Mean Sunspot Number Research Articles

Geomagnetic Storm Associated D‐Region Anomalies Estimated From VLF Observations at a Low‐Latitude Station, Suva, Fiji

AbstractA comprehensive analysis of subionospheric Very Low Frequency (VLF) signals from transmitters with call‐signs NWC, NPM, and NLK monitored at a low‐latitude station, Suva, Fiji, has been carried out to determine the D‐region effects of the intense geomagnetic storms for a period of 7 years (2011–2016 and 2018). The periods of 2011–2016 and 2018 fall under the 24th solar cycle with yearly mean sunspot numbers 80.8–39.8 and 7.0, respectively. Seven out of 12 geomagnetic storms revealed VLF anomalies during the storm main phase day and storm recovery phase days. A pronounced decrease in the VLF amplitude and phase for the storms having main phase onset in the nighttime was found. Out of seven geomagnetic storms, a storm of St Patrick's Day of 17–18 March 2013 is presented as a case study and the same analysis was done for all the storms. The anomalies in the VLF amplitude and phase have been modeled using Long‐Wave Propagation Capability code V2.1 to obtain the changes in Wait parameters (reference height, H′ and sharpness factor, ꞵ) and changes in the D‐region electron density. In most cases of storms, an increase in the H′ and a decrease in the ꞵ during the storm main and recovery phases were obtained which for the 17–18 March 2013 storm increased by 7.4–7.6 km and decreased from 0.03 to 0.07 km−1, respectively. Wavelet analysis of signal anomalies showed clear wave‐like spectra (0.05–0.18 mHz) of atmospheric gravity waves indicating traveling ionospheric disturbances propagating toward low latitudes due to storm‐associated Joule heating at high latitudes.

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.

Applying support vector machine (SVM) using GPS-TEC and Space Weather parameters to distinguish ionospheric disturbances possibly related to earthquakes

The effort to identify and comprehend potential earthquake-related phenomena shares a common goal: successful earthquake forecasting. Advancements in science and technology have made this goal multidisciplinary. Currently, possibly earthquake-related anomalies in the Vertical Total Electron Content (VTEC) of the Earth’s ionosphere are being investigated. Global Navigation Satellite Systems (GNSS) can be used to calculate this ionospheric parameter. In this research work, GPS VTEC was calculated for periods between the years 2015 and 2019. The selection of this periods considered both seismically active and non-seismically areas in Mexico. The Mw ≥ 5 earthquakes under study were registered by the National Seismological Service. Moreover, different geomagnetic storm and solar activity parameters, such as the geomagnetic equatorial Dst index and the F10.7 index, were analyzed. Additionally, the daily average and monthly mean number of sunspots (R, SSN, respectively) were included as a direct, long-term record of the development of the solar cycle. To the periods under study different statistical methods were applied, such as Mean-Square Error (MSE) and cross-correlation. The above aims to apply a machine learning technique capable of classifying between periods with seismic and non-seismic activity. The features were constructed using statistical data and results from the implemented analysis. Furthermore, Principal Component Analysis (PCA) was applied to reduce the feature vector dimensions, and accuracy scores were compute using k-fold cross-validation. The results from the Support Vector Machine (SVM) model indicated an accuracy of 88.9% for the training set, and an accuracy of 80% was obtained for the test set. One of the limitations of the current study was the sample size. However, the present initial approach for classifying seismic events from non-seismic periods using SVM demonstrated promising results when considering the indicated parameters and the days under study.

A statistical study of trans-equatorial loops from 2006 to 2020

ABSTRACT We studied trans-equatorial loops (TLs) statistically from December 2006 to 2020. During this period, 160 TLs were identified, and 12 per cent of all active regions are connected by TLs. The result shows that 74 per cent of TLs are connected to regions of preceding magnetic polarity (PTLs), and only 26 per cent of TLs are connected to regions of following magnetic polarity (FTLs). The dominance of PTLs is related to Joy’s law. Moreover, the average length of TLs is 20°, and their lengths decrease during the solar cycle. By comparing the number of TLs and the smoothed monthly mean number of sunspots from 2009 to 2018, 88 per cent of the TLs appeared near the solar maximum (2014 ± 2). We noted that the closer a sunspot is to the equator, the easier it is for a TL to form. Furthermore, the tilt angle and latitudinal asymmetry of TL foot-points are independent of the solar cycle.

