Details
| Original language | English |
|---|---|
| Title of host publication | 8th Hotine-Marussi Symposium on Mathematical Geodesy - Proceedings of the Symposium |
| Editors | Nico Sneeuw, Mattia Crespi, Fernando Sansò, Pavel Novák |
| Publisher | Springer Verlag |
| Pages | 75-80 |
| Number of pages | 6 |
| ISBN (print) | 9783319245485 |
| Publication status | Published - 2016 |
| Externally published | Yes |
| Event | 8th Hotine-Marussi Symposium on Mathematical Geodesy, 2013 - La Sapienza, Italy Duration: 17 Jun 2013 → 21 Jun 2013 |
Publication series
| Name | International Association of Geodesy Symposia |
|---|---|
| Volume | 142 |
| ISSN (Print) | 0939-9585 |
Abstract
Seasonal signals (annual and semi-annual) in GPS time series are of great importance for understanding the evolution of regional mass, e.g. ice and hydrology. Conventionally, these signals are derived by least-squares fitting of harmonic terms with a constant amplitude and phase. In reality, however, such seasonal signals are modulated, i.e., they have time variable amplitudes and phases. Davis et al. (J Geophys Res 117(B1):B01,403, 2012) used a Kalman filtering (KF) based approach to investigate seasonal behavior of geodetic time series. Singular spectrum analysis (SSA) is a data-driven method that also allows to derive time-variable periodic signals from the GPS time series. In Chen et al. (J Geodyn 72:25–35, 2013), we compared time-varying seasonal signals obtained from SSA and KF for two GPS stations and received comparable results. In this paper, we apply SSA to a global set of 79 GPS stations and further confirm that SSA is a viable tool for deriving time variable periodic signals from the GPS time series. Moreover, we compare the SSA-derived periodic signals with the seasonal signals from KF with two different input process noise variances. Through the comparison, we find both SSA and KF obtain promising results from the stations with strong seasonal signals. While for the stations dominated by the long-term variations, SSA seems to be superior. We also find that KF with input process noise variance based on variance rates performs better than KF with the input process noise variance based on simulations.
Keywords
- Kalman filtering, Singular spectrum analysis, Time variable seasonal signals
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
- Earth and Planetary Sciences(all)
- Geophysics
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8th Hotine-Marussi Symposium on Mathematical Geodesy - Proceedings of the Symposium. ed. / Nico Sneeuw; Mattia Crespi; Fernando Sansò; Pavel Novák. Springer Verlag, 2016. p. 75-80 (International Association of Geodesy Symposia; Vol. 142).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - On time-variable seasonal signals
T2 - 8th Hotine-Marussi Symposium on Mathematical Geodesy, 2013
AU - Chen, Q.
AU - Weigelt, M.
AU - Sneeuw, N.
AU - van Dam, T.
N1 - Funding information: We acknowledge the International GNSS Service (IGS), especially Xavier Collilieux (IGN, France), for providing the original GPS coordinate time series. We appreciate J. L. Davis and two anonymous reviewers for their valuable comments. Qiang Chen acknowledges the Chinese Scholarship Council for supporting his PhD study.
PY - 2016
Y1 - 2016
N2 - Seasonal signals (annual and semi-annual) in GPS time series are of great importance for understanding the evolution of regional mass, e.g. ice and hydrology. Conventionally, these signals are derived by least-squares fitting of harmonic terms with a constant amplitude and phase. In reality, however, such seasonal signals are modulated, i.e., they have time variable amplitudes and phases. Davis et al. (J Geophys Res 117(B1):B01,403, 2012) used a Kalman filtering (KF) based approach to investigate seasonal behavior of geodetic time series. Singular spectrum analysis (SSA) is a data-driven method that also allows to derive time-variable periodic signals from the GPS time series. In Chen et al. (J Geodyn 72:25–35, 2013), we compared time-varying seasonal signals obtained from SSA and KF for two GPS stations and received comparable results. In this paper, we apply SSA to a global set of 79 GPS stations and further confirm that SSA is a viable tool for deriving time variable periodic signals from the GPS time series. Moreover, we compare the SSA-derived periodic signals with the seasonal signals from KF with two different input process noise variances. Through the comparison, we find both SSA and KF obtain promising results from the stations with strong seasonal signals. While for the stations dominated by the long-term variations, SSA seems to be superior. We also find that KF with input process noise variance based on variance rates performs better than KF with the input process noise variance based on simulations.
AB - Seasonal signals (annual and semi-annual) in GPS time series are of great importance for understanding the evolution of regional mass, e.g. ice and hydrology. Conventionally, these signals are derived by least-squares fitting of harmonic terms with a constant amplitude and phase. In reality, however, such seasonal signals are modulated, i.e., they have time variable amplitudes and phases. Davis et al. (J Geophys Res 117(B1):B01,403, 2012) used a Kalman filtering (KF) based approach to investigate seasonal behavior of geodetic time series. Singular spectrum analysis (SSA) is a data-driven method that also allows to derive time-variable periodic signals from the GPS time series. In Chen et al. (J Geodyn 72:25–35, 2013), we compared time-varying seasonal signals obtained from SSA and KF for two GPS stations and received comparable results. In this paper, we apply SSA to a global set of 79 GPS stations and further confirm that SSA is a viable tool for deriving time variable periodic signals from the GPS time series. Moreover, we compare the SSA-derived periodic signals with the seasonal signals from KF with two different input process noise variances. Through the comparison, we find both SSA and KF obtain promising results from the stations with strong seasonal signals. While for the stations dominated by the long-term variations, SSA seems to be superior. We also find that KF with input process noise variance based on variance rates performs better than KF with the input process noise variance based on simulations.
KW - Kalman filtering
KW - Singular spectrum analysis
KW - Time variable seasonal signals
UR - http://www.scopus.com/inward/record.url?scp=84971401674&partnerID=8YFLogxK
U2 - 10.1007/1345_2015_4
DO - 10.1007/1345_2015_4
M3 - Conference contribution
AN - SCOPUS:84971401674
SN - 9783319245485
T3 - International Association of Geodesy Symposia
SP - 75
EP - 80
BT - 8th Hotine-Marussi Symposium on Mathematical Geodesy - Proceedings of the Symposium
A2 - Sneeuw, Nico
A2 - Crespi, Mattia
A2 - Sansò, Fernando
A2 - Novák, Pavel
PB - Springer Verlag
Y2 - 17 June 2013 through 21 June 2013
ER -