An Examination of SuperDARN Backscatter Modes Using Machine Learning Guided by Ray‐Tracing
An Examination of SuperDARN Backscatter Modes Using Machine Learning Guided by Ray‐Tracing
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DOI：
10.1029/2022sw003130
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发表时间：
2022-09
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影响因子：
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通讯作者：
B. Kunduri;J. Baker;J. Ruohoniemi;E. G. Thomas;S. Shepherd
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作者：
B. Kunduri;J. Baker;J. Ruohoniemi;E. G. Thomas;S. Shepherd
超级双极光雷达网络（SuperDARN）是一个高频雷达网络，通常用于监测地球电离层中的等离子体对流。SuperDARN的大部分后向散射可大致分为三类：（a）电离层E和F区等离子体不规则反射引起的电离层散射，（B）电离层反射后地面/海面反射引起的地面散射，（c）流星体进入地球大气层时留下的流星轨迹的后向散射。由于HF传播和中纬度电动力学的复杂性，通常无法直接区分SuperDARN观察到的不同模式的后向散射。在这项研究中，我们提出了一种新的两阶段机器学习算法，用于识别SuperDARN数据中的不同后向散射模式。在第一阶段，使用“模仿”射线跟踪的神经网络预测给定位置沿着发生电离层和地面散射的概率，同时使用仰角、反射高度等参数。网络的输入包括控制HF传播的参数，如信号频率、季节、UT时间和地磁活动水平。在第二阶段，来自神经网络和实际SuperDARN数据的输出概率被聚类在一起以确定后向散射的类别。我们的模型可以区分流星散射，1/2跳E-/F-区域电离层以及地面/海洋散射。我们通过比较预测的仰角与在SuperDARN雷达测量的仰角来验证我们的模型。
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.