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<p>The accuracy of Universal Time (UT1) and Length of Day (LOD) forecasts can be improved by the z-component of short-term Earth effective angular momentum (EAM) forecasts. This study evaluates the accuracy of EAM forecasts from two key providers, German Research Centre for Geosciences (GFZ) and Eidgenossische Technische Hochschule Zürich (ETH), using Mean Absolute Error (MAE) on a comprehensive dataset spanning over 4&#xa0;years. The results reveal different strengths. ETH’s forecasts are more stable, showing less impact from outliers in the mass terms of Atmospheric Angular Momentum (AAM) and Sea Level Angular Momentum (SLAM). With the original data, ETH’s accuracy was significantly higher across both 6-day and 10-day forecast lengths, outperforming GFZ by over 85%. However, the trend reverses after removing a few significant outliers. On this screened data, GFZ's forecast accuracy becomes approximately 37% higher than ETH's, a finding consistent across both forecast horizons. Overall, these results indicate that ETH offers greater raw stability, while GFZ can achieve superior accuracy if data is pre-processed to handle outliers.</p> Graphical abstract <p></p>

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Accuracy evaluation of short-term EAM forecast products from GFZ and ETH

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月13日

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  • Kai Nan,
  • Xishun Li,
  • Jose Manuel Ferrandiz,
  • Harald Schuh,
  • Pei Wei,
  • Xuhai Yang,
  • Shougang Zhang

摘要

The accuracy of Universal Time (UT1) and Length of Day (LOD) forecasts can be improved by the z-component of short-term Earth effective angular momentum (EAM) forecasts. This study evaluates the accuracy of EAM forecasts from two key providers, German Research Centre for Geosciences (GFZ) and Eidgenossische Technische Hochschule Zürich (ETH), using Mean Absolute Error (MAE) on a comprehensive dataset spanning over 4 years. The results reveal different strengths. ETH’s forecasts are more stable, showing less impact from outliers in the mass terms of Atmospheric Angular Momentum (AAM) and Sea Level Angular Momentum (SLAM). With the original data, ETH’s accuracy was significantly higher across both 6-day and 10-day forecast lengths, outperforming GFZ by over 85%. However, the trend reverses after removing a few significant outliers. On this screened data, GFZ's forecast accuracy becomes approximately 37% higher than ETH's, a finding consistent across both forecast horizons. Overall, these results indicate that ETH offers greater raw stability, while GFZ can achieve superior accuracy if data is pre-processed to handle outliers.

Graphical abstract