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Title: Performance Evaluation of an Automatic GPS Ionospheric Phase Scintillation Detector Using a Machine-Learning Algorithm: Scintillation detection with machine learning
Author(s) / Creator(s):
 ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Page Range / eLocation ID:
391 to 402
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Abstract

    We examined evolution of Global Positioning System (GPS) scintillation during a substorm in the nightside high latitude ionosphere, using 1‐s phase and amplitude scintillation indices from the Canadian High Arctic Ionospheric Network (CHAIN) network. The traditional 1‐min scintillation indices showed that the phase scintillation was dominant, while the amplitude scintillation was weak. However, the 1‐s amplitude scintillation occurred more often in association with major auroral structures (polar cap arc, growth phase arc, onset arc, poleward expanding arc, poleward boundary intensification, and diffuse aurora) that were detected by the THEMIS all‐sky imagers (ASIs). The 1‐min index missed much of the amplitude fluctuations because they only lasted ∼10 s near a local peak or at the gradients of the auroral structures. The 1‐s phase scintillation was concurrent with the amplitude scintillation but was much weaker than the 1‐min phase scintillation. The frequency spectral analysis showed that the spectral power above ∼1 Hz was diffractive and below ∼1 Hz was refractive. We suggest that the amplitude scintillation in the high‐latitude ionosphere is much more common than previously considered, and that a short time window of the order of 1 s should be used to detect the scintillation. The 1‐min phase scintillation index is largely influenced by refractive effects due to total electron content (TEC) variations, and the spectral power below ∼1 Hz should be removed to identify diffractive scintillation.

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