S7

Aside from main geodetic applications, the Global Navigation Satellite System (GNSS) is now an established atmospheric observing system for water vapour with high spatiotemporal resolution. Water vapour is under-sampled in the current meteorological and climate-observing systems, so obtaining and exploiting more high-quality humidity observations is essential to weather forecasting and climate monitoring.

Operational GNSS data processing at GFZ provides all tropospherical products: zenith total delays (ZTD), precipitable water vapour (PWV), slant total delays (STD) and tropospheric gradients in the frameworks of different meteorological projects like E-GVAP ("The EUMETNET EIG GNSS Water Vapour Programme", http://egvap.dmi). E-GVAP is in charge of the collection and quality control of operational GNSS tropospheric products for numerical weather prediction. GFZ is one of the E-GVAP Analysis Centres and processes about 600 GNSS stations in near real-time. The tropospheric products provided by GFZ are used by European weather services for operational forecasts.

GFZ contributes to climate research within the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) with its expertise in processing of ground-based GNSS network data to generate precise PWV products. Since 2013, GFZ hosts a GRUAN central processing facility for the GNSS-PWV. GFZ is responsible for the installation of GNSS hardware, data transfer, processing and archiving, derivation of GNSS-PWV products according to GRUAN requirements including PWV uncertainty estimation. GNSS-PWV products and results of selected validation studies will be presented.

How to cite: Dick, G., Zus, F., Wickert, J., and Wilgan, K.: GNSS-derived Tropospheric Products for Weather Forecast and Climate Research, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-39, https://doi.org/10.5194/iag-comm4-2022-39, 2022.

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. This is a work in progress and the presentation will focus on the process and early results.

How to cite: Santos, M., Rees, J., Balidakis, K., Klos, A., and Pacione, R.: A report on JWG C.2: Quality control methods for climate applications of geodetic tropospheric parameters , 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-26, https://doi.org/10.5194/iag-comm4-2022-26, 2022.

Due to climate change, intensive storms and severe precipitation will continue to happen, causing destructive flooding. These natural hazards have dramatic impact on humanity. In July 2021, a series of storms with prolonged rain episodes took place over Europe. Several countries were affected by severe floods that followed that rainfall, causing many deaths and material damage. A good understanding and forecasting of severe weather events is thus of uttermost importance.

This study is a combined effort of two research projects: Advanced MUlti-GNSS Array for Monitoring Severe Weather Events (AMUSE) funded by the German Research Foundation DFG and the ALARM äH2020 SESAR project from EU (https://alarm-project.eu). We highlight the interest of multi-GNSS tomography for the 3D-modelling of the neutral atmosphere refractivity and the water vapour density. We obtain these tropospheric parameters for the July 2021 flood in Germany and four tomographic solutions with different constraining options and time resolutions using either GPS only or multi-GNSS estimates. In the constrained solution, we take hourly a priori information from the numerical weather model ICON-D2 (provided by the German Weather Service, DWD), while in the stand-alone solution, the a priori is used only to initiate the tomography. Our investigations show that the stand-alone solution is producing more patterns of refractivity, especially for the multi-GNSS solution, while is it also temporally more stable. We compare the tomographic results with external observations such as radiosondes and GNSS radio occultations from Metop-A and Metop-B satellites. The results show that tomography is producing wetter conditions than the reference data sources. However, we can see the precursor information of the initiation of deep convection in the ground-based GNSS technique.

How to cite: Wilgan, K., Brenot, H., Bender, M., Biondi, R., Dick, G., Zus, F., and Wickert, J.: Multi-GNSS tomography: Case study of the flood in Europe in July 2021, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-30, https://doi.org/10.5194/iag-comm4-2022-30, 2022.

