Sunspot Number Research Articles

Correlation analysis between the occurrence of epidemic in ancient China and solar activity.

As the globe has witnessed the pandemic, epidemic diseases exert a strong impact on human beings and ecosystems. Since the Sun is the primary energy source of the Earth, some scientific pioneers attempted to search for the discernible relation between solar activity and the incidence of epidemics. In this study, the periodic changes and trends of ancient Chinese epidemic data were analyzed in comparison with those of sunspot numbers, a solar activity proxy. The results show that the epidemic and solar activity changes are in good agreement to a certain extent, especially during the Gleissberg and the de Vries cycles. The wavelet coherence shows that the frequency of the epidemic data and sunspot numbers are highly associated. In addition, results from the ensemble empirical mode decomposition illustrate consistent variations in low-frequency decompositions. This study has important implications for further understanding of the potential impact of solar activity on Earth's biosphere, the underlying mechanism of which needs further exploration.

PREDICTION OF THE AMPLITUDE OF 25 TH SOLAR CYCLE USING THE RATE OF INCREASE OF SOLAR ACTIVITY

A prediction of the amplitude of the 25th cycle of solar activity is proposed based on the analysis of data on 24 previous solar cycles, which relate to the statistical relationship between the rate of increase in the number of sunspots in the phase of the growth curve and the amplitude of the cycle. It turned out that the forecasting result depends on which section of the growth curve is taken as the basis for forecasting, as well as whether all 24 cycles are taken into account, or only the odd ones. The prediction result is also affected by the initial assumption about monotonicity or non-monotonicity of the growth phase. A comparison of the rates of sunspot growth in different parts of the growth phase of different cycles shows that the current cycle #25 does not show early signs of non monotonic growth similar to those observed in the 24th cycle. It was concluded that, most likely, the maximum smoothed number of sunspots in the 25th cycle W max (25) should be equal to 185 ± 18 units in the new system, which corresponds to the average power of the solar cycle, with the implementation of the Hnievyshev-Ohl rule. However, if cycle #25 will still have a non-monotonic curve of the growth phase, similar to such a curve in the previous cycle #24, then W max (25) » 130. With such parameters of this cycle, there are no signs of approaching the deep minimum of the age cycle in the middle 21st century. This does not exclude the fact that this deep age minimum can occur suddenly and sharply immediately after the 25th cycle, as was the case, for example, in the Dalton minimum.

Variability of Helioclimate Factors and Applicability to the Emergency Service Population of the Northern Region

