On the Diurnal, Annual, and Solar Cycle Variations of Slant Total Electron Content in the Korean Peninsula

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(1)JPNT. JPNT 5(2), 87-96 (2016) http://dx.doi.org/10.11003/JPNT.2016.5.2.087. Journal of Positioning, Navigation, and Timing. On the Diurnal, Annual, and Solar Cycle Variations of Slant Total Electron Content in the Korean Peninsula Woong-Jun Yoon1†, Kwan-Dong Park2 Department of Geoinformatic Engineering, Inha University, 100 Inha-ro, Nam-gu, Incheon 22212, Korea Jipyong Space Inc., #39-307, 100 Inha-ro, Nam-gu, Incheon 22212, Korea. 1 2. ABSTRACT The ionospheric error, which is one of many error elements considered during the Global Navigation Satellite System (GNSS) positioning, is hard to be predicted due to the influence of geomagnetic activity and irregular solar activities. Thus, the present study analyzed a change pattern in the ionosphere through Global Ionosphere Map (GIM) data for 12 years from 2003 to 2014 and a variation in the Slant Total Electron Content (STEC) between Sinuiju and Busan which was the longest range in the southeastern direction of the Korean Peninsula. The variation in the STEC verified the diurnal, annual, and solar cycle variations due to the influence of solar activity. The diurnal variation was characterized that the variation in the STEC started to increase from 6–7 am and reached the maximum at 13–14 pm followed by being decreased. The seasonal variation was characterized that the variation in the STEC was high in spring and autumn whereas it was low in summer and winter. The solar cycle variation revealed that the variation in the STEC increased during solar maximum and decreased during solar minimum. The variation in the STEC was up to 20 Total Electron Content Unit (TECU) during the solar minimum and up to 60 TECU during solar maximum.. Keywords: STEC, diurnal, annual, solar cycle. 1. INTRODUCTION The Global Navigation Satellite System (GNSS) was developed initially for military purpose but it was allowed to be used in private purpose since 1996 thereby being utilized in many fields including not only military purpose but also vehicle, ships, and airplanes. Positioning using the GNSS includes a number of errors, which were caused by ionospheric delay, tropospheric delay, multi-path error, and clock errors. In recent years, a large number of studies on reduction in error within 1m distance during positioning via a low-price single receiver. The ionospheric error among Received April 11, 2016 Revised May 09, 2016 Accepted May 12, 2016 †Corresponding Author E-mail: [email protected] Tel: +82-32-873-4305 Fax: +82-2-6455-4305. Copyright © The Korean GNSS Society. them is about 5–15m at day time and 1–3m at night in the mid-latitude region on the basis of the zenith direction. This error is very difficult to be predicted because of the influence of solar and geomagnetic activities (Misra & Enge 2011). The ionosphere reflects or refracts radio waves from extraterrestrial sources thereby creating an ionospheric error when GNSS signals are passed through the ionosphere. The ionospheric delay error can be eliminated up to 99% using a difference in refraction rate between L1 and L2 signals (Hofmann-Wellenhof et al. 2008). However, it is difficult to ensure high accuracy of positioning for low-price receivers that use a single frequency due to the ionospheric delay error. The relative positioning such as Differential Global Positioning System or Real Time Kinematic (RTK), which are much utilized in recent years, can minimize various errors including the ionospheric delay error by receiving correction information from the reference station even with. http://www.gnss.or.kr Print ISSN: 2288-8187 Online ISSN: 2289-0866.

(2) 88. JPNT 5(2), 87-96 (2016). single frequency receivers thereby ensuring accuracy of 1–3 m in the case of DGPS and 1–3 cm in the case of RTK. The DGPS creates the Pseudo Range Correction (PRC) including the correction of ionospheric delay error at the reference station, which knows the precise positioning, and then transfers the created PRC to the user thereby correcting the user positioning error. The RTK can provide high positioning accuracy by offsetting a common error of user and the reference station which knows the precise position during positioning using a carrier wave. However, as a distance between user and reference station becomes farther, the ionospheric error in the common error components varies so that a single frequency receiver is limited to relative positioning of long baseline (Kang et al. 1996). Thus, the present study aimed to determine the effect of the ionospheric error on positioning during long baseline relative positioning using a single frequency quantitatively and analyzed the maximum of the variation in the Slant Total Electron Content (STEC) between Busan and Sinuiju, which is the longest line (675 km) in the southeastern direction in the Korean Peninsula for a long term from 2003 to 2014 to identify the change in the ionosphere. For the comparison of the ionosphere between Busan and Sinuiju, Vertical Total Electron Content (VTEC) values in the corresponding region provided through the Global Ionosphere Map (GIM) model were used and converted to STEC values that were used in real positioning thereby analyzing the variations in STEC and periodicity due to the diurnal, annual, and solar cycle variations.. in the ionosphere approximately using ION alpha and ION beta coefficients transferred from a GPS navigation system by users who use single frequency receivers. It can correct errors up to 50%–60% (Klobuchar 1987). This model sets the ION alpha and ION beta coefficients considering the observation date and a mean sun flux at the central control station and calculates the total electron content in the ionosphere assuming that electrons are concentrated at a virtual layer whose thickness is 0 at an altitude of 350 km using the coefficients. A unit of electron contents is Total Electron Content Unit (TECU). One TECU means that there are 1×1016 electrons at a column whose base area is 1 m2. It is assumed that the TEC in the ionosphere calculated through the Klobuchar model is highest at 14 pm and constant as 9.24 TECU between 22 pm and 06 am (Klobuchar 1987).. 