Peak Of Solar Cycle Research Articles

Ionospheric tomography for SWARM satellite orbit determination using single-frequency GNSS data

Ionospheric tomography offers three-dimensional (3D) description of the electron density distribution, enabling the direct incorporation of electron density data into the slant total electron content (STEC) computation. As a result, STEC derived from tomography helps mitigate the ionospheric delay experienced in the line of sight between global navigation satellite systems (GNSS) and satellites positioned in low Earth orbits (LEO). Tomography can therefore be effectively employed to correct single-frequency GNSS observations and allow enhanced positioning of spaceborne platforms. We demonstrate the accuracy and performance of a global-scale ionospheric tomography method for determining satellite orbits, utilizing single-frequency GNSS measurements combined with a precise point positioning (PPP) algorithm. We compare the tomographic outcomes against orbit determination derived from the GRoup and PHase ionospheric correction (GRAPHIC) observable and based on an ionospheric climatological model. Near the peak of solar cycle 24, the overall accuracy achieved with tomography was around 3.8 m. notably, compared to the background climatological model, tomography demonstrated improvements ranging from 15 to 20%. The GRAPHIC method outperformed tomography, achieving an accuracy of 0.7 m, whereas we obtained around 7 m accuracy when no ionospheric model is employed. Although the developed ionospheric tomography has yet to match the precision of GRAPHIC, our results bring us relatively closer to this objective.

Comprehensive analysis of different GNSS receivers performance based on PPP-AR and positioning accuracy during 22 geomagnetic storms in 2023

Geomagnetic storms induced ionospheric disturbances can degrade the positioning accuracy and Ambiguity Resolution (PPP-AR) of GPS Precise Point Positioning (PPP), and this negative effect varies among different GNSS receivers. Under current conditions with frequent geomagnetic storms occurrences during the peak of solar cycle 25, selecting GNSS receivers with strong resistance to ionospheric disturbances is meaningful for precise GNSS positioning and ionospheric research. Therefore, it is necessary to conduct a comprehensive comparison and evaluation of the performance for different GNSS receivers under geomagnetic storms. Based on 22 geomagnetic storms in 2023, we investigated the three-dimensional root mean square error (3D RMS) and ambiguity resolved percentage (ARP), which is defined as the ratio of the number of carrier-phase observations with fixed ambiguities to the total number of phase measurements of GPS kinematic PPP, across ten groups of collocated stations around the world. For the groups using different receiver brands, the performance ranking for positioning accuracy and PPP-AR during geomagnetic storms are as follows: TPS > JVAVD > SEPTENTRIO > TRIMBLE. The station with a larger average ARP generally has a smaller average 3D RMS error than the another GNSS station. The average 3D RMS error between collocated stations using different receiver brands is typically greater than 0.010 m and even larger. The largest 3D RMS error difference is observed between collocated stations with SEPTENTRIO and JAVAD receivers, with 3D RMS errors of 0.089 m and 0.019 m, respectively. These identical-brand receivers with different types are as follows: TPS NET-G5 > TPS NET-G3A, TRIMBLE ALLOY > TRIMBLE NETRS, SEPT POLARX5TR > SEPT POLARX5S, and LEICA GR10 > LEICA GR30, respectively. The average differences in 3D RMS and ARP can reach up to 0.021 m and 6.0%, respectively. We found that the choice of antennas does not significantly affect PPP positioning performance during storms, with differences in average 3D RMS below 0.005 m and ARP differences below 0.2%.. Higher latitudes have more satellites affected by ionospheric disturbances, while this effect is typically only observed during strong storms (Dstmin≤-100nT) in mid-latitudes. Adequate available GPS satellites are crucial for achieving high PPP-AR and positioning accuracy during storms. The variations of disturbed satellites and the average Rate of Total Electron Content Index (ROTI) for collocated stations are generally consistent. However, there are differences in the average of ROTI values for different receivers under geomagnetic storms. The differences in PPP-AR performance and ROTI values among different receivers under geomagnetic storms can be attributed to the variations in satellite tracking algorithms, tracking loop designs, firmware, and multipath mitigation methods used by different receivers. These findings can provide a reference value and future research direction for selection, application and enhancement for different receivers to achieve GNSS high precision positioning under complex space weather conditions.

Our Stormy Solar Future: Space weather is heating up in our current solar cycle peak.

