Quasi-stationary substructure within a sporadic E layer observed by the Low Frequency Array (LOFAR)

Observations made with the Low Frequency Array (LOFAR) have been used to infer the presence of variations in a sporadic E layer on a spatial scale of several kilometres and a temporal scale of ~10 minutes. LOFAR stations across the Netherlands observed Cygnus A between 17 UT and 18 UT on 14th July 2018 at frequencies between 24.9 MHz and 64.0 MHz. Variations in the relative signal intensity, together with consideration of geometric optics, were used to infer the presence of a plasma structure. Spatial variations between the stations and the dispersive nature of the observations suggested that this plasma structure was located within the ionosphere. Independent confirmation of the presence of a sporadic E layer, and variation within it, was obtained from observations made by the Juliusruh ionosonde (54.6° N, 13.4° E), which observed reflection of radio waves at an altitude of ~120 km and from frequencies of up to ~6 MHz. The large number (38) of LOFAR stations across the Netherlands together with the sub-second temporal resolution and broadband frequency coverage of the observations enabled the fine details of the spatial variation and the evolution of the structure to be determined. The structure was quasi-stationary, moving at ~12 m s-1, and it exhibited significant variation on spatial scales of a few kilometres. The observations were consistent with the steepening of a plasma density gradient at the edge of the feature over time due to an instability process. A 1-D numerical model showed that the observations were consistent with an electron density enhancement in the sporadic E layer with a density change of 2x1011 m-3 and a spatial scale of several kilometres. Collectively, these results show the ability of LOFAR to observe substructure within sporadic E layers and how this substructure varies with time. They also show the potential value of such datasets to constrain models of instability processes, or to discriminate between competing models.