2. TYPES OF THE IONOSPHERE MODEL. 2.3 GIM Model. A type of the ionosphere model that is used to correct the ionospheric delay error is various according to the use purpose and analysis method. Among them, Klobuchar model, International Reference Ionosphere (IRI) model, and GIM model are widely used in the GNSS (Choi 2009). Each of the models has different use methods and error correction rate. Although the Klobuchar model can be used in real time positioning, its correction rate is just 50–60%. On the other hand, a correction rate of the IRI model is higher than that of the Klobuchar model but it is not used in real time positioning because it is an empirical model. As such, an optimum model among the ionospheric models shall be selected depending on positioning circumstance.. The GIM is a map that shows the TEC inside the ionosphere around the earth, which is provided by the International GNSS Service (IGS). It is created using base functions such as triangular grid interpolation and spherical harmonics by collecting information obtained from 1 hundred or more observatories around the world. The GIM model provides a value in the grid whose size is latitude 2.5° and longitude 5° via the IONEX format and data are updated in every two hour (Schaer et al. 1998). The GIM model provides only VTEC so it is needed to be changed to STEC by using obliquity factor when it is used in the GNSS positioning. In this study, the GIM model was selected to analyze ionospheric information accurately among the Klobuchar model, IRI model, and GIM model because it had the highest correction rate (Choi 2009).. 2.1 Klobuchar Model The Klobuchar model estimates total electron content http://dx.doi.org/10.11003/JPNT.2016.5.2.087. 2.2 IRI Model The IRI model is an empirical ionospheric model based on past data, which can calculate not only TEC in the ionosphere but also ion composition and temperature using various equations and 100 or more parameters according to conditions (Choi 2009). The IRI model is provided via the FORTRAN code and user-preferred date, time, latitude, longitude, and altitude are used as input variables thereby calculating the TEC inside the ionosphere up to 50–2000 km. The IRI model is updated consistently through research and improvement and the most-up-to-date version is IRI2012, which can correct errors up to 75% (Choi 2009)..

(3) Woong-Jun Yoon & Kwan-Dong Park Variations of Slant Total Electron Content in the Korean Peninsula. 89. Fig. 1. Changes in electron contents at January 1 2014. Fig. 3. Change in TEC from 1998 to 2014.. Fig. 2. Variation in electron contents between January and December in 2014.. 3. CYCLE OF THE IONOSPHERE VARIATION Prior to analyzing the trend of deviation in the STEC in the Korean peninsula, periodicity of the GIM mode was analyzed according to the diurnal, annual, and solar cycle variations in order to determine the characteristics of the GIM model. The solar meridian altitude, season, and solar cycle change the intensity of solar radiation that affects the ionosphere and variations in TEC values that are provided by the GIM model show periodicity due to the periodical change. In order to verify the periodicity, a cycle of variation in the TEC was checked at an arbitrary user location (latitude 30° and longitude 0°) using the GIM model from 2003 to 2014. A unit of the ionosphere in the GIM model provided in every two hour was converted to TECU. Fig. 1 shows the GIM model data at the user location (latitude 30°, longitude 0°) for 24 hours from UTC 00:00 to 24:00 of January 1 in 2014. As shown in Fig. 1, a change in the ionosphere in a day was the lowest at 6 am as 9 TECU and electron content was increased rapidly from 6 to 7 am when the sun rose and reached the highest as 42 TECU at 13 to 14 pm. This result means that solar radiation on the atmosphere is increased as the solar meridian altitude is higher and then as the solar meridian altitude is lower, electron content is also reduced. Fig. 2 shows the GIM model data as a weekly mean value for one year from January to December in 2014 in order to analyze. Fig. 4. Change in the number of sunspots yearly (NASA Solar cycle prediction 2016).. a seasonal change in the ionosphere. The TEC was the highest as about 50 TECU in April and May in spring and reduced to about 30 TECU in July and August in summer. It was increased to about 45 TECU in October and November in autumn and then reduced to about 30 TECU in December and January in winter. Due to the characteristic of the ionosphere which was affected much by solar activities, the highest TEC was expected during summer but the highest TEC was revealed during spring and autumn on the contrary. This result was consistent with the monthly periodicity of the TEC in the ionosphere observed in regular observatories in Cheorwon, Suwon, and Jeju using the Klobuchar model in 2009 (Lee et al. 2010). The same periodicity was also found in the GIM model. The reason for this was not clearly disclosed yet but there was a study on suppression of ionization in the ionosphere as free electrons moved along the magnetic field lines in the earth from the northern to southern hemisphere during the summer in the northern hemisphere whereas free electrons moved from the southern to northern hemisphere during the summer in the southern hemisphere (Rishbeth 1972). The solar magnetic activity is a periodic 11-year change and the cycle can be found via various phenomena such as change in the number of sunspots and solar flare. Fig. 3 shows the weekly mean value of the ionosphere at the aforementioned user location calculated using the GIM model thereby showing a time-series change for 16 years from 1999 to 2014. Fig. 4 shows a graph of the solar cycle http://www.gnss.or.kr.

