Equatorial Plasma Bubbles Identification with Single Frequency GNSS Receiver Data

Introduction

Global Navigation Satellite System (GNSS) is a satellite system that is used to pinpoint the location of a user’s receiver anywhere in the world. International GPS Service (IGS) is a worldwide network of receivers tracking the transmissions of GNSS satellites is a valuable source of ionospheric data that is globally distributed and continuously available.¹ The services provided by GNSS are used in massive number of applications, both civilian and military. However, these signals are prone to several sources of disturbances, causing errors which result in degradation of positioning accuracy.

Ionosphere is the layer of the earth’s atmosphere extending from 80 to 1,000 km above the earth’s surface containing a high concentration of ions and free electrons and is able to reflect radio waves. Free electrons and ions are produced by solar radiation which causes group delay and phase advancement of GNSS signals resulting in positioning errors in order of tens of meters. Ionosphere is the highest contributor of GNSS positioning error.² The ionospheric F-region extending from 250 to 400 kms contains the greatest concentration of free electrons. The plasma disturbances in the F-region scatter radio waves and generate rapid fluctuations in the amplitude and phase of GNSS signals called scintillations. The disturbances in ionospheric plasma are most frequent and severe in the geomagnetic equatorial belt from 20°N to 20°S, particularly during the post-sunset hours. These scintillations depend on the ionospheric Total Electron Content (TEC) which is the total number of free electrons in a cylindrical volume of one square meter cross section, extending from the receiver to the satellite.³

Low-latitude ionospheric scintillations are season- dependent and are primarily limited to local night-time hours.⁴ The ionospheric plasma irregularities vary greatly in spatial extent and drift with the background plasma at speeds of 50 to 150 m/sec. Ionospheric effects on GNSS signals are complex and difficult to mitigate given the inhomogeneous and anisotropic nature of the medium.⁵

Equatorial Plasma Bubbles (EPBs) represent plasma depletions with respect to the background ionosphere and are the most important sources for electron density irregularities in the F-region.⁶ EPBs are the ionospheric phenomenon near the earth’s geomagnetic equator which makeup one of the distinguishing phenomena that disrupt radio and satellite communications. EPBs are generated through the nonlinear evolution of the Rayleigh-Taylor instability (RTI). These EPBs form post sunset when the solar radiation ceases to ionise the ionosphere. During post-sunset period in the ionosphere, the absence of solar radiations causes rapid decay of the lower ionosphere, and a steep vertical density gradient develops on the bottom side of the raised F-layer.⁷

The EPB occurrences may be affected by inter-hemispheric neutral winds (meridional winds) blowing from summer hemisphere to winter hemisphere. Apart from the night-time EPBs occurrences, the daytime occurrence also happens but their cases are very exceptional. The daytime plasma density irregularities is expected to be caused by the irregularities associated with the sporadic E layers. Using the spread-F data has found a small percentage of occurrences of plasma bubbles between sunrise and local noon time.

The presence of EPBs can degrade the quantity and quality of the user and reference station measurements thereby disrupting the communication from GNSS.^8,9 The EPBs can affect the radio-based technologies by scattering and diffracting radio wave signals that pass through them resulting in amplitude and phase scintillations in GNSS signals.⁵

There have been many studies on the occurrence characteristics of EPBs using localized observational data such as ionosondes, topside sounders, radio scintillations and in situ measurements.⁸ A new observational technique of EPBs is ground-based GNSS receiver’s data due to its wide coverage. The time derivative of TEC is often used to measure small-scale ionospheric fluctuations. Time differential TEC is called Rate of TEC (ROT).⁸ The standard gradient of ROT gives us the Rate of TEC Index (ROTI). In this paper, ROTI is used to identify the occurrence of plasma bubble using the ground-based GPS-TEC data. Hence by using the data from IGS website and by finding the corresponding ROTI values, the EPBs occurrence has been identified.

