An autocovariance forecasting procedure for single location ionospheric characteristics is presented. Its accuracy is illustrated as a function of the amount of time extrapolation for selected European stations under quiet and disturbed conditions

Stanislawska, I.

Zbyszynski, Z.

Earth-prints Repository

ANNALS OF GEOPHYSICS, VOL. 45, N. 1, February 2002
169
Forecasting of ionospheric characteristics
during quiet and disturbed conditions
Iwona Stanisl/awska and Zbigniew Zbyszyn´ski
Space Research Centre, Polish Academy of Sciences, Warsaw, Poland
Abstract
An autocovariance forecasting procedure for single location ionospheric characteristics is presented. Its accuracy
is illustrated as a function of the amount of time extrapolation for selected European stations under quiet and
disturbed conditions.
1. Introduction
Day-to-day and hour-to-hour ionospheric
variations are generally irregular. Irregular
ionospheric variations are variations that cannot
be predicted by any linear prediction method.
They are caused by irregular changes in am-
plitudes of ionospheric parameters as well as
their time spread. In order to predict them much
interest and efforts have been dedicated in the
past. Various non-linear techniques have been
proposed. The latest publications deal with a
statistical approach (Muhtaov and Kutiev, 1999),
or with a modern neural network technique
(Wintoft and Cander, 1999, 2000; Tulunay et al.,
2000). This paper presents a continuation of this
work (Stanisl/awska and Zbyszyn´ski, 2001) in
the application of the autocovariance prediction
method for ionospheric purposes.
2.  The autocovariance prediction method
application
The autocovariance prediction method was
originally elaborated at the Space Research
Centre for prediction of irregular variations in
Earth rotation (Kosek, 1993, 1997). In this
method the first prediction point outside the data
time interval in the future and in the past is
computed and added at the beginning or at the
end of data, respectively, so the next prediction
point can be computed. The difference between
the prediction and data at a particular time in
the future computed at different starting
prediction epochs reveals unpredictable or
irregular variations of the considered ionospheric
parameter. Along this line the analysis of
potential application of this method in the
ionosphere was presented in Stanisl/awska
(1994). The forecasting capabilities of the
method for f0F2 parameter were shown in
Stanisl/awska and Zbyszyn´ski (2001). This
paper presents a similar analysis for other
ionospheric characteristics as well as the forecast
dependence on the time range considered.
One of the advantages of the autocovariance
method is that it does not require any addition-
al parameters, which describe solar and/or
geophysical conditions. It also means that any
Mailing address: Dr. Iwona Stanisl/awska, Space Re-
search Centre PAS, 00-716 Warsaw, Bartycka 18a, Poland;
e-mail: stanis@cbk.waw.pl
Key  words   forecasting of ionospheric characteristics
170
Iwona Stanisl/awska and Zbigniew Zbyszyn´ski
additional uncertainty connected with the need
to use a prediction of these is avoided. The only
information needed is a long enough period of
observation. Another advantage is that it can be
used for forecasting without any knowledge of
the morphological and physical processes in a
medium, such as the ionosphere.
3.  Results and conclusions
The present study investigates the f0F2, f 0F1,f0E, and M(3000)F2 parameters. Data have been
taken from RAL-CD-ROM, prepared by the
Rutherford Appleton Laboratory, United Kingdom,
for European Cooperation in the Field of Scientific
and Technical Research (COST) Action 251
(Hanbaba, 1999) and from the Ionospheric
Despatch Centre in Europe (Stanisl/awska et al.,
1999) (http://www.cbk.waw.pl/rwc/idce.html),
that is a COST 251 initiative. A list of relevant
stations is presented in table I.
In this method the prediction estimation is
computed as a function of an observed variable
(different ionospheric characteristic) from several
ionospheric stations only. The sampling interval
of ionospheric characteristics, in our case, is 1 h.
In this paper, 1-, 2-, 4-, 8-, 12-, 24- and 48-h-ahead
forecast have been obtained. Particular purposes
of this application are to consider the needs of the
forecast obtaining for instantaneous situation by
operational service of the ionospheric situation.
To deal with a real situation any specific method
for gaps filling has been used. Any data gaps were
replaced by 7-days-smoothed average only. Also
the requirements of the time period of available
data were limited. Data from separate periods
within September - November 1998 were used.
The number of data used in the computation for
the presented statistics is shown in table II.
For the calculations of the short time forecast
(up to 12 h ahead) 73 past values (3 days) hour by
hour, have been taken, while for a longer time
forecast (24-48 h ahead) 28 values from the
previous 28 days, for each hour separately, have
been taken. To satisfy the requirements of the
method, as input data we used deviations of the
measurements from the 7-days-smoothed
Table  I. Stations and their geographical coordinates.
Location Station Latitude, °N Longitude, °E
Tortosa EB040 40.8   0.5
Rome RO041 41.9 12.5
Juliusruh JR055 54.6 13.4
Sofia SQ143 42.7 23.4
Warsaw MZ152 52.2 21.2
Uppsala UP158 59.8 17.6
Lycksele LY164 64.6 18.8
Kiruna KI167 67.8 20.4
Table  II. Number of considered data points.
Disturbed Quiet Total
f0E 1900 3900 5800
f0F1 500 900 1400
f0F2 6100 9200 15300
M(3000)F2 6100 8500 14600
171
Forecasting of ionospheric characteristics during quiet and disturbed conditions
Fig.  1. Bar chart created for RMS errors for f0F2 characteristic, for disturbed data (upper panel), quiet data(middle panel), and for all data together (lower panel). Autocovariance forecasting method: For1, 1 h ahead For2,
2 h ahead, etc. Persistence: P1.
Fig.  2.  The same as fig. 1 for f0F1 characteristic.
Fig.  3.  The same as fig. 1 for f0E characteristic.
Fig.  4.  The same as fig. 1 for M(3000)F2 characteristic.
foF2 Disturbed Data
F
o
r1
F
o
r2
F
o
r4
F
o
r8
F
o
r1
2
F
o
r2
4
F
o
r4
8
P
1 IT
U
-R
0.2
0.8
1.4
 
