Optimizing PPP Performance by Incorporating ZWD Constraints Derived From Data Assimilation

Abstract

One of the primary error sources limiting the performance of the Precise Point Positioning (PPP) technique is the atmospheric wet delay, caused by the presence of water vapor in the lower atmosphere. Accurately representing this parameter is crucial for improving the initialization and accuracy of satellite-based positioning techniques. However, existing empirical models have struggled to capture the severe spatial and temporal variations of this parameter, thereby limiting their effectiveness in high-precision applications. To address these challenges, this study introduces a sequential Calibration and Data Assimilation (C/DA) approach to enhance the estimation and prediction of Zenith Wet Delay (ZWD) values. For this aim, an empirical regional atmospheric wet delay model was constructed using Principal Component Analysis (PCA), serving as the background model for the C/DA method. The methodology involves calibrating this empirical ZWD model using the Ensemble Kalman Filter (EnKF) method, wherein observed ZWD values from approximately 309 GNSS stations across the central Europe are assimilated into the model. The calibrated model parameters were then used to estimate ZWD values, which were subsequently applied as constraints in the PPP method (referred to as PPP-DA) at 10 GNSS test stations within the study area. The study compares the positioning accuracy and convergence time achieved using the PPP-DA method with those obtained from traditional PPP approaches and PPP utilizing ZWD constraints from the GFS model (PPP-GFS). The results demonstrate a significant enhancement, with the PPP-DA method achieving an average improvement of 2 mm in positioning accuracy across all considered stations (representing a 21% reduction compared to the conventional PPP method), along with an average decrease in convergence time of approximately 16%. These findings highlight the potential of integrating C\DA techniques to refine the accuracy and efficiency of satellite-based positioning.