gWaveNet: Classification of Gravity Waves from Noisy Satellite Data using Custom Kernel Integrated Deep Learning Method

TL;DR

gWaveNet is a deep learning model with a custom kernel that effectively detects atmospheric gravity waves from noisy satellite images, outperforming existing methods with over 98% training accuracy and 94% test accuracy.

Contribution

This paper introduces a novel kernel integrated into a deep CNN for gravity wave detection in satellite data without noise removal, achieving state-of-the-art accuracy.

Findings

Achieved over 98% training accuracy

Achieved over 94% test accuracy

Outperforms existing gravity wave detection methods

Abstract

Atmospheric gravity waves occur in the Earths atmosphere caused by an interplay between gravity and buoyancy forces. These waves have profound impacts on various aspects of the atmosphere, including the patterns of precipitation, cloud formation, ozone distribution, aerosols, and pollutant dispersion. Therefore, understanding gravity waves is essential to comprehend and monitor changes in a wide range of atmospheric behaviors. Limited studies have been conducted to identify gravity waves from satellite data using machine learning techniques. Particularly, without applying noise removal techniques, it remains an underexplored area of research. This study presents a novel kernel design aimed at identifying gravity waves within satellite images. The proposed kernel is seamlessly integrated into a deep convolutional neural network, denoted as gWaveNet. Our proposed model exhibits impressive proficiency in detecting images containing gravity waves from noisy satellite data without any feature engineering. The empirical results show our model outperforms related approaches by achieving over 98% training accuracy and over 94% test accuracy which is known to be the best result for gravity waves detection up to the time of this work. We open sourced our code at https://rb.gy/qn68ku.