Abstract
The Super Dual Auroral Radar Network (SuperDARN) currently consists of
more than thirty high-frequency (HF, 3–30 MHz) radars covering
mid-latitude to polar regions in both hemispheres. Their major task is
to map ionospheric plasma circulation which provides information about
the interactions between the solar wind and the near-Earth’s space
plasma environment. One of the major factors defining radar data quality
is the signal-to-noise ratio (SNR), which requires an accurate
characterisation of the HF noise. The standard SuperDARN data analysis
software uses the SNR as part of a set of empirical procedures designed
to remove low-quality data from further analysis. In this study we found
that the currently used empirical algorithm systematically
underestimates the noise level by up to 40%. Based on comparison of
theoretical and observational noise statistics, we resolve this issue by
designing and validating a procedure for accurate background noise level
estimation. We then propose a simple SNR threshold to replace the
existing criteria for excluding low-quality data. In addition, we show
that several aspects of the radar operational regime design, as well as
short-lived anthropogenic radio interference, can adversely affect the
quality of the noise estimates at selected radar sites, and we propose
ways to mitigate these problems.