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Ionospheric variations over Chinese EIA region using foF2 and comparison with IRI-2016 model
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ScienceDirect Advances in Space Research 62 (2018) 84–93 www.elsevier.com/locate/asr
Ionospheric variations over Chinese EIA region using foF2 and comparison with IRI-2016 model S.S. Rao a,⇑, Monti Chakraborty b, R. Pandey a b
a Department of Physics, MLS University, Udaipur 313001, India Department of Electronics and Communication Engineering, Tripura University, Agartala 799022, India
Received 5 January 2018; received in revised form 7 April 2018; accepted 10 April 2018 Available online 21 April 2018
Abstract In the present work, we have analyzed data of critical frequency of the F2 region (foF2) for the period, 2008–2013 over low latitude Chinese station Guangzhou (Geog. Lat. 23.10°N, Geog. Long. 113.40°E, dip, Lat. 13.49°N) and results thereof have been compared with IRI-2016 model. foF2 data set of the present study encompasses period of unusual and extended solar minimum, i.e., the years 2008–2009 and rising phase of solar cycle 24. IRI data have been obtained by choosing topside electron density profile IRI-NeQuick for two F peak models, CCIR and URSI. It is found that the general trend of variation in foF2 closely follows the trend of the solar flux during the period of study. A linear regression analysis gave a correlation coefficient of 0.98 which shows strong dependence of foF2 variation over solar flux variation. Semi-annual and annual oscillations are clearly brought out in the foF2 data using the Lomb-Scargle periodogram. A presence of semiannual and winter anomaly in observed as well as modeled foF2 at Guangzhou have found to be consistent throughout the period 2008–2013 irrespective of the phases of the solar activity. Our results also show the stronger presence of winter anomaly during the years of higher solar flux and it has been confirmed by normalizing the difference of winter to summer foF2 values for each year. Comparative results of ionosonde observation and IRI-2016 model show a significant discrepancy with regard to values of foF2 in different seasons and local time variations. IRI 2016 model underestimates the foF2 values in winter and equinoxes and overestimates foF2 values in summer. IRI modeled foF2 values using CCIR and URSI F peak models were found greater during forenoon hours and smaller during afternoon hours than the observed foF2 values throughout the period 2008–2013. Ó 2018 COSPAR. Published by Elsevier Ltd. All rights reserved.
Keywords: Equatorial ionization anomaly; Ionosonde data; IRI-2016 model; Critical frequency of the F2 region; Winter anomaly
1. Introduction The F2 region is the most potential part of the ionosphere having immense effect on the radio wave propagation. Ionospheric irregularities that occur in the F2 region during quiet as well as during disturbed periods may also disturb the satellite based communication by introducing significant time delays and range errors in high frequency ⇑ Corresponding author.
E-mail addresses: [email protected] (S.S. Rao), [email protected] (M. Chakraborty), pandey.rj@gmail. com (R. Pandey). https://doi.org/10.1016/j.asr.2018.04.009 0273-1177/Ó 2018 COSPAR. Published by Elsevier Ltd. All rights reserved.
(HF) radio signal. With the increasing demand of application of trans-ionospheric radio wave propagations and satellite communications, the F2 region is a topic of acute research interest for the several decades. Though numerous earlier works (Appleton and Barnett, 1925; Rishbeth and Setty, 1961; Forbes et al., 2000; Schunk and Nagy, 2000; Rao et al., 2014 and references therein) are available for the present understanding of the F2 region variability, yet it is still poorly understood and quantified. Features like the winter anomaly, equinoctial asymmetry and solar cycle variations are less understood due to complex electrodynamics of the equatorial ionization anomaly (EIA) zone,
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unavailability of continuous data and limited converge of the ionosondes in the region. Unavailability of data for periods of the order of a solar cycle is a big issue to model the ionosphere and to quantify the ionospheric variations. Particularly, the equatorial ionosphere over the South China Sea is complex because its variability considerably changes with time and location (Wen and Xun-jie, 1999). Many researchers (e.g., Chandra and Rastogi, 1971; Pasricha et al., 1987; Sharma et al., 1999; Forbes et al., 2000; Rishbeth and Mendillo 2001; Zhang et al., 2004; Dabas et al., 2006; Wang et al., 2009; Chandra et al., 2009; Chen and Liu, 2010; Yadav et al., 2010; Ma et al., 2012; Chaitanya et al., 2015) have studied the variability of the critical frequency of F2 region (foF2) over low latitude region. The ionospheric variabilities in F2 region are better demonstrated using International Reference Ionosphere (IRI) model (Bilitza and Reinisch, 2008). The worldwide network of the ionosondes, the powerful incoherent scatter radars (e.g., the ones at Jicamarca, Arecibo, Millstone Hill, Malvern and St. Santin), the ISIS and Alouette topside sounders, in-situ instruments on several satellites and rockets are the major sources of data for this model program (Aggarwal, 2011). Many workers (e.g., Batista and Abdu, 2004, Adeneyi et al., 2007; Sethi et al., 2008; Tamer et al., 2009) have done comparative studies of observed foF2 with IRI model from different sectors of the globe. Particularly, in the low latitude region of Southeast Asia and South China Sea region, only a few studies of foF2 variability were attempted and their brief account is given in following. For the Taiwanese region, Chuo and Lee (2008) have reported a comparative study between observed and IRI-2001 modeled foF2 for the period 1994–1999 and found that the value of daytime foF2 in the IRI model produced a good agreement during low solar activity, but underestimated during the high solar activity. They further noted that the percentage deviation of the observed foF2 values with respect to the IRI model varied from 5% to 80% during nighttime and 2–17% during the daytime. Using ionosonde measurements obtained at two low latitude stations in Thailand, Wichaipanich et al. (2012) studied the variation of the F2-layer peak electron density (NmF2) during low solar activity and results obtained thereof were compared with IRI-2007 model. Their results show that the diurnal and seasonal variations of NmF2 predicted by the IRI model generally shows the same features as in observed NmF2. In most of cases, IRI model underestimates the observed NmF2 except during the September equinox and the December solstice at 10.72°N (dip, 3.0°N) and in September equinox and March equinox at 18.76°N (dip, 12.7°N). A study of Wang et al. (2009) presented comparison of foF2 obtained from DPS4 digisonde with IRI-2001 model at low latitude station ‘Hainan’ during high to low phase of solar activity-23. They reported that the general trend of IRI predictions are in consonant with the observations for the diurnal, seasonal and solar cycle variation in foF2, however a deviation
