and Forecast (WRF) model predictions , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10831, https://doi.org/10.5194/egusphere-egu2020-10831, 2020.

Global Navigation Satellite Systems (GNSS) have revolutionized positioning, navigation, and timing, becoming a common part of our everyday life. Aside from these well-known civilian and commercial applications, GNSS is currently established as a powerful and versatile observation tool for geosciences. An outstanding application in this context is the operational monitoring of atmospheric water vapor with high spatiotemporal resolution. The water vapor is the most abundant greenhouse gas, which accounts for about 70% of atmospheric warming and plays a key role in the atmospheric energy exchange. The precise knowledge of its highly variable spatial and temporal distribution is a precondition for precise modeling of the atmospheric state as a base for numerical weather forecasts especially with focus to the strong precipitation and severe weather events.

The data from European GNSS networks are widely operationally used to improve regional weather forecasts in several countries. However, the impact of the currently provided data products to the forecast systems is still limited due to the exclusively focusing on GPS-only based data products; to the limited atmospheric information content, which is provided mostly in the zenith direction and to the time delay between measurement and providing the data products, which is currently about one hour.

AMUSE is a recent research project, funded by the DFG (German Research Council) and performed in close cooperation of TUB, GFZ and DWD during 2020-2022. The project foci are the major limitations of currently operationally used generation of GNSS-based water vapor data. AMUSE will pioneer the development of next generation data products. Main addressed innovations are:  1) Developments to provide multi-GNSS instead of GPS-only data, including GLONASS, Galileo and BeiDou; 2) Developments to provide high quality slant observations, containing water vapor information along the line-of-sight from the respective ground stations; 3) Developments to shorten the delay between measurements and the provision of the products to the meteorological services.

This GNSS-focused work of AMUSE will be complemented by the contribution of German Weather Service DWD to investigate in detail and to quantify the forecast improvement, which can be reached by the new generation GNSS-based meteorology data. Several dedicated forecast experiments will be conducted with focus on one of the most challenging issues, the precipitation forecast in case of severe weather events. These studies will support the future assimilation of the new generation data to the regional forecast system of DWD and potentially also to other European weather services.

How to cite: Wilgan, K., Wickert, J., Dick, G., Zus, F., Schmidt, T., and Potthast, R.: Advanced MUlti-GNSS Array for Monitoring Severe Weather Events (AMUSE): Project overview, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9669, https://doi.org/10.5194/egusphere-egu2020-9669, 2020.

One of the objectives of the H2020 project TWIGA - Transforming Weather Water data into value-added Information services for sustainable Growth in Africa - is the improvement of heavy rainfall prediction in Africa. In this area, the scarcity of data to support such predictions makes it fundamental to enhance the monitoring of atmospheric parameters.

In this project, GNSS observations and SAR images from Sentinel missions are used to produce water vapor products to be assimilated into Numerical Weather Prediction Models (NWPs).

GNSS observations, collected by ad-hoc networks of geodetic and low-cost stations, are processed to obtain near real-time (NRT) Zenith Total Delay (ZTD) time series, while Sentinel-1 SAR images are used to derive Atmospheric Phase Screens, APSs. The free and open source GNSS software goGPS, developed by the Politecnico di Milano spin-off Geomatics Research and Development (GReD), is used for the retrieval of ZTDs time series.

After proper calibration and validation procedures, the delay maps from SAR and the delay time series from GNSS will be finally assimilated into NWP models to improve the prediction of heavy rainfall.

The GNSS-related activities will be presented in terms of network deployment and processing settings evaluation. A network of 5 single-frequency low-cost GNSS stations was installed in Uganda, and a new network of dual-frequency low-cost stations is going to be installed in Kenya. To improve the outputs provided by these networks, preliminary tests on ionospheric delay corrections at various distances were performed. Different methods, focused on the reconstruction of a synthetic L2 observation for the single-frequency receivers, were employed and evaluated with the aim to define the optimal approach.

In order to demonstrate the capability to achieve GNSS NRT processing within TWIGA, an automated procedure was set up to estimate hourly ZTDs from two geodetic permanent stations located in South Africa (Cape Town and Southerland) and to upload them to the TWIGA project web portal.