foF2 Quiet Data
F
o
r1
F
o
r2
F
o
r4
F
o
r8
F
o
r1
2
F
o
r2
4
F
o
r4
8
P
1
IT
U
-R
0.2
0.8
1.4
 
foF2 All Data
F
o
r1
F
o
r2
F
o
r4
F
o
r8
F
o
r1
2
F
o
r2
4
F
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r4
8
P
1
IT
U
-R
0.2
0.8
1.4
 
 
foF1 Disturbed Data
F
o
r1
F
o
r2
F
o
r4
F
o
r8
F
o
r1
2
F
o
r2
4
F
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r4
8
P
1
IT
U
-R
0.1
0.3
0.5
 
foF1 Quiet Data
F
o
r1
F
o
r2
F
o
r4
F
o
r8
F
o
r1
2
F
o
r2
4
F
o
r4
8 P
1
IT
U
-R
0.1
0.3
0.5
 
foF1 All Data
F
o
r1
F
o
r2
F
o
r4
F
o
r8
F
o
r1
2
F
o
r2
4
F
o
r4
8
P
1
IT
U
-R
0.1
0.3
0.5
 
foE Disturbed Data
F
o
r1
F
o
r2
F
o
r4
F
o
r8
F
o
r1
2
F
o
r2
4
F
o
r4
8 P
1
IT
U
-R
0.0
0.2
0.4
 
foE Quiet Data
F
o
r1
F
o
r2
F
o
r4
F
o
r8
F
o
r1
2
F
o
r2
4
F
o
r4
8 P
1
IT
U
-R
0.0
0.2
0.4
 
foE All Data
F
o
r1
F
o
r2
F
o
r4
F
o
r8
F
o
r1
2
F
o
r2
4
F
o
r4
8 P
1
IT
U
-R
0.0
0.2
0.4
 
