Preliminary results on the within-the-hour ionospheric variability

Phys. Chem. Earth (C), Vol. 26, No. 5, pp 315-318, 2001

( ~

Pergamon

© 2001 Elsevier Science Ltd. All rights reserved 1464-1917/01/$ - see front matter

PII: S1464-1917(01)00005-8

Preliminary Results on the Within-the-Hour Ionospheric Variability D. N. Fotiadis I, S. S. Kouris' and B. ZolesP 'Aristole University of Thessaloniki, Department of Electrical Engineering and Computer Engineering, Telecommunications Division, GR-540 06 Thessaloniki, Greece 'Istituto Nazionale di Geofisica, Via di Vigna Murata 605, 00143, Rome Italy Received 14 June 2000; accepted 31 October 2000

Abstract. Studying the variations of the within-the-hour

reserved

hour variability and how this varies with the time of day, the season and latitude. The study of the within-the-hour variability can offer precious information from many aspects; the potential detection of storm precursor phenomena, greater reliability of I-IF communication systems for a greater percentage of time and ultimately the definition of a quiet ionosphere. Aim of this work is to provide quantitative prediction bounds of the variability within-the-hour for all hours of the day and different seasons. Moreover, a preliminary investigation of any latitudinal dependences of the withinthe hour variability is being attempted.

1 Introduction

2 Data and method of analysis

In HF communications users try to detect at a very short time interval, any solar and geomagnetic phenomena likely to cause a perturbation and followingly to estimate its grade of impact in the ionospheric plasma using the most recent ionospheric information which is usually the measurement of the previous exact hour (or that of 15 minutes ago, but only in the case of a small number of ionospheric stations). Such short-term prediction techniques are provided by ITU in some Reports (CCIR 1990a; CCIR 1990b). On the other hand, even when quiet conditions prevail the ionosphere presents an inherent variability, an "intrinsic noise" (Kouris et al., 2000), which has to be overcome in order to assure reliable, high quality HF services under any conditions and especially for radar and surveillance applications. Moreover, increased within-the-hour variability can be considerable at dawn and dusk due to the presence of traveling disturbances (COST 251 Final Report, 1999). Therefore, further improvements in the current ionospheric real-time prediction models can only be achieved by increasing our knowledge on the within-the-

Daily 5-minute interval measurements of the ionospheric characteristics foE, foF2, and the propagation factor M(3000)F2 were collected at the stations of Rome (41 8°N. 12.5°E) and Juliusruh (54.6°N, 13.4°E) as shown in Table 1. In the case of Rome station using a DPS-4 Lowell digisondes the ionograms were automatically scaled by

relative deviations of daily 5-minutes measurements of the critical frequencies foF2, foE and the propagation factor M(3000)F2, it is shown that the deviations of these parameters follow a different statistical distribution in different percentages of the time in each month. Latitudinal dependences of the within-the-hour variability are investigated. Finally comparisons between distributions lead to preliminary quantitative specifications of the within-the-hour variability for each of the examined ionospheric parameters. © 2001 Elsevier Science Ltd. All nghts

Correspondence to: D.N. Fotiadis; e-mail: [email protected]

Table 1. Daily 5-minute measurements used inthis analysis. Symbols []

indtcate the f~F'2, A the M(3000)F2and O the foE data, respectively.

Rome (41.8~V, 12.5 ~) Month

1997

January February March

1998 AO DAO []AO

April May June July

[]AO []AO

August September October

November December

[]AO AO

Juhusruh (54.6q7, 134°E) 199"/

1998 DA UA [~A

D. N. Fotiadis et al.. Within-the-Hour Ionospheric Variability

316

implementing the ARTIST software (Reinlsch and Huang, 1983). The scaling error was statistically estimated by Jodogne (1998) as less than 1 to 1.5%. However a certain scaling error cannot be completely erased by the application of a manual validation, but may also depend on the sample frequency and the efficiency of the equipment being used at a given location. In fact disturbances and radionoise may influence the performance of the autoscaling method and thus affect the scaling error. A rough statistical analys~s has shown that the error introduced by the ARTIST software regards more frequently the occurrence of missing values, especially in the lower layers as the E- and Fl-layer, than a different estimation of the data between automatic and manual scaling. It is found that in 90% of the analysed ionograms the standard deviation of the differences is about 2%. In order to investigate the variability within-the-hour, the differences between each 5-minute daily measured value within an hour and the corresponding exact hourly daily value were normalized according to the following expression: _- X ~ - X ~

