2023
DOI: 10.1029/2023sw003430
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Statistical Characteristics of Nighttime Medium‐Scale Traveling Ionospheric Disturbances From 10‐Years of Airglow Observation by the Machine Learning Method

Abstract: For the first time, we used the machine learning method to analyze the statistical occurrence and propagation characteristics of nighttime medium‐scale traveling ionospheric disturbances (MSTIDs) from October 2011 to December 2021 observed by the all‐sky airglow imager deployed at Xinglong (40.4°N, 117.6°E, 30.5° MLAT), China. We developed a program code using the algorithms to identify and extract the propagation and morphological features of MSTIDs in 630 nm airglow images automatically. The classification m… Show more

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Cited by 5 publications
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“…These TIDs are unrelated to the typhoon and likely reflect background TIDs caused by Perkins instability in middle latitude region. Lai et al (2023) conducted a statistical analysis of the spatiotemporal distribution of ionospheric TIDs in the mid-latitude region of China using airglow observations from 2011 to 2021. They found that these TIDs predominantly occur in the summer and propagate in the southwest direction.…”
Section: Space Weathermentioning
confidence: 99%
“…These TIDs are unrelated to the typhoon and likely reflect background TIDs caused by Perkins instability in middle latitude region. Lai et al (2023) conducted a statistical analysis of the spatiotemporal distribution of ionospheric TIDs in the mid-latitude region of China using airglow observations from 2011 to 2021. They found that these TIDs predominantly occur in the summer and propagate in the southwest direction.…”
Section: Space Weathermentioning
confidence: 99%