Earth, Planets and Space (Jul 2024)

Findings on celestial pole offsets predictions in the second earth orientation parameters prediction comparison campaign (2nd EOP PCC)

  • Małgorzata Wińska,
  • Tomasz Kur,
  • Justyna Śliwińska-Bronowicz,
  • Jolanta Nastula,
  • Henryk Dobslaw,
  • Aleksander Partyka,
  • Santiago Belda,
  • Christian Bizouard,
  • Dale Boggs,
  • Mike Chin,
  • Sujata Dhar,
  • Jose M. Ferrandiz,
  • Junyang Gou,
  • Richard Gross,
  • Sonia Guessoum,
  • Robert Heinkelmann,
  • Sadegh Modiri,
  • Todd Ratcliff,
  • Shrishail Raut,
  • Matthias Schartner,
  • Harald Schuh,
  • Mostafa Kiani Shahvandi,
  • Benedikt Soja,
  • Daniela Thaller,
  • Yuanwei Wu,
  • Xueqing Xu,
  • Xinyu Yang,
  • Xin Zhao

DOI
https://doi.org/10.1186/s40623-024-02042-3
Journal volume & issue
Vol. 76, no. 1
pp. 1 – 21

Abstract

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Abstract In 2021, the International Earth Rotation and Reference Systems Service (IERS) established a working group tasked with conducting the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) to assess the current accuracy of EOP forecasts. From September 2021 to December 2022, EOP predictions submitted by participants from various institutes worldwide were systematically collected and evaluated. This article summarizes the campaign's outcomes, concentrating on the forecasts of the dX, dY, and dψ, dε components of celestial pole offsets (CPO). After detailing the campaign participants and the methodologies employed, we conduct an in-depth analysis of the collected forecasts. We examine the discrepancies between observed and predicted CPO values and analyze their statistical characteristics such as mean, standard deviation, and range. To evaluate CPO forecasts, we computed the mean absolute error (MAE) using the IERS EOP 14 C04 solution as the reference dataset. We then compared the results obtained with forecasts provided by the IERS. The main goal of this study was to show the influence of different methods used on predictions accuracy. Depending on the evaluated prediction approach, the MAE values computed for day 10 of forecast were between 0.03 and 0.16 mas for dX, between 0.03 and 0.12 mas for dY, between 0.07 and 0.91 mas for dψ, and between 0.04 and 0.41 mas for dε. For day 30 of prediction, the corresponding MAE values ranged between 0.03 and 0.12 for dX, and between 0.03 and 0.14 mas for dY. This research shows that machine learning algorithms are the most promising approach in CPO forecasting and provide the highest prediction accuracy (0.06 mas for dX and 0.08 mas for dY for day 10 of prediction). Graphical abstract

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