A solar cycle is the amount of magnetic flux that rises up to the Sun’s surface varies with time in a cycle. This cycle lasts 11 years on average. This cycle is sometimes referred to as the sunspot cycle. Our Sun is a huge ball of electrically-charged hot gas. This charged gas moves, generating a powerful magnetic field. The Sun’s magnetic field goes through a cycle, called the solar cycle. Every 11 years or so, the Sun’s magnetic field completely flips. This means that the Sun’s north and south poles switch places. Then it takes about another 11 years for the Sun’s north and south poles to flip back again. The solar cycle affects activity on the surface of the Sun, such as sunspots which are caused by the Sun’s magnetic fields. As the magnetic fields change, so does the amount of activity on the Sun’s surface. This period of time is considered as cycle 24 which have started in the year 2008 and might end between mid-2019 and late 2020.

Methods

GNSS data from IISC station Bangalore, Karnataka, India (latitude- 13.0219°N, longitude-77.5671°E) is collected from SOPAC (SCRIPPS ORBIT AND PERMANENT ARRAY CENTER) archives for the duration 1^st January, 2014 to 31^st December, 2019 of the 24^th solar cycle. SOPAC is a major participant in association with the International GPS Service (IGS), serving as a Global Data Center and a Global Analysis Center. The data is downloaded one day at a time in RINEX (Receiver Independent Exchange) format.¹⁰ RINEX is a data interchange format for raw satellite navigation system data in a compressed form. The downloaded RINEX data file is converted to an easily understandable.cmn format using the GPS-TEC application software developed by Dr. Gopi Krishna Seemala.¹¹ The converted data consists of time, PRN, Slant TEC (STEC), Vertical TEC (VTEC), elevation angle and azimuth angle. Only data corresponding to elevation angles greater than 40° were considered as lower elevation angles are associated with increased multipath error. The ROTI based EPB detection algorithm is discussed in section 3.

Roti based EPB Detection Algorithm

The occurrence of EPBs depends on factors such as local time, PRN, Slant TEC (STEC), elevation angle, etc. These parameters refer to occurrence rate, intensity of depletion, depth of depletion and size of EPBs.

Results

The processing and analysis of ROTI algorithm was carried out in MATLAB version 2019a software using the tool Research Resource Identifier (RRID) SCR_001622.¹³

The data was collected from IGS receiver located at IISC Bangalore, Karnataka, India of year 2016. The time, PRN, STEC, elevation angle were extracted from the collected data. ROTI values were found to detect EPBs as explained in the Methodology section. Figure 1 shows the process flow of the EPB detection algorithm. This study investigates the casual linkage between EPB’s and TEC using solar maximum and minimum period.¹⁴

The analysis of EPBs has been demonstrated for two geomagnetically quiet days and two geomagnetically distributed days. IGS data collected from IISC station on the 6^th and 7^th February, 2016 were considered to demonstrate the performance of the ROTI based EPB detector for geomagnetically calm days as the average Kp index on those days was 2.

K_p is an excellent indicator of disturbances in the Earth’s magnetic field and is used by Space Weather Prediction Centre (SWPC) to decide whether geomagnetic alerts and warnings need to be issued for users who are affected by these disturbances. The Kp-index or Planetary K-index characterizes the magnitude of geomagnetic storms.³ The total day is divided into 8 slots of 3hours each. Figure 2 has been taken from www.magneticstormsonline.com, which depicts the Kp index values on 6^th and 7^th February, 2016. The average value of Kp index is 2 for both the days indicating geomagnetic calm. Kp index values exceeding 5 indicate a geomagnetic storm.

Figure 1. Flowchart representing the EPB Detection algorithm.

Figure 2. Kp index values on 6^th and 7^th February 2016 at the IISC station, Bangalore.

Figure 3 shows the STEC, ROT and ROTI values on 6^th February, 2020 at IISC station for PRN 7. The data from PRN 7 was available from 19.40 hours to 22.40 hours local time. From Figure 3, it can be observed that the STEC values showed a very smooth variation during the entire period with no abrupt variations with a maximum value of 50 TECU. The calculated value of ROT reached a maximum value of 0.6 TECU/min and ROTI values varied from 0 to 0.05 TECU/min. From the smooth curve of STEC and small values of ROTI and ROT it can be hypothesised that 6^th February, 2016 was a geomagnetically quiet day.

Figure 3. STEC, ROT and ROTI values on 6^th February, 2020 at IISC Station, Bangalore from PRN 7.

Figure 4. ROTI values for 6^th February, 2016 at IISC station, Bangalore.

