Estimating Time-varying Trends from Geodetic Time Series

Abstract:

Modeling signal and noise in geodetic time series is crucial for the proper interpretation of the data. Depending on how the functional and stochastic models are defined, the estimated trend and corresponding uncertainties can vary significantly, emphasizing the need for a robust tool for their estimation. In this study, instead of using the traditional deterministic approach where seasonal signals are estimated with fixed amplitudes and phases and the trend is assumed to be linear, an alternate approach is presented in which these signals are modeled stochastically. The benefit of this approach is that it allows for physically natural variations of the various signal constituents over time. To accomplish this, state space models are defined and solved through the use of a Kalman filter. Since the appropriate choice of the noise parameters is at the heart of the proposed approach, a robust method for their estimation is developed. The performance of the methodology is demonstrated using Gravity Recovery and Climate Experiment (GRACE) and the Global Positioning System (GPS) data at the CAS1 station located in East Antarctica and compared to commonly used least-squares adjustment techniques. The results show that the developed technique allows for a more reliable trend estimation as well as for more physically valuable interpretations while validating independent geodetic observing systems. Moreover, the results suggest that the pursued methodology should become the standard in particular when analyzing climatologic data.