Panasenko et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-021-00441-8

R E S E A R C H ARTICLE(2021) 8:45

Open Access

Coupled investigations of ionospherevariations over European and Japaneseregions: observations, comparative analysis,
and validation of models and facilities
Sergii V. Panasenko1* , Dmytro V. Kotov1 , Yuichi Otsuka2 , Mamoru Yamamoto3 , Hiroyuki Hashiguchi3 ,Philip G. Richards4 , Vladimir Truhlik5 , Oleksandr V. Bogomaz1 , Maryna O. Shulha1 , Taras G. Zhivolup1
and Igor F. Domnin1
Abstract
This paper presents the results of a coordinated measurement campaign with ground based and satellite
observations over European and Japanese regions during September 5–6, 2017. Two incoherent scatter radars, twosatellite missions, International Reference Ionosphere (IRI-2016) empirical model, and Field Line Interhemispheric
Plasma (FLIP) physical model were employed to examine the regular behavior of the F2-layer peak height and density
and the topside ionosphere electron density, electron, and ion temperatures as well as traveling ionosphericdisturbances (TIDs). The daily ionospheric variations over Kharkiv and Shigaraki exhibited similar behavior qualitatively
and quantitatively. The results show that none of the empirical IRI-2016 models of F2-layer peak height, topside
electron density, and temperature can be preferred for predicting the key qualitative features of variations in
ionospheric plasma parameters over Kharkiv and Shigaraki. The likely reason is rapid day to day changes in solar
activity and series of moderate enhancements of magnetic activity occurring in the observation period and preceding
days. Compared with IRI-2016 model, the FLIP physical model was shown to provide the best agreement with theobservations when constrained to follow the observed diurnal variations of F2-layer peak height both over Europe
and Japan. This paper presents the first direct comparison of the mid-latitude electron density measured by the
Swarm satellite with incoherent scatter radar data and it confirms the high quality of the space-borne data. For the
first time, evidence of the possible need to increase the neutral hydrogen density in NRLMSISE-00 model by at least afactor of 2 was obtained for the Asian longitudinal sector. The TIDs, which have predominant periods of about 50 min
over Europe and 80 min over Japan, were detected, likely caused by passage of the solar terminator. Such a differencein the periods could indicate regional features and is the topic for further research.
Keywords: Ionospheric observations, European and Japanese sectors, Background, storm-time, and wave-likevariations, Model and facility validation*Correspondence: sergii.v.panasenko@gmail.com
Institute of Ionosphere, 16 Kyrpychova, Kharkiv 61001, Ukraine
Full list of author information is available at the end of the article

1© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Panasenko et al. Progress in Earth and Planetary Science(2021) 8:45

1 Introduction
Knowledge about the characteristics of geospace environment all over the world is necessary for understanding and predicting space weather phenomena during
different solar and magnetic activity conditions. Strong
space weather events such as severe and extreme magnetic storms are known to result in significant variations in ionosphere and thermosphere (IT) parameters
and often have negative effects on human infrastructure, especially, on space-based assets (see, e.g., Hapgood(2011); Tsurutani et al. (2012)). Moreover, there is evidence that even weak changes in parameters of the solar
wind and interplanetary magnetic field can profoundly
affect the ionosphere and plasmasphere. In particular,
Buresova et al. (2014) revealed that minor and moderate storms occurring during very low solar activity
conditions can cause variations in the F2 peak electron
density (Nm F2) and height (hm F2) that can rival thoseobserved during strong magnetic activity. Kotov et al.
(2018); Kotov et al. (2019) reported a significant decrease(factor of 2) in plasmaspheric density that was induced
by a very weak magnetic disturbance and strong modulation of the ionosphere-plasmasphere H + ion fluxes thatcauses strong variations in the ion composition of the topside ionosphere. The magnitude of the observed effects
depends highly on the amount of energy transferred tothe magnetosphere and ionosphere and also on the previous and current state of the IT system, its regionalfeatures, and time of storm onset. Trigger mechanismsof energy release can also play a decisive role due to thehighly nonlinear IT response to the external influence(Chernogor 2011).
Tides and gravity waves propagating from low altitudes
also contribute to ionospheric variability. For example,
solar tides propagating into the low latitude dynamo
region induce electric fields that produce the so-calledwave 4 crest structure of enhanced neutral and plasma
density (Sagawa et al. 2005; England et al. 2006).
In situ and remote sensing provide reliable information about the IT characteristics which can be measured
but the data are sparse in both the temporal and/or spatial domain. On the other hand, models can reproduce
the temporal variations of IT parameters over a definite location, but they may not reflect reality. This iscaused by the lack of the data on which empirical models are based or incomplete knowledge about input orembedded parameter values used for physical models.
For example, there are discrepancies between International Reference Ionosphere (IRI) model predictions andexperimental results occurring for definite regions (see,e.g., Liu et al. (2019); Patel et al. (2019)). One way to
reduce these differences is to use data assimilation models that account for diurnal and regional variability (e.g.,
Schunk et al. (2014)). Recent investigations confirmed that

Page 2 of 14

the Field Line Interhemispheric Plasma (FLIP) model can
reproduce the ionospheric and plasmaspheric variability
over the East-European region for all seasons (Kotov et al.
2015, 2016, 2018, 2019). It is important that in those studies the FLIP model was constrained to follow the observed
hm F2 variations.
Numerous experimental and theoretical studies haveestablished that there are strong latitudinal differencesof IT processes, thereby clearly separating the aeronomy,
dynamics, and electrodynamics of polar, mid-latitude andequatorial regions (e.g., Heelis (2004); Kim et al. (2020)).
Such variability arises from latitudinal changes of solar
radiative heating, atmosphere composition, and circulation, as well as the geomagnetic field configuration. Evenlongitudinal dependencies of the IT processes occurring
during the same local time are not fully understood since
they are greatly influenced by the displacement of geographic and geomagnetic poles. The East-Siberian magnetic anomaly is known to be responsible for strong
longitudinal variations of Nm F2 in the Northern Hemisphere polar ionosphere (Pirog et al. 2001), whereas theelectron density in the Southern Hemisphere has regionalfeatures such as the Weddell Sea anomaly (Luan and Dou
2013; Richards et al. 2017). Zhang et al. (2011); Zhang
et al. (2012) detected an east-west difference in the electron density over the continental United States using
GPS receiver and Millstone Hill incoherent scatter radar(ISR) data.
Panasenko et al. (2018) studied background and wavelike variations in ionospheric characteristics over the EastAmerican and European sectors (distant by ∼108 degrees
of longitude) from joint measurements using two midlatitude Kharkiv and Millstone-Hill ISRs. They found considerable differences in the magnitude of both diurnal andquasi-periodic variations in electron density and ion andelectron temperatures over ISR locations which cannot be
simply interpreted in terms of dependence on magnetic
and/or geographic coordinates.
This work is an extension of our investigation of regional
differences in the mid-latitude ionosphere. Here, we
present a comparative analysis for European and Japaneseregions (separated by ∼100 degrees of longitude). The
results from ground based and satellite observations aswell as ionospheric and plasmaspheric model simulations
are analyzed with the aim of revealing of the regional
differences and validating the empirical and physicalmodels.
The advantage of this study is the use of data from a
special joint observation campaign conducted with ISRs
located at Kharkiv (Europe) and Shigaraki (Japan). Bothradars were the only mid-latitude European and Asian
ISRs that operated simultaneously during the periodof 5–6 September 2017. As magnetic activity moderately increased on September 6, it enabled a study ofPanasenko et al. Progress in Earth and Planetary Science

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the longitudinal effects of a weak magnetic storm andtested the capability of models to reproduce the storm
effects.

