Choice of the Definition Method for the Total Electron Content to
Describe the Conditions in the Ionosphere
Olga Maltseva
Institute of Physics Southern Federal University, Stachki, 194, Rostov-on-Don, Russia
mal@ip.rsu.ru
Keywords: GPS. Total electron content TEC. Ionospheric models. Radio wave propagation. Geomagnetic disturbances.
Abstract: Conditions of radio-wave propagation in the ionosphere, influencing functioning of the modern navigation
and communication systems, are defined by the critical frequency foF2 and an electron density distribution
termed the N(h)- profile. In the given paper, the experimental values of the total electron content TEC(obs)
are used for their determination. It is shown that the median of the equivalent slab thickness of the
ionosphere is the good calibration factor, allowing to obtain values of foF2 from TEC(obs) of any global
map though in most cases values of foF2, the closest to foF2(obs), are provided with the JPL map. For
coordination of the N(h)-profile with values of TEC(obs), coefficient K(PL), modifying a plasmaspheric
part of a profile, is entered (up to heights of navigation and geostationary satellites). In this case, the CODE
map is the best one. It is necessary to have models of the ТЕС parameter to support navigation system
operation. It is shown that the big progress in modeling of this parameter is reached during the last years:
appearance of various models allows us to compare and use them at forecast ТЕС for any level of solar
activity. It is especially important, because values of solar spots and the F10.7 parameter and also
geomagnetic indexes of Kp, Dst, АЕ are well enough predicted.
1 INTRODUCTION
The ionosphere plays an important role in the life of
mankind: it mitigates the blows of solar wind and
provides wave propagation of various frequency
bands. The systems connected to the ionosphere are
most full presented in Tab. 1 from the paper
(Goodman, 2005).
Category 1 involves those systems that depend
upon the ionosphere (i.e., involve the ionosphere as
part of the system), and category 2 involves those
systems for which the ionosphere is simply a
nuisance. The special role for description of the
ionospheric conditions is played by models, and the
model, capable to provide high accuracy of
description of ionospheric characteristic distribution,
should be adapted for the experimental information
in a real time mode.
Table 1: Categories of radio systems in terms of
ionospheric dependence
Category 1: systems
that depend upon the
ionosphere
Category 2: systems for
which the ionosphere is
simply a nuisance
VLF–LF
communication and
navigation
Satellite
communication
MF communication
Satellite navigation
(e.g., GPS&GLONASS)
HF communication
Space-based radar and
imaging
HF broadcasting
(‘‘short-wave’’ listening)
Terrestrial radar
surveillance and tracking
OTH radar
surveillance
Meteor-burst
communication
HFDF and
HF SIGINT
Any other system for
which the ionosphere is not
necessary for conveyance
51
Olga M.
Choice of the Definition Method for the Total Electron Content to Describe the Conditions in the Ionosphere.
DOI: 10.5220/0005421200510061
In Proceedings of the Third International Conference on Telecommunications and Remote Sensing (ICTRS 2014), pages 51-61
ISBN: 978-989-758-033-8
Copyright
c
2014 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
The data which can be used in such an approach
should be available and operatively updated.
Traditional parameters meeting such
requirements are the critical frequency foF2 and
maximum height hmF2. Because the total electron
content TEC is the most important parameter of the
ionosphere for the operation of technological
systems, in the given paper it is used for this
purpose. TEC data are available and is updated in
several Internet archives. From huge number of
possible applications of ТЕС, in the present paper
the preference is given for an estimation of
possibility of determination of propagation
conditions. It means usage of ТЕС for determination
of foF2 (or too NmF2) and N(h)- profiles. The huge
need exists in the forecast of these parameters, and,
hence, of ТЕС. It is possible to select three methods
of the TEC determination: (1) measurements, (2)
empirical modeling, (3) integration of theoretical or
empirical N(h)-profiles. Information and results are
given for each of these methods.
