On the Noise Estimation in Super Dual Auroral Radar Network Data

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 characterization 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. Plain Language Summary The Super Dual Auroral Radar Network (SuperDARN) consists of more than 30 high‐frequency (3–30 MHz) ground‐based radars designed for studying high‐latitude plasma processes driven by the sun. We have found that the conventional radar software underestimates the noise level in SuperDARN echoes, which may lead to a significant data quality deterioration. To resolve this issue, atmospheric noise characteristics have been studied in detail both experimentally and theoretically. This allowed us to identify the cause of the problem, develop an effective correction algorithm, and improve the data quality assessment. In addition, we analyze several radar operation aspects that may affect the quality of the SuperDARN noise estimates and propose ways to mitigate these problems. Key Points Statistical characteristics of the noise received by Super Dual Auroral Radar Network radars are studied in detail experimentally and theoretically A new technique is developed to provide accurate noise level values which are currently underestimated by the conventional software Hardware and operational factors affecting the noise es