Preprints
https://doi.org/10.5194/egusphere-2025-4594
https://doi.org/10.5194/egusphere-2025-4594
02 Oct 2025
 | 02 Oct 2025
Status: this preprint is open for discussion and under review for Annales Geophysicae (ANGEO).

Storm-Time Asymmetries at Magnetic Conjugate Points: A Distribution-Aware Benchmark for GNSS

Serhat Korlaelci, Ramazan Atici, and Selcuk Sagir

Abstract. Geomagnetic storms disrupt the Global Navigation Satellite System (GNSS) and transionospheric links through rapid asymmetric ionospheric variability. In this study, three widely used empirical models (IRI-2016, IRI-Plas, and NeQuick2) were used against GNSS-derived Total Electron Content (TEC) at two magnetic conjugate pairs (mid- and low-latitude) during the geomagnetic storm of August 25–27, 2018. Rather than assessing storm-time predictability, these models were employed as quiet-time reference baselines to quantify storm-time deviations and hemispheric asymmetry. Model performance was evaluated using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and distribution-aware Kullback–Leibler divergence (KLD). This study introduces a novel conjugate-point validation framework augmented by KLD that uniquely captures both magnitude errors and structural distributional mismatches between hemispheres. This is a critical aspect of GNSS reliability that is overlooked by conventional metrics. The results indicate a phase-dependent performance: all models exhibit degradation during the main phase, with the largest errors and structural mismatches occurring at the equator. KLD reveals distributional distortions (variance, skewness, tails) that MAE and RMSE cannot, particularly at the storm onset. NeQuick2 demonstrates superior performance only during the recovery phase, which is consistent with its solar-flux-driven parameterization but limited topside representation. By integrating a conjugate-point framework with distribution-aware validation, this study elucidates where empirical baselines fail under storm conditions, and why hemispheric responses diverge. This approach clarifies the model limitations relevant to GNSS reliability and motivates the development of hybrid data-assimilative schemes that incorporate dynamic drivers while being evaluated with both magnitude- and structure-sensitive metrics.

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Serhat Korlaelci, Ramazan Atici, and Selcuk Sagir

Status: open (until 13 Nov 2025)

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Serhat Korlaelci, Ramazan Atici, and Selcuk Sagir
Serhat Korlaelci, Ramazan Atici, and Selcuk Sagir
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Short summary
Ionospheric storms affect GNSS differently in conjugate hemispheres. Using GNSS-derived TEC during the 25–27 Aug 2018 storm, we assess IRI-2016, IRI-Plas, and NeQuick2 as quiet-time baselines. Magnitude errors (MAE, RMSE) and structure-sensitive KLD show phase-dependent degradation, with equatorial asymmetries strongest. This framework reveals hidden mismatches overlooked by conventional metrics and improves GNSS reliability assessment.
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