scholarly journals Prediction of solar activity on the basis of spectral characteristics of sunspot number

2004 ◽  
Vol 22 (6) ◽  
pp. 2239-2243 ◽  
Author(s):  
E. Echer ◽  
N. R. Rigozo ◽  
D. J. R. Nordemann ◽  
L. E. A. Vieira
Keyword(s):  
Solar Activity ◽  
Sunspot Number ◽  
Confidence Level ◽  
Spectral Characteristics ◽  
Solar Cycles ◽  
Sine Series ◽  
Maximum Peak ◽  
Spectral Components ◽  
Sunspot Series

Abstract. Prediction of solar activity strength for solar cycles 23 and 24 is performed on the basis of extrapolation of sunspot number spectral components. Sunspot number data during 1933-1996 periods (solar cycles 17-22) are searched for periodicities by iterative regression. The periods significant at the 95% confidence level were used in a sum of sine series to reconstruct sunspot series, to predict the strength of solar cycles 23 and 24. The maximum peak of solar cycles is adequately predicted (cycle 21: 158±13.2 against an observed peak of 155.4; cycle 22: 178

ESTABLISHING SOLAR ACTIVITY TREND FOR SOLAR CYCLES 21 – 24

2021 ◽  
Vol 44 ◽  
pp. 100-106
Author(s):  
A.K. Singh ◽  
◽  
A. Bhargawa ◽  
Keyword(s):  
Solar Activity ◽  
Solar Cycle ◽  
Sunspot Number ◽  
Cosmic Ray ◽  
Occurrence Rate ◽  
The Sun ◽  
Solar Cycles ◽  
Lyman Alpha ◽  
Rate Versus ◽  
Alpha Index

Solar-terrestrial environment is manifested primarily by the physical conditions of solar interior, solar atmosphere and eruptive solar plasma. Each parameter gives unique information about the Sun and its activity according to its defined characteristics. Hence the variability of solar parameters is of interest from the point of view of plasma dynamics on the Sun and in the interplanetary space as well as for the solar-terrestrial physics. In this study, we have analysed various solar transients and parameters to establish the recent trends of solar activity during solar cycles 21, 22, 23 and 24. The correlation coefficients of linear regression of F10.7 cm index, Lyman alpha index, Mg II index, cosmic ray intensity, number of M & X class flares and coronal mass ejections (CMEs) occurrence rate versus sunspot number was examined for last four solar cycles. A running cross-correlation method has been used to study the momentary relationship among the above mentioned solar activity parameters. Solar cycle 21 witnessed the highest value of correlation for F10.7 cm index, Lyman alpha index and number of M-class and X-class flares versus sunspot number among all the considered solar cycles which were 0.979, 0.935 and 0.964 respectively. Solar cycle 22 recorded the highest correlation in case of Mg II index, Ap index and CMEs occurrence rate versus sunspot number among all the considered solar cycles (0.964, 0.384 and 0.972 respectively). Solar cycle 23 and 24 did not witness any highest correlation compared to solar cycle 21 and 22. Further the record values (highest value compared to other solar three cycles) of each solar activity parameters for each of the four solar cycles have been studied. Here solar cycle 24 has no record text at all, this simply indicating that this cycle was a weakest cycle compared to the three previous ones. We have concluded that in every domain solar 24 was weaker to its three predecessors.

Nonlinear Prediction of Solar Cycle 25

2018 ◽  
Vol 13 (S340) ◽  
pp. 321-322
Author(s):  
Volkan Sarp ◽  
Ali Kılçık
Keyword(s):  
Solar Activity ◽  
Sunspot Number ◽  
Prediction Method ◽  
Solar Cycles ◽  
Nonlinear Prediction ◽  
Cycle 24 ◽  
And Performance ◽  
Prediction Approach ◽  
Good Agreement

AbstractSolar activity is a chaotic process and there are various approximations to forecast its long term and short term variations. But there is no prediction method that predicts the solar activity exactly. In this study, a nonlinear prediction approach was applied to international sunspot numbers and performance of predictions was tested for the last 5 solar cycles. These predictions are in good agreement with observed values of the tested solar cycles. According to these results, end of cycle 24 is expected at February, 2020 with 7.7 smoothed monthly mean sunspot number and maximum of cyle 25 is expected at May, 2024 with 119.6 smoothed monthly mean sunspot number.

scholarly journals Modification of the solar activity indices in the International Reference Ionosphere IRI and IRI-Plas models due to recent revision of sunspot number time series

2016 ◽  
Vol 2 (3) ◽  
pp. 59-68 ◽  
Author(s):  
Тамара Гуляева ◽  
Tamara Gulyaeva
Keyword(s):  
Time Series ◽  
Solar Activity ◽  
Sunspot Number ◽  
International Reference Ionosphere ◽  
Electron Content ◽  
Prediction Errors ◽  
Solar Cycles ◽  
Total Electron ◽  
International Reference ◽  
Ionospheric Models

