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The relationship between TEC and Earth’s magnetic field during quiet and disturbed days over Istanbul, Turkey
Journal Pre-proofs The relationship between TEC and Earth’s magnetic field during quiet and disturbed days over Istanbul, Turkey O. Özcan, S. Sağ ır, R. Atıcı PII: DOI: Reference:
S0273-1177(20)30069-7 https://doi.org/10.1016/j.asr.2020.01.035 JASR 14632
To appear in:
Advances in Space Research
Received Date: Revised Date: Accepted Date:
29 November 2018 24 January 2020 27 January 2020
Please cite this article as: Özcan, O., Sağ ır, S., Atıcı, R., The relationship between TEC and Earth’s magnetic field during quiet and disturbed days over Istanbul, Turkey, Advances in Space Research (2020), doi: https:// doi.org/10.1016/j.asr.2020.01.035
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2020 COSPAR. Published by Elsevier Ltd. All rights reserved.
The relationship between TEC and Earth’s magnetic field during quiet and disturbed days over Istanbul, Turkey O. Özcan1, S. Sağır2*, and R. Atıcı3 1Department
of Physics, Faculty of Sciences, Firat University, Elazig 23000, Turkey
2Department
of Electronics and Automation, Mus Alparslan University, Mus 49250, Turkey
3Faculty
of Education, Mus Alparslan University, Mus 49250, Turkey
*Corresponding author: S. Sağır ([email protected]) Abstract In this study, the impact of Earth’s magnetic field on total electron content (TEC) was studied by using statistical multiple linear regression model and co-integration method. TEC values were measured over the Turkey-Istanbul (ista) station using date of global positioning system (GPS), and the magnetic field components of the Earth were measured from Boğaziçi University, Kandilli Observatory and Earthquake Research Institute, Geomagnetic observatory Istanbul (ISK) station. This examination has been carried out during the dates of March 14-19, 2015 covering the dates of March 17-18, 2015 known in the literature as St. Patrick's Day geomagnetic storm. The three days before the storm (March 14-16) were named as quiet days, whereas the other days (March 17-19) were named as disturbed days after which the two periods were examined separately. It was observed as a result of the examination that the x-component (southnorth direction) of the magnetic field had a negative impact on TEC on quiet days, whereas the impact was positive on disturbed days. However, the y-component (east-west direction) of the magnetic field had an inverse relationship of the x-component on the quiet and disturbed days. In addition, it was deduced that the impact coefficient of the x and y-component of the magnetic field was higher on disturbed days in comparison with those on quiet days. The correlation coefficient between the TEC and the components of the Earth’s magnetic field was 0.11 on quiet days and 0.95 on disturbed days. Therefore, it can be stated that the relationship of the TEC values with the geomagnetic field are higher on disturbed days. Keywords: Earth’s magnetic field, ionospheric TEC, multiple linear regression model 1. Introduction A geomagnetic storm is a very complex process that begins in the near-Earth space as the high energy released by different phenomena, e.g. coronal mass ejection (CME) after flare, a corotating interaction region (CIR), a high speed solar wind originating from a coronal hole, compresses the magnetosphere and condenses the ring current. This process is expressed by a change in the Disturbance storm time (Dst) index (Sugiura, 1963; Sugiura et al., 1991). The main 1
reason for the complexity of this process is the coupling between the regions (magnetospherethermosphere-ionosphere) where the different processes are effective (Kalita et al., 2016). Study of the response of the ionosphere to geomagnetic storm is important not only for scientific purposes, but also for daily practical purposes as it can affect various satellite-based communication and positioning systems such as telecommunications and navigation (Ansari et al., 2019; Astafyeva et al., 2014; Basu et al., 2008; Heelis et al., 2009; Pavlov, 1994; Prölss, 1987; Reddybattula et al., 2019). Numerous studies addressed the effects of the geomagnetic storm over the ionosphere on March 17-18, 2015, also called Saint Patrick’s Day storm in the literature (Astafyeva et al., 2015; Astafyeva et al., 2018; Cherniak et al., 2015; Kumar and Kumar, 2019; Liu et al., 2016; Nava et al., 2016; Zakharenkova et al., 2016). Liu et al. (2016) found that the storm-enhanced density (SED) leading the Main Ionospheric Through (MIT) towards the equator was unusually low latitudes. It was found that the most intense ionospheric irregularities in high and mid-latitudes lasted more than 24 hours starting from 07:00 UT on 17 March 2015 (Cherniak et al., 2015). In addition, reverse hemispheric asymmetries occurring at different longitudes were observed in mid-latitudes. It is stated that observed asymmetries can be explained partly by thermospheric composition changes and partly by hemispherically different non-dipolar parts of the geomagnetic field (Astafyeva et al., 2015). We also address this storm. Thus, unlike published studies in this study, the impact of the components of the earth’s magnetic field on the total electron content (TEC) of the ionosphere in the middle latitude region has been examined by the different statistical methods. This examination has been taken into consideration separately for geomagnetically disturbed and quiet days. 2. Description of the dataset used for the analysis In this study, the relationship between TEC and Earth’s magnetic field components measured for Istanbul/Turkey have been studied statistically. TEC data were calculated for the Istanbul (ista) station (41.10 N, 29.01 E, geomagnetic coordinates 38.37 N, 109.41 E) at 30 s time resolution using program provided by Hacettepe University IONOLAB group (http://www.ionolab.org). IONOLAB-TEC combines data from all the GPS satellites that are above 10o elevation angle (horizon limit) of the GPS station with a temporal resolution of 30 seconds. The method calculates VTEC (Vertical Total Electron Content) per satellite and combines them using a weighting function based on satellite positions which reduce the contamination caused by multipath effects (Arikan et al., 2008; Nayir et al., 2007). The receiver differential code bias is estimated using the method described in Arikan et al. (2008). The magnetic field components of the Earth have been obtained from the geomagnetic observatory Istanbul (ISK) station for Boğaziçi University, Kandilli Observatory and Earthquake Research Institute (41.03N, 29.01E, geomagnetic coordinates 38,5oN 107,5oE) (http://www.koeri.boun.edu.tr/jeomanyetizma).
