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Variation of ionospheric range errors for L1 frequency GPS users during the 23rd solar cycle over BAHR IGS station
Available online at www.sciencedirect.com
ScienceDirect Advances in Space Research 64 (2019) 1034–1045 www.elsevier.com/locate/asr
Variation of ionospheric range errors for L1 frequency GPS users during the 23rd solar cycle over BAHR IGS station Abdollah Masoud Darya a,b,⇑, Muhammad Mubasshir Shaikh a,c, Ilias Fernini a,c a
Ionospheric Research Laboratory, Sharjah Center for Astronomy and Space Sciences, Sharjah, United Arab Emirates b Department of Electrical and Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates c Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, United Arab Emirates Received 10 July 2018; received in revised form 25 May 2019; accepted 27 May 2019 Available online 4 June 2019
Abstract Errors induced by the ionosphere on global navigation satellite systems (GNSS) signal propagation significantly affect the positioning calculation done by ground receivers. These ionospheric errors may end up reaching tens of meters in the final positioning calculation. In this study, the ionospheric range error (IRE) was monitored over the local ionosphere of BAHR, Bahrain (26.209N, 50.608E) during the period of the 23rd solar cycle. IRE values were obtained through observation data derived from RINEX files and compared with NeQuick 2 (NQ2) model calculations. It was found that, for the region of study, NQ2 overestimated the total electron content (TEC) values as compared to observation data, resulting in higher IRE values of up to 12 m. However, IRE derived using GNSS observations and NQ2 follow similar trends over the course of the solar cycle. IRE values were also compared to the smoothed sunspot number (SSN) and F10.7 indexes which resulted in significant correlation between the seasonal calculation of IRE and solar activity. Throughout the 23rd solar cycle, the highest IRE values were found during the equinoxes and the lowest during solstices. The largest IRE value was observed in the vernal equinox of 2000 (19.13 m), while the lowest IRE value was observed in the winter solstice of 1998 (0.276 m). Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
Keywords: Ionosphere; NeQuick; Vertical total electron content; Smoothed sunspot number; Global positioning system; Global navigation satellite systems
1. Introduction With the rise of new modes of transportation such as autonomous air taxis and self driving cars, acquiring accurate positioning has never been more critical (Gleason, 2009; Sadlier et al., 2017). To obtain a receiver’s position using the global positioing system (GPS), the distance between the satellite and the receiver must be determined. This distance is calculated by multiplying the time of prop-
⇑ Corresponding author.
E-mail addresses: [email protected] (A.M. Darya), [email protected] (M.M. Shaikh), [email protected] (I. Fernini). https://doi.org/10.1016/j.asr.2019.05.044 0273-1177/Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
agation (the time taken by the signal to travel from the satellite to the receiver) by the speed of light. Errors in the propagation time increase or decrease the computed range which, in turn, affects the positioning calculation. As a rough approximation, an error of 1 ms (1 106 s) in propagation time can produce a ranging error of 300 m (Bidikar et al., 2014). As the GPS signal passes through the atmosphere, it is adversely affected. Through refraction and diffraction, the atmosphere alters the direction of the signal, increasing its propagation time, which results in ranging errors. The layer of the atmosphere that most influences the transmission of global navigation satellite system (GNSS) signals is the ionosphere. It is a layer of
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the upper atmosphere, ionized by solar and cosmic radiation, creating a region of free electrons, ranging from 50 to 1000 km above the Earth’s surface. These free electrons affect radio wave propagation in various ways by modifying characteristic wave parameters such as amplitude, phase and polarization. This marks the ionosphere as the most significant source of error for single frequency GPS users (Hoque and Jakowski, 2012). To effectively eliminate ionospheric error from the signal, the number of free electrons present along the signal’s propagation path must be calculated or estimated accurately. Total electron content (TEC) is a dynamic quantity that depends on many geographical and geophysical parameters such as local time, season, solar activity, longitude and latitude coordinates. With TEC values typically ranging between 1016 and 1018, ranging errors could be in the order of meters (Hoque and Jakowski, 2011; Ya’acob et al., 2008). During the peak of solar cycles, however, the value of the TEC could exceed 1018 which could scale up the error to tens of meters. Accurate measurement and modeling of TEC is an active and predominant area of space weather research around the globe, particularly, for low latitude and polar regions where ionospheric effects on radio signals are most adverse. Mass market GPS receivers mostly operate on a single frequency and experience the greatest effect of ionospheric delays (Yuan and Ou, 2001). There are two methods to compensate for this delay. Either by using a model that represents the expected ionospheric delay such as the NeQuick 2 (NQ2) model, or through real time observation and mapping. NQ2 is the most recent version of the NeQuick ionosphere electron density model (Nava et al., 2008). It is a three dimensional and time dependent ionospheric electron density model that allows for calculation of TEC along any ground to satellite path through numerical integration. Real time observations offer greater accuracy. However, they are limited by the need of a regional network of dual frequency GNSS receivers (Allain and Mitchell, 2008). The idea of using dual frequency GNSS receivers to study regional ionospheric variations started decades ago (Jorgensen, 1978; Mannucci et al., 1998). Since then, many similar studies have been performed in various regions (Ansari et al., 2017; Breed et al., 1998; Edemskiy et al., 2018; Leong et al., 2009; Ya’acob et al., 2009) using the same technique. The current study is based on data retrieved from a local area receiver to study the effect of 23rd solar cycle on ionospheric errors experienced by L1 single frequency GPS users. It aims to serve as a reference for measuring ionospheric errors experienced by single frequency GPS users in a low to mid latitude, equatorial ionization anomaly (EIA) region (Bahrain). It also compares between results obtained through Receiver Independent Exchange Format (RINEX) observations and the NQ2 model, to gain greater insight into ionospheric analysis for the local region. The seasonal trend of Ionospheric Range Error (IRE) throughout the 23rd solar cycle is also analysed and discussed. The
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paper is organized as follows: in section II, the developed methodology will be presented. In section III, measurement results are presented and discussed along with details of the data used in this case study. Finally, in section IV, the conclusion is presented with possible future work to be done in continuation of this study. 2. Methodology The ionosphere is a dispersive medium that delays radio signals. A dual frequency GPS receiver can measure the ionospheric delays between two GPS transmitted frequencies; L1 (at 1.575 GHz) and L2 (at 1.227 GHz). GNSS data from receiver independent exchange format (RINEX) files is used to calculate the slant TEC (STEC) between satellite and receiver. STEC is defined as the integral of the electron density along the path of the signal between the satellite and the receiver through a column of 1 m2 cross sectional area. STEC is calculated as follows: " # 1 f 21 f 22 ð P 2 P 1 Þ þ bR þ bS ð1Þ STEC ¼ 40:3 f 21 f 22 where f1 is the L1 frequency (1.575 GHz), f2 is the L2 frequency (1.227 GHz), P2 P1 is the group delay obtained from the difference between the precision code derived pseudo ranges measured in the L2 and L1 frequencies respectively, and bR and bS are the receiver and satellite biases respectively (Jorgensen, 1978; Wang et al., 2016). STEC has also been extracted using the NQ2 model. The NQ2 model requires several inputs; the location of the receiver and the satellites, the average daily F10.7 solar index (omniweb.gsfc.nasa.gov/form/dx1.html), time of day and date. To relate the STEC value to a geographical location, STEC measurements are converted into VTEC using the single layer model (SLM). In SLM, the ionosphere is approximated as a spherical shell around the Earth at a chosen altitude above the Earth’s surface. The intersection of this shell and the line of sight (LoS) between the satellite and the receiver is known as the ionospheric pierce point (IPP). The zenith angle of the satellite with respect to the IPP is used to map the measured STEC to VTEC. In this study, the median height of the ionosphere is assumed to be at 350 km above the Earth’s surface. This facilitates the conversion of STEC to VTEC at the IPP (Gao and Liu, 2002). STEC is converted to VTEC using the following mathematical relation: VTEC ¼ STEC Cosv
ð2Þ
where v is the angle of incidence between the nadir and LoS at the IPP (Breed et al., 1998). In this study the mean VTEC is considered. That is, the averaged VTEC derived from multiple satellite passes at a single point in time. To better understand the precision of the measurements and the deviation from the mean measured value, Fig. 1 has been plotted to show the most active and least active days
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Fig. 1. Mean (blue line) and the deviation from the mean (error bars) of VTEC. (a) Day with highest F10.7 value (257), autumnal equinox of 2001. (b) Day with lowest F10.7 value (67.3), summer solstice of 2008. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
of the study period in terms of F10.7 index. The error bars show the range of VTEC values derived at a single point in time (dotted line) and the mean value considered (solid blue line). The final step after obtaining the VTEC would be placing it into Eq. (3) where IRE is calculated using the L1 or L2 frequency as: I¼
40:3 TEC f2
ð3Þ
where f is the L band frequency of the signal and I is IRE in meters (Ya’acob et al., 2008). As is seen from Eq. (3), the IRE is directly proportional to the TEC. Since this study is concerned with L1 GPS users, L1 has been used to calculate IRE. The severity of the ionospheric effects on GPS signals is mostly reliant on the signal time of flight through it. A signal originating from a satellite near the observer’s horizon must pass through a larger part of the ionosphere to reach the receiver than does a signal from a satellite near the observer’s zenith. Additionally, for lower elevation angles, multipath errors also increase significantly. Therefore, in this study, data measured at elevation angles greater than 30° were only considered, to avoid unwanted multipath errors (Takahashi et al., 2016). For this study, the pseudo-ranges were obtained from daily RINEX files produced by the BAHR dual frequency GPS receiver, while the satellite and receiver biases were obtained from IONEX files provided by the Chinese Academy of Sciences (CAS). All previously mentioned datasets are accessible through NASA’s archive of space geodesy data (cddis.gsfc.nasa. gov/gnss/data/daily).