The purpose of this study was to study the influence of the variability of weather and heliophysical factors on the appeal to the Ambulance service for different groups of nosologies in the population of the Russian North. Information about calls to the emergency medical service of Khanty-Mansiysk was obtained from the database of calls for the period from 2001 to 2021 by disease classes (ICD-10). To assess the dynamics of weather factors, data from the All-Russian Research Institute of Hydrometeorological Information and materials from the weather station of Khanty-Mansiysk were used. The dynamics of air temperature; barometric pressure; relative humidity; baric trend; maximum wind speed; weight oxygen content in the air are analyzed. Data on the relative daily number of sunspots are obtained from the materials of the Royal Observatory of Belgium. The average daily values of the level of planetary geomagnetic activity, expressed by the Ap index, are copied from the website of the National Center for Geophysical Data of the USA (Boulder). Mathematical processing was carried out using wavelet analysis. The graph of temperature fluctuations for the period from 2001 to 2021 shows a significant circadian cyclicity and a semi-annual rhythm with a high level of trend. Significant rhythms of barometric pressure with a period of 5 years, 1 year and rhythms close to semi-annual, seasonal and near-monthly are observed. Humidity changes significantly in the circadian and intra-annual rhythms. The baric tendency has significant rhythms: 3 years, 1 year, 6, 3, 2 months. Changes in the maximum wind speed do not have a circadian rhythm, but intra-annual monthly variations are observed. The value of the weight oxygen content has circadian; two-year, five-year; two- and near-monthly rhythms. The relative number of sunspots (W) for the period from 2001 to 2021 is characterized by a five-, two-year and circadian rhythm. When considering the variations of the number W for the period from 1818 to 2017, in addition to the classic eleven-year, annual and near-monthly, rhythms with a period of 42.5 years, 18.1 years and 2.1 years are found. The index of planetary geomagnetic activity (Ar) has a rhythm with a period of 4 years, two-year and near-annual. The significance of all rhythms is p=0.001. When analyzing fluctuations in geomagnetic activity from 1932 to 2016, long-term rhythms are added: 35.00 years; 16.06 years; 10.88 years; semi-annual and three-month rhythms. Despite numerous hypotheses of the search for cosmobiological harmony in the form of evolutionary synchronisms, rhythmic cascades, golden section, the problem of the interaction of exogenous natural rhythms and endogenous rhythms of the human body remains largely unresolved. Comparison of the results of various studies is difficult due to methodological and mathematical approaches. With observation periods of 100-200 years or more, a long-period component is monitored, and in clinical studies (from a day to a week), short-period components are detected. Changing the background in the form of helioclimatic and social variations also does not add accuracy. The use of various methods of time series analysis involves obtaining information of different resolution levels (Fourier transform, SWANN, wavelet analysis). Nevertheless, the results obtained track the classical rhythms of solar and geomagnetic activity with periods of about eleven years, a year and a month. There is also a 35-year-old rhythm, a 42.5-year-old rhythm close to the 44.16-year rhythm identified by Bezrukova A.Ya., at 18.1 years (long-period components of the lunar tide) and other intra-annual rhythms with periods from two weeks to 9 months. The effect of helioclimatic factors on human health indicates the absence of constant coherence (in our case, the consistency of these processes over time, manifested when they are combined), due to the presence of a large number of mega- and mesorhythms, which is manifested by amplitude and phase desynchronization of heliogeophysical indicators and health status.

Toroidal Flux Loss due to Flux Emergence Explains why Solar Cycles Rise Differently but Decay in a Similar Way.

A striking feature of the solar cycle is that at the beginning, sunspots appear around midlatitudes, and over time the latitudes of emergences migrate toward the equator. The maximum level of activity (e.g., sunspot number) varies from cycle to cycle. For strong cycles, the activity begins early and at higher latitudes with wider sunspot distributions than for weak cycles. The activity and the width of sunspot belts increase rapidly and begin to decline when the belts are still at high latitudes. Surprisingly, it has been reported that in the late stages of the cycle the level of activity (sunspot number) as well as the widths and centers of the butterfly wings all have the same statistical properties independent of how strong the cycle was during its rise and maximum phases. We have modeled these features using a Babcock-Leighton type dynamo model and show that the flux loss through magnetic buoyancy is an essential nonlinearity in the solar dynamo. Our Letter shows that the nonlinearity is effective if the flux emergence becomes efficient at the mean-field strength of the order of 10^{4} G in the lower part of the convection zone.

Variations in the Maximum Electron Density of the F<sub>2</sub> Layer (NmF<sub>2</sub>) over the Middle Latitude Station of Grahamstown, South Africa, during Solar Cycle 23

Ultraviolet (UV) and X-ray radiation are the primary causes of ionization that produce electron density in sufficient quantities to promote the propagation of satellite radio signals in the ionosphere. The electron densities suffer from spatio-temporal variations, and this poses different degrees of threats to satellite radio signals propagating through the ionosphere. We aimed to characterize the maximum electron density of the F<sub>2</sub> layer (N<sub>m</sub>F<sub>2</sub>) in the middle-latitude ionosphere over Grahamstown, South Africa (Geographic latitude: 33.30°S, Geographic longitude: 26.50°E; Geomagnetic Latitude: 33.92°S, Geomagnetic Longitude: 89.37°E). The mean N<sub>m</sub>F<sub>2</sub> data for solar cycle 23 (1998–2008) were used for the studies. The data were grouped into the high solar activity (HSA: 2000–2002), moderate solar activity (MSA: 1998–1999, 2003–2005), and low solar activity (LSA: 2006–2008) years. N<sub>m</sub>F<sub>2</sub> variations were characterized based on the diurnal, seasonal, monthly, and annual data. Also, the correlation between N<sub>m</sub>F<sub>2</sub> and the sunspot number was investigated. Results on diurnal and seasonal variations revealed that noontime bite-out of N<sub>m</sub>F<sub>2</sub> was observed during the June solstice every year. However, it was not observed in the other three seasons. Equinoctial asymmetry is observed to show insignificant annual and solar cycle variations. The seasonal and annual variations of N<sub>m</sub>F<sub>2</sub> with sunspot number were linear (exception: June solstice for MSA, the year 1999; HSA, years 2000–2001). The results reveal that the correlation between N<sub>m</sub>F<sub>2</sub> and the sunspot number was insignificant under conditions of the annual, solar cycle, and latitudinal variations (exception: MSA, the year 2005; negative correlation (0.64)).

