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GNSS tropospheric tomography is a striking remote-sensing tool to monitor the spatiotemporal behaviour of the troposphere, specifically of highly variable water vapour. This technique is generally advantageous compared to conventional data sources such as radiosonde and water vapour radiometers (WVR) due to its capability to continuous monitor Earth’s atmosphere during all weather conditions. Hence, the products of tropospheric tomography can play an influential role in meteorological and climatological studies. In July 2021, a series of severe storms and intense rainfall led to floods in European countries like Germany, Belgium and Luxembourg. This costed hundreds of lives and caused widespread damage to various structures. Therefore, this research aims to define a tomography model with a varying temporal resolution based on the physical atmospheric conditions and satellite’s geometry. Thereby, two steps are employed to optimize the temporal resolution of the tomography model. First, we compute the average spatiotemporal correlation between horizontal gradients of tropospheric delay. Then, the temporal resolution of the model is chosen by dependence on the calculated correlations in three different similarity thresholds, namely 85 %, 90 %, and 95 %. Due to the intense rainfall during 13-15 July, we consider two days before and after this core period to assess the temporal variability of the tomography model before, during and after this period. Finally, the discrepancy between the retrieved wet refractivity tomography profiles and the calculated radiosonde profiles is computed to investigate the performance of the tomography model during the experimental period., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Geodetic GNSS networks are the backbone for tropospheric tomography studies. In addition, dense low-cost GNSS networks, InSAR interferograms or GNSS radio occultation profiles are valuable assets, which can provide important complementary information for stabilizing the tomography system and further increase the spatio-temporal resolution of the reconstructed water vapor field. Therefore, the IAG tomography working group was establishing a benchmark dataset covering the meteorological conditions before, during and after the Central European floods in July 2021. The created dataset consists of a suite of complementing observing techniques and thus, was designed to contribute to developing and testing of new tomography-based concepts for sensing small-scale structures in the troposphere. In this presentation, we thereby focus on the achievements made in the last four years in supporting the analysis and prediction of extreme weather events with 3D water vapour products available in (near) real-time; improving the spatio-temporal resolution of derived tomography products by sensor fusion of ground-based GNSS, InSAR and radio occultation measurements; and establishing new standards in dynamic model parameterization., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

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., ISSN:https://zenodo.org/communities/iag-comm4-symp-2022