Classifying Signatures of Sudden Ionospheric Disturbances

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Abstract/Contents

Abstract: Solar activity, such as flares, produce bursts of high-energy radiation that temporarily enhance the D-region of the ionosphere and attenuate low-frequency radio waves. To track these Sudden Ionospheric Disturbances (SIDs), which disrupt communication signals and perturb satellite orbits, Scherrer et al. (2008) developed an international, ground-based network of around 500 SID monitors that measure the signal strength of low-frequency radio waves. However, these monitors suffer from a host of noise contamination issues that preclude their use for rigorous scientific analysis. As such, we attempt to create an algorithm to automatically distinguish noisy, contaminated SID data sets from clean ones. To do so, we develop a set of features to characterize time series measurements from SID monitors and use these features, along with a binary classifier called a support vector machine, to automatically assess the quality of the SID data. We compute the True Skill Score, a performance verification metric, and find that it is 0.75 ± 0.06. We find features characterizing the difference between the daytime and nighttime signal strength of low-frequency radio waves most effectively discern noisy data sets from clean ones.

Type of resource	software, multimedia
Date created	August 17, 2018

Subject	Columbia University Department of Physics
Subject	Stanford University Hansen Experimental Physics Laboratory
Subject	machine learning
Subject	Sun
Subject	flare
Subject	solar activity
Subject	Sudden Ionospheric Disturbance
Subject	ionosphere
Subject	SID Monitor

Related Publication	Scherrer, D., Cohen, M., Hoeksema, T., et al. 2008, Advances in Space Research, 42, 1777. https://doi.org/10.1016/j.asr.2007.12.013.
Location	https://purl.stanford.edu/cs332mr4558

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Preferred Citation: Sahil Hegde, Monica G. Bobra, Philip H. Scherrer. "Classifying Signatures of Sudden Ionospheric Disturbances." 2018, RNAAS, 2, 162.
Paper: https://doi.org/10.3847/2515-5172/aade47
Code: https://purl.stanford.edu/cs332mr4558

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