2 MEASUREMENT METHODS
The most widespread are global maps JPL, CODE,
UPC, ESA, created by Jet Propulsion Laboratory of
California Institute of Technology (Pasadena, USA),
the Center for Orbit Determination in Europe
(CODE) of the International GPS Service for
Geodynamics (Switzerland), Astronomy and
Geomatics of the Polytechnical University of
Catalonia, Barcelona, Spain (UPC), European Space
Agency (Germany) respectively as TEC
experimental data (e.g. Schaer et al., 1995;
Mannucci et al., 1998; Hernandez-Pajares et al.,
1997; Sardon et al., 1994; Jakowski et al., 1996). For
specific coordinates and time, the maps can be
derived from the IONEX (IONosphere map
Exchange) files (ftp://cddis.gsfc.nasa.gov/pub/
gps/products/ionex/). Owing to the big differences of
methods (on determination of biases, approximating
functions, etc.), values of ТЕС for various maps and
other methods of determination ТЕС are strongly
differed. Traditional examples of such differences
are Figures from the paper (Arikan et al., 2003).
Now, the GPS community selected the average
IGS values as the standard (Hernandez et al., 2009),
therefore in the given paper all maps, including IGS,
are used. These maps are given on the same site and
already start to be used, e.g. (Lean et al., 2011). We
use all maps for comparison. However, under the
valid remark of (Lastovicka, 2013), such selection
does not remove the restrictions inherent in each
method. JPL is used in paper (Gulyaeva and
Stanislawska, 2008), CODE is used in paper
(Jakowski et al, 2006). Other methods also yield
comparable results.
Figure 1: Comparison of the estimated TEC values from
the algorithm with other models of TEC for Kiruna,
estimated VTEC from the developed algorithm (solid
line), RCRU (dashed line), IRI model (pluses),
gAGE/UPC (triangles and a solid line), JPL-GNISD
(diamonds and a solid line), ESA/ESOC (circles and a
solid line), NRCan (stars and a solid line), CODE (squares
and a solid line). (a) 25 April 2001 and (b) 28 April 2001.
3 METHODS OF EMPIRICAL
MODELING
Empirical modeling plays an important role both for
the forecast of ionospheric parameters, and for
validation of models. For modeling of ТЕС,
basically, the method of orthogonal components is
used (Zhang et al., 2012; Ivanov et al., 2011),
however authors do not give appropriate coefficients
and functions. Besides, there is a difficulty of the
forecast at an output for temporal boundaries of the
used data, therefore the main attention is given for
avaiable and new models. Many long years, the most
simple model of Klobuchar (Klobuchar, 1987) was
unique for adjustment of delay of signals in the
ionosphere and till now is widely used for systems
with single-frequency receivers though the authors
using it note a row of shortcomings, for example
(Chen and Gao, 2005). The model (Kakinami, 2009)
is an example of models for specific station which
should possess the high accuracy. The model is
based on values of the instrumental biases given by
the JPL laboratory. Results of the test of this model
Third International Conference on Telecommunications and Remote Sensing
52
in the form of correspondence between model and
experimental values are given on Fig. 2.
Figure 2: Comparison of model and observational ТЕС for
the Taiwan model near to a maximum of solar activity.
Seasonal course of ТЕС at the given latitude and
the full conformity of model and observational ТЕС
for autumn and winter months is perfectly seen. In
the spring and in the summer, the model
underestimates values. Range of the root mean
square (RMS) deviation makes 4-14 TECU, the
relative RMSD makes 6-18 %. On Fig. 3, results for
a minimum of solar activity are yielded.
Additionaly, values of IGS are shown by asterisks.
Figure 3: Comparison of model and observational ТЕС for
the Taiwan model near to a minimum of solar activity.
It is seen that values of ТЕС can be in 2-3 times
less, than in a maximum of solar activity. The model
can both to underestimate and to overestimate the
observational values. The range of absolute
deviations has made 1-10 TECU. Absolute (σ,
TECU) and relative (σ, %) RMSD are presented to
Tab. 2 for four months of three years.
Table 2: Absolute and relative RMS deviations for the
model (Kakinami, 2009).
σ,
TECU
Mar
Jun
Sep
Dec
2002
13.5
8.0
3.5
4.0
2006
3.0
1.7
2.6
2.2
2010
5.6
2.8
5.9
5.6
σ, %
Mar
Jun
Sep
Dec
2002
15.8
17.2
6.0
8.9
2006
14.7
9.5
14.6
16.2
2010
24.3
16.3
26.2
24.3
If to compare these results with 50 % -
estimation for the model (Klobuchar, 1987) then
improving in 2-5 times may be got. Important
property of the model is dependence of ТЕС on a
daily index. The previous Figures 2 and 3 showed
results for medians. The following Fig. 4 gives
comparison of daily model and experimental values
for August 2002.