2 Geospace state, facilities, models, and methods
Coordinated observations of the background, wave-like,and storm-induced ionospheric variations were conducted using the Kharkiv ISR (KhISR) and the Shigaraki MU radar (MUR) during 5–6 September 2017.
These stations are at mid geographic latitudes but different magnetic latitudes. KhISR is located in the northeast of Ukraine at (49.6° N, 36.3° E). The Shigaraki
MUR is located at Shigaraki MU observatory, Shigaraki, Japan at (34.9° N, 136.1° E). The Kharkiv magnetic latitude is 45.5°, with a dip angle of 66.6°. The
Shigaraki magnetic latitude is 26.2° with a 49.1° dip
angle.

2.1 Solar and magnetic conditions

The measurements were made 5–6 September 2017prior to the onset of the severe magnetic storm of 7–8
September 2017. Though the observations were near theminimum phase of the solar cycle (average F10.7 index∼83), September 1–6 were characterized by enhanced
F10.7 solar flux and sunspot number values with the F10.7
index maximizing at 183 at 20 UT on September 4 (Fig. 1).
Figure 1 also shows that magnetic disturbances occurredwith maximum K and Kp values equal to 5 late on
September 4. A series of separate bursts of the AE index
occurred during September 5–6 reaching up to ∼800 nT
on September 6 morning.
2.2 Radar measurements

The Kharkiv radar characteristics, operating modes, andsoftware suites for data analysis were described by

Fig. 1 Variations of sunspot number, solar flux F10.7 , planetary index Kp , and AE index during 1–6 September 2017. Green shaded area denotes theionospheric measuring campaign period. Time is UT

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Domnin et al. (2014) and Bogomaz et al. (2017) and are on
the website (http://iion.org.ua).
For this study, KhISR operated in a composite twofrequency radio pulse mode. The first simple pulse
has a length of about 650 μs (the carrier frequency
f0 =158 MHz), providing an altitude resolution of∼100 km, while the second simple one has the pulse
length of about 135 μs (the frequency f1 =(158+0.1) MHz)and yields the 20-km altitude resolution. As a resultof receiving and processing of the first signal element
scattered by the ionosphere, the electron density, theelectron and ion temperatures, the vertical componentof the plasma velocity, and the ion composition are measured for the altitudes near the peak of the ionosphericF2 layer and in the upper ionosphere. The return signal
from the second pulse element allows the determination of the altitude profile of the IS signal power (Ps )at the altitudes h=100–550 km for correction of thealtitude electron density profile and estimation of vertical propagation characteristics of traveling ionosphericdisturbances (TIDs) such as vertical phase velocity andwavelength.
The Shigaraki MUR operated at a frequency of
46.5 MHz (Fukao et al. 1985, 1985; Hassenpflug et al.
2008). It is a fully active phased array system which
employs up to four independent beams and realizes
sequential sensing toward the magnetic north, east, south
and west. Standard IS modes are usually used for ionospheric parameter retrieval of the electron density and ion
drift velocity as well as ion and electron temperatures (see,e.g., Table 1 from Balan et al. (1998)).
During the joint measuring campaign, the MU radar
was operated in a modified mode with only one beam
directed toward the magnetic south to provide consistency between the ISR and MU results. Two different
pulse patterns (7-element code) were transmitted. The
pattern “1000000,” where “1” means a presence and “0”means an absence of a single subpulse being of 96 μs
width, was employed for signal power. Another pattern“1100101” enabled the estimation of autocorrelation functions for retrieving ion and electron temperatures. The
patterns alternated approximately every 5 min (52 setsof 512 scans with 11 ms interpulse period). Data wereacquired starting at 1333 μs after the pattern transmission
onset. This provided a lower boundary at a distance of
200 km corresponding to 188 km altitude for an antenna
beam zenith angle is 20°. Only data obtained during the“1000000” pattern transmission were analyzed further inthis paper.
The electron densities measured by both radars
were calibrated using the data of ionosondes locatednearby.

2.3 Satellite data
2.3.1 DMSP satellite data

DMSP is a mission of the United States Air Force that currently consist of four satellites (F15–F18) flying in circularSun-synchronous, polar orbits at altitudes between 820
and 860 km (Hairston et al. 2018).
The DMSP spacecraft measure several plasma parameters in the topside ionosphere with the special sensor-ion,
electron, scintillation (SSIES) package consisting of thefollowing thermal plasma instruments: Langmuir probe,
retarding potential analyzer, scintillation monitor, and ion
drift meter (Heelis and Hanson 1998; Rich 1994; Brace1998).
This study used the electron density, and electron andion temperatures from all F15–F18 DMSP satellites. Only
the data points with good quality flags were used. The
DMSP F15 electron density data quality was previously
validated for the KhISR region (Kotov et al. 2018).
2.3.2 Swarm Langmuir probe data

Swarm is a mission of the European Space Agency primarily intended to study the Earth’s magnetic field. The
mission has a constellation of three satellites named A, B,
and C which were deployed into circular near-polar low
Earth orbits at the end of 2013.
Swarm A and C orbit side-by-side at the same altitudeat ∼ 460 km (inclination 87.4°) and Swarm B flies at analtitude of about 530 km (inclination 88°). All the satellites
are equipped with identical instruments. This study used
Level 1b 2 Hz time resolution data from the Langmuirprobes (Knudsen et al. 2017). The data are downloadablefrom https://swarm-diss.eo.esa.int/.
This study uses the high-quality electron density datafrom all three Swarm satellites. These data were recently
validated by comparison with ISR measurements (excluding KhISR and MUR) by Lomidze et al. (2018). Other
satellite instruments and models (IRI) indicated that
Swarm typically underestimates the electron density by
10–15%.
The Swarm electron temperatures were not used forthis study as recent validation studies have reported that
the Swarm Langmuir probe electron temperatures can begreatly overestimated. Lomidze et al. (2018) proposed acalibration of measured electron temperature using comparisons with ISR measurements. However, some problems with the data quality remain including unpredictablejumps along the satellite orbits.
2.4 Ionospheric models
2.4.1 FLIP model

The field line interhemispheric plasma (FLIP) model is a
one-dimensional model that calculates the plasma den-Panasenko et al. Progress in Earth and Planetary Science

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sities and temperatures along magnetic flux tubes frombelow 100 km in the Northern Hemisphere through theplasmasphere to below 100 km in the Southern Hemisphere (Richards 2001; Richards et al. 2010). A detailed
description of the FLIP model can be found in the paperof Kotov et al. (2015).
Due to a lack of direct measurements, neutral winds are
one of the greatest uncertainties for modeling ionosphere
electron densities. The FLIP model overcomes this problem by adjusting the neutral winds to accurately reproduce
the observed hm F2 when available (Richards 1991). The
FLIP model can also use empirical model hm F2 or neutral
winds such as from the HWM-14 wind model (Drob et al.
2015). All the model runs were conducted with the actual
values of solar and magnetic activity indices.
2.4.2 Models of electron and ion temperatures

The International Reference Ionosphere model (IRI)
includes a global description of the electron temperature(Te ). Beginning from IRI-2012, the TBT-2012 model is thedefault option for the topside (Truhlik et al. 2012; Bilitza
et al. 2014). The model employs a spherical harmonics
representation of the electron temperature at several fixed
altitudes where most satellite data is available (350, 550,
850, 1400, and 2000 km) providing different sets of coefficients for different seasons. As the modeling coordinates,
the magnetic local time and a special latitudinal magnetic coordinate invdip were used (Truhlík et al. 2001). A
Booker function (Booker 1977) was employed to describe
the altitude variation of Te . The TBT-2012 model provides a more detailed description of the diurnal variation,
including the morning overshoot, than previous models
and includes an option to describe the variation of theelectron temperature with solar activity (TBT-2012+SA).
The ion temperature (Ti ) model in IRI was describedby Bilitza (1990). The model is based on the latitudinal profiles from AEROS-A satellite measured at 430 km
altitude.
2.4.3 F2 peak models

