Articles | Volume 31, issue 1
Research article
21 Feb 2024
Research article |  | 21 Feb 2024

Extraction of periodic signals in Global Navigation Satellite System (GNSS) vertical coordinate time series using the adaptive ensemble empirical modal decomposition method

Weiwei Li and Jing Guo

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Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Solid earth, continental surface, biogeochemistry | Techniques: Theory
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Cited articles

Agnieszka, W. and Dawid, K.: Modeling seasonal oscillations in GNSS time series with Complementary Ensemble Empirical Mode Decomposition, GPS Solut., 26, 101,, 2022. 
Abraha, K. E., Teferle, F. N., Hunegnaw, A., and Dach, R.: GNSS related periodic signals in coordinate time-series from Precise Point Positioning, Geophys. J. Int., 208, 1449–1464,, 2017. 
Australian Burcau of Meteorology:, last access: July 2023. 
Bao, Z., Chang, G., Zhang, L., Chen, G., and Zhang, S.: Filling missing values of multi-station GNSS coordinate time series based on matrix completion, Measurement, 183, 109862,, 2021. 
Bennett, R. A.: Instantaneous deformation from continuous GPS: Contributions from quasi-periodic loads, Geophys. J. Int., 174, 1052–1064,, 2008. 
Short summary
Improper handling of missing data and offsets will affect the accuracy of a signal of interest. The trend in GNSS belonging to GLOSS is key to getting the absolute sea level. However, this is affected by the periodic signals that are included. Although adaptive EEMD is capable of extracting periodic signals, missing data and offsets are ignored in previous work. Meanwhile, the time-varying periodic characteristics derived by adaptive EEMD are more conducive to analyzing the driving factors.