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Temporal solar irradiance variability analysis using neural networks

Published online by Cambridge University Press:  09 September 2016

Ambelu Tebabal ,
Baylie Damtie  and
Melessew Nigussie
Ambelu Tebabal
Affiliation:
Washera Geospace and Radar Science Laboratory (WaGRL), Bahir Dar University, Bahir Dar, Ethiopia email: [email protected]
Baylie Damtie
Affiliation:
Washera Geospace and Radar Science Laboratory (WaGRL), Bahir Dar University, Bahir Dar, Ethiopia email: [email protected]
Melessew Nigussie
Affiliation:
Washera Geospace and Radar Science Laboratory (WaGRL), Bahir Dar University, Bahir Dar, Ethiopia email: [email protected]
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Abstract

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A feed-forward neural network which can account for nonlinear relationship was used to model total solar irradiance (TSI). A single layer feed-forward neural network with Levenberg-marquardt back-propagation algorithm have been implemented for modeling daily total solar irradiance from daily photometric sunspot index, and core-to-wing ratio of Mg II index data. In order to obtain the optimum neural network for TSI modeling, the root mean square error (RMSE) and mean absolute error (MAE) have been taken into account. The modeled and measured TSI have the correlation coefficient of about R=0.97. The neural networks (NNs) model output indicates that reconstructed TSI from solar proxies (photometric sunspot index and Mg II) can explain 94% of the variance of TSI. This modeled TSI using NNs further strengthens the view that surface magnetism indeed plays a dominant role in modulating solar irradiance.

Keywords

SunspotsNeural networks
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 11 , Symposium S320: Solar and Stellar Flares and their Effects on Planets , August 2015 , pp. 333 - 338
Copyright
Copyright © International Astronomical Union 2016 

References

Pap, J., M. & Fox, P. 2004, Geophysical Monographs Series, 11 Google Scholar
Lean, J. 2001, Physics Today, 58, 6 Google Scholar
Balmaceda, L. A., Solanki, S. K., & Krivova, N. A. 2009, J. Geophys. Res., 114, A07104 Google Scholar
Deland, M. T. & Cebula, R. P. 1993, J. Geophys. Res., 98 Google Scholar
Rottman, G. 2006, Space. Sci. Rev., 125, 39 CrossRefGoogle Scholar
Fröhlich, C. & Lean, J. 2004, A&AR, 4, 12 Google Scholar
Balmaceda, L., Krivova, N. A., & Solanki, S. K. 2007, Adv. Sp. Res., 40, 986 CrossRefGoogle Scholar
Fröhlich, C. & Lean, J. 1998, Geophys. Res. Lett., 25, 4377 Google Scholar
Ashamari, O., Qahwaji, R., Ipson, S., Schöll, M., Nibouche, O., & Haberreiter, M. 2015, astro-ph.SR Google Scholar
Hagan, M.T., Demuth, H., & Beale, M. 1995, PWS Publishing Company, Boston, MA Google Scholar
Hornik, K., Stinchcombe, M., & White, H. 1989, Neural Networks, 2, 359 Google Scholar
Habarulema, J. B., McKinnell, L.-A., & Opperman, B. D. L. 2007, JASTP, 72, 1842 Google Scholar
Habarulema, J. B.,& McKinnell, L.-A. 2012, Ann. Geophys., 30, 857 CrossRefGoogle Scholar
Tebabal, A., Baylie, D., Melessew, N., Bires, A., & Yizengaw, E. 2015, JASTP, 135, 64 Google Scholar
Srivastava, N., Hinton, G., Krizhevsky, A., ISutskever, I., & Salakhutdinov, R. 2014, Journal of Machine Learning Research, 15, 1929 Google Scholar
Demuth, H. & Beale, M. 2000, The MathWorks, Inc. Google Scholar
Hoppe, P., Ott, U., & Lugmair, G. W. 2004, New Astron. Revs, 48, 171 Google Scholar
Fröhlich, C. & Lean, J. 2004, AAR, 4, 273 Google Scholar
Kopp, G. & Lean, J. L. 2011, Geophys. Res. Lett., 28, 1706 Google Scholar