Meanwhile, first sets of WRF NWP model parameterizations have been defined for both South Africa and Kenya. A cooperation has been established with the Kenya Meteorological Department on the exploitation of 3DVAR tool for water vapor data assimilation into WRF. Studies to define a strategy for ZTD maps retrieval from InSAR APS have been performed on Italian datasets and further investigations on TWIGA-collected African datasets will follow.

How to cite: Mascitelli, A., Meroni, A. N., Barindelli, S., Manzoni, M., Tagliaferro, G., Gatti, A., Realini, E., Venuti, G., and Monti Guarnieri, A.: TWIGA project activities for the enhancement of heavy rainfall predictions in Africa: low-cost GNSS network deployment and NWP model parameterization., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16122, https://doi.org/10.5194/egusphere-egu2020-16122, 2020.

E-GVAP (the EIG EUMETNET GNSS Water Vapour Programme) is an operational service providing atmospheric delay estimates for use in operational meteorology in near real-time. This is done in a close collaboration between geodetic and meteorological institutions. The use of the GNSS delay estimates is found to increase the skill of weather forecasts. By the start of 2019 E-GVAP did, along with EUMETNET itself, entered a new phase. In E-GVAP 4 the main product will still be zenith total delays (ZTD), with a focus on improving timeliness, in support of the high resolution, local weather models with frequent updates being set up these years. But in addition there will be focus on GNSS derived slant total delay (STD) estimates. Several of the weather models used in Europe are being prepared for STD assimilation. The STDs provide additional information, on atmospheric asymmetries, on top of the information contained in a single ZTD estimate

How to cite: Vedel (1), H., Jones (2), J., Lewis (2), O., and de Haan (3), S.: E-GVAP Status and future, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22264, https://doi.org/10.5194/egusphere-egu2020-22264, 2020.

The ionosphere is the dominant source of the errors in the Global Navigation Satellite Systems  (GNSS), which causes delays and degradation of the GNSS signal. These errors have an impact on many terrestrial and space applications that rely on GNSS. The key parameter for the study of the ionosphere is the Total Electron Content (TEC). In an effort to eliminate the impact of delayed GNSS signal caused by the ionospheric refraction on the accuracy of GNSS positioning and navigation, the researchers made significant advances and began other ionospheric research. This paper studies the variability of GNSS derived TEC values in the International quiet and disturbed days, but also in periods when three tropical-like cyclones in the Mediterranean developed. However, the term tropical-like cyclone distinguishes tropical cyclones developing outside the tropics (like in the Mediterranean Basin) from those developing inside the tropics. Mediterranean tropical cyclones, known as a Medicane, show no difference to other tropical cyclones and can be developed into a hurricane.

Hence, the variability of GNSS derived TEC values time series were analyzed during periods when three Medicanes happened in the fall of 2014, 2016, 2017. Data from eight GNSS stations of the European Permanent Network (EPN) were used and TEC calculations were performed using the VShell program. The results demonstrated that the TEC variability is reflected in daily variations within one month, for three different years of consideration. When the state of the ionosphere was disturbed by external influences, such as the space weather storms, the results demonstrated extreme changes in the number of electrons in the ionosphere. Variations of the TEC and parameter VTEC*sigma were analyzed in the weeks before and after three subtropical cyclones in the Mediterranean Sea, recorded in November 2014, November 2016 and November 2017. Special attention was given to the time series analysis of the variable VTEC*sigma for the GNSS stations located nearby the area where the Medicane developed and stations in regions away from the storm.

The results demonstrated higher VTEC values derived from GNSS stations in the area of the storm on the storm days, as well as the days before and after. Also, the results for the storm in November 2014 showed higher VTEC values compared to the other two tropical-like cyclones. The recorded events of space weather are in correlation with the days when three analyzed Medicanes developed. Therefore, it is difficult to distinguish whether the TEC variability was caused by the space weather storm or the Medicane.

How to cite: Mulić, M., Halilović, D., and Lavić, A.: Correlation between tropical-like cyclones in the Mediterranean Sea and the space weather, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22293, https://doi.org/10.5194/egusphere-egu2020-22293, 2020.

