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...
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Wiley
2022-09-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2022SW003130 |
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| author | B. S. R. Kunduri J. B. H. Baker J. M. Ruohoniemi E. G. Thomas S. G. Shepherd |
| author_facet | B. S. R. Kunduri J. B. H. Baker J. M. Ruohoniemi E. G. Thomas S. G. Shepherd |
| author_sort | B. S. R. Kunduri |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dbb7b4fdfb6c4b1997185683b801a3b7 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-dbb7b4fdfb6c4b1997185683b801a3b72025-01-14T16:31:13ZengWileySpace Weather1542-73902022-09-01209n/an/a10.1029/2022SW003130An Examination of SuperDARN Backscatter Modes Using Machine Learning Guided by Ray‐TracingB. S. R. Kunduri0J. B. H. Baker1J. M. Ruohoniemi2E. G. Thomas3S. G. Shepherd4Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USABradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USABradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USAThayer School of Engineering Dartmouth College Hanover NH USAThayer School of Engineering Dartmouth College Hanover NH USAAbstract 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.https://doi.org/10.1029/2022SW003130Machine learningSuperDARNRay‐tracing |
| spellingShingle | B. S. R. Kunduri J. B. H. Baker J. M. Ruohoniemi E. G. Thomas S. G. Shepherd An Examination of SuperDARN Backscatter Modes Using Machine Learning Guided by Ray‐Tracing Space Weather Machine learning SuperDARN Ray‐tracing |
| title | An Examination of SuperDARN Backscatter Modes Using Machine Learning Guided by Ray‐Tracing |
| title_full | An Examination of SuperDARN Backscatter Modes Using Machine Learning Guided by Ray‐Tracing |
| title_fullStr | An Examination of SuperDARN Backscatter Modes Using Machine Learning Guided by Ray‐Tracing |
| title_full_unstemmed | An Examination of SuperDARN Backscatter Modes Using Machine Learning Guided by Ray‐Tracing |
| title_short | An Examination of SuperDARN Backscatter Modes Using Machine Learning Guided by Ray‐Tracing |
| title_sort | examination of superdarn backscatter modes using machine learning guided by ray tracing |
| topic | Machine learning SuperDARN Ray‐tracing |
| url | https://doi.org/10.1029/2022SW003130 |
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