Figure 5. ROTI values for 7^th February, 2016 at IISC station, Bangalore.

The Figure 4 and 5 show the R_eve, R_day, (R_eve - R_day) values and the threshold of 0.05 TECU/min for 6^th and 7^th February, 2016 respectively at the IISC station, Bangalore. It can be seen from Figures 4 and 5 that the (R_eve -R_day) on both 6^th and 7^th February, 2016 did not exceed the threshold of 0.05 TECU/min, hence declaring them as EPB free days. The results shown in Figures 4 and 5 are in perfect correlation with the Kp index values of Figure 2.

Figure 6 depicts the Kp index values on 16^th and 17^th February, 2016 at IISC station, Bangalore. The average value of Kp index on 16^th and 17^th February, 2016 was found to be 5 which indicates the occurrence of a geomagnetic storm on those two days.

Figure 6. Kp index values on 16^th and 17^th February 2016 at the IISC station, Bangalore.

Figure 7 shows the STEC, ROT and ROTI values on 16^th February, 2020 at IISC Station, Bangalore from PRN 7. The data from PRN 7 was available from 18.70 hours to 21.70 hours, local time. From Figure 7, it is apparent that the STEC curve depicts ups and downs unlike the smooth curve in Figure 3. The maximum value of STEC recorded on 16th Feb, 2020 was 80 TECU. The STEC value decreased to 0 TECU at 20.40 hours followed by an increase and decrease. The ROT values varied from 0 to 2 TECU/min reaching the maximum at 20.40 hours. The ROTI value reached a maximum of 0.6 TECU/min at 20.40 hours. The variations in STEC, ROT and ROTI during the post sunset hours evidently indicate geomagnetic disturbances on 16^th February, 2020 which is in correlation with Figure 6.

Figure 7. STEC, ROT and ROTI values on 16^th February, 2020 at IISC Station, Bangalore from PRN 7.

Figures 8 and 9 illustrate the R_eve, R_day, (R_eve - R_day) values along with the threshold of 0.05 TECU/min. It is evident from the Figures 8 and 9 that the R_eve values on both 16^th and 17^th February, 2016 are considerably higher than the R_day values such that the difference (R_eve - R_day) exceeded 0.05 TECU/min, thus declaring the presence of EPBs on both the days.

Figure 8. ROTI values for 16^thFebruary, 2016 at IISC station, Bangalore.

Figure 9. ROTI values for 17^th February, 2016 at IISC station, Bangalore.

To study the seasonal EPBs occurrence, the monthly occurrences of 2016 year have been grouped into four seasons namely Vernal/Spring equinox months (February to April), Summer solstice months (May to July), Autumnal equinox months (August to October) and Winter solstice months (November to January).

The decision criterion (R_eve -R_day) values, for the detection of EPBs, for every day of the four seasons, Spring, Summer, Autumn and Winter are shown in Figures 10 to 13 respectively.

Figure 10. Detection of EPBs during Vernal Equinox months of February, March and April, using ROTI based detector for the year 2016 at IISC station, Bangalore.

Figure 11. Detection of EPBs during Summer solstice months of May, June and July, using ROTI based detector for the year 2016 at IISC station, Bangalore.

Figure 12. Detection of EPBs during Autumnal Equinox months of August, September and October, using ROTI based detector for the year 2016 at IISC station, Bangalore.

Figure 13. Detection of EPBs during Winter Solstice months of November, December and January, using ROTI based detector for the year 2016 at IISC station, Bangalore.

It can be observed from Figure 10, that the number of disturbances in the month of March is more when compared to those in February and April.

Figure 11 shows the days on which EPBs were detected during the summer solstice months of May, June and July. It is apparent from Figure 11 that there were non EPBs detected during the summer solstice months. During these three months, the decision criterion (R_eve-R_day) equal to zero.

Figure 12 depicts the (R_eve-R_day) values during the Autumnal equinox months of August, September and October. As can be noticed in Figure 12, the number of days on which EPBs were detected is less than those detected during the Vernal equinox months indicating an equinoctial asymmetry.

