Long-term polar motion prediction based on diurnal variation parameters of polar motion

This paper proposes a long-term polar motion prediction method using two derived diurnal parameters: daily polar motion amplitude (DR) and phase (DA). In particular, this study proposes a processing method to reorganize DA into a continuous curve. Suitable models and fitting-window lengths are selected for the derived parameters. Using IERS 20C04 observations, we conducted 365 forecast experiments with horizons up to 500 days. In this retrospective evaluation, the proposed scheme improves average prediction accuracy over LS+AR by 39.86% for the X pole and 40.17% for the Y pole. These results indicate the potentially achievable performance under the selected settings.

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Publication Details

Journal
Journal of Spatial Science
Published
2026-10-28
DOI
https://doi.org/10.1080/14498596.2026.2748981
Primary Topic
Geophysics and Gravity Measurements
Type
article
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article

Long-term polar motion prediction based on diurnal variation parameters of polar motion

Leyang Wang, Guobin Chang, Yu Mei, Yufeng Dai et al.
Journal of Spatial Science
Geophysics and Gravity Measurements
article

Long-term polar motion prediction based on diurnal variation parameters of polar motion

Leyang Wang, Guobin Chang, Yu Mei, Yufeng Dai, Lintao Tao, Zhiping Liu, Fei Wu
article en

Abstract

This paper proposes a long-term polar motion prediction method using two derived diurnal parameters: daily polar motion amplitude (DR) and phase (DA). In particular, this study proposes a processing method to reorganize DA into a continuous curve. Suitable models and fitting-window lengths are selected for the derived parameters. Using IERS 20C04 observations, we conducted 365 forecast experiments with horizons up to 500 days. In this retrospective evaluation, the proposed scheme improves average prediction accuracy over LS+AR by 39.86% for the X pole and 40.17% for the Y pole. These results indicate the potentially achievable performance under the selected settings.

Journal of Spatial Science
China University of Mining and Technology (CN), East China University of Technology (CN)
Openalex Percentile: Top 17%
Geophysics and Gravity Measurements
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