(1)

where Xh is the hourly daily value of the ionospheric parameter measured at the exact hour time and X5 is the every 5-minute measured value of the same parameter witlun the interval from h to h +1 hour. The exact hour measured value Xh is used as value of reference, since in most cases only the previous exact hour value is available to users in real-time. Only recently there is an effort to establish a global ionospheric data base of higher resolution data, measured as frequently as 15 or even 5 minutes (R. Conkright -private communication). Thus, the ratio in equation (1) nmy be considered as an efficient estimator of the within-the-hour variability. The present statistical analysis provides quantitative estimations of the within-the-hour variability in each hour of the day, at different percentages of the time in each month, thus permitting to estimate different monthly percentiles of the within-the-hour variability and check its monthly distribution. In this way, useful comparisons between the absolute variabih.ty levels at different hours, seasons and geographic locations are accomplished for each ionospheric parameter examined. Then, the calculation of the monthly mean value, standard error and standard deviatmn of the corresponding for each hour and day dXsh values and their comparisons from one hour to another permit us to discern zones of similar within-the-hour variability, in a winter (i.e. from November to FebruaD,) or summer (i e. the available months of June and July) day, for every available ionospheric parameter, Moreover, it has to be noted that no distinction of measurements during periods of quiet/ disturbed ionospheric or geomagnetic conditions has been made in this study.

3

Results and discussion

Taking into account every dX5h value within the hour, and for each hour separately, we have calculated the absolute level of the within-an-hour variability which is not exceeded for different percentages of monthly time. Table 2 presents some of the respective results for February 1998 for the stations of Rome and Jnliusruh. It may be observed that very little difference exists between corresponding Table 2

Levels o f f , F2 within-the-hour variability in absolute value for different percentages of monthly firn~ during February 1998 at Rome (upper) and ffuliusruh (bottom panel).

LT 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 0 .07 .06 .05 .05 .04 04 I .05 .07 .07 .05 .05 .05 2 .08 .07 .06 .05 .04 .04 3 .07 .06 .05 .04 .04 .04 4 .08 .07 .07 .06 .05 .04 5 .09 .08 6 7 .10 .09 8 •07 .07 .06 .05 04 04 >I2 .10 .09 .08 07 06 05 07 .06 .06 .05 10 ,41 ,l~ ,tt" .09 .08 O5 04 11 ,~:'~ ~t7 A t .09 •08 .07 .06 .06 05 05 04 12 a~ ~ lO .09 •08 .07 .07 .06 .05 .05 04 13 ,,.~6 , ~ .I~ ~. ÷i~ .t~" .10 .09 .0g .07 .06 .05 14 ~,~%,:~[ ",+!~', , ~ , .10 .09 .0g .07 .06 05 05 15 ~ ¢ .'l:~,~"%]~a, " ~'~ , I I .10 .09 .08 .07 .06 .05 .10 .08 .07 .... •~ ,,,.~ .>.~. .'-,/# ,,.,~-".- ..tl .10 .09 IS . ~ ' ' ~ z ' ~ , t , <.i~ .lO .09 .os .07 .06 06 .09 .08 08 :0 ,,):-,~!~"~?~'~ .lO .09 .0~ .07 .06 .06 .05 lO 09 08 .07 06 .05 22 ~ I r " ~ ~ . ~ , .10 .09 .08 .07 .06 .05 .05 23 ~:¢4t " ~ ' ~ £ ~ ? : .10 .09 .08 .07 .06 .06 .05 0 1

:.k~ ~15"

,t~,l

.10

.07 .08

.06 .06

.04 .04

.03 .04

03 .03

2

,~

.lO

.09 .08 .07 .06 .06 .05

3 4

.~'L .1'$, - ~F~.~'~i" .09 ",~(~,",15 C'iD,',," •I0 .08

.05 .05

.04 .04

.04

.04

.08 .07

.07 .06

.06 .05

.06 .04

05 04

09 .09 .07 .05 .06 .08 .07 .07

.08 .08 .06 .05 .06 .06 .07 .07

.07 .07 .06 .04 .06 .06 .06 .06

.06 .06 .05 .04 .05 .05 .05 .06

05 .05 .05 04 05 .05 .05 05

a*°?',,,i~''~'ii:-'-.:~.u, .lo

.09

.08

07

-I;~'~:,':i':.¢[~t; .08 06 06 .07 .06 .05 .06 .05 .04 .05 .04 .04

.10 .05 .05 .04 .04

09 .04 .04 03 .03

8 ,I:~ .14 .I~ ".I,~ 9 ,~ .I6" ~.~ .10 10 ,I~ ,]5 .lg" .09 11 a ~ , , . I ~ .08 .06 12 , i , t . 6 , , , ~ : .10 .08 ,~I~, .09 14,~6 "~'.~ .10 15, " , I ~ ' ~ , Z ~ .10 .09 17,,[:Z9" ,~9