Figure 13 shows the decision criterion values during the Winter solstice months of November, December and January of the year 2016 at IISC station, Bangalore. From Figure 13, it can be observed that the month of January recorded the maximum number of EPB events in 2016. As per the pre-established EPB climatology, the ionospheric disturbances during equinoctial months are greater than those during the solstice months.¹⁵

However, an increased number of EPB detections in January indicate a winter anomaly, which also is quite common in low latitude ionosphere.

The analysis of monthly EPBs occurrences for half of the 24^th solar cycle has been carried out, which are shown in Figures 14 to 19. The observed EPBs occurrences were found maximum during month of January, February, March, April, August.

Figure 14. Month wise EPB detection for the year 2014 at IISC Bangalore station.

Figure 15. Month wise EPB detection for the year 2015 at IISC Bangalore station.

Figure 16. Month wise EPB detection for the year 2016 at IISC Bangalore station.

Figure 17. Month wise EPB detection for the year 2017 at IISC Bangalore station.

Figure 18. Month wise EPB detection for the year 2018 at IISC Bangalore station.

Figure 19. Month wise EPB detection for the year 2019 at IISC Bangalore station.

Figure 14 indicates the month wise EPB detection of year 2014 of IISC Bangalore Station. It is observed that the maximum detection of EPBs were in the months of March, February, April and January.

Figure 15 indicates the month wise EPB detection of year 2015 of IISC Bangalore Station. It is observed that the maximum detection of EPBs were in the months of March, April, January, September, August, February, and May.

Figure 16 indicates the month wise EPB detection of year 2016 of IISC Bangalore Station. It is observed that the maximum detection of EPBs were in the months of March, January, February and August.

Figure 17 indicates the month wise EPB detection of year 2017 of IISC Bangalore Station. It is observed that the maximum detection of EPBs were in the months of February, March, April and November.

Figure 18 indicates the month wise EPB detection of year 2018 of IISC Bangalore Station. It is observed that no EPB is detected in this year.

Figure 19 indicates the month wise EPB detection of year 2019 of IISC Bangalore Station. It is observed that October, November and December are the months detected with EPBs.

During this half solar cycle period, maximum EPBs occurrences were observed during January. Moreover, the EPBs occurrences during February, March, April and August were also found to be significantly larger than in September, October, November.

There are no EPBs occurrences for the month May, June, July, December. Occurrence rate of EPBs are expected to be high during the period when the sunset time lag is small, in other words, when the sunsets of the geomagnetic conjugate points are synchronized. This is “magnetic equinox”. The solstice periods are detected with zero and less EPBs. The asymmetric equinox periods are detected with more EPBs. The sunset time lag effect plays an important role for the monthly variation.

Conclusions

In this paper the IGS data has been analysed for half solar cycle period from 2014-2019 year for IISC Bangalore, Karnataka, India. The ionospheric errors over low-latitudinal regions has been discussed. The algorithm which can be used to detect the EPBs over low-latitudes has been discussed. The proposed ROTI algorithm detects the occurrence of EPBs using IGS data. Here, we have considered two quiet days and two disturbed days and plotted them. SWPC data is used for comparison of whether geomagnetic alerts and warnings need to be issued for users who are affected by these disturbances and can be concluded that the particular days have geomagnetic disturbances (storms) or not. As the solar cycle is of 11 years, we have considered a half solar cycle for detection of EPBs using ROTI algorithm. The middle years of the solar cycle are detected to have more solar spots and hence years 2014 and 2015 are detected with more no. of EPBs. And the no. of EPBs detected reduced by the end of the solar cycle. This shows that ROTI algorithm is beneficial in detecting EPBs. This ROTI algorithm can be used over GAGAN data to detect EPBs.

Author contributions

Swapna Raghunath and Ch. Ushakumari: Conception and design of study. Swapna Raghunath, T Padma: Acquisition, analysis and/or interpretation of data. Swapna Raghunath, T Padma, Ch. Usha Kumari: Drafting the manuscript and revising the manuscript critically for important intellectual content.

Ethical approval and consent

Hereby, we Swapna Raghunath and Ch. Usha kumari consciously assure that for the manuscript Equatorial Plasma Bubbles Identification with Single Frequency GNSS Receiver Data is the authors’ own original work, which has not been previously published elsewhere and the paper is not currently being considered for publication elsewhere.