Impacts of Stochastic Modeling on GPS-derived ZTD Estimations

TL;DR

This study investigates how different stochastic models affect GPS-derived ZTD estimations, demonstrating that appropriate modeling, especially elevation angle-based, can significantly improve accuracy for weather forecasting.

Contribution

It introduces and compares various stochastic modeling strategies for GPS ZTD estimation, highlighting the superiority of elevation angle-based models.

Findings

Elevation angle-based stochastic model outperforms others

ZTD estimation accuracy can vary by up to 1cm with different models

Improved ZTD modeling enhances weather prediction reliability

Abstract

GPS-derived ZTD (Zenith Tropospheric Delay) 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 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 modeling methods on GPS-derived ZTD estimations using Australian IGS data. The results show that the accuracy of GPS-derived ZTD can be improved using a suitable stochastic model for the GPS measurements. The stochastic model using satellite elevation angle-based cosine function is better than other investigated stochastic models. It is noted that, when different stochastic modeling strategies 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 other tropospheric delay corrections.