Statistical Characteristics of Nighttime Medium‐Scale Traveling Ionospheric Disturbances From 10‐Years of Airglow Observation by the Machine Learning Method

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 model and detection model have accuracies of 96.9% and 70%–85%, respectively. We identified 611 MSTID events from 749,888 airglow images, and obtained the following statistical results: (a) the MSTIDs occurrence peaked at 2200–2300 local time in summer and 2300–2400 in winter; (b) the annual average of horizontal wavelength and velocity are 160–311 km and 98–133 m/s, respectively; (c) among 611 events, 589 MSTIDs propagated southwestward. Fifteen events are northeastward and all of them are periodic MSTIDs, most of which occurred between April and August; (d) the annual trend of relative intensity perturbation (%) shows a negative correlation with the horizontal phase speed; (e) horizontal wavelengths of MSTIDs are independent of the solar activity. Further analyses found those southwestward propagating MSTIDs are consistent with the Es‐Perkins coupling theory, while those non‐southwestward ones could be related to the atmospheric gravity waves and other possible sources. The northeastward events exhibit morphological and seasonal characteristics, which cannot be explained by the Perkins instability, more simultaneous observations (GPS‐TEC, OH airglow, etc.) are required to reveal the mechanism behind these characteristics. Plain Language Summary The traveling ionospheric disturbances are typical perturbations in the middle and upper atmosphere. The waveforms in the airglow layer of oxygen atoms triggered by these perturbations can be tracked by all‐sky airglow imagers. The imager at Xinlong station has captured 749,888 airglow images for almost a solar cycle from 2011 to 2021. The manual statistics of such massive data are time‐consuming and laborious. We developed an auto detection program based on machine learning. The program can categorize the airglow image according to the shooting environment and locate the possible waveforms in the projected airglow images. Then the identified waveforms are fitted with straight lines to extract the propagating