Comparative analysis of the associations between solar activity and trends in the incidence of haemoblastoses in Russia, the USA and Canada

Background. Identification of the links between hemoblastoses and environmental factors is necessary for the development of preventive measures.
 Aim. Comparative analysis of the link between hemoblastosis trends in Russia, the USA and Canada and solar activity.
 Material and methods. The open data on the incidence of hemoblastoses in Canada (19832012), the USA (19982012) and Russia (19932018) was used. To estimate solar activity, the average annual number of sunspots (Wolff numbers) was taken. The relationship between the frequency of hemoblastoses and solar activity was studied using the Pearson correlation analysis in 11 iterations (lag 010 years). The results obtained are compared with the data of an earlier study on Russia.
 Results. In Canada, an association with Wolf numbers was found in the frequency of Hodgkin's lymphoma in men (r=0.431, p=0.017, lag 2 years), multiple myeloma in women (r=0.542, p=0.037, lag 1 year), non-Hodgkin's lymphomas in men (r =0.538, p=0.038, lag 5 years) and women (r=0.750, p=0.001, lag 5 years), leukemia in men (r=0.707, p=0022, lag 7 years) and women (r=0.817, p=0.004, lag 7 years). In the United States, a relationship with solar activity was established for fluctuations in the incidence of Hodgkin's lymphoma in men (r=0.577, p=0.050, lag 5 years) and women (r=0.641, p=0.025, lag 5 years), non-Hodgkin's lymphomas in men (r=0.809, p 0.001, lag 3 years) and women (r=0.844, p 0.001, lag 2 years), leukemia in men (r=0.619, p=0.032, lag 1 year). In the Russian population, a relationship between solar activity and the frequency of all forms of hemoblastoses, was previously revealed. Fluctuations in the frequency of leukemia and non-Hodgkin's lymphomas in the populations of Russia, the USA and Canada have a strong positive relationship with solar activity. The strength of this association in Hodgkin's lymphoma and multiple myeloma ranges from moderate to significant. In all the countries studied, an increase in the incidence of multiple myeloma and leukemia was found, and an increase in the incidence of non-Hodgkin's lymphomas was found in Russia and Canada. The trend in the incidence of Hodgkin's lymphoma in Russia and Canada has a significant downward trend.
 Conclusion. Trends in hemoblastoses in Russia, the USA and Canada are strongly related to solar activity.

Locally weighted regression for sunspots estimation and prediction

Locally weighted regression (LOESS) is a modern non-parametric regression method designed for treating cases where classical procedures are not highly efficient or cannot applied efficiently. Sunspots are the darker areas of the solar sphere's surface relative to other regions and are an important indicator of solar activity .The aim of this paper is to model and predict the number of sunspots because of their very importance to understanding the terrestrial consequences of solar activity and its direct impact on weather and communication systems on Earth, which may lead to damage to satellites. In this paper, the number of sunspots represented by annual data for the period from 1900 to 2021 (122 years) as well as monthly data for the period from January 1900 to January 2022 (1465 months) was obtained from the global data center (Sunspot Index and Long-term Solar Observations) (SILSO). The LOESS regression used for estimating and predicting the number of monthly and annual sunspots. The smoothing parameter, as well as the degree of the polynomial that fulfills the lowest for Akaike corrected information criterion. The analysis showed the ability of the LOESS to represent sunspot data by passing diagnostic tests as well as its high predictive ability. From the predictive values for the monthly data, it found that the maximum average number of sunspots will be 123.7 in July 2022, and the lowest average will be in February with 61.3 sunspots. Regarding the annual data, it found from the predictive values that the maximum average number of sunspots will be in the year 2023 with an average of 161.7 sunspots, and the lowest average will be in the year 2029 with an average of 16.1. Keywords: Locally weighted regression; sunspot; solar cycle; prediction.