2
3. Description of the method The relationship between TEC values and Earth’s magnetic field components was studied by multiple linear regression (MLR) model. MLR analysis is a statistical technique used to estimate the relationship with independent variables of a dependent variable that responds to any independent variable. The purpose of MLR is to model the relationship between independent and dependent variables. The first condition for the MLR analysis is to determine the stability of variables by the unit root test. Since the unit root test is very important at regression analysis, this process is completed by three tests (Phillips-Perron (PP), the Augmented-Dickey Fuller (ADF) and the Kwiatkowski- Phillips-Schmidt-Shin (KPSS)). After the stability of the variables is ensured, the next stage is to detect whether there is a long-term relationship between the dependent variables and independent variables or not. This condition is provided by the co-integration test. If there is a long-term relationship between the variables, the last stage is to establish the MLR model to determine the effect on TEC of magnetic field components. For more detailed information, associated with these tests, see the references (Kurt et al., 2016; Sağir et al., 2018; Sağır et al., 2015). The equation in which the lagged values of the dependent variable are included in the model can be formulated as follows; 𝑘
(1)
∆𝑦𝑡 = 𝜇 + 𝛼𝑡 + 𝛿𝑦𝑡 ― 1 + ∑𝑗 = 1𝛽𝑗∆𝑦𝑡 ― 𝑗 + 𝜀𝑡
where y is the dependent variable, is the mean value, 𝛼 is the coefficient of time trend, 𝛿 is the difference processor, t is the time trend, is the error term, 𝛽 is the coefficient of the dependent variable and k is the number of lags (Enders, 2008; Sağir et al., 2018). In general notation, the regression equation in the MLR model is given by 𝑦𝑖 = 𝛽0 + 𝛽1𝑥𝑖1 + 𝛽2𝑥𝑖2 +… + 𝛽𝑝𝑥𝑖𝑝 + ε
where i = 1,2, ..., n
(2)
where, 𝑦𝑖 is dependent variable (namely, TEC), 𝑥𝑖 is independent variable (namely, Bx, By and Bz), 𝛽0 is constant, 𝛽1, 𝛽2, …, 𝛽𝑝 are regression coefficients and ε is error term. Regression equations obtained for quiet and disturbed days according to the stationarity of the variables are given in Equation (3) and (4), respectively. 4. Results The relationship between the TEC values and the geomagnetic field components of the Earth measured over Istanbul/Turkey has been studied statistically. The days examined in this study are divided into quiet, weakly disturbed and disturbed. However, in order to make the comparison better in the calculations, we considered the day of weakly disturbed as a quiet day. Thus, this examination was carried out for days when the geomagnetic activity was quiet (March 14,15 and 16, 2015), and disturbed (March 17, 18 and 19, 2015). Figure 1 shows the x-, y- and z3
components of the field along with the change in TEC. SYM-H index was used to obtain information about the state of the geomagnetic storm. The change of all these parameters during quiet and disturbed days is shown in Figure 1. As can be seen in Figure 1, there is a gap in the values of the x and y components of the magnetic field at some times on March 17-18. This gap is due to the lack of measured values during these hours. Since the dependent (TEC) and independent (Bx and By) variables must be the same size in the statistical calculations, the TEC values at the relevant hours are also not included in the statistical calculations. Therefore, the gap seen in the Bx and By components in the figure will not affect the statistical results. The variation of SYM-H (a), components of the magnetic field of Earth (Bz (b), By (c) and Bx (d)) and ionospheric TEC (e) during March 14-19, 2015 is shown in Figure 1. The storm sudden commencement (SSC) was recorded on March 17 at approximately 04:45 UT. At this time, the SYM-H value increased suddenly from the steady state to 53 nT. There is also a sudden increase in the x and y components of the Earth's magnetic field. Then, the development of the storm was divided into three phases: the initial phase (~ 04: 45-07: 30 UT, the region between the red and black line), the main phase (~ 07: 30-22: 45 UT, the region between the black and the blue line) and the recovery phase (after 22:45 UT ). While TEC values follow the quiet diurnal pattern under quiet geomagnetic conditions, deviations from the quiet diurnal pattern occurred under disturbed geomagnetic conditions. 14 Mar.
