Exploring Machine Learning based Atmospheric Gravity Wave Detection

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BigDataREU2022Team1.pdf (2.53 MB)

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https://hpcf-files.umbc.edu/research/papers/BigDataREU2022Team1.pdf

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http://hdl.handle.net/11603/26248

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UMBC Biological Sciences Department
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UMBC GESTAR II
UMBC Information Systems Department
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https://orcid.org/0000-0002-9933-1170

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technical reports
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UMBC High Performance Computing Facility (HPCF)

Abstract

Atmospheric gravity waves are produced when gravity attempts to restore disturbances through stable layers in the atmosphere. This phenomena should be considered when predicting weather due to their association with weather fronts, wind currents, and extreme weather events. Despite their importance, little research has been conducted on how to computationally detect gravity waves. In this study, we explored various methods of preprocessing and transfer learning in order to work around the small size of our labeled dataset. We pre-trained an autoencoder on unlabeled data before training it to classify labeled data. We also created a CNN by combining certain pre-trained layers from the InceptionV3 Model trained on ImageNet with custom layers and a custom learning rate scheduler.