Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach

Global Navigation Satellite System (GNSS) long-term residual height time series exhibit signals related to environmental influences. These can partly b explained through environmental surface loads, which are described with physical models. In this work, a model is computed to connect the GNSS residuals with raw meteorological parameters. A Temporal Convolutional Network (TCN) is trained on 206 GNSS stations in central Europe, and applied to 68 test stations in the same area. The resulting Root Mean Square (RMS) error reduction is on average 0.8% lower for the TCN modeled time series, compared to using physical models for the reduction. In a further experiment, the TCN is trained on the GNSS time series after reducing those by the surface loading models. The aim is a further increase of RMS reduction, which is achieved with 2.7% on average, resulting in an overall mean reduction of 28.6%. The results suggest that with meteorological features as input data, TCN modeled reductions are able to compete with reductions derived from physical models. Trained on the residuals reduced by environmental loading models, the TCN is able to slightly increase the overall reduction of variations in the GNSS station position time series.