The IRI model provides two choices for the descriptionof the F2 critical frequency fo F2: (1) the model recommended by the International Radio Consultative Committee (CCIR, 1967) and (2) the one proposed by theInternational Union of Radio Science (URSI) (Rush 1989).
In this paper, we compare the observed peak electron
density with values calculated with URSI version set as
default at IRI-2016 site. The model predictions depend on
the solar activity. Additionally, the F peak storm model(Fuller-Rowell et al. 1998, 2000) was turned on to reflect
average storms behavior in Nm F2 based on the Ap history
over the preceding 33 h.
The F2 peak height is represented in IRI-2016 by three
options: (1) from the correlation with the propagation

parameter M(3000)F2 (Bilitza et al. 1979), (2) the F2peak height data from the worldwide ionosonde network(Altadill et al. 2013) (ATMB-2013), and (3) from the
COSMIC radio occultation measurements (Shubin 2015)(SHU-2015). The SHU-2015 option seems to be the best
choice following from several recent comparison studies. In this study, we compared the observations with the
SHU-2015 and ATMB-2013 models. Both the models aregoverned by solar activity indices and they are insensitive
to the changes in magnetic activity.
2.4.4 Topside electron density models

The IRI model includes three options for the electron density from the F2 peak up to 2000 km altitude with anextension to high altitudes into the plasmasphere for possible comparisons with GPS TEC. The electron density
profiles from the topside models are normalized to Nm F2.
The first option (IRI-2001) is based on the Bent model(Bent et al. 1972). The Bent model construction employeda limited amount of topside data that was available at thetime (40,000 Alouetter-1 profiles). Numerous studies withother data have found it significantly overestimates theelectron density in the upper topside (above ∼ 500 km
above hm F2) increasing with altitude and reaching a factor of ∼ 3 at 1000 km above the peak. For this reason,
the IRI-01corr was introduced by Bilitza (2004) as a correction to the original version as a second option. The
third option, which has been included since IRI-2007 as
the default option for the topside ionosphere, adopts thetopside electron density formulation of the NeQuik model(Nava et al. 2008).
2.5 Data analysis

The KhISR and MUR data were subjected to several processing stages using similar methods and procedures toensure consistency between results. The data were first
filtered to remove narrow bursts or spikes caused by thesignal reflections from man-made objects like satellites,
space debris, and from temporal variations in KhISR and
MUR signal. We primarily estimated a signal-to-noise
ratio (ratio of powers of incoherent scatter signal andnoise, SNR) and an electron density vs time and altitude. Figure 2 shows that the SNR for the MUR data
does not reach 0.1 and is up to two orders of magnitude less than that for KhISR. This causes the need toaverage the measured MUR signal parameters over substantially longer time intervals than those usually used for
KhISR. To provide uniformity of the data of both radars
in terms of temporal resolutions that is important forthe correct comparison of the observational results, weaveraged the ISR data over 3-h intervals. We ran FLIP
model with hm F2 variations obtained by KhISR with usual
20 min resolution and after 3-h averaging to check thepossible additional errors in the FLIP plasma parameters

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Fig. 2 Signal-to-noise ratio (top) and electron density (bottom) behavior over Kharkiv and Shigaraki during joint measuring campaign on
5–6 September 2017. LT is solar apparent time

related to the averaging. The errors are typically 3–5% andnever exceed 10%. It is likely that the same applies for
MUR data.
We applied a program package called UPRISE (Bogomaz et al. 2017) to retrieve ion and electron temperaturesfrom the KhISR data but the MUR SNR was too low
to retrieve temperatures (see above). As with the electron density averaging, the temporal variations of theion and electron temperatures were smoothed over 3-hintervals as were all plasma parameters computed by theionospheric models.
The KhISR electron and ion temperatures wereretrieved from the auto-correlation functions. In the caseof MUR, the temperatures were provided by the FLIP
model, which are consistent with those obtained by DMSP
satellites as seen below.
The electron density was evaluated using the measured
IS signal power as well as electron and ion temperaturesby algorithm described in the detail by Evans (1969).
Apart from background variations in ionosphericparameters, we detected quasiperiodic fluctuations in
the electron density using spectral analysis. For this,the relative variations of the KhISR and MUR power
were obtained as differences between the initial data
and those after 3-h averaging, which were normalized to these average values. The adaptive Fouriertransform, which uses a sliding window with a variable width to be constantly equal to three harmonic
period lengths (Chernogor 2008), was done to estimate the periods of predominant oscillations. The data
analysis stages are described in detail by Panasenko
et al. (2018), but 2-h data averaging was used in that
paper.

3 Results
3.1 Comparison of observations in the European andJapanese longitudinal sectors

Figure 2 shows the diurnal and altitude variations of electron density obtained over Kharkiv and Shigaraki. Despite
the extremely low SNR for the MUR data, the 3-h averaged Ne variations over Shigaraki demonstrate physicallyrealistic behavior. The peak electron density exhibited
similar values (∼1–5)×105 cm−3 over both sites. The
main difference was observed in the local time morning
on September 6 (near 56 LT) where the Shigaraki Ne wasup to a factor of 2 greater than Ne over Kharkiv.
Figure 3 shows diurnal variations of the key ionosphericparameters – hm F2 and Nm F2. The magnitude of the diurnal change of hm F2 is largest for Shigaraki. During thedaytime, the hm F2 at Kharkiv is up to ∼20 km higher thanat Shigaraki, whereas during the nighttime, the reverse istrue. Another prominent difference is the much stronger

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Fig. 3 Smoothed diurnal variations of Nm F2 (a) and hm F2 (b). Gray circles denote 3-h averaged ionosonde Nm F2 and ISR hm F2 observations. Solid
red lines show the FLIP model simulations with the ISR hm F2 constraint together with doubled NRLMSISE-00 H density. The dashed blue lines are theFLIP simulations with the wind from the HWM-14 model. The dotted brown hm F2 lines are from AMTB-2013 and dotted brown Nm F2 lines are fromthe FLIP model simulation using the AMTB-2013 hm F2 variation as the constraint. The black dash-dot lines show similar results when the SHU-2015
hm F2 is used in the FLIP model. The small green dotted Nm F2 lines are from the IRI-2016 model. The solid arrows on the bottom panels denote thetime of a maximum AE index burst (∼800 nT). The dashed arrows show the same local time on the preceding day when the AE index was ∼50 nT

variability of hm F2 on a time scale of several hours for Shigaraki. The nighttime values of Nm F2 are close for both
sectors (∼ 2×105 cm−3 ) while the daytime Nm F2 is ∼30%
higher for Shigaraki. As in the case of hm F2, the Nm F2
variability on a time scale of several hours is stronger for
Shigaraki. At Kharkiv, the measured Nm F2 is up to 50%
lower for 12–18 UT than for 36–42 UT.
In the topside ionosphere, continuous diurnal variations of electron density were measured with KhISR but
not with MUR because of the low SNR. Fortunately, four
DMSP satellites provided daytime and nighttime data overboth the European and Japanese regions at 850 km altitude (Fig. 4). At that altitude, the electron density isclose to the FLIP model values over Kharkiv and Shigaraki for the daytime (∼ 2.5 × 104 cm−3 ) and nighttime(∼ 1.5 × 104 cm−3 ). The daytime DMSP electron temperature over Kharkiv is up to ∼ 25% higher than over
Shigaraki. The daytime DMSP and FLIP ion temperatures
are also close for both sites (∼2400–2700 K). Several data
points for the electron density observed at lower orbits(508 km and 445 km) by three Swarm satellites show
excellent agreement with the KhISR electron density data
(Fig. 4d, e).
3.2 Comparison of observations with empirical model
predictions

Figures 3 and 4 include a comparison of the observed
variations of ionospheric plasma parameters with thepredictions of the sub-models of International Reference
Ionosphere model.
The diurnal variation of the F2-layer peak density predicted by the Nm F2 model in IRI (Fig. 3a) is consistent
with the Shigaraki ionosonde data except after 33 UT.
Over Kharkiv, the IRI model underestimates Nm F2 by∼70–100% during nighttime and by ∼ 30% during daytime on September 6 but not on September 5.
Both IRI hm F2 models provide reasonable qualitative agreement with the observations in the European
and Japanese regions (Fig. 3b). However, neither one
can be preferred in terms of quantitative agreementwith the data. The SHU-2015 model variation is muchcloser to the observations during daytime but it underestimates hm F2 by ∼20–30 km during nighttime. In
contrast, the AMTB-2013 model predictions are better for the nighttime (especially for Europe) while thedaytime hm F2 values are ∼ 25 km overestimated forJapan.
The two IRI electron temperature models underestimate the DMSP Te during the daytime (Fig. 4a). In theEuropean sector, the underestimation is ∼ 600 − 1100 K
for TBT-2012 without the solar activity (SA) option and∼1000–1500 K with the option. In the Japanese sector, the TBT-2012 model underestimates Te by ∼300–
350 K while the SA option underestimates Te by ∼ 650–
1100 K. The absence of nighttime DMSP Te data precludes the evaluation of the model accuracy at Kharkiv.

