Skip to main content
SpringerLink
Log in

Forecasting Medium-Term F10.7 Using the Deep-Learning Informer Model

  • Research
  • Published:
Solar Physics Aims and scope Submit manuscript

Abstract

The daily 10.7-cm solar radio flux (F10.7) is one of the most important solar activity indices and has been widely applied in various space environment modeling as a crucial parameter. In this study, we adopt a deep-learning Informer model, based on the transformer architecture to predict the medium-term F10.7 index, which uses 48 historical daily F10.7 indices as input to directly forecast the following 1 – 27 days’ F10.7 index. The model is demonstrated to be effective and to have superior performance compared with other widely-used forecasting techniques: two statistical methods provided by British Geological Survey (BGS), Space Weather Prediction Center (SWPC), and a multiflux neural network method provided by Collecte Localisation Satellites (CLS). In comparison, the Informer model significantly improves the forecast accuracy for the prediction horizon larger than 6 days, especially during the solar activity descending phase and at the solar activity minimum. For its effectiveness, accurate prediction capability and the advantage in F10.7 forecasting with longer horizon, the Informer could be potentially used as a candidate model for space weather operational forecasting.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10

Similar content being viewed by others

Data Availability

The yearly average total sunspot numbers are available at Solar Influences Data Analysis Center (https://www.sidc.be/SILSO/dayssnplot). The observed values of F10.7 are downloaded from OMNIWeb service (https://omniweb.gsfc.nasa.gov/form/dx1.html). The prediction data of BGS and SWPC can be obtained through the European Space Agency (ESA) data platform (https://swe.ssa.esa.int/web/guest/forind-federated). The CLS model’s prediction data can be found at the official website (https://spaceweather.cls.fr/services/radioflux/).

References

  • Bilitza, D.: 2018, IRI the international standard for the ionosphere. Adv. Radio Sci. 16, 1.

    Article  ADS  Google Scholar 

  • Bowman, B., Tobiska, W.K., Marcos, F., Huang, C., Lin, C., Burke, W.: 2008, A new empirical thermospheric density model JB2008 using new solar and geomagnetic indices. In: AIAA/AAS Astrodynamics Specialist Conf., 6438.

    Google Scholar 

  • Bruinsma, S., Boniface, C.: 2021, The operational and research DTM-2020 thermosphere models. J. Space Weather Space Clim. 11, 47.

    Article  ADS  Google Scholar 

  • Covington, A.E.: 1969, Solar radio emission at 10.7 cm, 1947-1968. J. Roy. Astron. Soc. Can. 63, 125.

    ADS  Google Scholar 

  • Du, Z.: 2020, Forecasting the daily 10.7 cm solar radio flux using an autoregressive model. Solar Phys. 295, 125.

    Article  ADS  Google Scholar 

  • Elvidge, S., Themens, D.R., Brown, M.K., Donegan-Lawley, E.: 2023, What to do when the F10. 7 goes out? Space Weather 21, e2022SW003392.

    Article  ADS  Google Scholar 

  • Feynman, J., Gabriel, S.: 2000, On space weather consequences and predictions. J. Geophys. Res. 105, 10543.

    Article  ADS  Google Scholar 

  • Gulati, A., Qin, J., Chiu, C.-C., Parmar, N., Zhang, Y., Yu, J., Han, W., Wang, S., Zhang, Z., Wu, Y., et al.: 2020. Conformer: convolution-augmented transformer for speech recognition. arXiv.

  • Kalyan, K.S., Rajasekharan, A., Sangeetha, S.: 2021, Ammus: a survey of transformer-based pretrained models in natural language processing. arXiv.

  • Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., Shah, M.: 2022 Transformers in vision: a survey. ACM Comput. Surv. 54 1.

    Article  Google Scholar 

  • Lei, L., Zhong, Q., Wang, J., Shi, L., Liu, S., et al.: 2019, The mid-term forecast method of f 10.7 based on extreme ultraviolet images. Adv. Astron. 2019 Article ID 5604092.

    Article  ADS  Google Scholar 

  • Liu, C.-A., Zhao, X.-H., Chen, T., Li, H.-C.: 2018, Predicting short-term F 10.7 with transport models. Astrophys. Space Sci. 363, 266.

    Article  ADS  Google Scholar 

  • Oreshkin, B.N., Carpov, D., Chapados, N., Bengio, Y.: 2019, N-BEATS: neural basis expansion analysis for interpretable time series forecasting. arXiv.

  • Picone, J., Hedin, A., Drob, D.P., Aikin, A.: 2002, NRLMSISE-00 empirical model of the atmosphere: statistical comparisons and scientific issues. J. Geophys. Res. 107, SIA 15-1.

    Google Scholar 

  • Riley, P., Baker, D., Liu, Y.D., Verronen, P., Singer, H., Güdel, M.: 2018, Extreme space weather events: from cradle to grave. Space Sci. Rev. 214, 1.

    Article  ADS  Google Scholar 

  • Stevenson, E., Rodriguez-Fernandez, V., Minisci, E., Camacho, D.: 2022, A deep learning approach to solar radio flux forecasting. Acta Astronaut. 193, 595.

