Extraction of periodic signals in GNSS vertical coordinate time series using adaptive Ensemble Empirical Modal Decomposition method

Abstract

Abstract. Ensemble Empirical Mode Decomposition (EEMD) has been widely used in the data analysis. Adaptive EEMD further improves computational efficiency through the adaptability in the white noise amplitude and set average number. However, its effectiveness of the periodic signal extraction in Global Navigation Satellite System (GNSS) coordinate time series regarding on the inevitable missing data and offsets issues have not been comprehensively validated. It is verified with 5- year time series through 300 simulations for each case. The results show that high accuracy could reach for the overall random missing rate below 15 % and avoiding consecutive missing epochs exceeding 30. Meanwhile, offsets should be corrected in advance regardless of their magnitudes. The analysis of vertical component of 13 stations within the Australian Global Sea Level Observing System (GLOSS) monitoring network, demonstrates the advantage in revealing the time-varying characteristics of periodic signals. From the perspectives of correlation coefficients, power spectral density (PSD) indices and signal noise ratio (SNR), the means for adaptive EEMD are 0.36, -0.18 and 0.48, respectively, while for least squares (LS), they are 0.27, -0.50 and 0.23. Meanwhile, significance test of the residuals further substantiate the effectiveness in periodic signal extraction, which shows there is no annual signal remained. Also, the longer the series, the higher accuracy of extracted periodic signal is reasonable concluded via the significance test. Furthermore, the time-varying periodic characteristics is more conducive to analyze the driving factors. Overall, the application of adaptive EEMD could achieve high accuracy in analyzing GNSS time series, but it should be based on the proper dealing with missing data and offsets.