Nanosatellite technology opens up new possibilities for Earth observation. We expect that in the next decade large satellite constellations will arise with hundred, up to thousand satellites in low Earth orbit. While most constellations will be dedicated to internet of things and global communication, a larger number of satellites will be also equipped with rather low-cost sensors, such as GNSS receivers, suited for monitoring of the Earth’s atmosphere. However, future evolution of atmospheric science leans not only on densified observing systems but also on new, more complex analysis methods. In this respect, tomographic principles provide a unique opportunity for sensor fusion and therefore, contribute to a better understanding of the specific sensor characteristics and to exploit their full potential for sensing the three-dimensional atmospheric state. One difficulty in performing the conversion of integral measurements into three-dimensional images is that the signal ray path is not a straight line but rather dependent on the object properties along the signal path. Another challenge is related to the limited number of radio sources and detectors with respect to the size of the object of interest. Therefore, the inverse problem is either solved linearly or iterative non-linear. In this chapter, the individual solving techniques for the tomographic problem are presented, including strategies for removing deficiencies of the ill-posed problem by using truncated singular value decomposition and L-curve technique. Applied to dense nanosatellite formations, a new quality in the reconstruction of the 3D water vapor distribution is obtained, which has the potential for leading to further advances in atmospheric science.

How to cite: Moeller, G. and Ao, C. O.: Nanosatellites: The next big chapter in atmospheric tomography?, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-16, https://doi.org/10.5194/iag-comm4-2022-16, 2022.

Tropospheric delay is one of the important error sources in GNSS positioning and is caused when radio signals broadcasted by GNSS satellites propagate into the neutral atmosphere.

This delay is typically divided into wet and hydrostatic components.  ZTD is described as the sum of the Zenith Hydrostatic Delay (ZHD) and the Zenith Wet Delay (ZWD) and can be combined with surface pressure and temperature to estimate the integrated content of water vapour above GNSS station.

The main objective of this study is to compute the tropospheric zenith delay from ERA 5 reanalysis for 3 stations in Algeria which have different types of climate. We opt for using integration method for different level of pressure with ERA5.

The values of tropospheric delay are also compared with delays obtained from radiosondes profiles.

The results of this work shows good agreement with a mean correlation of 0.70, a mean bias of 3 mm and a RMS of 4 mm. We plan to extend this work for GNSS station recording for long periods.

How to cite: Houaria, N.:  Tropospheric zenith delay as derived from Era-5 reanalysis in Algeria., 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-36, https://doi.org/10.5194/iag-comm4-2022-36, 2022.

Precipitable water vapor (PWV) is a key parameter of the global climate system and the hydrologic cycle. Accurately sensing PWV is of significant importance to understanding the process of global climate change and the hydrologic cycle as well as improving severe weather forecasting. Currently, PWV can be measured and retrieved with various techniques of different accuracies as well as spatial and temporal resolutions, while PWV products still show diverse limitations. In this study, we aim to achieve PWV estimates of high accuracy and resolutions by fusing PWV values derived from the moderate resolution imaging spectroradiometer (MODIS), the fifth generation of the European Centre for Medium-Range Forecasts (ECMWF) global reanalyzes (ERA5) and Global Navigation Satellite System (GNSS) through a convolutional neural network (CNN). Specifically, a CNN-based PWV fusion model which is capable of extracting and fusing information from input features is established to improve the accuracy and spatial resolution of PWV. The region is focused on the west coast of America and the experimental duration lasts for the whole year of 2020. The fused PWV values reveal good agreement with the GNSS PWV values taken as the references, showing the mean absolute error (MAE) of 0.54-0.65 mm and the root mean square error (RMSE) of 0.73-0.90 mm. Compared to MODIS PWV estimates, the fused PWV demonstrates significant improvement in accuracy, with the MAE and RMSE reduced by 79.4% and 76.8%. Meanwhile, they are also superior to the ERA5 PWV, revealing decreases of 35.3% in RMSE and 33.7% in MAE, respectively. Furthermore, the PWV estimates derived from the fusion model are less affected by seasonal variations in terms of accuracy and can provide more detailed and reasonable spatial variation features. The fused PWV values retrieved from the proposed approach contribute to exploiting the full potential of MODIS and ERA5 products and offer promising potential for meteorological applications.

How to cite: Zhang, Y., Lu, C., Zheng, Y., and Wu, Z.: Precipitable water vapor fusion of MODIS and ERA5 based on convolutional neural network, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-7, https://doi.org/10.5194/iag-comm4-2022-7, 2022.