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Fig. 4 Diurnal variations of electron and ion temperature, and electron density at 850 km (a–c), electron density at 508 km (d), and at 445 km (e).
Solid red line is the FLIP model simulation with the hm F2 constraint together with doubled NRLMSISE-00 H density, dashed pink line – the same butwith NRLMSISE-00 H density. Gray circles denote KhISR, blue triangles – DMSP, green diamonds – SWARM-A, orange squares – SWARM-B, cyan
hexagons – SWARM-C observations respectively. On panel (a), dashed brown and dash-dotted green lines denote Te variation from TBT-2012+SA
and TBT-2012 models respectively. Dash-dotted black line on panel (b) shows Ti variation from IRI-2016 model. On panels (c–e), dashed black line
denotes NeQuick, dotted green – IRI-2001, dash-dotted brown lines – IRI-01corr models calculations respectively. The models predictions andKharkiv ISR data are 3-h averaged

For Shigaraki, several pre-sunrise DMSP data points ineach of two nights agree better with the TBT-2012+SA
model predictions (the model overestimates Te by ∼200–
300 K); overestimation of Te by TBT-2012 model
reaches ∼600 K.
The overall agreement of the IRI model with DMSP
observations is much better for ion temperature (Fig. 4b).
During the day, the IRI model overestimates Ti by several
hundred kelvins. No reliable conclusion about the modeldata agreement for the nighttime can be made due to thelack of data.

Electron density observations in the topside ionosphere
were compared with three optional models of IRI at
three altitudes (Fig. 4c–e). The default NeQuick model
underestimates the DMSP Ne at 850 km in the European
and Japanese sectors during the night by a factor of ∼3.
On the other hand, at the lower altitudes of 508 km and445 km, the model-data agreement at night is excellent
over Kharkiv. There were no data to compare with the
NeQuick predictions over Shigaraki. During the daytime,the model-data agreement at 850 km is good for Shigaraki.
For Kharkiv, the agreement is good for September 5 but

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not for September 6 when NeQuick underestimates Ne by
a factor of ∼2. Also, for this date, notable underestimation by NeQuick is seen for Kharkiv at 508 km (a factorof ∼1.5) and at 445 km (a factor of ∼1.3). For Shigaraki,
daytime agreement of NeQuick with Swarm data point is
excellent at 508 km.
The IRI-2001 model of Ne provides better predictions
at 850 km for both longitudinal sectors and for bothday and night compared with NeQuick. The model-data
agreement is especially improved for Shigaraki while theunderestimation is still a factor of ∼1.5 for Kharkiv. At
508 km and 445 km altitudes, the IRI-2001 predictions arevery close to those of the NeQuick model.
The IRI-01corr model predictions are the same as
those of IRI-2001 at 445 km and 508 km. At 850 km,
the IRI-01corr variation is close to that of the NeQuickmodel while for Shigaraki the IRI-01corr Ne variation lies
approximately in the middle of the NeQuick and IRI-2001
variations.
3.3 Comparison of observations with FLIP physical model
simulations

One of the key parameters for a successful simulation of
the mid-latitude ionosphere is the diurnal variation of thehorizontal thermospheric wind velocity, which is rarely
available from data. The main effect of neutral winds is
to change the entire electron density profile by changing
the height (hm F2) of the F2 density peak (Nm F2). Some
global thermosphere physical models calculate wind vectors self-consistently and there are global empirical windmodels. In both models, the velocity uncertainties are still
too large for accurate ionosphere modeling (Drob et al.
2015). To overcome the wind uncertainty, the FLIP physical model uses an algorithm that adjusts the winds toaccurately reproduce the observed hm F2 as it steps in time
(Richards 1991).
In this work, we present FLIP model simulations of F2layer peak density with three types of input hm F2 and with
empirical models (Fig. 3). First, the hm F2 input was thevariation observed by the ISRs. At Kharkiv, this produced
simulated Nm F2 variations that are in good agreementwith the observations on September 6 (Fig. 3a). However, the model is up to a factor of ∼2 too high at
Kharkiv in the afternoon on September 5. For Shigaraki,
the largest model-data disagreement is ∼ 40% for thenight of September 6.
The FLIP model with hm F2 from the AMTB-2013
and SHU-2015 models produces overall good model-data
agreement for Kharkiv and Shigaraki. The largest deviation is ∼ 100% nighttime underestimation of Nm F2 over
Kharkiv by FLIP with the SHU-2015 model. As in thecase of using the ISR hm F2, the FLIP model overestimates
Nm F2 for Kharkiv near sunset on September 5 by ∼ 80%
with both IRI models. Another difference is that the model

Nm F2 decay starts ∼1.5 h too early in the evening of
September 6 over Shigaraki. That is not the case when
FLIP uses measured hm F2.
The final simulation was with the horizontal thermospheric winds from the HWM-14 model. For both
longitudinal sectors the model-data agreement is worse
than with any of the hm F2 inputs. Over Kharkiv, using
HWM-14 produces a ∼ 60% overestimation of Nm F2 inthe evening of September 6. Over Shigaraki, a factor of ∼2
overestimation is seen for the nighttime.
For the model comparisons with the observations in
the topside ionosphere that are shown in Fig. 4, the FLIP
model was constrained to follow the ISR hm F2 variations
and the neutral hydrogen density in the NRLMSISE-00
atmospheric model was doubled in accordance with therecent findings of Kotov et al. (2015), (2016), (2018), and(2019) for the European region. It is seen that the calculated electron temperatures agree well with the DMSP
observations excluding the morning hours of September 6
over Kharkiv when the model underestimated Te by up
to ∼1000 K (Fig. 4a). There is also very good model-data
agreement for the ion temperature (Fig. 4b) and electron
density at all the three considered altitudes in both longitudinal sectors (Fig. 4c–d). As with Nm F2, the exception isthe evening of September 5 over Kharkiv where the model
overestimates Ne by a factor of ∼1.5–1.7 with the tendency of increasing model-data deviation with decreasingof altitude.
Additional simulations made with the NRLMSISE-00
standard neutral hydrogen density (Fig. 4c) clearly showsthat the doubling of H density is really needed to provide
matching the model to data not only for Kharkiv but for
Shigaraki too.
3.4 Wave processes

Figure 5 demonstrates comparative results of wave characteristics and their localization in the temporal and periodical domains. Over both regions, we found an enhancement in the wave activity near the local solar terminator.
Some increase in the power spectral density over Kharkiv
also occurred near 09:00 and 16:00 LT, while that density was slightly amplified around 10:00 and 14:00 LT over
Shigaraki (see Fig. 5b). Figure 5c shows the substantially
downward phase progression. This is likely to indicate thatmajority of the TIDs are the ionospheric manifestations ofinternal gravity waves propagating from below.
Notably, the sunset terminator induced TIDs over Shigaraki at the same time that strong quasi-periodic fluctuations in hm F2 ensued from the enhancement in magnetic
activity (cf. Fig. 3, 34–38 UT that corresponds to 19–23 LT
in Fig. 5). Separation of these two sources is facilitatedby the difference in oscillation periods. As seen in Fig. 3,
hm F2 fluctuated with a predominant period of about 4 h,while that for the TIDs was about 80 min.