Good correspondence of dynamics of TEC
variations is seen. That proves to be true by
quantitative estimations of absolute deviations 6.4
TECU, absolute RMS deviations 8.3 TECU and
relative RMSD 16.4 %. These results show high
efficiency of the model and a way of its
construction. It can be used for validation of other
models.
One more aspect of use of models is connected
with reconstruction of ТЕС values for those periods
when there were no regular measurements of ТЕС
(before 1998). The possibility of such reconstruction
is illustrated on Fig. 5 according to GPS
measurements at the Taiwan station in the morning
since September 1996 till August 1997 (Wu et al.,
2004). Icon ТЕС represents measurements, TW -
values of the Taiwan model.
It is seen that results are satisfactory as a first
approximation. They give representation also about
possibilities of forecast forward.
Choice of the Definition Method for the Total Electron Content to Describe the Conditions in the Ionosphere
53
Figure 4: Comparison of daily model and experimental ТЕС for August 2002.
Figure 5: Comparison of model and observational ТЕС in
years 1996-1997.
The Neustrelitz Global Model (NGM) unlike the
Taiwan model is global one (Jakowski et al., 2011).
Except the ТЕС model, it includes models of other
parameters (NmF2, hmF2) (Hoque and Jakowski,
2011, 2012). Authors of this model have fulfilled
own validation however it is not enough for certain
conclusions about efficiency of their model. Results
of more extensive validation are given in (Maltseva
et al., 2013c) for a middle-latitude region and in
(Maltseva et al., 2013b) for low-latitude area in
which the greatest advantages were expected.
Results of additional validation for low-latitude
stations Niue and Sao Luis are given on Fig. 6, for
high-altitude stations are given on Fig. 7.
Figure 6: Cases of the NGM model advantage at definition
of foF2 (two low-latitude stations, 2010, 2004, 2002).
Third International Conference on Telecommunications and Remote Sensing
54
Figure 7: Cases of the NGM model advantage at definition
of foF2 (three high-altitude stations, 2010, 2007).
In these cases, periods of the NGM model
advantages are seen, however the major statistics on
all regions shows that the NGM model not always
yields the best results, than the IRI model.
Discrepancy between radio occultation and
ionosonde values of NmF2 may be one of the
reasons why insert of great number of radio
occultation measurements has not led to
improvement of the NGM model, e.g. (Hajj and
Romans, 1998; Tsai and Tsai, 2004). Nevertheless, it
can be recommended for use in low- and high-
latitude areas.
Process of model development continues
constantly that is additional confirmation of an
urgency of this process. The latest model is the
model of authors (Mukhtarov et al., 2013a, b). It, on
the one hand, is the most physically proved, on the
other hand, according to estimates of authors, their
model is two times more exact, than the NGM
model. In papers (Mukhtarov et al., 2013a, b), not
only the ТЕС model is developed, but also model of
its error (Mukhtarov et al., 2013b). Difference from
the NGM model is the consideration not only
components, caused by sunlight, but also the regular
wave structure of the tidal nature influencing from
the lower atmosphere. The model is constructed
according to the CODE map for 1999-2011. As the
starting parameter, not only coefficient F10.7 is
chosen, but also its linear velocity of change K
F
.
It is
one more difference of this model from all previous
options. For all array of the used data, the following
estimates are obtained: mean (systematic) error
МЕ=0.003TECU, at such МЕ, root of mean square
error (RMSE) and an error of a standard deviation
(STDE) were equal and have made
RMSE=STDE=3.387TECU. These estimates are
compared to estimates for the TEC(NGM) model
(Jakowski et al., 2011): ME =-0.3TECU,
RMSE=7.5TECU. Thus, the Bulgarian model has a
smaller error in two times. However it is worth to
note that both models are climatological, i.e. they
describe a mean state in quiet geomagnetic
conditions, and the difference in number of
coefficients (12 against 4374) is underlined. Authors
of (Mukhtarov et al., 2013a) absolutely validly do
not consider a great number of coefficients as a
model deficiency because these coefficients are
calculated once, however they are inaccessible.
Coefficients of the TEC(NGM) model are published
and may be used by any user. In turn, we can note
that there are "tails" in an error distribution of any
model. It is important to determine, what latitudinal
areas and to what conditions of solar activity they
concern. As any model cannot work equally well in
all latitudinal areas and meet the possible
requirements, validation of models does not cease to
be an actual problem. These requirements are
connected with limitation of approaches, the used
data, distinction of physical processes.
