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CNN-Based Deep Learning Model for Solar Wind Forecasting

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Abstract

This article implements a Convolutional Neural Network (CNN)-based deep-learning model for solar-wind prediction. Images from the Atmospheric Imaging Assembly (AIA) at 193 Å wavelength are used for training. Solar-wind speed is taken from the Advanced Composition Explorer (ACE) located at the Lagrangian L1 point. The proposed CNN architecture is designed from scratch for training with four years’ data. The solar-wind has been ballistically traced back to the Sun assuming a constant speed during propagation, to obtain the corresponding coronal-intensity data from AIA images. This forecasting scheme can predict the solar-wind speed well with a RMSE of 76.3 ± 1.87 km s−1 and an overall correlation coefficient of 0.57 ± 0.02 for the year 2018, while significantly outperforming benchmark models. The threat score for the model is around 0.46 in identifying the HSEs with zero false alarms.

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References

  • Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. tensorflow.org.

  • Bailey, R.L., Reiß, M., Arge, C.N., Möstl, C., Henney, C.J., Owens, M.J., Amerstorfer, U.V., Amerstorfer, T., Weiss, A.J., Hinterreiter, J.: 2021, Using gradient boosting regression to improve ambient solar wind model predictions. Space Weather 9, e2020SW002673. DOI.

    Article  ADS  Google Scholar 

  • Bartels, J.: 1932, Terrestrial-magnetic activity and its relations to solar phenomena. Terr. Magn. Atmos. Electr. 37, 1. DOI.

    Article  MATH  Google Scholar 

  • Bjorck, N., Gomes, C.P., Selman, B., Weinberger, K.Q.: 2018, Understanding batch normalization. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Adv. Neural Infor. Proc. Syst. 31, Curran Associates, Red Hook, 7694.

    Google Scholar 

  • Bu, X., Luo, B., Shen, C., Liu, S., Gong, J., Cao, Y., Wang, H.: 2019, Forecasting high-speed solar wind streams based on solar extreme ultraviolet images. Space Weather 17, 1040. DOI.

    Article  ADS  Google Scholar 

  • Cranmer, S.R.: 2009, Coronal holes. Living Rev. Solar Phys. 6, 3. DOI.

    Article  ADS  Google Scholar 

  • Echer, E., Gonzalez, W.D., Tsurutani, B.T., Gonzalez, A.L.C.: 2008, Interplanetary conditions causing intense geomagnetic storms (Dst ≤ −100 nT) during solar cycle 23 (1996–2006). J. Geophys. Res. 113, A05221. DOI.

    Article  ADS  Google Scholar 

  • Gal, Y., Ghahramani, Z.: 2016, Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proc. 33rd Internat. Conf. Machine Learning, Proc. Machine Learning Res. 48, 1050. New York. proceedings.mlr.press/v48/gal16.html.

    Google Scholar 

  • Galvez, R., Fouhey, D.F., **, M., Szenicer, A., Muñoz-Jaramillo, A., Cheung, M.C.M., Wright, P.J., Bobra, M.G., Liu, Y., Mason, J., et al.: 2019, A machine-learning data set prepared from the NASA solar dynamics observatory mission. Astron. Astrophys. Suppl. Ser. 242, 7. DOI.

    Article  ADS  Google Scholar 

  • Gonzalez, W.D., Joselyn, J.A., Kamide, Y., Kroehl, H.W., Rostoker, G., Tsurutani, B.T., Vasyliunas, V.M.: 1994, What is a geomagnetic storm? J. Geophys. Res. 99, 5771. DOI.

    Article  ADS  Google Scholar 

  • Gosling, J.T., Pizzo, V.J.: 1999, Formation and evolution of corotating interaction regions and their three dimensional structure. Space Sci. Rev. 89, 21. DOI.

    Article  ADS  Google Scholar 

  • Hansen, R.T., Hansen, S.F., Sawyer, C.: 1976, Long-lived coronal structures and recurrent geomagnetic patterns in 1974. Planet. Space Sci. 24, 381. DOI.

    Article  ADS  Google Scholar 

  • Harris, C.R., Millman, K.J., van der Walt, S.J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N.J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M.H., Brett, M., Haldane, A., del Río, J.F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., Oliphant, T.E.: 2020, Array programming with NumPy. Nature 585, 357. DOI.

    Article  ADS  Google Scholar 

  • Harvey, J.W., Sheeley, N.R.: 1979, Coronal holes and solar magnetic fields. Space Sci. Rev. 23, 139. DOI.