3. Data Analysis The current case study concerns the TEC measurement over the local ionosphere at the BAHR IGS station (see Table 1) during the 23rd solar cycle (1998–2008). Due to data availability restrictions, the first data point selected was the autumnal equinox of 1998 and the last was the summer solstice of 2008, adding up to an even period of 10 years. The TEC data was retrieved from the Bahrain (BAHR) GNSS station which is operated by the US National Geospatial Intelligence Agency (NGA). Daily analysis was conducted for all four seasons by selecting the first day of each season (vernal equinox, summer solstice, autumnal equinox and winter solstice) from the autumnal equinox of 1998 to the summer solstice of 2008. These daily calculations were performed using data sampled at 15 min intervals. This study focuses on the effects of the 23rd solar cycle on Ionospheric Range Errors (IRE) experienced by L1 frequency GPS users. In general, the solar cycle is monitored by two parameters; the solar radio noise flux at 10.7 cm or 2800 MHz (F10.7) and the smoothed sunspot number (SSN), both being the longest
Table 1 Details regarding the BAHR station used as this study’s data source. Parameter
Value
Site Name Four Character ID Country City Geographic Coordinates, LLA (deg, deg, m) Geomagnetic Coordinates, LLA (deg, deg, m)
Bahrain GPS Station BAHR Bahrain Manama 26.209, 50.608, 17.03 20.55, 126.92, 17.03
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running records of solar activity (www.swpc.noaa.gov). These indices are commonly used in research studies related to space weather monitoring (Greenkorn, 2012; Lampropoulos et al., 2016; Le Moue¨l et al., 2012; Li et al., 2014). The F10.7 index correlates well with the SSN as well as a number of Ultraviolet (UV) and visible solar irradiance records. This work studies the correlation between the SSN/F10.7 and the IRE. Fig. 2 shows the variation of smoothed sunspot number (SSN) and the F10.7 index for the period from 1993 to 2013. It is clearly observed that the 23rd solar cycle was double peaked (Georgieva, 2011). Both SSN and the F10.7 started increasing around the end of 1996 until they formed their first peak in 2000, with the second peak being in the year 2002. After the second peak, the values of SSN and the F10.7 decreased to their minimum in 2009. Figs. 3–6 show the daily variation of the IRE for the two equinoxes and two solstices for the 23rd solar cycle (1998 to 2008). IRE has been calculated for the 24 h period from 1998 to 2008 on March 21, June 21, September 21 and December 21 for vernal equinox, summer solstice, autumnal equinox and winter solstice, respectively, or one day before/after, depending on data availability. In each plot, the x axis shows the time of observation in UTC (UTC + 3 for local time), while the y axis shows the IRE value in meters. Fig. 3(a) shows the daily variation of IRE for the vernal equinoxes from 1999 to 2008 observed at BAHR. IRE measured in the year 2000 (red curve) and 2002 (blue curve) are the maximum of the whole study period and coincide with the twin peaks of the 23rd solar cycle (Fig. 2). The IRE values decrease from these points onwards, except for a slight increase in 2004 (dark magenta curve) at around 10:00 (UTC). IRE was lower in 2001 (light magenta curve) than the values of the two peaks in 2000 and 2002 as was also observed by (Mansoori et al., 2013). Fig. 3(b) shows similar IRE calculation using NQ2 as a background ionospheric model. It is clearly observed that
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NQ2 is overestimating the IRE values in all seasons and, generally, for the whole solar cycle. Nevertheless, the pattern of the curves display similar features, showing that NQ2 estimates the average ionospheric conditions well, given that the correct solar activity parameter (F10.7) is used. For high solar activity years, the post sunset enhancement of TEC (Balan and Rao, 1984; Jain, 1987) on IRE is also visible (Fig. 3(b)). This feature is not so prominent in the observation values for the vernal equinox (Fig. 3(a)). Fig. 3(c) shows the difference between IRE values of the model and observations. What can be seen is that for the first 5 h of the day the difference between the model and observation readings are minimal, while at midday they are the highest. A prominent feature are the differences at around 15–20 UTC for the the years of 2000 and 2002. For all cases displayed in the figure it can be clearly seen that the model IRE exceeds that of observations, for most part of the day. The maximum amount where the model has exceeded the observation is around 10 m, with the years 2000 and 2002 being the ones with the largest difference. Fig. 4(a) shows the daily variation of IRE for the autumnal equinoxes from 1998 to 2007 observed at BAHR. The IRE values peaked in 2000 (red curve) coinciding with the first peak of the 23rd solar cycle and again in 2001 (light magenta curve) and 2002 (blue curve) to show the same twin peak trend as seen in Fig. 2, however they are not as prominent. The values of IRE declined afterward until their minimum in 2007. In comparison with the vernal equinox, the values of IRE for autumnal equinox are relatively lower for most of the 23rd solar cycle. This is believed to be an effect of the equinoctial asymmetry in the topside electron density at low to mid latitudes which has also been observed by (Bailey et al., 2000). Fig. 4(b) shows the NQ2 results. The overestimation of the model is evident again. A discrepancy in the NQ2 values appears for the cases of years 1998, 1999, 2002 and 2003, where they appear to have similar trends and magni-
Fig. 2. Variation of the smoothed sunspot number (red curve) and the F10.7 index (blue curve) for the period from 1993 to 2013. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Fig. 3. Daily IRE measurements for the vernal equinoxes (coloured curves) from 1999 to 2008. The y axis represents the magnitude of the IRE in meters while the x axis represents the time in UTC hours. (a) Observation data, (b) NeQuick 2 model data. (c) Difference between model and measurement IRE.
tudes, however that is not the case in the observation. This is because of the generalization of the empirical model which smoothen short term changes in the ionosphere. The post sunset enhancement effect is clearly visible in high solar activity years in both experimental and model data. Similar to Fig. 3(c) the model IRE of Fig. 4(c) exceeds that of observations except for the case of the beginning of the 1999 observation where the observation reading exceeded that of the model by more than 5 m. Similar to Fig. 3(c) the maximum amount where the model has exceeded the observation is around 10 m at the case of the year 2000.
