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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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Super Dual Auroral Radar Network Expansion and Its Influence on the Derived Ionospheric Convection Pattern
Abstract The Super Dual Auroral Radar Network (SuperDARN) was built to study ionospheric convection and has in recent years been expanded geographically. Alongside software developments, this has resulted in many different versions of the convection maps data set being available. Using data from 2012 to 2018, we produce five different versions of the widely used convection maps, using limited backscatter ranges, background models and the exclusion/inclusion of data from specific radar groups such as the StormDARN radars. This enables us to simulate how much information was missing from older SuperDARN research. We study changes in the Heppner‐Maynard boundary (HMB), the cross polar cap potential (CPCP), the number of backscatter echoes (n) and theχ2/nstatistic which is a measure of the global agreement between the measured and fitted velocities. We find that the CPCP is reduced when the PolarDARN radars are introduced, but then increases again when the StormDARN radars are added. When the background model is changed from the RG96 model, to the most recent TS18 model, the CPCP tends to decrease for lower values, but tends to increase for higher values. When comparing to geomagnetic indices, we find that there is on average a linear relationship between the HMB and the geomagnetic indices, as well asn, which breaks when the HMB is located at latitudes below ∼50° due to the low observational density. Whilstnis important in constraining the maps (maps withn > 400 data points are unlikely to differ), it is insufficient as the sole measure of quality.
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- Award ID(s):
- 1934997
- PAR ID:
- 10366903
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Space Physics
- Volume:
- 127
- Issue:
- 2
- ISSN:
- 2169-9380
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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