(4) 90. JPNT 5(2), 87-96 (2016). Fig. 5. Geometrical diagram of the IPP.. Fig. 6. Configuration of the grid in the IONEX interpolation.. prediction provided by the NASA, which predicted the change in the number of sunspots by year from 1985 to 2020. These two graphs from 1999 to 2014 show a very similar pattern (NASA Solar cycle prediction 2016). Thus, we can infer that the number of sunspots is closely related to the TEC in the ionosphere. At the solar maximum from 2002 to 2003 where the number of sunspots was increased, a mean value of the ionosphere was increased to around 50 TECU and a mean value of the ionosphere was decreased to around 12 TECU at the solar minimum in 2008.. 4. METHOD OF ANALYSIS ON STEC VARIATION Relative positioning such as DGPS and RTK is highly accurate within an error of 1 m and 1 cm, respectively (Kim et al. 2012). However, relative positioning has a shortcoming that positioning error is increased as a baseline distance between reference station and user becomes farther. A STEC variation between reference station and user, which is one of the relative positioning error components, is http://dx.doi.org/10.11003/JPNT.2016.5.2.087. affected not only by the baseline distance but also by solar elevation angle, seasons, and solar magnetic activities. Accordingly, we selected Sinuiju and Busan as comparison locations, which produced the longest baseline in the southeastern direction in the Korean Peninsula, thereby The solar magnetic activity is a periodic 11-year change and the cycle can be found via various studying the activity change periodicity ofand STEC variation by via various The solar magnetic is a and periodic 11-year change the cycle can be found phenomena such as change in the number of sunspots and solar flare. Fig. 3 shows the weekly mean phenomena diurnal, such as change in the number of sunspots andvariations. solar flare. Fig. 3 shows the weekly mean annual, and solar The value ofthe the ionosphere at the aforementioned usercycle location calculated using thelatitude GIM model thereby value of the ionosphere at the aforementioned user location calculated using the GIM model thereby showing a time-series change for 16 years from 1999 to 2014. Fig. 4 shows a graph of the solar cycle and longitude of for Busan and are Fig. 35.15 N and 129.05 showing a time-series change 16 years fromSinuiju 1999 to 2014. 4 shows a graph of the solar cycle prediction provided by the NASA, which predicted the change in the number of sunspots by year from prediction provided by the NASA, which predicted the change in the number of sunspots yearcycle from 1985 toE2020. These two graphs from 1999 to 2014 show a very similar pattern (NASA and 40.10 N and 124.40 E. For ionospheric data, GIM databySolar 1985 to 2020. These two from 1999 2014 show a veryissimilar Solarincycle prediction 2016). Thus, wegraphs can infer that the to number of sunspots closelypattern related(NASA to the TEC the provided by we thecanIGS which wererelated obtained prediction 2016). Thus, inferwere that theemployed, number of sunspots is closely to the TEC in the ionosphere. At the solar maximum from 2002 to 2003 where the number of sunspots was increased, a ionosphere. At the solar maximum from 2002 to 2003 where the number of sunspots was increased, a mean value of the ionosphere increased to around 50 TECU and a data mean value of the ionosphere for 12 years fromwas 2003 to 2014. Since the GIM provided mean value of the ionosphere was increased to around 50 TECU and a mean value of the ionosphere was decreased to around 12 TECU at the solar minimum in 2008. 2.5˚ × 5˚ latitude-longitude VTEC gridindata was decreased to around 12 TECU at the solar minimum 2008. in every two hour, 4. METHOD ANALYSIS ONtoSTEC the dataOF were converted STECVARIATION for each satellite. Fig. 5 4. METHOD OF ANALYSIS ON STEC VARIATION shows the geometrical characteristics of Ionosphere Pierce Relative positioning such as DGPS and RTK is highly accurate within an error of 1 m and 1 cm, Relative positioning such as DGPS and RTK is highly accurate within an error of 1 m and 1 cm, respectively (Kim et al.between 2012). However, relative positioning has a shortcoming that positioning error Point (IPP) satellite and user location, in which the respectively (Kim et al. 2012). However, relative positioning has a shortcoming that positioning error is increased as a baseline distance between reference station and user becomes farther. A STEC IPP is where the satellite is passed through is increased asaalocation baseline distance between reference signal station and user becomes farther. A STEC variation between reference station and user, which is one of the relative positioning error variation between reference station and user, which is one of the relative positioning error components, is affectedionospheric not only by the band baselinewhose distance but also by is solar elevation angle, seasons, the virtual altitude 350 km and components, is affected not only by the baseline distance but also solar as elevation angle, seasons, and solar magnetic activities. Accordingly, we selected Sinuiju andby Busan comparison locations, and solar magnetic Accordingly, wetoselected Sinuiju and as comparison λinrefer thickness is 0. φbaseline and a latitude and a longitude to locations, which produced the activities. longest the southeastern direction in Busan the Korean Peninsula, thereby which produced the longest baseline in the southeastern direction in the Korean Peninsula, thereby studying the change user and