Additional flight delays and magnetospheric–ionospheric disturbances during solar storms

Although the sun is really far away from us, some solar activities could still influence the performance and reliability of space-borne and ground-based technological systems on Earth. Those time-varying conditions in space caused by the sun are also called solar storm or space weather. It is known that aviation activities can be affected during solar storms, but the exact effects of space weather on aviation are still unclear. Especially how the flight delays, the top topic concerned by most people, will be affected by space weather has never been thoroughly researched. By analyzing huge amount of flight data (~ 4 × 106 records), for the first time, we quantitatively investigate the flight delays during space weather events. It is found that compared to the quiet periods, the average arrival delay time and 30-min delay rate during space weather events are significantly increased by 81.34% and 21.45% respectively. The evident negative correlation between the yearly flight regularity rate and the yearly mean total sunspot number during 22 years also confirms such correlation. Further studies show that the flight delay time and delay rate will monotonically increase with the geomagnetic field fluctuations and ionospheric disturbances. These results indicate that the interferences in communication and navigation during space weather events may be the most probable reason accounting for the increased flight delays. The above analyses expand the traditional field of space weather research and could also provide us with brand new views for improving the flight delay predications.

Prediction for the amplitude and second maximum of Solar Cycle 25 and a comparison of the predictions based on strength of polar magnetic field and low-latitude sunspot area

ABSTRACTThe maximum of a solar cycle contains two or more peaks, known as Gnevyshev peaks. Studies of this property of solar cycles may help in better understanding the solar dynamo mechanism. We analysed the 13-month smoothed monthly mean Version-2 international sunspot number (SN) during the period 1874–2017 and found that there exists a good correlation between the amplitude (value of the main and highest peak) and the value of the second maximum (value of the second highest peak) during the maximum of a solar cycle. Using this relationship and the earlier predicted value 86 ± 18 (92 ± 11) of the amplitude of Solar Cycle 25, here we predict a value 73 ± 15 (79 ± 15) for the second maximum of Solar Cycle 25. The ratio of the predicted second maximum to the amplitude is found to be 0.85, almost the same as that of Solar Cycle 24. The least-square cosine fits to the values of the peaks that occurred first and second during the maxima of Solar Cycles 12–24 suggest that in Solar Cycle 25 the second maximum would occur before the main maximum, the same as in Solar Cycle 24. However, these fits suggest ≈106 and ≈119 for the second maximum and the amplitude of Solar Cycle 25, respectively. Earlier, we analysed the combined Greenwich and Debrecen sunspot-group data during 1874–2017 and predicted the amplitude of Solar Cycle 25 from the activity just after the maximum of Solar Cycle 24 in the equatorial latitudes of the Sun’s Southern hemisphere. Here, from the hindsight of the results we found the earlier prediction is reasonably reliable. We analysed the polar-fields data measured in Wilcox Observatory during Solar Cycles 20–24 and obtained a value 125 ± 7 for the amplitude of Solar Cycle 25. This is slightly larger – whereas the value ≈86 (≈92) predicted from the activity in the equatorial latitudes is slightly smaller – than the observed amplitude of Solar Cycle 24. This difference is discussed briefly.

A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map

In order to achieve the high-accuracy prediction of the total electron content (TEC) of the regional ionosphere for supporting the application of satellite navigation, positioning, measurement, and controlling, we proposed a modeling method based on machine learning (ML) and use this method to establish an empirical prediction model of TEC for parts of Europe. The model has three main characteristics: (1) The principal component analysis (PCA) is used to separate TEC’s temporal and spatial variation characteristics and to establish its corresponding map, (2) the solar activity parameters of the 12-month mean flux of the solar radio waves at 10.7 cm (F10.712) and the 12-month mean sunspot number (R12) are introduced into the temporal map as independent variables to reflect the temporal variation characteristics of TEC, and (3) The modified Kriging spatial interpolation method is used to achieve the spatial reconstruction of TEC. Finally, the regression learning method is used to determine the coefficients and harmonic numbers of the model by using the root mean square error (RMSE) and its relative value (RRMSE) as the evaluation standard. Specially, the modeling process is easy to understand, and the determined model parameters are interpretable. The statistical results show that the monthly mean values of TEC predicted by the proposed model in this paper are highly consistent with the observed values curve of TEC, and the RRMSE of the predicted results is 12.76%. Furthermore, comparing the proposed model with the IRI model, it can be found that the prediction accuracy of TEC by the proposed model is much higher than that of the IRI model either with CCIR or URSI coefficients, and the improvement is 38.63% and 35.79%, respectively.

