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Advances in Space Research 53 (2014) 635–646

Monthly mean foF2 model for an African low-latitude stationand comparison with IRI

S.O. Ikubanni a,⇑, J.O. Adeniyi b, O.K. Obrou c

a Department of Physics, Landmark University, P.M.B. 1001, Omu Aran, Kwara State, Nigeriab Department of Physics, University of Ilorin, P.M.B. 1515, Ilorin, Kwara State, Nigeria

c Laboratorie de physique de l’atmosphere, Universite de cocody, 22 BP 582, Abidjan 22 Cote d’Ivoire

Received 25 July 2013; received in revised form 6 December 2013; accepted 14 December 2013Available online 24 December 2013

Abstract

We have employed the hourly values of the ionospheric F-region critical frequency (foF2) obtained from Ouagadougou ionosonde,Burkina Faso (geographic coordinates 12� N, 1.8� W) during the interval of 1985–1995 (solar cycle 22) and solar radio flux of 10 cmwavelength (F10.7) to develop a local model (LM) for the African low-latitude station. The model was developed from regression anal-ysis method, using the two-segmented regression analysis. We validated LM with foF2 data from Korhogo observatory, Cote d’Ivorie(geographical coordinates 9.3� N, 5.4� W). LM as well as the International Reference Ionosphere (IRI) agrees well with observations.LM gave some improvement on the IRI-predicted foF2 values at the sunrise (06 LT) at all solar flux levels and in all seasons except Junesolstice. The performance of the models at the representing the salient features of the equatorial foF2 was presented. Considering day-time and nighttime performances, LM and IRI are comparable in low solar activity (LSA), LM performed better than IRI in moderatesolar activity (MSA), while IRI performed better than LM in high solar activity (HSA). CCIR has a root mean square error (r.m.s.e),which is only 0.10 MHz lower than that of LM while LM has r.m.s.e, which is about 0.05 MHz lower than that of URSI. In general, ourresult shows that performance of IRI, especially the CCIR option of the IRI, is quite comparable with the LM. The improved perfor-mance of IRI is a reflection of the numerous contributions of ionospheric physicists in the African region, larger volume of data for theIRI and the diversity of data sources, as well as the successes of the IRI task force activities.� 2013 COSPAR. Published by Elsevier Ltd. All rights reserved.

Keywords: Critical frequency (foF2); Low-latitude; IRI; F10.7; Ouagadougou

1. Introduction

The importance of the F2-peak ionospheric parameterssuch as the critical frequency (foF2) for terrestrial-to-ter-restrial and terrestrial-to-space radio-wave propagationprompted several researches that have been carried outand other numerous ongoing ones. A very important aimof ionospheric studies is to provide a near real-timedescription of the ionosphere and to improve the efficiencyof its use by radio high frequency (HF) users. In describing

0273-1177/$36.00 � 2013 COSPAR. Published by Elsevier Ltd. All rights rese

http://dx.doi.org/10.1016/j.asr.2013.12.016

⇑ Corresponding author. Tel.: +234 703 226 1146.E-mail addresses: stevewolex1@yahoo.com, s.ikubanni@gmail.com,

ikubanni.stephen@lmu.edu.ng (S.O. Ikubanni).

the ionosphere, several scientists had reviewed previousstudies and had been able to create ionospheric modelswith good ionospheric predictive ability. However, thesemodels needs frequent updating to improve their output.

Ionospheric models are generally classified as global orregional, based on their coverage. The most widely usedglobal ionospheric model called the International Refer-ence Ionosphere (IRI) is a project of the joint workingcommittee of Committee on Space Research (COSPAR)and Union Radio Scientifique International (URSI). TheIRI is a statistical model (Xu et al., 2008) or an empiricalmodel (Bilitza et al., 2004). It was developed from collec-tions of observational data from different regions of theworld and still being updated on a continually basis as

rved.

636 S.O. Ikubanni et al. / Advances in Space Research 53 (2014) 635–646

recent data and information becomes available. The IRIwas designed to be the climatological standard for the ion-osphere and was accorded the status by the InternationalStandardization Organization (ISO) in 2009 with the tech-nical specification number (ISO TS 16457:2009) (http://www.spacewx.com/Docs/ISO_TS_16457_2009_E.pdf).

