Published March 9, 2026
| Version v1
Dataset
Open
Automated Detection and Climatological Analysis of Ripple-Scale Gravity Wave Instabilities Using a Squeeze-and-Excitation Convolutional Neural Network
Authors/Creators
-
1.
Embry-Riddle Aeronautical University Daytona Beach Florida Campus
Contributors
Supervisors:
-
1.
Embry-Riddle Aeronautical University Daytona Beach Florida Campus
Description
This dataset contains the manually labeled ripple training dataset and the derived automated ripple event catalog used in:
Hu et al., Automated Detection and Climatological Analysis of Ripple-Scale Gravity Wave Instabilities Using a Squeeze-and-Excitation Convolutional Neural Network, Submitted to EGU Atmospheric Measurement Techniques.
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).
Files
Files
(24.5 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:2140b6af484248c2a787882ab4dffb71
|
12.2 MB | Download |
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md5:ea126d3b305c8be8c082c62d1f039fff
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12.3 MB | Download |
Additional details
Funding
- National Aeronautics and Space Administration
- 80NSSC24K0124
- NSF National Geophysical Facility
- AGS-2327914
Software
- Repository URL
- https://github.com/Multi-Scale-Wave-Dynamics-Group/automated-ripple-recognition.git
- Programming language
- Python