periodicity of STEC variation by the diurnal, annual, and solar cycle λ represent location (φ , ) and IPP location (φ , user variation user IPP solar cycle studying the change and periodicity of STEC by the diurnal, annual, and variations. The latitude and longitude of Busan and Sinuiju are 35.15 N and 129.05 E and 40.10 N and variations. The latitude and longitude of Busan and Sinuiju are 35.15 N and 129.05 E and 40.10 N and λ ), E refers to an elevation angle of satellite, R is a radius e 124.40 E.IPPFor ionospheric data, GIM data provided by the IGS were employed, which were obtained 124.40 E. For ionospheric data, GIM data provided by the IGS were employed, which were obtained for 12 years from 2003 to 2014. Since the GIM data provided 2.5˚ × 5˚ latitude-longitude VTEC of the earth to(6378.137 km), H refers to an× 5˚ altitude of theVTECgrid for 12 2014. Since theconverted GIM data to provided data in years everyfrom two2003 hour, the data were STEC 2.5˚ for each latitude-longitude satellite. Fig. 5 shows grid the data inIPP every two km), hour, the data were to converted to (IPP) STEC for each satellite. shows the ψofIPPIonosphere (350 refers an Point angle between user location geometrical characteristics Pierce between satellite and Fig. user 5location, in geometrical characteristics of Ionosphere Pierce Point (IPP) between satellite and user location, in which the IPPIPP is a location wherecenter the satellite signal is passed and through the virtualto ionospheric band and from the of the earth, A refers an which the IPP is a location where the satellite signal is passed through the virtual ionospheric band whose altitude is 350 km and thickness is 0. φ and λ refer to a latitude and a longitude to represent whoseazimuth altitude is 350 km and thickness Eq. is 0. (1) φ and λ refer to a latitude and abetween longitude to represent of 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 the satellite. calculates an angle ) and IPP location (φ user location (φ𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 , λ𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 𝐼𝐼𝐼𝐼𝐼𝐼 , λ𝐼𝐼𝐼𝐼𝐼𝐼 𝐼𝐼𝐼𝐼𝐼𝐼 ), E refers to an elevation angle of satellite, 𝐼𝐼𝐼𝐼𝐼𝐼 user location (φ𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 , λ𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 ) and IPP location (φ𝐼𝐼𝐼𝐼𝐼𝐼 , λ𝐼𝐼𝐼𝐼𝐼𝐼 ), E refers to an elevation angle of satellite, radiuslocation of the earth and (6378.137 km), H refers to an altitude of the IPP (350 km), Eq. ψ𝐼𝐼𝐼𝐼𝐼𝐼 R 𝑒𝑒𝑒𝑒 is auser 𝐼𝐼𝐼𝐼𝐼𝐼 refers to IPP km), from the center of the and H refers to an altitude of theearth IPP (350 km), ψ𝐼𝐼𝐼𝐼𝐼𝐼 refers to R is a radius of the earth (6378.137 an𝑒𝑒angle between user location and IPP from the center of the earth, and A refers to an azimuth of the an angle between user location and between IPP of from the location center and ofand theIPP earth, andthe A refers of to the an azimuth the (2) calculates latitude the IPP, Eq.from (3) calculates a andofEq. satellite. Eq. (1) calculates an angle user center earth satellite. Eq. (1) calculates angle between user location and IPP of from of the earth (2) calculates latitude of the thean IPP, and(Klobuchar Eq. (3) calculates a longitude thethe IPPcenter (Klobuchar 1987).and Eq. longitude of IPP 1987). (2) calculates latitude of the IPP, and Eq. (3) calculates a longitude of the IPP (Klobuchar 1987). 𝜋𝜋. R. 𝜋𝜋 R𝑒𝑒 −1 ψ𝐼𝐼𝐼𝐼𝐼𝐼 = 𝜋𝜋− 𝐸𝐸 − sin −1( R𝑒𝑒𝑒𝑒 cos 𝐸𝐸) (1) (1) ψ𝐼𝐼𝐼𝐼𝐼𝐼 = 2 − 𝐸𝐸 − sin (R𝑒𝑒𝑒𝑒+𝐻𝐻 cos 𝐸𝐸) (1) 2. R𝑒𝑒 +𝐻𝐻. φ𝐼𝐼𝐼𝐼𝐼𝐼 = sin−1 ( sin φ𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 cos ψ𝐼𝐼𝐼𝐼𝐼𝐼 + cos φ𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 sin ψ𝐼𝐼𝐼𝐼𝐼𝐼 cos 𝐴𝐴) (2) (2) φ𝐼𝐼𝐼𝐼𝐼𝐼 = sin−1 ( sin φ𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 cos ψ𝐼𝐼𝐼𝐼𝐼𝐼 + cos φ𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 sin ψ𝐼𝐼𝐼𝐼𝐼𝐼 cos 𝐴𝐴) (2) sin ψ. sin 𝐴𝐴. sin ψ 𝐼𝐼𝐼𝐼𝐼𝐼 sin 𝐴𝐴 λ𝐼𝐼𝐼𝐼𝐼𝐼 = λ𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 + sin−1 ( sin ψ𝐼𝐼𝐼𝐼𝐼𝐼 sin 𝐴𝐴) (3) φ𝐼𝐼𝐼𝐼𝐼𝐼 𝐼𝐼𝐼𝐼𝐼𝐼 λ𝐼𝐼𝐼𝐼𝐼𝐼 = λ𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 + sin−1 ( cos𝐼𝐼𝐼𝐼𝐼𝐼 ) (3) (3) cos φ𝐼𝐼𝐼𝐼𝐼𝐼. the value VTEC value at the calculated IPPwas location, In theInVTEC at the calculated IPP location, interpolation performed using an In the VTEC value at the calculated IPP location, interpolation was performed using an interpolation method (Eq. 4) proposed by the IONEX with surrounding four grid points given at the interpolation was performed using an interpolation interpolation method (Eq. 4) proposed by the IONEX with surrounding four grid points given at the GIM data. After selecting four surrounding grid points at the IPP latitude and longitude, VTEC was GIM data. After selecting surrounding by gridthe points at the IPPwith latitude and longitude, VTEC was method (Eq. 4)fourproposed IONEX surrounding four grid points given at the GIM data. After selecting four surrounding grid points at the IPP latitude and longitude, VTEC was calculated at the IPP based on the VTEC in the four grid points. In Eq. (4), q and p refer to y and x values in the grid as shown in Fig. 6 and current user location E is distanced away from E0,0 as much as Δλ by latitude and Δβ by longitude. The VTEC shall be converted to STEC for each satellite using a Mapping Function in order to be used in real positioning. In this study, the VTEC was converted to STEC in order to analyze the ionospheric error that affected positioning. A Mapping Function (MF) is shown in Eq. (5) and a method of conversion of VTEC to STEC is shown in Eq. (6) (Sohn 2015)..

