Abstract
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.
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Acknowledgements
The authors thank http://sidc.oma.be/silso/datafiles (SILSO data/image, Royal Observatory of Belgium, Brussels) for providing SSN data. This work is supported by the National Basic Research Program of China (2012CB957801). We are very grateful to the reviewer for his/her pertinent and detailed comments and suggestions on our manuscript, which were very useful for its improvement of the manuscript.
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Han, Y.B., Yin, Z.Q. A Decline Phase Modeling for the Prediction of Solar Cycle 25. Sol Phys 294, 107 (2019). https://doi.org/10.1007/s11207-019-1494-7
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DOI: https://doi.org/10.1007/s11207-019-1494-7