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

   https://doi.org/10.1029/2022SW003130  

   Kunduri, B. S. R.; Baker, J. B. H.; Ruohoniemi, J. M.; Thomas, E. G.; Shepherd, S. G. (September 2022, Space Weather)

   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. 

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2. HyperTools: A Python toolbox for visualizing and manipulating high-dimensional data

   Heusser, Andrew C; Ziman, Kirsten; Owen, Lucy LW; Manning, Jeremy R (January 2017, arXiv.org)

   Data visualizations can reveal trends and patterns that are not otherwise obvious from the raw data or summary statistics. While visualizing low-dimensional data is relatively straightforward (for example, plotting the change in a variable over time as (x,y) coordinates on a graph), it is not always obvious how to visualize high-dimensional datasets in a similarly intuitive way. Here we present HypeTools, a Python toolbox for visualizing and manipulating large, high-dimensional datasets. Our primary approach is to use dimensionality reduction techniques (Pearson, 1901; Tipping & Bishop, 1999) to embed high-dimensional datasets in a lower-dimensional space, and plot the data using a simple (yet powerful) API with many options for data manipulation [e.g. hyperalignment (Haxby et al., 2011), clustering, normalizing, etc.] and plot styling. The toolbox is designed around the notion of data trajectories and point clouds. Just as the position of an object moving through space can be visualized as a 3D trajectory, HyperTools uses dimensionality reduction algorithms to create similar 2D and 3D trajectories for time series of high-dimensional observations. The trajectories may be plotted as interactive static plots or visualized as animations. These same dimensionality reduction and alignment algorithms can also reveal structure in static datasets (e.g. collections of observations or attributes). We present several examples showcasing how using our toolbox to explore data through trajectories and low-dimensional embeddings can reveal deep insights into datasets across a wide variety of domains. 

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3. ImpDAR: an open-source impulse radar processor

   https://doi.org/10.1017/aog.2020.44  

   Lilien, David A.; Hills, Benjamin H.; Driscol, Joshua; Jacobel, Robert; Christianson, Knut (October 2020, Annals of Glaciology)

   Abstract Despite widespread use of radio-echo sounding (RES) in glaciology and broad distribution of processed radar products, the glaciological community has no standard software for processing impulse RES data. Dependable, fast and collection-system/platform-independent processing flows could facilitate comparison between datasets and allow full utilization of large impulse RES data archives and new data. Here, we present ImpDAR, an open-source, cross-platform, impulse radar processor and interpreter, written primarily in Python. The utility of this software lies in its collection of established tools into a single, open-source framework. ImpDAR aims to provide a versatile standard that is accessible to radar-processing novices and useful to specialists. It can read data from common commercial ground-penetrating radars (GPRs) and some custom-built RES systems. It performs all the standard processing steps, including bandpass and horizontal filtering, time correction for antenna spacing, geolocation and migration. After processing data, ImpDAR's interpreter includes several plotting functions, digitization of reflecting horizons, calculation of reflector strength and export of interpreted layers. We demonstrate these capabilities on two datasets: deep (~3000 m depth) data collected with a custom (3 MHz) system in northeast Greenland and shallow (<100 m depth, 500 MHz) data collected with a commercial GPR on South Cascade Glacier in Washington. 

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4. Reliability of Matching AMPERE Field‐Aligned Current Boundaries With SuperDARN Lower Latitude Ionospheric Convection Boundaries During Geomagnetic Storms

   https://doi.org/10.1029/2024JA033253  

   Walach, M‐T; Fogg, A_R; Coxon, J_C; Grocott, A.; Milan, S_E; Sangha, H_K; McWilliams, K_A; Vines, S_K; Lester, M.; Anderson, B_J (January 2025, Journal of Geophysical Research: Space Physics)

   Abstract High‐latitude ionospheric convection is a useful diagnostic of solar wind‐magnetosphere interactions and nightside activity in the magnetotail. For decades, the high‐latitude convection pattern has been mapped using the Super Dual Auroral Radar Network (SuperDARN), a distribution of ground‐based radars which are capable of measuring line‐of‐sight (l‐o‐s) ionospheric flows. From the l‐o‐s measurements an estimate of the global convection can be obtained. As the SuperDARN coverage is not truly global, it is necessary to constrain the maps when the map fitting is performed. The lower latitude boundary of the convection, known as the Heppner‐Maynard boundary (HMB), provides one such constraint. In the standard SuperDARN fitting, the HMB location is determined directly from the data, but data gaps can make this challenging. In this study we evaluate if the HMB placement can be improved using data from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), in particular for active time periods when the HMB moves to latitudes below . We find that the boundary as defined by SuperDARN and AMPERE are not always co‐located. SuperDARN performs better when the AMPERE currents are very weak (e.g., during non‐active times) and AMPERE can provide a boundary when there is no SuperDARN scatter. Using three geomagnetic storm events, we show that there is agreement between the SuperDARN and AMPERE boundaries but the SuperDARN‐derived convection boundary mostly lies equatorward of the AMPERE‐derived boundary. We find that disagreements primarily arise due to geometrical factors and a time lag in expansions and contractions of the patterns. 

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5. Virtual Height Characteristics of Ionospheric and Ground Scatter Observed by Mid‐Latitude SuperDARN HF Radars

   https://doi.org/10.1029/2022RS007429  

   Thomas, E. G.; Shepherd, S. G. (June 2022, Radio Science)

   Abstract Propagation of high‐frequency (HF) radio signals is strongly dependent on the ionospheric electron density structure along a communications link. The ground‐based, HF space weather radars of the Super Dual Auroral Radar Network (SuperDARN) utilize the ionospheric refraction of transmitted signals to monitor the global circulation ofE‐ andF‐region plasma irregularities. Previous studies have assessed the propagation characteristics of backscatter echoes from ionospheric irregularities in the auroral and polar regions of the Earth's ionosphere. By default, the geographic location of these echoes are found using empirical models which estimate the virtual backscattering height from the measured range along the radar signal path. However, the performance of these virtual height models has not yet been evaluated for mid‐latitude SuperDARN radar observations or for ground scatter (GS) propagation modes. In this study, we derive a virtual height model suitable for mid‐latitude SuperDARN observations using 5 years of data from the Christmas Valley East and West radars. This empirical model can be applied to both ionospheric and GS observations and provides an improved estimate of the ground range to the backscatter location compared to existing high‐latitude virtual height models. We also identify a region of overlapping half‐hopF‐region ionospheric scatter and one‐hopE‐region GS where the measured radar parameters (e.g., velocity, spectral width, elevation angle) are insufficient to discriminate between the two scatter types. Further studies are required to determine whether these backscatter echoes of ambiguous origin are observed by other mid‐latitude SuperDARN radars and their potential impact on scatter classification schemes. 

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