Classifying Signatures of Sudden Ionospheric Disturbances
- 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
|August 17, 2018
|Bobra, Monica G.
|Scherrer, Philip H.
|Columbia University Department of Physics
|Stanford University Hansen Experimental Physics Laboratory
|Sudden Ionospheric Disturbance
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