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with regard to time of occurrence of foF2 peak and overestimation/underestimation depended upon the local time and season. At the same station Hainan, Zhang et al. (2007) have also compared F2 peak parameters with those obtained from IRI-2001 model. Lynn et al. (2006) have used the ionosonde observations over New Guinea and Indonesia to study the relationship among foF2, hmF2, ionospheric slab thickness and Doppler velocity during the nightime EIA. Maruyama et al. (2007) have analyzed the ionosonde observations from four locations in the Thailand, Indonesia and Vietnam to study the nighttime ionospheric height variations. Wichaipanich et al. (2013) have done comparative study of foF2 and hmF2 variations with IRI-2007 model for the period January 2004 to February 2007 over the conjugate locations; Chiang Mai, Thailand (Geog. Lat. 18.76°N, dip, 12.7°N) and Kototabang, Indonesia (Geog. Lat. 0.2°S, dip, 10.1°S) and at near equatorial station; Chumphon, Thailand (Geog. Lat. 10.72°N, dip 3.0°N). For foF2, most of their results show that the IRI model overestimates the observed foF2 at the Chumphon, underestimates at Chiang Mai and close to the measured ones at the Kototabang. Considering the same stations, Wichaipanich et al. (2017) have done comparative study of neural network-based predictions of foF2 (NN) and IRI-2012 model with observed foF2 for the period 2004–2012 (except for the year 2009). In general, their results show the same trends in foF2 variations between the models (NN and IRI-2012) and the ionosonde observations. Zain et al. (2008) have analyzed foF1 and foF2 data determined through the digital ionosonde installed at University Tun Hussain Onn, Parit Raja, Malaysia (Geog. Lat. 1.52°N, dip, 8.15°S) and showed the existence of F3 layer. They have found that the foF2 decreases with the appearance of the F3-layer. Over the same location, Abdullah and Zain (2009) have reported solar activity effect on foF2 variability for the years 2005 to 2007. Malik et al. (2016) have studied diurnal, seasonal and sunspot effect on the variability in maximum usable frequency (MUF) over peninsular Malaysia and compared results thereof to IRI-2012 model, for the years 2009–2011. Their results reveal that the variability in the equatorial and low latitude regions decreases when solar activity increases. However, studies using the GPS derived TEC and IRI modeled TEC were extensively done and documented for the Malaysian region (e.g., Leong et al., 2015; Bahari and Abdullah, 2018 and references therein). From the above discussion, it follows that the comparative study of the foF2 variability obtained using the ionosonde and its comparison with IRI model, particularly for the EIA zone in the Southeast Asia and South China region, is scant. Therefore, in the present work we have analyzed foF2 data for the period, 2008–2013 over low latitude Chinese station Guangzhou (Geog. Lat. 23.10°N, Geog. Long. 113.40°E, dip, 13.49°N) and results thereof have been compared with IRI-2016 model. foF2 data set of the present study encompasses period of unusual and
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extended solar minimum, i.e., the years 2008–2009. The ionosphere during this solar minimum was contracted to a thin shell, with the O+/H+ transition height and ion temperature reaching very low values (Heelis et al., 2009) following the contraction of the thermosphere to record low level (Solomon et al., 2010). Thus, study of the low latitude ionosphere during this prolonged solar cycle 23/24 and during the rising phase of solar cycle 24 is an important contribution. For this, foF2 data for the period 2008– 2013 over the Guangzhou station have been obtained from the website: https://www.ukssdc.ac.uk. This data is provided by the United Kingdom Solar System Data Center (UK SSDC) retrieved from the analog ionosonde GU421, installed at Guangzhou. IRI-2016 model foF2 data using topside electron density profile option IRI-NeQuick for F peak models CCIR and URSI have been downloaded from the website https://cohoweb.gsfc.nasa.gov. The monthly mean absolute values of solar flux at F10.7 cm have been downloaded from the link-ftp://ftp.ngdc.noaa.gov. 2. Result and discussion 2.1. Variation of monthly mean foF2 The variation of daytime maximum foF2 over Guangzhou during the period 2008–2013 along with solar flux at F10.7 cm is presented in Fig. 1. A green curve in Fig. 1 shows variation of monthly mean absolute solar flux at F10.7 cm and the blue curve show the variation of monthly mean daytime maximum foF2 for the period 2008–2013.
The scale of absolute value of solar flux at F10.7 cm (in sfu) is shown on right ordinate. It is seen from Fig. 1 that the level of solar flux remained almost constant, 70 sfu, for the period January 2008 – January 2010. Thereafter it showed rising trend and became largest in December 2011whereafter it had humps and dumps and in December 2013 its magnitude was 130 sfu. The solar cycle 24 reached its maxima (170 sfu) in the months of January 2014 (not shown in figure) followed by a declining phase of the solar cycle and it is now expected that minima would appear in the years 2018–2019. It can be seen from Fig. 1 that the general trend of variation in foF2 has a positive slope which closely follows the trend of the solar flux during the period of study. Our results are in agreement with earlier studies in different sectors as is shown in the works of Araujo-Pradere et al. (2011), Liu et al. (2011), and Yang et al. (2012) who reported that the global values of F region critical frequency (foF2) and GPS-TEC reached lower level in the solar minimum (years, 2008–2009) compared to the minimum of previous solar cycles. Since, Mielich and Bremer (2013) and Lasˇtovicˇka et al. (2006) have suggested to use solar flux at F10.7 cm to derive reliable ionospheric trends, we have also attempted a linear regression analysis to determine solar flux dependence of foF2 variation (see, Fig. 2). It is known that the F10.7 cm solar flux has no seasonal dependence whereas foF2 variation has. To make the seasonal independent foF2 data, we have employed annual averaging of foF2 data. We have calculated correlation coefficient (R) between annually averaged noontime
Fig. 1. Variation of monthly mean daytime maximum foF2 (blue curve) over low latitude station Guangzhou along with variation of absolute solar flux at F10.7 cm (green curve) is shown for the period 2008–2013. The general trend of the variation of foF2 is seen to be in tandem with that of the solar flux in low, rising and high solar activity phase. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 2. Linear regression analysis curve between annually averaged monthly mean daytime maximum foF2 and absolute solar flux at F10.7 cm is shown. The correlation coefficient for the two parameters is found to be 0.98 which showed strong dependence of foF2 variation on solar flux variation.