Our Stormy Solar Future: Space weather is heating up in our current solar cycle peak.

Automated detection and analysis of coronal active region structures across solar cycle 24

ABSTRACT Observations from NASA’s Solar Dynamic Observatory Atmospheric Imaging Assembly were employed to investigate targeted physical properties of coronal active region structures across the majority of solar cycle 24 (From 2010 May to end of 2020 December). This is the largest consistent study to date which analyses emergent trends in structural width, location, and occurrence rate by performing an automatic and long-term examination of observable coronal and chromospheric limb features within equatorial active region belts across four extreme ultraviolet wavelengths (171, 193, 211, and 304 Å). This has resulted in over 30 000 observed coronal structures and hence allows for the production of spatial and temporal distributions focused upon the rise, peak, and decay activity phases of solar cycle 24. Employing a self-organized-criticality approach as a descriptor of coronal structure formation, power-law slopes of structural widths versus frequency are determined, ranging from -1.6 to -3.3 with variations of up to 0.7 found between differing periods of the solar cycle, compared to a predicted Fractal Diffusive Self-Organized Criticality (FD-SOC) value of -1.5. The North–South hemispheric asymmetry of these structures was also examined with the Northern hemisphere exhibiting activity that is peaking earlier and decaying slower than the Southern hemisphere, with a characteristic ‘butterfly’ pattern of coronal structures detected. This represents the first survey of coronal structures performed across an entire solar cycle, demonstrating new techniques available to examine the composition of the corona by latitude in varying wavelengths.

IONOSPHERIC IRREGULARITIES MEASURED BY GROUND-BASED AND SATELLITE-EMBEDDED RECEIVERS: ANALYSIS OF S4 INDEX IN A LOW LATITUDE REGION

Abstract. Ionosphere is an important layer of Earth’s atmosphere which can interfere in the transmission of radio signals, affecting several activities, such as the ones related to global navigation satellite systems (GNSS) positioning. In this intend, many efforts have been made in the last decades in order to better understand and model this region of atmosphere. The observation of ionosphere and its irregularities can be performed by several techniques, including classic methods such as ionosondes. Besides that, the use of GNSS signals as source of atmospheric observations have allowed the development of different methodologies and techniques to obtain ionospheric information. Among those, the use of data collected by receivers embedded in low earth orbiting (LEO) satellites, such as the ones used in radio occultation (RO) missions can be relevant due to the amount and distribution of the data obtained. Besides that, GNSS receivers has also been used in ground stations networks to continuously monitor the ionosphere. In this sense, the present work aims to compare irregularities observed by two different techniques using data from ground-based (GNSS monitoring stations) and satellite-embedded (RO mission) receivers. The study is performed over one of the most challenging scenarios, the Brazilian region, considering seasonal variability in two months (low and high solar flux) from 2014 (solar cycle peak). The results obtained show agreement in the general behavior of the ionospheric irregularities observed by the two different techniques.

New Space Companies Meet a “Normal” Solar Maximum

AbstractThe monthly mean sunspot number has been larger in June–July 2023 than the double peak of solar cycle 24 (146 in February 2014 and 139 in November 2011) and brings us back to the sunspot level of solar cycle 23. However, the number of rocket launches, satellites in orbit and private space companies has increased dramatically in the past 20 years. Additionally, there is a growing interest for space exploration beyond Earth's orbit, to the Moon and beyond, which comes with higher risk of being affected by space weather. Here, we discuss some of these trends and the role of the journal to improve awareness of space weather impacts.

Low latitude ionospheric TEC response to the solar flares during the peak of solar cycle 24

We describe the newly discovered response of differential vertical total electron content (DVTEC) in the low-equatorial latitude ionosphere to the solar flares during the peak of solar cycle 24 i.e., the year 2014. GPS TEC data for the existing investigation has been acquired from the international GNSS services network station in Bangalore, India (geomagnetic latitude 4.58οN). For the year 2014, we inspected five X-class solar flares, forty-nine M-class solar flares, and three hundred ninety-six C-class solar flares in the current study. To ascertain the relationship between ΔDVTEC and the ΔX-ray and ΔEUV fluxes, the two main ionizing radiation fluxes during flare events are used. Additionally, to identify electron density change in the ionosphere due to solar flares, we used two well-known methodologies, the baseline method and the mean method. The baseline method provides a better correlation between ΔDVTEC and ΔX-ray for X-, M-, and C-class flares. For M- and C- class flares, both methods do not show any meaningful correlation. The Solar disc location effect shows an enhanced (0.573) correlation between ΔDVTEC and ΔX-ray*cos (CMD) for the X-class flares, whereas for M- and C-class flares, no significant change was observed, which indicates feeble or no CMD effect for lower class solar flares while it is opposite for X-class flares. Here we suggest that the high solar activity, and location of the observation station to the EIA trough region might be the possible reason for the observed results.

