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

TL;DR

This study uses automated classification of Mount Wilson solar images to model and reconstruct total solar irradiance with high accuracy, enabling better understanding of solar variability and long-term trends.

Contribution

It introduces a novel application of Bayesian classification to classify solar surface pixels and accurately model TSI variations from ground-based data.

Findings

Achieved correlation of better than 0.96 in TSI modeling

Created spatially-resolved solar images related to TSI variations

Potential to fill gaps in satellite TSI records and analyze long-term trends

Abstract

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.