15 Mar.
16 Mar.
100
17 Mar.
18 Mar.
19 Mar.
(a)
0
SYM-H (nT)
-100 -200
Main Phase
SSC
40300 -300 40280 40260 40240 40220 40200 40180 2300
Recovery Phase
Bz (nT)
(b)
By (nT)
(c)
2250 2200 25200 25100 25000
Bx (nT)
24900
(d)
24800 24700 60
TEC (TECU)
(e)
40 20 0
0
12
24
12
24
12
24
12
24
12
24
12
24
Time (UT)
Figure 1. SYM-H index (a), the Bz (b), By (c), Bx (d) components of geomagnetic field and TEC (e) variations during March 14-19, 2015 over Istanbul, Turkey.
All variables should be stable either at level value or their first differences to be able to carry out statistical calculations between them. We also took into account the condition that the 4
variables should be stationary in either the baseline level or the first differences at the 1% significance ratio of at least two tests in order to establish the regression model. To be stationary at 1% significance level of a variable , the values obtained for each test must be greater than the critical value of MacKinnon at the 1% level, which is absolute (MacKinnon, 1996). Therefore, the vertical component (Bz) of the magnetic field from among the variables in Table 1 has not been taken into consideration for regression calculations since it was stable neither at level value nor its first differences with other variables on both disturbed and quiet days for all tests (namely, ADF, PP and KPPS tests). However, the x-component of the magnetic field is at both the baseline level (4.48> 4.03 for ADF test; 4.17> 3.99 for PP test; 0.36> 0.22 for KPSS test) and on the first differences (23.77> 4.03 for ADF test; 30.70> 3.99 for The PP test; 0.01 <0.22 for KPSS test) is stationary at 1% significance level for at least two tests. On disturbed days, in the baseline level (58.27> 4.03 for ADF test; 58.27> 3.99 for PP test; 0.05 <0.22 for KPSS test) was only stationary at 1% significance level for at least two tests. By component of the magnetic field is stationary for two tests (24.43> 4.03 for ADF test; 33.37> 3.99 for PP test; 0.05 <0.22 for KPSS test) with 1% significance rate at first differences on quiet days. On disturbed days, it was stationary for two tests (58.26> 4.03 for ADF test; 58.26> 3.99 for PP test; 0.05 <0.22 for KPSS test) with 1% significance rate at baseline level. The details of the method can be found in (Kurt et al., 2016; Sağir et al., 2018; Sağır et al., 2015). Thus, the following regression expressions were obtained by using the linear multiple regression equation for disturbed days and Dynamic Least Squares (DOLS) method for quiet days according to the stability of the variables. Table 1. The unit root test results for variables. Variables
for disturbed days ADF PP KPSS Bx (X component of -58.27 -58.27 0.05 B) By (Y component of -58.26 -58.26 0.05 B) Bz (Z component of -1.8 -2.09 0.35 B) TEC -6.29 -6.66 0.18 D( Bz) -16.38 -54.01 0.07 D( Bx) D( By) D(TEC) The level of significance MacKinnon (1996) critical values ADF PP 1% -4.03 -3.99 5% -3.44 -3.42 10% -3.13 -3.12
5
for quite days ADF PP -4.48 -4.17
KPSS 0.36
-1.92
-2.23
0.13
-3.88
-2.03
0.14
-3.73
-5.81
0.14
-23.77 -24.43 -34.12
-30.70 -33.37 -65.64
0.01 0.05 0.00
KPSS 0.22 0.15 0.12
The regression equation obtained for quiet days is the following: TEC= -53245 - (0.19) Bx + (0.20) By
(3)
The Adjusted R2 value, showing the statistical relationship between variables, is 0.11 for this regression equation. This value indicates a weak relationship between the TEC and Bx, By depending on the established model in addition to putting forth that 11 % of the changes in TEC can be explained by the By and Bx components. While there is a positive relationship between TEC and By according to the acquired regression equation, the relationship between TEC and Bx is negative. The increase/decrease of 1 nT in By results in an increase/decrease of 0.20 TECU in TEC. The increase/decrease of 1 nT in Bx results in a decrease / an increase of 0.19 TECU in TEC. The regression equation obtained for disturbed days is the following: TEC= 4938.79 + (0.20) Bx – (0.21) By
(4)