Panasenko et al. Progress in Earth and Planetary Science(2021) 8:45

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Fig. 5 Relative variations of incoherent scatter power δ P (a) measured by KhISR at the altitude of 200 km (left) and MUR at the altitude of 220 km(right) as well as corresponding results of spectral analysis (b) and band-pass filtering (c). Subfigure (b) contains a power spectral density S indicatedby color level plotted vs time and period as well as average power E plotted vs period. Both S and E values were normalized to their maximums.
Subfigure (c) shows altitude-time plots of δ P band-pass filtered in the range of 40–80 min and 60–120 min for KhISR and MUR data, respectively. The
solid lines mark the time of local sunrise and sunset terminator passage at the current altitude. LT is solar apparent time

The main difference between these KhISR and MUR
results is the greater value of predominant oscillation
period over Japanese region. Indeed, as seen in the average power plots, the maximum values of E(T) are attainedat periods of about 50 min and 80 min over Kharkiv andShigaraki, respectively. Unfortunately, the abrupt decreasein the SNR with altitude for the MUR data precludes anadvanced comparative analysis of TID propagation suchas an estimation of TID vertical and horizontal phasevelocities as was done by Panasenko et al. (2019) in characterizing solar eclipse induced TIDs.

4 Discussion
This study provided an opportunity to assess the distinctive regional features of the ionospheric plasma variations
in two distinct regions, to test capabilities of the empirical and physical models, and to compare the capabilities
of different models and research facilities.
The observed differences between the daytime valuesof Nm F2 and Te in the topside ionosphere over the European and Japanese longitudinal sectors are attributed totheir magnetic latitudinal differences (45.5° for Kharkiv
and 26.2° for Shigaraki). It is well known that the daytime Nm F2 is higher at low-to-middle latitudes than at
mid-latitudes (e.g., Hoque and Jakowski (2011)), and thisis the case for the Kharkiv and Shigaraki data. The higher
daytime Te values over Kharkiv agree with the commonly
accepted knowledge that electron temperature increases
with latitude (e.g., Truhlik et al. (2012)). The closeness ofthe observed daytime Ti values in the topside ionosphere
over the two regions is also consistent with our knowledgeof global climatology for these parameters (Bilitza 1990).
In contrast, the closeness of the topside Ne over Kharkiv
and Shigaraki seen from DMSP data disagrees with thepredictions of all three climatological Ne models whichshow a significant increase of the topside density towards
lower latitudes (Bilitza 2004; Nava et al. 2008).
The rapid day to day changes in solar activity andseries of moderate enhancements of magnetic activity
on September 5–6 and on preceding days (see Fig. 1)
make this period difficult for both physical and empiricalmodels.
The hm F2 predictions over Kharkiv and Shigaraki fromthe IRI models (AMTB-2013 and SHU-2015) follow theobservations reasonably well but there are systematicdeviations between the models and observations that canreach several tens of kilometers, which leads to unacceptable bias when Nm F2 is simulated by physical models. The
HWM-14 model neutral winds significantly overestimate
hm F2 and Nm F2 most of the time in both regions. This
may imply some bias in the thermospheric wind velocityof HWM-14 for the European and Japanese sectors during
the period of the study.
However, even using of the measured hm F2 variations asa neutral wind proxy for the FLIP model does not always
guarantee better quality of Nm F2 reproduction. For example, the measured Nm F2 at Kharkiv is up to 50% lower
for 12–18 UT than for 36–42 UT and such a low densityPanasenko et al. Progress in Earth and Planetary Science

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was not reproduced by the FLIP model. This is most likely
because the NRLMSISE-00 model is underestimating theeffect of the enhanced magnetic activity on the neutralcomposition during the preceding hours.
At Shigaraki, there is evidence of a weak magnetic disturbance that affects the F2-layer peak in the local evening(UT=32–34) of September 6 as indicated by the solid
arrow in Fig. 3. The effect is clearly visible with hm F2 rising reaching up to 60 km higher than on the previous
evening (UT=8–10). The apparent cause of this behavior was the increase of AE from ∼50 nT up to ∼800 nT
within ∼3 h interval (at ∼31–34 UT), which came following ∼10 h of low magnetic activity. This significant
uplifting over Shigaraki could be classified as a positive
ionosphere storm from the passage of a traveling atmospheric disturbance (TAD) generated in the auroral zone.
The FLIP model generates a clear rise in Nm F2 thatwould be expected from the TAD. The weaker increasein Nm F2 may be caused by storm related changes in neutral composition which were not correctly reflected by
the NRLMSISE-00 model. It is interesting that the magnitude of the hm F2 uplifting for Shigaraki is close to thoseobtained for the same (Asian) longitudinal sector in 1980s
by Hajkowicz (1999). The FLIP model enhancement ofelectron density in the topside ionosphere over Shigaraki
occurred just after the hm F2 uplift. A similar enhancement was observed by KhISR and reproduced by the FLIP
model for the similar event prior to sunset of September 25, 2016 (Kotov et al. 2019). The magnitudes of hm F2
uplifting and related enhancements of Nm F2 and topside electron density were close to those we see here for
Shigaraki.
The ionosphere over Kharkiv does not show similar
TAD like behavior during this period (UT=32–34). Thisdifferent ionosphere response could be a local time effect(e.g., Balan and Rao (1990)) either because of the longitude of the auroral impulse or the direction of the neutral
wind. Towards the late afternoon, the thermospheric windchanges from poleward to equatorward. In such conditions, a magnetic disturbance may enhance the normal
equatorward wind that leads to a more intense plasma
uplifting along the magnetic field line. This behavior wasless likely for Kharkiv where near-noon conditions normally correspond to a stable poleward wind. In such acase, a short-time enhancement of auroral activity was
unable to produce a significant wind surge and uplift theionosphere over Kharkiv.
In the topside ionosphere, the best agreement withthe observations is with the FLIP physical model simulations for all the considered plasma parameters. Empirical models still need much improvement for describing the diurnal plasma parameter variation on a givenday. This need is especially clear for the electron density models. None of the three Ne options of IRI canprovide acceptable agreement with the observations ineither region. The models of electron temperature provide
overly smoothed diurnal variations of Te . Also, the modeldata comparison reveals the need to increase the magnitude of day-to-night change of Te in the IRI TBT-2012
model.
The direct electron density comparison of Swarm andKharkiv ISR observations is the first such comparison
for the European sector and for the typical magnetic
mid-latitudes. All the previous comparisons (Lomidze etal. 2018) were made for American sector with the dataof ISRs in Jicamarca (equatorial), Arecibo (low-middle),
and Millstone Hill (sub-auroral). This comparison supports the high quality of Swarm electron density data for
mid-latitudes.
An important new result from this study is that it provides evidence of the need to increase the neutral hydrogen density in NRLMSISE-00 model by at least a factor of
2 for the Japanese longitudinal sector (Fig. 4c). Such a need
was previously deduced from different techniques for theAmerican (Nossal et al. 2012, 2019) and European (Kotov
et al. 2015, 2016, 2018, 2019) sectors. Here, we provide thefirst such evidence for Asian sector. This need should bevalidated by more model/data comparisons for different
seasons and conditions.
The TID events observed over Kharkiv and Shigaraki
were likely caused by the solar terminator passage because
the enhancement in wave activity mostly occurred in themorning and evening hours (see Fig. 5). Thus, our results
indicate that the solar terminator can be an effective
source of ionospheric disturbances for different longitudinal regions. The detected TID periods are also ingood agreement with the results of previous studies (see,e.g. Oliver et al. (1997); Song et al. (2013); Nygrén et al.
(2015); Panasenko et al. (2018)). They exhibit a greater
value for Japanese (about 80 min) than European (about
50 min) region. Notably, the very similar periodicities of
59±11 min for large scaled TIDs over Europe (Borries
et al. 2009) and 80±29 min for those over Japan (Tsugawa et al. 2004) were reported. Such similarity could be
simply a coincidence but could also reflect the regionalfeatures of wave processes and is the issue for further
research.