monthly mean foF2 values and absolute values of solar flux that is found to be 0.98. This shows strong solar cycle dependency of foF2 variation. It can be seen from Fig. 1 that throughout the period 2008–2013, monthly mean noontime foF2 value showed maxima during March-April and September-October with minimum in June. Thus, semi annual oscillation is present for all the years encompassing different phases of solar activity. Therefore, the semi-annual oscillation in foF2 is found to be a consistent feature with variation in solar flux. This is in agreement with earlier works from this region (Zhang et al., 2007; Wang et al., 2009). Regarding the foF2 values during solstices, it can be seen from Fig. 1 that the foF2 value in summer solstice of any given year is lower than that of the following winter months. For example, it was 8 MHz in June and 10 MHz in December for the years 2008 and 2009. What is worth noting is the fact that while the solar flux was almost constant (70 sfu) during the period 2008–2009, winter anomaly in foF2 still persisted. This is consistent with the work of Rao et al. (2013) who have stressed that the true feature of winter anomaly could only be identified by its appearance when level of solar flux is same during the summer and winter solstices of a year as this automatically removes the seasonal variation of ionospheric parameter (foF2 in the present case). Hence, the prolonged solar minimum of solar cycle 23/24 provided a unique opportunity to study the phenomenon of winter anomaly in foF2. Further, Rao et al. (2013) have argued that the appearance of winter anomaly in TEC is intimately related to the level of solar flux in the summer and winter solstices of a year. In variance with winter anomaly in TEC variation, presence of winter anomaly in foF2, during the rising phase of a solar cycle, irrespec-
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tive of level of solar flux, during the summer and winter solstice, is a consistent feature. For exploring the periodicity, we have used the LombScargle periodogram technique (Scargle, 1982) on the daily noontime (15 LT) peak foF2 data for the duration of 2008 – 2013. Periodogram analysis using this method is exactly equivalent to the least-square fitting of sine curves to the data and the technique has many advantages over conventional Fast Fourier Transform method as it is suitable for analysis of unevenly spaced data, that is, it can take the edge off missing data problem (Horne and Baliunas, 1986). Fig. 3 reveals that the underlying periodicities of foF2 data that are 14, 182, 230 and 350 days (with False Alarm Peak of 0.1%). Although many authors have reported the 27 day periodicity as a most prominent periodicity of foF2 data, it is not prominent in our analysis. Hence, semi-annual and annual oscillations are clearly brought out. 2.2. Comparative study of foF2 variation with IRI-2016 model We have also attempted to find out the extent to which the ionosonde foF2 values for Guangzhou station are comparable with the ones predicted by the IRI-2016 model. The contour plot of monthly mean foF2 obtained from the ionosonde and from the IRI model as a function of local time (during 0700–1900 LT) for the period 2008– 2013 is given in Fig. 4. We have analyzed model foF2 data for topside electron density profile options IRI-NeQuick, IRI-2001 and IRI01-corr for the URSI and CCIR coefficients. Our analysis showed exactly the similar variation of foF2 in all three electron density profiles for each coefficient. Therefore we have presented results only for IRINeQuick option using URSI and CCIR coefficients. The upper panel of Fig. 4 presents the foF2 variations from the ionosonde and the middle one is the variation from IRI-NeQuick model using the URSI coefficient and the lower one presents the IRI-NeQuick model using the CCIR coefficients. The abscissa of each panel of Fig. 4 gives months of the respective year and the color bar on the right of the figure gives scale of foF2. It can be seen from Fig. 4(a-c) that the general feature of modeled foF2 variation for URSI and CCIR coefficients is almost similar. The only difference that is found is regarding the magnitude of foF2 values. Fig. 4 shows that the noon time foF2 from the CCIR option had greater magnitude than the URSI option during the high solar activity. foF2 variation from the CCIR option had longer duration of daytime peak compare to the URSI option. It can be seen from Fig. 4(a) that the maximum value of observed foF2 for all the seasons starts to increase from the year 2008 and is highest during the winter months of the year 2011. Thereafter it decreases slightly, by 1 MHz, but was still higher compared to its values prior to the year 2011. Thus, the foF2 variation closely follows the variation in solar flux for the period 2008–2013. Similar
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Fig. 3. Lomb-Scargle periodogram for the foF2 data with false alarm peaks.
Fig. 4. (a) Contour plot of observed monthly mean foF2 over low latitude station Gunagzhou. (b) Contour plot of IRI 2016 modeled foF2 over low latitude station Gunagzhou obtained using URSI F peak model for IRI-NeQuick option. (c) Contour plot of IRI 2016 modeled foF2 over low latitude station Gunagzhou obtained using CCIR F peak model for IRI-NeQuick option.
to observed foF2, the variation of model foF2 obtained for an option IRI NeQuick using URSI and CCIR model, presented in Fig. 4 (b) and Fig. 4(c) respectively, also follows the solar flux variation during the period 2008–2013, it was lower in the year 2008 and 2009 and then increased with the progress of solar cycle 24. It can be seen from Fig. 4(a-c) that the noontime observed foF2 was largest around 15LT ± 1 whereas the noontime modeled foF2 was largest 1330 h throughout
the period 2008–2013. Thus, there is a slight local time difference with regard to the peaking hour of foF2. Comparison of Fig. 4(a) with Fig. 4(b) and Fig. 4(c) shows that the observed values are lower than the model values in the forenoon hours. This result is in agreement with Wang et al. (2009) wherein they have reported that the IRI predictions generally overestimate the observed ones during a few hours around sunrise (0600–0900 LT). It can be also seen from Fig. 4 that the noontime difference
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in values decreases with increase in solar activity. This showed discrepancy with regard to local time variation of modeled foF2. A comparative study of local time variation of observed and IRI modeled foF2 during solar cycle 23 over low latitude by Chaitanya et al. 2015 showed that the IRI model overestimated foF2 values irrespective of local time. In Fig. 4(a), the observed foF2 shows two well defined maxima during equinoxes and minimum during summer solstice during all the years for the period 2008–2013. It can be seen from the Fig. 4(a) that the second maxima is strongest compared to first one, during the rising phase, years 2010–2013. For the low solar activity year 2008, the vernal equinox was stronger compared to autumn equinox but for the low solar activity year 2009 both equinoxes are seen to be comparable. The model results in Fig. 4(b) and 4 (c) also shows clear cut presence of double peaks in foF2 during equinoctial months of each year. Thus, model also shows a consistent presence of semi-annual anomaly in low latitude foF2 throughout the period 2008–2013. The strength of the two peaks was more or less similar for the years 2008–2011 but an asymmetry is clearly seen for the period 2012–2013. For the year 2012, the strength of a first maximum was stronger than second one, while for the year 2013; second maximum was stronger than first one. It can be seen from Fig. 4(a–c) that foF2 values in the winter months (December/January) were larger than those in summer months (June/July) throughout the period 2008–2013. This indicates the presence of winter anomaly in foF2 over low latitude during low, rising and high phases of the solar activity. A careful look at Fig. 4(a) reveals that the difference of summer-winter foF2 values was smaller
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during low solar activity years and higher during high solar activity years. Thus, winter anomaly feature in a foF2 is a consistent feature during the period 2008–2013 but it was more prominent during the high solar activity year 2011– 2013. For the model results, Fig. 4(b) and Fig. 4(c) shows that the values of modeled foF2 during the summer and winter months were comparable during the years 2008 and 2009 but for the years 2010–2013, the modeled foF2 values during the winter months were significantly larger than those in summer months. Thus, IRI model also shows consistent presence of winter anomaly during the period 2008–2013 wherein its presence during rising and high solar activity years is prominent while during the low solar activity its presence seems to be a weak. The stronger winter anomaly during high sunspot years is expected as foF2 is more during high sunspot years; the winter to summer difference could be more than that in low sunspot years. This is discussed separately in the Section 2.3 wherein difference of winter to summer noontime foF2 is normalized to constant level of solar flux using regression analysis. A comparison of observed and modeled foF2 is better illustrated in Fig. 5 wherein difference of largest daytime monthly mean foF2 have been calculated and presented. The upper panel of Fig. 5 gives the difference of observed foF2 and modeled foF2 calculated using URSI option and lower panel gives the difference of observed foF2 and modeled foF2 calculated using CCIR option. The abscissa of Fig. 5 shows the months of the respective years of the period 2008–2013 and left ordinate shows the difference of observed and model monthly mean of peak time foF2 values (DfoF2). Fig. 5 give positive gradients (+DfoF2) during the winter and equinoctial months which
Fig. 5. A comparison of observed and modeled foF2 is shown wherein difference of largest daytime monthly mean foF2 has been calculated. The upper panel and lower panel gives the difference of observed foF2 and modeled foF2 calculated using URSI and CCIR option, respectively.