Occurrence Rate of Radio-Loud and Halo CMEs in Solar Cycle 25: Prediction Using their Correlation with the Sunspot Number

The coronal mass ejections (CMEs) from the Sun are known for their space weather and geomagnetic consequences. Among all CMEs, so-called radio-loud (RL) and halo CMEs are considered the most energetic in the sense that they are usually faster and wider than the general population of CMEs. Hence the study of RL and halo CMEs has become important and the prediction of their occurrence rate in a future cycle will give a warning in advance. In the present paper, the occurrence rates of RL and halo CMEs in solar cycle (SC) 25 are predicted. For this, we obtained good correlations between the numbers of RL and halo CMEs in each year and the yearly mean sunspot numbers in the previous two cycles. The predicted values of sunspot numbers in SC 25 by NOAA/NASA were considered as representative indices and the corresponding numbers of RL and halo CMEs have been determined using linear relations. Our results show that the maximum number of RL and halo CMEs will be around 39 $/pm$ 3 and 45 $/pm$ 4, respectively. Removing backside events, a set of front-side events was also considered separately and the front-side events alone in SC 25 are predicted again. The peak values of front-side RL and halo events have been estimated to be around 31 $/pm$ 3 and 29 $/pm$ 3 respectively. These results are discussed in comparison with the predicted values of sunspots by different authors.

Evolution of Dynamical Complexities in Geospace as Captured by Dst Over Four Solar Cycles 1964–2008.

AbstractThe dynamical complexities of geospace has been examined by investigating the characteristic chaotic properties of hourly Dst values obtained from the World Data Center for Geomagnetism, Kyoto, over four solar cycles (1964–2008) using nonlinear methods: sample entropy, Lyapunov exponents, correlation dimension, recurrence quantification analysis, and multifractal detrended fluctuation. Throughout the solar cycles considered, the space environment was observed to be chaotic based on Lyapunov exponents obtained. The order of increasing complexity in the solar cycles based on recurrence rate (RR) values is Cycle 21 < 22 < 23 < 20. Similar patterns and trend were observed in the correlation dimension values for the different solar cycles. The peak of each solar cycle is associated with high chaoticity. In all the analysis considered, high values of sample entropy, Lyapunov exponent, correlation dimension, correspond to increasing chaoticity. However, lower value of RR signifies more chaotic activity. The solar cycle 20 with the greatest values of mean sunspot number (651.62) and standard deviation of sunspot number (1,656.90) has the greatest chaoticity. The chaoticity in the magnetosphere increases with increasing geomagnetic activity as captured by Dst index. Similar trends were not observed in BZ and solar wind dynamic pressure which showed that the observed chaoticity is due to internal variations. The multifractal spectrum of Dst at all solar cycles considered revealed that the chaotic activities in the magnetosphere are not influenced by the local fluctuations in the region. The degree of multifractality were found to be in the order Cycle 20 > Cycle 23 > Cycle 21 > Cycle 22.

Predicting the Amplitude of Solar Cycle 25 Using the Value 39 Months Before the Solar Minimum

Yoshida and Yamagishi (Ann. Geophys. 28, 417, 2010) found that the rate of decrease in the smoothed monthly mean sunspot number [ $R_{\mathrm{I}}$ ] over the final three years of a solar cycle or $R_{\mathrm{I}}$ itself at three years before the solar minimum [ $R_{ \mathrm{min}}$ ] can be used as a precursor for the ensuing maximum amplitude [ $R_{\mathrm{m}}$ ] if the time of $R_{\mathrm{min}}$ can be predicted in advance. The $R_{\mathrm{I}}$ series of the new version is employed to carefully analyze the decrease rate [ $\beta $ ] at different months [ $m$ ] before $R_{\mathrm{min}}$ and over different time intervals [ $\Delta m$ ] in this study. It is found that $R_{\mathrm{m}}(n)$ of Solar Cycle $n$ is best correlated ( $r=0.831$ ) with the preceding $\beta (n-1, m, \Delta m)$ over the final $\Delta m=m=39$ months. In addition, $R_{\mathrm{m}}$ is found to be best correlated ( $r=0.834$ ) with the $R_{\mathrm{I}}$ value 39 months before the preceding minimum. For the even- (odd-)numbered cycles, $R_{\mathrm{m}}$ is best correlated, $r=0.956$ (0.747), with the rate of decrease over $\Delta m= 38 (43)$ months interval from $m=39(47)$ months before the solar minimum, and $R_{\mathrm{m}}$ is best correlated, $r=0.964(0.739)$ , with the $R_{\mathrm{I}}$ -value 39 (46) months before the solar minimum. Based on this method and the inferred end time of Solar Cycle 24, the amplitude of Solar Cycle 25 is predicted to be $R_{\mathrm{m}}=130.0\pm 31.9$ , occurring around October 2024 $\pm 13$ (months).