5 Conclusions
This coordinated observational campaign using two incoherent scatter radars, two satellite missions, during 5–
6 September of 2017 and appropriate model simulations
provided the following key results.
1. The regular variations of the main plasma parametersat the F2-layer peak and in the topside ionosphere (electron density, electron and ion temperatures) for Kharkiv
are close to those for Shigaraki qualitatively and quantitatively. Most of the slight differences seen from thePanasenko et al. Progress in Earth and Planetary Science

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observations may be explained by the differences in theirgeomagnetic latitudes.
2. The International Reference Ionosphere empiricalmodel is capable of predicting the qualitative featuresof the variations in the key ionospheric plasma parameters over Kharkiv and Shigaraki. However, the predictions
still need to be improved quantitatively. None of theoptional IRI sub-models of the F2-layer peak height, topside electron density, and temperature can be considered
preferable.
3. The FLIP physical model provides the best agreementwith the observations when being constrained to follow
the observed diurnal variations of hm F2. Using the winds
from the HWM-14 model in FLIP leads to an overestimation of hm F2 during the whole day for both regions, whichcauses biases in Nm F2 calculations.
4. The high quality of Swarm electron density data is
confirmed for the first time by a direct comparison with
the ISR located at typical mid-latitudes (Kharkiv).
5. It was pointed that the rapid day to day changes
in solar activity and series of moderate enhancementsof magnetic activity on September 5–6 and in the preceding days made this period difficult for both physical and empirical modeling. The ionospheric responseto the weak magnetic disturbance following ∼10 h ofmagnetically quiet conditions (in the UT morning of
September 6, 2017) was studied. Significant regional
differences were found, apparently caused by the wellknown LT effect of magnetic storms. This effect was
confirmed by simulations with the FLIP model. The
magnitude of the storm-induced hm F2 enhancement
observed over Shigaraki is found to be close to estimatesobtained for the same longitudinal sector in 1980s by
Hajkowicz (1999).
6. For the first time, evidence of a need to increase
the neutral hydrogen density in NRLMSISE-00 modelby a factor of at least 2 was obtained for the Asianlongitudinal sector. More studies should be done tocheck if the same need is valid for other seasons andconditions.
7. Traveling ionospheric disturbances, likely induced bysolar terminator passage, were detected over both Kharkiv
and Shigaraki. They exhibit different dominant periodsof about 50 min and 80 min over Europe and Japan,
respectively. This could reflect differences in local timeof occurrence as well as differences in latitude, longitude,and magnetic field inclination/declination that need to befurther investigated.
Abbreviations
IT: Ionosphere and thermosphere; ISR: Incoherent scatter radar; IRI:International Reference Ionosphere; FLIP: Field Line Interhemispheric Plasma;
KhISR: Kharkiv incoherent scatter radar; MUR: Middle and upper atmosphereradar; TID: Traveling ionospheric disturbance; DMSP: Defense Meteorological

Satellite Program; URSI: International Union of Radio Science; SNR:
Signal-to-noise ratio; HWM: Horizontal wind model
Acknowledgements
We acknowledge Open Madrigal Initiative for providing DMSP data andpersonally thank Patricia Doherty (Boston College) and Marc Hairston(University of Texas at Dallas, W. B. Hanson Center for Space Sciences). The
authors are grateful to the European Space Agency for Swarm data providing.
The authors from Institute of ionosphere are thankful to Dr. L. Ya. Emelyanov
and Ya. M. Chepurnyy for conducting KhISR observations.
Authors’ contributions
SVP was a principal investigator of the collaborative project and analyzed thewave processes in both longitudinal sectors. DVK contributed to the conceptof the study and participated in interpretation of the results on the neutralhydrogen density and storm-related effects. YO collaborated in this study andconsulted about TID propagation. MY and HH organized MUR observations
and consulted on MUR data analysis. PGR consulted on the simulations by FLIP
model and participated in the interpretation of the FLIP based results. VT
consulted on the empirical models and satellite data, participated ininterpretation of the results. OVB developed the software for KhISR, MUR,
Swarm, and DMSP data processing, as well as edited the manuscript in LaTeX.
MOS conducted simulations by FLIP models and participated in the analysis ofstorm-related effects. TGZ manually analyzed all the ionograms andcontributed in result discussion. IFD proposed a topic and designed aconception of the study. All authors read and approved the final manuscript.
Funding
SVP, DVK, YO, MY, HH, and PGR were supported by Collaborative Researchbased on the MU Radar and EAR Project “Coordinated observations of lightions and TIDs with Shigaraki MU and Kharkiv IS radars.” The work of SVP and
DVK was partially funded through a number of Ukrainian scientific projects(0117U004133, 0120U102038). OVB and MOS were supported by the project
0119U100032 “Investigations of long-term changes of the plasmasphere: newresults for security into the space and on Earth” funded by the Ministry of
Education and Science of Ukraine. VT was supported by grant LTAUSA17100 of
the Ministry of Education, Youth and Sports of the Czech Republic.
Availability of data and materials
The ionospheric parameters based on rough analysis of KhISR data are available
through the Institute of ionosphere Database (http://database.iion.org.ua/).
The results of the thorough analysis of KhISR data can be obtained from SVP or
DVK upon request. MUR data can be provided by MY and HH upon request.
DMSP data are available at http://cedar.openmadrigal.org. Swarm data are
obtained from https://swarm-diss.eo.esa.int/. The FLIP model laptop version isfreely available from PGR upon request. International Reference Ionospheremodel is available for on-line runs at
https://ccmc.gsfc.nasa.gov/modelweb/models/iri2016_vitmo.php.
Geomagnetic indices (A, Ap , K, Kp ) are downloaded from the Space Weather
Prediction Center data archive(ftp://ftp.swpc.noaa.gov/pub/indices/old_indices/). AE indices are taken at
Goddard Space Flight Center SPDF site (https://omniweb.gsfc.nasa.gov/).

Declarations
Competing interests
The authors declare that they have no competing interests.
Author details

1 Institute of Ionosphere, 16 Kyrpychova, Kharkiv 61001, Ukraine. 2 Institute forSpace-Earth Environmental Research, Nagoya University, Furo-cho, Chikusa-ku,
Nagoya City, Aichi Prefecture 464-8601, Japan. 3 Research Institute for
Sustainable Humanosphere, Kyoto University, Gokasho, Uji City, Kyoto
Prefecture 611-0011, Japan. 4 University of Alabama in Huntsville, Shelby
Center for Science and Technology, 301 Sparkman Dr NW, Huntsville, AL
35899, USA. 5 Institute of Atmospheric Physics of the Czech Academy of
Science, Boční II 1401/1A, Prague 14100, Czech Republic.
Received: 19 March 2021 Accepted: 8 July 2021