    Article  ADS  Google Scholar 

  • Hunter, J.D.: 2007, Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90.

    Article  Google Scholar 

  • Jian, L.K., MacNeice, P.J., Taktakishvili, A., Odstrcil, D., Jackson, B., Yu, H.-S., Riley, P., Sokolov, I.V., Evans, R.M.: 2015, Validation for solar wind prediction at Earth: comparison of coronal and heliospheric models installed at the CCMC. Space Weather 13, 316. DOI.

    Article  ADS  Google Scholar 

  • Kingma, D.P., Ba, J.: 2015, Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd Internat. Conf. Learning Rep. ADS. ar**v.

    Google Scholar 

  • Krieger, A.S., Timothy, A.F., Roelof, E.C.: 1973, A coronal hole and its identification as the source of a high velocity solar wind stream. Solar Phys. 29, 505. DOI.

    Article  ADS  Google Scholar 

  • Krizhevsky, A., Sutskever, I., Hinton, G.: 2012, ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Proc. Syst. 25. DOI.

  • Lemen, J.R., Title, A.M., Akin, D.J., Boerner, P.F., Chou, C., Drake, J.F., Duncan, D.W., Edwards, C.G., Friedlaender, F.M., Heyman, G.F., Hurlburt, N.E., Katz, N.L., Kushner, G.D., Levay, M., Lindgren, R.W., Mathur, D.P., McFeaters, E.L., Mitchell, S., Rehse, R.A., Schrijver, C.J., Springer, L.A., Stern, R.A., Tarbell, T.D., Wuelser, J.-P., Wolfson, C.J., Yanari, C., Bookbinder, J.A., Cheimets, P.N., Caldwell, D., Deluca, E.E., Gates, R., Golub, L., Park, S., Podgorski, W.A., Bush, R.I., Scherrer, P.H., Gummin, M.A., Smith, P., Auker, G., Jerram, P., Pool, P., Soufli, R., Windt, D.L., Beardsley, S., Clapp, M., Lang, J., Waltham, N.: 2012, The Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO). Solar Phys. 275, 17. DOI.

    Article  ADS  Google Scholar 

  • Liu, D.D., Huang, C., Lu, J.Y., Wang, J.S.: 2011, The hourly average solar wind velocity prediction based on support vector regression method. Mon. Not. Roy. Astron. Soc. 413, 2877. DOI.

    Article  ADS  Google Scholar 

  • Liu, Y., Racah, E., Prabhat, Correa, J., Khosrowshahi, A., Lavers, D., Kunkel, K., Wehner, M.F., Collins, W.D.: 2016, Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets. CoRR, ar**v.

  • MacNeice, P.: 2009, Validation of community models: identifying events in space weather model timelines. Space Weather 7, S06004. DOI.

    Article  ADS  Google Scholar 

  • Neupert, W.M., Pizzo, V.: 1974, Solar coronal holes as sources of recurrent geomagnetic disturbances. J. Geophys. Res. (1896–1977) 79, 3701. DOI.

    Article  ADS  Google Scholar 

  • Owens, M., Challen, R., Methven, J., Henley, E., Jackson, D.: 2013, A 27 day persistence model of near-Earth solar wind conditions: a long lead-time forecast and a benchmark for dynamical models. Space Weather 11, 225. DOI.

    Article  ADS  Google Scholar 

  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: 2011, Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825.

    MathSciNet  MATH  Google Scholar 

  • Pesnell, W.D., Thompson, B.J., Chamberlin, P.C.: 2012, The Solar Dynamics Observatory (SDO). Solar Phys. 275, 3. DOI. ADS.

    Article  ADS  Google Scholar 

  • Poduval, B., Zhao, X.: 2014, Validating solar wind prediction using the current sheet source surface model. Astrophys. J. Lett. 782, L22. DOI.

    Article  ADS  Google Scholar 

  • Rama Rao, P.V.S., Gopi Krishna, S., Vara Prasad, J., Prasad, S.N.V.S., Prasad, D.S.V.V.D., Niranjan, K.: 2009, Geomagnetic storm effects on GPS based navigation. Ann. Geophys. 27, 2101. DOI.

    Article  ADS  Google Scholar 

  • Rotter, T., Veronig, A.M., Temmer, M., Vrsnak, B.: 2012, Relation between coronal hole areas on the Sun and the solar wind parameters at 1 AU. Solar Phys. 281, 793. DOI.