The Adjusted R2 value is 0.95 for the regression equation given above. This value indicates a strong relationship between the TEC and Bx and By depending on the established model. While there is a negative relationship between TEC and By according to the obtained regression equation, there is a positive relationship between TEC and Bx. The increase/decrease in By of about 1 nT results in a decrease / increase of 0.21 TECU in TEC. The increase/decrease in Bx of about 1 nT results in an increase/decrease of 0.20 TECU in TEC. 5. Discussions and Conclusions The relationship between TEC and Geomagnetic field components for Istanbul/Turkey located on the middle latitude has been studied by the linear multiple regression model for disturbed days and DOLS model for quiet days. Since Bz was not statistically stable, it was not included in the calculations. It was revealed that there is a positive relationship between TEC and By and a negative relationship between TEC and Bx on quiet days, whereas a negative relationship was revealed between TEC and By a positive relationship was revealed between TEC and Bx on disturbed days. It was revealed that the relationship on disturbed days is stronger than the relationship on quiet days. The regression coefficients for both Bx and By were revealed to be higher on disturbed days. It can be due to the fluctuations in the geomagnetic field in all phases of the storm during disturbed periods as was present in the study by (De Michelis et al., 2016) even though they studied the higher latitude regions. In addition, it is also known that there is a significant amount of energy input to the higher latitudes during geomagnetic storms (Elias and de Adler, 2006). Thus, the middle latitude ionospheric electron density during disturbed periods is varied by both ionizing the particles in the environment and the changes in the geomagnetic field. Since the energy input is related to the geomagnetic field geometry, the change in the geomagnetic field will have an impact on the thermosphere composition and on the production and loss of ions and electrons, and therefore on the total electron content (Laundal et 6
al., 2017). In addition, the correlation coefficient between magnetic field components and TEC in this study was 0.11 on quiet days and 0.95 on disturbed days. Therefore, it can be said that the change in TEC on disturbed days is more related to the magnetic field than quiet days for this geomagnetic storm. Acknowledgments The authors declare no conflicts of interest. We would like to thank the IONOLAB group for TEC data and Boğaziçi University for magnetic field data. References Ansari, K., Park, K.-D., Panda, S. K. J. A. A., 2019. Empirical Orthogonal Function analysis and modeling of ionospheric TEC over South Korean region. Acta Astronautica, 161, 313324. doi:https://doi.org/10.1016/j.actaastro.2019.05.044 Arikan, F., Nayir, H., Sezen, U., Arikan, O., 2008. Estimation of single station interfrequency receiver bias using GPS‐TEC. Radio Science 43, RS4004. doi:https://doi.org/10.1029/2007RS003785 Astafyeva, E., Yasyukevich, Y., Maksikov, A., Zhivetiev, I., 2014. Geomagnetic storms, superstorms, and their impacts on GPS-based navigation systems. Space Weather, 12(7), 508525. doi:10.1002/2014SW001072 Astafyeva, E., Zakharenkova, I., Förster, M., 2015. Ionospheric response to the 2015 St. Patrick's Day storm: A global multi‐instrumental overview. Journal of Geophysical Research: Space Physics, 120(10), 9023-9037. doi:https://doi.org/10.1002/2015JA021629 Astafyeva, E., Zakharenkova, I., Hozumi, K., Alken, P., Coïsson, P., Hairston, M., Coley, W., 2018. Study of the Equatorial and Low‐Latitude Electrodynamic and Ionospheric Disturbances During the 22–23 June 2015 Geomagnetic Storm Using Ground‐Based and Spaceborne Techniques. Journal of Geophysical Research: Space Physics, 123(3), 24242440. doi:https://doi.org/10.1002/2017JA024981 Basu, S., Makela, J. J., MacKenzie, E., Doherty, P., Wright, J., Rich, F., . . . Coster, A. (2008). Large magnetic storm‐induced nighttime ionospheric flows at midlatitudes and their impacts on GPS‐based navigation systems. Journal of Geophysical Research: Space Physics, 113, A00A06. doi:10.1029/2008JA013076 Cherniak, I., Zakharenkova, I., Redmon, R. J., 2015. Dynamics of the high-latitude ionospheric irregularities during the 17 March 2015St. Patrick's Day storm: Ground-based GPS measurements. Space Weather, 13(9), 585-597. doi:10.1002/2015SW001237 De Michelis, P., Consolini, G., Tozzi, R., Marcucci, M. F., 2016. Observations of high-latitude geomagnetic field fluctuations during St. Patrick’s Day storm: Swarm and SuperDARN measurements. Earth, Planets and Space, 68(1), 105. doi:https://doi.org/10.1186/s40623016-0476-3 Elias, A. G., de Adler, N. O., 2006. Earth magnetic field and geomagnetic activity effects on long-term trends in the F2 layer at mid-high latitudes. Journal of Atmospheric and SolarTerrestrial physics, 68(17), 1871-1878. doi:https://doi.org/10.1016/j.jastp.2006.02.008 Enders, W., 2008. Applied econometric time series: John Wiley & Sons, New York. Heelis, R., Sojka, J. J., David, M., Schunk, R., 2009. Storm time density enhancements in the middle‐latitude dayside ionosphere. Journal of Geophysical Research: Space Physics, 114(A3), A03315. doi:10.1029/2008JA013690 7
