Coincident Observations by the Kharkiv IS Radar and Ionosonde, DMSP and Arase (ERG) Satellites, and FLIP Model Simulations: Implications for the NRLMSISE‐00 Hydrogen Density, Plasmasphere, and Ionosphere

Kotov, Dmytro; Richards, P. G.; Truhlík, Vladimír; Bogomaz, Oleksandr; Shulha, Maryna; Maruyama, Naomi; Hairston, M. R.; Miyoshi, Yoshizumi; Kasahara, Yoshiya; Kumamoto, A.; Tsuchiya, F.; Matsuoka, A.; Shinohara, I.; Hernández‐Pajares, M.; Domnin, I. F.; Zhivolup, T. G.; Emelyanov, L. Ya.; Chepurnyy, Ya. M.

doi:10.1029/2018gl079206

Cited by 20 publications

(25 citation statements)

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“…Lower down, the model that is primarily used to represent the thermospheric hydrogen density profile is NRLMSISE‐00, which is based on Atmospheric Explorer charge exchange calculations from the 1970s (Hedin, ). However, multiple studies have shown that NRLMSISE‐00 does not provide an accurate measure of hydrogen density and is often a factor of ∼2 lower than observed values (Kotov et al, , ; Nossal et al, ).…”

Section: Introductionmentioning

confidence: 97%

“…Atomic hydrogen density in the upper thermosphere is an important input parameter in ionospheric and plasmaspheric modeling studies (Korenkov et al, ), making it an integral player in the big picture of solar terrestrial interaction. Resonant charge exchange reactions between atomic hydrogen and atomic oxygen are paramount to the formation and refilling of the plasmasphere after geomagnetic storms (Kotov et al, , ; Krall et al, ). Hydrogen that exists in the geocorona also influences the ring current decay that occurs after geomagnetic storms (Daglis et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Signatures of Thermospheric‐Exospheric Coupling of Hydrogen in Observed Seasonal Trends of H α Intensity

et al. 2019

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Analysis of an established geocoronal H data set indicates a seasonal trend in observed dusk-to-dawn intensity variation, consistent with a diurnal variation in the underlying thermospheric hydrogen density. Observations were obtained at Pine Bluff Observatory, WI, from 2000 to 2001 using a high spectral resolution (R ∼80,000) Fabry-Perot annular summing spectrometer. This dusk-to-dawn asymmetry in intensity is highest in winter months with a difference of ∼2.7 Rayleighs and smallest in summer months with a difference of ∼0.5 Rayleighs; observations near equinox show a dusk-to-dawn difference in intensity close to ∼1.3 Rayleighs. Comparisons between modeled and observed dusk-to-dawn intensity variation show good agreement near the equinoxes. The modeled intensity was generated using the lyao_rt radiative transport code of Bishop (1999, https://doi.org/10.1016/S0022-4073(98)00031-4), employing NRLMSISE-00 thermospheric hydrogen profiles extended into the exosphere via the evaporative case of the Bishop analytic exosphere. Near the equinoxes and summer solstice, the model tends to agree with observations. Near the winter solstice, the model underestimates the dusk-to-dawn asymmetry by 1.5-2 Rayleighs. Overall, modeled H intensity generated with NRLMSISE-00 as the thermospheric input is shown to be consistently lower than observed intensity by a factor of ∼2.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Signatures of Thermospheric‐Exospheric Coupling of Hydrogen in Observed Seasonal Trends of H α Intensity

et al. 2019

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“…In particular, IRI model shows the nighttime enhancement but it is not as clear as in the observation. The likely reason is that IRI N m F 2 is built on the data collected during the period with moderately enhanced magnetic activity when the flux tube could be partially emptied that not provided strong enough downward nighttime plasma fluxes (Kotov et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

“…NeQuick model is an ionospheric electron density model particularly designed for trans-ionospheric applications which allows calculating the electron density distribution in both the bottomside and topside of the ionosphere (Nava et al, 2008;. To describe the electron density of the ionosphere up to the F2 layer peak, NeQuick uses a profile formulation which includes semi-Epstein layers with a height-dependent thickness parameter empirically determined (Hochegger et al, 2000;Leitinger et al, 2005). AMTB-2013 model is based on data from 26 digisonde stations from the Global Ionosphere Radio Observatory (GIRO) network (Reinisch, Galkin, 2011).…”

