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.
Highlights
Gravity waves (GWs) are generated by the disturbance of the atmosphere parcel due to the unbalance between the gravity force and buoyancy force on the parcel
The identification of GWs from the massive all-sky airglow imager (ASAI) images remains a challenging task for manual operations
This program has two advantages: a unified criterion to avoid accidental errors caused by manual GW extraction, and high efficiency to process ASAI images for climatological studies of GWs
Summary
Gravity waves (GWs) are generated by the disturbance of the atmosphere parcel due to the unbalance between the gravity force and buoyancy force on the parcel. Matsuda et al [16] developed a statistical analysis method with three-dimensional (3D) Fourier transformation of GW images of time series They used this method to extract the characteristics of the GW captured in a period of more than one month [17]. Inspired by the cat’s eye, Hubel and Wiesel [19] first proposed the convolutional neural network (CNN) for image processing in machine learning. The GW-containing images are selected and the regions of wave pattern are framed with rectangles This program has two advantages: a unified criterion to avoid accidental errors caused by manual GW extraction, and high efficiency to process ASAI images for climatological studies of GWs. This paper is organized as follows: Section 2 briefly introduces the ASAI data used in this study.
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