Thus, it is possible to specify major progress in
modeling of parameter ТЕС: occurrence of various
models allows us to compare and use them at
forecast of ТЕС for any level of solar activity. It is
especially important because values of solar spots
and parameter F10.7, and also geomagnetic
coefficients of Kp, Dst, АЕ are well enough
predicted (e.g. Pesnell, 2012; Tobiska et al., 2013).
4 DEFINITION OF FoF2 ON
CURRENT VALUES OF ТЕС
Definition of foF2 on current values of ТЕС in the
this paper is based on use of median of the
equivalent slab thickness of the ionosphere τ.
Empirical models of τ have appeared earlier, than
empirical models of ТЕС. Definition of ТЕС under
formula TEC=τ*NmF2, where the independent
empirical model of τ should be used, was one of
Choice of the Definition Method for the Total Electron Content to Describe the Conditions in the Ionosphere
55
main applications of the τ model. NmF2 is possible
to take from the IRI model or any another. The
simple relation for τ =ТЕС/NmF2 shows that τ is
width of a slab in the form of a rectangle with
constant concentration NmF2. For definition of ТЕС
and NmF2, the τ(IRI) model was traditionally used
(e.g. Houminer and Soicher, 1996; Gulyaeva, 2003)
though it also is not empirical in the same sense in
what the ТЕС(IRI) model is not empirical model of
ТЕС. On the basis of expression foF2=foF2(IRI)*
SQRT(TEC(obs)/TEC(IRI)), GIM-TEC adaptive
ionospheric weather assessment and forecast system
was constructed (Gulyaeva et al., 2013). It is easy to
show that value which can be designated τ(obs,IRI)
is used in this case. It means that model values of
NmF2 and the observational values of ТЕС are used
at definition of τ(obs,IRI). It differs as from τ(IRI,
IRI), and from τ(obs, obs) which are designated
τ(IRI) and τ(obs) for brevity of records. Papers
(Maltseva et al., 2012a, b) are devoted results of use
of median τ(med) from values τ(obs). Empirical
models of ionospheric parameters are known to
include median or mean values hence they
characterize a mean state, close to the quiet.
Advantage of median τ(med) is that it allows to
determine foF2 on current values of ТЕС. These
foF2 values differ from averages and are closer to
the real. In paper (Maltseva et al., 2012a), it is
shown that the median τ(med) allows to determine
foF2 during disturbances or to fill gaps of the foF2
data. The estimate of efficiency of use of values τ is
done by means of calculation of deviations |ΔfoF2|
of calculated foF2 from the observational ones. The
observational values of ТЕС form the whole array:
JPL, CODE, UPC, ESA, La Plata, IONOLab TEC,
RAL and others. To each of these values, the various
values of τ correspond. The example of τ behavior
for the JPL map is shown on Fig. 8 for the Juliusruh
station for April 2000 (near to a maximum of solar
activity) and April 2009 (near to a minimum of solar
activity).
Figure 8: An example of comparison of τ for various
options of ТЕС and NmF2.
Example of the foF2 definition by means of
various τ is given on Fig. 9 together with
experimental values of ТЕС for the JPL map. These
values are shown together with medians. This
picture specifies presence of disturbance.
Tab. 3 shows results of |ΔfoF2| calculation for
four global maps JPL, CODE, UPC, ESA. These are
monthly average values of deviations for the
instantaneous quantities foF2(ins). Values of |ΔfoF2|
for τ(IRI) are given for comparison.
The table illustrates the most general regularities:
the greatest deviations are proper τ(IRI) in the initial
model, the little smaller deviations correspond to
τ(obs, IRI). The best conformity is given by median
of τ(obs). From four maps, the best conformity
concerns the JPL map in this case. And though it
gives the best conformity in most cases, there are
conditions and regions in which the best conformity
can be given and by other maps. More often it is
CODE, sometimes - UPC. And even there was a
station (Sao Luis) for which the best conformity is
given by the ESA map in certain cases. The huge
statistics of calculations for more, than 30 stations
and 10 years, shows that deviations of the calculated
frequencies from the observational values have the
greatest quantity for τ(IRI), the least - for τ(obs), i.e.