    Article  ADS  Google Scholar 

  • Su, Y., Bailey, G., Fukao, S.: 1999, Altitude dependencies in the solar activity variations of the ionospheric electron density. J. Geophys. Res. 104, 14879.

    Article  ADS  Google Scholar 

  • Tapping, K.: 2013, The 10.7 cm solar radio flux (F10. 7). Space Weather 11, 394.

    Article  ADS  Google Scholar 

  • To, A.S., James, A.W., Bastian, T., van Driel-Gesztelyi, L., Long, D.M., Baker, D., Brooks, D.H., Lomuscio, S., Stansby, D., Valori, G.: 2023, Understanding the relationship between solar coronal abundances and F10. 7 cm radio emission. Astrophys. J. 948, 121.

    Article  ADS  Google Scholar 

  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: 2017, Attention is all you need. In: Adv. Neural Inf. Process. Syst. 30.

    Google Scholar 

  • Wang, H.B., Xiong, J.N., Zhao, C.Y.: 2015, The mid-term forecast method of solar radiation index. Chin. Astron. Astrophys. 39, 198.

    Article  ADS  Google Scholar 

  • Wang, Z., Hu, Q., Zhong, Q., Wang, Y.: 2018, Linear multistep F10. 7 forecasting based on task correlation and heteroscedasticity. Earth Space Sci. 5, 863.

    Article  ADS  Google Scholar 

  • Warren, H.P., Emmert, J.T., Crump, N.A.: 2017, Linear forecasting of the F 10.7 proxy for solar activity. Space Weather 15, 1039.

    Article  ADS  Google Scholar 

  • Worden, J., Harvey, J.: 2000, An evolving synoptic magnetic flux map and implications for the distribution of photospheric magnetic flux. Solar Phys. 195, 247.

    Article  ADS  Google Scholar 

  • Yaya, P., Hecker, L., de Wit, T.D., Le Fèvre, C., Bruinsma, S.: 2017, Solar radio proxies for improved satellite orbit prediction. J. Space Weather Space Clim. 7, A35.

    Article  Google Scholar 

  • Yeates, A., Mackay, D., van Ballegooijen, A.: 2007, Modelling the global solar corona: filament chirality observations and surface simulations. Solar Phys. 245, 87.

    Article  ADS  Google Scholar 

  • Zhang, W., Zhao, X., Feng, X., Liu, C., Xiang, N., Li, Z., Lu, W.: 2022, Predicting the daily 10.7-cm solar radio flux using the long short-term memory method. Universe 8, 30.

    Article  ADS  Google Scholar 

  • Zhao, J., Han, Y.-B.: 2008, Historical dataset reconstruction and a prediction method of solar 10.7 cm radio flux. Chin. J. Astron. Astrophys. 8, 472.

    Article  ADS  Google Scholar 

  • Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: 2021, Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence 35, 11106.

    Google Scholar 

Download references

Acknowledgments

We greatly acknowledge British Geological Survey (BGS), Collecte Localisation Satellites (CLS), and the Space Weather Prediction Center (SWPC), as well as the National Geophysical Data Center (NOAA) for providing the data necessary to carry out this work. We also thank Haoyi Zhou, the creator of the Informer model, for answering questions on the details and providing valuable insights.

Funding

This work is jointly supported by National Key Research and Development Program of China (grant No. 2022YFF0503904), the National Natural Science Foundation of China (grant No. 42074205), Guangdong Basic and Applied Basic Research Foundation (grant No. 2023B1515040021), Hong Kong–Macao Exchange Project of Harbin Institute of Technology, Shenzhen Key Laboratory Launching Project (grant No. ZDSYS20210702140800001), Shenzhen Science and Technology Program (grant No. JCYJ20220818102401003) and the Science and Technology Development Fund of Macao SAR (File No. 0048/2021/A).

Author information

Authors and Affiliations

  1. Shenzhen Key Laboratory of Numerical Prediction for Space Storm, Institute of Space Science and Applied Technology, Harbin Institute of Technology, Shenzhen, China

    Kun Zhang, Pingbing Zuo, Xueshang Feng, Yanshi Huang, Fengsi Wei & Hao Yang

  2. Key Laboratory of Solar Activity and Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing, China

    Kun Zhang, Pingbing Zuo, Xueshang Feng, Yanshi Huang, Fengsi Wei & Hao Yang

  3. State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology, Macau, China

    Zhengyang Zou

  4. Intelligence & Space Tech, Inc., Beijing, China

    Shijin Wang

Authors
  1. Kun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pingbing Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhengyang Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xueshang Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yanshi Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shijin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Fengsi Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Zhang, Zuo and Zou wrote the main manuscript text and Feng prepared Figures 1-5. Huang and Wang prepared Figures 5-10. Wei and Yang prepared Tables 1-4. All authors reviewed the manuscript.

Corresponding author

Correspondence to Pingbing Zuo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, K., Zuo, P., Zou, Z. et al. Forecasting Medium-Term F10.7 Using the Deep-Learning Informer Model. Sol Phys 299, 47 (2024). https://doi.org/10.1007/s11207-024-02284-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11207-024-02284-0

Keywords

Search

Navigation