(5) Woong-Jun Yoon & Kwan-Dong Park Variations of Slant Total Electron Content in the Korean Peninsula. 91. Fig. 7. STEC variation (upper) and difference (lower) between Busan and Sinuiju at DOY 1–6 and PRN No. 8 in 2003.. calculated at the IPP based on the VTEC in the four grid points. In Eq. (4), q and p refer to y and x values in the gridVTEC as shown Fig.grid 6 and current user(4), location is distanced from 𝐸𝐸0,0 as calculated at the IPP based on the in theinfour points. In Eq. q and p𝐸𝐸 refer to y and away x calculated at Fig. the IPP based on the in the four grid points. In Eq. (4), q and refer to and x much as ∆𝜆𝜆in and byVTEC VTEC shall befrom converted foryeach asto pSTEC valuesininthe thefour gridgrid as shown 6 and current user location is distanced away 𝐸𝐸0,0 based on the VTEC points. Inby Eq.latitude (4), q and p∆𝛽𝛽 refer tolongitude. y and x 𝐸𝐸The as values using in the grid as shown inThe Fig. 6 and current user location 𝐸𝐸 is In distanced away from 𝐸𝐸0,0 satellite a Mapping Function in order to be used in real positioning. this study, the VTEC was much as ∆𝜆𝜆 by latitude and ∆𝛽𝛽 by longitude. VTEC shall be converted to STEC for each shown in Fig. 6 and current user location is distanced away from 𝐸𝐸0,0 as (lower) between Busan and Sinuiju at DOY 1–6 and all satellites Fig. 8. 𝐸𝐸STEC variation (upper) and difference much as ∆𝜆𝜆 by latitude and ∆𝛽𝛽 by longitude. The VTEC shall be converted to STEC for each converted to STEC in order to analyze the ionospheric error that affected positioning. A Mapping satellite using The a Mapping order to be in real positioning. In this study, the VTEC was 2003. tude and ∆𝛽𝛽 by longitude. VTECinFunction shall be inconverted to used STEC for each satellite using Mapping Function in aorder to beofaffected used in real positioning. In this study, the in VTEC was Function (MF) isa shown instudy, Eq. (5)theand method conversion of VTECAtoMapping STEC is shown Eq. (6) to used STEC in order to analyze the ionospheric error that positioning. ping Function inconverted order to be in real positioning. In this VTEC was converted to(5)STEC inmethod order of to conversion analyze theofionospheric error is that affected positioning. A Mapping (Sohn 2015). Function (MF) is shown in Eq. and a VTEC to STEC shown in Eq. (6) n order to analyze the ionospheric error that affected positioning. A Mapping Function (MF) is shown in Eq. (5) and a method of conversion of VTEC to STEC is shown in Eq. (6) (Sohn 2015). of conversion wn in Eq. (5) and a method of VTEC to STEC is shown in Eq. (6) analyzed for the diurnal analysis. (Sohn 2015). 𝐸𝐸(𝜆𝜆0 + 𝑝𝑝∆𝜆𝜆, 𝛽𝛽 + 𝑞𝑞∆𝛽𝛽) = (1 − 𝑝𝑝)(1 − 𝑞𝑞)𝐸𝐸0,0 + 𝑝𝑝(1 − 𝑞𝑞)𝐸𝐸 (4) For the annual analysis, 1,0 + 𝑞𝑞(1 − 𝑝𝑝)𝐸𝐸0,1 + 𝑝𝑝𝑝𝑝𝐸𝐸1,1 six+days in 2003 𝐸𝐸(𝜆𝜆0 + 𝑝𝑝∆𝜆𝜆, 𝛽𝛽 + 𝑞𝑞∆𝛽𝛽) = (1 − 𝑝𝑝)(1 − 𝑞𝑞)𝐸𝐸0,0 + 𝑝𝑝(1 − 𝑞𝑞)𝐸𝐸1,0 + 𝑞𝑞(1 − 𝑝𝑝)𝐸𝐸0,1 𝑝𝑝𝑝𝑝𝐸𝐸1,1 (4) that corresponded to spring, summer, 𝐸𝐸(𝜆𝜆0 + 𝑝𝑝∆𝜆𝜆, 𝛽𝛽 + 𝑞𝑞∆𝛽𝛽) = (1 − 𝑝𝑝)(1 − 𝑞𝑞)𝐸𝐸0,0 + 𝑝𝑝(1 − 𝑞𝑞)𝐸𝐸1,0 + 𝑞𝑞(1 − 𝑝𝑝)𝐸𝐸0,1 + 𝑝𝑝𝑝𝑝𝐸𝐸1,1 (4) autumn, and winter were selected respectively thereby 𝑞𝑞∆𝛽𝛽) = (1 − 𝑝𝑝)(1 − 𝑞𝑞)𝐸𝐸0,0 + 𝑝𝑝(1 − 𝑞𝑞)𝐸𝐸1,0 + 𝑞𝑞(11 − 𝑝𝑝)𝐸𝐸0,1 + 𝑝𝑝𝑝𝑝𝐸𝐸1,1 (4) (4) 𝑀𝑀𝑀𝑀 = (5) 2 R𝑒𝑒 1 analyzing the change due to season. For the solar cycle √1−( cos 𝐸𝐸) R𝑒𝑒 +𝐻𝐻 𝑀𝑀𝑀𝑀 = (5) 1 2 𝑀𝑀𝑀𝑀 = (5) solar minimum in 2008,. (5) √1−( R𝑒𝑒 cos 𝐸𝐸) analysis, solar maximum in 2003, 2 R +𝐻𝐻. s 𝐸𝐸). 2. 𝑒𝑒. √1−( R𝑒𝑒 cos 𝐸𝐸) R𝑒𝑒 +𝐻𝐻. (5). and Day of Year (DOY) 141-146 during solar maximum in. STEC = MF × VTEC (6) 2014 were selected to verify (6) the effect of the 11-year solar STEC = MF × VTEC (6) cycle on the STEC variation. magnetic activity STEC = MF × VTEC (6) EC (6) Eq.to (5)calculate was used STEC to calculate STEC for eachinsatellite in Busan and Sinuiju using the VTEC Eq. (5) was used for each satellite provided by the GIM data. STEC was in calculated onlySinuiju when ausing specific Eq. (5) wasSinuiju used to using calculate forThe each satellite Busan and the satellite VTEC was observed at Busan and theSTEC VTEC provided by the GIM Eq. The (5) STEC was used toand calculate STEC for each satellite in Busan and Busan Sinuiju using the VTEC the same time in Busan Sinuiju and a difference in STEC between Sinuiju was