The amount of water vapor in the atmosphere is highly variable and not easy to measure. One of the methods to provide reliable information about the amount and distribution of the humidity in the troposphere is GNSS (Global Navigation Satellite Systems) tomography. The GNSS tomography uses the observations of signal delays between satellites and ground-based receivers over the field covered by a GNSS network. This method enables deriving the 3D distribution of wet refractivity at a low cost in all weather conditions, with high temporal and spatial resolution.

The first applications of the GNSS tomography data in the Weather Research and Forecasting Data Assimilation (WRF DA) system were performed by the adaptation of the GPSREF observation operator. In this study, we present a new tool, namely the TOMOREF observation operator, which consists of three parts: forward, tangent linear, and adjoint operators. As the input data in the assimilation process, the wet refractivity fields from two tomographic models (TUW, WUELS) are used. The analysis is carried out for a 2-week long period (May 29 – June 14, 2013) in Central Europe when severe weather conditions occurred, including heavy precipitation events. The data assimilation results are verified against radiosonde observations, synoptic data, and ERA5 reanalysis. Moreover, the performance of the TOMOREF and GPSREF operators is examined. For the forecasts of relative humidity (RH) at a pressure level of 300 hPa, the implementation of the TOMOREF operator vanishes the negative impact caused by the GPSREF operator. Additionally, the improvement of the root mean square error of the forecasts of RH up to 0.5% is observed. Comparing to the assimilation of Zenith Total Delay observations, the application of the tomographic data has overall a greater influence on the WRF model. Consequently, the GNSS tomography data can be valuable in operational weather forecasting.

How to cite: Hanna, N., Trzcina, E., Kryza, M., and Rohm, W.: TOMOREF operator for assimilation of GNSS tomography wet refractivity fields in WRF DA system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5339, https://doi.org/10.5194/egusphere-egu2020-5339, 2020.

Within the International Association of Geodesy (IAG), a new working group was formed with the intention to bring together researchers and professionals working on tomography-based concepts for sensing the neutral atmosphere with space-geodetic techniques. Hereby the focus lies on Global Navigation Satellite Systems (GNSS) but also on complementary observation techniques, like Interferometric Synthetic Aperature Radar (InSAR) or microwave radiometers, sensitive to the water vapor distribution in the lower atmosphere.

In the next four years (2019-2023), we will address current challenges in tropospheric tomography with focus on ground-based and space-based measurements, the combination of measurement techniques and the design of new observation concepts using tomographic principles. While geodetic GNSS networks are nowadays the backbone for troposphere tomography studies, further local densifications, e.g. at airports, cities or fundamental stations are necessary to achieve very fine spatial and temporal resolution. Besides, the combination of ground-based GNSS with other microwave techniques like radio occultation or InSAR seems to be beneficial due their complementary nature. Therefore, several further developments in the field of tropospheric tomography are required. This includes more dynamical tomography models - adaptable to varying input data, advanced ray-tracing algorithms for the reconstruction of space-based observations and the coordination of a benchmark campaign.

In this presentation, we will give an overview about the current challenges in tropospheric tomography and the objectives of working group. The latter will also include standards for data exchange and therefore, make tomographic products available for the assimilation into numerical weather prediction models but also for various other disciplines, which rely on accurate wet refractivities or derived products like tropospheric signal delays.

How to cite: Moeller, G., Ao, C., Adavi, Z., Brenot, H., Sá, A., Hajj, G., Hanna, N., Kitpracha, C., Pottiaux, E., Rohm, W., Shehaj, E., Trzcina, E., Wang, K.-N., Wilgan, K., and Zhang, K.: Sensing small-scale structures in the troposphere with tomographic principles (IAG working group), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8469, https://doi.org/10.5194/egusphere-egu2020-8469, 2020.