.05 .06

.I0 09 .09 .06 .07 .08 .08 .08

19 ,~li/ie,o",~i ,i~::[::$~;5:,%',i'~, 20 ~ ~ ,zt~ :-;',:1~,'~,',~]-]' .09 21 ,~g ,.14 . f l .09 .07 22 .2,4 ,t3 .09 .07 .07 23 .23 ,I~ .08 .06 .06

O2 03 .03 04 .03 .10 ,28 10 O5 O5 O4 O3 04 .04 .05 O5 06 O7 07 O8 .04 04 O3 O3

D. N. Fotiadis et aL: Within-the-Hour Ionospheric Variability

317

absolute dXsh levels per hour and percentage of time at the two locations, although we should confess that the withinthe-hour variability in Rome seems to be overall slightly greater. Most differences are observed at higher percentages of time, for example more than 80% of time, while almost all other hours converge within the scaling errors of the measurements. This implies that the withinthe-hour variability seems to be of the same magnitude at the two geographical locations, but further analysis should be undertaken in order to arrive at safe conclusions. The higher within-the-hour foF2 variability of Rome may be attributed to the different scaling of measurements of the two stations (manual for Juliusruh; automatic scaling for Rome station). Furthermore, day-time measurements of the Juliusruh station present a lower resolution (i.e. of 15 minutes). Similar tables were produced for all available 5-minutes data. Table 3 summarizes the most important findings, giving an average level of the within-the-hour variability in absolute value that is not exceeded for the respective

than ~0.20 (Kouris et al., 2000) and have to be studied separately. Furthermore, months with campaigns of less than 15 days are not reported in Table 3 since they do not have a sample size of statistical significance, when examined separately. It is clear from Table 3 that for 8085% of the monthly time all hours -except sunrise and sunset- present a variability of £-0.10 in the case offoF2, whereas the variability of foE and the propagation factor M(3000)F2 remains less than _-Z-0.10for a shghtly greater percentage of monthly time. At the 50% of monthly time, an inherent variability of the order of 4-5% is always present and no virtual difference of the level of the variability is observed between the different ionospheric parameters. Now, by calculating the usual statistical measures (monthly mean value, standard error and standard deviation) for the corresponding hour and day dX5h values, we are able to compare the within-the-hour variability for each hour of the day. In order to group the hours which present the same probabilistic behaviour of within-the-hour variabili.ty,

Table 3 Average level ofwithm-an-hour variability in absolute value that is not exceeded for the corresponding percentage of monthly tune. Hours around sunrise and sunset are not included.

Table 4 Speicifications (in tea'ms of/x.'lzs.e.,a) of the within-the-hour level of variability in time intervals presenting a uniform probabihstic behaviour.

df, F2sh

90%

80%

70%

50%

13

10

08

.06

January Juliusruh .

.

.

.

.

.

.

.

.

.

.

.

.

.

.

February March .

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

13 10 .

.

.

.

.

.

.

.

.

.

.

.

.

09 08 .

.

.

.

.

.

.

.

.

.

.

07 .06 .

.

.

.

.

.

.

.

.

.

.

.

.05 04 .

.

.

.

.

.

.

.

.

.

.

.

.

November

15

11

09

06

February

14

10

.08

.05

Rome

March

16

11

08

04

June

.15

.10

.07

03

July

13

09

.06

03

90%

80%

70%

50%

January February

.10 .08

07 .07

.06 05

.03 .03

March

.08

06

.05

.03

November December

12 13

09 10

.07 .08

04 .05

ltt + s.c. (%)

(%)

+5

7

10-14 & 23-4

5-7

8

tare- and post sunrise and sunset (5, 9, 15-16, 21-22)

15

6-8 & 17-20

df, F2~b .

.

.

.

Winter

0 to 15-30 +3-5

dM(3000)F2s b

Juliusruh

Rome

January

15

10

.08

.05

February

11

09

07

.04

March

12

10

07

04

12

08

.06

.03

July

12

08

.06

03

dfoEsh

Rome

811%

70%

50%

November December

90%

11 11

09 10

07 07

04 .03

January

.08

05

.04

03

February

.11

09

06

04

March June July

.12 .15 .12

10 11 10

.08 .09 .09

.05 .06 .06

-8 to 0

12

rmdnight (23,0)

0to 10-15

15

5-7 & 20-22

7

8-16 & 22-3

dM(3000)F2s h

.