The International Reference Ionosphere (IRI) imports global effective ionospheric IG12 index based on ionosonde measurements of the critical frequency foF2 as a proxy of solar activity. Similarly, the global electron content (GEC), smoothed by the sliding 12-months window (GEC12), is used as a solar proxy in the ionospheric and plasmaspheric model IRI-Plas. GEC has been calculated from global ionospheric maps of total electron content (TEC) since 1998 whereas its productions for the preceding years and predictions for the future are made with the empirical model of the linear dependence of GEC on solar activity. At present there is a need to re-evaluate solar and ionospheric indices in the ionospheric models due to the recent revision of sunspot number (SSN2) time series, which has been conducted since 1st July, 2015 [Clette et al., 2014]. Implementation of SSN2 instead of the former SSN1 series with the ionospheric model could increase model prediction errors. A formula is proposed to transform the smoothed SSN212 series to the proxy of the former basic SSN112=R12 index, which is used by IRI and IRI-Plas models for long-term ionospheric predictions. Regression relationships are established between GEC12, the sunspot number R12, and the proxy solar index of 10.7 cm microwave radio flux, F10.712. Comparison of calculations by the IRI-Plas and IRI models with observations and predictions for Moscow during solar cycles 23 and 24 has shown the advantage of implementation of GEC12 index with the IRI-Plas model.

scholarly journals Prediction of solar activity cycles by assimilating sunspot data into a dynamo model

2009 ◽  
Vol 5 (S264) ◽  
pp. 202-209 ◽  
Author(s):  
Irina N. Kitiashvili ◽  
Alexander G. Kosovichev
Keyword(s):  
Magnetic Field ◽  
Solar Activity ◽  
Solar Cycle ◽  
Sunspot Number ◽  
Magnetic Activity ◽  
Magnetic Helicity ◽  
Dynamo Model ◽  
Solar Cycles ◽  
Sunspot Data ◽  
Cycle 24

AbstractSolar activity is a determining factor for space climate of the Solar system. Thus, predicting the magnetic activity of the Sun is very important. However, our incomplete knowledge about the dynamo processes of generation and transport of magnetic fields inside Sun does not allow us to make an accurate forecast. For predicting the solar cycle properties use the Ensemble Kalman Filter (EnKF) to assimilate the sunspot data into a simple dynamo model. This method takes into account uncertainties of both the dynamo model and the observed sunspot number series. The method has been tested by calculating predictions of the past cycles using the observed annual sunspot numbers only until the start of these cycles, and showed a reasonable agreement between the predicted and actual data. After this, we have calculated a prediction for the upcoming solar cycle 24, and found that it will be approximately 30% weaker than the previous one, confirming some previous expectations. In addition, we have investigated the properties of the dynamo model during the solar minima, and their relationship to the strength of the following solar cycles. The results show that prior the weak cycles, 20 and 23, and the upcoming cycle, 24, the vector-potential of the poloidal component of magnetic field and the magnetic helicity substantial decrease. The decrease of the poloidal field corresponds to the well-known correlation between the polar magnetic field strength at the minimum and the sunspot number at the maximum. However, the correlation between the magnetic helicity and the future cycle strength is new, and should be further investigated.

scholarly journals Modification of the solar activity indices in the International Reference Ionosphere IRI and IRI-Plas models due to recent revision of sunspot number time series

2016 ◽  
Vol 2 (3) ◽  
pp. 87-98 ◽  
Author(s):  
Тамара Гуляева ◽  
Tamara Gulyaeva
Keyword(s):  
Time Series ◽  
Solar Activity ◽  
Sunspot Number ◽  
International Reference Ionosphere ◽  
Electron Content ◽  
Prediction Errors ◽  
Solar Cycles ◽  
Total Electron ◽  
International Reference ◽  
Ionospheric Models

The International Reference Ionosphere (IRI) imports global effective ionospheric IG12 index based on ionosonde measurements of the critical frequency foF2 as a proxy of solar activity. Similarly, the global electron content (GEC), smoothed by the sliding 12-months window (GEC12), is used as a solar proxy in the ionospheric and plasmaspheric model IRI-Plas. GEC has been calculated from global ionospheric maps of total electron content (TEC) since 1998 whereas its productions for the preceding years and predictions for the future are made with the empirical model of the linear dependence of GEC on solar activity. At present there is a need to re-evaluate solar and ionospheric indices in the ionospheric models due to the recent revision of sunspot number (SSN2) time series, which has been conducted since July 1, 2015 [Clette et al., 2014]. Implementation of SSN2 instead of the former SSN1 series with the ionospheric model could increase model prediction errors. A formula is proposed to transform the smoothed SSN212 series to the proxy of the former basic SSN112=R12 index, which is used by the IRI and IRI-Plas models for long-term ionospheric predictions. Regression relationships are established between GEC12, the sunspot number R12, and the proxy solar index of 10.7 cm microwave radio flux, F10.712. Comparison of calculations by the IRI-Plas and IRI models with observations and predictions for Moscow during solar cycles 23 and 24 has shown the advantage of implementation of GEC12 index with the IRI-Plas model.

