Optimising EISCAT Radar Observations with AI-Powered Scheduling

EISCAT radar operators have to decide how best to use their expensive resources. The decision of “which experiment to run when” depends on space weather (such as when auroras occur) as well as terrestrial weather conditions (i.e. if these auroras are observable by ground based optics). Numerous external real-time data sets from satellites and ground observations have to be analysed to make the optimal decision.

This is where RI-SCALE steps in to empower EISCAT with an intelligent decision support system to optimise the use of radars. 

By training AI models on historical data and integrating real-time monitoring, the system will learn to predict the optimal timing for each experiment. This allows EISCAT to optimise the scheduling of their radar observations, reducing energy waste, costs, and operational inefficiencies associated with poorly timed experiments while maximising the research results for EISCAT’s users. To ensure its accuracy and reliability, the model will be thoroughly validated using different space conditions.

This use demonstrates how RI-SCALE enables EISCAT operators to streamline radar operations through AI-driven decision-making. 

The AI-supported scheduling system will increase the likelihood of radar experiments being conducted under desired conditions, improving data reliability and reducing the operational inefficiencies often tied to poorly timed radar experiments. In parallel, it provides an intelligent tool to manage increasingly complex scheduling demands while reducing wasted energy and personnel effort.