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Forecast of the Decadal Average Sunspot Number

机译:十月平均黑子数预测

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摘要

The forecast of the decadal average sunspot number (SN) becomes possible with an extension of telescopic observations based on proxy reconstructions using the tree ring radiocarbon data during the Holocene. These decadal numbers (SNRC) provide a powerful statistic to verify the forecasting methods. Complicated dynamics of long-term solar activity and noise of proxy-based reconstruction make the one-step-ahead forecast challenging for any forecasting method. Here we construct a continuous data set of SNRC which extends the group sunspot number and the international sunspot number. The known technique of nonlinear forecast, the local linear approximation, is adapted to estimate the coming SN. Both the method and the continuous data set were tested and tuned to obtain the minimum of a normalized average prediction error (E) during the last millennium using several past millennia as a training data set. E=0.58σ D is achieved to forecast the SN successive differences whose standard deviation is σ D=7.39 for the period of training. This corresponds to the correlation (r=0.97) between true and forecasted SN. This error is significantly smaller than the prediction error when the surrogate data were used for the training data set, and proves the nonlinearity in the decadal SN. The estimated coming SN is smaller than the previous one.
机译:借助全新世期间使用树环放射性碳数据的代理重建,通过望远镜观测的扩展,可以预测十年平均太阳黑子数(SN)。这些十进制数(SN RC )提供了强大的统计数据来验证预测方法。长期太阳活动的复杂动态和基于代理的重建的噪声使任何一种预测方法都难以进行一步一步的预测。在这里,我们构造了一个连续的SN RC 数据集,该数据集扩展了组黑子数和国际黑子数。非线性预测的已知技术(局部线性逼近)适用于估计即将到来的SN。方法和连续数据集都经过测试和调整,以获取最近一个千年中使用过去几千年作为训练数据集的归一化平均预测误差(E)的最小值。通过E =0.58σ D 可以预测训练期间标准差为σ D = 7.39的SN连续差。这对应于真实SN与预测SN之间的相关性(r = 0.97)。当将替代数据用于训练数据集时,此误差明显小于预测误差,并证明了十年序列SN中的非线性。估计的即将到来的SN小于前一个。

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