Physics
Scientific paper
Dec 2008
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008agufmsh23a1631p&link_type=abstract
American Geophysical Union, Fall Meeting 2008, abstract #SH23A-1631
Physics
1650 Solar Variability (7537), 7524 Magnetic Fields, 7536 Solar Activity Cycle (2162), 7537 Solar And Stellar Variability (1650), 7538 Solar Irradiance
Scientific paper
This poster presents the results of using the AutoClass software, a Bayesian finite mixture model based pattern recognition program developed by Cheeseman and Stutz (1996), on Mount Wilson Solar Observatory (MWO) intensity and magnetogram images to identify spatially resolved areas on the solar surface associated with TSI emissions. Using indices based on the resolved patterns identified by AutoClass from MWO images, and a linear regression fit of those indices to satellite observations of TSI, we were able to model the satellite observations from the MWO data with a correlation of better than 0.96 for the period 1996 to 2007. The association of the spatial surface regional patterns identified by AutoClass with the indices developed from them also allows construction of spatially resolved images of the Sun as it would be "seen" by TSI measuring instruments like Virgo if they were able to capture resolved images. This 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, the spatial resolution of these "images" should assist in identifying with greater accuracy 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 may be possible to construct an independent estimate of TSI emission at several solar minima to ascertain if there has been any significant increase or decrease, a topic of significance to determining what part, if any, solar TSI variations play in global warming. Cheeseman, P. & Stutz, J.,1996, in Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamny (Eds.). (AAAI Press), p.61
Bertello Luca
Boyden John E.
Pap Judit M.
Parker Gary D.
Ulrich Richard K.
No associations
LandOfFree
Modeling Tsi Variations Using Automated Pattern Recognition Software On Mount Wilson Data does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Modeling Tsi Variations Using Automated Pattern Recognition Software On Mount Wilson Data, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Modeling Tsi Variations Using Automated Pattern Recognition Software On Mount Wilson Data will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-1247265