Publications in Scientific Journals:
Z. Adavi, R. Weber, M. Glaner:
"Assessment of regularization techniques in GNSS tropospheric tomography based on single- and dual-frequency observations";
Water vapor is one of the most variable components in the earth's atmosphere and has a significant role in forming 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 natural hazards. In recent years, GNSS has turned out to be a valuable tool for remotely sensing the atmosphere. In this context, GNSS tomography evolved to an extremely promising technique to reconstruct the spatiotemporal structure of the troposphere. However, locating dual-frequency (DF) receivers with a spatial resolution of a few tens of kilometers sufficient for GNSS tomography is not economically feasible. Therefore, in this research, the feasibility of using single-frequency (SF) observations in GNSS tomography as an alternative approach has been investigated. The algebraic reconstruction technique (ART) and the total variation (TV) method are examined to reconstruct a regularized solution. The accuracy of the reconstructed water vapor distribution model using low-cost receivers is verified by 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 DoY 232-245, 2019. The results indicate that irrespective of the investigated ART and TV techniques, the quality of the reconstructed wet refractivity field is comparable for both SF and DF schemes. However, in the SF scheme the MAE with respect to the radiosonde measurements for ART + NWM and ART + TV can reach up to 10 ppm during noontime. Despite that, all statistical results demonstrate the degradation of the retrieved wet refractivity field of only 10-40% when applying the SF scheme in the presence of the initial guess.
GNSS, Tropospheric tomography, Single-frequency observations, Total variation method
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Created from the Publication Database of the Vienna University of Technology.