Source-region characteristics of anemone active regions in the ascending phase of solar cycle 24

Context. Active regions in close proximity to coronal holes, also known as anemone regions, are the best candidates for studying the interaction between closed and open magnetic field topologies at the Sun. Statistical investigation of their source-region characteristics can provide vital clues regarding their possible association with energetic events, relevant from space weather perspectives. Aims. The main goal of our study is to understand the distinct properties of flaring and non-flaring anemone active regions and their host coronal holes, by examining spatial and magnetic field distributions during the rise phase of the solar cycle, in the years 2011–2014. Methods. Anemone regions were identified from the minimum-distance threshold, estimated using the data available in the online catalogs for on-disk active regions and coronal holes. Along with the source-region area and magnetic field characteristics, associated filament and flare cases were also located. Regions with and without flare events were further selected for a detailed statistical examination to understand the major properties of the energetic events, both eruptive and confined, at the anemone-type active regions. Results. Identified anemone regions showed weak asymmetry in their spatial distribution over the solar disk, with yearly average independent from mean sunspot number trend, during the rise phase of solar cycle 24. With the progression in solar cycle, the area and minimum-distance parameters indicated a decreasing trend in their magnitudes, while the magnetic field characteristics indicated an increase in their estimated magnitudes. More than half of the regions in our database had an association with a filament structure, and nearly a third were linked with a magnetic reconnection (flare) event. Anemone regions with and without flares had clear distinctions in their source-region characteristics evident from the distribution of their properties and density analysis. The key differences included larger area and magnetic field magnitudes for flaring anemone regions, along with smaller distances between the centers of the active region and its host coronal hole.

Cosmic-ray propagation around the Sun: investigating the influence of the solar magnetic field on the cosmic-ray Sun shadow

The cosmic-ray Sun shadow, which is caused by high-energy charged cosmic rays being blocked and deflected by the Sun and its magnetic field, has been observed by various experiments, such as Argo-YBJ, Tibet, HAWC, and IceCube. Most notably, the shadow’s size and depth was recently shown to correlate with the 11-year solar cycle. The interpretation of such measurements, which help to bridge the gap between solar physics and high-energy particle astrophysics, requires a solid theoretical understanding of cosmic-ray propagation in the coronal magnetic field. It is the aim of this paper to establish theoretical predictions for the cosmic-ray Sun shadow in order to identify observables that can be used to study this link in more detail. To determine the cosmic-ray Sun shadow, we numerically compute trajectories of charged cosmic rays in the energy range of 5−316 TeV for five different mass numbers. We present and analyze the resulting shadow images for protons and iron, as well as for typically measured cosmic-ray compositions. We confirm the observationally established correlation between the magnitude of the shadowing effect and both the mean sunspot number and the polarity of the magnetic field during the solar cycle. We also show that during low solar activity, the Sun’s shadow behaves similarly to that of a dipole, for which we find a non-monotonous dependence on energy. In particular, the shadow can become significantly more pronounced than the geometrical disk expected for a totally unmagnetized Sun. For times of high solar activity, we instead predict the shadow to depend monotonously on energy and to be generally weaker than the geometrical shadow for all tested energies. These effects should become visible in energy-resolved measurements of the Sun shadow, and may in the future become an independent measure for the level of disorder in the solar magnetic field.

Negative correlation between the number of sunspots and the occurrence of 7Be and 22Na in the surface air and their contribution to radiation doses.