The IRI describes parameters such as the peak electrondensity, the critical frequency, the maximum height, elec-tron and ion temperatures, ion composition and othersfor any hour, day, season, geographical location, solaractivity level, and under disturbed ionospheric conditions.Since the inception in 1969, the IRI as an operationalmodel has been constantly updated as newer data and bet-ter mathematical descriptions of global and temporal vari-ation patterns became available (Bilitza and Reinisch,2008). A full decription of the improvements made on theIRI can be seen in literatures (Bilitza and Reinisch, 2008;Bilitza, 2006), as well as its applications in the later. TheIRI has however been limited in some region of the world.The limitations has been attributed to the sparse data dueto inadequate coverage of these regions by observationalequipments such as the ionosonde, incoherent scatter radar(ISR), satellites and rockets, and partly to the harsh climateconditions especially at equatorial and auroral latitudes(Bilitza and Reinisch, 2008). Some past works, especiallyin the African sector (e.g. Adeniyi and Adimula, 1995;Adeniyi, 1996, 1997; Bilitza et al., 2000; Adeniyi et al.,2003) have investigated the predictability of the IRI inthe region. Some of their results and recommendationshave been reviewed, as it is usually done in IRI Task ForceActivities (e.g. Radicella et al., 1998), and included in theIRI-model update. Likewise, there are recent works suchas Adeniyi et al. (2007), Adebesin et al. (2013), Adewaleet al. (2012), Oyeyemi et al. (2013), Klenzing et al.(2013), Satya Srinivas et al. (2013) and numerous ongoingdirected at validating the ionospheric parameters in theIRI, and especially during geomagnetic-disturbed periods.

Adeniyi et al. (2003, 2007) used hourly averages of foF2of different years but from the same station to validate theURSI and CCIR options of the IRI-2000 model. Theyreported very good IRI representation at low and moderatesolar activity for both day and night when the CCIRoption is used. In high solar activity level, the CCIR optionalso presented a very good representation for daytime onlywhile the URSI options gave a considerably good represen-tation for nighttime.

Bertoni et al. (2006) validated the IRI-2001 for two sta-tions in the Brazilian sector, using data from two CanadianAdvanced Digital Ionosondes (CADIs). One of the iono-sonde was located near the trough of the equatorial ioniza-tion anomaly (EIA) while the other lied just under the EIAcrest, both in the southern hemisphere. One of the stationlies towards the trough of the EIA while the other lies justunder the EIA crest, both in the southern hemisphere. Thedata used were for geomagnetically quiet days for moder-ate and high solar activity. They reported that IRI under-estimates foF2 during daytime and nighttime at the EIA

crest station. The IRI-2001 predictions by both submodels– CCIR and URSI – are better at the EIA trough than atthe EIA crest. At both the trough and crest, CCIR hassome advantage over URSI. They also reported that thepre-reversal peak of the vertical drift (at 2100LT) nearthe trough of the magnetic EIA is not well simulated byIRI-2001. Generally, the model overestimates the EIAtrough, in essence, underestimating foF2.

Luhr and Xiong (2010) compared IRI-2007 with obser-vations by satellite CHAMP between 2000 and 2009. Theyobserve that IRI overestimation increases progressivelywith decreasing solar activity. Therefore, they investigatedthe IRI-2007 predictability of the extreme solar minimumperiods (2008–2009). They reported that IRI overestimatesthe electron density at a larger magnitude. They observelargest differences at magnetic low latitudes ±30� duringthe daytime. They reported smaller differences betweenIRI and CHAMP observations across all seasons betweenthe post-midnight hours and 0600 LT. The overestimationby IRI was attributed to a very strong vertical plasma drift.At noon, average drift velocities are 25 m/s for IRI and17 m/s for CHAMP.

Regional models were developed as alternatives to IRIto better describe the local ionosphere. Regional modelsare usually developed from one ionospheric parameter ata particular station or stations in a particular region. Manyresearchers were reported to have described monthly med-ian models of ionospheric parameters from single iono-sonde stations located in the middle to high latituderegions (see Pancheva and Mukhtarov (1996); who alsodid it for Sofia: 42.7� N, 23.4� E).

In further appreciation of regional models, Xu et al.(2008) recently described the monthly median foF2 fromionosonde data at Chongqing station, China (a stationlying at the extension of the EIA crest). They reported thattheir single-station model (SSM) is in good agreement withobservations and performs better with a standard deviationof 0.31 MHz lower than that of the IRI.

The limitations of the IRI, the successes and advantagesof the regional models and the lack of any regional foF2model in this region are the main motivations for thiswork. Of interest to this work is the equatorial or low lat-itude part of this region of the ionosphere because of itsdistinct features and dynamics. This work thereforedescribes a model for the African low latitude station andvalidates it by means of comparison with the IRI-2007and data from another African equatorial station.

2. Data and methodology

The monthly mean foF2 model was developed usingfoF2 measurements retrieved from ionograms generatedby the Ionospheric Prediction Service IPS-42 located atOuagadougou, Burkina Faso (Geog. lat. 12� N, long.1.8� W). The Ouagadougou ionosonde is located at theinner flank of the EIA crest of the northern hemisphere.Hourly regression plots of the F2-critical frequency

Table 1The yearly mean solar radio flux (in solar flux units, sfu) for the threeselected years.