τ(med). Deviations for τ(obs, IRI) lie between them,
closer to |ΔfoF2| for τ(IRI) more often. New results,
including the data for values IGS, are given in Tab.
4 for high- and low-latitude stations which
determine boundaries of values |ΔfoF2|, because
values for middle-latitude stations are always less.
Values, averaging for 2013, are given.
Figure 9: An example of use of median of τ for definition
of foF2.
Third International Conference on Telecommunications and Remote Sensing
56
Table 3: Deviations |ΔfoF2| for various options of τ definition.
|∆foF2|,
April
2000
April
2009
MHz
τ(IRI)→
1.034
τ(IRI)→
0.554
τ
(obs,IRI)
τ(obs)
τ
(obs,IRI)
τ(obs)
JPL
0.660
0.452
0.497
0.205
CODE
0.677
0.484
0.501
0.222
UPC
0.731
0.544
0.472
0.237
ESA
1.013
0.907
0.496
0.242
Table 4: Correspondence between experimental and calculated foF2 according to three stations.
|ΔfoF2ǀ
τ(IRI)
τ(obs, IRI)
τ(obs)
station
IRI
JPL
CODE
UPC
ESA
IGS
JPL
CODE
UPC
ESA
IGS
Thule
0.82
0.71
0.75
0.71
0.75
0.71
0.41
0.45
0.41
0.45
0.41
Longyear
0.69
0.61
0.67
0.59
0.63
0.62
0.40
0.49
0.38
0.43
0.41
Niue
2.19
2.09
2.16
2.15
2.13
2.13
1.01
1.10
1.09
1.06
1.04
Value for IGS is inscribed in the general
statistics and more often there is a little above, than a
value for the best map. It is worth to note that the
proximity of values |ΔfoF2| for various maps
testifies that τ is good calibration coefficient for
ТЕС.
Figure 10: Comparison of results of foF2 definition with
use of various τ.
In connection with such results, there is a
question on empirical model of τ. One of
possibilities is to pay attention to the NGM model
which allows to calculate NmF2(NGM) and
TEC(NGM). Having values NmF2(NGM) and
TEC(NGM), it is possible to calculate values of the
equivalent slab thickness of the ionosphere τ(NGM)
=TEC(NGM)/NmF2(NGM). The purpose is to
estimate, how much the model τ(NGM) is closer to
τ(obs) than τ(IRI). Such estimate is done by
comparison of instantaneous values |ΔfoF2(ins)|. For
the NGM model, values of foF2(ins) are obtained
from values ТЕС(CODE) with use of τ(NGM). On
Fig. 10, examples of comparison of critical
frequencies for two global maps are given ("best"
and "worst" from the point of view of definition of
foF2 in each specific case) and two models (IRI and
NGM) for middle-latitude and two high-altitude
stations in the conditions of various solar activity.
It is seen that near to a maximum of activity
(2001) the NGM model yields the best results than
the IRI model and the ESA map. In the conditions of
low activity (2006) at middle-latitude station, it
yields results, close to IRI. At high latitudes, the
NGM model gives major deviations in winter and
autumn, and it is seen that the CODE map yields the
worst results in these cases. At an increase of solar
activity in 2011 and the corresponding increase of
the ТЕС, the NGM model again starts to yield
results, the best than the IRI model. Thus, in most
cases τ(NGM) provides results, the best than τ(IRI),
however its deviations do not come nearer anywhere
to the values given by τ(JPL). In the conditions of a
minimum of activity, the NGM model has no
advantages to low-latitude stations.
Choice of the Definition Method for the Total Electron Content to Describe the Conditions in the Ionosphere
57
As a whole, it is possible to tell that τ(NGM)
may carry out a role of the empirical model of τ. At
use of other map instead of the CODE map,
probably, results would be better.
4 N(h)-PROFILE S OF THE
IONOSPHERE AND VALUES
OF ТЕС
As it is known, conditions in the ionosphere are
determined by distribution of concentration, or N(h)-
profile. N(h)-profile can be divided into three parts:
bottom side, topside and plasmaspheric. The bottom
side is determined by the experimental critical
frequency foF2. The topside is improved by means
of the plasma frequencies measured on satellites, but
there is a residual of ТЕС. It is possible to use
coefficient K(PL) which is selected for the full
conformity with the observational ТЕС. However
there is no data for development and validation of
the K(PL) model yet. Having such model, it will be
possible even to improve determination of foF2.
