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\begin{document}

\title{On the noise estimation in Super Dual Auroral Radar Network data}



\author[1]{Pavlo V. Ponomarenko}%
\author[2]{Emma Christine Bland}%
\author[1]{Kathryn A McWilliams}%
\author[3]{Nozomu Nishitani}%
\affil[1]{University of Saskatchewan}%
\affil[2]{The University Centre in Svalbard}%
\affil[3]{Nagoya University}%


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  \date{May 27, 2024}


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\begin{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.%
\end{abstract}%



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