Autonomous identification and classification of ionospheric sporadicEin digital ionograms

Abstract

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.

Factorial inferential grid grouping and representativeness analysis for a systematic selection of representative grids

“…Due to this advantage, FIGGRA may be an advanced and reliable alternative algorithm for Earth imagery classification and the associated diverse studies or practices [Jiang and Shekhar, 2017;Rauniyar et al, 2017;Schwenk et al, 2017]. In addition, FIGGRA-based spatial/temporal heterogeneity analysis may facilitate improvement of various quantitative analysis approaches for investigating many problems in Earth and space sciences, e.g., monitoring network design [Mishra et al, 2016;Gleason et al, 2017], urban ecology [Bardhan et al, 2016], representative-days selection [Rife et al, 2013], O 3 distribution detection [Parrish et al, 2016], spatial tracking or navigation [Fuchs et al, 2015;Palmer et al, 2016], tsunamis modeling [Grawe and Makela, 2015], atmospheric process analyses [Weisz et al, 2015], seafloor venting detection [Smart et al, 2017], sporadic E propagation [Ghosh and Berkey, 2015], or eco-system analysis [Zhang et al, 2017]. The potentiality of FIGGRA in addressing the abovementioned problems would be assessed in the future studies.…”

Multidimensional Feature Explorer for Unbalanced Spatiotemporal Data

“…The rapid accumulation of geoscience spatiotemporal data is leading to the “big data era of geoscience” (ChengBin et al, ; Li et al, ). Modern signal processing methods, such as principal component analysis (PCA; Buhr et al, ; Ghosh & Berkey, ), empirical orthogonal function (Kaihatu et al, ; Kayano et al, ), Fourier analysis, and spectral decomposition (Houtveen & Molenaar, ) have been widely applied to spatiotemporal analysis, template matching, vector field analysis, and so on (Pereira & Pun, ; Yuan et al, ). However, the accurate extraction and pattern analysis of nonlinear, weak, and quasiperiodic signals such as the El Niño–Southern Oscillation (ENSO), which is a complex climate phenomenon, are still a challenge.…”