Panasenko et al. Progress in Earth and Planetary Science(2021) 8:45

References
Altadill D, Magdaleno S, Torta J, Blanch E (2013) Global empirical models of thedensity peak height and of the equivalent scale height for quiet conditions.
Adv Space Res 52(10):1756–1769. https://doi.org/10.1016/j.asr.2012.11.018
Balan N, Otsuka Y, Bailey G, Fukao S (1998) Equinoctial asymmetries in theionosphere and thermosphere observed by the MU radar. J Geophys Res:
Space Phys 103(A5):9481–9495. https://doi.org/10.1029/97JA03137
Balan N, Rao P (1990) Dependence of ionospheric response on the local time ofsudden commencement and the intensity of geomagnetic storms. J Atmos
Terr Phys 52(4):269–275. https://doi.org/10.1016/0021-9169(90)90094-4
Bent R, Llewellyn S, Schmid P (1972) Description and evaluation of the Bent
ionospheric model, vol 1–3. National Information Service, Springfield,
Virginia. Technical report, AD-753-081,-082,-083
Bilitza D (1990) International Reference Ionosphere 1990, National Space
Science Data Center. NSSDC/WDC-AR &S 90-22
Bilitza D (2004) A correction for the IRI topside electron density model basedon Alouette/ISIS topside sounder data. Adv Space Res 33(6):838–843.
https://doi.org/10.1016/j.asr.2003.07.009
Bilitza D, Altadill D, Zhang Y, Mertens C, Truhlik V, Richards P, McKinnell L-A,
Reinisch B (2014) The International Reference Ionosphere 2012 – a modelof international collaboration. J Space Weather Space Clim 4(A07). https://doi.org/10.1051/swsc/2014004
Bilitza D, Eyfrig R, Sheikh N (1979) A global model for the height of the F2-peakusing M3000 values from the CCIR numerical map. ITU Telecommun J
46:549–553
Bogomaz O, Kotov D, Panasenko S, Emelyanov L (2017) Advances in software
for analysis of Kharkiv incoherent scatter radar data. In: 2017 International
Conference on Information and Telecommunication Technologies andRadio Electronics (UkrMiCo). IEEE. pp 1–5. https://doi.org/10.1109/
UkrMiCo.2017.8095425
Booker HG (1977) Fitting of multi-region ionospheric profiles of electron
density by a single analytic function of height. J Atmos Terr Phys
39(5):619–623. https://doi.org/10.1016/0021-9169(77)90072-1
Borries C, Jakowski N, Wilken V (2009) Storm induced large scale TIDs observedin GPS derived TEC. Ann Geophys 27(4):1605–1612. Copernicus GmbH
Brace LH (1998) Langmuir probe measurements in the ionosphere. Geophys
Monogr-Am Geophys Union 102:23–35. https://doi.org/10.1029/
GM102p0023
Buresova D, Lastovicka J, Hejda P, Bochnicek J (2014) Ionospheric disturbances
under low solar activity conditions. Adv Space Res 54(2):185–196. https://doi.org/10.1016/j.asr.2014.04.007
Chernogor LF (2008) Advanced methods of spectral analysis of quasiperiodic
wave-like processes in the ionosphere: specific features and experimental
results. Geomagn Aeron 48(5):652–673. https://doi.org/10.1134/
S0016793208050101
Chernogor LF (2011) The Earth–atmosphere–geospace system: main
properties and processes. Int J Remote Sens 32(11):3199–3218. https://doi.
org/10.1080/01431161.2010.541510
Domnin IF, Chepurnyy YM, Emelyanov LY, Chernyaev SV, Kononenko AF, Kotov
DV, Bogomaz OV, Iskra DA (2014) Kharkiv incoherent scatter facility. Bulletinof the National Technical University “Kharkiv Polytechnic Institute”. Series“Radiophysics and ionosphere” 471089:28–42
Drob DP, Emmert JT, Meriwether JW, Makela JJ, Doornbos E, Conde M,
Hernandez G, Noto J, Zawdie KA, McDonald SE, et al. (2015) An update to
the Horizontal Wind Model (HWM): the quiet time thermosphere. EarthSpace Sci 2(7):301–319. https://doi.org/10.1002/2014EA000089
England S, Maus S, Immel T, Mende S (2006) Longitudinal variation of theE-region electric fields caused by atmospheric tides. Geophys Res Lett
33(21). https://doi.org/10.1029/2006GL027465
Evans J (1969) Theory and practice of ionosphere study by Thomson scatterradar. Proc IEEE 57(4):496–530. https://doi.org/10.1109/PROC.1969.7005
Fukao S, Sato T, Tsuda T, Kato S, Wakasugi K, Makihira T (1985) The MU radarwith an active phased array system: 1. Antenna and power amplifiers.
Radio Sci 20(6):1155–1168. https://doi.org/10.1029/RS020i006p01155
Fukao S, Tsuda T, Sato T, Kato S, Wakasugi K, Makihira T (1985) The MU radarwith an active phased array system: 2. In-house equipment. Radio Sci
20(6):1169–1176. https://doi.org/10.1029/RS020i006p01169
Fuller-Rowell T, Araujo-Pradere E, Codrescu M (2000) An empirical ionosphericstorm-time correction model. Adv Space Res 25(1):139–146. https://doi.
org/10.1016/S0273-1177(99)00911-4

Page 13 of 14

Fuller-Rowell T, Codrescu M, Araujo-Pradere E, Kutiev I (1998) Progress indeveloping a storm-time ionospheric correction model. Adv Space Res
22(6):821–827. https://doi.org/10.1016/S0273-1177(98)00105-7
Hairston MR, Mrak S, Coley W, Burrell A, Holt B, Perdue M, Depew M, Power R(2018) Topside ionospheric electron temperature observations of the 21
August 2017 eclipse by DMSP spacecraft. Geophys Res Lett
45(15):7242–7247. https://doi.org/10.1029/2018GL077381
Hajkowicz LA (1999) Monitoring ionospheric response to auroral electrojet
activity from sub-auroral to equatorial latitudes in the East Asian-Australian
longitudinal sector over a solar cycle (1978–1986). J Atmos Sol-Terr Phys
61(11):857–866. https://doi.org/10.1016/S1364-6826(99)00034-6
Hapgood M (2011) Towards a scientific understanding of the risk fromextreme space weather. Adv Space Res 47(12):2059–2072. https://doi.org/
10.1016/j.asr.2010.02.007
Hassenpflug G, Yamamoto M, Luce H, Fukao S (2008) Description anddemonstration of the new middle and upper atmosphere radar imaging
system: 1-D, 2-D, and 3-D imaging of troposphere and stratosphere. RadioSci 43(02):1–24. https://doi.org/10.1029/2006RS003603
Heelis R (2004) Electrodynamics in the low and middle latitude ionosphere: atutorial. J Atmos Sol-Terr Phys 66(10):825–838
Heelis R, Hanson W (1998) Measurements of thermal ion drift velocity andtemperature using planar sensors. Geophys Monogr-Am Geophys Union
102:61–72
Hoque MM, Jakowski N (2011) A new global empirical NmF2 model for
operational use in radio systems. Radio Sci 46(06):RS6015. https://doi.org/
10.1029/2011RS004807
Kim E, Jee G, Ji E-Y, Kim YH, Lee C, Kwak Y-S, Shim J-S (2020) Climatology ofpolar ionospheric density profile in comparison with mid-latitude
ionosphere from long-term observations of incoherent scatter radars: A
review. J Atmos Sol-Terr Phys:105449
Knudsen D, Burchill J, Buchert S, Eriksson A, Gill R, Wahlund J-E, Åhlén L, Smith
M, Moffat B (2017) Thermal ion imagers and Langmuir probes in the
Swarm electric field instruments. J Geophys Res: Space Phys
122(2):2655–2673. https://doi.org/10.1002/2016JA022571
Kotov D, Richards P, Bogomaz O, Chernogor L, Truhlik V, Emelyanov LY,
Chepurnyy YM, Domnin I (2016) The importance of neutral hydrogen forthe maintenance of the midlatitude winter nighttime ionosphere:
evidence from IS observations at Kharkiv, Ukraine, and field line
interhemispheric plasma model simulations. J Geophys Res: Space Phys
121(7):7013–7025. https://doi.org/10.1002/2016JA022442
Kotov D, Richards PG, Truhlík V, Bogomaz O, Shulha M, Maruyama N, Hairston
M, Miyoshi Y, Kasahara Y, Kumamoto A, et al. (2018) 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. Geophys Res Lett
45(16):8062–8071. https://doi.org/10.1029/2018GL079206
Kotov D, Richards PG, Truhlík V, Maruyama N, Fedrizzi M, Shulha M, Bogomaz
O, Lichtenberger J, Hernández-Pajares M, Chernogor L, et al. (2019) Weakmagnetic storms can modulate ionosphere-plasmasphere interaction
significantly: mechanisms and manifestations at mid-latitudes. J Geophys
Res: Space Phys 124(11):9665–9675. https://doi.org/10.1029/2019JA027076
Kotov DV, Truhlík V, Richards PG, Stankov S, Bogomaz OV, Chernogor LF,
Domnin IF (2015) Night-time light ion transition height behaviour over the
Kharkiv (50 N, 36 E) IS radar during the equinoxes of 2006–2010. J Atmos
Sol-Terr Phys 132:1–12. https://doi.org/10.1016/j.jastp.2015.06.004
Liu Z, Fang H, Weng L, Wang S, Niu J, Meng X (2019) A comparison ofionosonde measured foF2 and IRI-2016 predictions over China. Adv SpaceRes 63(6):1926–1936. https://doi.org/10.1016/j.asr.2019.01.017
Lomidze L, Knudsen DJ, Burchill J, Kouznetsov A, Buchert SC (2018) Calibrationand validation of swarm plasma densities and electron temperatures using
ground-based radars and satellite radio occultation measurements. RadioSci 53(1):15–36. https://doi.org/10.1002/2017RS006415
Luan X, Dou X (2013) Seasonal dependence of the longitudinal variations ofnighttime ionospheric electron density and equivalent winds at southern
midlatitudes. Ann Geophys 31:1699–1708. https://doi.org/10.5194/angeo31-1699-2013
Nava B, Coisson P, Radicella S (2008) A new version of the nequick ionosphere
electron density model. J Atmos Sol-Terr Phys 70(15):1856–1862. https://doi.org/10.1016/j.jastp.2008.01.015