Section: Methods and Toolsmentioning

confidence: 99%

Ionosphere over Ukrainian Antarctic Akademik Vernadsky station under minima of solar and magnetic activities, and daily insolation: case study for June 2019

Bogomaz¹,

Shulha²,

Kotov³

et al. 2020

UAJ

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We present ionospheric observational results obtained over Ukrainian Antarctic Akademik Vernadsky station. Ionospheric parameters (peak electron density and height, electron density and electron and ion temperatures in the topside) during the period near the local winter solstice in the Southern Hemisphere (June 28-29, 2019) are considered. The main objective is to show distinctive features of variations in ionospheric parameters during a prolonged period with very low solar and magnetic activities, and minimal daily insolation. Methods. F2 layer peak electron density and height were calculated from ionograms obtained with ionosonde installed at the station with subsequent profile inversion. Defense Meteorological Satellite Program (DMSP) and Swarm data acquired over the station are used as well. Diurnal variations of electron density, and electron and ion temperatures at the altitude of the satellites' orbits were calculated using a set of sub-models of the International Reference Ionosphere-2016 (IRI-2016) model. Results. We found a very good agreement between the observed and model variations of F2 layer peak electron density. Significant (by ~2 times) nighttime enhancement of electron density was observed on June 29. Electron density models show the similar increase of the density at the same time interval but this enhancement is much more smoothed comparing with the observations. Peak height values obtained using ionosonde are very close to ones calculated with the IRI-2016 sub-models. Satellite data are in a good consistency with the IRI model predictions, especially for electron density obtained by Swarm satellite. Conclusions. Multi-instrumental observations revealed a number of unique features of the ionosphere over Antarctic Peninsula under minima of solar and magnetic activities, and daily insolation.

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“…For example, Shinbori et al [10] investigated the temporal and spatial variations of the ionospheric trough during a geomagnetic storm using in-situ electron density calculated from UHR frequencies observed by the PWE/HFA and from the global navigation satellite system total electron content (GNSS-TEC) data. Kotov et al [11] compared the electron density at Arase's altitude and measured the topside ionosphere using the incoherent scatter radar at Kharkiv, Ukraine.…”

Section: Introductionmentioning

confidence: 99%

Automatic Electron Density Determination by Using a Convolutional Neural Network

et al. 2019

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In this study, we developed a technique for automatically determining upper hybrid resonance (UHR) frequencies using a convolutional neural network (CNN) to derive the electron density along the orbit of the Arase satellite. We used three CNN models (AlexNet, VGG16 and ResNet) to determine the UHR frequencies without additional features based on an expert's knowledge. We also reproduced the multi-layer perceptron (MLP) model that had been used for the Van Allen probes mission, which requires observed electric field spectra and additional five features (i.e., decimal logarithm of electron cyclotron frequency (log 10 f ce), L-value, geomagnetic index (K p), magnetic local time, and frequency bin with the highest power spectral density from the electric field spectra (f bin max)). We confirmed that the proposed method using CNN more accurately determined the UHR frequencies than did the conventional method. The mean absolute error (MAE) of the VGG16 model was 3.478 bins when the input vector comprised both the observed electric field spectrum and the additional five features. In contrast, the MAE of the conventional method was 5.986 bins (72.1% worse). Moreover, we confirmed that the proposed method achieves a high accuracy regardless of the use of the additional five features (the MAE of the ResNet model was 3.664 bins when excluding the additional five features). This suggests that the feature map of the ResNet model acquired a representation ability beyond the five features. INDEX TERMS Computer aided analysis, machine intelligence, magnetosphere, plasma waves.

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Coincident Observations by the Kharkiv IS Radar and Ionosonde, DMSP and Arase (ERG) Satellites, and FLIP Model Simulations: Implications for the NRLMSISE‐00 Hydrogen Density, Plasmasphere, and Ionosphere

Cited by 20 publications

References 30 publications

Signatures of Thermospheric‐Exospheric Coupling of Hydrogen in Observed Seasonal Trends of H α Intensity

Signatures of Thermospheric‐Exospheric Coupling of Hydrogen in Observed Seasonal Trends of H α Intensity

Ionosphere over Ukrainian Antarctic Akademik Vernadsky station under minima of solar and magnetic activities, and daily insolation: case study for June 2019

Automatic Electron Density Determination by Using a Convolutional Neural Network

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