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shows smaller modeled foF2 values compared to observed values while in the summer months of period 2008–2013, Fig. 5 shows a negative gradient (-DfoF2) wherein modeled foF2 values were greater than the observed ones. Thus, IRI 2016 model underestimates the foF2 values in winter and equinoxes and overestimates foF2 values in summer. It can be easily seen from Fig. 5 that the DfoF2 increased in winter and equinoxes whereas decreased in summer with the progress of the solar cycle. Many workers have reported a good agreement between ionospheric observations with the previous versions of IRI models, namely, IRI-2001, 2007 and 2012 at different levels of solar activity (Zhang et al., 2007; Wang et al., 2009; Chuo and Lee, 2008; Sethi et al., 2008), but discrepancies between the model and observed data are also reported (Bilitza et al., 2012 and references therein). Although IRI 2016 model has been upgraded with many new and improved descriptions of ion composition at different levels of solar activity along with the new developed model for F2 peak heights (Bilitza et al., 2017), the present study found discrepancy with regard to values of foF2. As the station Guangzhou is located in the EIA crest region and therefore the variation in foF2 is also influenced by the electric field in the magnetic equator region. Equatorial and low latitude ionosphere is strongly depended on the strength of the equitorial electrojet, EEJ (Dabas et al., 1984; Chandra et al., 2009) and neutral winds (Gurubaran and Sridharan, 1993). IRI model fails to account for the variation in the ionosphere which is driven by these two factors. This fact is supported by results from the Indian EIA region as discussed hereafter. Sharma et al. (1999) have examined the foF2 over the EIA crest location
Ahmedabad and found that the long term changes in foF2 are related to the long term changes in the zonal electric field or the secular changes in the position of dip equator. Upadhayaya and Mahajan (2013) have analyzed foF2 data of equatorial station Trivandrum and EIA crest location Bhopal in the Indian region and then compared it with strength of EEJ. They have reported that the variability in low and equatorial ionosphere is strongly related to the EEJ. Using spectral analysis, they showed 13–14 days periodicity both in EEJ strength and in foF2 for the EIA location Bhopal. We have also observed 14 day periodicity in noontime foF2 data for the EIA crest location Guangzhou during the period 2008–2013 (see, Fig. 3). NmF2 variation during the rising phase of solar cycle 24 over the low latitude station Dibrugarh (27.5°N, 17°N dip) measured using the Canadian Advanced Digital Ionosonde (CADI), have been studied by Kalita et al. (2015). They have shown the good correlation of NmF2 with solar activity and EEJ. Also due to the low coverage of the ionosondes in the low latitudes, particularly in the Southeast Asia and South China region, an essential data source for the IRI model (Bilitza and Reinisch, 2008) is inadequate. Further, the availability of data for period of the order of a solar cycle is a big issue to model the ionosphere. 2.3. Regression analysis of foF2 with solar flux Since ionization in the ionosphere depends on the solar flux, it is obvious that the amount of foF2 and variation thereof should, besides other factors (EEJ and neutral winds), also depend on the solar flux and its variation, particularly on the 11-year solar cycle. As discussed in
Fig. 6. Seasonal difference of noontime monthly mean foF2 over Guangzhou during the period 2008–2013, is plotted wherein the top panel shows the winter to summer difference of noon time foF2 that is attributable to the solar flux and all other sources, like the electrojet etc. The bottom panel gives the winter to summer difference of solar flux independent noon time foF2 calculated using regression analysis.
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the previous Section 2.2, the winter anomaly in foF2 found to be stronger during high solar activity years and weaker during low solar activity years. This is obvious, as foF2 is more during high sunspot years; the seasonal difference could be more than that in low sunspot years. It is indeed the case as has been found by normalizing the difference of winter to summer foF2 values for each year. Results of this analysis are given in Fig. 6. In this figure the top panel shows the difference of noon time foF2 during winter and summer for each year. This difference is attributable to the solar flux and all other sources, like the EEJ etc. In order to make the foF2 independent of the solar flux, we have fitted a polynomial between foF2 and the solar flux. (From the goodness of fit, we found that a straight line is as good a fit as is a polynomial of higher degree.) Since the relationship between foF2 and solar flux is linear, one could get rid of the solar flux dependence of foF2. During the year 2008 as the solar flux values during winter and summer were nearly same, we have used the solar flux of the year as the base line and have calibrated the difference foF2 values for the rest of the years. The bottom panel of Fig. 6 give the difference of noon time foF2 during winter and summer which is independent of the solar flux. It is seen from the Fig. 6 that while the winter anomaly is still seen, it magnitude is independent of the level of solar flux. During the year 2012, when the flux was high, the difference is the least. Therefore, the presence of winter anomaly in foF2 is a consistent with the different phases of solar cycle; its presence is found to be stronger during high solar activity years. Thus, our results show that the winter anomaly is a consistent feature during various phases of solar cycle in low latitude foF2, either it is solar flux dependent or independent. The winter anomaly in low latitude foF2 in the present work is found to be consistent with the results of Rao et al. 2014 wherein they have reported a consistent feature of mid latitude foF2 during various phases of solar cycle 23. Lee et al. 2011 have also reported that the winter anomaly is found to be associated with narrow altitude range near the F-peak height at northern middle latitudes. 3. Conclusions 1. The general trend of variation in observed and IRI-2016 modeled foF2 closely follows the trend of the solar flux during the period 2008–2013 but the discrepancy with regard to local time of peaking hour of foF2 is found. 2. Lomb-Scargle periodogram technique on the daily noontime (1500 LT) peak foF2 data over low latitude station Guangzhou for the years 2008–2013 showed 14, 182, 230 and 350 days periodicity. 3. Observed foF2 and IRI 2016 modeled foF2 over low latitude station Guangzhou showed consistent presence of semiannual variation and winter anomaly feature irrespective of the phase of solar activity during the period 2008–2013. We have also performed a regression analysis of foF2 with the incident solar flux to extract the foF2
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that is independent of the solar flux variation. Results on foF2 variability, obtained after the regression analysis conform that during the years of high solar activity, the presence of winter anomaly is stronger than that in years of low solar activity. 4. A significant discrepancy with regard to values of foF2 has been observed in IRI 2016 model compare to ionosonde observations. IRI 2016 model underestimates the foF2 values in winter and equinoxes and overestimates foF2 values in summer. It is also found that the DfoF2 increased in winter and equinoxes whereas decreased in summer with the progress of the solar cycle. Model also showed discrepancy with regard to local time variation of foF2 wherein model foF2 values were greater during forenoon hours and smaller during afternoon hours than the observed foF2 values throughout the period 2008–2013. The daytime peaking time of foF2 values is also found to be earlier in model compared to observed foF2.