M(3000)F2 Disturbed Data
F
o
r1
F
o
r2
F
o
r4
F
o
r8
F
o
r1
2
F
o
r2
4
F
o
r4
8
P
1
IT
U
-R
0.05
0.15
0.25
 
M(3000)F2 Quiet Data
F
o
r1
F
o
r2
F
o
r4
F
o
r8
F
o
r1
2
F
o
r2
4
F
o
r4
8
P
1
IT
U
-R
0.05
0.15
0.25
 
M(3000)F2 All Data
F
o
r1
F
o
r2
F
o
r4
F
o
r8
F
o
r1
2
F
o
r2
4
F
o
r4
8
P
1
IT
U
-R
0.05
0.15
0.25
 
1 2
3 4
172
Iwona Stanisl/awska and Zbigniew Zbyszyn´ski
average value calculated for each hour sep-
arately. An additional correction factor for 1-h-
ahead forecast is introduced in the following
manner: for the forecast at time t, parameters at
times t-1 and t-2 are calculated. If the values in
t-1 and t-2 differ from the measurements by
more than 30%, the current calculated forecast
is changed exactly by the last value of the
gradient. The forecast was analysed for the
disturbed and quiet periods separately. For the
distinction between quiet and disturbed
conditions, the catalogue of disturbances (Kouris
et al., 1998) observed at selected ionospheric
stations was used. The catalogue is available at
the Ionospheric Despatch Centre in Europe.
The prediction error is presented as an RMS
error and percentage deviation of the prediction
against the measurements. For comparison with
the forecast results using the ITU-R-predicted
median value and the measurement from the
previous hours (persistence) have also been
given.
Figures 1 to 4 show the bar charts of the RMS
error for four considered characteristics for quiet
and disturbed conditions separately, as well as
for all data together. The method has been used
Fig.   5.  1- and 24-h-ahead forecast, and measurements for f0F2 characteristic for three 5-day-periods in September
1998 at Kiruna station.
173
Forecasting of ionospheric characteristics during quiet and disturbed conditions
Fig.  6.  The same as fig. 5 for f0E characteristic at Uppsala station.
for 1-, 2-h ahead, etc. Samples of 1-, and 24-h-
ahead forecast for f0F2, f0 E and M(3000)F2
characteristics for the most quiet, incidentally
chosen moderate disturbed and the most
disturbed periods are shown in figs. 5 to 7. Five
day periods in October and September 1998
observations, 1- and 24-h ahead, are presented.
Incidentally chosen, but representative for the
numerical experiments performed, stations from
mid- and high latitudes are presented. Only the
plot for f0F1 characteristic (fig. 8) is presented
for a 5-day-period in September 1998 for a
mixed quiet and negatively disturbed period for
the mid-latitude Warsaw station only.
For all the considered data the forecast shows
much better results than medians and per-
sistence. Except for the most disturbed data for
f0F2 characteristic, when the persistence is better
than the forecast 2-, 4-, etc. hours ahead. But
obviously, while considering the forecast for
longer than 1-h-ahead period, the persistence
does not exist. It ought to be mentioned that
ITU-R prediction is never available with such
accuracy, as used in this paper, because of the
actual solar activity parameters used (not
prediction). For M(3000)F2 characteristic,
increasing the time range of the forecast also
increases the errors. This increase is not so
174
Iwona Stanisl/awska and Zbigniew Zbyszyn´ski
Fig.  7. The same as fig. 5 for M(3000)F2 characteristic for three 5-day-periods in October 1998 at Juliusruh
station.
Fig.  8.  The same as fig. 5 for f0F1 characteristic for one 5-day-period in September 1998 at Warsaw station.
175
Forecasting of ionospheric characteristics during quiet and disturbed conditions
pronounced as for f0 F2. For f0 F1 and f0 E
characteristics the situation is quite different.
The impact of the data from sunrise and sunset
