Sidereal filtering is an effective method for mitigating multipath error in static GPS positioning. Using accurate estimates of multipath repeat time (MRT) in sidereal filtering can further improve the performance of the filter. There are three commonly used methods for estimating the MRT: Orbit Repeat Time Method (ORTM), Aspect Repeat Time Adjustment (ARTA), and Residual Correlation Method (RCM). This study utilizes advanced sidereal filtering (ASF) adopting the MRT estimates derived by the three methods to mitigate the multipath in observation domain, then evaluates the three methods in term of multipath reduction in both coordinate and observation domain. Normally, the differences between the MRT estimates from the three methods are less than 1.2 s on average. The three methods are basically identical in multipath reduction, with RCM being slightly better than the other two methods, whereas for a satellite affected by orbit maneuver (satellite number 13 in this study), the MRT estimated by the three methods differ by up to tens of seconds, and the RCM- and ARTA-derived MRT estimates are better than ORTM-derived ones for ASF multipath reduction. The RCM shows a slight advantage in multipath mitigation, while ORTM is the one of lowest computation and ARTA is the optimal one for real-time ASF. Thus, the best MRT estimation method for practical applications depends on which criterion overweighs the others.

Advanced sidereal filtering (ASF) is an observation-domain sidereal filtering that adopts the repeat time of each individual satellite separately rather than the mean repeat time, adopted by the modified sidereal filtering (MSF). To evaluate the performance of ASF, we apply the method to filter the multipath for a short baseline based on a dual-antenna Global Navigation Satellite System (GNSS) receiver. The errors from satellite and receiver clocks, satellite orbit, troposphere, ionosphere, and antenna phase center variations are greatly eliminated by single difference between the two antennas because they are connected to the same receiver clock. The performances of ASF are compared with MSF to evaluate the gain for multipath mitigation. Comparisons indicate that ASF slightly outperforms MSF when the repeat time values of all satellites incorporated in data processing are within the normal range (86,145–86,165 s), but the difference of variance reduction rate between ASF and MSF is statistically significant. When the data of a satellite with repeat time outside the normal range are included, the performances of MSF become much worse, but ASF is almost not affected. This advantage of ASF over MSF is important because the proportion of the days on which at least one satellite’s repeat time exceeds the normal range reaches 71.19% based on the statistics on the data of 2014 and 2015. After applying ASF multipath corrections on the test days, the averages of standard deviations of north, east, and up component are reduced from 3.8 to 2.1 mm, 3.2 to 1.7 mm, and 7.6 to 4.3 mm, respectively. Comparison between applying ASF with the single-day model and with the seven-day model indicates that the former is generally more effective in multipath reduction.

Multipath hemispherical map (MHM) is a kind of multipath mitigation approach that takes advantage of the spatial repeatability of the multipath effect under an unchanged environment. This approach is not only suitable for static environments, but also for some kinematic platforms, such as a moving ship and airplane, where the dominant multipath effects come from the platform itself and the multipath effects from the surrounding environment are considered minor or negligible. Previous studies have verified the feasibility of the MHM approach in static environments. In this study, we expanded the MHM approach to a kinematic shipborne environment. Both static and kinematic tests were carried out to demonstrate the feasibility of the MHM approach. The results indicate that, after MHM multipath mitigation, the root mean square (RMS) of baseline length deviations are reduced by 10.47% and 10.57%, and the RMS of residual values are reduced by 39.89% and 21.91% for the static and kinematic tests, respectively. Power spectrum analysis has shown that the MHM approach is more effective in mitigating multipath in low-frequency bands; the high-frequency multipath effects still exist, and are indistinguishable from observation noise. Taking the observation noise into account, the residual reductions increase to 41.68% and 24.51% in static and kinematic tests, respectively. To further improve the performance of MHM for kinematic platforms, we also analyzed the influence of spatial coverage and resolution on residual reduction.

Atmospheric rivers (ARs) are long, narrow, and transient corridors of strong horizontal water vapor transport that can result in heavy precipitation. Measuring the movement of these concentrated water vapor bands is important in gaining better insight into AR characteristics and forecasts of AR‐caused precipitation. We describe a method to dynamically map the movement of landfalling ARs. The method utilizes high‐rate GPS observations from a dense network to derive isochrones that represent the AR arrival time over specific locations. The generated isochrones show that the three ARs, during landfall over Southern California in January 2017, moved southeastward and took about 10 hr to pass over the study area. Overlaying the topography with isochrones reveals that the Peninsular Ranges slow the movement of the landfalling ARs. The large spacing between two adjacent isochrones, reflecting fast AR movement, is closely related to the increased hourly rain rate.