This article presents yearly mean concentrations of cosmogenic radionuclides 7Be and 22Na occurring in dry and wet depositions (fallout) and aerosols. Time dependencies negatively correlated with the yearly mean number of sunspots. Activity concentrations of 7Be and 22Na in aerosols in the surface air had a correlation of near-unity. 7Be in aerosols exhibited a smoother time dependence than 22Na, implying that the production of 22Na is more sensitive to the solar activity than the production of 7Be. The effect of the measured doses on the general population through internal and external exposure to radiation from cosmogenic radionuclides was small.

Investigation of HZE particle fluxes as a space radiation hazard for future Mars missions

Manned Mars missions planned in the near future of very low solar activity period and hence higher than acceptable radiation doses due mainly to the Galactic Cosmic Rays (GCR), would require special techniques and technological development for maintaining the good health of the astronauts. The present study is an attempt to make an assessment and characterise the coming years in terms of solar activity and space radiation environment especially due to the abundance of highly energetic heavy ions (known as HZE charged particles). These HZE particle fluxes constitute a major hazard to the astronauts and also to the critical electronic components of the spacecraft. Recent data on the HZE species (from B to Ni) obtained from ACE spacecraft shows a clear enhancement of the particle fluxes between the solar cycle 23 and solar cycle 24 (~between SSN peaks 2002 and 2014) due to the persisting low sunspot numbers of the latter cycle. The peak values of these cosmic ray fluxes occur with a time lag of about a year of the corresponding minimum value of the sunspots of a particular 11-year cycle which is pseudo-periodic in nature. This is demonstrated by the Fourier and Wavelet transform analyses of the long duration (1700–2018) yearly mean sunspot number data. The same time series data is also used to train a Hybrid Regression Neural Network (HRNN) model to generate the predicted yearly mean sunspot numbers for the solar cycle 25 (~2019–2031). The wavelet analysis of this new series of annual sunspot numbers including the predictions up to the end of 2031 shows a clear trend of continuation of the low solar activity and hence continuation of very high HZE fluxes prevailing in Solar cycle 24 into the solar cycle 25 and perhaps beyond.

Study of Exospheric Neutral Composition of Mars observed from Indian Mars Orbiter Mission

The exospheric composition data of Mars for the period 2014–15 has been extracted and analysed using the observations carried out by the MENCA (Mars Exospheric Neutral Composition Analyser) payload on-board the Mars Orbiter Mission (MOM) launched by India. The latitude, longitude, altitude and solar zenith angle coverage of the partial pressure values of different exospheric constituents are determined and assigned to create a new data-set with orbit-wise data assimilation particularly between 260 and 600 km altitude range. Apart from getting the results on mean individual orbits’ partial pressure profiles, the variations of the total as well as partial pressures are studied with respect to the distribution of the major atmospheric constituents and their dependence on solar activity. In particular, CO2 and O variations are considered together for any differential effects due to photolysis and photo-ionisation. The results on the gradual reduction in densities due to decreasing daily mean sunspot numbers and strong response of CO2 and O pressures to solar energetic particle events like that of 24 December 2014 are presented.

A Decline Phase Modeling for the Prediction of Solar Cycle 25

We use the Vondrak smoothing method and the V2.0 version of the monthly mean sunspot number (SSN) to produce a series of smoothed SSN (denoted SSN-VS), which closely mimics the 13-month running mean SSN. SSN-VS is then used to determine the characteristics of solar cycles. In particular, we find that our simulations for the past seven solar cycles yield predictions with relatively small errors. Applying the technique to the descending portion (i.e. the 20 months following SSN-VS = 70) of the present Solar Cycle 24, we make predictions for the next Solar Cycle 25. In particular, we find, assuming that Solar Cycle 25 is not a statistical outlier, that: 1) the sunspot minimum occurrence is expected around $2019.188 \pm 0.98$ years, 2) the sunspot maximum occurrence is expected around $2023.918 \pm 1.64$ years, and 3) a sunspot maximum value of about $228.8 \pm 40.5$ units of sunspot number is expected.

