  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 identify noisy, contaminated SID data sets
from clean ones. To do so, we develop a set of features to characterize times
series measurements from SID monitors and use these features, along with a
binary classifer called a support vector machine, to automatically assess the
quality of the SID data. We compute the True Skill Score, a metric that
measures the performance of our classifier, 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.
