Day-to-day Variability of Ionospheric Pre-reversal Enhancement Under Constant Solar and Geomagnetic Conditions

Abstract:

Equatorial plasma bubbles (EPB) result from ionospheric plasma instability and can adversely impact radio communication and GPS signals. Understanding the physical processes behind their spatial and temporal variability--in particular the day-to-day variability--is an important topic for low-latitude space weather studies. The occurrence frequency of post-sunset EPB displays a clear spatial and seasonal pattern, with peak values around the Atlantic sector in December and January, multiple peaks at the African sector (usually the strongest), the American sector, and the Pacific sector around March and September equinoxes, and reduced peak values in the African and Pacific sector around June solstice. The lowest occurrence frequency values are found in the America/Atlantic and the India sectors during boreal summer. This pattern is very similar to the spatial and seasonal variability of the equatorial pre-reversal enhancement (PRE), which is believed to be key driver of EPB. Using NCAR Whole Atmosphere Community Climate Model extended (WACCM-X) version 2, which interactively solves ionospheric wind dynamo and F-region O+ transport, we can reproduce the spatial and seasonal variation of PRE. In addition to the seasonal variation, the simulated PRE also varies significantly from day-to-day in simulations with constant solar and geomagnetic forcing. Analysis of simulation results show that the day-to-day variability of wind dynamo in the summer side E-region, primarily caused by changes in large-scale neutral dynamics, is largely responsible for the PRE variability. Furthermore, we can deduce EPB occurrence from the simulated PRE, using empirical relation between PRE and EPB rate. The deduced EPB rate is in good agreement with the observed rate. This suggests that the day-to-day variability of PRE could be an important cause of the day-to-day variability of EPB, and that large-scale neutral dynamics could provide information to improve the skills in probabilistic forecast of EPB.