Fig. 5(a) shows the daily variation of IRE for the winter solstice from 1998 to 2007. The winter solstice shows a rather low trend where the only significant peak is observed for the year 2001. In fact, the winter solstice peak is much lower than the peak values observed for the equinoxes (see Figs. 3 and 4). This phenomenon has been reported by multiple sources, and appears to be more prevalent during winter and summer seasons (Bailey et al., 2000; Balan et al., 1998; Balan et al.,2000; Richards, 2001; Torr and Torr, 1973). The previous sources state that the reason for this are the equatorial anomalies, occurring as a result of variations in the maximum electron density of the F2 layer of
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Fig. 4. Daily IRE measurements for the autumnal equinoxes (coloured curves) from 1998 to 2007. The y axis represents the magnitude of the IRE in meters while the x axis represents the time in UTC hours. (a) Observation data, (b) NeQuick 2 model data. (c) Difference between model and measurement IRE.
the ionosphere for equatorial or near equatorial regions. The post sunset anomaly is evident in all years; more prominent in the peak years of the solar cycle and less prominent in quiet years of the solar cycle. This observation has been reported previously as a regular feature of TEC in winter (Balan and Rao, 1984; Jain, 1987). However, NQ2 was not able to produce this feature as prominent (see Fig. 5b) in the case of the winter solstice as it had in the cases of vernal and autumnal equinoxes (Figs. 3 (b) and 4(b)). Following the same trend set previously, NQ2 overestimated the IRE values for each year when compared to experimental observations.
Fig. 5(c) shows a larger difference between model and observations as compared to Figs. 3 and 4(c), mainly visible in the cases of the years 1999 and 2000, where it exceeds 12 m. Similar to the previous cases the model values exceed those of observations throughout the study period. Fig. 6 shows the daily variation of IRE for summer solstice from 1999 to 2008. The summer solstice IRE values are clearly seen to be significantly lower than those observed at the equinoxes (Figs. 3 and 4) and winter solstice (Fig. 4). This anomaly has been observed by multiple studies (Bailey et al., 2000; Balan et al., 1998; Balan et al.,2000; Richards, 2001; Torr and Torr, 1973). It is
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Fig. 5. Daily IRE measurements for the winter solstice (coloured curves) from 1998 to 2007. The y axis represents the magnitude of IRE in meters while the x axis represents the time in UTC hours. (a) Observation data, (b) NeQuick 2 model data. (c) Difference between model and measurement IRE.
believed to be the result of variations in the maximum electron density of the F2 layer (NmF2) of the ionosphere, where the daytime NmF2 values are much higher in winter than in summer and less than that of the two equinoxes (Gerzen et al., 2013; Liu et al., 2007; Xingliang et al., 2005). Similar to the winter solstice, the IRE values for the summer solstices peak in the year 2001 and then decrease to a minimum in 2008. The model results presented in Fig. 6(a) are in line with observation results where it shows the lowest IRE values of all seasons. Additionally, no post sunset enhancement is evident in summer for model as well as for observation data.
Similar to Figs. 3–5(c), Fig. 6(c) shows that the model IRE readings exceed those of observations throughout the study period, with the highest case being that of the year 2000. Figs. 7 and 8 show a summary of the seasonal variation, for all four seasons, of IRE maxima (Fig. 7) and minima (Fig. 8) for the 23rd solar cycle. These are the maximum and minimum values of the daily IRE values already presented in Figs. 3–6. From Fig. 7(a), it is observed that the IRE trends of the autumnal and vernal equinoxes closely resemble each other not only for their peaks and troughs but also for their overall behavior. The only excep-
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Fig. 6. Daily IRE measurements for the summer solstice (coloured curves) from 1999 to 2008. The y axis represents the magnitude of the IRE in meters while the x axis represents the time in UTC hours. (a) Observation data, (b) NeQuick 2 model data. (c) Difference between model and measurement IRE.
tion is a small peak in 2004 for the vernal equinox. Similarly, the behavior of observed IRE for summer and winter solstices follow each other as well. However, the solstices seem to disagree with the equinoxes. This mismatch between solstices and equinoxes is interesting since the vernal and autumnal equinoxes are similar as they almost have the same length of day and night but the summer solstice witnesses the longest day of the year while the winter solstice coincides with the longest night of the year. Additionally, vernal and autumnal equinoxes for our region of study have the largest magnitude of IRE maxima in 2000 and 2002, with the vernal equinox peaking at a slightly greater value than the autumnal equinox; following the trend of
the double peak of the 23rd solar cycle. In contrast, the solstices seem to disagree with the solar activity trend with a peak in 2001 where there is a dip in solar activity (see Fig. 2). There is a definite difference in the peak IRE values obtained seasonally. The IRE values reached a maximum of 19.13 m for the vernal equinox followed closely by the autumnal equinox (18.64 m) in 2000. Whereas, the solstices peaked at a lower value, 15.32 m and 12.38 m for winter and summer respectively in 2001. This large difference between the IRE values of the solstices is due to the seasonal variations in the maximum electron density of the F2 layer (NmF2) of the ionosphere, as has been observed
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Fig. 7. IRE maxima trends for the 23rd solar cycle. The y axis represents the magnitude of IRE maxima in meters while the x axis represents time in years. (a) Observation data, (b) NeQuick 2 model data.
Fig. 8. IRE minima trends for the 23rd solar cycle. The y axis represents the magnitude of IRE minima in meters while the x axis represents time in years. (a) Observation data, (b) NeQuick 2 model data.
by (Bailey et al., 2000; Balan et al., 1998; Balan et al.,2000; Richards, 2001; Torr and Torr, 1973). In Fig. 7(b), NeQuick2 results are presented which generally follow the same trend of increase and decrease but with notably larger values of IRE compared to the experimental values, as shown previously in Figs. 3–6. The two peak trend following the solar activity is observed in NQ2 results for the vernal equinox but not the autumnal equinox, as contrast to the experimental observations. One interesting similarity between experimental and model data is for the case of the 2004 vernal equinox, where both NQ2 and experimental values show a sudden surge in IRE values. Here, it is important to understand that only one day data for each season/year has been considered in this study.
Fig. 8(a) shows the trend of the IRE minima throughout the 23rd solar cycle. It is observed that, for the most part, the vernal equinox has the largest values followed by the summer solstice and the autumnal equinox. The winter solstice has the smallest magnitude of minima peaking at 2.5 m. In contrast to maxima trends shown in Fig. 7 where large differences are observed between the IRE values of equinoxes and solstices, the obtained minima values are very close to each other. The IRE minima is noted to be approximately 25% of the value of the IRE maxima for almost all seasons. Similar results can be seen with NQ2 (see Fig. 8(b)) with minor differences in the IRE magnitude. This shows that NQ2 is able to represent the ionosphere quite well in low solar activity and quiet periods.