Year F10.7 (sfu) Epoch

1995 77.0 L.S.A.1993 109.7 M.S.A.1999 153.5 H.S.A

S.O. Ikubanni et al. / Advances in Space Research 53 (2014) 635–646 637

(foF2) in megahertz (MHz) against the solar radio flux of10.7 cm wavelength (F10.7) in solar flux unit (sfu) over asolar cycle (SC 22: 1985–1995) were obtained for each sea-son and statistically tested for significance. Rishbeth andMendillo (2001) had earlier revealed that long-termchanges in the ionospheric parameters are majorly due tosolar sources. Xu et al. (2008) validated this by reportinginsiginificant changes in the single-station model of themonthly median foF2 developed as a function of sunspotnumber from the one developed as a function of both sun-spot number, R and geomagnetic index, Ap. Although thelong-term solar activity changes in foF2 has been estab-lished to be quadratic rather than lnear, the segmented fitwas introduced by Chen et al. (2000) and Liu et al.(2003) to decribe the long-term relationship between foF2and F10.7. Based on this assumption, Ikubanni and Ade-niyi (2013) reported that the segmented fits better describethe long-term solar activity influence on the foF2 at theAfrican equatorial station of Ouagadougou, Burkina Faso.

Therefore, instead of the quadratic, segmented regres-sion analysis was used on the pair of variables – foF2(MHz) and F10.7 (sfu). Saturation effect in the iono-spheric-F2 electron density is a feature of the low-latitudeionosphere (Liu et al., 2003; Sethi et al., 2002; Ma et al.,2009). For modeling purposes, there is need to obtain thelevel of solar radiation influx at which saturation occurs.A recent work that made use of the same set of data as thiswork obtained this critical point in which saturation effectis observed (Adeniyi and Ikubanni, 2013), this they termedthe F10.7 threshold value. The segmented regression anal-ysis is such that two jointed straight lines give the best fitfor the plotted pair of variables. Therefore, the segmentedregression analysis has two regression equations and theyare given as:

y ¼ a1xþ b1; for ðx < BP Þ ð1Þ

y ¼ a2xþ b2; for ðx P BP Þ ð2Þ

The expression labeled equation (1) is the regressionequation for the first part of the two-segmented fit,whereby the F10.7 index value is below the threshold value.The expression in equation (2) is the regression equationfor the second part of the segmented fit, whereby theF10.7 index value is either equal or beyond the thresholdvalue. The independent variable, x, is the solar radio flux,F10.7 (sfu), while the dependent variable, y, is the F2-crit-ical frequency (MHz). The acronym ‘BP’ stands for break-point of the two-segmented fit, which is the F10.7 thresholdvalue where saturation effect in foF2 is seen. The regressioncoefficients a1; a2; b1 and b2 are constants for specific localtime and season. The coefficient, a1, is the response rateof the critical frequency (foF2) to solar activity before sat-uration while the coefficient, a2, is the response rate of thecritical frequency (foF2) to solar activity at saturation. Apositive value of a1 or a2 implies that the F2-critical fre-quency is directly proportional to solar activity, that is,electron density increases as solar flux level increases and

vice versa. A negative a1 or a2 value means that the F2-crit-ical frequency is inversely proportional to solar activity; inessence, electron density decreases as solar flux levelincreases and vice versa. There is no response of foF2 tosolar activity whenever a1 or a2 is zero. Thus, the regressioncoefficients for each hour and season (subscripts h and s inequation (3)) can be written as:

y ¼ aðh;sÞxþ bðh;sÞ ð3Þ

Statistically significant coefficients from the analysis ofthe segmented fits were used as the model. Data from theionosonde at Korhogo, Cote-d’Ivoire (Geog. lat. 9.3� N,geog. lat. 5.4� W) was used to validate our model; likewisethe performance of our the model was compared with theIRI. Korhogo is also in the northern hemisphere, but liesnear the magnetic equator. Three years of Korhogo data,each from the three solar epochs, were used. This is pre-sented in Table 1.

The year 1999 falls in SC 23. In each year, a representa-tive month for each season was chosen (March, June, Octo-ber, and December for March equinox, June Solstice,September equinox, and December solstice, respectively).The choice of the representative month is such that thesame month is chosen across the years and that the F10.7values for such months fall in the range for the respectivesolar epochs (Table 2).

The F10.7 values for the months in the used Korhogodata was used as in the input for both our model and theIRI. Note that there is no month in the March Equinoxfor the high solar activity year (1999) that has F10.7 valuethat fits our classification. Of the three months that falls inMarch equinox in HSA, March has the highest F10.7 value(126.3 sfu); it however, does not fall into the high solaractivity range of F10.7 values. The classification is as fol-lows: low solar activity (F10.7 < 100 sfu), moderate solaractivity (100 6 F10.7 < 155 sfu), and high solar activity(F10.7 P 155 sfu) Hence, the March equinox observationfor HSA is not represented. The IRI-2007 values – forthe location, months and year that corresponds with Kor-hogo data – were obtained online via the NASA’s webinterface for computing and plotting IRI (http://model-web.gsfc.nasa.gov/models/iri.html). The validation wasdone with visual inspection of the curves, the deviation ofthe models from the observations at Korhogo and theroot-mean-square (r.m.s.) error.

