Automatic Extraction of Gravity Waves from All-Sky Airglow Image Based on Machine Learning

Abstract

With the development of ground-based all-sky airglow imager (ASAI) technology, a large amount of airglow image data needs to be processed for studying atmospheric gravity waves. We developed a program to automatically extract gravity wave patterns in the ASAI images. The auto-extraction program includes a classification model based on convolutional neural network (CNN) and an object detection model based on faster region-based convolutional neural network (Faster R-CNN). The classification model selects the images of clear nights from all ASAI raw images. The object detection model locates the region of wave patterns. Then, the wave parameters (horizontal wavelength, period, direction, etc.) can be calculated within the region of the wave patterns. Besides auto-extraction, we applied a wavelength check to remove the interference of wavelike mist near the imager. To validate the auto-extraction program, a case study was conducted on the images captured in 2014 at Linqu (36.2°N, 118.7°E), China. Compared to the result of the manual check, the auto-extraction recognized less (28.9% of manual result) wave-containing images due to the strict threshold, but the result shows the same seasonal variation as the references. The auto-extraction program applies a uniform criterion to avoid the accidental error in manual distinction of gravity waves and offers a reliable method to process large ASAI images for efficiently studying the climatology of atmospheric gravity waves.

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“…This new technique can quantitatively indicate GW interaction with the background mean wind and temperature during their vertical propagation from below (Matsuda et al 2017;Perwitasari et al 2018). On the other hand, various approaches, such as machine-learning (Lai et al 2019), for extracting GW events have also been tried.…”

Observation of atmospheric gravity waves using a Raspberry Pi camera module on board the International Space Station

“…The former ones are usually known as mountain or lee waves. Throughout their propagation, they transport energy and momentum to the upper layers of the atmosphere [6,8,9]. Atmospheric gravity waves can be detected by visual patterns created in the atmosphere, for instance, in meteor trails, airglow, and clouds.…”

Extraction of Quasi-Monochromatic Gravity Waves from an Airglow Imager Network

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