| 02 Oct 2025
Storm-Time Asymmetries at Magnetic Conjugate Points: A Distribution-Aware Benchmark for GNSS
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.
The paper evaluates three empirical ionospheric models (IRI-2016, IRI-Plas, NeQuick2) during a geomagnetic storm. Combining magnetic conjugate-point analysis with a distribution-aware metric (Kullback–Leibler Divergence, KLD) is useful, though not entirely novel. The approach allows the authors to quantify magnitude errors (via MAE and RMSE) and reveal structural differences in TEC distributions between hemispheres. However, the manuscript’s contribution is not yet significant. I recommend the following revisions:
The authors utilize IRI-2016. However, given that the IRI model is continuously improved, the IRI-2020 version has been available for some years. Since IRI-2020 incorporates significant updates, it would be more worthwhile and relevant to investigate this improved version to understand if its advancements mitigate the storm-time discrepancies your framework so effectively reveals.
One significant point is the LT confounding factor. The authors correctly note (lines 273-277, 433-439) that conjugate points do not share the same Local Time at a given Universal Time. Since TEC is heavily influenced by solar illumination (i.e., local time), a portion of the observed "hemispheric asymmetry" could simply be a diurnal effect. While the authors state this will be addressed in future work, the current study's conclusions about hemispheric asymmetry would be significantly stronger if this factor had been controlled for by using LT-aligned data (this could be an important scientific improvement).
The analysis is based on a single storm event. The authors explicitly state that future work will expand to a statistical ensemble of storms (lines 530-533), which is necessary to establish the generalizability of the findings across different seasons and solar cycle conditions. Doing this in the present manuscript (especially since there is no major hinderance to doing so) could also add more value to the manuscript.
The IRI model includes a storm-time option, while NeQuick does not. There doesn't seem to be a justification for using the NeQuick for such storm-time study. The authors should clarify the model settings used and justify applying NeQuick for storm-time analysis.