The root-mean-square (r.m.s.) errors were obtainedfrom the expression in equation (4).

Table 2The solar radio flux (in sfu) of each of the representative months for eachsolar epoch.

1995 1993 1999

March 85.1 136.4 126.3June 75.7 109.3 169.8October 77.9 100.3 164.8December 72.6 104.8 169.8

638 S.O. Ikubanni et al. / Advances in Space Research 53 (2014) 635–646

r:m:s:e¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

N

XN

1

ðfoF2observed� foF2modelÞ2vuut ðunit in MHzÞ

ð4Þ

Where foF2observed is the measurement of foF2 at Kor-hogo, foF2model is either the foF2 generated by LM orany of the options of the IRI, and N is the number of datapairs.

3. Results and comparison with IRI

The results of LM and its comparison with IRI andobservations is presented with a view to validating it.Figs. 1, 3, and 5 present the morphology of foF2 observa-tions as well as foF2 predicted by LM and IRI for the fourdifferent seasons in each solar activity phases, showing theagreement between the trend of the models and observa-tions. Figs. 2, 4, and 6 present the plot of the differencesbetween the hourly values at Korhogo observations andthose of the model (that is subtracting the values obtained

Fig. 1. The diurnal plots of foF2 observed and foF2 predicted by LM (solid dMarch equinox, (b) June solstice, (c) September equinox, and (d) December s

from the models from the observations at Korhogo), whichwe referred to as the deviation. In the diurnal plot of thedeviations of the model-predictions from observations.Any data point on the horizontal line implies that at thecorresponding local time, there is no difference betweenthe observation and model. A positive value implies thatthe model (either IRI or LM) underestimates the observa-tions while a negative value implies an overestimation.

3.1. Low solar activity (LSA)

The low solar activity is a time the Sun is at its not orlittle active. This time is characterized by appearance ofno or little sunspot number and subsequently, a low radia-tion flux. Fig. 1 presents the diurnal plots of the foF2 pre-dictions and the observations for the four seasons duringlow solar activity.

The foF2 morphology in the equatorial region is distinctwith two peaks – one before noon, called the pre-noonpeak and the other after the noon, called the post-noonpeak – thereby forming a trough of foF2 value aroundnoon. This trough of value at noon referred to as the noon-time bite-out. The IRI predicted and LM predicted foF2values followed the trend of the observed values. Thepost-noon peak is usually more pronounced than the pre-noon peak at low solar flux while the appearance of thepost-noon peak diminishes with increasing solar flux.

For clarity and easy study of the diurnal performance ofLM in comparison with IRI, the diurnal plot of the

iamond), URSI (solid triangle) and CCIR (star) for different seasons [(a)olstice] during LSA.

-3-2-10123

0 2 4 6 8 10 12 14 16 18 20 22

(a) Mar. Equ. (LSA)

-3-2-10123

0 2 4 6 8 10 12 14 16 18 20 22

(b) Jun. Sols. (LSA)LM URSI CCIR

-3-2-1012

3

0 2 4 6 8 10 12 14 16 18 20 22

(c) Sept. Equ. (LSA)

-3-2-1012

3

0 2 4 6 8 10 12 14 16 18 20 22

(d) Dec. Sols. (LSA)

Dev

iati

on

sof

mod

els

from

obs

erva

tions

(M

Hz)

Local Time (HR)

Fig. 2. The deviations of the models from Korhogo observations at LSA. (a) March equinox, (b) June solstice, (c) September equinox, and (d) Decembersolstice.

S.O. Ikubanni et al. / Advances in Space Research 53 (2014) 635–646 639

deviation of the model predictions from observations ispresented in Fig. 2.

The measurements for the prenoon (9–11 LT) and noon-time (12 LT) periods in all seasons except December sol-stice (Fig. 2) are better represented by LM while theCCIR did better for those of the December solstice. Also,the CCIR performed better than LM, while LM did betterthan URSI for the postnoon period (14–17 LT) in Marchequinox and June solstice. The URSI performed betterthan LM in the postnoon, and LM did better than CCIRin September equinox while both IRI options performedbetter LM in December equinox.

3.2. Moderate solar activity (MSA)

The period of moderate solar activity is a period whenthe solar activity moves from low to high (increasing phase)or from high to low (decreasing phase). The year 1993 usedin this study falls in the decreasing phase of SC 22. Fig. 3presents the diurnal plots of the foF2 prediction and theobservations for the different seasons during a year ofMSA.

Similar to LSA, the diurnal performance of LM in com-parison with IRI during moderate solar activity is pre-sented in the diurnal plot of the deviations of the modelpredictions from observations (Fig. 4).