scholarly journals On the Prediction of Solar Cycles

Solar Physics ◽  
2021 ◽  
Vol 296 (1) ◽  
Author(s):  
V. Courtillot ◽  
F. Lopes ◽  
J. L. Le Mouël
Keyword(s):  
Solar Activity ◽  
Solar Cycle ◽  
Transfer Function ◽  
Singular Spectrum Analysis ◽  
Activity Cycle ◽  
Solar Activity Cycle ◽  
Planetary Motion ◽  
Data Sets ◽  
Solar Cycles ◽  
Jovian Planets

AbstractThis article deals with the prediction of the upcoming solar activity cycle, Solar Cycle 25. We propose that astronomical ephemeris, specifically taken from the catalogs of aphelia of the four Jovian planets, could be drivers of variations in solar activity, represented by the series of sunspot numbers (SSN) from 1749 to 2020. We use singular spectrum analysis (SSA) to associate components with similar periods in the ephemeris and SSN. We determine the transfer function between the two data sets. We improve the match in successive steps: first with Jupiter only, then with the four Jovian planets and finally including commensurable periods of pairs and pairs of pairs of the Jovian planets (following Mörth and Schlamminger in Planetary Motion, Sunspots and Climate, Solar-Terrestrial Influences on Weather and Climate, 193, 1979). The transfer function can be applied to the ephemeris to predict future cycles. We test this with success using the “hindcast prediction” of Solar Cycles 21 to 24, using only data preceding these cycles, and by analyzing separately two 130 and 140 year-long halves of the original series. We conclude with a prediction of Solar Cycle 25 that can be compared to a dozen predictions by other authors: the maximum would occur in 2026.2 (± 1 yr) and reach an amplitude of 97.6 (± 7.8), similar to that of Solar Cycle 24, therefore sketching a new “Modern minimum”, following the Dalton and Gleissberg minima.

Solar-type periodicities in the climate variability of Northern Fennoscandia during the last three centuries: Real influence of solar activity or natural instability in the climate system

The Holocene ◽  
2021 ◽  
pp. 095968362110604
Author(s):  
Maxim Ogurtsov ◽  
Samuli Helama ◽  
Risto Jalkanen ◽  
Högne Jungner ◽  
Markus Lindholm ◽  
...  
Keyword(s):  
Solar Activity ◽  
Climate Variability ◽  
Western Siberia ◽  
Climate System ◽  
Solar Cycles ◽  
Activity Data ◽  
The Past ◽  
Proxy Records ◽  
Northern Fennoscandia ◽  
Solar Type

Fifteen proxy records of summer temperature in Fennoscandia, Northern Europe and in Yamal and Taymir Peninsulas (Western Siberia) were analyzed for the AD 1700–2000 period. Century-long (70–100 year) and quasi bi-decadal periodicities were found from proxy records representing different parts of Fennoscandia. Decadal variation was revealed in a smaller number of records. Statistically significant correlations were revealed between the timescale-dependent components of temperature variability and solar cycles of Schwabe (~11 year), Hale (~22 year), and Gleissberg (сentury-long) as recorded in solar activity data. Combining the results from our correlation analysis with the evidence of solar-climatic linkages over the Northern Fennoscandia obtained over the past 20 years suggest that there are two possible explanations for the obtained solar-proxy relations: (a) the Sun’s activity actually influences the climate variability in Northern Fennoscandia and in some regions of the Northern Hemisphere albeit the mechanism of such solar-climatic linkages are yet to be detailed; (b) the revealed solar-type periodicities result from natural instability of climate system and, in such a case, the correlations may appear purely by chance. Multiple lines of evidence support the first assumption but we note that the second one cannot be yet rejected. Guidelines for further research to elucidate this question are proposed including the Fisher’s combined probability test in the presence of solar signal in multiple proxy records.

scholarly journals Construction of a century solar chromosphere data set for solar activity related research

2017 ◽  
Vol 3 (2) ◽  
pp. 5-8
Author(s):  
Линь Ганхуа ◽  
Lin Ganghua ◽  
Ван Сяо-Фань ◽  
Wang Xiao Fan ◽  
Ян Сяо ◽  
...  
Keyword(s):  
Data Mining ◽  
Solar Activity ◽  
Space Weather ◽  
Large Scale ◽  
Abnormal Behavior ◽  
Solar Chromosphere ◽  
Solar Cycles ◽  
Data Set ◽  
Related Research ◽  
Data Mining Algorithms

