Potential Contributors to CME and Optimal Noise Model Analysis in the Chinese Region Based on Different HYDL Models

Abstract

Optimizing the noise model for global navigation satellite system (GNSS) vertical time series is vital to obtain reliable uplift (or subsidence) deformation velocity fields and assess the associated uncertainties. In this study, by thoroughly considering the effects of hydrological loading (HYDL) that dominates the seasonal fluctuations and common mode error (CME), we analyzed the optimal noise characteristics of GNSS vertical time series at 39 stations spanning from January 2011 to August 2019 in the Chuandian region, southeast of the Qinghai–Tibet Plateau. Our results showed that the optimal noise models without HYDL correction were white noise plus flicker noise (WN + FN), white noise plus power law noise (WN + PL), and white noise plus Gauss–Markov noise (WN + GGM), which accounted for 87%, 10%, and 3% of GNSS stations, respectively. By contrast, the optimal noise models at all stations were WN + FN and WN + PL after correction by different HYDLs. The correlation between CME and HYDL provided by the School and Observatory of Earth Sciences (EOST), namely EOST_HYDL, was 0.63~0.8 and the value of RMS reduction was 18.9~40.3% after removing EOST_HYDL time series from the CME, with a mean value of 31.8%, there is a good correlation and consistency between CME and EOST_HYDL. The absolute value of vertical velocity and its uncertainty with and without EOST_HYDL correction varied from 0.11 to 0.55 mm/a and 0 to 0.23 mm/a, respectively, implying that the effect of HYDL should not be neglected when performing optimal noise model analysis for GNSS vertical time series in the Chuandian region.

A Novel Method for Analyzing the Spatiotemporal Characteristics of GNSS Time Series: A Case Study in Sichuan Province, China

“…The GNSS coordinate time series exhibits time-dependent background noise with significant spatial correlation [15]. The errors caused by the incomplete modeling in the position residuals, such as satellite orbits, environmental loading effects [16] or incorrect modeling (GNSS processing strategy), are called common mode errors (CME). CME affect the accuracy of station coordinates and velocity solutions [17] and constitute one of the largest sources of the errors in the accuracy of regional network time series [18].…”