With its relatively low cost, high availability and continuous observation ability, zenith delays from GPS combined with mapping function have been used in satellite tracking media calibration since early 2000. The mapping functions are used to model elevation dependency of radio wave delays in the troposphere. It assumes that the ratio of signal slant delay over zenith delay is less variable w.r.t time and location than the signal delay itself. Thus the parameters of signal delay elevation dependency can be modeled and unknowns of the tropospheric delay were reduced. However, the parameterization comes with a loss of accuracy. For example, the state-of-art VMF series mapping functions have a time resolution of 6 hours, which means variations that took place in less than 6 hours are smoothed. Nowadays GPS has evolved to multi-constellation GNSS with many more satellites in visibility. Here we propose a single station GNSS tomography algorithm for radio wave delay correction by directly using slant delays. This algorithm can extract the information of the troposphere variations in all the signal directions of GNSS observations with high time resolution. Thus it will be beneficial to the radio wave delay correction of precise satellite tracking. We assess the performance of this algorithm with a collocated water vapor radiometer.

How to cite: Peng, F., Fei, L., Barriot, J.-P., Jianguo, Y., Fangzhao, Z., and Mao, Y.: Single station GNSS tomography as a replacement of mapping function for high precision troposphere radio wave delay correction, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12133, https://doi.org/10.5194/egusphere-egu2020-12133, 2020.

Water vapor is one of the most variable components in the Earth’s atmosphere, which has a significant role in the formation of clouds, rain and snow, air pollution and acid rain. Therefore, increasing the accuracy of estimated water vapor can lead to more accurate predictions of severe weather, upcoming storms, and reducing natural hazards. In recent years, GNSS has turned out to be a valuable tool for remotely sensing the atmosphere. GNSS tomography is one of the most valuable tools to reconstruct the Spatio-temporal structure of the troposphere. However, locating dual-frequency receivers with a sufficient spatial resolution for GNSS tomography of a few tens of kilometers is not economically feasible. Therefore, in this research, the feasibility of using single-frequency receivers in GNSS tomography as a possible alternative approach has been investigated. The accuracy of the reconstructed model of water-vapor distribution using low-cost receivers is verified using radiosonde measurements in the area of the EPOSA (Echtzeit Positionierung Austria) GNSS network, which is mostly located in the east part of Austria for the period DoYs 233-246 in 2019.

How to cite: Weber, R., Adavi, Z., and Glaner, M. F.: Assessment of single-frequency observations in GNSS Tropospheric Tomography, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15460, https://doi.org/10.5194/egusphere-egu2020-15460, 2020.

Rapid sea level rise, a severe consequence of global warming, could significantly damage the lives and properties of numerous human beings living in low-lying coastal areas. Therefore, realizing and monitoring coastal sea level variations are of great importance for human society. Conventionally, sea level heights are measured by using tide gauges; however, the records are contaminated by vertical land motions which are difficult to be separated. Recently, Global Navigation Satellite System Reflectometry (GNSS-R) technology has been proved to effectively monitor the coastal sea level changes from GNSS signal-to-noise ratio (SNR) data. However, the generation of detrended SNR ( SNR) depending on different satellite elevation angle intervals via a quadratic fitting, considerably influences the accuracy of sea level retrievals. Moreover, the quadratic fitting cannot perfectly describe the trend of SNR data. Therefore, we proposed a method combining ensemble empirical mode decomposition (EEMD) and ocean tide model to compute SLHs. EEMD can decompose the original SNR data into several intrinsic mode functions (IMFs) corresponding to specific frequencies. Then, Lomb-Scargle Periodogram (LSP) is applied to calculate the dominant frequency of the IMF with maximum spectral power. EEMD is not only suitable for dealing with nonlinear and nonstationary data but also eliminates the mode mixing problem of empirical mode decomposition (EMD) by adding white noises. In addition, we set an empirical SLH interval from ocean tide model as a quality control. In this study, the existing GNSS stations at the coasts of Taiwan are used to examine the proposed approach and then compare the results with those from the traditional quadratic fitting. Finally, the measurements from co-located or nearby traditional tide gauges are served as ground truth to evaluate the accuracy and stability of the mentioned methods.

How to cite: Lee, C. M., Hung, S. L., Kuo, C. Y., Sun, J., Tseng, T. P., Chen, K. H., Shum, C., Yi, Y., and Ching, K. E.: Determination of Coastal Sea Level Heights around Taiwan by improved GNSS Reflectometry, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12060, https://doi.org/10.5194/egusphere-egu2020-12060, 2020.

