On Improving Slant Wet delays for Tracking Severe Weather Events: An evaluation During Two Storms in Europe

Climate change has led to an increase in the frequency and severity of weather events with intense precipitation and, subsequently, a greater susceptibility of communities around the world to flash flooding. Networks of ground-based Global Navigation Satellite System (GNSS) stations enable the measurement of integrated water vapor along slant pathways, providing three-dimensional (3D) water vapor distributions at low-cost and in real-time. This makes these data a valuable complementary source of information for tracking storm events and predicting their paths. However, it is well established that residual modelling errors and multipath (MP) effects at GNSS stations do impact incoming signals, especially at low elevations and during storms when the atmospheric conditions change rapidly. Until now, the bulk of GNSS products for meteorology are estimates of the more conventional zenith total delays and horizontal gradients, but these products may not be most appropriate for determining 3D distributions of water vapor during convective storm events. In this study we investigate the impact of residual-phase-corrected and multipath-corrected slant wet delay (SWD) estimates on tracking extreme weather events using two events in Europe that led to flooding, damage to property and loss of life. We employed Precise Point Positioning (PPP) with integer ambiguity resolution to generate station-specific MP correction maps. The spatial stacking was carried out in congruent cells with an optimal resolution in elevation and azimuth at the local horizon but with decreasing azimuth resolution as the elevation angle increases. This permits an approximately equal number of observations allocated to each cell. In our analysis we recovered the one-way SWD by adding GNSS post-fit phase residuals, representing the non-isotropic component of the SWD, i.e., the higher-order inhomogeneity. Using the derived MP maps in a final step, the one-way SWD were improved to employ them for the analysis of the weather event. Moreover, we validated the SWD between ground-based water-vapor radiometry and GNSS-derived SWD for different elevation angles. Furthermore, the spatio-temporal fluctuations in the SWD as measured by GNSS closely mirrored the moisture field from the ERA5 re-analysis associated with this severe weather event