Details of use of ТЕС for determination of N(h)-
profile to heights of navigation satellites are the
following. In paper (Maltseva et al., 2013c), it has
been shown that use of the plasma frequencies
measured on satellites allows to improve the shape
of the topside side. As a result, values of ТЕС for the
several N(h)-profiles transiting through the critical
frequency foF2 and a various combination of plasma
frequencies are obtained: (1) satellite s1, (2) satellite
s2, (3) both satellites s1 and s2. Two first options are
realized in most cases. The third option is realized in
case of simultaneous passage of two satellites over
the given point. The illustration of obtained values
of ТЕС is given for the Juliusruh station on Fig. 11.
Simultaneous passages took place for 6 days
specified in Tab. 5 in UT=13 and UT=23. Values for
the initial IRI model are shown by black circles. Red
circles show the observational values. Green
triangles show values for the first option, by violet
crosses - for the second one, blue asterisks – for the
third case. Orange circles show ТЕС for the N(h)-
profiles transiting through both plasma frequencies
and adapting by coefficient K(PL). All values are
given for four maps in decreasing order of values.
This order is specified in Tab. 5.
Figure 11: TEC, calculated for N(h)-profiles, transiting
through the critical frequency foF2 and a various
combination of plasma frequencies.
Table 5: Days, hours and names of maps in decreasing
order of ТЕС for simultaneous passages of satellites over
the Juliusruh station in April 2001.
day
hour
map
10
13
ESA
JPL
UPC
CODE
12
13
JPL
UPC
CODE
ESA
13
13
JPL
CODE
UPC
ESA
20
13
JPL
UPC
CODE
ESA
25
23
UPC
JPL
ESA
CODE
30
23
JPL
UPC
CODE
ESA
Big difference between ТЕС for the corrected
N(h)-profiles and ТЕС for the initial model,
corresponding to quiet conditions, speaks about
influence of disturbances. N(h)-profiles transiting
through frequency of one of satellites, are close each
other. That can testify both to "interchangeability" of
profiles, and about their ambiguity. In most cases,
orange circles coincide with red points. It testifies
that the N(h)-profile, transiting through plasma
frequencies of both satellites, provides the
observational value of ТЕС. It is reached by
selection of coefficient K(PL) shown on Fig. 12 also
for four maps.
Figure 12: Behavior of coefficient K(PL) for four global
maps of ТЕС in cases of simultaneous passages of
satellites.
Third International Conference on Telecommunications and Remote Sensing
58
It is seen that values of K(PL) decrease with
decreasing TEC(obs). Relation K(PL) =1 specifies
the full conformity of model ТЕС and TEC(obs). It
is obvious that it is possible to select the ТЕС value
to which relation K(PL) =1 corresponds. There are
some cases with the negative value K(PL) =-0.001.
They can be identified by misfit of orange and red
circles on Fig. 13. It means that the N(h)-profile,
providing TEC(obs), is not found. It occurs when
ТЕС for N(h)-profiles s1 exceed TEC(obs).
The N(h)-profiles corresponding to these ТЕС
are given on Fig. 13.
Figure 13: Topside and plasmaspheric parts of N(h)-
profiles corresponding various global maps of TEC.
4 CONCLUSIONS
In recent years, the TEC has become an important
parameter to describe the state of the propagation
medium. However, its use encounters certain
difficulties associated with a variety of values. This
diversity leads to ambiguity of parameters
determined through TEC and models. This paper
makes the following recommendations. 1. The best
method of TEC modeling is the EOC. 2. To
determine foF2, it is possible to use τ, which is a
good calibration factor, including τ(NGM). 3. Using
plasma frequencies measured on satellites allows us
to construct N(h)-profiles, closer to the real part in
the topside part. 4. For full proximity of N (h)-
profiles with experimental TEC(obs), we must enter
the factor K(PL), modifying plasmaspheric part of
profile. Its value depends on the choice of TEC(obs).
In this regard, we can focus on IGS.
Choice of the Definition Method for the Total Electron Content to Describe the Conditions in the Ionosphere
59
ACKNOWLEDGEMENTS
Author thanks organizations and scientists who are
developing the IRI model, providing data of SPIDR,
JPL, CODE, UPC, ESA, and Dr M. Hoque for
detailed comments on the NGM model.