The Variation of the Solar Wind Correlation Scale and Taylor Scale Upstream of Mars Observed by MAVEN

The correlation scale and Taylor scale, which characterize the turbulence and dissipation levels, of the solar wind upstream of Mars are determined using the Mars atmosphere and volatile evolution magnetic field data from 2015 to 2020, which covers half of a solar cycle from the solar maximum to the solar minimum. Our analysis suggests that the correlation scale varies between 10 and 20 hr and the Taylor scale between 0.3 and 1 s. Applying the frozen-in flow approximation, we convert the two temporal scales to the two spatial scales, which are about (1.5–3.0) × 107 km and 150–500 km, respectively. We further compare the correlation scale and Taylor scale to the sunspot number (SSN) to study the impacts of solar activity. The highest correlation coefficient between the correlation scale and the SSN is 0.78, where the two data sets are shifted by 16 months with the correlation scale behind the SSN. For the Taylor scale, the highest correlation coefficient is 0.52 with the time lag of 17 months. We also analyze the effective magnetic Reynolds number that is the square of the ratio of the two scales. It is more than 3 × 109, suggesting the good assumption of the frozen-in flow. However, its correlation with the SSN is weak.

Galactic Cosmic Rays at Mars and Venus: Temporal Variations from Hours to Decades Measured as the Background Signal of Onboard Microchannel Plates

A microchannel plate (MCP) is a component widely used for counting particles in space. Using the background counts from MCPs on the Mars Express and Venus Express orbiters—operating over 17 yr and 8 yr, respectively—we investigated the galactic cosmic ray (GCR) characteristics of the inner solar system. The MCP background counts at Mars and Venus, on a solar cycle timescale, exhibited clear anticorrelation with the sunspot number. We concluded that the measured MCP background counts contained GCR information. The GCR characteristics measured using the MCP background counts at Mars showed features consistent with measurements on Earth in Solar Cycle 24. The time lag between the sunspot number and the MCP background counts was found to be ∼9 months at Mars. The shorter-term background data recorded along the orbits (with a timescale of several hours) also showed evident depletion of the background counts, due to absorption of the GCR particles by the planets. Thanks to the visible planetary size change along an orbit, we developed a model to separate the GCR contribution to the MCP background counts from the internal contribution caused by the β-decay of radioactive elements in the MCP glass. Our statistical analysis of the GCR absorption signatures at Mars implies that the effective absorption radius of Mars for the GCR particles is >100 km larger than the radius of the planet. However, the cause remains an open question.

