Gauging the Sensitivity of GNSS for Resolving Vertical Land Motion Over Europe

Through a parametric fit to daily vertical displacement time series from European Permanent GNSS stations, we conducted a statistical sensitivity analysis focusing on Vertical Land Motion (VLM) – specifically, station velocity (linear trend). We compared two independent corrections to raw observed displacements: non-tidal atmospheric, oceanic, and hydrological loading displacements, as well as a correction for common mode errors (CME). Our methodology involved selecting the most realistic stochastic models based on information criteria, analyzing GNSS-observed displacements and identifying discrepancies with loading model predictions. We also employed restricted maximum likelihood estimation (RMLE) to mitigate low-frequency noise biases, enhancing the reliability of velocity uncertainty estimates. Our results demonstrate that 1) an autoregressive, power-law, and white noise model combination is preferred for uncorrected GNSS VLM data, 2) when compared to the corrected cases, this model choice yields lower improvement rates in trend sensitivity than previously reported, and 3) RMLE reveals that for many stations, noise is optimally modeled by a combination of random-walk, flicker-noise, and white noise. We report median trend sensitivity and detection rates of about 0.5 mm/year (with best results for the CME-corrected case), approaching the GGOS goal of a 0.1 mm/year precision, crucial for sea level studies and other applications.