K. P. Raju, Jagdev Singh, B. Ravindra, Muthu Priyal
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引用次数: 0
摘要
在太阳图像中看到的网络结构是超粒化的轮廓,超粒化是太阳中的大尺度对流,其大小约为 30 毫米,寿命为 24 小时。我们从 Ca ii K 分光日线图图像窗口的自相关函数中获得了超粒道宽度。这些图像来自 100 年的 Kodaikanal 数据,其中包含超过九个太阳周期的信息。车道宽度与太阳黑子数量呈正相关。现在发现,在太阳黑子周期最小值期间,在中纬度附近获得的道宽与随后的太阳黑子数最大值密切相关。直线拟合充分描述了这一变化。研究还发现,在其他时间,相关性较弱或不明显。因此,这两个参数的强相关性为提前约 4-5 年预测太阳黑子数最大值提供了一种简单的方法。这些结果对于空间天气预报和太阳辐照度变化具有重要意义。
Correlation between Supergranular Lane Widths and Sunspot Number; A Simple Way to Predict the Amplitude of Sunspot Cycle
The network structure seen in the solar images is the outline of supergranulation, which is the large-scale convection in the Sun with a size of about 30 Mm and a lifetime of 24 hr. We have obtained the supergranulation lane widths from the autocorrelation function of image windows from the Ca ii K spectroheliograms. The images are obtained from the 100 yr Kodaikanal data, which contains information on more than nine solar cycles. The lane widths are known to show a positive correlation with the sunspot number. It is now found that the lane widths, obtained near the mid-latitudes during the sunspot cycle minima, are strongly correlated to the following sunspot number maxima. A straight-line fit adequately describes the variation. It is also found that the correlation is weak or insignificant at other times. The strong correlation of the two parameters thus provides a simple way to predict the maximum sunspot number about 4–5 yr in advance. The results are important in space weather predictions and solar irradiance variations.