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arXiv 0909.2392astro-ph.SR
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威尔逊山太阳总辐照度变化的自动分类模拟

Modeling Total Solar Irradiance Variations Using Automated Classification Software On Mount Wilson Data

R. K. Ulrich, D. Parker, L. Bertello, J. Boyden

摘要:

我们目前的结果使用AutoClass分析应用程序可在美国宇航局/艾姆斯智能系统司。(2002),其是由Cheeseman和Stutz(1996)开发的贝叶斯有限混合模型分类系统。我们将这个系统应用到威尔逊山太阳天文台(MWO)的强度和磁图图像,并对太阳表面上的单个像素进行分类,以计算每日指数,然后将其与太阳总辐照度(TSI)相关联,以产生一组回归系数。这种方法使我们能够对1996年至2007年期间的TSI进行建模,其相关性优于0.96。这些回归系数应用于观察到的太阳表面上的分类像素,可以构建太阳的图像,因为它可以通过TSI测量仪器看到,如Foukal等人最近飞行的太阳辐射热成像仪,(2004年)。由于非常高的相关性,我们实现了在再现TSI记录,我们的方法提供了可能性,创造一个持续的,准确的,独立的估计TSI变化从地面观测,可用于比较,并确定分歧的来源,TSI观测从各种卫星仪器,并填补在卫星记录的空白。此外,我们的空间分辨图像应该有助于表征与TSI变化相关的特定太阳表面区域。此外,由于这一分析所依据的特定MWO数据集至少可以追溯到1985年,并且在此之前是间歇性的,因此可以估计由于在几个太阳极小期确定的太阳表面特征而引起的TSI排放,以限制表面磁效应对太阳能输出的长期趋势的作用。

英文摘要:

We present results using the AutoClass analysis application available at NASA/Ames Intelligent Systems Div. (2002) which is a Bayesian, finite mixture model classification system developed by Cheeseman and Stutz (1996). We apply this system to Mount Wilson Solar Observatory (MWO) intensity and magnetogram images and classify individual pixels on the solar surface to calculate daily indices that are then correlated with total solar irradiance (TSI) to yield a set of regression coefficients. This approach allows us to model the TSI with a correlation of better than 0.96 for the period 1996 to 2007. These regression coefficients applied to classified pixels on the observed solar surface allow the construction of images of the Sun as it would be seen by TSI measuring instruments like the Solar Bolometric Imager recently flown by Foukal et al., (2004). As a consequence of the very high correlation we achieve in reproducing the TSI record, our approach holds out the possibility of creating an on-going, accurate, independent estimate of TSI variations fromground-based observations which could be used to compare, and identify the sources of disagreement among, TSI observations from the various satellite instruments and to fill in gaps in the satellite record. Further, our spatially-resolved images should assist in characterizing the particular solar surface regions associated with TSI variations. Also, since the particular set of MWO data on which this analysis is based is available on a daily basis back to at least 1985, and on an intermittent basis before then, it will be possible to estimate the TSI emission due to identified solar surface features at several solar minima to constrain the role surface magnetic effects have on long-term trends in solar energy output.

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