Measurement of soil moisture content on a global scale have gained increased interest over the years, due to its essential role in agriculture and most importantly in predicting the occurrence of natural disasters. This paper is dedicated to a study on GNSS Reflectometry (GNSS-R) using a low-altitude airborne carrier for soil moisture estimation with 1 ms rate of carrier-to-noise ratio observations. The principle of GNSS-R is the exploitation of L-band navigation signals as sources of opportunity to characterize the earth surface, because the reflected signals are often affected by the nature of the reflective surface. To scan large regional surface areas and quickly reach the areas to monitor, a dynamic GNSS-R system is considered.
The GNSS-R setup used in this study consists of RHCP and LHCP antennas mounted on the nose of a gyrocopter and of a Syntony front-end GNSS receiver. In addition, the gyrocopter is equipped with a signal digitizer and mass storage devices for digitizing and storing the base-band GNSS direct and reflected signals along the flight. A drone sensors board is also attached to the gyrocopter, which records the gyrocopter’s attitude and position at 1000 Hz rate along its trajectory. To cope with the rapid displacement of the satellites’ footprints along the receiver trajectory, high rate (1 ms) of carrier-to-noise ratio observations are processed with the data collection of the base-band RHCP and LHCP signals.
In the context of the study, it is very important to localize the reflective surfaces (satellites’ footprints) from which each processed signal has reflected, and thus detect which areas were scanned during the flight. The link between the reflected signals and the satellites' footprints is based on the GPS time, attitude and position provided by the drone board and the GPS time extracted from the digitized GNSS signals. We show that these parameters allow to determine, at ms rate, the satellites’ footprints locations (i.e. the surface areas) from which each signal has reflected at a specific GPS Time. A Geographic Information System is developed based on this principle to map the measurements obtained from the GNSS-R airborne setup along a real receiver trajectory. The ultimate aim of this study is to link the obtained GNSS-R measurements with the scanned surfaces to provide a soil moisture mapping of the studied area.

How to cite: Issa, H., Stienne, G., Reboul, S., Semmling, M., Raad, M., Faour, G., and Wickert, J.: Airborne Experiment for Soil Moisture Retrieval using GNSS Reflectometry, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16032, https://doi.org/10.5194/egusphere-egu2020-16032, 2020.

Global Satellite Navigation Systems (GNSS) applications like navigation and positioning generally focus on the use of the direct radio signal broadcasted by the navigation satellites. From these signals, very highly precise coordinates can be obtained. However, there is a proportion of signals, that do not reach the receivers directly, that is, the signals that are reflected off Earth’s surface before reaching the receivers. That phenomenon gave way to one of the techniques that is taking an important role in the scope of GNSS remote sensing called GNSS-Reflectometry (GNSS-R). Due to the high reflection coefficient of the water and its importance within the climate system, the ocean is one of the surfaces with greatest interest in GNSS-R research projects. The objective of this study is to retrieve information about ocean height measured through the delay of the signal, and sea state and wind retrieval (ocean surface roughness) from the analysis of the signal amplitude.

During this study, GNSS-R measurements were executed along the North Sea coast between the cities of Calais and Boulogne, France, onboard of a gyrocopter. The setup consisted of a front-end data recorder with a right-handed circular polarization (RHCP) antenna. The campaign was conducted in July 2019 within a total of 9h 40m flight time. Each flight was performed at an altitude of about 800 m above sea level going on two legs forth and back along the coast. The legs differed in the distance from the coastline, of 700 m and 2 km, respectively.

Reflectometry signal processing involves three data levels. Level (0): The raw data samples of Syntony front-end receiver. Level (1): The Delay-Doppler Map (DDM) of the correlated reflected signal and the carrier phase, from which geophysical information can be derived. And Level (2): height estimation (from signal correlation in delay and frequency domain) and roughness estimation (from signal amplitude).

By using the DDM and the carrier phase delay the sea state shall be assessed including the achievable precision and reliability of estimates. An additional aim is also to validate the configuration in terms of the used platform, antenna setup, and flight design.