The observations for the prenoon period (9–11 LT),noontime (12 LT) and the postnoon period (13–18 LT)during March equinox (Fig. 4(a)) as well as the sunrise per-iod (06–09 LT) during the September equinox are best rep-resented by LM. Moreover, the LM did better than IRI in

representing the the post sunset period (18–23 LT) duringthe solstices (Fig. 4(b) and (d)). The LM June solstice post-noon peak (13–14 LT) better than the IRI. These arereflected in the r.m.s error values presented in Table 4.The URSI represented the prenoon period better thanCCIR (which did better than LM) during the June solsticeand the September equinox. In December solstice, CCIRperformed better than URSI (which did better than LM)in the prenoon and noontime periods. The URSI per-formed better than CCIR, and CCIR did better than LMin postnoon period during the September equinox andDecember solstice.

3.3. High solar activity (HSA)

The period of high solar solar activity is characterizedby emission of large quantity of solar flux as a result ofthe increase in visible sunspot and sunspot groups on thesolar disk, and subsequently the solar radio flux. The per-iod of high solar activity is usually around the mid of thesolar cycle. The year 1999 used in this study falls in SC23. Fig. 5 presents the diurnal plots of the foF2 predictionand the observations for the different seasons during a yearof HSA.

Similar to the other two solar epochs, the diurnalperformance of LM in comparison with IRI in HSA yearis presented in the diurnal plot of the deviations of themodel-predictions from observations each season (Fig. 6).

There seems to be little differences in the performance ofthe three models at the prenoon period (9–11 LT) and thepost-sunset period (18–22 LT) during June solstice

Fig. 3. Same as Fig. 1, but for MSA.

Fig. 4. Same as Fig. 2, but for MSA.

640 S.O. Ikubanni et al. / Advances in Space Research 53 (2014) 635–646

(Fig. 6(b)), and noontime (12 LT) during September equi-nox (Fig. 6(c)). Around sunrise (05–07 LT), the deviationsof the URSI and CCIR is highest during the Septemberequinox while deviation of LM is highest during the June

solstice and lowest during the December solstice. Likewise,the prenoon (09–11 LT) is best represented by URSI whilethe noon and postnoon periods (13–16 LT) is best repre-sented by the CCIR during the December solstice.

Fig. 5. Same as Fig. 1, but for HSA (March equinox, not presented).

-5-4-3-2-1012345

0 2 4 6 8 10 12 14 16 18 20 22

(b) Jun. Sols. (HSA) LM URSI CCIR

-4

-2

0

2

4

0 2 4 6 8 10 12 14 16 18 20 22

(c) Sept. Equ. (HSA)

-4

-2

0

2

4

0 2 4 6 8 10 12 14 16 18 20 22

(d) Dec. Sols. (HSA)

Dev

iatio

ns o

f m

odel

s fr

om o

bser

vatio

ns (

MH

z)

Local Time (HR)

Fig. 6. Same as Fig. 2, but for HSA (March equinox, not presented).

S.O. Ikubanni et al. / Advances in Space Research 53 (2014) 635–646 641

In Table 3, the bold values are the smallest r.m.s.e. valuein each row (daytime: 06–18 LT and nighttime: 18–06 LTconsidered separately) while the asterisked values are thesmallest in each column (the three solar activity phasesconsidered separately). For example, 0.47 MHz is ther.m.s.e. of LM and it is bolded for March equinox duringLSA; it implies that LM performed better than both URSIand CCIR in the corresponding period. Generally, thesmaller the r.m.s.e, the better the model’s performance.

According to results presented in Table 3, it can gener-ally be stated that for low solar activity, the three modelsgave a better prediction in daytime (75% of the cases) thannighttime (25% of the cases). Likewise, all three models(LM, URSI, and CCIR) performed better in the daytimethan the nighttime during the two equinoxes and June sol-stices while they performed better in nighttime during theDecember solstice. Our model (LM) performed better thanIRI in the daytime equinoxes and the nighttime solsticeswhile the CCIR submodel of the IRI performed better thanLM in the daytime solstices and the nighttime equinoxes.The LM and the IRI submodels (URSI and CCIR) gave

the best daytime prediction for Korhogo during the Junesolstice (asterisked values). Likewise, LM, URSI andCCIR gave the best nighttime representation of the obser-vations in December solstice (asterisked values).

For moderate solar activity, the three models gave a bet-ter prediction in daytime (75% of the cases) than nighttime(25% of the cases). The LM performed better in the daytimethan the nighttime during the two equinoxes and June sol-stices while it performed better in nighttime during theDecember solstice. The URSI performed better in daytimethan nighttime across all seasons. The CCIR performed bet-ter in daytime than nighttime during the March equinoxand December solstice while it performed better in night-time than daytime during June solstice and September equi-nox. The best daytime prediction by LM and CCIR isduring the March equinox while that of URSI is duringthe June solstice (asterisked values). Likewise, the nighttime

Table 3Daytime and nighttime comparisons of LM with both options of IRI-2007 in different seasons during the different solar activity epoch. The bold values arethe r.m.s. error (MHz) for the model that performed best in each season.

