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<p>Global Ionospheric Maps (GIMs) provide ionospheric corrections together with associated Root-Mean-Square (RMS) maps intended to characterize model uncertainty. However, the interpretation and reliability of these RMS values vary among the Ionospheric Associated Analysis Centers (IAACs) of the International GNSS Service (IGS) due to their generation methodologies. This study investigates the statistical characterisrics of unmodeled ionospheric errors in GIM products from IGS IAACs during 2016–2021.The results show that the residuals generally follow symmetric but leptokurtic distributions. After excluding outliers, the residuals can be well approximated by a normal distribution. Spectral and Allan variance analyses further reveal the presence of deterministic components with dominant periodicities of 1/6, 1/3, and 2/3&#xa0;day. To improve the consistency between GIM-provided RMS values and actual ionospheric errors, we propose the Factor-Adjusted Ionospheric Residual Statistics (FAIRS) approach, which adaptively scales RMS values based on the residual distributions at contributing GNSS stations.The proposed FAIRS method has been implemented in generating the RMS maps of Chinese Academy of Sciences (CAS) GIM products. The validation using the Ionospheric Errors-Accuracy Diagram (IEAD) and RMS Bounding Percentage (RMSBP) demonstrates that FAIRS provides more realistic and responsive precision information than the original CAS RMS generation method, which employs a function fitting and global inflation approach. Furthermore, when applied to Precise Point Positioning (PPP) with ionospheric constraints, FAIRS reduces convergence time while maintaining reliable error bounding.</p>

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FAIRS: a factor-adjusted ionospheric residual statistics approach for global ionospheric RMS mapping

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月15日

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  • Ang Liu,
  • Ningbo Wang,
  • Zishen Li,
  • Andreas Brack,
  • Manuel Hernández-Pajares,
  • Andrzej Krankowski,
  • Francesco Gini,
  • Reza Ghoddousi-Fard,
  • Qi Liu,
  • Liang Wang,
  • Adam Froń,
  • Ang Li,
  • Runqing Liu,
  • Mingqiang Xie

摘要

Global Ionospheric Maps (GIMs) provide ionospheric corrections together with associated Root-Mean-Square (RMS) maps intended to characterize model uncertainty. However, the interpretation and reliability of these RMS values vary among the Ionospheric Associated Analysis Centers (IAACs) of the International GNSS Service (IGS) due to their generation methodologies. This study investigates the statistical characterisrics of unmodeled ionospheric errors in GIM products from IGS IAACs during 2016–2021.The results show that the residuals generally follow symmetric but leptokurtic distributions. After excluding outliers, the residuals can be well approximated by a normal distribution. Spectral and Allan variance analyses further reveal the presence of deterministic components with dominant periodicities of 1/6, 1/3, and 2/3 day. To improve the consistency between GIM-provided RMS values and actual ionospheric errors, we propose the Factor-Adjusted Ionospheric Residual Statistics (FAIRS) approach, which adaptively scales RMS values based on the residual distributions at contributing GNSS stations.The proposed FAIRS method has been implemented in generating the RMS maps of Chinese Academy of Sciences (CAS) GIM products. The validation using the Ionospheric Errors-Accuracy Diagram (IEAD) and RMS Bounding Percentage (RMSBP) demonstrates that FAIRS provides more realistic and responsive precision information than the original CAS RMS generation method, which employs a function fitting and global inflation approach. Furthermore, when applied to Precise Point Positioning (PPP) with ionospheric constraints, FAIRS reduces convergence time while maintaining reliable error bounding.