scholarly journals A Novel Bimodal Forecasting Model for Solar Cycle 25

2022 ◽  
Vol 924 (2) ◽  
pp. 59
Author(s):  
J. Y. Lu ◽  
Y. T. Xiong ◽  
K. Zhao ◽  
M. Wang ◽  
J. Y. Li ◽  
...  
Keyword(s):  
Solar Cycle ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Sunspot Cycle ◽  
Missing Information ◽  
Geometric Information ◽  
Precursor Method ◽  
Peak Structure ◽  
Cycle 24 ◽  
Precursor Information

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.

scholarly journals Size of the coming solar cycle 24 based on Ohl's Precursor Method, final estimate

2010 ◽  
Vol 28 (7) ◽  
pp. 1463-1466 ◽  
Author(s):  
R. P. Kane
Keyword(s):  
Solar Cycle ◽  
Sunspot Cycle ◽  
Preliminary Estimate ◽  
Precursor Method ◽  
Solar Cycle 24 ◽  
False Signal ◽  
Final Estimate ◽  
Cycle 24 ◽  
Maximum Sunspot ◽  
Aa Index

Abstract. In Ohl's Precursor Method (Ohl, 1966, 1976), the geomagnetic activity during the declining phase of a sunspot cycle is shown to be well correlated with the size (maximum sunspot number Rz(max)) of the next cycle. For solar cycle 24, Kane (2007a) used aa(min)=15.5 (12-month running mean), which occurred during March–May of 2006 and made a preliminary estimate Rz(max)=124±26 (12-month running mean). However, in the next few months, the aa index first increased and then decreased to a new low value of 14.8 in July 2007. With this new low value, the prediction was Rz(max)=117±26 (12-month running mean). However, even this proved a false signal. Since then, the aa values have decreased considerably and the last 12-monthly value is 8.7, centered at May 2009. For solar cycle 24, using aa(min)=8.7, the latest prediction is, Rz(max)=58.0±25.0.

Forecasting Solar Cycle 25 using comprehensive precursor combination and multiple regression technique

2021 ◽  
Author(s):  
Yating Xiong ◽  
Jianyong Lu ◽  
Kai Zhao ◽  
Meng Sun ◽  
Yang Gao
Keyword(s):  
Solar Cycle ◽  
Multiple Regression ◽  
Moving Average ◽  
Sunspot Cycle ◽  
Original Data ◽  
Regression Technique ◽  
New Model ◽  
Multiple Parameters ◽  
Multiple Regression Technique ◽  
Precursor Information

Abstract In this paper, we propose a new model to predict the complete sunspot cycle based on the comprehensive precursor information (peak, skewness, and maximum geomagnetic index aa of the previous cycle, and start value of predicted cycle). The monthly average sunspot original data are processed by Gaussian smoothing and the new model is validated by the observed sunspots of cycle 24. Compared with the traditional 13-month moving average, the Gaussian filter has less missing information and is better to describe the overall trend of the raw data. Through the permutation and combination of multiple parameters in precursor methods of solar cycle forecasting, the multiple regression technique is used to successfully achieve the peak prediction. The regression coefficient (R) of the empirical model established in this paper can reach 0.95. By adding a new parameter to the original HWR function, we provide a complete solar cycle profile showing unimodal structure. It shows that the peak value of cycle 25 will come in March 2024, with a peak of 140.2.

scholarly journals Estimating the maximum of the smoothed highest 3-hourly <i>a</i><i>a</i> index in 3 d by the preceding minimum for the solar cycle

2020 ◽  
Vol 38 (6) ◽  
pp. 1237-1245
Author(s):  
Zhanle Du
Keyword(s):  
Solar Cycle ◽  
Geomagnetic Activity ◽  
Sunspot Cycle ◽  
Maximum Amplitude ◽  
Maximum Intensity ◽  
Activity Intensity ◽  
Future Space ◽  
Cycle 24 ◽  
Ap Index ◽  
Aa Index

Abstract. Predicting the maximum intensity of geomagnetic activity for an upcoming solar cycle is important in space weather service and for planning future space missions. This study analyzed the highest and lowest 3-hourly aa index (aaH∕aaL) in a 3 d interval, smoothed by 363 d to analyze their variation with the 11-year solar cycle. It is found that the maximum of aaH (aaHmax) is well correlated with the preceding minimum of either aaH (aaHmin, r=0.85) or aaL (aaLmin, r=0.89) for the solar cycle. Based on these relationships, the intensity of aaHmax for solar cycle 25 is estimated to be aaHmax(25)=83.7±6.9 (nT), about 29 % stronger than that of solar cycle 24. This value is equivalent to the ap index of apmax(25)=47.4±4.4 (nT) if employing the high correlation between ap and aa (r=0.93). The maximum of aaL (aaLmax) is also well correlated with the preceding aaHmin (r=0.80). The maximum amplitude of the sunspot cycle (Rm) is much better correlated with high geomagnetic activity (aaHmax, r=0.79) than with low geomagnetic activity (aaLmax, r=0.37). The rise time from aaHmin to aaHmax is weakly anti-correlated to the following aaHmax (r=-0.42). Similar correlations are also found for the 13-month smoothed monthly mean aa index. These results are expected to be useful in understanding the geomagnetic activity intensity of solar cycle 25.

scholarly journals Predicting amplitude of solar cycle 24 based on a new precursor method

2010 ◽  
Vol 28 (2) ◽  
pp. 417-425 ◽  
Author(s):  
A. Yoshida ◽  
H. Yamagishi
Keyword(s):  
Magnetic Field ◽  
Solar Cycle ◽  
Solar Minimum ◽  
Physical Phenomenon ◽  
Late Phase ◽  
The Sun ◽  
Precursor Method ◽  
Solar Cycle 24 ◽  
Cycle 24 ◽  
The Imf

