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.