Acknowledgements This work is supported from research grant received from Council of Scientific and Industrial Research (CSIR)-New Delhi, India under Emeritus Scientist scheme vide sanction no. 21(954)/13/EMR-II. References Abdullah, S., Zain, A.F.M., 2009. Diurnal and seasonal variations of critical frequency in Malaysia from 2005 to 2007. Appl. Mech. Mater. 225, 448–452. https://doi.org/10.4028/www.scientific.net/AMM 225.448. Adeneyi, J.O., Oladipo, O.A., Radicella, S.M., 2007. Variability of foF2 for an equatorial station and comparasion with the foF2 maps in IRI model. J. Atmos. Sol. Terr. Phys. 69, 721–733. https://doi.org/10.1016/ j.jastp.2006.12.001. Aggarwal, M., 2011. TEC variability near northern EIA crest and comparison with IRI model. Adv. Space Res. 48, 1221–1231, j. asr.2011.05.037. Appleton, V., Barnett, A.F.M., 1925. Local reflection of wireless waves from the upper atmosphere. Nature 115, 333–334. https://doi.org/ 10.1038/115333a0. Araujo-Pradere, E.A., Redmon, R., Fedrizzi, M., Viereck, R., FullerRowell, T.J., 2011. Some characteristics of the ionospheric behaviour during the solar cycle 23–24 minimum. Solar Phys. 274 (1–2), 439. https://doi.org/10.1007/s11207-011-9728-3. Bahari, S.A., Abdullah, M., 2018. A Brief Review: Response of the Ionosphere to Solar Activity over Malaysia. In: Suparta W., Abdullah M., Ismail M. (Eds.) Space Science and Communication for Sustainability. Springer, Singapore. 47-56, ISBN 978-981-10-6573-6. Doi.org/ 10.1007/978-981-10-6574-3-5. Batista, I.S., Abdu, M.A., 2004. Ionospheric variability at Brazilian low and equatorial latitudes: comparison between observations and IRI model. Adv. Space Res. 34 (9), 1894–1900. https://doi.org/10.1016/j. asr.2004.04.012. Bilitza, D., Reinisch, B.W., 2008. International Reference Ionosphere 2007: improvements and new parameters. Adv. Space Res. 42, 599– 609. https://doi.org/10.1016/j.asr.2007.07.048. Bilitza, D., Steven, A.B., Matthew, Y.W., Jonas, R.S., Patrick, A.R., 2012. Measurements and IRI model predictions during the recent solar
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Ionospheric variations over Chinese EIA region using foF2 and comparison with IRI-2016 model S.S. Rao a,⇑, Monti Chakraborty b, R. Pandey a b
a Department of Physics, MLS University, Udaipur 313001, India Department of Electronics and Communication Engineering, Tripura University, Agartala 799022, India
Received 5 January 2018; received in revised form 7 April 2018; accepted 10 April 2018 Available online 21 April 2018
Abstract In the present work, we have analyzed data of critical frequency of the F2 region (foF2) for the period, 2008–2013 over low latitude Chinese station Guangzhou (Geog. Lat. 23.10°N, Geog. Long. 113.40°E, dip, Lat. 13.49°N) and results thereof have been compared with IRI-2016 model. foF2 data set of the present study encompasses period of unusual and extended solar minimum, i.e., the years 2008–2009 and rising phase of solar cycle 24. IRI data have been obtained by choosing topside electron density profile IRI-NeQuick for two F peak models, CCIR and URSI. It is found that the general trend of variation in foF2 closely follows the trend of the solar flux during the period of study. A linear regression analysis gave a correlation coefficient of 0.98 which shows strong dependence of foF2 variation over solar flux variation. Semi-annual and annual oscillations are clearly brought out in the foF2 data using the Lomb-Scargle periodogram. A presence of semiannual and winter anomaly in observed as well as modeled foF2 at Guangzhou have found to be consistent throughout the period 2008–2013 irrespective of the phases of the solar activity. Our results also show the stronger presence of winter anomaly during the years of higher solar flux and it has been confirmed by normalizing the difference of winter to summer foF2 values for each year. Comparative results of ionosonde observation and IRI-2016 model show a significant discrepancy with regard to values of foF2 in different seasons and local time variations. IRI 2016 model underestimates the foF2 values in winter and equinoxes and overestimates foF2 values in summer. IRI modeled foF2 values using CCIR and URSI F peak models were found greater during forenoon hours and smaller during afternoon hours than the observed foF2 values throughout the period 2008–2013. Ó 2018 COSPAR. Published by Elsevier Ltd. All rights reserved.
Keywords: Equatorial ionization anomaly; Ionosonde data; IRI-2016 model; Critical frequency of the F2 region; Winter anomaly
1. Introduction The F2 region is the most potential part of the ionosphere having immense effect on the radio wave propagation. Ionospheric irregularities that occur in the F2 region during quiet as well as during disturbed periods may also disturb the satellite based communication by introducing significant time delays and range errors in high frequency ⇑ Corresponding author.
E-mail addresses: [email protected] (S.S. Rao), [email protected] (M. Chakraborty), pandey.rj@gmail. com (R. Pandey). https://doi.org/10.1016/j.asr.2018.04.009 0273-1177/Ó 2018 COSPAR. Published by Elsevier Ltd. All rights reserved.
(HF) radio signal. With the increasing demand of application of trans-ionospheric radio wave propagations and satellite communications, the F2 region is a topic of acute research interest for the several decades. Though numerous earlier works (Appleton and Barnett, 1925; Rishbeth and Setty, 1961; Forbes et al., 2000; Schunk and Nagy, 2000; Rao et al., 2014 and references therein) are available for the present understanding of the F2 region variability, yet it is still poorly understood and quantified. Features like the winter anomaly, equinoctial asymmetry and solar cycle variations are less understood due to complex electrodynamics of the equatorial ionization anomaly (EIA) zone,
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unavailability of continuous data and limited converge of the ionosondes in the region. Unavailability of data for periods of the order of a solar cycle is a big issue to model the ionosphere and to quantify the ionospheric variations. Particularly, the equatorial ionosphere over the South China Sea is complex because its variability considerably changes with time and location (Wen and Xun-jie, 1999). Many researchers (e.g., Chandra and Rastogi, 1971; Pasricha et al., 1987; Sharma et al., 1999; Forbes et al., 2000; Rishbeth and Mendillo 2001; Zhang et al., 2004; Dabas et al., 2006; Wang et al., 2009; Chandra et al., 2009; Chen and Liu, 2010; Yadav et al., 2010; Ma et al., 2012; Chaitanya et al., 2015) have studied the variability of the critical frequency of F2 region (foF2) over low latitude region. The ionospheric variabilities in F2 region are better demonstrated using International Reference Ionosphere (IRI) model (Bilitza and Reinisch, 2008). The worldwide network of the ionosondes, the powerful incoherent scatter radars (e.g., the ones at Jicamarca, Arecibo, Millstone Hill, Malvern and St. Santin), the ISIS and Alouette topside sounders, in-situ instruments on several satellites and rockets are the major sources of data for this model program (Aggarwal, 2011). Many workers (e.g., Batista and Abdu, 2004, Adeneyi et al., 2007; Sethi et al., 2008; Tamer et al., 2009) have done comparative studies of observed foF2 with IRI model from different sectors of the globe. Particularly, in the low latitude region of Southeast Asia and South China Sea region, only a few studies of foF2 variability were attempted and their brief account is given in following. For the Taiwanese region, Chuo and Lee (2008) have reported a comparative study between observed and IRI-2001 modeled foF2 for the period 1994–1999 and found that the value of daytime foF2 in the IRI model produced a good agreement during low solar activity, but underestimated during the high solar activity. They further noted that the percentage deviation of the observed foF2 values with respect to the IRI model varied from 5% to 80% during nighttime and 2–17% during the daytime. Using ionosonde measurements obtained at two low latitude stations in Thailand, Wichaipanich et al. (2012) studied the variation of the F2-layer peak electron density (NmF2) during low solar activity and results obtained thereof were compared with IRI-2007 model. Their results show that the diurnal and seasonal variations of NmF2 predicted by the IRI model generally shows the same features as in observed NmF2. In most of cases, IRI model underestimates the observed NmF2 except during the September equinox and the December solstice at 10.72°N (dip, 3.0°N) and in September equinox and March equinox at 18.76°N (dip, 12.7°N). A study of Wang et al. (2009) presented comparison of foF2 obtained from DPS4 digisonde with IRI-2001 model at low latitude station ‘Hainan’ during high to low phase of solar activity-23. They reported that the general trend of IRI predictions are in consonant with the observations for the diurnal, seasonal and solar cycle variation in foF2, however a deviation
