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

摘要

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