Panasenko et al. Progress in Earth and Planetary Science

Page 14 of 14(2021) 8:45

Nossal S, Mierkiewicz E, Roesler F (2012) Observed and modeled solar cycle
variation in geocoronal hydrogen using NRLMSISE-00 thermosphereconditions and the bishop analytic exosphere model. J Geophys Res:
Space Phys 117(A3). https://doi.org/10.1029/2011JA017074
Nossal S, Mierkiewicz E, Roesler F, Woodward R, Gardner D, Haffner L (2019)
Geocoronal hydrogen emission variation over two solar cycles. J Geophys
Res: Space Phys 124(12):10674–10689. https://doi.org/10.1029/
2019JA026903
Nygrén T, Aikio AT, Voiculescu M, Cai L (2015) Radar observations ofsimultaneous traveling ionospheric disturbances and atmospheric gravity
waves. J Geophys Res: Space Phys 120(5):3949–3960. https://doi.org/10.
1002/2014JA020794
Oliver WL, Otsuka Y, Sato M, Takami T, Fukao S (1997) A climatology of F region
gravity wave propagation over the middle and upper atmosphere radar. J
Geophys Res 102:14499–14512. https://doi.org/10.1029/97JA00491
Panasenko SV, Goncharenko LP, Erickson PJ, Aksonova KD, Domnin IF (2018)
Traveling ionospheric disturbances observed by Kharkiv and Millstone Hill
incoherent scatter radars near vernal equinox and summer solstice. J
Atmos Sol-Terr Phys 172:10–23. https://doi.org/10.1016/j.jastp.2018.03.001
Panasenko SV, Otsuka Y, van de Kamp M, Chernogor LF, Shinbori A, Tsugawa T,
Nishioka M (2019) Observation and characterization of travelingionospheric disturbances induced by solar eclipse of 20 March 2015 using
incoherent scatter radars and GPS networks. J Atmos Sol-Terr Phys
191:105051. https://doi.org/10.1016/j.jastp.2019.05.015
Patel N, Karia S, Pathak K (2019) Evaluation of the improvement of IRI-2016
over IRI-2012 at the India low-latitude region during the ascending phaseof cycle 24. Adv Space Res 63(6):1860–1881. https://doi.org/10.1016/j.asr.
2018.10.008
Pirog O, Polekh N, Chistyakova L (2001) Longitudinal variation of criticalfrequencies in polar F-region. Adv Space Res 27(8):1395–1398. https://doi.
org/10.1016/S0273-1177(01)00038-2
Rich F (1994) Users guide for the topside ionospheric plasma monitor (SSIES,SSIES-2 and SSIES-3) on spacecraft of the Defense Meteorological Satellite
Program, Volume 1: Technical description. Technical report, Air Force
Phillips Laboratory, Hanscom AFB. https://apps.dtic.mil/sti/pdfs/
ADA315731.pdf
Richards P (1991) An improved algorithm for determining neutral winds fromthe height of the F2 peak electron density. J Geophys Res: Space Phys
96(A10):17839–17846. https://doi.org/10.1029/91JA01467
Richards P (2001) Seasonal and solar cycle variations of the ionospheric peakelectron density: comparison of measurement and models. J Geophys Res:
Space Phys 106(A7):12803–12819. https://doi.org/10.1029/2000JA000365
Richards P, Meier R, Chen S-P, Drob D, Dandenault P (2017) Investigation of thecauses of the longitudinal variation of the electron density in the Weddell
Sea Anomaly. J Geophys Res: Space Phys 122(6):6562–6583. https://doi.
org/10.1002/2016JA023565
Richards P, Meier R, Wilkinson P (2010) On the consistency of satellite
measurements of thermospheric composition and solar EUV irradiancewith Australian ionosonde electron density data. J Geophys Res: Space
Phys 115(A10). https://doi.org/10.1029/2010JA015368
Rush C (1989) Ionospheric mapping-an update of foF2 coefficients. Telecomm
J 56:179–182
Sagawa E, Immel T, Frey H, Mende S (2005) Longitudinal structure of theequatorial anomaly in the nighttime ionosphere observed by IMAGE/FUV.
J Geophys Res: Space Phys 110(A11). https://doi.org/10.1029/
2004JA010848
Schunk RW, Scherliess L, Eccles V, Gardner L, Sojka JJ, Zhu L, Pi X, Mannucci A,
Wilson B, Komjathy A, et al. (2014) Ensemble modeling with data
assimilation models: a new strategy for space weather specifications,forecasts, and science. Space Weather 12(3):123–126. https://doi.org/10.
1002/2014SW001050
Shubin V (2015) Global median model of the F2-layer peak height based on
ionospheric radio-occultation and ground-based Digisonde observations.
Adv Space Res 56(5):916–928. https://doi.org/10.1016/j.asr.2015.05.029
Song Q, Ding F, Wan W, Ning B, Liu L, Zhao B, Li Q, Zhang R (2013) Statistical
study of large-scale traveling ionospheric disturbances generated by thesolar terminator over China. J Geophys Res: Space Phys 118(7):4583–4593.
https://doi.org/10.1002/jgra.50423
Truhlik V, Bilitza D, Triskova L (2012) A new global empirical model of theelectron temperature with the inclusion of the solar activity variations for
IRI. Earth Planets Space 64(6):531. https://doi.org/10.5047/eps.2011.10.016

Truhlík V, Třísková L, Šmilauer J (2001) Improved electron temperature modeland comparison with satellite data. Adv Space Res 27(1):101–109. https://doi.org/10.1016/S0273-1177(00)00144-7
Tsugawa T, Saito A, Otsuka Y (2004) A statistical study of large-scale travelingionospheric disturbances using the GPS network in Japan. J Geophys Res:
Space Phys 109(A6). https://doi.org/10.1029/2003JA010302
Tsurutani BT, Verkhoglyadova OP, Mannucci AJ, Lakhina GS, Huba JD (2012)
Extreme changes in the dayside ionosphere during a Carrington-typemagnetic storm. J Space Weather Space Clim 2:05. https://doi.org/10.
1051/swsc/2012004
Zhang S, Coster AJ, Holt JM, Foster JC, Erickson P (2012) Ionosphericlongitudinal variations at midlatitudes: incoherent scatter radarobservation at Millstone Hill. Sci China Technol Sci 55(5):1153–1160.
https://doi.org/10.1007/s11431-012-4784-yZhang S-R, Foster JC, Coster AJ, Erickson PJ (2011) East-West Coast differencesin total electron content over the continental US. Geophys Res Lett
38(19):L19101. https://doi.org/10.1029/2011GL049116

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