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with regard to time of occurrence of foF2 peak and overestimation/underestimation depended upon the local time and season. At the same station Hainan, Zhang et al. (2007) have also compared F2 peak parameters with those obtained from IRI-2001 model. Lynn et al. (2006) have used the ionosonde observations over New Guinea and Indonesia to study the relationship among foF2, hmF2, ionospheric slab thickness and Doppler velocity during the nightime EIA. Maruyama et al. (2007) have analyzed the ionosonde observations from four locations in the Thailand, Indonesia and Vietnam to study the nighttime ionospheric height variations. Wichaipanich et al. (2013) have done comparative study of foF2 and hmF2 variations with IRI-2007 model for the period January 2004 to February 2007 over the conjugate locations; Chiang Mai, Thailand (Geog. Lat. 18.76°N, dip, 12.7°N) and Kototabang, Indonesia (Geog. Lat. 0.2°S, dip, 10.1°S) and at near equatorial station; Chumphon, Thailand (Geog. Lat. 10.72°N, dip 3.0°N). For foF2, most of their results show that the IRI model overestimates the observed foF2 at the Chumphon, underestimates at Chiang Mai and close to the measured ones at the Kototabang. Considering the same stations, Wichaipanich et al. (2017) have done comparative study of neural network-based predictions of foF2 (NN) and IRI-2012 model with observed foF2 for the period 2004–2012 (except for the year 2009). In general, their results show the same trends in foF2 variations between the models (NN and IRI-2012) and the ionosonde observations. Zain et al. (2008) have analyzed foF1 and foF2 data determined through the digital ionosonde installed at University Tun Hussain Onn, Parit Raja, Malaysia (Geog. Lat. 1.52°N, dip, 8.15°S) and showed the existence of F3 layer. They have found that the foF2 decreases with the appearance of the F3-layer. Over the same location, Abdullah and Zain (2009) have reported solar activity effect on foF2 variability for the years 2005 to 2007. Malik et al. (2016) have studied diurnal, seasonal and sunspot effect on the variability in maximum usable frequency (MUF) over peninsular Malaysia and compared results thereof to IRI-2012 model, for the years 2009–2011. Their results reveal that the variability in the equatorial and low latitude regions decreases when solar activity increases. However, studies using the GPS derived TEC and IRI modeled TEC were extensively done and documented for the Malaysian region (e.g., Leong et al., 2015; Bahari and Abdullah, 2018 and references therein). From the above discussion, it follows that the comparative study of the foF2 variability obtained using the ionosonde and its comparison with IRI model, particularly for the EIA zone in the Southeast Asia and South China region, is scant. Therefore, in the present work we have analyzed foF2 data for the period, 2008–2013 over low latitude Chinese station Guangzhou (Geog. Lat. 23.10°N, Geog. Long. 113.40°E, dip, 13.49°N) and results thereof have been compared with IRI-2016 model. foF2 data set of the present study encompasses period of unusual and
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extended solar minimum, i.e., the years 2008–2009. The ionosphere during this solar minimum was contracted to a thin shell, with the O+/H+ transition height and ion temperature reaching very low values (Heelis et al., 2009) following the contraction of the thermosphere to record low level (Solomon et al., 2010). Thus, study of the low latitude ionosphere during this prolonged solar cycle 23/24 and during the rising phase of solar cycle 24 is an important contribution. For this, foF2 data for the period 2008– 2013 over the Guangzhou station have been obtained from the website: https://www.ukssdc.ac.uk. This data is provided by the United Kingdom Solar System Data Center (UK SSDC) retrieved from the analog ionosonde GU421, installed at Guangzhou. IRI-2016 model foF2 data using topside electron density profile option IRI-NeQuick for F peak models CCIR and URSI have been downloaded from the website https://cohoweb.gsfc.nasa.gov. The monthly mean absolute values of solar flux at F10.7 cm have been downloaded from the link-ftp://ftp.ngdc.noaa.gov. 2. Result and discussion 2.1. Variation of monthly mean foF2 The variation of daytime maximum foF2 over Guangzhou during the period 2008–2013 along with solar flux at F10.7 cm is presented in Fig. 1. A green curve in Fig. 1 shows variation of monthly mean absolute solar flux at F10.7 cm and the blue curve show the variation of monthly mean daytime maximum foF2 for the period 2008–2013.
The scale of absolute value of solar flux at F10.7 cm (in sfu) is shown on right ordinate. It is seen from Fig. 1 that the level of solar flux remained almost constant, 70 sfu, for the period January 2008 – January 2010. Thereafter it showed rising trend and became largest in December 2011whereafter it had humps and dumps and in December 2013 its magnitude was 130 sfu. The solar cycle 24 reached its maxima (170 sfu) in the months of January 2014 (not shown in figure) followed by a declining phase of the solar cycle and it is now expected that minima would appear in the years 2018–2019. It can be seen from Fig. 1 that the general trend of variation in foF2 has a positive slope which closely follows the trend of the solar flux during the period of study. Our results are in agreement with earlier studies in different sectors as is shown in the works of Araujo-Pradere et al. (2011), Liu et al. (2011), and Yang et al. (2012) who reported that the global values of F region critical frequency (foF2) and GPS-TEC reached lower level in the solar minimum (years, 2008–2009) compared to the minimum of previous solar cycles. Since, Mielich and Bremer (2013) and Lasˇtovicˇka et al. (2006) have suggested to use solar flux at F10.7 cm to derive reliable ionospheric trends, we have also attempted a linear regression analysis to determine solar flux dependence of foF2 variation (see, Fig. 2). It is known that the F10.7 cm solar flux has no seasonal dependence whereas foF2 variation has. To make the seasonal independent foF2 data, we have employed annual averaging of foF2 data. We have calculated correlation coefficient (R) between annually averaged noontime
Fig. 1. Variation of monthly mean daytime maximum foF2 (blue curve) over low latitude station Guangzhou along with variation of absolute solar flux at F10.7 cm (green curve) is shown for the period 2008–2013. The general trend of the variation of foF2 is seen to be in tandem with that of the solar flux in low, rising and high solar activity phase. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 2. Linear regression analysis curve between annually averaged monthly mean daytime maximum foF2 and absolute solar flux at F10.7 cm is shown. The correlation coefficient for the two parameters is found to be 0.98 which showed strong dependence of foF2 variation on solar flux variation.