Advanced Warning of Threatening Equatorial Plasma Bubbles to Support GBAS in Low Latitudes

In low-latitude regions such as Brazil, the single-frequency Ground-Based Augmentation System (GBAS) is not yet fully operational due to the influence of ionospheric variability. The most critical issue for GBAS is the occurrence of ionospheric gradients large enough to compromise the system's integrity. This can result in unsafe operation because a single GBAS ground station cannot identify all threatening gradients in real time. This paper develops and validates a strategy to detect and alert in advance the presence and effects of equatorial plasma bubbles (EPBs), which create the largest threat to GBAS operations in low latitudes. This alerting system uses surrounding stations in the vicinity of the GBAS facilities being supported to monitor the ionosphere state in real-time and send alerts to the served GBAS facility when threatening conditions are detected on one or more tracked satellites. Time-step measurements of ionospheric gradients and data availability at these surrounding stations are used to generate these alerts when needed. An architecture is proposed for such a system along with a complete validation of the method. Validation uses a reliable dataset from the peak of ionospheric Solar Cycle 24 containing post-processed station-pair gradients in time, amplitude scintillation indices, and ionospheric maps. All gradients from the dataset classified as threatening to GBAS operation were encompassed by the alerts issued by the real-time methodology within the adopted success criteria. Based on these results, an EPB alerting approach based on this method presented and validated in this paper can significantly enhance single-frequency GBAS integrity and availability in low latitudes.

Prediction of solar cycle 25 using deep learning based long short-term memory forecasting technique

• Peak & timing of solar cycle 25 has been predicted using sunspot number data. • The concept of Deep Learning algorithm has been used for forecasting. • The proposed model showed good performance on various time intervals with low RMSE value. • Validation of model performance has been done by predicting past solar cycles 20–24. • The model captured non-linearity and cyclic trend within the data with good RMSE. In the current work we have used the deep learning based long short-term memory model to predict the strength and peak time of solar cycle 25 by employing the monthly smoothed sunspot number data obtained from WDC-SILSO, Royal Observatory of Belgium, Brussels. We have used the stacked LSTM forecasting model to predict the upcoming cycle 25. From our analysis it has been shown that our proposed model is capable of capturing long term dependencies as well as trend within the data. For cycle 20 and 21 the error difference between predicted as well as observed peak value is 2.3 and 0.7 respectively while the peak prediction error is 1.47% and 0.30%. The RMSE of the model for cycle 20 and 21 is 3.97 and 4.34 respectively. For cycle 22, the AE and RE is 4.6 and 2.16% while the RMSE of the model for this case is 4.50. The predicted peak amplitude of solar cycle 23 and 24 from our proposed model has a relative error of 1.75% and 1.99% respectively from the observed value while the RMSE is 3.4 for cycle 23 and 4.2 for cycle 24. Our projected prediction of cycle 25 using the proposed LSTM model, says that it will be stronger than cycle 24 and weaker than cycle 23. The solar cycle 25 will peak with an amplitude of sunspot number at 171.9 ± 3.4 and will be 47 % stronger than cycle 24. The solar cycle 25 will reach its peak in August 2023 ± 2 months.

A Novel Bimodal Forecasting Model for Solar Cycle 25

Abstract In this paper, a novel bimodal model to predict a complete sunspot cycle based on comprehensive precursor information is proposed. We compare the traditional 13 month moving average with the Gaussian filter and find that the latter has less missing information and can better describe the overall trend of the raw data. Unlike the previous models that usually only use one precursor, here we combine the implicit and geometric information of the solar cycle (peak and skewness of the previous cycle and start value of the predicted cycle) with the traditional precursor method based on the geomagnetic index and adopt a multivariate linear approach with a higher goodness of fit (>0.85) in the fitting. Verifications for cycles 22–24 demonstrate that the model has good performance in predicting the peak and peak occurrence time. It also successfully predicts the complete bimodal structure for cycle 22 and cycle 24, showing a certain ability to predict whether the next solar cycle is unimodal or bimodal. It shows that cycle 25 is a single-peak structure and that the peak will come in 2024 October with a peak of 145.3.

