Enhancing Atmospheric Gravity Wave Studies through Cloud Classification in Satellite Infrared Imagery: A Contribution to the AWE Mission

The Atmospheric Waves Experiment (AWE) mission, supported by NASA, focuses on understanding how Earth's weather influences space weather by studying atmospheric gravity waves (AGWs). AWE investigates how these waves are distributed globally, how they change with the seasons, and how they move through the upper atmosphere, aiming to improve our knowledge of their effects on space weather. AWE uses the Advanced Mesospheric Temperature Mapper (AMTM), an infrared imager operating on the International Space Station (ISS), to observe AGWs as they move through the OH airglow layer around 87 km above Earth.
The work presented here plays a key role in the AWE mission by tackling the essential task of sorting AMTM images into two categories: 'Cloud' and 'No-cloud'. Accurately determining whether clouds are present in the image data is critical for ensuring the AWE temperature measurements are reliable.
To enhance this classification process, we are developing a machine learning model that combines techniques from simple logistic regression to advanced convolutional neural networks (CNNs) and transfer learning. This approach will allow us to accurately classify images, enhancing the precision of atmospheric studies.