REFERENCES
Arikan, F., Erol C.B., Arikan O., Regularized estimation
of vertical total electron content from Global
Positioning System data. 2003. J. Geophys. Res., 108,
A12, 1469, doi:10.1029/2002JA009605, 12p.
Chen, K., Gao Y. Real-time precise point positioning
using single frequency data. Proceedings of IONGNSS
18
th
International Technical Meeting of the Satellite
Division, Long Beach, CA, 2005, 1514-1523.
Goodman, J.M., 2005. Operational communication
systems and relationships to the ionosphere and space
weather. Adv. Space Res., 36, 2241–2252.
Gulyaeva, T.L., 2011. Storm time behavior of topside
scale height inferred from the ionosphere-
plasmosphere model driven by the F2 layer peak and
GPS-TEC observations. Adv. Space Res., 47, 913-920.
Gulyaeva, T.L., Arikan F., Hernandez-Pajares M.,
Stanislawska I. 2013. GIM-TEC adaptive ionospheric
weather assessment and forecast system. J. Atm.
Solar-Terr. Phys., 102, 329-340.
Gulyaeva, T.L., Bilitza D., 2011. Towards ISO standard
Earth ionosphere and plasmasphere model. In: Larsen,
R.J. (Ed.), New developments in the standard model.
NOVA Publishers, USA, 11–64.
Gulyaeva, T.L., Stanislawska, I., 2008. Derivation of a
planetary ionospheric storm index. Ann. Geophys., 26,
2645–2648.
Hajj, G.A., Romans L.J., 1998. Ionospheric electron
density profiles obtained with the Global Positioning
System: results from the GPS/MET experiment. Radio
Sci., 33, 175–190.
Hernandez-Pajares, M., Juan, J. M., Orus, R., Garcia-Rigo,
A., Feltens, J., Komjathy, A., Schaer, S. C., and
Krankowski, A.: The IGS VTEC maps: a reliable
source of ionospheric information since 1998. J.
Geod., 2009, 83, 263–275.
Hernandez-Pajares, M., Juan, J.M., Sanz, J., 1997. High-
resolution TEC monitoring method using permanent
ground GPS receivers, Geophys. Res. Lett., 24, 1643-
1646.
Hoque, M.M., Jakowski N., 2011. A new global empirical
NmF2 model for operational use in radio systems.
Radio Sci., 46, RS6015, 1-13.
Hoque, M.M., Jakowski N., 2012. A new global model for
the ionospheric F2 peak height for radio wave
propagation. Ann. Geophys., 30, 797-809.
Houminer, Z., Soicher H., 1996. Improved short –term
predictions of foF2 using GPS time delay
measurements. Radio Sci., 31(5), 1099-1108.
Ivanov, V.B., Gefan G.D., Gorbachev O.A., 2011. Global
empirical modeling of the total electron content of the
ionosphere for satellite radio navigation systems. J.
Atm. Solar-Terr. Phys., 73, 1703–1708.
Jakowski, N., Hoque M.M., Mayer C. A new global TEC
model for estimating transionospheric radio wave
propagation errors, Journal of Geodesy, 2011, 85(12),
965-974.
Jakowski, N., Sardon E., Engler E., Jungstand A., Klahn
D., 1996. Relationships between GPS-signal
propagation errors and EISCAT observations. Ann.
Geophys., 14, 1429-1436.
Jakowski, N., Stankov S.M., Schlueter S., Klaehn D.,
2006. On developing a new ionospheric perturbation
index for space weather operations. Adv. Space Res.,
38, 2596-2600.
Kakinami, Y., Chen C.H., Liu J.Y., Oyama K.-I., Yang
W.H., Abe S., 2009. Empirical models of total
electron content based on functional fitting over
Taiwan during geomagnetic quiet condition. Ann.
Geophys., 27, 3321-3333.
Klobuchar, J.A., 1987. Ionospheric time-delay algorithm
for single-frequency GPS users. IEEE Transactions on
aerospace and electronic systems. 1987AES-23(3),
325-331.
Lastovicka, J. Are trends in total electron content (TEC)
really positive? J. Geophys. Res.: Space Physics,
2013, 118, 3831–3835, doi:10.1002/jgra.50261.
Lean, J., Emmert J.T., Picone J.M., Meier R. R. Global
and regional trends in ionospheric electron content. J.
