On the Handling of Outliers in the GNSS Time Series by Means of the Noise and Probability Analysis

The data pre-analysis plays a significant role in the noise determination. The most important issue is to find an optimum criterion for outliers removal, since their existence can affect any further analysis. The noises in the GNSS time series are characterized by spectral index and amplitudes that can be determined with a few different methods. In this research, the Maximum Likelihood Estimation (MLE) was used. The noise amplitudes as well as spectral indices were obtained for the topocentric coordinates with daily changes from few selected EPN (EUREF Permanent Network) stations. The data were obtained within the EPN re-processing made by the Military University of Technology Local Analysis Centre (MUT LAC). The outliers were removed from the most noisy 12 EPN stations with the criteria of 3 and 5 times the standard deviations (3σ, 5σ) as well as Median Absolute Deviation (MAD) to investigate how they affect noise parameters. The results show that the removal of outliers is necessary before any further analysis, otherwise one may obtain quite odd and unrealistic values. The probability analysis with skewness and kurtosis was also performed beyond the noise analysis. The values of skewness and kurtosis show that assuming a wrong criterion of outliers removal leads to the wrong results in case of probability distribution. On the basis of the results, we propose to use the MAD method for the outliers removal in the GNSS time series.