Kalita, B. R., Hazarika, R., Kakoti, G., et al., 2016. Conjugate hemisphere ionospheric response to the St. Patrick's Day storms of 2013 and 2015 in the 100° E longitude sector. Journal of Geophysical Research: Space Physics, 121(11), 11, 364-311,390. doi:https://doi.org/10.1002/2016JA023119 Kumar, S., Kumar, V. V., 2019. Ionospheric Response to the St. Patrick's Day Space Weather Events in March 2012, 2013, and 2015 at Southern Low and Middle Latitudes. Journal of Geophysical Research: Space Physics, 124(1), 584-602. doi:https://doi.org/10.1029/2018JA025674 Kurt, K., Yeşil, A., Sağir, S., Atici, R., 2016. The Relationship of Stratospheric QBO with the Difference of Measured and Calculated NmF2. Acta Geophysica, 64(6), 2781-2793. doi:https://doi.org/10.1515/acgeo-2016-0061 Laundal, K. M., Cnossen, I., Milan, S. E. et al., 2017. North–south asymmetries in earth’s magnetic field. Space Science Reviews, 206(1-4), 225-257. doi:https://doi.org/10.1007/s11214-016-0273-0 Liu, J., Wang, W., Burns, A., Yue, X., Zhang, S., Zhang, Y., Huang, C., 2016. Profiles of ionospheric storm‐enhanced density during the 17 March 2015 great storm. Journal of Geophysical Research: Space Physics, 121(1), 727-744. doi:https://doi.org/10.1002/2015JA021832 MacKinnon, J. G., 1996. Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11(6), 601-618. doi:https://doi.org/10.1002/(SICI)1099-1255(199611)11:6<601::AID-JAE417>3.0.CO;2T Nava, B., Rodríguez‐Zuluaga, J., Alazo‐Cuartas, K., Kashcheyev, et al., 2016. Middle‐and low‐latitude ionosphere response to 2015 St. Patrick's Day geomagnetic storm. Journal of Geophysical Research: Space Physics, 121(4), 3421-3438. doi: https://doi.org/10.1002/2015JA022299 Nayir, H., Arikan, F., Arikan, O., Erol, C., 2007. Total electron content estimation with Reg‐Est. Journal of Geophysical Research: Space Physics, 112(A11), A11313. doi:10.1029/2007JA012459 Pavlov, A., 1994. The role of vibrationally excited nitrogen in the formation of the mid-latitude negative ionospheric storms. Annales Geophysicae, 12, 554-564. Prölss, G., 1987. Storm-induced changes in the thermospheric composition at middle latitudes. Planetary and Space Science, 35(6), 807-811. doi:https://doi.org/10.1016/00320633(87)90041-9 Reddybattula, K. D., Panda, S. K., Ansari, K., Peddi, V. S. R. J. A. A., 2019. Analysis of ionospheric TEC from GPS, GIM and global ionosphere models during moderate, strong, and extreme geomagnetic storms over Indian region. 161, 283-292. doi:https://doi.org/10.1016/j.actaastro.2019.05.042 Sağir, S., Atici, R., Özcan, O., 2018. The possible effect of stratospheric quasi-biennial oscillation on the critical frequency of the ionospheric F2-layer. Pramana-Journal of Physics, 91(4), 54. doi:https://doi.org/10.1007/s12043-018-1627-9 Sağır, S., Atıcı, R., Özcan, O., Yüksel, N., 2015. The effect of the stratospheric QBO on the neutral density of the D region. Annals of Geophysics, 58(3), 0331. doi: https://doi.org/10.4401/ag-6491
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Sugiura, M., 1963. Hourly values of equatorial Dst for the IGY. NASA-National Academy of Sciences-National Research CounciI Senior Pat-Doctoral Resident Research Associate on leave of absence from Geophysical Institute, University of Alaska. . Sugiura, M., Kamei, T. J. b. A. B., Menville, M., 1991. Equatorial Dst index 1957–1986, IAGA Bull., 40. Zakharenkova, I., Astafyeva, E., Cherniak, I., 2016. GPS and GLONASS observations of large‐scale traveling ionospheric disturbances during the 2015 St. Patrick's Day storm. Journal of Geophysical Research: Space Physics, 121(12), 12,138–112,156. doi:10.1002/2016JA023332
9
S0273-1177(20)30069-7 https://doi.org/10.1016/j.asr.2020.01.035 JASR 14632
To appear in:
Advances in Space Research
Received Date: Revised Date: Accepted Date:
29 November 2018 24 January 2020 27 January 2020
Please cite this article as: Özcan, O., Sağ ır, S., Atıcı, R., The relationship between TEC and Earth’s magnetic field during quiet and disturbed days over Istanbul, Turkey, Advances in Space Research (2020), doi: https:// doi.org/10.1016/j.asr.2020.01.035
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2020 COSPAR. Published by Elsevier Ltd. All rights reserved.