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Figs. 9 and 10 show the variation of the IRE maxima and minima, respectively, as well as the smoothed monthly mean sunspot number (SSN) from two separate data archives, for the 23rd solar cycle. These two figures summarise the findings of this paper by highlighting the IRE
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maxima and minima for all datasets in order of occurrence, starting with the autumnal equinox of 1998 and ending with the summer solstice of 2008. The blue dotted curve represents the Smoothed Brussels International Sunspot Number (RI) obtained from the Solar Influences Data
Fig. 9. IRE maxima and SSN trends for the 23rd solar cycle. The blue y axis represents SSN values while the red y axis represents IRE values. The x axis represents time in years. Blue dotted curve represents the Smoothed Brussels International Sunspot Number (RI) obtained from the Solar Influences Data Analysis Center (S.I.D.C.), while the blue dashed curve represents the Smoothed Sunspot Number obtained from Space Weather Prediction Center (SWPC), Space Weather Operations (SWO). The red dash dot curve represents the IRE found through the NeQuick 2 model while the red solid curve represents the IRE found through observation data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 10. IRE minima and SSN trends for the 23rd solar cycle. The blue y axis represents SSN values while the red y axis represents IRE values. The x axis represents time in years. The blue dotted curve represents the Smoothed Brussels International Sunspot Number (RI) obtained from the Solar Influences Data Analysis Center (S.I.D.C.), while the blue dashed curve represents the Smoothed Sunspot Number obtained from Space Weather Prediction Center (SWPC), Space Weather Operations (SWO). The red dash dot curve represents the IRE found through the NeQuick 2 model while the red solid curve represents the IRE found through observation data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Analysis Center (www.sidc.be/silso) whereas the blue dashed curve represents the Smoothed Sunspot Number obtained from the Space Weather Prediction Center (SWPC), Space Weather Operations (www.swpc.noaa.gov). The red line represents the IRE maxima and minima (in meters) for Figs. 9 and 10, respectively. The dot dash red curve represent IRE derived using NQ2 while the solid red curve shows IRE derived from observation data. As can be seen in Figs. 9 and 10, the trend of IRE maxima and minima follow the pattern of increase and decrease of both SSN variables. The two peaks of the 23rd solar cycle in 2000 and 2002 and dip in 2001 are closely followed by the IRE values in the envelope of two SSN indices, except for an unexpected peak in 2004 for the case of maxima (Fig. 9). These two figures show an association between the trend of the IRE values and solar activity parameters (SSN) for the 23rd solar cycle. Once compared to the NQ2 result, the overestimation of IRE values in the maxima plot is clearly seen, with all values of NQ2 exceeding those of observation data. However, the shape of both IRE curves in the maxima plot closely resemble each other. For the minima plot (Fig. 10), the two curves are more similar, both in their magnitudes and form. 4. Conclusion This study measures the Ionospheric Range Error (IRE) over Bahrain (IGS station BAHR) after calculating daily VTEC trend for different seasonal points throughout the 23rd solar cycle. IRE values have been compared with the smoothed sunspot number (SSN) and F10.7 indices in different seasons to study seasonal variations as well as their minima and maxima for the 23rd solar cycle. It has been observed that there is significant correlation between the IRE and solar activity throughout the 23rd solar cycle. The largest IRE values are observed for the vernal equinox in 2000 while the lowest IRE values are observed for the winter solstice in 1998. The two peaks of the 23rd solar cycle and the dip in solar activity between them had a noticeable effect on the IRE calculation. It is worth noting that, overall, for the whole study period, the two equinoxes have shown the highest values of IRE, while the winter and summer solstices have shown the lowest. A similar data set was obtained by simulating NeQuick 2 as a background ionosphere model. It is clear that NeQuick 2 overestimated the TEC values which resulted in higher IRE values. However, the pattern of the curves show similar features showing that the NeQuick 2 is very good at estimating the average ionospheric condition, especially during solar minimums, given that the correct solar activity parameter (F10.7) is used. This study is one of the first observations performed on ionospheric measurements and their influence on the positioning errors in the South Eastern Arabian Peninsula region. Some interesting and important observations have been found for the local area ionosphere. A connection has been observed between the IRE and the solar activity
throughout the 23rd solar cycle by using the data from IGS station BAHR. In future, the study would be extended to understand the trends of the 24th solar cycle as well as the 22nd solar cycle. Additionally, further studies can be performed to analyse the seasonal anomalies that are found to be most prominent at near equatorial regions, as well as the sudden peak of IRE seen in the vernal equinox of 2004. Although the geographical location of this part of the Arabian Peninsula is crucial for space weather and ionospheric studies since it lies under the northern crest of the equatorial ionization anomaly, not much has been observed and analysed in this area of scientific research. With an ambitious plan of establishing a state of the art ionospheric laboratory, the Sharjah Center of Astronomy and Space Sciences (SCASS) in Sharjah (U.A.E) will soon be able to provide continuous monitoring of the ionosphere in the local region and help overcome local area data gaps for the observations of solar terrestrial interaction. Acknowledgments The authors are grateful to all the data providers for this study: Solar Influences Data Analysis Center (www.sidc. be/silso), Space Weather Prediction Center (www.swpc. noaa.gov) and NASA’s archive of space geodesy data (http://cddis.nasa.gov). The authors are also thankful to the reviewers and Noora Alameri (Research Assistant at SCASS) for their valuable comments which helped improved the quality of the paper significantly. References Allain, D., Mitchell, C., 2008. Ionospheric delay corrections for singlefrequency GPS receivers over Europe using tomographic mapping. GPS Solut. 13 (2), 141–151. https://doi.org/10.1007/s10291-008-0107-y. Ansari, K., Panda, S., Althuwaynee, O., Corumluoglu, O., 2017. Ionospheric TEC from the Turkish Permanent GNSS Network (TPGN) and comparison with ARMA and IRI models. Astrophys. Space Sci. 362 (9). https://doi.org/10.1007/s10509-017-3159-z. Bailey, G., Su, Y., Oyama, K., 2000. Yearly variations in the low-latitude topside Ionosphere. Ann. Geophysicae 18 (7), 789–798. https://doi. org/10.1007/s005859900159. Balan, N., Rao, P., 1984. Relationship between nighttime total electron content enhancements and VHF scintillations at the equator. J. Geophys. Res. 89 (A10), 9009–9013. Balan, N., Otsuka, Y., Bailey, G., Fukao, S., 1998. Equinoctial asymmetries in the Ionosphere and Thermosphere observed by the MU radar. J. Geophys. Res. Space Phys. 103 (A5), 9481–9495. https://doi.org/ 10.1029/97ja03137. Balan, N., Otsuka, Y., Fukao, S., Abdu, M., Bailey, G., 2000. Annual variations of the Ionosphere: a review based on MU radar observations. Adv. Space Res. 25 (1), 153–162. https://doi.org/10.1016/s02731177(99)00913-8. Bidikar, B., Sasibhushana Rao, G., Ganesh, L., Santosh Kumar, M., 2014. Satellite clock error and orbital solution error estimation for precise navigation applications. Positioning 05 (01), 22–26. https://doi. org/10.4236/pos.2014.51003. Breed, A., Goodwin, G., Silby, J., 1998. Total electron content measurements in the southern hemisphere using GPS satellites, 1991 to 1995. Radio Sci. 33 (6), 1705–1726. https://doi.org/10.1029/98rs01856.
A.M. Darya et al. / Advances in Space Research 64 (2019) 1034–1045 Edemskiy, I., Lastovicka, J., Buresova, D., Habarulema, J., Nepomnyashchikh, I., 2018. Unexpected Southern Hemisphere ionospheric response to geomagnetic storm of 15 August 2015. Ann. Geophysicae 36 (1), 71–79. Gao, Y., Liu, Z., 2002. Precise ionosphere modeling using regional GPS network data. J. Glob. Position. Syst. 1 (1), 18–24. https://doi.org/ 10.5081/jgps.1.1.18. Georgieva, K., 2011. Why the sunspot cycle is double-peaked. ISRN Astron. Astrophys. 2011, 1–11. https://doi.org/10.5402/2011/437838. Gerzen, T., Jakowski, N., Wilken, V., Hoque, M., 2013. Reconstruction of F2 layer peak electron density based on operational vertical total electron content maps. Ann. Geophysicae 31 (7), 1241–1249. https:// doi.org/10.5194/angeo-31-1241-2013. Gleason, S., 2009. GNSS Applications and Methods. Boston [u.a.]. Artech House. Greenkorn, R., 2012. A comparison of the 10.7-cm radio flux values and the international sunspot numbers for solar activity cycles 19, 20, and 21. Sol. Phys. 280 (1), 205–221. https://doi.org/10.1007/s11207-0120043-4. Hoque, M., Jakowski, N., 2011. Ionospheric bending correction for GNSS radio occultation signals. Radio Sci. 46 (6). https://doi.org/10.1029/ 2010rs004583. Hoque, M., Jakowski, N., 2012. Ionospheric propagation effects on GNSS signals and new correction approaches, global navigation satellite systems. IntechOpen. https://doi.org/10.5772/30090. Jain, A., 1987. Reversal of E B drift & post-sunset enhancement of the ionospheric total electron content at equatorial latitudes. Ind. J. Radio Space Phys. 16, 267–272. Jorgensen, P., 1978. Ionospheric Measurements from NAVSTAR Satellites. The Aerospace Corporation, Los Angeles. Lampropoulos, G., Mavromichalaki, H., Tritakis, V., 2016. Possible estimation of the solar cycle characteristic parameters by the 10.7 cm solar radio flux. Sol. Phys. 291 (3), 989–1002. https://doi.org/10.1007/ s11207-016-0859-4. Le Moue¨l, J., Blanter, E., Shnirman, M., Courtillot, V., 2012. On secular changes of correlation between geomagnetic indices and variations in solar activity. J. Geophys. Res.: Space Phys. 117 (A9), n/a-n/a. https:// doi.org/10.1029/2012ja017643. Leong, S., Musa, T., Abdullah, K., Othman, R., Lim, S., Rizos, C., 2009. GPS-derived local TEC mapping over peninsula Malaysia during solar minimum of sunspot cycle 24. South East Asian Survey Congress. Li, K., Kong, D., Liang, H., Feng, W., 2014. What do the solar activity indices represent? Astronomische Nachrichten 335 (4), 371–377. https://doi.org/10.1002/asna.201312016.