.

.

.

+5

Winter .

.

.

.

.

.

.

.

.

Summer

df,E5h Wmter ..................

Summer

percentage of time in each month. Sunrise and sunset hours were not considered in this table as they account for very large within-the-hour variabih .ty of the order of more

8-19 & I-4

8-12

Summer

.

June

Hours in LT

o

.

.

.

.

.

.

.

.

.

10 .

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

9 .

.

.

.

.

.

.

.

.

.

.

.

4-7 & 17-21 .

.

.

.

.

.

.

.

.

.

.

.

:1:5

10

20-8

+6

15

9-14

0 to +6

12

15-19

0 to +6

7

9-12

-10 to 0

6

13-15

20 ...........

1 0

__.7:8_,~.16_

.

.

.

.

.

.

.

.

.

..........

+4

7

9-12

10

7

6-8 & 13-16

20

12

4-5 & 17-18

.

318

D. N. Fotiadis et al.: Within-the-HourIonosphericVariability

all box'plots for each hour and month and station are plotted and a worstcase criterion is applied with major concern on the trend of ~A:se. and the size of standard deviation. This empirical method of visual inspection may well introduce some personal biases, but the pattern recognition of human brain is unsurpassed (Loewe and Proelss, 1997). Nevertheless, considering more high resolution data and performing a more standard statistical analysis is a goal for the near future. Table 4 reports the results of the procedure described above. For reader's facilitation it has to be noted that sunrise and sunset p:ts.e values do not have a sign since they correspond to solely positive and negative trends of variability, respectively. A typical winter or summer day can be divided into three sectors in the case of foF2 within-the-hour variability. However, this is not the case for M(3000)F2, where a uniform statistical behaviour of its within-the-hour variability is observed for all hours of the day. Sunrise and sunset hours do not affect the level of dM(3000)F2 in the same way in a typical wmter or summer day. On the contrary, dfoE presents a symmetrical behaviour, either in a winter or a summer day, and although this is mostly a deterministic layer controlled by the zenith solar angle, an inherent variability of the order of 4% is present here even in midday hours. However we must take into account that the scaling error in the case of foE measurements could be greater than in the case o f f oF2.

4 Conclusions

This preliminary statistical investigation from 5-minutes measured data has shown that there is practically no latitude dependence of the within-the-hour variability for the 80% of the monthly time or less. At 50% of the monthly time no differences at the average level of the within-the-hour variability for all hours except sunrise and sunset are observed between all examined ionospheric

parameters; thus, an intrinsic variability of about 4% is always present, independently of season. Finally, a three-sector day of statistically similar withinthe hour variability can be deduced forfoF2, whereas this is not possible for the propagation factor M(3000)F2. The latter result needs a more comprehensive investigation in order to arrive at more accurate specifications of the within-the-hour variability.

Acknowledgments

The research work reported here is part of a joint GreekItalian research program supported by the cultural collaboration (5 th and 6 th protocol) between the two countries. The authors would like to thank Dr. J. Bremer for providing 5-minutes V-I data observed at the ionospheric station of Juliusruh, Germany.

References

CCIR, Short-term forecasting of critical ffequenctes, operational maximum usable frequencies and total electron content, Report 888-2, InternationalTelecommunicationUnion, Geneva, 1990a~ CCIR, Real-time channel evaluation of HF ionospheric radio clrcmts, Report889-2, InternationalTelecommunicationUnion, Geneva, 1990b COST 251 "Improved Quality of Service in Ionosphenc TelecommunicationSystemsPlanning and Operation", Final Report, Edt. R. Haababa,p. 52, 1999. Jodogne J.C., "Manual versus automatic computer processing from yeats of hourly data and comparison",Report UAG-105 Computer aided Processing of lonograms and tonosonde records WCD-A for STP, pp. 16-21, 1998. Kouris S.S., Zolesi B., Fotiadis D.N., and Bianchi C., "On the

variability within-the hour and from hour-to-hour of the F-region characteristicsaboveRome",Physics and Chemlstry of the Earth. Part C: Solar-Terrestrtal and Planetary Science, Vol. 25(4), pp. 347-351, 2000 Loewe C.A. and Prodss G.W., "Classificationand mean behaviourof magnetic storms",Journal of Geophyswal Research. Vol. 102(.47), pp 14209-14213, 1997. Reinisch B. and Huang X., "Automaticcalculationof electron denstty profilesfromdigital ionograms",Radio Sctence, Vol. 18(3). p. 477-492. 1983