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Long-term prediction of polar motion using a combined SSA and ARMA model

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Abstract

To meet the need for real-time and high-accuracy predictions of polar motion (PM), the singular spectrum analysis (SSA) and the autoregressive moving average (ARMA) model are combined for short- and long-term PM prediction. According to the SSA results for PM and the SSA prediction algorithm, the principal components of PM were predicted by SSA, and the remaining components were predicted by the ARMA model. In applying this proposed method, multiple sets of PM predictions were made with lead times of two years, based on an IERS 08 C04 series. The observations and predictions of the principal components correlated well, and the SSA \(+\) ARMA model effectively predicted the PM. For 360-day lead time predictions, the root-mean-square errors (RMSEs) of PMx and PMy were 20.67 and 20.42 mas, respectively, which were less than the 24.46 and 24.78 mas predicted by IERS Bulletin A. The RMSEs of PMx and PMy in the 720-day lead time predictions were 28.61 and 27.95 mas, respectively.

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Acknowledgements

We thank the anonymous reviewers for their helpful comments and suggestions. We are very grateful to the International Earth Rotation and Reference Systems Service (IERS) for providing the polar motion data. This research was supported by the National Natural Science Foundation of China (Grant Nos. 41374009 and 41774001), the Basic Science and Technology Project of China (Grant No. 2015FY310200), and the SDUST Research Fund (Grant No. 2014TDJH101).

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Authors and Affiliations

  1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China

    Yi Shen, Jinyun Guo, Xin Liu, Qiaoli Kong & Linxi Guo

  2. State Key Laboratory of Mining Disaster Prevention and Control Cofounded by Shandong Province and Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao, 266590, China

    Jinyun Guo & Qiaoli Kong

  3. School of Earth Science and Engineering, Hohai University, Nanjing, 211100, China

    Wang Li

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  1. Yi Shen
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  2. Jinyun Guo
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  3. Xin Liu
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Correspondence to Jinyun Guo.

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Shen, Y., Guo, J., Liu, X. et al. Long-term prediction of polar motion using a combined SSA and ARMA model. J Geod 92, 333–343 (2018). https://doi.org/10.1007/s00190-017-1065-3

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