The relationship between TEC and Earth’s magnetic field during quiet and disturbed days over Istanbul, Turkey O. Özcan1, S. Sağır2*, and R. Atıcı3 1Department
of Physics, Faculty of Sciences, Firat University, Elazig 23000, Turkey
2Department
of Electronics and Automation, Mus Alparslan University, Mus 49250, Turkey
3Faculty
of Education, Mus Alparslan University, Mus 49250, Turkey
*Corresponding author: S. Sağır ([email protected]) Abstract In this study, the impact of Earth’s magnetic field on total electron content (TEC) was studied by using statistical multiple linear regression model and co-integration method. TEC values were measured over the Turkey-Istanbul (ista) station using date of global positioning system (GPS), and the magnetic field components of the Earth were measured from Boğaziçi University, Kandilli Observatory and Earthquake Research Institute, Geomagnetic observatory Istanbul (ISK) station. This examination has been carried out during the dates of March 14-19, 2015 covering the dates of March 17-18, 2015 known in the literature as St. Patrick's Day geomagnetic storm. The three days before the storm (March 14-16) were named as quiet days, whereas the other days (March 17-19) were named as disturbed days after which the two periods were examined separately. It was observed as a result of the examination that the x-component (southnorth direction) of the magnetic field had a negative impact on TEC on quiet days, whereas the impact was positive on disturbed days. However, the y-component (east-west direction) of the magnetic field had an inverse relationship of the x-component on the quiet and disturbed days. In addition, it was deduced that the impact coefficient of the x and y-component of the magnetic field was higher on disturbed days in comparison with those on quiet days. The correlation coefficient between the TEC and the components of the Earth’s magnetic field was 0.11 on quiet days and 0.95 on disturbed days. Therefore, it can be stated that the relationship of the TEC values with the geomagnetic field are higher on disturbed days. Keywords: Earth’s magnetic field, ionospheric TEC, multiple linear regression model 1. Introduction A geomagnetic storm is a very complex process that begins in the near-Earth space as the high energy released by different phenomena, e.g. coronal mass ejection (CME) after flare, a corotating interaction region (CIR), a high speed solar wind originating from a coronal hole, compresses the magnetosphere and condenses the ring current. This process is expressed by a change in the Disturbance storm time (Dst) index (Sugiura, 1963; Sugiura et al., 1991). The main 1
reason for the complexity of this process is the coupling between the regions (magnetospherethermosphere-ionosphere) where the different processes are effective (Kalita et al., 2016). Study of the response of the ionosphere to geomagnetic storm is important not only for scientific purposes, but also for daily practical purposes as it can affect various satellite-based communication and positioning systems such as telecommunications and navigation (Ansari et al., 2019; Astafyeva et al., 2014; Basu et al., 2008; Heelis et al., 2009; Pavlov, 1994; Prölss, 1987; Reddybattula et al., 2019). Numerous studies addressed the effects of the geomagnetic storm over the ionosphere on March 17-18, 2015, also called Saint Patrick’s Day storm in the literature (Astafyeva et al., 2015; Astafyeva et al., 2018; Cherniak et al., 2015; Kumar and Kumar, 2019; Liu et al., 2016; Nava et al., 2016; Zakharenkova et al., 2016). Liu et al. (2016) found that the storm-enhanced density (SED) leading the Main Ionospheric Through (MIT) towards the equator was unusually low latitudes. It was found that the most intense ionospheric irregularities in high and mid-latitudes lasted more than 24 hours starting from 07:00 UT on 17 March 2015 (Cherniak et al., 2015). In addition, reverse hemispheric asymmetries occurring at different longitudes were observed in mid-latitudes. It is stated that observed asymmetries can be explained partly by thermospheric composition changes and partly by hemispherically different non-dipolar parts of the geomagnetic field (Astafyeva et al., 2015). We also address this storm. Thus, unlike published studies in this study, the impact of the components of the earth’s magnetic field on the total electron content (TEC) of the ionosphere in the middle latitude region has been examined by the different statistical methods. This examination has been taken into consideration separately for geomagnetically disturbed and quiet days. 2. Description of the dataset used for the analysis In this study, the relationship between TEC and Earth’s magnetic field components measured for Istanbul/Turkey have been studied statistically. TEC data were calculated for the Istanbul (ista) station (41.10 N, 29.01 E, geomagnetic coordinates 38.37 N, 109.41 E) at 30 s time resolution using program provided by Hacettepe University IONOLAB group (http://www.ionolab.org). IONOLAB-TEC combines data from all the GPS satellites that are above 10o elevation angle (horizon limit) of the GPS station with a temporal resolution of 30 seconds. The method calculates VTEC (Vertical Total Electron Content) per satellite and combines them using a weighting function based on satellite positions which reduce the contamination caused by multipath effects (Arikan et al., 2008; Nayir et al., 2007). The receiver differential code bias is estimated using the method described in Arikan et al. (2008). The magnetic field components of the Earth have been obtained from the geomagnetic observatory Istanbul (ISK) station for Boğaziçi University, Kandilli Observatory and Earthquake Research Institute (41.03N, 29.01E, geomagnetic coordinates 38,5oN 107,5oE) (http://www.koeri.boun.edu.tr/jeomanyetizma).
