A Running Average Method for Predicting the Size and Length of a Solar Cycle

Z. Du, Huaning Wang, Liyun Zhang
{"title":"A Running Average Method for Predicting the Size and Length of a Solar Cycle","authors":"Z. Du, Huaning Wang, Liyun Zhang","doi":"10.1088/1009-9271/8/4/12","DOIUrl":null,"url":null,"abstract":"The running correlation coefficient between the solar cycle amplitudes and the max-max cycle lengths at a given cycle lag is found to vary roughly in a cyclical wave with the cycle number, based on the smoothed monthly mean Group sunspot numbers available since 1610. A running average method is proposed to predict the size and length of a solar cycle by the use of the varying trend of the coefficients. It is found that, when a condition (that the correlation becomes stronger) is satisfied, the mean prediction error (16.1) is much smaller than when the condition is not satisfied (38.7). This result can be explained by the fact that the prediction must fall on the regression line and increase the strength of the correlation. The method itself can also indicate whether the prediction is reasonable or not. To obtain a reasonable prediction, it is more important to search for a running correlation coefficient whose varying trend satisfies the proposed condition, and the result does not depend so much on the size of the correlation coefficient. As an application, the peak sunspot number of cycle 24 is estimated as 140.4±15.7, and the peak as May 2012±11 months.","PeriodicalId":124495,"journal":{"name":"Chinese Journal of Astronomy and Astrophysics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Journal of Astronomy and Astrophysics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/1009-9271/8/4/12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23

Abstract

The running correlation coefficient between the solar cycle amplitudes and the max-max cycle lengths at a given cycle lag is found to vary roughly in a cyclical wave with the cycle number, based on the smoothed monthly mean Group sunspot numbers available since 1610. A running average method is proposed to predict the size and length of a solar cycle by the use of the varying trend of the coefficients. It is found that, when a condition (that the correlation becomes stronger) is satisfied, the mean prediction error (16.1) is much smaller than when the condition is not satisfied (38.7). This result can be explained by the fact that the prediction must fall on the regression line and increase the strength of the correlation. The method itself can also indicate whether the prediction is reasonable or not. To obtain a reasonable prediction, it is more important to search for a running correlation coefficient whose varying trend satisfies the proposed condition, and the result does not depend so much on the size of the correlation coefficient. As an application, the peak sunspot number of cycle 24 is estimated as 140.4±15.7, and the peak as May 2012±11 months.
预测太阳活动周期大小和长度的运行平均法
基于1610年以来的平滑月平均太阳黑子群数,在给定的周期滞后下,太阳周期振幅和最大-最大周期长度之间的运行相关系数随着周期数大致变化。提出了一种利用系数变化趋势预测太阳周期大小和长度的运行平均法。研究发现,当满足相关性变强的条件时,平均预测误差(16.1)远小于不满足该条件时的平均预测误差(38.7)。这一结果可以用预测必须落在回归线上并增加相关性强度来解释。方法本身也可以表明预测是否合理。为了获得合理的预测,更重要的是寻找一个变化趋势满足所提出条件的运行相关系数,并且结果不太依赖于相关系数的大小。作为应用,估计第24周期的太阳黑子峰值为140.4±15.7,峰值为2012年5月±11个月。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信