Open AccessProbabilistic forecasting of solar flares from vector magnetogram data
Author(s) -
Barnes G.,
Leka K. D.,
Schumer E. A.,
DellaRose D. J.
Publication year - 2007
Publication title -
space weather
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.254
H-Index - 56
ISSN - 1542-7390
DOI - 10.1029/2007sw000317
Subject(s) - magnetogram , flare , solar flare , space weather , physics , probabilistic logic , probabilistic forecasting , solar cycle 24 , sunspot , bayesian probability , solar physics , remote sensing , algorithm , solar cycle , magnetic field , computer science , astrophysics , meteorology , artificial intelligence , geography , solar wind , magnetic flux , quantum mechanics
Discriminant analysis is a statistical approach for assigning a measurement to one of several mutually exclusive groups. Presented here is an application of the approach to solar flare forecasting, adapted to provide the probability that a measurement belongs to either group, the groups in this case being solar active regions which produced a flare within 24 hours and those that remained flare quiet. The technique is demonstrated for a large database of vector magnetic field measurements obtained by the University of Hawai'i Imaging Vector Magnetograph. For a large combination of variables characterizing the photospheric magnetic field, the results are compared to a Bayesian approach for solar flare prediction, and to the method employed by the U.S. Space Environment Center (SEC). Although quantitative comparison is difficult as the present application provides active region (rather than whole‐Sun) forecasts, and the present database covers only part of one solar cycle, the performance of the method appears comparable to the other approaches.