The Occurrence of Sporadic E during the IGY

Leighton, H. I.; Shapley, A. H.; Smith, Ernest K.

doi:10.1016/b978-0-08-009744-2.50018-7

Cited by 26 publications

(25 citation statements)

References 2 publications

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“…The E s occurrence percentages (Figure , top row) are obtained by multiplying the y axis by 100. The results shown here are consistent with previously reported studies such as that of Leighton et al [], who also found that the peaks of the diurnal variation of the f , c , h , and l subtypes of E s occurred between 9 and 10 LT and again near 21 LT.…”

Section: E Region Trace Identificationsupporting

confidence: 93%

“…Their study was derived from 16 years of equatorial data from Fortaleza, which analyzed the occurrence characteristics of the q subtype, as well as the c , l , and h subtypes, which they attributed to ionospheric wind or wind shear mechanisms. Additional studies of the various subtypes of E s have been carried out using ionosonde measurements obtained from equatorial, low‐latitude, and midlatitude sites [ Leighton et al , ; Shi and Wang , ; Vryonides et al , ; Prasad et al , ]. For example, using ionogram data from an equatorial station near Hainan, China (8.47 ∘ N), Shi and Wang [] analyzed E s data acquired from March 2002 to February 2008.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous identification and classification of ionospheric sporadicEin digital ionograms

Ghosh

Berkey

2015

Earth and Space Science

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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.

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Section: E Region Trace Identificationsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Autonomous identification and classification of ionospheric sporadicEin digital ionograms

Ghosh

Berkey

2015

Earth and Space Science

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“…Es on ionograms can have a Leighton et al, 1962). For this study we have not attempted to classify the Es echoes as a particular type.…”

Section: Observationsmentioning

confidence: 96%

Sporadic E at cusp latitudes

MacDougall

Jayachandran

2005

Journal of Atmospheric and Solar-Terrestrial Physics

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“…However, Chadwick (1962), after a very careful study, found that at Washington, DC, the occurrence of Es was very significantly negatively correlated with sunspot number. Further, Leighton et al (1962) concluded that sporadic E occurrence (foEs > 5 MHz) was slightly positively correlated during the day and slightly negatively correlated during the night. Otherwise, Reddy and Matsushita (1968) used fbEs and concluded that the occurrence of fbEs > 4 MHz was increased as R increased, whereas the occurrence of fbEs < 4 MHz decreased as R increased.…”

Section: Introductionmentioning

confidence: 96%