Forecasting Solar Cycle 25 with a Principled Bayesian Two-stage Statistical Model

Abstract We update the Bayesian analysis of sunspot numbers (SSNs) developed by Yu et al. prior to the peak of Solar Cycle 24 to account for the recalibration of the SSN index. We show that the model yields an accurate hindcast of Cycle 24 with the revised SSN, and use the data acquired since then to predict the strength and peak of Cycle 25. We predict the behavior of Cycle 25 to be moderate to weak, with a peak SSN of 120±27 to occur c. 2025 July (±7 months).

Statistical and Solar Cycle Distribution of Daily Flux $\ge 10^{9}\mbox{ cm}^{-2}$ d−1 sr−1 for $E>2$ MeV Electrons Observed by GOES During 1987 – 2019

We studied the statistical and solar cycle distribution of daily flux $\ge 10^{9}\mbox{ cm}^{-2}$ d−1 sr−1 for $E>2$ MeV electrons Observed by GOES from 1987 to 2019. There were 282 days with daily flux $\ge 10^{9}\mbox{ cm}^{-2}$ d−1 sr−1 for $E>2$ MeV electrons during this period. Of the 282 days, 71.63% of them have the daily flux <2.0 $\times 10^{9}\mbox{ cm}^{-1}$ d−1sr−1, 19.86% of them with the daily flux fall in the interval ≥2.0 $\times 10^{9}\mbox{ cm}^{-2}$ d−1 sr−1 and <3.0 $\times 10^{9}\mbox{ cm}^{-2}$ d−1 sr−1, 4.26% of them have the daily flux in the interval between ≥3.0 $\times 10^{9}\mbox{ cm}^{-2}$ d−1 sr−1 and <4.0 $\times 10^{9}\mbox{ cm}^{-2}$ d−1 sr−1, and 4.26% of them have the daily flux ≥4.0 $\times 10^{9}\mbox{ cm}^{-2}$ d−1 sr−1. The daily flux $\ge 10^{9}\mbox{ cm}^{-2}$ d−1 sr−1 for $E>2$ MeV electrons that lasted for 1, 2, 3, 4, 5, 6, 7 and 10 days accounted for 18.44%, 17.02%, 20.21%, 15.6%, 8.87%, 6.38%, 9.93% and 3.55%, respectively. Among all the daily fluxes $\ge 10^{9}\mbox{ cm}^{-2}$ d−1 sr−1, 47.79% of them occurred in the northern hemisphere spring, 36.03% of them occurred in the fall, and 16.18% of them occurred in the other months. More than 82% of the daily flux $\ge 10^{9}\mbox{ cm}^{-2}$ d−1 sr−1 for $E>2$ MeV electrons appeared in the descending phase for each solar cycle. The greater percentage of the electrons of daily flux $\ge 10^{9}\mbox{ cm}^{-2}$ d−1 sr−1 for $E>2$ MeV occurred during the descending phase for the solar cycle with higher amplitude. Also, for the solar cycle with higher amplitude, the lower the percentage of the electrons of daily flux $\ge 10^{9}\mbox{ cm}^{-2}$ d−1 sr−1 of $E>2$ MeV occurred in the period from two years before to three years after the solar cycle peak.

Solar cycle prediction using a long short-term memory deep learning model

In this paper, we propose a long short-term memory (LSTM) deep learning model to deal with the smoothed monthly sunspot number (SSN), aiming to address the problem whereby the prediction results of the existing sunspot prediction methods are not uniform and have large deviations. Our method optimizes the number of hidden nodes and batch sizes of the LSTM network structures to 19 and 20, respectively. The best length of time series and the value of the timesteps were then determined for the network training, and one-step and multi-step predictions for Cycle 22 to Cycle 24 were made using the well-established network. The results showed that the maximum root-mean-square error (RMSE) of the one-step prediction model was 6.12 and the minimum was only 2.45. The maximum amplitude prediction error of the multi-step prediction was 17.2% and the minimum was only 3.0%. Finally, the next solar cycle (Cycle 25) peak amplitude was predicted to occur around 2023, with a peak value of about 114.3. The accuracy of this prediction method is better than that of the other commonly used methods, and the method has high applicability.

