| Citation: | Zhao Li, Kehao Yu, Kunpeng Shi, Justyna Śliwińska-Bronowicz, Xiaoya Wang, Jian Wang, Kai Liu, Zhou Wu, Weiping Jiang. A New Short-Term Polar Motion Prediction Method Based on Combination of LS Model with Time-Varying Characteristics and Arima Model. Journal of Earth Science, 2026, 37(2): 870-881. doi: 10.1007/s12583-025-0319-x
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Accurate and rapid short-term (up to 30 days in advance) polar motion (PM) predictions are critical for real-time applications like earthquake monitoring and early warning, global navigation satellite system (GNSS) meteorology, etc. Traditional prediction models, such as the least squares (LS) model, primarily rely on empirical periodic signals with constant amplitude and phase for extrapolation. However, due to complicated internal and external geophysical processes, these signals exhibit irregular variations rather than remaining constant, making it challenging for traditional methods to resolve them autonomously, especially in short-term predictions. To address this issue, we propose a method that combines the LS model with time-varying PM characteristics (TVLS) using the Prony method and the autoregressive integrated moving average (ARIMA) model, along with the effective angular momentum (EAM) data, to enhance the accuracy of short-term PM prediction. Compared with the official predictions disseminated by the International Earth Rotation and Reference Systems Service (IERS), the proposed method improves the prediction accuracy of PMX and PMY by up to 60.84% and 56.70%, respectively. Our method also outperforms the LS + AR + EAM forecast models from the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC), ranking first for forecast horizons beyond 7 days for our predicted PMX and 12 days for PMY. The improvement can be attributed to the core feature of the TVLS model, which constructs a model for the main components of the PM periodic signal based on the Prony method, effectively capturing the non-stationary characteristics by addressing amplitude and phase variations. Therefore, we conclude that the proposed method could significantly enhance short-term PM prediction accuracy and has potential applications in the fields such as real-time satellite orbit determination, precise positioning and navigation.
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