Advancing polar motion prediction with derivative information

Maciej Michalczak; Marcin Ligas; Santiago Belda; José M. Ferrándiz; Sadegh Modiri

doi:10.1515/jag-2024-0046

Article

Advancing polar motion prediction with derivative information

Maciej Michalczak , Marcin Ligas , Santiago Belda , José M. Ferrándiz and Sadegh Modiri

Published/Copyright: August 22, 2024

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From the journal Journal of Applied Geodesy Volume 19 Issue 1

Abstract

Earth Orientation Parameters (EOP) are essential for monitoring Earth’s rotational irregularities, impacting satellite navigation, space exploration, and climate forecasting. This study introduces a hybrid prediction model combining least-squares (LS) and vector autoregression (VAR) to improve Earth’s Pole Coordinates (x, y) forecast accuracy. Using daily sampled IERS EOP 20 C04 data from 2013 to 2023, we conducted 1,000 yearly random trials, performing 48 forecasts per year. Our method evaluates six data combinations, including primary variables (x, y) and their derivatives ( x ̇ , y ̇ ). Results show a systematic improvement in prediction accuracy, especially for ultra-short-term forecasts (10 days into future), with derivative information stabilizing the solutions. The best-performing combination ( x , y , x ̇ , y ̇ ) achieved a mean absolute prediction error (MAPE) reduction (with respect to the reference data combination – x, y) of up to 8 % for the y and 7 % for the x over a whole 30-day forecast horizon. These findings highlight the effectiveness of incorporating derivatives of polar motion time series into prediction procedure.

Keywords: earth orientation parameters; polar motion; derivative time series; vector autoregression; prediction

Corresponding author: Maciej Michalczak, Department of Integrated Geodesy and Cartography, Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, AGH University of Krakow, Krakow, Poland, E-mail: mmichalc@agh.edu.pl

Funding source: Generalitat Valenciana

Award Identifier / Grant number: PROMETEO/2021/030

Award Identifier / Grant number: SEJIGENT/2021/001

Funding source: Ministerio de Ciencia e Innovación

Award Identifier / Grant number: MCIN/AEI/10.13039/501100011033

Funding source: “Excellence initiative – research university” for the AGH University of Krakow

Award Identifier / Grant number: 6360

Funding source: The European Union-NextGenerationEU

Award Identifier / Grant number: ZAMBRANO 21-04

Funding source: Statutory research grant at the Department of Integrated Geodesy and Cartography, AGH University of Krakow

Award Identifier / Grant number: 16.16.150.545

Acknowledgments

We gratefully acknowledge Poland’s high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2022/015968.

Research ethics: Not applicable.
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission. ML proposed the general idea and methodology of this contribution, did the computations and wrote the draft version. MM proposed the general idea, wrote the draft version and prepared figures. SB, JMF, SM proposed the general idea, commented regularly on the results and gave suggestions.
Competing interests: The authors state no conflict of interest.
Research funding: Research project partly supported by program “Excellence initiative – research university” for the AGH University of Krakow and it is also a result of research on geospatial methods carried out within the statutory research grant no. 16.16.150.545 at the Department of Integrated Geodesy and Cartography, AGH University of Krakow. Santiago Belda and Jose M. Ferrándiz research was partially supported by Generalitat Valenciana (PROMETEO/2021/030, SEJIGENT/2021/001), the European Union-NextGenerationEU (ZAMBRANO 21-04) and by the Project PID2020-119383GB-I00 funded by Ministerio de Ciencia e Innovación (MCIN/AEI/10.13039/501100011033/).
Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2024-05-28

Accepted: 2024-07-31

Published Online: 2024-08-22

Published in Print: 2025-01-29

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https://doi.org/10.1515/jag-2024-0046

Keywords for this article

earth orientation parameters; polar motion; derivative time series; vector autoregression; prediction