Assessment of Global Ionospheric Maps Performance by Means of Ionosonde Data

This work presents a new method for assessing global ionospheric maps (GIM) using ionosonde data. The method is based on the critical frequency at the F2 layer directly measured by ionosondes to validate VTEC (vertical total electron content) values from GIMs. The analysis considered four different approaches to using foF2. The study was performed over one of the most challenging scenarios, the Brazilian region, considering four ionosondes (combined in six pairs) and thirteen GIM products available at CDDIS (Crustal Dynamics Data Information System). Analysis was conducted using daily, weekly, one year (2015), and four years (2014–2017) of data. Additional information from the ionosphere was estimated to complement the daily analysis, such as slab thickness and shape function peak. Results indicated that slab thickness and shape function peak could be used as alternative indicators of periods and regions where this method could be applied. The weekly analysis indicated the squared frequency ratio with local time correction as the best approach of using foF2, between the ones evaluated. The analysis of one-year data (2015) was performed considering thirteen GIMs, where CODG and UQRG were the two GIMs that presented the best performance. The four-year time series (2014–2017) were analyzed considering these two products. Regional and temporal ionospheric influences could be noticed in the results, with expected larger errors during the solar cycle peak in 2014 and at locations with pairs of ionosondes with the larger distance apart. Therefore, we have confirmed the viability of the developed approach as an assessment method to analyze GIMs quality based on ionosonde data.

Heliospheric Maps from Cassini INCA Early in the Cruise to Saturn

Abstract We present new energetic neutral atom (ENA) maps from the Ion and Neutral Camera (INCA) instrument on Cassini from the year 2000, prior to Cassini’s encounter with Jupiter. These maps are the first produced for the year 2000 and are the only maps with comprehensive spatial coverage from the peak of solar cycle 23. These ENA maps span the energy range from 5.2 to 55 keV covering the pickup to suprathermal energy range. These maps represent a novel glimpse into the influence of the solar cycle on the structure of the outer heliosphere, specifically on the heliosheath where pickup and suprathermal ions dominate. The observations are consistent with the picture of the heliosheath from previous observations by the Cassini, Interstellar Boundary Explorer (IBEX), and Voyager missions. These maps have some consistent spatial features to maps produced by Cassini during solar cycle 24 such as reduced intensities in the mid-latitude basins. These maps also have distinct spatial features such as enhanced intensities at the poles and reduced intensities at the low-latitude flanks. These maps do not indicate a strong intensity increase in the regions adjacent to the nose and also show an intensity increase in the regions adjacent to the tailward direction.

Remeasurement of Solar Observing Optical Network sunspot areas

ABSTRACT The United States Air Force solar observing optical network (SOON) sunspot areas have been reported by several researchers over many years to be underestimated by as much as 50 per cent. Here, the areas of sunspots from scanned SOON disc drawings have been accurately remeasured for a period of two months from 2014 October and November – this being near the peak of Solar Cycle 24 and which includes the largest sunspot group of that cycle. The remeasured sunspot areas are now comparable with areas in sunspot catalogues.

The solar cycle: predicting the peak of solar cycle 25

Motivated by a successful prediction on the peak of solar cycle 24 (81.7, comparable to the observed 81.9, Du in Astrophys. Space Sci. 338:9, 2012), based on the logarithmic relationship between the maximum amplitude ( $R_{ \mathrm{m}}$ ) of a solar cycle and the preceding minimum $aa$ geomagnetic index ( $aa_{\mathrm{min}}$ ), we perform a prediction on the peak of the upcoming cycle 25 using the sunspot number of the new version instead. If the suggested error in $aa$ (3 nT) before 1957 is corrected, the correlation between $\ln R_{\mathrm{m}}$ and $\ln aa_{\mathrm{min}}$ ( $r=0.92$ ) is stronger than that not corrected ( $r=0.86$ ). Based on this relationship, the peak value of cycle 25 is predicted to be $R_{\mathrm{m}}(25)\simeq 151.1\pm 16.9$ , about 30% stronger than cycle 24. Employing the ‘Waldmeier effect’ that the rise time of a cycle is well anti-correlated to its amplitude, we estimated the rise time, $T_{\mathrm{a}}(25)=4.3\pm 0.2\pm 0.6$ , and the peak time of cycle 25, $2024.1 \pm 0.8 $ (years), which is during April 2023 and November 2024.