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Liu, L., Zhao, B., Wan, W., Venkartraman, S., Zhang, M., Yue, X., 2007. Yearly variations of global plasma densities in the topside Ionosphere at middle and low latitudes. J. Geophys. Res.: Space Phys. 112 (A7), n/a-n/a. https://doi.org/10.1029/2007ja012283. Mannucci, A., Wilson, B., Yuan, D., Ho, C., Lindqwister, U., Runge, T., 1998. A global mapping technique for GPS-derived ionospheric total electron content measurements. Radio Sci. 33 (3), 565–582. https://doi. org/10.1029/97RS02707. Mansoori, A., Khan, P., Bhawre, P., Gwal, A., Purohit, P., 2013. Variability of TEC at mid-latitude with solar activity during the solar cycle 23 and 24. International Conference on Space Science and Communication. Melaka, Malaysia. IEEE. Nava, B., Coı¨sson, P., Radicella, S., 2008. A new version of the NeQuick ionosphere electron density model. J. Atmos. Sol. Terr. Phys. 70 (15), 1856–1862. Richards, P., 2001. Seasonal and solar cycle variations of the ionospheric peak electron density: comparison of measurement and models. J. Geophys. Res. Space Phys. 106 (A7), 12803–12819. https://doi.org/ 10.1029/2000ja000365. Sadlier, G., Flytkjær, R., Sabri, F., Herr, D., 2017. The Economic Impact on the UK of a Disruption to GNSS. London Economics, London. Takahashi, H., Wrasse, C., Denardini, C., Pa´dua, M., de Paula, E., Costa, S., et al., 2016. Ionospheric TEC weather map over South America. Space Weather 14 (11), 937–949. https://doi.org/10.1002/2016sw001474. Torr, M., Torr, D., 1973. The seasonal behaviour of the F2-layer of the Ionosphere. J. Atmos. Terr. Phys. 35 (12), 2237–2251. https://doi.org/ 10.1016/0021-9169(73)90140-2. Wang, N., Yuan, Y., Li, Z., Montenbruck, O., Tan, B., 2016. Determination of differential code biases with multi-GNSS observations. J. Geod. 90 (3), 209–228. Xingliang, H., Yunbin, Y., Jikun, O., Debao, W., Xiaowen, L., 2005. The diurnal variations, semiannual and winter anomalies of the ionospheric TEC based on GPS date in China. Prog. Nat. Sci. 15 (1), 56–60. Yaacob, N., Abdullah, M., Ismail, M., 2008. Leveling process of total electron content (TEC) using malaysian global positioning system (GPS) data. Am. J. Eng. Appl. Sci. 1 (3), 223–229. https://doi.org/ 10.3844/ajeassp.2008.223.229. Ya’acob, N., Abdullah, M., Ismail, M., 2009. GPS vertical total electron content (TEC) dual frequency technique and TEC map at ionosphere layer using malaysian data. In: International Conference on Future Computer and Communication. IEEE, Kuala Lumpar, pp. 589–593. Yuan, Y., Ou, J., 2001. An improvement to ionospheric delay correction for single-frequency GPS users–the APR-I scheme. J. Geod. 75 (5–6), 331–336.
ScienceDirect Advances in Space Research 64 (2019) 1034–1045 www.elsevier.com/locate/asr
Variation of ionospheric range errors for L1 frequency GPS users during the 23rd solar cycle over BAHR IGS station Abdollah Masoud Darya a,b,⇑, Muhammad Mubasshir Shaikh a,c, Ilias Fernini a,c a
Ionospheric Research Laboratory, Sharjah Center for Astronomy and Space Sciences, Sharjah, United Arab Emirates b Department of Electrical and Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates c Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, United Arab Emirates Received 10 July 2018; received in revised form 25 May 2019; accepted 27 May 2019 Available online 4 June 2019
Abstract Errors induced by the ionosphere on global navigation satellite systems (GNSS) signal propagation significantly affect the positioning calculation done by ground receivers. These ionospheric errors may end up reaching tens of meters in the final positioning calculation. In this study, the ionospheric range error (IRE) was monitored over the local ionosphere of BAHR, Bahrain (26.209N, 50.608E) during the period of the 23rd solar cycle. IRE values were obtained through observation data derived from RINEX files and compared with NeQuick 2 (NQ2) model calculations. It was found that, for the region of study, NQ2 overestimated the total electron content (TEC) values as compared to observation data, resulting in higher IRE values of up to 12 m. However, IRE derived using GNSS observations and NQ2 follow similar trends over the course of the solar cycle. IRE values were also compared to the smoothed sunspot number (SSN) and F10.7 indexes which resulted in significant correlation between the seasonal calculation of IRE and solar activity. Throughout the 23rd solar cycle, the highest IRE values were found during the equinoxes and the lowest during solstices. The largest IRE value was observed in the vernal equinox of 2000 (19.13 m), while the lowest IRE value was observed in the winter solstice of 1998 (0.276 m). Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
Keywords: Ionosphere; NeQuick; Vertical total electron content; Smoothed sunspot number; Global positioning system; Global navigation satellite systems
1. Introduction With the rise of new modes of transportation such as autonomous air taxis and self driving cars, acquiring accurate positioning has never been more critical (Gleason, 2009; Sadlier et al., 2017). To obtain a receiver’s position using the global positioing system (GPS), the distance between the satellite and the receiver must be determined. This distance is calculated by multiplying the time of prop-
⇑ Corresponding author.
E-mail addresses: [email protected] (A.M. Darya), [email protected] (M.M. Shaikh), [email protected] (I. Fernini). https://doi.org/10.1016/j.asr.2019.05.044 0273-1177/Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
agation (the time taken by the signal to travel from the satellite to the receiver) by the speed of light. Errors in the propagation time increase or decrease the computed range which, in turn, affects the positioning calculation. As a rough approximation, an error of 1 ms (1 106 s) in propagation time can produce a ranging error of 300 m (Bidikar et al., 2014). As the GPS signal passes through the atmosphere, it is adversely affected. Through refraction and diffraction, the atmosphere alters the direction of the signal, increasing its propagation time, which results in ranging errors. The layer of the atmosphere that most influences the transmission of global navigation satellite system (GNSS) signals is the ionosphere. It is a layer of
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the upper atmosphere, ionized by solar and cosmic radiation, creating a region of free electrons, ranging from 50 to 1000 km above the Earth’s surface. These free electrons affect radio wave propagation in various ways by modifying characteristic wave parameters such as amplitude, phase and polarization. This marks the ionosphere as the most significant source of error for single frequency GPS users (Hoque and Jakowski, 2012). To effectively eliminate ionospheric error from the signal, the number of free electrons present along the signal’s propagation path must be calculated or estimated accurately. Total electron content (TEC) is a dynamic quantity that depends on many geographical and geophysical parameters such as local time, season, solar activity, longitude and latitude coordinates. With TEC values typically ranging between 1016 and 1018, ranging errors could be in the order of meters (Hoque and Jakowski, 2011; Ya’acob et al., 2008). During the peak of solar cycles, however, the value of the TEC could exceed 1018 which could scale up the error to tens of meters. Accurate measurement and modeling of TEC is an active and predominant area of space weather research around the globe, particularly, for low latitude and polar regions where ionospheric effects on radio signals are most adverse. Mass market GPS receivers mostly operate on a single frequency and experience the greatest effect of ionospheric delays (Yuan and Ou, 2001). There are two methods to compensate for this delay. Either by using a model that represents the expected ionospheric delay such as the NeQuick 2 (NQ2) model, or through real time observation and mapping. NQ2 is the most recent version of the NeQuick ionosphere electron density model (Nava et al., 2008). It is a three dimensional and time dependent ionospheric electron density model that allows for calculation of TEC along any ground to satellite path through numerical integration. Real time observations offer greater accuracy. However, they are limited by the need of a regional network of dual frequency GNSS receivers (Allain and Mitchell, 2008). The idea of using dual frequency GNSS receivers to study regional ionospheric variations started decades ago (Jorgensen, 1978; Mannucci et al., 1998). Since then, many similar studies have been performed in various regions (Ansari et al., 2017; Breed et al., 1998; Edemskiy et al., 2018; Leong et al., 2009; Ya’acob et al., 2009) using the same technique. The current study is based on data retrieved from a local area receiver to study the effect of 23rd solar cycle on ionospheric errors experienced by L1 single frequency GPS users. It aims to serve as a reference for measuring ionospheric errors experienced by single frequency GPS users in a low to mid latitude, equatorial ionization anomaly (EIA) region (Bahrain). It also compares between results obtained through Receiver Independent Exchange Format (RINEX) observations and the NQ2 model, to gain greater insight into ionospheric analysis for the local region. The seasonal trend of Ionospheric Range Error (IRE) throughout the 23rd solar cycle is also analysed and discussed. The