Seasons Daytime Nighttime

LM URSI CCIR LM URSI CCIR

Low solar activity Mar. equ. 0.47 0.89 0.63 1.13 1.55 0.99

Jun. sols. 0.45* 0.70* 0.35* 0.76 1.17 1.21Sept. equ. 0.63 0.95 0.82 1.52 1.27 1.22

Dec. sols. 0.83 0.89 0.67 0.47* 0.79* 0.66*

Moderate solar activity Mar. equ. 0.42* 1.01 1.13* 2.09 2.12 1.85

Jun. sols. 0.66 0.77* 1.40 0.77 1.40 0.99*

Sept. equ. 0.50 0.90 1.25 1.39 1.25* 1.11

Dec. sols. 0.83 0.85 1.30 0.50* 1.30 1.31

High solar activity Mar. equ. – – – – – –Jun. sols. 1.33 0.88 0.63 1.87 1.23 1.01

Sept. equ. 1.01 1.22 0.89 1.42 1.75 1.27

Dec. sols. 1.01* 0.75* 0.38* 0.59* 0.84* 0.88*

* The asterisk indicates the season in which the each model performed better than other seasons.

642 S.O. Ikubanni et al. / Advances in Space Research 53 (2014) 635–646

seasonal performance of the models shows that LM per-formed best in December solstice than other seasons, CCIRperformed best during the June solstice, and URSI duringSeptember equinox (asterisked values).

For high solar activity, as presented alongside othersolar activity phases in Table 3, it can be observed thatthe three models gave a better prediction in daytime (89%of the cases) than nighttime (11% of the cases). The LMperformed better in daytime than nighttime during Junesolstice and September equinox while it performed betterin nighttime than daytime during the December solstice.Both the CCIR and URSI performed better in daytimethan nighttime across the three represented seasons (bothsolstices and the September equinox). The best daytimeperformance by LM is during the December solstice (aster-isked value) as well as the September equinox (having samevalue as the December solstice) whiles the best daytime per-formance by both URSI and CCIR are during the Decem-ber solstice (asterisked values). Likewise, the nighttimeseasonal performance of the three models shows that theyall better in the December solstice than other seasons(asterisked values). In essence, we can say that the threemodels were better during the December solstice than otherseasons both at day and night.

The overall performance of LM, from Table 1 is around50%, specifically in the low and moderate solar activityphases.

3.4. Other observations

Observations presented in Figs. 2, 4, and 6, show thatthe models either underestimated or overestimated theKorhogo foF2 values during the salient features of theequatorial F2-morphology. For easy comparison, someimportant hours have been selected and the deviations ofthe models examined and presented in Fig. 7. These periodsare the prenoon peak (09–11 LT), noon (12 LT), the post-noon peak (14–15 LT), and sunrise (06 LT). The deviations

of the each of the models from observation values are pre-sented. A positive value implies an overestimation ofKorhogo observations while a negative value impliesunderestimation.

The results presented in Table 3 has shown and con-firmed that CCIR is a better option for the African Equa-torial station, especially during the daytime. However,Fig. 7(a) revealed that CCIR underestimated the prenoonvalues of foF2 during March equinox, and the magnitudeof the underestimation increases with increasing solar flux.In contrast, LM correctly estimated the prenoon valuesduring the equinoxes while overestimation of the prenoonvalues was highest during the December solstice, and themagnitude of the overestimation increases with increasingsolar flux. At noon (Fig. 7(b)), CCIR underestimated dur-ing March equinox, especially at MSA and did better thanLM across all seasons in HSA. The LM correctly estimatedthe noon foF2 values during the equinoxes at LSA andMSA, and overestimated during the solstices regardlessof the solar flux levels. The postnoon peak was consideredand CCIR was observed to have done well in March Equi-nox and June solstice in LSA, December solstice in MSA,and across all seasons in HSA (Fig. 7(c)). LM also did wellin March equinox and June solstice in LSA, March equi-nox in MSA (where CCIR had its highest underestimation)but was poor in estimating the postnoon peak values athigh solar flux (Fig. 7(c)). The sunrise (06 LT) values offoF2 across all seasons, with an highest overestimationwas observed during the September equinox. The overesti-mation ranges between 0.4 and 3.9 MHz, and it increases inwith increasing solar flux. Also from Fig. 7(d), it could beobserved that LM was nearly accurate with the sunrise(06 LT) foF2 prediction during the December solstice,regardless of the solar flux level. Although the LM alsooverestimated the sunrise foF2 values in other seasons, itperformed better than IRI in each solar epoch across theseasons except during June solstice. In June solstice, theCCIR performed better than the LM and the magnitude

Fig. 7. Deviations of the models from Korhogo observations for the seasons at different solar flux levels during (a) prenoon, (b) noon, (c) postnoon, and(d) sunrise.