Extreme ionospheric spatial decorrelation observed during the March 1, 2014, equatorial plasma bubble event

The ground-based augmentation system must make provisions to being sufficiently robustness to ionospheric anomalies through the development of an ionospheric anomaly threat model. For developing the threat model in Brazil, earlier work found that ionospheric spatial decorrelations larger than those in the midlatitude regions were frequently observed during the peak of Solar Cycle #24 (current cycle). We provide details of a study of the extreme ionospheric spatial decorrelation observed over Brazil during the March 1, 2014, equatorial plasma bubble (EPB) event. As viewed by two Brazilian GNSS reference stations in Sao Jose dos Campos, PRN 03 descended to an elevation angle of about 19° in the northern sky. A spatial decorrelation of 850.7 mm/km at the GPS L1 signal at 01:04:00 UT between the two stations SJCU (23.21° S, 45.96° W) and SSJC (23.20° S, 45.86° W) over a baseline of 9.72 km was discovered, when the line of sight of PRN 03 passed through the transition zone of the EPB. Since the EPB-induced ionospheric scintillation can corrupt the ionospheric gradient estimates, multiple gradient observations were made from multiple stations and satellites to verify the largest gradient observation. Severe gradients discovered at other station–satellite pairs support that the event of PRN 03 is a real anomaly as opposed to a receiver fault or the result of post-processing errors. Since the availability loss was estimated to be 41.7% with the Brazilian threat model, remedies to reduce over-estimated ionospheric impact when evaluating and mitigating ionospheric integrity risk are presented.

The Properties of Source Locations and Solar Cycle Distribution of GLEs During 1942–2017

During 1942–2017, 72 ground level events (GLEs) have been registered. Source location and solar cycle distribution of GLEs during 1942–2017 have been investigated. The GLEs that occurred during 1942–2017 are mainly distributed in the longitudinal area ranged from E30 to W150. The northern hemisphere has many more GLEs than the southern hemisphere during Solar Cycles 19 and 20, while the northern hemisphere has slightly more GLEs than the southern hemisphere of the Sun during Solar Cycles 21 and 22. However, the southern hemisphere has more GLEs than the northern hemisphere during Solar Cycle 23. The latitudinal area ranged from N20 to N40 of the northern hemisphere of the Sun has many more GLEs than the latitudinal area ranging from S20 to S40 of the southern hemisphere of the Sun. The latitudinal area ranging from N0 to N20 of the northern hemisphere of the Sun has slightly more GLEs than the latitudinal area ranged from S0 to S20 of the southern hemisphere of the Sun. 46 GLEs came from the northern hemisphere, while 26 GLEs came from the southern hemisphere of the Sun, suggesting that GLEs dominated in the northern hemisphere of the Sun during 1942–2017. Statistical results show that 44.3% and 55.7% of the GLEs appeared in the ascending and descending phases of the solar cycles, respectively, and about 83% of the GLEs appeared during the period from the two years before solar cycle peak and the three years after solar cycle peak time. The number of GLEs during a solar cycle has a poor correlation with the amplitude of the solar cycle.

Occurrence characteristics of equatorial plasma bubbles and total electron content during solar cycle peak 23rd to peak 24th over Bangalore (13.02∘ N, 77.57∘ E)

We present the monthly, seasonal and annual variation in the ionospheric total electron content (TEC) and % occurrence rate of Equatorial plasma bubble (EPBs) during the lowest to highest solar activity phase for the period of 2002–2013. The Total Electron Content (TEC) is computed using Global Positing System (GPS) from Bangalore (13.02∘ N, 77.57∘ E) IGS station for the period 2002 to 2013. The total 4383 days during the period 2002–2013, out of which 4229 days GPS data were analyzed i.e. 96.48%. Total 1175 days data shows signature of EPBs. The average occurrence rates of EPBs were 5.93% during the disturbed period and 47.07% during quiet period. This shows that the EPBs occurrence rate is higher in quiet period than that of disturbed period. We also found that both the average GPS-TEC and % occurrence rate of plasma bubbles are positively correlated with solar flux for the entire 12 year period. This study investigates the causal linkage between EPBs and TEC using their statistics during the solar minimum and maximum period. The studies on dynamics of EPBs are essential because they affects the satellite communication system, mainly due to depletions in the Total Electron Content (TEC) which encounters most of the effects on GPS signals.