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paper is organized as follows: in section II, the developed methodology will be presented. In section III, measurement results are presented and discussed along with details of the data used in this case study. Finally, in section IV, the conclusion is presented with possible future work to be done in continuation of this study. 2. Methodology The ionosphere is a dispersive medium that delays radio signals. A dual frequency GPS receiver can measure the ionospheric delays between two GPS transmitted frequencies; L1 (at 1.575 GHz) and L2 (at 1.227 GHz). GNSS data from receiver independent exchange format (RINEX) files is used to calculate the slant TEC (STEC) between satellite and receiver. STEC is defined as the integral of the electron density along the path of the signal between the satellite and the receiver through a column of 1 m2 cross sectional area. STEC is calculated as follows: " # 1 f 21 f 22 ð P 2 P 1 Þ þ bR þ bS ð1Þ STEC ¼ 40:3 f 21 f 22 where f1 is the L1 frequency (1.575 GHz), f2 is the L2 frequency (1.227 GHz), P2 P1 is the group delay obtained from the difference between the precision code derived pseudo ranges measured in the L2 and L1 frequencies respectively, and bR and bS are the receiver and satellite biases respectively (Jorgensen, 1978; Wang et al., 2016). STEC has also been extracted using the NQ2 model. The NQ2 model requires several inputs; the location of the receiver and the satellites, the average daily F10.7 solar index (omniweb.gsfc.nasa.gov/form/dx1.html), time of day and date. To relate the STEC value to a geographical location, STEC measurements are converted into VTEC using the single layer model (SLM). In SLM, the ionosphere is approximated as a spherical shell around the Earth at a chosen altitude above the Earth’s surface. The intersection of this shell and the line of sight (LoS) between the satellite and the receiver is known as the ionospheric pierce point (IPP). The zenith angle of the satellite with respect to the IPP is used to map the measured STEC to VTEC. In this study, the median height of the ionosphere is assumed to be at 350 km above the Earth’s surface. This facilitates the conversion of STEC to VTEC at the IPP (Gao and Liu, 2002). STEC is converted to VTEC using the following mathematical relation: VTEC ¼ STEC Cosv
ð2Þ
where v is the angle of incidence between the nadir and LoS at the IPP (Breed et al., 1998). In this study the mean VTEC is considered. That is, the averaged VTEC derived from multiple satellite passes at a single point in time. To better understand the precision of the measurements and the deviation from the mean measured value, Fig. 1 has been plotted to show the most active and least active days
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Fig. 1. Mean (blue line) and the deviation from the mean (error bars) of VTEC. (a) Day with highest F10.7 value (257), autumnal equinox of 2001. (b) Day with lowest F10.7 value (67.3), summer solstice of 2008. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
of the study period in terms of F10.7 index. The error bars show the range of VTEC values derived at a single point in time (dotted line) and the mean value considered (solid blue line). The final step after obtaining the VTEC would be placing it into Eq. (3) where IRE is calculated using the L1 or L2 frequency as: I¼
40:3 TEC f2
ð3Þ
where f is the L band frequency of the signal and I is IRE in meters (Ya’acob et al., 2008). As is seen from Eq. (3), the IRE is directly proportional to the TEC. Since this study is concerned with L1 GPS users, L1 has been used to calculate IRE. The severity of the ionospheric effects on GPS signals is mostly reliant on the signal time of flight through it. A signal originating from a satellite near the observer’s horizon must pass through a larger part of the ionosphere to reach the receiver than does a signal from a satellite near the observer’s zenith. Additionally, for lower elevation angles, multipath errors also increase significantly. Therefore, in this study, data measured at elevation angles greater than 30° were only considered, to avoid unwanted multipath errors (Takahashi et al., 2016). For this study, the pseudo-ranges were obtained from daily RINEX files produced by the BAHR dual frequency GPS receiver, while the satellite and receiver biases were obtained from IONEX files provided by the Chinese Academy of Sciences (CAS). All previously mentioned datasets are accessible through NASA’s archive of space geodesy data (cddis.gsfc.nasa. gov/gnss/data/daily).
3. Data Analysis The current case study concerns the TEC measurement over the local ionosphere at the BAHR IGS station (see Table 1) during the 23rd solar cycle (1998–2008). Due to data availability restrictions, the first data point selected was the autumnal equinox of 1998 and the last was the summer solstice of 2008, adding up to an even period of 10 years. The TEC data was retrieved from the Bahrain (BAHR) GNSS station which is operated by the US National Geospatial Intelligence Agency (NGA). Daily analysis was conducted for all four seasons by selecting the first day of each season (vernal equinox, summer solstice, autumnal equinox and winter solstice) from the autumnal equinox of 1998 to the summer solstice of 2008. These daily calculations were performed using data sampled at 15 min intervals. This study focuses on the effects of the 23rd solar cycle on Ionospheric Range Errors (IRE) experienced by L1 frequency GPS users. In general, the solar cycle is monitored by two parameters; the solar radio noise flux at 10.7 cm or 2800 MHz (F10.7) and the smoothed sunspot number (SSN), both being the longest
Table 1 Details regarding the BAHR station used as this study’s data source. Parameter
Value
Site Name Four Character ID Country City Geographic Coordinates, LLA (deg, deg, m) Geomagnetic Coordinates, LLA (deg, deg, m)
Bahrain GPS Station BAHR Bahrain Manama 26.209, 50.608, 17.03 20.55, 126.92, 17.03
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running records of solar activity (www.swpc.noaa.gov). These indices are commonly used in research studies related to space weather monitoring (Greenkorn, 2012; Lampropoulos et al., 2016; Le Moue¨l et al., 2012; Li et al., 2014). The F10.7 index correlates well with the SSN as well as a number of Ultraviolet (UV) and visible solar irradiance records. This work studies the correlation between the SSN/F10.7 and the IRE. Fig. 2 shows the variation of smoothed sunspot number (SSN) and the F10.7 index for the period from 1993 to 2013. It is clearly observed that the 23rd solar cycle was double peaked (Georgieva, 2011). Both SSN and the F10.7 started increasing around the end of 1996 until they formed their first peak in 2000, with the second peak being in the year 2002. After the second peak, the values of SSN and the F10.7 decreased to their minimum in 2009. Figs. 3–6 show the daily variation of the IRE for the two equinoxes and two solstices for the 23rd solar cycle (1998 to 2008). IRE has been calculated for the 24 h period from 1998 to 2008 on March 21, June 21, September 21 and December 21 for vernal equinox, summer solstice, autumnal equinox and winter solstice, respectively, or one day before/after, depending on data availability. In each plot, the x axis shows the time of observation in UTC (UTC + 3 for local time), while the y axis shows the IRE value in meters. Fig. 3(a) shows the daily variation of IRE for the vernal equinoxes from 1999 to 2008 observed at BAHR. IRE measured in the year 2000 (red curve) and 2002 (blue curve) are the maximum of the whole study period and coincide with the twin peaks of the 23rd solar cycle (Fig. 2). The IRE values decrease from these points onwards, except for a slight increase in 2004 (dark magenta curve) at around 10:00 (UTC). IRE was lower in 2001 (light magenta curve) than the values of the two peaks in 2000 and 2002 as was also observed by (Mansoori et al., 2013). Fig. 3(b) shows similar IRE calculation using NQ2 as a background ionospheric model. It is clearly observed that
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NQ2 is overestimating the IRE values in all seasons and, generally, for the whole solar cycle. Nevertheless, the pattern of the curves display similar features, showing that NQ2 estimates the average ionospheric conditions well, given that the correct solar activity parameter (F10.7) is used. For high solar activity years, the post sunset enhancement of TEC (Balan and Rao, 1984; Jain, 1987) on IRE is also visible (Fig. 3(b)). This feature is not so prominent in the observation values for the vernal equinox (Fig. 3(a)). Fig. 3(c) shows the difference between IRE values of the model and observations. What can be seen is that for the first 5 h of the day the difference between the model and observation readings are minimal, while at midday they are the highest. A prominent feature are the differences at around 15–20 UTC for the the years of 2000 and 2002. For all cases displayed in the figure it can be clearly seen that the model IRE exceeds that of observations, for most part of the day. The maximum amount where the model has exceeded the observation is around 10 m, with the years 2000 and 2002 being the ones with the largest difference. Fig. 4(a) shows the daily variation of IRE for the autumnal equinoxes from 1998 to 2007 observed at BAHR. The IRE values peaked in 2000 (red curve) coinciding with the first peak of the 23rd solar cycle and again in 2001 (light magenta curve) and 2002 (blue curve) to show the same twin peak trend as seen in Fig. 2, however they are not as prominent. The values of IRE declined afterward until their minimum in 2007. In comparison with the vernal equinox, the values of IRE for autumnal equinox are relatively lower for most of the 23rd solar cycle. This is believed to be an effect of the equinoctial asymmetry in the topside electron density at low to mid latitudes which has also been observed by (Bailey et al., 2000). Fig. 4(b) shows the NQ2 results. The overestimation of the model is evident again. A discrepancy in the NQ2 values appears for the cases of years 1998, 1999, 2002 and 2003, where they appear to have similar trends and magni-
Fig. 2. Variation of the smoothed sunspot number (red curve) and the F10.7 index (blue curve) for the period from 1993 to 2013. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Fig. 3. Daily IRE measurements for the vernal equinoxes (coloured curves) from 1999 to 2008. The y axis represents the magnitude of the IRE in meters while the x axis represents the time in UTC hours. (a) Observation data, (b) NeQuick 2 model data. (c) Difference between model and measurement IRE.
