An improved prediction algorithm for Earth’s polar motion with considering the retrograde annual and semi-annual wobbles based on least squares and autoregressive model

  • Zhangzhen Sun
  • Tianhe XuEmail author
  • Chunhua Jiang
  • Yuguo Yang
  • Nan Jiang
Original Study


Earth Rotation Parameters (ERP) are indispensable in the transformation between the Celestial Reference Frame and the Terrestrial Reference Frame, and significant for high-precision space navigation and positioning. As a key parameter in ERP, Polar Motion (PM) is of great importance in analyzing and understanding the dynamic interaction between solid Earth, atmosphere, ocean and other geophysical fluids. The diverse excitations, as well as complex motion mechanisms of PM, make it more difficult for its high-precision prediction. In this study, the characteristics of PM from 1962 to 2018 are firstly analyzed. The main period term of the PM is extracted and reconstructed by the Fourier Transform Band-Pass Filter, which indicates Chandler’s amplitude has decayed to its lowest state in 2016 and then enters into the next growth stage. More importantly, a Retrograde Semi-annual Wobble (RSAW) is detected and confirmed for the first time. Secondly, the contributions of Retrograde Annual Wobble (RAW) and RSAW terms to PM are analyzed and compared. Results demonstrate that the magnitudes of RAW and RSAW terms to PM from 1962 to 2018 are about 3–8 mas. Finally, in view of the existence of RAW and RSAW in PM, an improved PM prediction algorithm with considering the influence of RAW and RSAW based on least squares and autoregressive model (LS + AR) is developed. The results show that the inclusion of RAW term can effectively improve the accuracy of the LS + AR model in the prediction span of 1–360 days for both components of PM. Besides, considering the RSAW term, the prediction accuracy can be further improved in the prediction spans of 50–310 days for x component of PM, and in the prediction spans of 50–180 days for y component of PM.


Polar Motion FTBPF Retrograde annual wobble Retrograde Semi-annual wobble Prediction 



The authors are grateful to IERS for the EOP 14C04 solution. This study is funded by the National Natural Science Foundation of China (Grant Nos. 41874032, 41574013 and 41731069), the National Key Research & Development Program of China (2016YFB0501701 and 2016YFB0501900), and the Open Fund of State Key Laboratory of Geo-information Engineering (SKLGIE2016-M-1-1).


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Copyright information

© Akadémiai Kiadó 2019

Authors and Affiliations

  • Zhangzhen Sun
    • 1
  • Tianhe Xu
    • 1
    Email author
  • Chunhua Jiang
    • 1
  • Yuguo Yang
    • 1
  • Nan Jiang
    • 1
  1. 1.Institute of Space ScienceShandong UniversityWeihaiChina

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