Ionospheric Space Weather Targets

Cander, Ljiljana R.

doi:10.1007/978-3-319-99331-7_9

Cited by 4 publications

(8 citation statements)

References 36 publications

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“…Present ionospheric research is transiting towards ionospheric space weather that goes beyond the groundand space-based communication interruptions to influence decision-making communities on social, economical, and physical infrastructural policies. The enhancements in ionospheric plasma irregularities driven by space weather conditions demand an accurate characterization of the dynamical properties of the electron density and its complex nonlinear variation (Cander, 2019). With instruments operating over a substantial frequency domain, a study of plasma density irregularities provide insight into the underlying physical mechanism and its structural properties (Wernik et al, 2003;Muralikrishna et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Structural characterization of the equatorial F region plasma irregularities in the multifractal context

et al. 2020

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Abstract. In the emerging ionosphere–space–weather paradigm, investigating the dynamical properties of ionospheric plasma irregularities using advanced computational nonlinear algorithms provide new insights into their turbulent-seeming nature, for instance, the evidence of energy distribution via a multiplicative cascade. In this study, we present a multifractal analysis of the equatorial F region in situ data obtained from two different experiments performed at Alcântara (2.4∘ S, 44.4∘ W), Brazil, to explore their scaling structures. The first experiment observed several medium- to large-scale plasma bubbles whereas the second experiment observed vertical uplift of the base of the F region. The multifractal detrended fluctuation analysis and the p-model fit are used to analyze the plasma density fluctuation time series. The result shows the presence of multifractality with degree of multifractality 0.53–0.93 and 0.3≤p≤0.4 cascading probability for the first experiment. Other experimental data also exhibit multifractality with degree of multifractality 0.19–0.27 and 0.42≤p≤0.44 cascading probability in ionospheric plasma irregularities. Our results confirm the nonhomogeneous nature of plasma irregularities and characterize the underlying nonhomogeneous multiplicative cascade hypothesis in the ionospheric medium. Differences in terms of scaling and complexity in the data belonging to different types of phenomena are also addressed.

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Section: Introductionmentioning

confidence: 99%

Structural characterization of the equatorial F region plasma irregularities in the multifractal context

et al. 2020

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“…satellite communication signals. Scintillation represents the random fluctuation in the phase and amplitude of received signals (Cander, 2019). The signals propagate through the irregular plasma density structures and the diffraction and/or refraction leads to signal intensity drops and phase shifts.…”

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confidence: 99%

Automatic Spread‐F Detection Using Deep Learning

et al. 2022

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Spread‐F (SF) is a feature that can be visually observed on ionograms when the ionosonde signals are significantly impacted by plasma irregularities in the ionosphere. Depending on the scale of the plasma irregularities, radio waves of different frequencies are impacted differently when the signals pass through the ionosphere. An automated method for detecting SF in ionograms is presented in this study. Through detecting the existence of SF in ionograms, we can help identify instances of plasma irregularities that are potentially affecting the high‐frequency radio‐wave systems. The ionogram images from Jicamarca observatory in Peru, during the years 2008–2019, are used in this study. Three machine learning approaches have been carried out: supervised learning using Support Vector Machines, and two neural network‐based learning methods: autoencoder and transfer learning. Of these three methods, the transfer learning approach, which uses convolutional neural network architectures, demonstrates the best performance. The best existing architecture that is suitable for this problem appears to be the ResNet50. With respect to the training epoch number, the ResNet50 showed the greatest change in the metric values for the key metrics that we were tracking. Furthermore, on a test set of 2050 ionograms, the model based on the ResNet50 architecture provides an accuracy of 89%, recall of 87%, precision of 95%, as well as Area Under the Curve of 96%. The work also provides a labeled data set of around 28,000 ionograms, which is extremely useful for the community for future machine learning studies.

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“…This may result in changing the routes of airliners, which can lead to higher costs for the airline operators (Cander, 2019). In outer space environment, the satellites can get subjected to more energetic particles during solar storms, increasing likelihood of what are called single-event upsets, which when they do happen, may cause their electronics systems to malfunction (Cander, 2019). Finally, and most relevantly for this research, changes in the ionosphere can affect signal propagation through the ionosphere.…”

Section: The Effects Of Space Weathermentioning

confidence: 99%

“…The presence of charged particles means that the ionosphere does modulate the transmission, refraction and reflection of radio waves (Cander, 2019). These charged particles primarily result from the ionization of molecules and atoms that are present in the atmosphere.…”

Section: Ionospherementioning

confidence: 99%

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Using deep learning for studying spread-F in the ionosphere

Luwanga

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National Oceanic and Atmospheric Administration (NOAA) agency in the United States of America, has been the most phenomenal advisor. She constantly challenged me to develop a scientific mindset-to say what I can back up with evidence, to think about why things are this way or that, and above all to pay close attention even to the minutest details. I believe that developing a scientific worldview is one of my most significant outcomes from this work, for it is a skill I will apply in life in general.Thank you for the guidance and prodding.I am grateful to Associate Professor Erry Gunawan who provided support by reviewing my progress. He was also kind enough to take over as my supervisor later in my PhD journey. Thank you to Dr. Yu-Ju Lee who helped me with the technicalities of developing good machine learning models.Thank you to the Nanyang Technological University for the resources and environment to do this research, and to the Singapore government and its lovely people for welcoming me here in Singapore. vi At around the age that I am typing this, my mother had five children that she had to take care of on her own. She worried about whether I, or any of us, would make it to secondary school. I now appreciate much more what a giant of a woman she is to have enabled me to come this far. Her love and support have been immense. I dedicate this work to her. Taŵonga amama.

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Ionospheric Space Weather Targets

Cited by 4 publications

References 36 publications

Structural characterization of the equatorial F region plasma irregularities in the multifractal context

Structural characterization of the equatorial F region plasma irregularities in the multifractal context

Automatic Spread‐F Detection Using Deep Learning

Using deep learning for studying spread-F in the ionosphere

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