Autonomous identification and classification of ionospheric sporadic E in digital ionograms

This paper introduces a methodology to autonomously identify and classify ionospheric sporadic E layers (E s ) from digital ionograms acquired using a NOAA dynasonde operated at the Bear Lake Observatory (BLO) in northern Utah. This approach uses a windowed‐fuzzy clustering technique to group ionospheric echoes present in digital ionograms, employing the transitive property of equivalence. The algorithm introduces a variance constraint to automatically determine the number and size of the clusters present in the data set. Principal component analysis (PCA) of the cluster shapes is employed for curvature estimation and to enable classification of the data into the four principal types of temperate latitude sporadic E . A visual evaluation of the autonomously classified ionograms suggests that the algorithm accurately identifies ∼95% of the sporadic E echoes present in the BLO data set. The paper also includes a “proof‐of‐concept” analysis of the methodology, deriving E s parameters for summer and winter intervals, that explicitly characterizes the diurnal and seasonal variations present in the E region at 42 ∘ N. This analysis was derived from approximately 34,000 digital ionograms acquired over 4 months. While the experimental data show lower peak E region heights than predicted by modeling studies, they are generally consistent with GPS satellite occultation measurements. Our analysis of the temporal variation of f o E during summer and winter intervals is inconsistent with previous results that suggest a seasonal E region density anomaly. The results confirm, however, the ionospheric effect of increased NO densities in both the summer and winter E region, as predicted by modeling studies. The identification of sporadic E using the approach outlined in this work markedly facilitates the analysis of E s parameters, as well as eliminating any human biases from the ionogram analysis process.