This article introduces our ongoing project “Construction of a Century Solar Chromosphere Data Set for Solar Activity Related Research”. Solar activities are the major sources of space weather that affects human lives. Some of the serious space weather consequences, for instance, include interruption of space communication and navigation, compromising the safety of astronauts and satellites, and damaging power grids. Therefore, the solar activity research has both scientific and social impacts. The major database is built up from digitized and standardized film data obtained by several observatories around the world and covers a timespan more than 100 years. After careful calibration, we will develop feature extraction and data mining tools and provide them together with the comprehensive database for the astronomical community. Our final goal is to address several physical issues: filament behavior in solar cycles, abnormal behavior of solar cycle 24, large-scale solar eruptions, and sympathetic remote brightenings. Significant progresses are expected in data mining algorithms and software development, which will benefit the scientific analysis and eventually advance our understanding of solar cycles.

scholarly journals Several Populations of Sunspot Group Numbers – Resolving a Conundrum

2021 ◽  
Author(s):  
Leif Svalgaard
Keyword(s):  
Solar Activity ◽  
Sunspot Number ◽  
Degrees Of Freedom ◽  
18Th Century ◽  
Number Series ◽  
Solar Observations ◽  
Group Sunspot Number ◽  
Group Number ◽  
Working Groups

<p>The long-standing disparity between the sunspot number record and the Hoyt and Schatten (1998, H&S) Group Sunspot Number series was initially resolved by the Clette et al. (2014) revision of the sunspot number and the group number series. The revisions resulted in a flurry of dissenting group number series while the revised sunspot number series was generally accepted. Thus, the disparity persisted and confusion reigned, with the choice of solar activity dataset continuing to be a free parameter. A number of workshops and follow-up collaborative efforts by the community have not yet brought clarity. We review here several lines of evidence that validate the original revisions put forward by Clette et al. (2014) and suggest that the perceived conundrum no longer need to delay acceptance and general use of the revised series. We argue that the solar observations constitute several distinct populations with different properties which explain the various discontinuities in the series. This is supported by several proxies: diurnal variation of the geomagnetic field, geomagnetic signature of the strength of the heliomagnetic field, and variation of radionuclides. The Waldmeier effect shows that the sunspot number scale has not changed over the last 270 years and a mistaken scale factor between observers Wolf and Wolfer explains the disparity beginning in 1882 between the sunspot number and the H&S reconstruction of the group number. Observations with replica of 18th century telescopes (with similar optical flaws) validate the early sunspot number scale; while a reconstruction of the group number with monthly resolution (with many more degrees of freedom) validate the size of Solar Cycle 11 given by the revised series that the dissenting series fail to meet. Based on the evidence at hand, we urge the working groups tasked with producing community-vetted and agreed upon solar activity series to complete their work expeditiously.</p>

scholarly journals A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly

2021 ◽  
Vol 13 (22) ◽  
pp. 4559
Author(s):  
Marjolijn Adolfs ◽  
Mohammed Mainul Hoque
Keyword(s):  
Neural Network ◽  
Solar Activity ◽  
Geomagnetic Latitude ◽  
Machine Learning Techniques ◽  
High Solar Activity ◽  
Electron Content ◽  
Solar Cycles ◽  
Total Electron ◽  
The Neural Network ◽  
Winter Anomaly

With the availability of fast computing machines, as well as the advancement of machine learning techniques and Big Data algorithms, the development of a more sophisticated total electron content (TEC) model featuring the Nighttime Winter Anomaly (NWA) and other effects is possible and is presented here. The NWA is visible in the Northern Hemisphere for the American sector and in the Southern Hemisphere for the Asian longitude sector under solar minimum conditions. During the NWA, the mean ionization level is found to be higher in the winter nights compared to the summer nights. The approach proposed here is a fully connected neural network (NN) model trained with Global Ionosphere Maps (GIMs) data from the last two solar cycles. The day of year, universal time, geographic longitude, geomagnetic latitude, solar zenith angle, and solar activity proxy, F10.7, were used as the input parameters for the model. The model was tested with independent TEC datasets from the years 2015 and 2020, representing high solar activity (HSA) and low solar activity (LSA) conditions. Our investigation shows that the root mean squared (RMS) deviations are in the order of 6 and 2.5 TEC units during HSA and LSA period, respectively. Additionally, NN model results were compared with another model, the Neustrelitz TEC Model (NTCM). We found that the neural network model outperformed the NTCM by approximately 1 TEC unit. More importantly, the NN model can reproduce the evolution of the NWA effect during low solar activity, whereas the NTCM model cannot reproduce such effect in the TEC variation.

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