provided by the GIM data. was calculated only when a specific satellite was observed at and ed to calculate STEC for each satellite Busan andonly Sinuiju using the VTEC 5.when RESULT AND DISCUSSION data. The STEC was in calculated when aSTEC specific satellite provided byanalyze the GIM data. The was calculated only a specific satellite was observed at calculated to the STEC variation. The STEC variations according to diurnal, annual, and solar the same time in Busan and Sinuiju and a difference in STEC between Busan and Sinuiju was M data. The STEC was calculated only when a specific satellite was observed at the same time in Busan and Sinuiju and a difference in STEC between Busan and Sinuiju was was observed at the same time in Busan and Sinuiju cycle were analyzed. Arbitrary six days in 2003 were selected and analyzed for the diurnal analysis. calculated to analyze the STEC variation. The STEC according to diurnal, annual, and solar usan and Sinuiju and a difference in STEC between Busan and variations Sinuiju was calculated tosix analyze thesix STEC variation. The STEC variations according to diurnal, annual, and solar For theinannual analysis, days in 2003 that corresponded spring, summer, autumn, winter cycle were analyzed. Arbitrary days in 2003 were selected and analyzed fortoThe the diurnal analysis. analysis results onand STEC variation were as follows. andThe a STEC difference STEC between Busan and Sinuiju the STEC variation. variations according to diurnal, annual, and solar cycle were analyzed. Arbitrary sixanalyzing days into2003 were selected and analyzed forsolar the cycle diurnalanalysis, analysis. were selected respectively thereby the change due to season. For the For the annual analysis, six days in 2003 that corresponded spring, summer, autumn, and winter Arbitrary six days in 2003 were selected and analyzed for the diurnal analysis. Fig.to 7spring, shows STEC autumn, values and (upper) and STEC variations was calculated to the analyze the STEC variation. Thethat STEC For maximum annual in analysis, six in 2003 corresponded summer, winter solar 2003, solar minimum into2008, andForDay Year (DOY) 141-146 during solar selected respectively analyzing thedays change season. the of solar cycle analysis, sis, six days inwere 2003 that corresponded tothereby spring, summer, autumn, anddue winter variations according to diurnal, annual, and solar cycle (lower) of DOY 1-6 and PRN No. 8 satellite at Busan and were selected respectively thereby analyzing the change due to season. For the solar cycle analysis, in 2014 were selected to verify the of effect the 11-year solar during magnetic activity cycle on the solar maximum inmaximum 2003, solar minimum insolar 2008, and Day Yearof (DOY) 141-146 solar tively thereby analyzing the change due to season. For the cycle analysis, solar maximum in 2003, solar minimum in 2008, and Day of Year (DOY) 141-146 during solar were analyzed. Arbitrary six days in 2003 were selected and Sinuiju in 2003. During this period, PRN No. 8 satellite was STEC variation. maximum in 2014 were selected to verify effect ofduring the 11-year 003, solar minimum in 2008, and Day of Year (DOY)the141-146 solar solar magnetic activity cycle on the maximum in 2014 were selected to verify the effect of the 11-year solar magnetic activity cycle on the STEC variation. ere selected to verify the effect of the 11-year solar magnetic activity cycle on the variation. 5.STEC RESULT AND DISCUSSION. 5. RESULT AND DISCUSSION 5. The RESULT AND on DISCUSSION analysis results STEC variation were as follows. Fig. 7 shows STEC values (upper) and D DISCUSSION. DOYas1-6 and PRN 8 satellite Busan(upper) and Sinuiju The analysis STEC resultsvariations on STEC (lower) variationofwere follows. Fig.No. 7 shows STECatvalues and in 2003. During The analysis results on STEC variation as afollows. Fig.STEC shows STEC (upper) and this period, PRN No. 8 PRN satellite observed twice day. The value wasvalues increased as an STEC variations of DOY 1-6 and No.was 8(upper) satellite atwere Busan and Sinuiju in7 2003. During ults on STEC variation were as (lower) follows. Fig. 7 shows STEC values and. http://www.gnss.or.kr.

