A methodology to eliminate snow- and ice-contaminated solutions from GPS coordinate time series

Positions derived from continuously operating GPS sites are used throughout the world for geophysical research. These positions are estimated assuming that the GPS signals have not been obstructed by either snow or ice on the GPS antenna. Unfortunately, in many regions of the world, this assumption is not correct. Snow and ice attenuate and scatter the GPS signal in a way that leads to significant positioning errors. These positioning outliers are typically removed by assuming geophysical models of displacement. In this study an algorithm is developed that uses signal strength data to determine when the GPS signal has been impacted by snow or ice. This information is then used to remove outliers in GPS coordinate time series. The signal strength-based algorithm was tested on 6years of data from the EarthScope Plate Boundary Observatory network. The algorithm improves the precision of similar to 10% of these coordinate time series, with most of the improvement found for sites operating in Alaska.