J. Space Weather Space Clim.
Volume 5, 2015
Statistical Challenges in Solar Information Processing
Article Number A23
Number of page(s) 12
Published online 10 July 2015
  • Ahammer, H., J.M. Kröpfl, C. Hackl, and R. Sedivy. Image statistics and data mining of anal intraepithelial neoplasia. Pattern Recognit. Lett., 29 (16), 2189–2196, 2008, DOI: 10.1016/j.patrec.2008.08.008. [CrossRef]
  • Anscombe, F.J. The transformation of Poisson, binomial and negative-binomial data. Biometrika, 35, 246–254, 1948. [CrossRef]
  • Barra, V., V. Delouille, and J.F. Hochedez. Segmentation of extreme ultraviolet solar images using a multispectral data fusion process. In Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007, IEEE International, London, 1–6, 2007, DOI: 10.1109/FUZZY.2007.4295367.
  • Bezdek, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, Norwell, MA, USA, 1981. [CrossRef]
  • Bloomfield, D.S., P.A. Higgins, R.T.J. McAteer, and P.T. Gallagher. Toward reliable benchmarking of solar flare forecasting methods. ApJ, 747, L41, 2012, DOI: 10.1088/2041-8205/747/2/L41. [NASA ADS] [CrossRef]
  • Breiman, L. Arcing classifier. Ann. Statist., 26 (3), 801–849, 1998, DOI: 10.1214/aos/1024691079. [CrossRef] [MathSciNet]
  • Breiman, L., J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees, Chapman & Hall, New York, 1984.
  • Chang, C.-C., and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, 27:1–27:27, 2011. Software available at [NASA ADS] [CrossRef] [MathSciNet]
  • Cranmer, S.R. Coronal Holes. Living Rev. Sol. Phys., 6, 3, 2009, DOI: 10.12942/lrsp-2009-3.
  • de Toma, G. Evolution of coronal holes and implications for high-speed solar wind during the minimum between cycles 23 and 24. Sol. Phys., 274, 195–217, 2011, DOI: 10.1007/s11207-010-9677-2. [NASA ADS] [CrossRef]
  • Delouille, V., P. Chainais, and J.-F. Hochedez. Spatial and temporal noise in solar EUV observations. Sol. Phys., 248, 441–455, 2008, DOI: 10.1007/s11207-008-9131-x. [CrossRef]
  • Fan, R.-E., K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9, 1871–1874, 2008.
  • Gosling, J.T., and V.J. Pizzo. Formation and evolution of corotating interaction regions and their three dimensional structure. Space Sci. Rev., 89, 21–52, 1999, DOI: 10.1023/A:1005291711900. [NASA ADS] [CrossRef]
  • Hanssen, A., and W. Kuipers. On the Relationship Between the Frequency of Rain and Various Meteorological Parameters: (with Reference to the Problem Of Objective Forecasting). In: Koninkl. Nederlands Meterologisch Institut. Mededelingen en Verhandelingen, 's-Gravenhage: Staatsdrukkerij- en Uitgeverijbedrijf, 1965.
  • Haralick, R.M., K. Shanmugam, and I. Dinstein. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3 (6), 610–621, 1973, DOI: 10.1109/tsmc.1973.4309314. [CrossRef]
  • Hurlburt, N., M. Cheung, C. Schrijver, L. Chang, S. Freeland, et al. Heliophysics event knowledgebase for the solar dynamics observatory (SDO) and beyond. Sol. Phys., 275, 67–78, 2012, DOI: 10.1007/s11207-010-9624-2. [NASA ADS] [CrossRef]
  • Japkowics, N., and M. Shah. Evaluating Learning Algorithms: A Classification Perspective, Cambridge University Press, 2014.