monthly mean foF2 values and absolute values of solar flux that is found to be 0.98. This shows strong solar cycle dependency of foF2 variation. It can be seen from Fig. 1 that throughout the period 2008–2013, monthly mean noontime foF2 value showed maxima during March-April and September-October with minimum in June. Thus, semi annual oscillation is present for all the years encompassing different phases of solar activity. Therefore, the semi-annual oscillation in foF2 is found to be a consistent feature with variation in solar flux. This is in agreement with earlier works from this region (Zhang et al., 2007; Wang et al., 2009). Regarding the foF2 values during solstices, it can be seen from Fig. 1 that the foF2 value in summer solstice of any given year is lower than that of the following winter months. For example, it was 8 MHz in June and 10 MHz in December for the years 2008 and 2009. What is worth noting is the fact that while the solar flux was almost constant (70 sfu) during the period 2008–2009, winter anomaly in foF2 still persisted. This is consistent with the work of Rao et al. (2013) who have stressed that the true feature of winter anomaly could only be identified by its appearance when level of solar flux is same during the summer and winter solstices of a year as this automatically removes the seasonal variation of ionospheric parameter (foF2 in the present case). Hence, the prolonged solar minimum of solar cycle 23/24 provided a unique opportunity to study the phenomenon of winter anomaly in foF2. Further, Rao et al. (2013) have argued that the appearance of winter anomaly in TEC is intimately related to the level of solar flux in the summer and winter solstices of a year. In variance with winter anomaly in TEC variation, presence of winter anomaly in foF2, during the rising phase of a solar cycle, irrespec-
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tive of level of solar flux, during the summer and winter solstice, is a consistent feature. For exploring the periodicity, we have used the LombScargle periodogram technique (Scargle, 1982) on the daily noontime (15 LT) peak foF2 data for the duration of 2008 – 2013. Periodogram analysis using this method is exactly equivalent to the least-square fitting of sine curves to the data and the technique has many advantages over conventional Fast Fourier Transform method as it is suitable for analysis of unevenly spaced data, that is, it can take the edge off missing data problem (Horne and Baliunas, 1986). Fig. 3 reveals that the underlying periodicities of foF2 data that are 14, 182, 230 and 350 days (with False Alarm Peak of 0.1%). Although many authors have reported the 27 day periodicity as a most prominent periodicity of foF2 data, it is not prominent in our analysis. Hence, semi-annual and annual oscillations are clearly brought out. 2.2. Comparative study of foF2 variation with IRI-2016 model We have also attempted to find out the extent to which the ionosonde foF2 values for Guangzhou station are comparable with the ones predicted by the IRI-2016 model. The contour plot of monthly mean foF2 obtained from the ionosonde and from the IRI model as a function of local time (during 0700–1900 LT) for the period 2008– 2013 is given in Fig. 4. We have analyzed model foF2 data for topside electron density profile options IRI-NeQuick, IRI-2001 and IRI01-corr for the URSI and CCIR coefficients. Our analysis showed exactly the similar variation of foF2 in all three electron density profiles for each coefficient. Therefore we have presented results only for IRINeQuick option using URSI and CCIR coefficients. The upper panel of Fig. 4 presents the foF2 variations from the ionosonde and the middle one is the variation from IRI-NeQuick model using the URSI coefficient and the lower one presents the IRI-NeQuick model using the CCIR coefficients. The abscissa of each panel of Fig. 4 gives months of the respective year and the color bar on the right of the figure gives scale of foF2. It can be seen from Fig. 4(a-c) that the general feature of modeled foF2 variation for URSI and CCIR coefficients is almost similar. The only difference that is found is regarding the magnitude of foF2 values. Fig. 4 shows that the noon time foF2 from the CCIR option had greater magnitude than the URSI option during the high solar activity. foF2 variation from the CCIR option had longer duration of daytime peak compare to the URSI option. It can be seen from Fig. 4(a) that the maximum value of observed foF2 for all the seasons starts to increase from the year 2008 and is highest during the winter months of the year 2011. Thereafter it decreases slightly, by 1 MHz, but was still higher compared to its values prior to the year 2011. Thus, the foF2 variation closely follows the variation in solar flux for the period 2008–2013. Similar
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Fig. 3. Lomb-Scargle periodogram for the foF2 data with false alarm peaks.
Fig. 4. (a) Contour plot of observed monthly mean foF2 over low latitude station Gunagzhou. (b) Contour plot of IRI 2016 modeled foF2 over low latitude station Gunagzhou obtained using URSI F peak model for IRI-NeQuick option. (c) Contour plot of IRI 2016 modeled foF2 over low latitude station Gunagzhou obtained using CCIR F peak model for IRI-NeQuick option.
to observed foF2, the variation of model foF2 obtained for an option IRI NeQuick using URSI and CCIR model, presented in Fig. 4 (b) and Fig. 4(c) respectively, also follows the solar flux variation during the period 2008–2013, it was lower in the year 2008 and 2009 and then increased with the progress of solar cycle 24. It can be seen from Fig. 4(a-c) that the noontime observed foF2 was largest around 15LT ± 1 whereas the noontime modeled foF2 was largest 1330 h throughout
the period 2008–2013. Thus, there is a slight local time difference with regard to the peaking hour of foF2. Comparison of Fig. 4(a) with Fig. 4(b) and Fig. 4(c) shows that the observed values are lower than the model values in the forenoon hours. This result is in agreement with Wang et al. (2009) wherein they have reported that the IRI predictions generally overestimate the observed ones during a few hours around sunrise (0600–0900 LT). It can be also seen from Fig. 4 that the noontime difference
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in values decreases with increase in solar activity. This showed discrepancy with regard to local time variation of modeled foF2. A comparative study of local time variation of observed and IRI modeled foF2 during solar cycle 23 over low latitude by Chaitanya et al. 2015 showed that the IRI model overestimated foF2 values irrespective of local time. In Fig. 4(a), the observed foF2 shows two well defined maxima during equinoxes and minimum during summer solstice during all the years for the period 2008–2013. It can be seen from the Fig. 4(a) that the second maxima is strongest compared to first one, during the rising phase, years 2010–2013. For the low solar activity year 2008, the vernal equinox was stronger compared to autumn equinox but for the low solar activity year 2009 both equinoxes are seen to be comparable. The model results in Fig. 4(b) and 4 (c) also shows clear cut presence of double peaks in foF2 during equinoctial months of each year. Thus, model also shows a consistent presence of semi-annual anomaly in low latitude foF2 throughout the period 2008–2013. The strength of the two peaks was more or less similar for the years 2008–2011 but an asymmetry is clearly seen for the period 2012–2013. For the year 2012, the strength of a first maximum was stronger than second one, while for the year 2013; second maximum was stronger than first one. It can be seen from Fig. 4(a–c) that foF2 values in the winter months (December/January) were larger than those in summer months (June/July) throughout the period 2008–2013. This indicates the presence of winter anomaly in foF2 over low latitude during low, rising and high phases of the solar activity. A careful look at Fig. 4(a) reveals that the difference of summer-winter foF2 values was smaller
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during low solar activity years and higher during high solar activity years. Thus, winter anomaly feature in a foF2 is a consistent feature during the period 2008–2013 but it was more prominent during the high solar activity year 2011– 2013. For the model results, Fig. 4(b) and Fig. 4(c) shows that the values of modeled foF2 during the summer and winter months were comparable during the years 2008 and 2009 but for the years 2010–2013, the modeled foF2 values during the winter months were significantly larger than those in summer months. Thus, IRI model also shows consistent presence of winter anomaly during the period 2008–2013 wherein its presence during rising and high solar activity years is prominent while during the low solar activity its presence seems to be a weak. The stronger winter anomaly during high sunspot years is expected as foF2 is more during high sunspot years; the winter to summer difference could be more than that in low sunspot years. This is discussed separately in the Section 2.3 wherein difference of winter to summer noontime foF2 is normalized to constant level of solar flux using regression analysis. A comparison of observed and modeled foF2 is better illustrated in Fig. 5 wherein difference of largest daytime monthly mean foF2 have been calculated and presented. The upper panel of Fig. 5 gives the difference of observed foF2 and modeled foF2 calculated using URSI option and lower panel gives the difference of observed foF2 and modeled foF2 calculated using CCIR option. The abscissa of Fig. 5 shows the months of the respective years of the period 2008–2013 and left ordinate shows the difference of observed and model monthly mean of peak time foF2 values (DfoF2). Fig. 5 give positive gradients (+DfoF2) during the winter and equinoctial months which
Fig. 5. A comparison of observed and modeled foF2 is shown wherein difference of largest daytime monthly mean foF2 has been calculated. The upper panel and lower panel gives the difference of observed foF2 and modeled foF2 calculated using URSI and CCIR option, respectively.