tudes, however that is not the case in the observation. This is because of the generalization of the empirical model which smoothen short term changes in the ionosphere. The post sunset enhancement effect is clearly visible in high solar activity years in both experimental and model data. Similar to Fig. 3(c) the model IRE of Fig. 4(c) exceeds that of observations except for the case of the beginning of the 1999 observation where the observation reading exceeded that of the model by more than 5 m. Similar to Fig. 3(c) the maximum amount where the model has exceeded the observation is around 10 m at the case of the year 2000.
Fig. 5(a) shows the daily variation of IRE for the winter solstice from 1998 to 2007. The winter solstice shows a rather low trend where the only significant peak is observed for the year 2001. In fact, the winter solstice peak is much lower than the peak values observed for the equinoxes (see Figs. 3 and 4). This phenomenon has been reported by multiple sources, and appears to be more prevalent during winter and summer seasons (Bailey et al., 2000; Balan et al., 1998; Balan et al.,2000; Richards, 2001; Torr and Torr, 1973). The previous sources state that the reason for this are the equatorial anomalies, occurring as a result of variations in the maximum electron density of the F2 layer of
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Fig. 4. Daily IRE measurements for the autumnal equinoxes (coloured curves) from 1998 to 2007. The y axis represents the magnitude of the IRE in meters while the x axis represents the time in UTC hours. (a) Observation data, (b) NeQuick 2 model data. (c) Difference between model and measurement IRE.
the ionosphere for equatorial or near equatorial regions. The post sunset anomaly is evident in all years; more prominent in the peak years of the solar cycle and less prominent in quiet years of the solar cycle. This observation has been reported previously as a regular feature of TEC in winter (Balan and Rao, 1984; Jain, 1987). However, NQ2 was not able to produce this feature as prominent (see Fig. 5b) in the case of the winter solstice as it had in the cases of vernal and autumnal equinoxes (Figs. 3 (b) and 4(b)). Following the same trend set previously, NQ2 overestimated the IRE values for each year when compared to experimental observations.
Fig. 5(c) shows a larger difference between model and observations as compared to Figs. 3 and 4(c), mainly visible in the cases of the years 1999 and 2000, where it exceeds 12 m. Similar to the previous cases the model values exceed those of observations throughout the study period. Fig. 6 shows the daily variation of IRE for summer solstice from 1999 to 2008. The summer solstice IRE values are clearly seen to be significantly lower than those observed at the equinoxes (Figs. 3 and 4) and winter solstice (Fig. 4). This anomaly has been observed by multiple studies (Bailey et al., 2000; Balan et al., 1998; Balan et al.,2000; Richards, 2001; Torr and Torr, 1973). It is
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Fig. 5. Daily IRE measurements for the winter solstice (coloured curves) from 1998 to 2007. The y axis represents the magnitude of IRE in meters while the x axis represents the time in UTC hours. (a) Observation data, (b) NeQuick 2 model data. (c) Difference between model and measurement IRE.
believed to be the result of variations in the maximum electron density of the F2 layer (NmF2) of the ionosphere, where the daytime NmF2 values are much higher in winter than in summer and less than that of the two equinoxes (Gerzen et al., 2013; Liu et al., 2007; Xingliang et al., 2005). Similar to the winter solstice, the IRE values for the summer solstices peak in the year 2001 and then decrease to a minimum in 2008. The model results presented in Fig. 6(a) are in line with observation results where it shows the lowest IRE values of all seasons. Additionally, no post sunset enhancement is evident in summer for model as well as for observation data.
Similar to Figs. 3–5(c), Fig. 6(c) shows that the model IRE readings exceed those of observations throughout the study period, with the highest case being that of the year 2000. Figs. 7 and 8 show a summary of the seasonal variation, for all four seasons, of IRE maxima (Fig. 7) and minima (Fig. 8) for the 23rd solar cycle. These are the maximum and minimum values of the daily IRE values already presented in Figs. 3–6. From Fig. 7(a), it is observed that the IRE trends of the autumnal and vernal equinoxes closely resemble each other not only for their peaks and troughs but also for their overall behavior. The only excep-
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Fig. 6. Daily IRE measurements for the summer solstice (coloured curves) from 1999 to 2008. The y axis represents the magnitude of the IRE in meters while the x axis represents the time in UTC hours. (a) Observation data, (b) NeQuick 2 model data. (c) Difference between model and measurement IRE.
tion is a small peak in 2004 for the vernal equinox. Similarly, the behavior of observed IRE for summer and winter solstices follow each other as well. However, the solstices seem to disagree with the equinoxes. This mismatch between solstices and equinoxes is interesting since the vernal and autumnal equinoxes are similar as they almost have the same length of day and night but the summer solstice witnesses the longest day of the year while the winter solstice coincides with the longest night of the year. Additionally, vernal and autumnal equinoxes for our region of study have the largest magnitude of IRE maxima in 2000 and 2002, with the vernal equinox peaking at a slightly greater value than the autumnal equinox; following the trend of
the double peak of the 23rd solar cycle. In contrast, the solstices seem to disagree with the solar activity trend with a peak in 2001 where there is a dip in solar activity (see Fig. 2). There is a definite difference in the peak IRE values obtained seasonally. The IRE values reached a maximum of 19.13 m for the vernal equinox followed closely by the autumnal equinox (18.64 m) in 2000. Whereas, the solstices peaked at a lower value, 15.32 m and 12.38 m for winter and summer respectively in 2001. This large difference between the IRE values of the solstices is due to the seasonal variations in the maximum electron density of the F2 layer (NmF2) of the ionosphere, as has been observed
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Fig. 7. IRE maxima trends for the 23rd solar cycle. The y axis represents the magnitude of IRE maxima in meters while the x axis represents time in years. (a) Observation data, (b) NeQuick 2 model data.
Fig. 8. IRE minima trends for the 23rd solar cycle. The y axis represents the magnitude of IRE minima in meters while the x axis represents time in years. (a) Observation data, (b) NeQuick 2 model data.
by (Bailey et al., 2000; Balan et al., 1998; Balan et al.,2000; Richards, 2001; Torr and Torr, 1973). In Fig. 7(b), NeQuick2 results are presented which generally follow the same trend of increase and decrease but with notably larger values of IRE compared to the experimental values, as shown previously in Figs. 3–6. The two peak trend following the solar activity is observed in NQ2 results for the vernal equinox but not the autumnal equinox, as contrast to the experimental observations. One interesting similarity between experimental and model data is for the case of the 2004 vernal equinox, where both NQ2 and experimental values show a sudden surge in IRE values. Here, it is important to understand that only one day data for each season/year has been considered in this study.
