An Examination of SuperDARN Backscatter Modes Using Machine Learning Guided by Ray‐Tracing

Abstract

The Super Dual Auroral Radar Network (SuperDARN) is a network of High Frequency (HF) radars that are typically used for monitoring plasma convection in the Earth's ionosphere. A majority of SuperDARN backscatter can broadly be divided into three categories: (a) ionospheric scatter due to reflections from plasma irregularities in the E and F regions of the ionosphere, (b) ground scatter caused by reflections from the ground/sea surface following reflection in the ionosphere, and (c) backscatter from meteor trails left by meteoroids as they enter the Earth's atmosphere. Due to the complex nature of HF propagation and mid‐latitude electrodynamics, it is often not straightforward to distinguish between different modes of backscatter observed by SuperDARN. In this study, we present a new two‐stage machine learning algorithm for identifying different backscatter modes in SuperDARN data. In the first stage, a neural network that “mimics” ray‐tracing is used to predict the probability of ionospheric and ground scatter occurring at a given location along with parameters like the elevation angles, reflection heights etc. The inputs to the network include parameters that control HF propagation, such as signal frequency, season, UT time, and geomagnetic activity levels. In the second stage, the output probabilities from the neural network and actual SuperDARN data are clustered together to determine the category of the backscatter. Our model can distinguish between meteor scatter, 1/2 hop E‐/F‐region ionospheric as well as ground/sea scatter. We validate our model by comparing predicted elevation angles with those measured at a SuperDARN radar.

Dynamics of Mid‐Latitude Sporadic‐E and Its Impact on HF Propagation in the North American Sector

“…It is possible that some of the non‐grainy near range BKS observations might be direct backscatter from Es layers. Our previous study (Kunduri et al., 2022) showed that the E‐region backscatter and meteor scatter can occur in the same ranges and such backscatter increases during summer, when Es are most frequently observed. Identifying the source of the non‐grainy echoes is beyond the scope of our current analysis, but will be pursued during future experimental campaigns between the Millstone Hill ISR and SuperDARN.…”

Multi‐Frequency SuperDARN Interferometer Calibration

“…This is particularly an issue when a radar's observational geometry is perpendicular to the ionospheric flow direction, and also at mid-latitudes where the velocity of quiet-time ionospheric irregularities can be as low as a few tens of meters per second (Maimaiti et al, 2018;Ribeiro et al, 2012;Wang et al, 2022). Several techniques for improved classification of backscatter sources have been developed in recent years (e.g., Bland et al, 2014;Burrell et al, 2015;Kong et al, 2024;Kunduri et al, 2022;Ribeiro et al, 2011), which could be used to pre-process input data before applying the multifrequency calibration technique to obtain improved t diff estimates.…”

On the use of SuperDARN Ground Backscatter Measurements for Ionospheric Propagation Model Validation

“…DBSCAN has previously been employed for SuperDARN backscatter characterisation as part of Kunduri et al's (2022) machine learning framework with success. Echoes with elevations below and above 40° are clustered in separate instances, as this provides separation in cases where backscatter is observed across most range gates that may otherwise be incorrectly grouped.…”