Improved classification and geolocation of SuperDARN ground scatter

The ground-based, high-frequency (HF) space weather radars of the Super Dual Auroral Radar Network (SuperDARN) utilize ionospheric refraction to routinely measure the Doppler velocity of backscatter echoes from E- and F-region plasma irregularities out to ranges of several thousand kilometers. An important byproduct of this skywave propagation is the occurrence of ground scatter echoes from land and ocean surfaces along the radar signal path. While these ground scatter returns are often treated as noise when producing global maps of ionospheric plasma motion, they can be useful for monitoring different geophysical phenomena such as traveling ionospheric disturbances or HF absorption caused by solar flares. The standard approach for separating ionospheric and ground scatter echoes relies solely on empirical Doppler velocity and spectral width criteria. Angle of arrival information obtained with a secondary antenna array can be used to improve the identification of ground scatter echoes, however these interferometry measurements can be difficult to calibrate and are not available for all historical data. Furthermore, the standard SuperDARN mapping techniques (using either measured elevation angles or an empirical virtual height model) are optimized for geolocating ionospheric scatter, resulting in ground range errors of hundreds of kilometers when applied to ground scatter observations. We perform a statistical analysis of fitted line-of-sight parameters (e.g., velocity, spectral width, elevation angle, etc.) from selected radars located at mid-, auroral, and polar latitudes to derive improved empirical classification of ground scatter echoes and geolocation algorithms.