Automated Detection and Climatological Analysis of Ripple-Scale Gravity Wave Instabilities Using a Squeeze-and-Excitation Convolutional Neural Network

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Auteur principal: Jiahui, Hu
Format: Recurso digital
Langue:anglais
Publié: Zenodo 2026
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author Jiahui, Hu
author_facet Jiahui, Hu
contents <p>This dataset contains the manually labeled ripple training dataset and the derived automated ripple event catalog used in:</p> <blockquote> <p>Hu et al., <em>Automated Detection and Climatological Analysis of Ripple-Scale Gravity Wave Instabilities Using a Squeeze-and-Excitation Convolutional Neural Network</em>, Submitted to EGU Atmospheric Measurement Techniques.</p> </blockquote> <p>The dataset supports reproducible machine-learning-based detection of ripple-scale gravity wave instabilities observed in mesospheric OH airglow imagery over Yucca Ridge Field Station (YRFS), Colorado (40.7°N, 104.9°W).</p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_18927628
institution Zenodo
language eng
publishDate 2026
publisher Zenodo
record_format zenodo
spellingShingle Automated Detection and Climatological Analysis of Ripple-Scale Gravity Wave Instabilities Using a Squeeze-and-Excitation Convolutional Neural Network
Jiahui, Hu
Machine Learning/classification
Atmospheric science
<p>This dataset contains the manually labeled ripple training dataset and the derived automated ripple event catalog used in:</p> <blockquote> <p>Hu et al., <em>Automated Detection and Climatological Analysis of Ripple-Scale Gravity Wave Instabilities Using a Squeeze-and-Excitation Convolutional Neural Network</em>, Submitted to EGU Atmospheric Measurement Techniques.</p> </blockquote> <p>The dataset supports reproducible machine-learning-based detection of ripple-scale gravity wave instabilities observed in mesospheric OH airglow imagery over Yucca Ridge Field Station (YRFS), Colorado (40.7°N, 104.9°W).</p>
title Automated Detection and Climatological Analysis of Ripple-Scale Gravity Wave Instabilities Using a Squeeze-and-Excitation Convolutional Neural Network
topic Machine Learning/classification
Atmospheric science
url https://doi.org/10.5281/zenodo.18927628