Abstract
Long-term Global Navigation Satellite System (GNSS) height residual time series contain signals that are related to environmental influences. A big part of the residuals can be explained by environmental surface loadings, expressed through physical models. This work aims to find a model that connects raw meteorological parameters with the GNSS residuals. The approach is to train a Temporal Convolutional Network (TCN) on 206 GNSS stations in central Europe, after which the resulting model is applied to 68 test stations in the same area. When comparing the Root Mean Square (RMS) error reduction of the time series reduced by physical models, and, by the TCN model, the latter reduction rate is, on average, 0.8% lower. In a second experiment, the TCN is utilized to further reduce the RMS of the time series, of which the loading models were already subtracted. This yields additional 2.7% of RMS reduction on average, resulting in a mean RMS reduction of 28.6% overall. The results suggests that a TCN, using meteorological features as input data, is able to reconstruct the reductions almost on the same level as physical models. Trained on the residuals, reduced by environmental loadings, the TCN is still able to slightly increase the overall reduction of variations in the GNSS station position time series.
Highlights
The first static Global Navigation Satellite System (GNSS) stations have been established almost three decades ago
The results suggests that a Temporal Convolutional Network (TCN), using meteorological features as input data, is able to reconstruct the reductions almost on the same level as physical models
The average reduction rate is slightly smaller than when using the physical models for reduction, but, overall, 36 out of 68 stations (52.9%) have a higher reduction rate when modeling the signal with meteorological parameters through a TCN
Summary
The first static Global Navigation Satellite System (GNSS) stations have been established almost three decades ago. We profit from very long observation time series, where the height component allows us to resolve important information about the vertical movement of the Earth’s crust. This movement is affected by long-term trends, linear drifts, seasonal motions, and offsets. The seasonal variations are dominated by annual and semi-annual periodicities, that can partly be explained by the so-called environmental surface loadings. These can be categorized into hydrological (HYDL), non-tidal atmospheric (NTAL), and non-tidal oceanic loading (NTOL). In 1994, van Dam et al [1] started analyzing the impact of atmospheric pressure on the GNSS height variance and ( van Dam et al [2]) correlate atmospheric and oceanic leading with geoid deformation and further with GNSS height deviations
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