Statistical Characteristics of Mid‐Latitude Ionospheric Irregularities at Geomagnetic Quiet Time: Observations From the Jiamusi and Hokkaido East SuperDARN HF Radars

Wang, Wei; Zhang, J.J.; Wang, Chunqi; Watanabe, Midori; Yan, Jingye; Lan, Aiyu; Deng, Xianmo; Qiu, Hongbin

doi:10.1029/2021ja029502

Cited by 8 publications

(8 citation statements)

References 26 publications

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“…However, it is beyond the scope of the current work. The utility of our model in identifying low‐velocity sub‐auroral ionospheric scatter (Maimaiti et al., 2019; Wang et al., 2022) is shown in Figure 10. Similar to the Ribeiro et al.…”

Section: Discussionmentioning

confidence: 94%

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

et al. 2022

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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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Section: Discussionmentioning

confidence: 94%

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

et al. 2022

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“…The data presented in this study thus show a great deal of similarity in terms of how the WA deteriorates as we go from the North American sector to Australian sector and to South American sector (Yasyukevich et al., 2018). In this sense, it is of great interest to investigate whether the daytime winter enhancements in GS occurrence rate occur in the Asian sector where Russian HF radar at Ekaterinburg (Berngardt et al., 2015) and new Chinese HF SuperDARN radar (Wang et al., 2022) have accumulated sufficient amount of data.…”

Section: Discussionmentioning

confidence: 99%

Occurrence Rates of SuperDARN Ground Scatter Echoes and Electron Density in the Ionosphere

et al. 2022

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Ground scatter (GS) echoes in Super Dual Auroral Radar Network (SuperDARN) observations have been always expected to occur under high‐enough electron density in the ionosphere providing sufficient bending of high frequency radio wave paths toward the ground. In this study, we provide direct evidence statistically supporting this notion by comparing the GS occurrence rate for the Rankin Inlet SuperDARN radar and the F region peak electron density NmF2 ${N}_{m}{F}_{2}$ measured at Resolute Bay by the Canadian Advanced Digital Ionosonde and incoherent scatter radars RISR‐N/C. We show that the occurrence rate increases with NmF2 ${N}_{m}{F}_{2}$ roughly linearly up to about ∼4⋅1011m−3 $\sim 4\cdot {10}^{11}{\mathrm{m}}^{-3}$ and the trend saturates at larger NmF2 ${N}_{m}{F}_{2}$. One expected consequence of this relationship is correlation in seasonal and solar cycle variations of the GS echo occurrence rate and NmF2 ${N}_{m}{F}_{2}$. GS occurrence rates for a number of SuperDARN radars at middle latitudes, in the auroral zone and in the polar cap are considered separately for daytime and nighttime. The data indicate that the daytime occurrence rates are maximized in winter and nighttime occurrence rates are maximized in summer for middle latitude and auroral zone radars in the Northern Hemisphere, consistent with the winter anomaly (WA) phenomenon. The effect is most evident in the North American and Japanese sectors, and the quality of WA signatures deteriorates in the European and, especially, in the Australian sectors. The effect does not exist in the South American sector and in the polar caps of both hemispheres.

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“…The boresight of the Jiamusi radar points 44° apart from the geographic north. Wang et al (2022) and Zhang et al (2020) validated the data by comparing the data from the Jiamusi radar and the Hokkaido East radar during disturbed and quiet geomagnetic conditions. Parkinson et al (2003) define the scattering occurrence rate as the total number of echoes during 15 min intervals at each of the 75 ranges per beam, divided by the total number of soundings made for a particular category of season and Kp.…”

Section: Jiamusi Radarmentioning

confidence: 93%

Statistical Characteristics of Mid‐Latitude Ionospheric F‐Region Backscatter Observed by the SuperDARN Jiamusi Radar

et al. 2023

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The Jiamusi (JME) radar is the first high‐frequency coherent scatter radar independently developed in China. In this study, we investigate the statistical characteristics of the Jiamusi radar scattering occurrence rate from the F‐region ionosphere between 40°N and 65°N geomagnetic latitude (MLAT) from March 2018 to November 2019. Then, the diurnal and seasonal variations in scattering echoes and their dependence on geomagnetic conditions are statistically investigated. It is shown that the local time of the peak scattering occurrence rate varies depending on the seasons, that is, approximately 20–22.5 magnetic local time (MLT) in summer, 17.5–20.5 MLT in equinox, and 16–17.5 MLT in winter, which is closely associated with the time of sunset. The occurrence rate also increases with the enhancement of the Kp index. To further understand the mechanism of these features, we simulate the distribution of the gradient drift instability (GDI) indicator ∇n·trueV→/n $\nabla n\cdot \overrightarrow{V}/n$ by using the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIEGCM). The analysis results indicate that the GDI may be one of the factors that contribute to these characteristic features.

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Statistical Characteristics of Mid‐Latitude Ionospheric Irregularities at Geomagnetic Quiet Time: Observations From the Jiamusi and Hokkaido East SuperDARN HF Radars

Cited by 8 publications

References 26 publications

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

Occurrence Rates of SuperDARN Ground Scatter Echoes and Electron Density in the Ionosphere

Statistical Characteristics of Mid‐Latitude Ionospheric F‐Region Backscatter Observed by the SuperDARN Jiamusi Radar

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