Geophys. Res., 2011, 116, A00H04, doi:10.1029
/2010JA016378.
Maltseva, О.A., Mozhaeva N.S, Zhbankov G.A., 2012a. A
new model of the International Reference Ionosphere
IRI for telecommunication and navigation systems.
Proceedings of the First International Conference on
Telecommunications and Remote Sensing, Sofia,
Bulgaria 29-30 August 2012, 129-138,
http://www.math.bas.bg/ursi/ICTRS2012
proceedings.pdf
Maltseva, О.A., Mozhaeva N.S, Poltavsky O.S., Zhbankov
G.A., 2012b. Use of TEC global maps and the IRI
model to study ionospheric response to geomagnetic
disturbances. Adv. Space Res., 49, 1076-1087.
Maltseva, O. A., Zhbankov G. A., Mozhaeva N.S., 2013a.
Advantages of the new model of IRI (IRI-Plas) to
study ionospheric environment. Adv. Radio Sci., 11,
907–911, doi:10.5194/ars-11-1-2013.
Maltseva, О.A., Mozhaeva N.S, T.V. Nikitenko, 2013b.
Validation of the Neustrelitz Global Model according
to the low latitude ionosphere. Adv. Space Res.,
http://dx.doi.org/10.1016/j.asr.2013.11.005.
Maltseva, O., Mozhaeva N., Vinnik E., 2013c. Validation
of two new empirical ionospheric models IRI-Plas and
NGM describing conditions of radio wave propagation
in space. Proceedings of Second International
Conference on Telecommunications and Remote
Sensing, Noordwijkerhout, The Netherlands, 11-12
July, 109-118.
Mukhtarov, P., Pancheva D., Andonov B., Pashova L.,
2013a. Global TEC maps based on GNSS data: 1.
Third International Conference on Telecommunications and Remote Sensing
60
Empirical background TEC model. J. Geophys. Res.:
Space Physics, 118, 4594–4608, doi:10.1002/
jgra.50413.
Mukhtarov, P., Pancheva D., Andonov B., Pashova L.,
2013b. Global TEC maps based on GNNS data: 2.
Model evaluation. J. Geophys. Res.: Space Physics,
118, 4609–4617, doi:10.1002/jgra.50412.
Mannucci, A. J., Wilson B. D., Yuan D. N., Ho C. H.,
Lindqwister U.J., Runge T.F., 1998. A global mapping
technique for GPS-derived ionospheric total electron
content measurements. Radio Science, 33(3), 565-582.
Mukhtarov, P., Pancheva D., Andonov B., Pashova L.,
2013. Global TEC maps based on GNSS data: 1.
Empirical background TEC model. J. Geophys. Res.:
Space Physics, 118, 4594–4608, doi:10.1002/
jgra.50413.
Pesnell, W.D. Solar Cycle Predictions (Invited Review),
Solar Phys, 2012, 1-26, DOI 10.1007/s11207-012-
9997-5.
Sardon, E., Rius A., Zarraoa N., 1994. Estimation of the
receiver differential biases and the ionospheric total
electron content from Global Positioning System
observations. Radio Sci., 29, 577-586.
Schaer, S., Beutler G., Mervart L., Rothacher M., Wild U.,
1995. Global and regional ionosphere models using
the GPS double difference phase observable. IGS
Workshop, Potsdam, Germany, May 1995, 1-16.
Tobiska, W. K., D. Knipp, W. J. Burke, D. Bouwer, J.
Bailey, D. Odstrcil, M. P. Hagan, J. Gannon, and B. R.
Bowman, 2013. The Anemomilos prediction
methodology for Dst. SpaceWeather, 11, 490–508,
doi:10.1002/swe.20094.
Tsai, Lung-Chih, Tsai Wei-Hsiung, 2004. Improvement of
GPS/MET Ionospheric Profiling and Validation Using
the Chung-Li Ionosonde Measurements and the IRI
model. Terr. Atmos. Ocean. Sci., 15(4), 589-607.
Wu, Chin-Chun, Fryb C.D., Liuc J.-Y., Lioud K., Tseng
C.-L., 2004. Annual TEC variation in the equatorial
anomaly region during the solar minimum: September
1996–August 1997. J. Atm. Solar-Terr. Phys., 66,
199–207.
Choice of the Definition Method for the Total Electron Content to Describe the Conditions in the Ionosphere
61
