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

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

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

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

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

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

How to cite: Rozsa, S., Turak, B., and Khaldi, A.: Near realtime tomographic reconstruction of atmospheric water vapour using multi-GNSS observations in Central Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4465, https://doi.org/10.5194/egusphere-egu23-4465, 2023.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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