Abstract
An algorithm has been developed to isolate the gravity waves (GWs) of different scales from airglow images. Based on the discrete wavelet transform, the images are decomposed and then reconstructed in a series of mutually orthogonal spaces, each of which takes a Daubechies (db) wavelet of a certain scale as a basis vector. The GWs in the original airglow image are stripped to the peeled image reconstructed in each space, and the scale of wave patterns in a peeled image corresponds to the scale of the db wavelet as a basis vector. In each reconstructed image, the extracted GW is quasi-monochromatic. An adaptive band-pass filter is applied to enhance the GW structures. From an ensembled airglow image with a coverage of 2100 km × 1200 km using an all-sky airglow imager (ASAI) network, the quasi-monochromatic wave patterns are extracted using this algorithm. GWs range from ripples with short wavelength of 20 km to medium-scale GWs with a wavelength of 590 km. The images are denoised, and the propagating characteristics of GWs with different wavelengths are derived separately.
Highlights
Gravity waves (GWs) have gained research interest for decades, because of their important role in energy and momentum transport throughout the atmosphere [1,2,3,4]
We developed an algorithm based on discrete wavelet transform (DWT), and applied it to peel off sub images containing wave patterns of different scales from a large-area airglow image captured by an all-sky airglow imager (ASAI) network
The algorithm based on DWT worked effectively for isolating the GWs of different scales in
Summary
Gravity waves (GWs) have gained research interest for decades, because of their important role in energy and momentum transport throughout the atmosphere [1,2,3,4]. Atmosphere 2020, 11, 615 can capture large-scale GWs, but without temporal information [15,16] To overcome these limitations, Xu et al established a no gap ASAI observation network in Northern China, making the continuous measurement of medium-scale GWs from the ground possible [17]. 2D FFT focuses on frequency information only, so that it works effectively for extracting the large-scale waves existing in most areas of the airglow images, while it is less effective at isolating the GWs at small scales in some regions and in the local ripples. We developed an algorithm based on discrete wavelet transform (DWT), and applied it to peel off sub images containing wave patterns of different scales from a large-area airglow image captured by an ASAI network.
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