ENHANCED PREDICTABILITY OF GNSS DISTURBANCES CHALLENGE
Global Navigation Satellite System (GNSS) plays a significant role in modern communication, navigation, positioning, and timing systems. GNSS signals often get disrupted while passing through earth's ionosphere, which is a volatile region of charged particles continuously getting affected by solar storms. Small-scale irregularities developed in the ionosphere as a result of solar disturbances are responsible for GNSS signal disruptions, and incredibly challenging to predict at a given location and time. This challenge is to use the insight about what affects the ionospheric behavior, from the sun to the magnetosphere to aurora borealis, combined with machine learning approaches to predict GNSS signal disruptions at high-latitudes.