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Data · dataset · 2026

Temporal and spatial variability of multiscale neutral density perturbations during storm‐time: Insights from multi‐satellite observations

Listed in National Center for Atmospheric Research

The neutral density perturbations exhibit multiscale features during geomagnetic storms, playing a crucial role in ionosphere‐thermosphere (I‐T) dynamics.

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However, the variations across various temporal and spatial scales remain underexplored. This study compared Gravity Recovery and Climate Experiment (GRACE) satellite data with simulations of Global Ionosphere‐Thermosphere Model (GITM) during the 2015 St.

Patrick's Day storm. In general, GITM captures large‐ and mesoscale density structures well, with some underestimations of mesoscale variations due to inaccuracy in the geomagnetic forcing. GRACE‐A/B distinguish between temporal and spatial variations, revealing enhanced mesoscale structures at high latitudes over 35 s (temporal) and 220 km (spatial) scales.

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Virtual satellite results show that three latitudinally spaced satellites efficiently capture the longitudinal propagation of the large‐scale traveling atmospheric disturbances (LSTADs), while six longitudinally spaced satellites ( apart) significantly improve polar map accuracy. The logarithmic string‐of‐pearl configuration in the latitudinal plane adequately extracts neutral density variations across different temporal and spatial scales.

Temporal variations, , increase with the time scale with a threshold of 3 min for clear detection, while the change rate, , decreases with time. Spatial variation in magnitude increases with spatial scale with a threshold of or 418 km to generate clear variation (>10%). However, the neutral density gradient, , remains roughly the same on different spatial scales.

Coherence analysis was applied to assess the relationship between satellite distance and observed scales, highlighting the importance of multi‐satellite observations in understanding of multiscale thermosphere dynamics.

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Longitudinal study 65% · Satellite remote sensing 65%
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