    Article  ADS  Google Scholar 

  • Rotter, T., Veronig, A.M., Temmer, M., Vrsnak, B.: 2015, Real-time solar wind prediction based on SDO/AIA coronal hole data. Solar Phys. 290, 1355. DOI.

    Article  ADS  Google Scholar 

  • Sargent, H.H.: 1985, Recurrent geomagnetic activity: evidence for long-lived stability in solar wind structure. J. Geophys. Res. 90, 1425. DOI.

    Article  ADS  Google Scholar 

  • Schwenn, R.: 2006, Solar wind sources and their variations over the solar cycle. Space Sci. Rev. 124, 51. DOI.

    Article  ADS  Google Scholar 

  • Selvaraju, R.R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., Batra, D.: 2016, Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization. CoRR, ar**v.

  • Sheeley, N.R., Harvey, J.W., Feldman, W.C.: 1976, Coronal holes, solar wind streams, and recurrent geomagnetic disturbances: 1973–1976. Solar Phys. 49, 271. DOI.

    Article  ADS  Google Scholar 

  • Shugay, Y.S., Veselovsky, I.S., Slemzin, V.A., Yermolaev, Y.I., Rodkin, D.G.: 2017, Possible causes of the discrepancy between the predicted and observed parameters of high-speed solar wind streams. Cosm. Res. 55, 20. DOI.

    Article  ADS  Google Scholar 

  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: 2014, Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929. jmlr.org/papers/v15/srivastava14a.html.

    MathSciNet  MATH  Google Scholar 

  • Tokumaru, M., Satonaka, D., Fujiki, K., Hayashi, K., Hakamada, K.: 2017, Relation between coronal hole areas and solar wind speeds derived from interplanetary scintillation measurements. Solar Phys. 292, 41. DOI.

    Article  ADS  Google Scholar 

  • Tsurutani, B., Lakhina, G., Hajra, R.: 2020, The physics of space weather/solar-terrestrial physics (STP): what we know now and what the current and future challenges are. Nonlinear Process. Geophys. 27, 75. DOI.

    Article  ADS  Google Scholar 

  • Upendran, V., Cheung, M.C.M., Hanasoge, S., Krishnamurthi, G.: 2020, Solar wind prediction using deep learning. Space Weather, e2020SW002478. DOI.

  • Vršnak, B., Temmer, M., Veronig, A.M.: 2007, Coronal holes and solar wind high-speed streams: I. Forecasting the solar wind parameters. Solar Phys. 240, 315. DOI.

    Article  ADS  Google Scholar 

  • Wang, Y.-M., Sheeley, N.R. Jr.: 1990, Solar wind speed and coronal flux-tube expansion. Astrophys. J. 355, 726. DOI. ADS.

    Article  ADS  Google Scholar 

  • Yang, S., Zhang, J., Li, T., Liu, Y.: 2011, SDO observations of magnetic reconnection at coronal hole boundaries. Astrophys. J. Lett. 732, L7. DOI.

    Article  ADS  Google Scholar 

  • Yang, Y., Shen, F., Yang, Z., Feng, X.: 2018, Prediction of solar wind speed at 1 AU using an artificial neural network. Space Weather 16, 1227. DOI.

    Article  ADS  Google Scholar 

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Acknowledgements

The authors thankfully acknowledge the use of data courtesy of the SDO/AIA science teams and Galvez et al. (2019) for ML curated dataset. The authors also thankfully acknowledge NOAA/SWPC and the ACE Science Center for providing the ACE data. The authors would also like to thank B. Luo for providing solar-wind forecast data on request. The authors would also like to express their gratitude to the anonymous reviewer for their insightful remarks, which greatly improved the technical quality and organization of our manuscript. This research uses Python packages numpy (Harris et al., 2020), scikit-learn (Pedregosa et al., 2011), matplotlib (Hunter, 2007), and tensorflow (Abadi et al., 2015). For Grad-CAM visualization, we used Python package ELI5.

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  1. Department of Astronomy, Astrophysics and Space Engineering, Indian Institute of Technology, Indore, Simrol, Indore, 453552, India

    Hemapriya Raju & Saurabh Das

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  1. Hemapriya Raju
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Correspondence to Hemapriya Raju.

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Raju, H., Das, S. CNN-Based Deep Learning Model for Solar Wind Forecasting. Sol Phys 296, 134 (2021). https://doi.org/10.1007/s11207-021-01874-6

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