Abstract. It is shown that the monthly smoothed sunspot number (SSN) or its rate of decrease during the final years of a solar cycle is correlated with the amplitude of the succeeding cycle. Based on this relationship, the amplitude of solar cycle 24 is predicted to be 84.5±23.9, assuming that the monthly smoothed SSN reached its minimum in December 2008. It is further shown that the monthly SSN in the three-year period from 2006 through 2008 is well correlated with the monthly average of the intensity of the interplanetary magnetic field (IMF). This correlation indicates that the SSN in the final years of a solar cycle is a good proxy for the IMF, which is understood to reflect the magnetic field in the corona of the sun, and the IMF is expected to be smallest at the solar minimum. We believe that this finding illuminates a physical meaning underlying the well-known precursor method for forecasting the amplitude of the next solar cycle using the aa index at the solar minimum or its average value in the decaying phase of the solar cycle (e.g. Ohl, 1966), since it is known that the geomagnetic disturbance depends strongly on the intensity of the IMF. That is, the old empirical method is considered to be based on the fact that the intensity of the coronal magnetic field decreases in the late phase of a solar cycle in parallel with the SSN. It seems that the precursor method proposed by Schatten et al. (1978) and Svalgaard et al. (2005), which uses the intensity of the polar magnetic field of the sun several years before a solar minimum, is also based on the same physical phenomenon, but seen from a different angle.

A Prediction of Solar Cycle 24 Using a Modified Precursor Method

Solar Physics ◽  
2008 ◽  
Vol 250 (1) ◽  
pp. 171-181 ◽  
Author(s):  
R. S. Dabas ◽  
Kavita Sharma ◽  
Rupesh M. Das ◽  
K. G. M. Pillai ◽  
Parvati Chopra ◽  
...  
Keyword(s):  
Solar Cycle ◽  
Precursor Method ◽  
Solar Cycle 24 ◽  
Cycle 24

Prediction of future evolution of solar cycle 24 using machine learning techniques

2018 ◽  
Vol 13 (S340) ◽  
pp. 317-318
Author(s):  
Sumesh Gopinath ◽  
P. R. Prince
Keyword(s):  
Machine Learning ◽  
Solar Cycle ◽  
Sunspot Cycle ◽  
Machine Learning Techniques ◽  
Power Lines ◽  
Future Evolution ◽  
The Earth ◽  
Learning Techniques ◽  
Cycle 24 ◽  
Nonlinear Autoregressive

AbstractForecasting the solar activity is of great importance not only for its effect on the climate of the Earth but also on the telecommunications, power lines, space missions and satellite safety. In the present work, machine learning using Artificial Neural Networks (ANNs) called Nonlinear Autoregressive Network (NAR) with Exogenous Inputs (NARX) have been applied for the prediction of future evolution of the present sunspot cycle. NARX network is able to combine the performance of ANN algorithm with nonlinear autoregressive method to handle problems such as finding dependencies among solar indices and prediction of solar cycle evolution.

scholarly journals Lessons learned from predictions of Solar Cycle 24

2020 ◽  
Vol 10 ◽  
pp. 60
Author(s):  
W. Dean Pesnell
Keyword(s):  
Solar Activity ◽  
Solar Cycle ◽  
Coronal Mass Ejections ◽  
Sunspot Cycle ◽  
Lessons Learned ◽  
Machine Learning Techniques ◽  
Polar Regions ◽  
The Sun ◽  
Solar Cycle 24 ◽  
Cycle 24

Solar Cycle 24 has almost faded and the activity of Solar Cycle 25 is appearing. We have learned much about predicting solar activity in Solar Cycle 24, especially with the data provided by SDO and STEREO. Many advances have come in the short-term predictions of solar flares and coronal mass ejections, which have benefited from applying machine learning techniques to the new data. The arrival times of coronal mass ejections is a mid-range prediction whose accuracy has been improving, mostly due to a steady flow of data from SoHO, STEREO, and SDO. Longer term (greater than a year) predictions of solar activity have benefited from helioseismic studies of the plasma flows in the Sun. While these studies have complicated the dynamo models by introducing more complex internal flow patterns, the models should become more robust with the added information. But predictions made long before a sunspot cycle begins still rely on precursors. The success of some categories of the predictions of Solar Cycle 24 will be examined. The predictions in successful categories should be emphasized in future work. The SODA polar field precursor method, which has accurately predicted the last three cycles, is shown for Solar Cycle 25. Shape functions for the sunspot number and F10.7 are presented. What type of data is needed to better understand the polar regions of the Sun, the source of the most accurate precursor of long-term solar activity, will be discussed.

Regression Analysis of Sunspot Numbers for the Solar Cycle 24 in Comparison to Previous Three Cycles

2014 ◽  
Vol 4 (2) ◽  
pp. 477-483
Author(s):  
Debojyoti Halder
Keyword(s):  
Regression Analysis ◽  
Solar Cycle ◽  
Sunspot Number ◽  
The Sun ◽  
Sunspot Numbers ◽  
Solar Cycle 24 ◽  
Current Cycle ◽  
Cycle 24

Sunspots are temporary phenomena on the photosphere of the Sun which appear visibly as dark spots compared to surrounding regions. Sunspot populations usually rise fast but fall more slowly when observed for any particular solar cycle. The sunspot numbers for the current cycle 24 and the previous three cycles have been plotted for duration of first four years for each of them. It appears that the value of peak sunspot number for solar cycle 24 is smaller than the three preceding cycles. When regression analysis is made it exhibits a trend of slow rising phase of the cycle 24 compared to previous three cycles. Our analysis further shows that cycle 24 is approaching to a longer-period but with smaller occurrences of sunspot number.

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