(6) 92. JPNT 5(2), 87-96 (2016). Fig. 9. Variation in STEC difference by season in 2003.. Fig. 10. Variation in the STEC difference between Busan and Sinuiju in 2003, 2008, and 2014.. observed twice a day. The STEC value was increased as an elevation angle was reduced when a satellite was descended. As an elevation angle is lower, a length where the satellite signal is passed through the ionosphere is longer thereby increasing the STEC value. Fig. 8 shows a collective graph of STECs of all satellites from PRN 1 to 32 in contrast with Fig. 7 showing only STEC value of a single satellite. It shows STEC values and STEC variations of all satellites from DOY 1-6 and PRN 1-32. A diurnal variation, which was not revealed in STEC value in a single satellite as shown in Fig. 7, can be clearly shown when diurnal variations from all PRNs were represented. From 6 to 7 am when the sun arose, the STEC variation, which was within a range of 0 and 5 TECU, started to increase and some variation exceeded 20 TECU at 13 to 14 pm. A pattern of variation was similar with that of VTEC which was large during the day but was small during the night. http://dx.doi.org/10.11003/JPNT.2016.5.2.087. The variation in STEC difference was also revealed clearly by season. Fig. 9 shows STEC differences during spring DOY 121-126 (upper left), summer DOY 231-236 (upper right), autumn DOY 291-296 (lower left), and winter DOY 351-356 (lower right) in order to see seasonal variation in 2003. Only a range of -5–30 was shown to see the variation in difference easily although there was a value over 30 TECU. In the spring, a variation in STEC difference was large indicating up to 60 TECU difference and a mean for six days was 4.77 TECU. In the summer, a variation in STEC difference was smaller than that in the spring as shown in the figure. In fact, a variation in STEC difference was reduced gradually as season moved from spring to summer. The STEC difference in summer was up to 30 TECU and a mean was 2.95 TECU. In the autumn, a variation of STEC difference was increased again and its difference was up to 32 TECU and a mean was 3.56 TECU. In the winter, the least variance.

(7) Woong-Jun Yoon & Kwan-Dong Park Variations of Slant Total Electron Content in the Korean Peninsula. 93. Fig. 11. Monthly relative frequency whose STEC difference was 10 TECU or larger.. in STEC difference was revealed among four seasons and its difference was up to 22 TECU and a mean was 2.23 TECU. The analysis result on seasonal variation revealed that the STEC difference between Busan and Sinuiju became larger during spring and autumn but it was smaller during summer and winter compared to that in the spring and autumn. Fig. 10 shows the STEC difference of DOY 141-146 during solar maximum in 2003 (upper), solar minimum in 2008 (middle), and again solar maximum in 2014 (lower) in order to see the variation in STEC difference according to 11-year periodic solar magnetic activities. The STEC difference in 2003 during solar maximum was up to 40 TECU and a mean STEC for six days was 4.86 TECU. The STEC difference was reduced gradually as it went from the solar maximum to solar minimum and during solar minimum in 2008, the STEC difference was up to 11 TECU, indicating the variation was smaller. A mean of the STEC difference of DOY 141146 in 2008 was 1.81 TECU. The STEC difference started. to increase again as it went from the solar minimum to solar maximum and during solar maximum in 2014, the STEC difference was up to 25 TECU and a mean was 4.63 TECU. Based on the above results, a variation in the STEC difference was closely related to the solar cycle. In order to see the variation cycle of STEC difference between Busan and Sinuiju easily, values of STEC difference more than 10 TECU were collected thereby showing relative frequencies. Fig. 11 shows a ratio of monthly relative frequency whose STEC difference between Busan and Sinuiju was 10 or larger TECU from 2003 to 2014. The monthly ratio of 10 TECU or larger showed also variations according to annual and solar cycle as the same as shown in the above result. The relative frequency ratio was increased during spring and autumn and decreased during summer and winter. The relative frequency ratio was increased during solar maximum but decreased during solar minimum. During the spring of solar maximum, the ratio was up to 20%. During solar minimum, most relative http://www.gnss.or.kr.

(8) 94. JPNT 5(2), 87-96 (2016). Fig. 12. STEC difference of DOY 311–316 in 2004.. Fig. 13. KP Index at Nov. 8 in 2004, DOY 313 (Space Weather Live 2016).. frequency ratio was less than 1%. Among the STEC analysis, days of excessively increased STEC difference were observed regardless of the diurnal, annual, and solar cycle. As shown in Fig. 12, the STEC difference was increased up to 50 TECU by the largest increase at DOY 313 in 2004. To determine the reason, the KP Index (Planetary K index) was checked and we found that there was a geomagnetic storm at DOY 313 in 2004. The KP Index shows a geomagnetic change in every three hour by averaging data observed from many geomagnetic observatories located between geomagnetic latitude 4663 (Choi et al. 2005). The magnetic change is exhibited at the ground by the current around the earth due to charged particles emitted from the sun. The KP Index is classified into 10 grades from 0 to 9 according to disturbance intensity in order to express a level of disturbance in the magnetic http://dx.doi.org/10.11003/JPNT.2016.5.2.087. field due to the sun quantitatively. When strong solar storms reach the earth due to sunspot explosion and solar flare, it can cause significant disturbance in geomagnetic field. Six to Nine Grades indicate relatively severe geomagnetic storms and Zero to Three Grades indicate a relatively quiet state without geomagnetic storm. As shown in Fig. 12, days of excessively increased STEC difference were confirmed that they were due to the effect of the geomagnetic storms regardless of periodicity. Fig. 13 shows the KP Index of DOY 313 in 2004 (Space Weather Live 2016).. 6. CONCLUSION In this study, the variation pattern of the ionosphere and STEC difference between Busan and Sinuiju, which.