2
3. Description of the method The relationship between TEC values and Earth’s magnetic field components was studied by multiple linear regression (MLR) model. MLR analysis is a statistical technique used to estimate the relationship with independent variables of a dependent variable that responds to any independent variable. The purpose of MLR is to model the relationship between independent and dependent variables. The first condition for the MLR analysis is to determine the stability of variables by the unit root test. Since the unit root test is very important at regression analysis, this process is completed by three tests (Phillips-Perron (PP), the Augmented-Dickey Fuller (ADF) and the Kwiatkowski- Phillips-Schmidt-Shin (KPSS)). After the stability of the variables is ensured, the next stage is to detect whether there is a long-term relationship between the dependent variables and independent variables or not. This condition is provided by the co-integration test. If there is a long-term relationship between the variables, the last stage is to establish the MLR model to determine the effect on TEC of magnetic field components. For more detailed information, associated with these tests, see the references (Kurt et al., 2016; Sağir et al., 2018; Sağır et al., 2015). The equation in which the lagged values of the dependent variable are included in the model can be formulated as follows; 𝑘
(1)
∆𝑦𝑡 = 𝜇 + 𝛼𝑡 + 𝛿𝑦𝑡 ― 1 + ∑𝑗 = 1𝛽𝑗∆𝑦𝑡 ― 𝑗 + 𝜀𝑡
where y is the dependent variable, is the mean value, 𝛼 is the coefficient of time trend, 𝛿 is the difference processor, t is the time trend, is the error term, 𝛽 is the coefficient of the dependent variable and k is the number of lags (Enders, 2008; Sağir et al., 2018). In general notation, the regression equation in the MLR model is given by 𝑦𝑖 = 𝛽0 + 𝛽1𝑥𝑖1 + 𝛽2𝑥𝑖2 +… + 𝛽𝑝𝑥𝑖𝑝 + ε
where i = 1,2, ..., n
(2)
where, 𝑦𝑖 is dependent variable (namely, TEC), 𝑥𝑖 is independent variable (namely, Bx, By and Bz), 𝛽0 is constant, 𝛽1, 𝛽2, …, 𝛽𝑝 are regression coefficients and ε is error term. Regression equations obtained for quiet and disturbed days according to the stationarity of the variables are given in Equation (3) and (4), respectively. 4. Results The relationship between the TEC values and the geomagnetic field components of the Earth measured over Istanbul/Turkey has been studied statistically. The days examined in this study are divided into quiet, weakly disturbed and disturbed. However, in order to make the comparison better in the calculations, we considered the day of weakly disturbed as a quiet day. Thus, this examination was carried out for days when the geomagnetic activity was quiet (March 14,15 and 16, 2015), and disturbed (March 17, 18 and 19, 2015). Figure 1 shows the x-, y- and z3
components of the field along with the change in TEC. SYM-H index was used to obtain information about the state of the geomagnetic storm. The change of all these parameters during quiet and disturbed days is shown in Figure 1. As can be seen in Figure 1, there is a gap in the values of the x and y components of the magnetic field at some times on March 17-18. This gap is due to the lack of measured values during these hours. Since the dependent (TEC) and independent (Bx and By) variables must be the same size in the statistical calculations, the TEC values at the relevant hours are also not included in the statistical calculations. Therefore, the gap seen in the Bx and By components in the figure will not affect the statistical results. The variation of SYM-H (a), components of the magnetic field of Earth (Bz (b), By (c) and Bx (d)) and ionospheric TEC (e) during March 14-19, 2015 is shown in Figure 1. The storm sudden commencement (SSC) was recorded on March 17 at approximately 04:45 UT. At this time, the SYM-H value increased suddenly from the steady state to 53 nT. There is also a sudden increase in the x and y components of the Earth's magnetic field. Then, the development of the storm was divided into three phases: the initial phase (~ 04: 45-07: 30 UT, the region between the red and black line), the main phase (~ 07: 30-22: 45 UT, the region between the black and the blue line) and the recovery phase (after 22:45 UT ). While TEC values follow the quiet diurnal pattern under quiet geomagnetic conditions, deviations from the quiet diurnal pattern occurred under disturbed geomagnetic conditions. 14 Mar.
15 Mar.
16 Mar.
100
17 Mar.
18 Mar.
19 Mar.
(a)
0
SYM-H (nT)
-100 -200
Main Phase
SSC
40300 -300 40280 40260 40240 40220 40200 40180 2300
Recovery Phase
Bz (nT)
(b)
By (nT)
(c)
2250 2200 25200 25100 25000
Bx (nT)
24900
(d)
24800 24700 60
TEC (TECU)
(e)
40 20 0
0
12
24
12
24
12
24
12
24
12
24
12
24
Time (UT)
Figure 1. SYM-H index (a), the Bz (b), By (c), Bx (d) components of geomagnetic field and TEC (e) variations during March 14-19, 2015 over Istanbul, Turkey.