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shows smaller modeled foF2 values compared to observed values while in the summer months of period 2008–2013, Fig. 5 shows a negative gradient (-DfoF2) wherein modeled foF2 values were greater than the observed ones. Thus, IRI 2016 model underestimates the foF2 values in winter and equinoxes and overestimates foF2 values in summer. It can be easily seen from Fig. 5 that the DfoF2 increased in winter and equinoxes whereas decreased in summer with the progress of the solar cycle. Many workers have reported a good agreement between ionospheric observations with the previous versions of IRI models, namely, IRI-2001, 2007 and 2012 at different levels of solar activity (Zhang et al., 2007; Wang et al., 2009; Chuo and Lee, 2008; Sethi et al., 2008), but discrepancies between the model and observed data are also reported (Bilitza et al., 2012 and references therein). Although IRI 2016 model has been upgraded with many new and improved descriptions of ion composition at different levels of solar activity along with the new developed model for F2 peak heights (Bilitza et al., 2017), the present study found discrepancy with regard to values of foF2. As the station Guangzhou is located in the EIA crest region and therefore the variation in foF2 is also influenced by the electric field in the magnetic equator region. Equatorial and low latitude ionosphere is strongly depended on the strength of the equitorial electrojet, EEJ (Dabas et al., 1984; Chandra et al., 2009) and neutral winds (Gurubaran and Sridharan, 1993). IRI model fails to account for the variation in the ionosphere which is driven by these two factors. This fact is supported by results from the Indian EIA region as discussed hereafter. Sharma et al. (1999) have examined the foF2 over the EIA crest location
Ahmedabad and found that the long term changes in foF2 are related to the long term changes in the zonal electric field or the secular changes in the position of dip equator. Upadhayaya and Mahajan (2013) have analyzed foF2 data of equatorial station Trivandrum and EIA crest location Bhopal in the Indian region and then compared it with strength of EEJ. They have reported that the variability in low and equatorial ionosphere is strongly related to the EEJ. Using spectral analysis, they showed 13–14 days periodicity both in EEJ strength and in foF2 for the EIA location Bhopal. We have also observed 14 day periodicity in noontime foF2 data for the EIA crest location Guangzhou during the period 2008–2013 (see, Fig. 3). NmF2 variation during the rising phase of solar cycle 24 over the low latitude station Dibrugarh (27.5°N, 17°N dip) measured using the Canadian Advanced Digital Ionosonde (CADI), have been studied by Kalita et al. (2015). They have shown the good correlation of NmF2 with solar activity and EEJ. Also due to the low coverage of the ionosondes in the low latitudes, particularly in the Southeast Asia and South China region, an essential data source for the IRI model (Bilitza and Reinisch, 2008) is inadequate. Further, the availability of data for period of the order of a solar cycle is a big issue to model the ionosphere. 2.3. Regression analysis of foF2 with solar flux Since ionization in the ionosphere depends on the solar flux, it is obvious that the amount of foF2 and variation thereof should, besides other factors (EEJ and neutral winds), also depend on the solar flux and its variation, particularly on the 11-year solar cycle. As discussed in
Fig. 6. Seasonal difference of noontime monthly mean foF2 over Guangzhou during the period 2008–2013, is plotted wherein the top panel shows the winter to summer difference of noon time foF2 that is attributable to the solar flux and all other sources, like the electrojet etc. The bottom panel gives the winter to summer difference of solar flux independent noon time foF2 calculated using regression analysis.
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the previous Section 2.2, the winter anomaly in foF2 found to be stronger during high solar activity years and weaker during low solar activity years. This is obvious, as foF2 is more during high sunspot years; the seasonal difference could be more than that in low sunspot years. It is indeed the case as has been found by normalizing the difference of winter to summer foF2 values for each year. Results of this analysis are given in Fig. 6. In this figure the top panel shows the difference of noon time foF2 during winter and summer for each year. This difference is attributable to the solar flux and all other sources, like the EEJ etc. In order to make the foF2 independent of the solar flux, we have fitted a polynomial between foF2 and the solar flux. (From the goodness of fit, we found that a straight line is as good a fit as is a polynomial of higher degree.) Since the relationship between foF2 and solar flux is linear, one could get rid of the solar flux dependence of foF2. During the year 2008 as the solar flux values during winter and summer were nearly same, we have used the solar flux of the year as the base line and have calibrated the difference foF2 values for the rest of the years. The bottom panel of Fig. 6 give the difference of noon time foF2 during winter and summer which is independent of the solar flux. It is seen from the Fig. 6 that while the winter anomaly is still seen, it magnitude is independent of the level of solar flux. During the year 2012, when the flux was high, the difference is the least. Therefore, the presence of winter anomaly in foF2 is a consistent with the different phases of solar cycle; its presence is found to be stronger during high solar activity years. Thus, our results show that the winter anomaly is a consistent feature during various phases of solar cycle in low latitude foF2, either it is solar flux dependent or independent. The winter anomaly in low latitude foF2 in the present work is found to be consistent with the results of Rao et al. 2014 wherein they have reported a consistent feature of mid latitude foF2 during various phases of solar cycle 23. Lee et al. 2011 have also reported that the winter anomaly is found to be associated with narrow altitude range near the F-peak height at northern middle latitudes. 3. Conclusions 1. The general trend of variation in observed and IRI-2016 modeled foF2 closely follows the trend of the solar flux during the period 2008–2013 but the discrepancy with regard to local time of peaking hour of foF2 is found. 2. Lomb-Scargle periodogram technique on the daily noontime (1500 LT) peak foF2 data over low latitude station Guangzhou for the years 2008–2013 showed 14, 182, 230 and 350 days periodicity. 3. Observed foF2 and IRI 2016 modeled foF2 over low latitude station Guangzhou showed consistent presence of semiannual variation and winter anomaly feature irrespective of the phase of solar activity during the period 2008–2013. We have also performed a regression analysis of foF2 with the incident solar flux to extract the foF2
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that is independent of the solar flux variation. Results on foF2 variability, obtained after the regression analysis conform that during the years of high solar activity, the presence of winter anomaly is stronger than that in years of low solar activity. 4. A significant discrepancy with regard to values of foF2 has been observed in IRI 2016 model compare to ionosonde observations. IRI 2016 model underestimates the foF2 values in winter and equinoxes and overestimates foF2 values in summer. It is also found that the DfoF2 increased in winter and equinoxes whereas decreased in summer with the progress of the solar cycle. Model also showed discrepancy with regard to local time variation of foF2 wherein model foF2 values were greater during forenoon hours and smaller during afternoon hours than the observed foF2 values throughout the period 2008–2013. The daytime peaking time of foF2 values is also found to be earlier in model compared to observed foF2.
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