Fig. 8(a) shows the trend of the IRE minima throughout the 23rd solar cycle. It is observed that, for the most part, the vernal equinox has the largest values followed by the summer solstice and the autumnal equinox. The winter solstice has the smallest magnitude of minima peaking at 2.5 m. In contrast to maxima trends shown in Fig. 7 where large differences are observed between the IRE values of equinoxes and solstices, the obtained minima values are very close to each other. The IRE minima is noted to be approximately 25% of the value of the IRE maxima for almost all seasons. Similar results can be seen with NQ2 (see Fig. 8(b)) with minor differences in the IRE magnitude. This shows that NQ2 is able to represent the ionosphere quite well in low solar activity and quiet periods.
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Figs. 9 and 10 show the variation of the IRE maxima and minima, respectively, as well as the smoothed monthly mean sunspot number (SSN) from two separate data archives, for the 23rd solar cycle. These two figures summarise the findings of this paper by highlighting the IRE
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maxima and minima for all datasets in order of occurrence, starting with the autumnal equinox of 1998 and ending with the summer solstice of 2008. The blue dotted curve represents the Smoothed Brussels International Sunspot Number (RI) obtained from the Solar Influences Data
Fig. 9. IRE maxima and SSN trends for the 23rd solar cycle. The blue y axis represents SSN values while the red y axis represents IRE values. The x axis represents time in years. Blue dotted curve represents the Smoothed Brussels International Sunspot Number (RI) obtained from the Solar Influences Data Analysis Center (S.I.D.C.), while the blue dashed curve represents the Smoothed Sunspot Number obtained from Space Weather Prediction Center (SWPC), Space Weather Operations (SWO). The red dash dot curve represents the IRE found through the NeQuick 2 model while the red solid curve represents the IRE found through observation data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 10. IRE minima and SSN trends for the 23rd solar cycle. The blue y axis represents SSN values while the red y axis represents IRE values. The x axis represents time in years. The blue dotted curve represents the Smoothed Brussels International Sunspot Number (RI) obtained from the Solar Influences Data Analysis Center (S.I.D.C.), while the blue dashed curve represents the Smoothed Sunspot Number obtained from Space Weather Prediction Center (SWPC), Space Weather Operations (SWO). The red dash dot curve represents the IRE found through the NeQuick 2 model while the red solid curve represents the IRE found through observation data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Analysis Center (www.sidc.be/silso) whereas the blue dashed curve represents the Smoothed Sunspot Number obtained from the Space Weather Prediction Center (SWPC), Space Weather Operations (www.swpc.noaa.gov). The red line represents the IRE maxima and minima (in meters) for Figs. 9 and 10, respectively. The dot dash red curve represent IRE derived using NQ2 while the solid red curve shows IRE derived from observation data. As can be seen in Figs. 9 and 10, the trend of IRE maxima and minima follow the pattern of increase and decrease of both SSN variables. The two peaks of the 23rd solar cycle in 2000 and 2002 and dip in 2001 are closely followed by the IRE values in the envelope of two SSN indices, except for an unexpected peak in 2004 for the case of maxima (Fig. 9). These two figures show an association between the trend of the IRE values and solar activity parameters (SSN) for the 23rd solar cycle. Once compared to the NQ2 result, the overestimation of IRE values in the maxima plot is clearly seen, with all values of NQ2 exceeding those of observation data. However, the shape of both IRE curves in the maxima plot closely resemble each other. For the minima plot (Fig. 10), the two curves are more similar, both in their magnitudes and form. 4. Conclusion This study measures the Ionospheric Range Error (IRE) over Bahrain (IGS station BAHR) after calculating daily VTEC trend for different seasonal points throughout the 23rd solar cycle. IRE values have been compared with the smoothed sunspot number (SSN) and F10.7 indices in different seasons to study seasonal variations as well as their minima and maxima for the 23rd solar cycle. It has been observed that there is significant correlation between the IRE and solar activity throughout the 23rd solar cycle. The largest IRE values are observed for the vernal equinox in 2000 while the lowest IRE values are observed for the winter solstice in 1998. The two peaks of the 23rd solar cycle and the dip in solar activity between them had a noticeable effect on the IRE calculation. It is worth noting that, overall, for the whole study period, the two equinoxes have shown the highest values of IRE, while the winter and summer solstices have shown the lowest. A similar data set was obtained by simulating NeQuick 2 as a background ionosphere model. It is clear that NeQuick 2 overestimated the TEC values which resulted in higher IRE values. However, the pattern of the curves show similar features showing that the NeQuick 2 is very good at estimating the average ionospheric condition, especially during solar minimums, given that the correct solar activity parameter (F10.7) is used. This study is one of the first observations performed on ionospheric measurements and their influence on the positioning errors in the South Eastern Arabian Peninsula region. Some interesting and important observations have been found for the local area ionosphere. A connection has been observed between the IRE and the solar activity
throughout the 23rd solar cycle by using the data from IGS station BAHR. In future, the study would be extended to understand the trends of the 24th solar cycle as well as the 22nd solar cycle. Additionally, further studies can be performed to analyse the seasonal anomalies that are found to be most prominent at near equatorial regions, as well as the sudden peak of IRE seen in the vernal equinox of 2004. Although the geographical location of this part of the Arabian Peninsula is crucial for space weather and ionospheric studies since it lies under the northern crest of the equatorial ionization anomaly, not much has been observed and analysed in this area of scientific research. With an ambitious plan of establishing a state of the art ionospheric laboratory, the Sharjah Center of Astronomy and Space Sciences (SCASS) in Sharjah (U.A.E) will soon be able to provide continuous monitoring of the ionosphere in the local region and help overcome local area data gaps for the observations of solar terrestrial interaction. Acknowledgments The authors are grateful to all the data providers for this study: Solar Influences Data Analysis Center (www.sidc. be/silso), Space Weather Prediction Center (www.swpc. noaa.gov) and NASA’s archive of space geodesy data (http://cddis.nasa.gov). The authors are also thankful to the reviewers and Noora Alameri (Research Assistant at SCASS) for their valuable comments which helped improved the quality of the paper significantly. References Allain, D., Mitchell, C., 2008. Ionospheric delay corrections for singlefrequency GPS receivers over Europe using tomographic mapping. GPS Solut. 13 (2), 141–151. https://doi.org/10.1007/s10291-008-0107-y. Ansari, K., Panda, S., Althuwaynee, O., Corumluoglu, O., 2017. Ionospheric TEC from the Turkish Permanent GNSS Network (TPGN) and comparison with ARMA and IRI models. Astrophys. Space Sci. 362 (9). https://doi.org/10.1007/s10509-017-3159-z. Bailey, G., Su, Y., Oyama, K., 2000. Yearly variations in the low-latitude topside Ionosphere. Ann. Geophysicae 18 (7), 789–798. https://doi. org/10.1007/s005859900159. Balan, N., Rao, P., 1984. Relationship between nighttime total electron content enhancements and VHF scintillations at the equator. J. Geophys. Res. 89 (A10), 9009–9013. Balan, N., Otsuka, Y., Bailey, G., Fukao, S., 1998. Equinoctial asymmetries in the Ionosphere and Thermosphere observed by the MU radar. J. Geophys. Res. Space Phys. 103 (A5), 9481–9495. https://doi.org/ 10.1029/97ja03137. Balan, N., Otsuka, Y., Fukao, S., Abdu, M., Bailey, G., 2000. Annual variations of the Ionosphere: a review based on MU radar observations. Adv. Space Res. 25 (1), 153–162. https://doi.org/10.1016/s02731177(99)00913-8. Bidikar, B., Sasibhushana Rao, G., Ganesh, L., Santosh Kumar, M., 2014. Satellite clock error and orbital solution error estimation for precise navigation applications. Positioning 05 (01), 22–26. https://doi. org/10.4236/pos.2014.51003. Breed, A., Goodwin, G., Silby, J., 1998. Total electron content measurements in the southern hemisphere using GPS satellites, 1991 to 1995. Radio Sci. 33 (6), 1705–1726. https://doi.org/10.1029/98rs01856.
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