Quantitative Evaluation of Tropospheric Delay Modeling and Its Influence on GNSS Height Precision

Abstract

Tropospheric delay remains one of the largest error sources in Global Navigation Satellite System (GNSS) positioning, particularly in the vertical component, where unmodeled or mismodeled delays can introduce biases exceeding several centimeters. This study presents a comprehensive quantitative evaluation of widely used tropospheric delay models, including Saastamoinen, Hopfield, Niell Mapping Function (NMF), Vienna Mapping Function (VMF1), and Global Pressure and Temperature models (GPT2/GPT2w), to assess their influence on GNSS height precision across diverse climatic regimes. Using a combination of International GNSS Service (IGS) datasets, numerical weather model (NWM) outputs, radiosonde profiles, and water vapor radiometer (WVR) observations, the models were systematically validated. Results revealed that empirical models, although computationally efficient, tend to underestimate wet delays in tropical and high-humidity environments, resulting in height errors of up to 3–5 cm. In contrast, advanced mapping functions such as VMF1 and GPT2w, particularly when driven by NWM data, consistently reduced residuals by up to 40% compared with standard formulations. The analysis further demonstrated that hybrid approaches integrating GNSS-derived zenith total delay (ZTD) with auxiliary meteorological observations achieved the highest reliability, showing strong correlation with independent radiosonde measurements (R² > 0.95). These findings highlight that the choice of tropospheric model is not universal but should be context-dependent, with climate sensitivity and application requirements driving model selection. The study concludes that accurate tropospheric delay modeling is indispensable for improving GNSS height precision, with significant implications for applications such as precise point positioning (PPP), real-time kinematic (RTK) surveys, crustal deformation monitoring, and atmospheric sensing.