Prediction of maximum amplitude of solar cycle 25 using machine learning

Prediction of maximum amplitude of Solar Cycle 25 is obtained by using four different machine learning regression methods, i.e. Linear Regression (LR), Random Forest (RF), Radial Basis Function (RBF) and Support Vector Machine (SVM). Monthly mean sunspot number data during the 1856-June 2018 (solar cycles 10-24) from the World Data Center SILSO, Royal Observatory of Belgium, Brussels are used as machine learning inputs. According to LR, RF, RBF and SVM, the maximum of Solar Cycle 25 is predicted to occur in September 2023 (sunspot number of 159.4 ± 22.3), in December 2024 (sunspot number of 110.2 ± 12.8), in December 2024 (sunspot number of 95.5 ± 21.9) and in July 2024 (sunspot number of 93.7 ± 23.2), respectively. The prediction using LR method suggested that Solar Cycle 25 maximum will be slightly higher than the current cycle, while RBF and SVM suggested much lower cycles. RF prediction suggested a lower maximum with well-constructed double-peak. It was also found that the Solar Cycle 25 is predicted to begin in the late 2019 or early 2020 according to all four methods.

An SOC Approach to Study the Solar Wind‐Magnetosphere Energy Coupling

AbstractSolar wind‐magnetosphere interaction and the injection of large quantity of plasma particles into the Earth's magnetosphere are the primary reasons behind geomagnetic storm, auroral effects, and, in general, all the fluctuations observed in the terrestrial magnetic field. In this paper, we analyzed the perturbed magnetosphere as a sandpile‐like cellular automata model based on the concept of self‐organized criticality and many‐body interactive system and proposed a solar wind‐magnetosphere energy coupling function in terms of interplanetary magnetic field BZ, the zth component of interplanetary magnetic field. The function determines the cusp width W depending on the intensity of (−BZ − BTh) where BTh is the threshold value. The model generates two output series, which are the numerical representation of the real‐time Dst index and AE index series, respectively. For our study, the range of years 1997–2007 of the 23rd solar cycle had been considered here. The threshold value BTh plays a significant role in the analysis and exhibits a proportional relationship with the yearly mean total number of sunspots for each year of the range 1997–2007 of the 23rd solar cycle. For each year, the two resultant output time series of the model display high‐correlation coefficients with the real‐time Dst and AE indexes, respectively, which denotes the acceptability of the proposed energy coupling function and its relation with the solar activities.

Validation of NeQuick 2 model over West African equatorial region using GNSS-derived Total Electron Content data

The NeQuick 2 has been implemented as a climatological model with the goal to represent the typical median conditions of the ionosphere. Several investigations have been made to test its performances at global and regional scale. In order to provide a 3D specification of the electron density of the ionosphere at a given geographic location and time, specific data ingestion techniques, based on NeQuick 2 adaptation to Total Electron Content (TEC) data, have been introduced. In the present work, the performance of the NeQuick 2 model for climate prediction of TEC has been evaluated in the west African equatorial region. Several types of solar indexes such as the smoothed monthly mean sunspot number (R12), the daily solar radio flux at 10.7 cm wavelengths (F10.7) and the daily sunspot number (Rz) have been used as input parameters for the model. The monthly medians of the modeled vertical TEC (vTEC) have been compared to the corresponding monthly medians of the vTEC obtained from two Global Navigation Satellite System (GNSS) stations located in Côte d'Ivoire. Furthermore, to assess the “weather-like” capabilities of the model, a data ingestion technique has been implemented through the computation of an effective parameter Az at a single GNSS station in Côte d'Ivoire (Yamoussoukro). For geomagnetically disturbed and quiet days, this Az parameter has been used as input for NeQuick to estimate the vertical TEC values in another station (Abidjan) located at about 200 km from the previous one. Additional tests based on the evaluation of Az at the station of Cotonou in Benin allowed analysing the model capability of reconstructing TEC at different distances (up to 850 km from the reference station) according to the north-south and east-west directions of displacement. The statistical analysis has been based on relative frequencies distribution of the difference between reconstructed and the corresponding observed TEC.

The Solar Flux and Sunspot Number; A Long-Trend Analysis

We investigate the nature of monthly sunspot numbers and solar flux F10.7 by employing the linear and multiple regression techniques. We observed a brilliant correlation between monthly mean sunspot number and F10.7. We observed that even in deep solar minimum there exist some magnetic activities. We obtained the coefficient of determination R2 to be 0.9533. We estimated the correlation coefficient for solar flux F10.7 and sunspot number to be 0.97. We extrapolated the F10.7 back to the year 1700 and observed a good correspondence between the modelled F10.7 and sunspot nature. We also found a very good correspondence between the modelled and observed solar flux F10.7.