All variables should be stable either at level value or their first differences to be able to carry out statistical calculations between them. We also took into account the condition that the 4
variables should be stationary in either the baseline level or the first differences at the 1% significance ratio of at least two tests in order to establish the regression model. To be stationary at 1% significance level of a variable , the values obtained for each test must be greater than the critical value of MacKinnon at the 1% level, which is absolute (MacKinnon, 1996). Therefore, the vertical component (Bz) of the magnetic field from among the variables in Table 1 has not been taken into consideration for regression calculations since it was stable neither at level value nor its first differences with other variables on both disturbed and quiet days for all tests (namely, ADF, PP and KPPS tests). However, the x-component of the magnetic field is at both the baseline level (4.48> 4.03 for ADF test; 4.17> 3.99 for PP test; 0.36> 0.22 for KPSS test) and on the first differences (23.77> 4.03 for ADF test; 30.70> 3.99 for The PP test; 0.01 <0.22 for KPSS test) is stationary at 1% significance level for at least two tests. On disturbed days, in the baseline level (58.27> 4.03 for ADF test; 58.27> 3.99 for PP test; 0.05 <0.22 for KPSS test) was only stationary at 1% significance level for at least two tests. By component of the magnetic field is stationary for two tests (24.43> 4.03 for ADF test; 33.37> 3.99 for PP test; 0.05 <0.22 for KPSS test) with 1% significance rate at first differences on quiet days. On disturbed days, it was stationary for two tests (58.26> 4.03 for ADF test; 58.26> 3.99 for PP test; 0.05 <0.22 for KPSS test) with 1% significance rate at baseline level. The details of the method can be found in (Kurt et al., 2016; Sağir et al., 2018; Sağır et al., 2015). Thus, the following regression expressions were obtained by using the linear multiple regression equation for disturbed days and Dynamic Least Squares (DOLS) method for quiet days according to the stability of the variables. Table 1. The unit root test results for variables. Variables
for disturbed days ADF PP KPSS Bx (X component of -58.27 -58.27 0.05 B) By (Y component of -58.26 -58.26 0.05 B) Bz (Z component of -1.8 -2.09 0.35 B) TEC -6.29 -6.66 0.18 D( Bz) -16.38 -54.01 0.07 D( Bx) D( By) D(TEC) The level of significance MacKinnon (1996) critical values ADF PP 1% -4.03 -3.99 5% -3.44 -3.42 10% -3.13 -3.12
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for quite days ADF PP -4.48 -4.17
KPSS 0.36
-1.92
-2.23
0.13
-3.88
-2.03
0.14
-3.73
-5.81
0.14
-23.77 -24.43 -34.12
-30.70 -33.37 -65.64
0.01 0.05 0.00
KPSS 0.22 0.15 0.12
The regression equation obtained for quiet days is the following: TEC= -53245 - (0.19) Bx + (0.20) By
(3)
The Adjusted R2 value, showing the statistical relationship between variables, is 0.11 for this regression equation. This value indicates a weak relationship between the TEC and Bx, By depending on the established model in addition to putting forth that 11 % of the changes in TEC can be explained by the By and Bx components. While there is a positive relationship between TEC and By according to the acquired regression equation, the relationship between TEC and Bx is negative. The increase/decrease of 1 nT in By results in an increase/decrease of 0.20 TECU in TEC. The increase/decrease of 1 nT in Bx results in a decrease / an increase of 0.19 TECU in TEC. The regression equation obtained for disturbed days is the following: TEC= 4938.79 + (0.20) Bx – (0.21) By
(4)
The Adjusted R2 value is 0.95 for the regression equation given above. This value indicates a strong relationship between the TEC and Bx and By depending on the established model. While there is a negative relationship between TEC and By according to the obtained regression equation, there is a positive relationship between TEC and Bx. The increase/decrease in By of about 1 nT results in a decrease / increase of 0.21 TECU in TEC. The increase/decrease in Bx of about 1 nT results in an increase/decrease of 0.20 TECU in TEC. 5. Discussions and Conclusions The relationship between TEC and Geomagnetic field components for Istanbul/Turkey located on the middle latitude has been studied by the linear multiple regression model for disturbed days and DOLS model for quiet days. Since Bz was not statistically stable, it was not included in the calculations. It was revealed that there is a positive relationship between TEC and By and a negative relationship between TEC and Bx on quiet days, whereas a negative relationship was revealed between TEC and By a positive relationship was revealed between TEC and Bx on disturbed days. It was revealed that the relationship on disturbed days is stronger than the relationship on quiet days. The regression coefficients for both Bx and By were revealed to be higher on disturbed days. It can be due to the fluctuations in the geomagnetic field in all phases of the storm during disturbed periods as was present in the study by (De Michelis et al., 2016) even though they studied the higher latitude regions. In addition, it is also known that there is a significant amount of energy input to the higher latitudes during geomagnetic storms (Elias and de Adler, 2006). Thus, the middle latitude ionospheric electron density during disturbed periods is varied by both ionizing the particles in the environment and the changes in the geomagnetic field. Since the energy input is related to the geomagnetic field geometry, the change in the geomagnetic field will have an impact on the thermosphere composition and on the production and loss of ions and electrons, and therefore on the total electron content (Laundal et 6
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