Fig. 8. Linear regression fits between 1900 LT observations at Korhogoand the models.

S.O. Ikubanni et al. / Advances in Space Research 53 (2014) 635–646 643

of the differences between the values from the two modelsincreased with increasing solar flux levels.

The foF2 at 19 LT which corresponds to the pre-reversalenhancement (PRE) was extracted for the Korhogo obser-vations and the models. These values from each of themodel were then plotted against observations to study theircorrelation. LM was observed to correlate with observa-tions slightly better than URSI and CCIR (Fig. 8).

The performance of each of the models for the entire24 h in each season and for each of the solar activity epochis presented in Table 4. The LM performed better thanboth CCIR and URSI in June solstice only during lowsolar activity, while CCIR performed better in other sea-sons (bolded values). During moderate solar activity, LMperformed better than IRI (CCIR) in the solstices andthe March equinox (bolded values). However, IRI (CCIR)did better than LM in all the represented seasons duringhigh solar activity (bolded values).

Moreover, the performance of LM is better in June sol-stice than other seasons during LSA and MSA (asteriskedvalues). In addition, LM performed better in Decembersolstices than other seasons during HSA.

Table 5 presents the yearly and overall performance ofeach model. It also validates the presentation in Table 4.The LM performed better than URSI and CCIR in theyear of moderate solar activity (bolded value). The CCIRperformed better than LM in low and high solar activities(bolded values). However, the three models performed dur-ing low solar activity better than in moderate and highsolar activities (asterisked values). Overall, CCIR gave

the best performance with an r.m.s. error value0.95 MHz, which is 0.1 MHz lower than that of LM

Table 4Comparisons of LM with both options of IRI-2007 during different seasons in each solar activity epochs. The r.m.s. error (MHz) for the model thatperformed best in each season is bolded.

Seasons Low solar activity Moderate solar activity High solar activity

LM URSI CCIR LM URSI CCIR LM URSI CCIR

Mar. equ. 0.86 1.19 0.80 1.57 1.68 1.57 – – –Jun. sols. 0.60* 0.91 0.91 0.69* 1.07 0.78 1.59 1.00 0.82

Sept. equ. 1.20 1.04 1.04 1.07 1.01* 0.87 1.14 1.35 1.00

Dec. sols. 0.69 0.77* 0.63* 0.71 1.05 0.73* 0.86* 0.74* 0.67*

* The asterisk indicates the season in which the each model performed better than other seasons.

Table 5Comparisons of LM with both options of IRI-2007 during different solaractivity levels (for all seasons merged). The model that performed best ineach solar epoch is bolded. The values in the LM, URSI and CCIRcolumns are the r.m.s. (MHz) and the values in the column N are thenumber of data points.

LM URSI CCIR N

L.S.A. 0.79* 0.28* 0.22* 96M.S.A. 1.07 1.23 1.10 96H.S.A. 1.24 1.07 0.85 70All 1.05 1.10 0.95 263

* The asterisk values in each column indicate the solar epoch, in which thecorresponding model performed better than others.

644 S.O. Ikubanni et al. / Advances in Space Research 53 (2014) 635–646

(1.05 MHz) and 0.15 MHz lower than that of URSI(1.10 MHz).

4. Discussion

The morphology of the F2-layer critical frequency isalready known, especially its unique features such as thenoontime bite-out, the pre-noon and post-noon peak.The equatorial ionosphere is highly influenced by theaction of the south-north (northward) magnetic field andthe perpendicular eastward electric field (for daytime) orwestward electric field (for nighttime), which causes verticalplasma drift and leads to the equatorial ionization anomaly(EIA). The EIA is described as a daytime trough of elec-tron density at the magnetic equator and two coexistingcrests of electron density on either side of the equator(about ±15� magnetic latitude). As explained, the Earth’smagnetic field and the perpendicular daytime eastwardact to cause an upward lifting of the F2-layer height. Atthis higher altitude, the joint influence of the pressure gra-dient forces and gravity on the plasma or electron causes itto diffuse along the magnetic field lines. Therefore, since thetwo ionospheric-observing stations whose data were usedfor this work are both in the African sector, then phenom-ena of similar magnitude are expected to influence iono-sphere at both stations. However, the difference in theirlatitudes implies that the magnitude of the E x B drift willdiffer at both stations. Recall that ðE � B ¼ jEjjBj sin hÞ.The angle h decreases away from the equator; subse-quently, there are decreases in the magnitude of the drift.The action of the vertical E � B drift will be greatest at

the equator and decreases towards the crest of the EIAregion. We have considered the performance of LM, rela-tive to IRI, in different seasons during different solar epoch.

