Mitigation of severe weather events and TID impact on the interpolation of SSR atmospheric parameters. Issue 6 (15th September 2021) - Darugna, Francesco; Bolmgren, Karl H.A.; Schmitz, Martin; Schön, Steffen; Wübbena, Jannes B.; Wübbena, Gerhard; Bruno, Jon; Mitchell, Cathryn N.
Abstract: In Global Navigation Satellite (GNSS)-based positioning, a user within a region covered by a network of reference stations can take advantage of the network-estimated parameters. The use of State Space Representation (SSR) parameters as GNSS-augmentation is valuable for Network-Real Time Kinematic (N-RTK) positioning and enables the ambiguity resolution for Precise Point Positioning (PPP) in the so-called PPP-RTK. SSR atmospheric corrections, i.e. tropospheric and ionospheric delays, are commonly estimated for the approximate user position by interpolation from values estimated for the reference stations. Widely used techniques are Inverse Distance Weighted, Ordinary Kriging and Weighted Least Squares (WLS). In this work, we analyze the interpolation quality of such techniques during severe weather events and Traveling Ionospheric Disturbances (TID). Furthermore, we propose modified WLS methods taking advantage of the physical atmospheric behavior during such events. Here, we exploit the use of Numerical Weather Models for tropospheric horizontal gradients information, and estimated TID parameters like wavelength and direction of propagation. Firstly, the interpolation is assessed using simulations considering artificial and real network geometries. Secondly, the proposed techniques are evaluated in post-processing using real SSR parameters generated by network computation of GNSS measurements. As examples, two severe weather events in North Europe in 2017, and one TID event over Japan in 2019 have been analyzed. The interpolation of SSR tropospheric and ionospheric parameters is evaluated. Considering the reference station positions as rover locations, the application of the modified WLS approach reduces the root mean square error in up to 80 % of the cases during sharp weather fluctuations. Also, the average error can be decreased in 64 % of the cases during the TID event investigated. Improvements up to factors larger than two are observed. Furthermore, specific cases are isolated showing particular tropospheric variations where significant errors (e.g. larger than 1 cm) can be reduced up to 20 % of the total amount. Finally, tropospheric and ionospheric messages are proposed to transmit to the user the information needed to implement the suggested interpolation properly.