Shape of the 11-Year Cycle in the Number of Sunspots and Polar Faculae

Shape of the 11-Year Cycle in the Number of Sunspots and Polar Faculae

Optimising the Hα index for the identification of activity signals in FGK stars

Context. The Ca II H&K and Hα lines are two of the most used activity diagnostics for detecting stellar activity signals in the optical regime, and for inferring possible false positives in exoplanet detection with the radial velocity method. The flux in the two lines is known to follow the solar activity cycle, and to correlate well with sunspot number and other activity diagnostics. However, for other stars, the flux in these lines is known to have a wide range of correlations, increasing the difficulty in the interpretation of the signals observed with the Hα line. Aims. In this work we investigate the effect of the Hα bandpass width on the correlation between the Ca II and Hα indices with the aim of improving the Hα index to better identify and model the signals coming from activity variability. Methods. We used a sample of 152 FGK dwarfs observed with HARPS for more than 13 yr with enough cadence to be able to detect rotational modulations and cycles in activity proxies. We calculated the Ca II and Hα activity indices using a range of bandwidths for Hα between 0.1 and 2.0 Å. We studied the correlation between the indices’ time series at long and short timescales, and analysed the impact of stellar parameters, activity level, and variability on the correlations. Results. The correlation between Ca II and Hα, both at short and long timespans, is maximised when using narrow Hα bandwidths, with a maximum at 0.6 Å. For some inactive stars, as the activity level increases, the flux in the Hα line core increases, while the flux in the line wings decreases as the line becomes shallower and broader. The balance between these fluxes can cause stars to show the negative correlations observed in the literature when using a wide bandwidth on Hα. These anti-correlations may become positive correlations if using the 0.6 Å bandwidth. We demonstrate that rotationally modulated signals observed in SCa II, which appear flat or noisy when using 1.6 Å on SHα, can become more evident if a 0.6 Å bandpass is used instead. Low activity variability appears to be a contributing factor for the cases of weak or no correlations. Conclusions. Calculating the Hα index using a bandpass of 0.6 Å maximises the correlation between Ca II and Hα, both at short and long timescales. On the other hand, the use of the broader 1.6 Å, generally used in exoplanet detection to identify stellar activity signals, degrades the signal by including the flux in the line wings. In view of these results, we strongly recommend the use of a 0.6 Å bandwidth when computing the Hα index for the identification of activity rotational modulation and magnetic cycle signals in solar-type stars.

Solar Cycle 25 Prediction Using an Optimized Long Short-Term Memory Mode with F10.7

In this paper, an optimized long short-term memory (LSTM) model is proposed to deal with the smoothed monthly F_{10.7} data, aiming to predict the peak amplitude of F_{10.7} and the occurring time for Solar Cycle 25 (SC-25) to obtain the maximum amplitude of sunspot number (SSN) and the reaching time. The “re-prediction” process in the model uses the latest prediction results obtained from the previous prediction as the input for the next prediction calculation. The prediction errors between the predicted and observed peak amplitude of F_{10.7} for SC-23 and SC-24 are 2.87% and 1.09%, respectively. The predicted peak amplitude of F_{10.7} for SC-25 is 156.3, and the maximum value of SSN is calculated as 147.9, which implies that SC-25 will be stronger than SC-24. SC-25 will reach its peak in July 2025.

Machine Learning-based Prediction of Sunspots using Fourier Transform Analysis of the Time Series

The study of solar activity holds special importance since the changes in our star’s behavior affect both the Earth’s atmosphere and the conditions of the interplanetary environment. They can interfere with air navigation, space flight, satellites, radar, high-frequency communications, and overhead power lines, and can even negatively influence human health. We present here a machine learning-based prediction of the evolution of the current sunspot cycle (solar cycle 25). First, we analyze the Fourier Transform of the total time series (from 1749 to 2022) to find periodicities with which to lag this series and then add attributes (predictors) to the forecasting models to obtain the most accurate result possible. Consequently, we build a trained model of the series considering different starting points (from 1749 to 1940, with 1 yr steps), applying Random Forests, Support Vector Machines, Gaussian Processes, and Linear Regression. We find that the model with the lowest error in the test phase (cycle 24) arises with Random Forest and with 1915 as the start year of the time series (yielding a Root Mean Squared Error of 9.59 sunspots). Finally, for cycle 25 this model predicts that the maximum number of sunspots (90) will occur in 2025 March.

Correlation of Time Series of Wolf Numbers and their Derivatives

Correlation of Time Series of Wolf Numbers and their Derivatives

Cosmic ray intensity variation in relation with sunspot numbers and geomagnetic activity parameter Kp and Ap index during the period of 1997-2007

Cosmic ray intensity variation in relation with sunspot numbers and geomagnetic activity parameter Kp and Ap index during the period of 1997-2007

Temporal evolution and rigidity dependence of the solar modulation lag of Galactic cosmic rays

When traveling in the heliosphere, Galactic cosmic rays (GCRs) are subjected to the solar modulation effect, a quasiperiodical change of their intensity caused by the 11-year cycle of solar activity. Here we investigate the association of solar activity and cosmic radiation over five solar cycles, from 1965 to 2020, using a collection of multichannel data from neutron monitors, space missions, and solar observatories. In particular, we focus on the time lag between the monthly sunspot number and the GCR flux variations. We show that the modulation lag is subjected to a 22-year periodical variation, ranging from about 2 to 14 months and following the polarity cycle of the Sun's magnetic field. We also show that the lag is remarkably decreasing with increasing energy of the GCR particles. These results reflect the interplay of basic physics phenomena that cause the GCR modulation effect: the drift motion of charged particles in the interplanetary magnetic field, the latitudinal dependence of the solar wind, the energy dependence of their residence time in the heliosphere. Based on this interpretation, we end up with a global effective formula for the modulation lag and testable predictions for the flux evolution of cosmic particles and antiparticles over the solar cycle.

