A Comparison of Feature Classification Methods for Modeling Solar Irradiance Variation |
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| Authors: | H P Jones G A Chapman K L Harvey J M Pap D G Preminger M J Turmon S R Walton |
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| Institution: | (1) National Solar Observatory, P.O. Box 26732, Tucson, AZ 85726, USA;(2) Department of Physics and Astronomy, California State University Northridge, Northridge, CA, USA;(3) Solar Physics Research Corporation, Tucson, AZ, USA;(4) Goddard Earth Science and Technology Institute/UMBC, NASA’s Goddard Space Flight Center, Greenbelt, MD, USA;(5) NASA’s Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA |
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| Abstract: | Physical understanding of total and spectral solar irradiance variation depends upon establishing a connection between the
temporal variability of spatially resolved solar structures and spacecraft observations of irradiance. One difficulty in comparing
models derived from different data sets is that the many ways for identifying solar features such as faculae, sunspots, quiet
Sun, and various types of “network” are not necessarily consistent. To learn more about classification differences and how
they affect irradiance models, feature “masks” are compared as derived from five current methods: multidimensional histogram
analysis of NASA/National Solar Observatory/Kitt Peak spectromagnetograph data, statistical pattern recognition applied to
SOHO/Michelson Doppler Imager photograms and magnetograms, threshold masks allowing for influence of spatial surroundings
applied to NSO magnetograms, and “one-trigger” and “three-trigger” algorithms applied to California State University at Northridge
Cartesian Full Disk Telescope intensity observations. In general all of the methods point to the same areas of the Sun for
labeling sunspots and active-region faculae, and available time series of area measurements from the methods correlate well
with each other and with solar irradiance. However, some methods include larger label sets, and there are important differences
in detail, with measurements of sunspot area differing by as much as a factor of two. The methods differ substantially regarding
inclusion of fine spatial scale in the feature definitions. The implications of these differences for modeling solar irradiance
variation are discussed.
K.L. Harvey and S.R. Walton are deseased, to whom this paper is dedicated. |
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| Keywords: | Solar irradiance Sunspots Active regions Photosphere Data analysis |
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