Abstract

The Earth's upper atmosphere is a dynamic environment that is continuously affected by space weather from above and atmospheric processes from below. An effective way to observe this interface region is the monitoring of airglow. Since the 1950s, airglow emissions have been systematically measured by ground-based photometers in specific wavelength bands during the nighttime. The availability of the calibrated data from over 30 years of photometric airglow measurements from Georgia, at wavelengths of 557.7 nm and 630.0 nm, enable us to investigate if a data-driven model based on advanced machine learning techniques can be successfully employed for modeling airglow intensities. A regression task was performed using the time series of space weather indices and thermosphere-ionosphere parameters. We have found that the developed data-driven model has good consistency with the commonly used airglow model and also captures airglow variations caused by cycles of solar activity and changes of the seasons. This enables us to visualize the green and red airglow variations over a period of three solar cycles with a one-hour time resolution.