The three models (the two options of the foF2 used inIRI and our LM) exhibit the noontime bite-out, the pre-noon and post-noon peaks, however, with varying magni-tude. Electron density features in the F2 region height ofthe equatorial region is known to differ with changing solaractivity epoch (Radicella and Adeniyi, 1999). A plausibleobservation is that all three models present all or most ofthese features. These observations include the post-noonpeak, which is higher than the pre-noon peak in June sol-stice and equinoxes during LSA due to the vertical E � Bdrift. The vertical drift velocity in LSA is lower thanHSA and the daytime drift attains a daytime maximumas well as decreases earlier in LSA than in HSA (Radicellaand Adeniyi, 1999). The dynamics of the E � B drift veloc-ity also affect the features in HSA, and it accounts for theslight difference observed in the appearance of the post-noon peak with increasing solar activity. Our model repli-cate observations, in that at HSA, pre-noon peak is higherthan the post-noon peak. The higher pre-noon peak is dueto the higher drift velocity at HSA, later attainment ofmaximum in daytime and later decreases compared to driftin LSA.

The varying observations in the different solar epoch areattributable to the solar dependence of the EIA (Liu et al.2006). It is known that the plasma drift model for the IRIwas developed with data from Jicamarca, in the Americansector (located on the southern side of the equator and at alongitude far from Africa sector: Geog. lat. 11.95� S, long.76.87� W). There is a significant longitudinal differencebetween American and African equatorial stations. Fejeret al. (2008) revealed that vertical drift in the equatorialregion has longitudinal dependence, which is more evidentduring solstices especially at nighttime. In addition, morn-ing or daytime drift has very limited solar flux dependenceexcept during June solstice (Fejer et al., 1991). The longitu-dinal dependence of vertical drift during solstices may havecontributed to LM having a better performance than CCIRin both LSA and MSA.

All three models performed in the daytime better thanthe nighttime in most cases. This may be due to higher var-iability of the ionosphere at night. Past works havereported that the African equatorial ionosphere is more

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variable at night than day (e.g. Oladipo et al., 2008). Forbulk of the daytime hours, the ionosphere at F2 height iscontrolled by solar radiation influx while the nighttimeF2-ionosphere is sustained by the transport process. It isestablished that high variation is correlated with low elec-tron density (Oladipo et al., 2008) and periods around sun-rise (06 LT) are known to be periods with the lowestelectron density, while Akala et al. (2011) showed thatequatorial variability is highest around sunrise (06 LT).Therefore, it is not surprising that the IRI model couldnot perform well as this time (06 LT). However, our modeldid well at this time. This could be some of the IRI limita-tions, which could only be overcome by employing regionalor local models.

5. Conclusions

In this work, we describe a model developed from themonthly mean foF2 values obtained from ionograms gen-erated by an IPS-42 sounder located at Ouagadougou,Burkina Faso (Geog. lat. 12� N, long. 1.8� W) and vali-dated this model with data from Korhogo, Cote-d’Ivoire(Geog. lat. 9.3� N, long. 5.4� W). We considered the salientfeatures of the equatorial foF2, the daytime, nighttime, sea-sonal, and solar cycle performance of our model, in com-parison with the IRI. Our model (LM) performed betterthan both options of the IRI in daytime equinoxes andnighttime solstices during LSA; likewise, daytime acrossall seasons and nighttime solstices during MSA; also,nighttime December solstice during HSA. African iono-spheric physicists and the task force activities should beapplauded for the improved performance of IRI over theAfrican equatorial region. This work has shown that sev-eral efforts geared towards improving the IRI model havebeen positive. IRI can be said to be near perfect in predict-ing monthly mean (which is an equivalent of the quiet timefoF2 profile) of the African equatorial ionosphere, thanksto the input of several researchers from this region. Thebetter performance of IRI, especially the CCIR option,over LM in some seasons may not be unconnected withthe diversity of data source for IRI-model. In addition,the performance of our model may have also been ham-pered by the differences in the ionospheric characteristicsalong different latitudes. Ouagadougou, the station fromwhich the data used in developing the model was obtained,lies near the inner flank of the equatorial ionization anom-aly (EIA) crest in the northern hemisphere while Korhogo,the station whose data was used for validating the model,lies near the trough of the EIA. The ionosphere in the crestof the EIA is known to be highly variable than in thetrough, hence the trough region experiences little seasonalvariation. Variation of the ionosphere increases withincreasing latitude in the EIA region. Likewise, the EIAis strengthened as solar activity increases. Our workrevealed that there is need for improvement on predictionsof some key features in some seasons; this work may beconsidered as an input to achieving this.

Acknowledgments

The authors appreciate R. Hanbaba of Centre Nationald_Etudes des Telecommunications, Lannion, France, whosupplied the Ouagadougou data used for this study. Mr.Konea Emile, the technician of the Korhogo ionosphericstation who supplied the data of Korhogo is also appreci-ated. We thank the SPDF/Modelweb team for the avail-ability of the IRI-2007 model on the website http://omniweb.gsfc.nasa.gov/vitmo/iri-vitmo.html. The anony-mous reviewers are also appreciated for their attention todetails.

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