An Atmospheric Turbulence-Based GPS Stochastic Model

The precision of GPS position estimates depends on several error sources. One of the limiting GPS error sources is due to fluctuations of water vapor content in the atmosphere, which causes variations in slant-path propagation delays. In GPS data processing, atmospheric propagation delays are typically modeled using a simplified parametric model that does not fully account for water vapor fluctuations. We have developed a stochastic model for delay variations in GPS that is based on the fluctuations of the atmospheric refractive index. We have implemented this model in a least-squares method and, using GPS data, have thus the ability to estimate a site-dependent, time-varying turbulence strength parameter. Preliminary results are very promising and suggest that turbulence is an important error source in GPS. We will describe this variance-covariance model and the scaling turbulence variance estimates. These results may have implications for the interpretation of signals, and its separation from noise, in analysis of GPS time series, and may benefit both geodesists and meteorologists.