Generating training images of cloud/no-cloud conditions for the AWE mission

Atmospheric gravity waves (GWs) originating from the lower atmosphere can transport energy and momentum into the Earth’s upper atmosphere which can produce detrimental effects on navigation, communication, and surveillance systems. The NASA AWE (Atmospheric Waves Experiment) mission observes GWs near 87 km altitude. The instrument measures over a large field-of-view (90° or 600 km) the intensity of several emission lines of the hydroxyl (OH) emission. Reflections of clouds pose a problem in the interpretation of these observations. In an effort to identify cloudy conditions, a machine-learning model has been developed. The model requires training images of cloud/no-cloud conditions that are obtained from the AWE observations. In this poster, we will describe the AWE observations and the process of classifying the images into cloud and no-cloud conditions.