Taxonomic diversity and structure of phytoplankton in different-type lakes of Kulunda basin (Altai krai)

In the phytoplankton of different-type lakes of Kulunda basin 87 species and 1 variety of algae from 6 systematic divisions were identified: Cyanobacteria, Bacillariophyta, Cryptophyta, Euglenophyta, Chlorophyta and Ochrophyta. The most diverse in the number of families, genera and species is the Cyanobacteria (35 species, or 40.2 % of the total number of species), as well as Chlorophyta (26, or 29.9 %), Bacillariophyta (17, or 19.5 %). Most of the taxa ranked above the family belong to Bacillariophyta (2 classes, 3 subclasses, 9 orders) and Chlorophyta (3 classes, 8 orders). The two divisions are not numerous, they have only 2 (Cryptophyta) and 3 (Ochrophyta) species. The largest family is Oscillatori-aceae with 4 genera and 13 species. The leading genera include 5 genera, and 3 of them are from Cyanobacteria, 1 genus each belong to Chlorophyta (Ulothrix) and Bacillariophyta (Nitzschia). According to the results of the study, the dependence of the number of species in phytoplankton on the average mineralization and Wolf Number (the measure of solar activity) for salt lakes in Kulunda basin was revealed.

A preliminary assessment of net box traps to catch gray wolves in Lower Saxony, Germany

The number of gray wolves (Canis lupus) in Lower Saxony, Germany, has increased since they recolonized the region in 2007, and developing appropriate management strategies requires accurate information on wolf habitat use. As research on space use and foraging ecology necessitates capture, tagging, and tracking of wolves, we evaluated the feasibility of using cage traps to catch animals unharmed. Due to legal restrictions in Germany, especially concerning body-grasping traps (e.g., foothold traps and foot snares), we developed an alternative trapping system. From July 2018 to December 2020, we installed net box traps at different locations in Lower Saxony. However, our study was disrupted and traps were removed prematurely because of human activities and sabotage. Although we did not capture any wolves, we photographically documented wolf pups entering our net box traps on seven occasions and suggest that young wolves can potentially be captured in our net box traps. We believe our work opens the possibility for further trapping options to improve future research on European gray wolves.

Solar Signature in Climate Indices

The influence of solar/geomagnetic activity on climate variables still remains a fully unclarified problem, although many scientific efforts have been made to better understand it. In order to bring more information to this open problem, in the present study, we analyze the connection between solar/geomagnetic activity (predictors) and climate variables (predictands) by applying elements from information theory and wavelet transform analysis. The solar activity was highlighted by the Wolf number and geomagnetic activity was quantified by the aa index. For the climate variables, we considered seven Climate Indices (CIs) that influence atmospheric circulation on regional or global scales, such as the Greenland-Balkan Oscillation Index (GBOI), North Atlantic Oscillation Index (NAOI), Arctic Oscillation (AO), Atlantic Multidecadal Oscillation (AMO), Southern Oscillation Index (SOI), Bivariate ENSO Timeseries (BEST) and Trans-Niño Index (TNI). By using the difference between synergy and redundancy, a few cases were found where the two predictors can be considered together for CIs’ estimation. Coherence analysis through the wavelet transform for three variables, both through multiple and partial analysis, provides the time intervals and bands of periods, where the two considered predictors can be used together or separately. The results differ depending on the predictand, the season and the considered lags. Significant information is brought out by using the two predictors together, namely the summer season, for GBOI and NAOI, when the predictors were taken 2 years before, and the winter season, as AMO responds to the variations of both solar and geomagnetic activity after 4 years.

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.