Automation in Middle- and Upper-Atmosphere LIDAR Operations: A Maximum Rayleigh Altitude Prediction System Based on Night Sky Imagery

Abstract

A prediction system was developed to determine the maximum Rayleigh altitude (MRA) by improving the automated detection of LIDAR power-on conditions and adapting to advancements in middle- and upper-atmosphere LIDAR technology. The proposed system was developed using observational data and nighttime sky imagery collected from multiple LIDAR stations. To assess the accuracy of predictions, three key parameters were employed: mean square error, root mean square error, and mean absolute error. Among the three prediction models created through multivariate regression and autoregressive integrated moving average (ARIMA) analyses, the most suitable model was selected for predicting the MRA. One-month predictions demonstrated the accuracy of the MRA with a maximum error of no more than 5 km and an average error of less than 2 km. This technology has been successfully implemented in numerous LIDAR stations, enhancing their automation capabilities and providing key technical support for large-scale, unmanned, and operational deployments in the middle- and upper-atmosphere LIDAR systems.

Keywords

• MUA LIDAR
• the maximum Rayleigh altitude (MRA)
• night sky images
• prediction