Item

Abstract

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.