hours, particularly for f0F1 data, and night hours
for f0E, enlarge the errors, because the forecast
is sometimes given for non-existing F1 layer,
and vice versa. Generally, the quiet situation,
which is represented by much more smoothed
data than for the disturbances, is predicted with
higher accuracy. During the disturbance lasting
several hours, the method might generate some
rapid fluctuations. When the method gives too
high, or too low values, the correction by
gradients improves the forecast, but only for
the second, and higher measured values hours.
So the forecast curve for later hours is much
smoother and closer to observations. However,
for individual disturbances the errors might still
be substantial. This effect can be avoided using
data with higher resolution sampling, as 15, or
5 min. In such a case 1-h-ahead forecast will
follow the observations with higher accuracy
after half an hour, or 10 min, respectively.
Generally, the autocovariance method
shows the correctness of this approach for any
ionospheric characteristics. The autocovari-
ance method of ionospheric characteristics
forecasting provides an acceptable accuracy.
This might also be the crucial point for
predicting the electron concentration height
profiles. Its quite reliable results for quiet, as
well as for disturbed conditions allow us to
conclude that this method can be used in
operational services of ionospheric situation,
as used to update the limited-area ionospheric
forecast at the Regional Warning Centre
Warsaw of the International Space Environment
Service.
Acknowledgements
This research was partly supported by Polish
Committee of Scientific Research grant 2 P03C
006 17.
REFERENCES
HANBABA, R. (1999): COST 251 Final Report, SRC Print.
Off., Warsaw.
KOSEK, W. (1993): The autocovariance prediction of the Earth
rotation parameters, in 7th International Symposium
«Geodesy and Physics of the Earth», IAG Symposium
No. 112, Potsdam, Germany, October 5-10, 1992, edited
by H. MONTAG and CH. REIGBER (Springer Verlag),
443-446.
KOSEK, W. (1997): Autocovariance prediction method of short
period Earth rotation parameters, Artif. Earth Satell., 32
(2), 75-85.
KOURIS, S.S., D.N. FOTIADIS and T.D. XENOS (1998): On
the day-to-day variation on f0F2 and M(3000)F2, Adv.
Space Res., 22 (6), 873-876.
MUHTAROV, P. and I. KUTIEV (1999): Autocorrelation method
for temporal interpolation and short-term prediction of
ionospheric data, Radio Sci., 34 (2), 459-464.
STANISL/ AWSKA, I. (1994): A single-station prediction model
as a contribution to instantaneous mapping, Ann. Geofis.,
37 (2), 153-157.
STANISL/ AWSKA, I. and Z. ZBYSZYN´SKI (2001): Forecasting
of the ionospheric quiet and disturbed f0F2 values at single
location, Radio Sci., 36 (5), 1065-1071.
STANISL/ AWSKA, I., T.L. GULYAEVA and R. HANBABA (1999):
Ionospheric Despatch Centre in Europe, Phys. Chem.
Earth (C), 24 (4), 355-357.
TULUNAY, E., C. OZKAPTAN and Y. TULUNAY (2000):
Temporal and spatial forecasting of the f0F2 values up to
twenty four hours in advance, Phys. Chem. Earth (C),
25 (4), 281-285.
WINTOFT, P. and LJ.R. CANDER (1999): Short-term prediction
of f0F2 using time-delay neural network, Phys. Chem.
Earth (C), 24 (4), 342-347.
WINTOFT, P. and LJ.R. CANDER (2000): Ionospheric f0F2 storm
forecasting using neural networks, Phys. Chem. Earth
(C), 25 (4), 267-273.


Forecasting of ionospheric characteristics during quiet and disturbed conditions

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Forecasting of ionospheric characteristics during quiet and disturbed conditions

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