(9) Woong-Jun Yoon & Kwan-Dong Park Variations of Slant Total Electron Content in the Korean Peninsula. is the longest baseline in the southeastern direction in the Korean Peninsula, were analyzed through GIM data for 12 years from 2003 to 2014. The variation in the ionosphere was affected by solar activities and STEC difference between Busan and Sinuiju was also much affected by solar activities. Due to the influence of the sun, diurnal, annual, and solar cycle variations occurred. The diurnal variation showed that as the ionosphere value was increased at 6-7 am when the sun arose, the STEC difference between Busan and Sinuiju also started to increase. The STEC difference was the largest at 13-14 pm and then started to decrease. The seasonal variation was characterized that the variation in the STEC was high in spring and autumn whereas it was low in summer and winter. The solar cycle variation revealed that the variation in the STEC increased up to 60 TECU during solar maximum and decreased up to 20 TECU during solar minimum. When days of excessively increased STEC difference were observed without showing periodicity due to geomagnetic storms caused by sunspot explosion and solar flare, we found via comparison of the KP Index that they were due to geomagnetic storms. The relative frequency where the STEC difference between Busan and Sinuiju was more than 10 TECU showed a similar cycle with that of the ionospheric variation. The relative frequency was up to 20% during solar maximum and less than 1% during solar minimum. Through the analysis on the long baseline STEC difference, periodicity of STEC difference between Busan and Sinuiju according to the diurnal, annual, and solar cycle was verified. We expect that this study result contribute to foundation research on algorithm development that can predict the ionospheric error during relative positioning of long baseline where the ionospheric variation and maximum analysis are deviated from the reference network.. ACKNOWLEDGMENTS This research was supported by “Land, Infrastructure and Transport Technology Commercialization Support Business” of Ministry of Land, Infrastructure and Transport of Korean government.. REFERENCES Choi, B.-K. 2009, Development of a Regional Ionosphere Model for Improving Geodetic Performance in the Middle Range Baseline, PhD Thesis, Department of Electronic Engineering, Graduate School, Chungnam. 95. National University, Daejeon, Korea Choi, K.-C., Cho, K.-S., Moon, Y.-J., Kim, K.-H. Lee, D.-Y., et al. 2005, Near real-time estimation of geomagnetic local K Index from gyeongzu magnetometer, JASS, 22, 431440. http://dx.doi.org/10.5140/JASS.2005.22.4.431 Hofmann-Wellenhof, B., Lichtenegge, H., & Wasle, E. 2008, GNSS:Global navigation satellite systems:GPS, GLONASS, Galileo & More (NY: Springer) Kang, J.-M., Nim, Y.-B., Song, S.-H., & Park, J.-H. 1996, Analysis of Baseline Accurac y by GPS Relative Positioning, Journal of Korean Society for Geospatial Information System, 4, 15-22 Kim, H.-I., Kim, J.-H., Kim, K.-T., Park, K.-D., & Kim, D.S. 2012, Accuracy Evaluation of DGPS Service via Terrestrial Digital Multimedia Broadcasting, Journal of Navigation and Port Research, 36, 437-442. http:// dx.doi.org/10.5394/KINPR.2012.36.6.437 Klobuchar, J. A. 1987, Ionospheric time-delay algorithm for single-frequency GPS users, IEEE Transactions on Aerospace and Electronics Systems, AES, 23, 325-331. http://dx.doi.org/10.1109/TAES.1987.310829 Lee, C.-M., Park, K.-D., & Lee, S.-U. 2010, Comparison of Real-Time Ionospheric Delay Correction Models for Single-Frequency GNSS Receivers: Klobuchar Model and NeQuick Model, Journal of the Korean Society of Survey, Geodesy, Photogrammetry and Cartography, 28, 413-420. Misra, P. & Enge, P. 2011, Global positioning system: Signals, measurements, and performance, Revised Second Edition (MA: Ganga-Jamuna Press) NASA Solar cycle prediction 2016, [Internet], cited 2016 March 2. available from: http://solarscience.msfc.nasa. gov/predict.shtml Rishbeth, H. 1972, Superrotation of the upper atmosphere, Reviews of Geophysics, 10, 799-819. http://dx.doi. org/10.1029/RG010i003p00799 Schaer, S., Gurtner, W., & Feltens, J. 1998, IONEX: The IONosphere Map E xchange For mat Version 1, Proceedings of the IGS Analysis Centers Workshop, ESOC, Darmstadt, Germany, February 9-11, 1998 Sohn, D.-H. 2015, Characteristics of rapid phase variations of GPS signals in the northern auroral zone, PhD Thesis, Department of Geoinformatic Engineering, Graduate School, Inha University, Incheon, Korea Space Weather Live, KP index 2016, [Internet], cited 2 0 1 6 Ma rc h 1 0 , ava i l ab l e f ro m : ht t p s : / / w w w . spaceweatherlive.com/en/archive /2004/11/08/kp. http://www.gnss.or.kr.

(10) 96. JPNT 5(2), 87-96 (2016). Woong-Jun Yoon is a BS candidate in geoinformatic engineering from Inha University, Korea. He is currently at Inha University as a student. His research interests include ionosphere analysis.. Kwan-Dong Park received his Ph. D. degree from the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin, and he is currently at Inha University as a professor. His research interests include DGNSS/PPPRTK algorithm development and GNSS geodesy.. http://dx.doi.org/10.11003/JPNT.2016.5.2.087.

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