Impacts of Stochastic Modelling on GPS-Derived ZTD

摘要

GPS-derived Zenith Tropospheric Delay (ZTD) plays a key role in near real-time weather forecasting, especially in improving the precision of Numerical Weather Prediction (NWP) models. The ZTD is usually estimated using the first-order Gauss-Markov (GM) process with a fairly large correlation, and under the assumption that all the GPS measurements, carrier phases or pseudo-ranges, have the same accuracy. However, these assumptions are unrealistic. This paper aims to investigate the impact of several stochastic modelling approaches and using the 1st GM with different power densities on GPS-derived ZTD estimates. The results show that the accuracy of GPS-derived ZTD can be improved using a smaller power density and a suitable stochastic model for the GPS measurements. The stochastic model using a satellite elevation angle-based cosine function is the best amongst the investigated stochastic models. It is noted that, when different power densities of 1st GM and different stochastic modelling approaches are used, the variations in estimated ZTD can reach as much as 1cm. This improvement of ZTD estimation is certainly critical for reliable NWP and for other tropospheric delay applications.