Modeling Total Solar Irradiance Variations Using Automated Classification Software on Mount Wilson Data |
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Authors: | R K Ulrich D Parker L Bertello J Boyden |
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Institution: | 1.Department of Physics and Astronomy,University of California,Los Angeles,USA |
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Abstract: | We present the 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. (Astrophys. J. 611, L57, 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 from ground-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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