Performance metrics for the prediction of ionospheric scintillations caused by Equatorial Plasma Bubbles

Wednesday, 13 February 2019: 09:40
Fountain I/II (Westin Pasadena)
Brett A Carter, RMIT University, SPACE Research Centre, Melbourne, Australia, Julie Louise Currie, RMIT University, SPACE research Centre, Melbourne, VIC, Australia and Michael Terkildsen, Australian Bureau of Meteorology, Space Weather Services, Sydney, Australia
Abstract:
Global Navigation Satellite System (GNSS) signals are used for a wide variety of modern applications that rely on Positioning, Navigation and Timing; e.g., aviation, agriculture, mining and construction. With the increased number of applications and benefits come increased vulnerabilities in the event of GNSS signal unavailability/disruption. Plasma irregularities in the Earth’s ionosphere are one cause of GNSS signal disruption, so understanding how and when ionospheric irregularities occur is currently a key topic in space weather research. The most common example of GNSS-disrupting ionospheric irregularities is Equatorial Plasma Bubbles (EPBs); low-density plasma regions that rise into high-density plasma at higher altitudes above the magnetic equator. EPBs are caused by the Generalized Rayleigh-Taylor plasma instability, which activates as the E-region undergoes recombination after sunset and the F-region undergoes rapid vertical drift. The EPB structures that grow in these conditions give rise to a spectrum of plasma irregularities that act as a diffraction grating on the coherent radio signals that traverse them, such as those transmitted by GNSS. The task of predicting EPBs is complicated by the fact that EPB occurrence exhibits both a seasonal/longitudinal (long-term) variability and a day-to-day (short-term) variability. The long-term variability is relatively well understood and reproducible, but the short-term EPB occurrence variability is less understood. Some recent progress has been made towards the accurate prediction of the short-term EPB variability, but the question as to how to best measure and compare prediction/modelling “success” has not been fully addressed. This research examines some popular forecast skill metrics and investigates their applicability to compare and track the progress of ionospheric scintillation prediction models and techniques.