  • Kirk, M.S., W.D. Pesnell, C.A. Young, and S.A. Hess Weber. Automated detection of EUV polar coronal holes during solar cycle 23. Sol. Phys., 257, 99–112, 2009, DOI: 10.1007/s11207-009-9369-y. [NASA ADS] [CrossRef]
  • Krista, L.D., and P.T. Gallagher. Automated coronal hole detection using local intensity thresholding techniques. Sol. Phys., 256, 87–100, 2009, DOI: 10.1007/s11207-009-9357-2. [NASA ADS] [CrossRef]
  • Lemen, J.R., A.M. Title, D.J. Akin, P.F. Boerner, C. Chou, et al. The atmospheric imaging assembly (AIA) on the solar dynamics observatory (SDO). Sol. Phys., 275, 17–40, 2012, DOI: 10.1007/s11207-011-9776-8. [NASA ADS] [CrossRef]
  • Mackay, D.H., J.T. Karpen, J.L. Ballester, B. Schmieder, and G. Aulanier. Physics of solar prominences: II – Magnetic structure and dynamics. Space Sci. Rev., 151, 333–399, 2010, DOI: 10.1007/s11214-010-9628-0. [NASA ADS] [CrossRef]
  • Martens, P.C.H., G.D.R. Attrill, A.R. Davey, A. Engell, S. Farid, et al. Computer vision for the solar dynamics observatory (SDO). Sol. Phys., 275, 79–113, 2012, DOI: 10.1007/s11207-010-9697-y. [NASA ADS] [CrossRef]
  • Müller, D., B. Fleck, G. Dimitoglou, B.W. Caplins, D.E. Amadigwe, et al. JHelioviewer: visualizing large sets of solar images using JPEG 2000. Computing in Science and Engineering, 11 (5), 38–47, 2009. [CrossRef]
  • Munro, R.H., and G.L. Withbroe. Properties of a coronal “hole” derived from extreme-ultraviolet observations. ApJ, 176, 511, 1972, DOI: 10.1086/151653. [NASA ADS] [CrossRef]
  • Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, et al. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830, 2011.
  • Pötzi, W., A.M. Veronig, G. Riegler, U. Amerstorfer, T. Pock, M. Temmer, W. Polanec, and D. Baumgartner. Real-time flare detection in ground-based Hα imaging at Kanzelhohe observatory. Sol. Phys., 290 (3), 951–977, 2015, DOI: 10.1007/s11207-014-0640-5. [CrossRef]
  • Quinlan, J.R. C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, ISBN 1-55860-238-0, 1993.
  • Reiss, M.A., M. Temmer, T. Rotter, S.J. Hofmeister, and A.M. Veronig. Identification of coronal holes and filament channels in SDO/AIA 193Å images via geometrical classification methods. Central European Astrophysical Bulletin, 38, 95–104, 2014.
  • Rotter, T., A.M. Veronig, M. Temmer, and B. Vršnak. Relation between coronal hole areas on the sun and the solar wind parameters at 1 AU. Sol. Phys., 281, 793–813, 2012, DOI: 10.1007/s11207-012-0101-y. [NASA ADS] [CrossRef]
  • Rotter, T., A.M. Veronig, M. Temmer, and B. Vršnak. Real-time solar wind prediction based on SDO/AIA coronal hole data. Sol. Phys., 290 (5), 1355–1370, 2015, DOI: 10.1007/s11207-015-0680-5. [CrossRef]
  • Scherrer, P.H., J. Schou, R.I. Bush, A.G. Kosovichev, R.S. Bogart, et al. The helioseismic and magnetic imager (HMI) investigation for the solar dynamics observatory (SDO). Sol. Phys., 275, 207–227, 2012, DOI: 10.1007/s11207-011-9834-2. [NASA ADS] [CrossRef]
  • Tsurutani, B.T., W.D. Gonzalez, A.L.C. Gonzalez, F.L. Guarnieri, N. Gopalswamy, et al. Corotating solar wind streams and recurrent geomagnetic activity: a review. J. Geophys. Res. [Space Phys.], 111, A07S01, 2006, DOI: 10.1029/2005JA011273.
  • Vapnick, V. Statistical Learning Theory, Wiley, New York, 1998.
  • Verbanac, G., B. Vršnak, A.M. Veronig, and M. Temmer. Equatorial coronal holes, solar wind high-speed streams, and their geoeffectiveness. A&A, 526, A20, 2011, DOI: 10.1051/0004-6361/201014617. [NASA ADS] [CrossRef] [EDP Sciences]
  • Verbeeck, C., V. Delouille, B. Mampaey, and R. De Visscher. The SPoCA-suite: Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images. A&A, 561, A29, 2014, DOI: 10.1051/0004-6361/201321243. [NASA ADS] [CrossRef] [EDP Sciences]
  • Vršnak, B., M. Temmer, and A.M. Veronig. Coronal holes and solar wind high-speed streams: I. Forecasting the solar wind parameters. Sol. Phys., 240, 315–330, 2007, DOI: 10.1007/s11207-007-0285-8. [NASA ADS] [CrossRef]
  • Weyn, B., W. Tjalam, G.V. de Wouwer, A.V. Daele, P. Scheunders, W. Jacob, E.V. Marck, and D.V. Dyck. Validation of nuclear texture density, morphometry and tissue syntactic structure analysis as prognosticators of cervical carcinoma. Analytical and Quantitative Cytology and Histology, 22 (5), 373–382, 2000.
  • Woodcock, F. The evaluation of yes/no forecasts for scientific and administrative purposes. Monthly Weather Review, 104, 1209, 1976, DOI: 10.1175/1520-0493(1976)104<1209:TE0YFF>2.0.C0;2. [CrossRef]

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