On the Impact of GNSS Multipath Correction Maps on Slant Wet Delays for Tracking Severe Weather Events

Climate change has led to an increase in the frequency and severity of weather events with intense precipitation and subsequently a greater susceptibility to flash flooding of cities worldwide. As a result, accurate fore- and now-casting of imminent extreme precipitation has become critical for the warning and mitigation of these hydro-meteorological hazards. Networks of ground-based Global Navigation Satellite System (GNSS) stations enable the measurement of integrated water vapour along slant pathways, providing three-dimensional (3D) water vapour 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 multipath effects at GNSS stations do impact incoming signals, especially at low elevations. While the GNSS products for meteorology to date consist predominantly of estimates of zenith total delay and horizontal gradients, these products are not optimal for constraining the 3D distribution of water vapour above a station. The direct use of slant delays counteracts this lack of azimuthal information but is more susceptible to multipath errors at low elevations. This study investigates the impact of multipath-corrected slant wet delay (SWD) estimates on tracking extreme weather events using the convective storm event over Bulgaria, Greece and Turkey on July 27, 2017, which resulted in flash floods and significant property damage. First, 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. As the MP errors in the GNSS phase observables can significantly affect the SWD from individual satellites, we employed an averaging strategy for stacking the post-fit phase residuals obtained from our Precise Point Positioning (PPP) processing strategy 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. Using these MP correction maps in a final step, the one-way SWD were improved to employ them for the analysis of the weather event. We found that the non-isotropic component of the one-way SWD contributes up to 11% of the SWD estimates. Moreover, we validated the SWD between ground-based water-vapour 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 weather event. By employing an adequate windowing system for generating these MP correction maps in combination with high-precision real-time GNSS analysis, it is possible to provide improved SWD estimates for the tracking of severe weather events.