How to cite: Moreno, M., Semmling, M., Stienne, G., Reboul, S., and Wickert, J.: Airborne GNSS reflectometry for coastal monitoring of sea state, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19944, https://doi.org/10.5194/egusphere-egu2020-19944, 2020.

Dielectric constant describes the electrical properties of a material and is related to soil moisture. The latter is known as a critical parameter in hydrological and climate science; however, computing such dielectric constants is a challenging problem as many factors effect the constant values, e.g., soil type, texture and temperature. Global Navigation Satellite System-Reflectometry (GNSS-R) is a relatively new remote sensing technique being used to infer geophysical information by measuring not only the signals coming directly from the GNSS satellites, but also the reflected GNSS signals from the Earth’s surface. This research presents a new, straightforward approach for computing relative dielectric constant by means of reflectivity, which is the ratio between the signal-to-noise ratio (SNR) of direct waves and SNR of reflected waves. With the well-known relationship between the reflectivity, Fresnel coefficient, and surface roughness, the dielectric constant can be expressed as the combination of horizontal and vertical Fresnel coefficients. Dual, circular-polarized antennas in the zenith and nadir directions were used to capture electromagnetic waves emitted from GNSS satellites and transform them into electrical signals. The zenith direction antenna senses the direct signals which have right-hand circular polarization, and the nadir direction antenna senses right and left-hand circular polarization of reflected GNSS signals created by electromagnetic reflections on the surfaces. An in-house Software Defined Radio (SDR) receiver, coupled with a commercial radio frequency frontend were used to collect, store and analyze both direct and reflected signals. Data collection experiments were carried in areas of smooth surface, and the observed SNR values were applied to the method of quantitative dielectric constant. The computation results demonstrate that derived dielectric constants have the values around 10 and are independent on the incident angle of waves coming to the specular point. Applying the additional data processing to the results, it is relevant to the dielectric constant measured by Time Domain Reflectometry techniques used commercial soil moisture probes at the same time. There have been few attempts to establish the dielectric constant model using forward scattered electromagnetic signals, especially GNSS signals. The proposed calculation method is able to solve the difficulties in analyzing with respect to the incident angle, as well as the polarization. Therefore, it is expected that the inversion approach of the retrieval algorithm makes the GNSS-R applicable to not only the scientific but also the industrial applications. In the future, the dielectric constant will be enhanced to include roughness information of the Earth’s surface and to attempt to calibrate surface soil moisture measurements for various soil types.

How to cite: Lee, J., Bisnath, S., and Lee, R.: A straightforward approach for obtaining quantitative dielectric constant using reflected GNSS signals, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21372, https://doi.org/10.5194/egusphere-egu2020-21372, 2020.

Previously ship-borne GNSS-R (Global Navigation Satellite System Reflectometry) experiments have been carried out on the Kiloton scientific research vessel or above with relatively stable attitude. In this paper, we developed a GNSS-R platform based on the unmanned surface vessel(USV) for the first time, whose main functions include receiving, storing and processing BDS(BeiDou navigation satellite system) direct and reflected signal. In order to overcome the affect of rapidly changed attitude of the small ship, we designed and installed a three axis stabilizer to keep the antenna stable. Meanwhile, we made full use of the geostationary characteristics of BDS GEO satellite, and calculated the interference complex field (ICF) between the direct and reflected signal so as to estimate sea wind speed near the track of USV. The case study in Hengsha Island, Shanghai from June 9 to 11, 2019 showed that the RMS of wind speed is better than 0.50m/s by comparison with the hot-film anemometer measurement.

Key words: Wind speed; unmanned surface vessel; attitude;  BDS Reflectometry; hot-film anemometer

Acknowledgements:  This work is supported by Natural Science Foundation of Shanghai (No. 17ZR1435700), National Natural Science Foundation of China project (No. 41074019) and State Key Laboratory of Estuarine and Coastal Research, East China Normal University.

How to cite: Chen, Y., Wu, J., Guo, P., and Wang, X.: Sea Surface Wind Speed Estimation based on USV borne System by BDS Reflectometry, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21966, https://doi.org/10.5194/egusphere-egu2020-21966, 2020.