Skip to main content
SpringerLink
Log in

Feedforward Deep Neural Network-Based Model for the Forecast of Severe Convective Wind

  • Conference paper
  • First Online:
Advances in Artificial Intelligence and Security (ICAIS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1422))

Included in the following conference series:

  • 1166 Accesses

Abstract

In this study, we compared and analyzed the environmental characteristics in Shaanxi between 2016–2018 (before and after the occurrence of convective wind) based on both severe and non-severe wind samples extracted from the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) data and automatic weather station data from the China Meteorological Administration. Through the establishment of deep feedforward neural networks, these environmental characteristics were then used to define a model for the forecast of severe convective wind events. For \(\sim\)75% (or > 75%) severe convective wind samples, the differences between 1 h after the occurrence of severe convective wind and when it occurred, 1 h after it occurred and 1 h before it occurred in MLCAPE, MUCAPE, SBCAPE, MLCIN, MUCIN, SBCIN, MLEL, MUEL, SBEL, SBLCL and temperature were negative, while those in MLLI, MULI, SBLI, sea level pressure (SLP), and precipitation were positive. This suggests that, for \(\sim\)75% (or > 75%) severe convective wind samples, atmospheric stability increased or SLP increased, or temperature decreased; moreover, precipitation was found to occur after severe convective wind. Finally, a certain degree of differentiation was noted between the parameters associated with the severe and non-severe convective wind samples. We designed two schemes (each containing three kinds of experiments) and trained a feedforward deep neural network to predict severe convective wind events. The network experiment including the higher number of elements was found to provide the highest threat score (Ts).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Fang, C., Zheng, Y., Lin, Y., Zhu, W.: Classification and characteristics of cloud patterns triggering regional thunderstorm high winds. Meteorol. Monthly 40(8), 905–915 (2014)

    Google Scholar 

  2. Klimowski, B.A., Bunkers, M.J., Hjelmfelt, M.R., Covert, J.N.: Severe convective windstorms over the northern high plains of the United States. Weather Forecast. 18(3), 502–519 (2003)

    Article  Google Scholar 

  3. Schoen, J.M., Ashley, W.S.: A Climatology of fatal convective wind events by storm type. Weather Forecast. 26(1), 109–121 (2011)

    Article  Google Scholar 

  4. Przybylinski, R.W.: The bow echo: observations, numerical simulations, and severe weather detection methods. Weather Forecast. 10(2), 203–218 (1995)

    Article  Google Scholar 

  5. Kanak, J., Benko, M., Simon, A., Sokol, A.: Case study of the 9 May 2003 windstorm in southwestern Slovakia. Atmos. Res. 83(2), 162–175 (2007)

    Article  Google Scholar 

  6. Lennard, C.: Simulating an extreme wind event in a topographically complex region. Bound.-Layer Meteorol. 153(2), 237–250 (2014). https://doi.org/10.1007/s10546-014-9939-x

    Article  Google Scholar 

  7. Wang, X., Zhou, X., Yu, X.: Comparative study of environmental characteristics of a windstorm and their impacts on storm structures. Acta Meteor. Sin. 71(5), 839–852 (2013)

    Google Scholar 

  8. Johns, R.H., Doswell, C.A., III.: Severe local storms forecasting. Weather Forecast. 7(4), 588–612 (1992)

    Article  Google Scholar 

  9. Cohen, A.E., Coniglio, M.C., Corfidi, S.F., Corfidi, S.J.: Discrimination of mesoscale convective system environments using sounding observations. Weather Forecast. 22(5), 1045–1062 (2007)

    Article  Google Scholar 

  10. Meng, Z., Yan, D., Zhang, Y.: General features of squall lines in East China. Mon. Weather Rev. 141(5), 1629–1647 (2013)

    Article  Google Scholar 

  11. Fei, H., Wang, X., Zhou, X., Yu, X.: Climatic characteristics and environmental parameters of severe thunderstroms gales in China. Meteorol. Monthly 42(12), 1513–1521 (2016)

    Google Scholar 

  12. Chen, G.T.J., Chou, H.C.: General characteristics of squall lines observed in TAMEX. Mon. Weather Rev. 121(3), 726–733 (1993)

    Article  Google Scholar 

  13. Kuchera, E.L., Parker, M.D.: Severe convective wind environments. Weather Forecast. 21(4), 595–612 (2006)

    Article  Google Scholar 

  14. Bentley, M.L., Mote, T.L., Byrd, S.F.: A synoptic climatology of derecho producing mesoscale convective systems in the North-Central Plains. Int. J. Climatol. 20(11), 1329–1349 (2000)

    Article  Google Scholar 

  15. Ashley, W.S., Mote, T.L., Bentley, M.L.: On the episodic nature of derecho-producing convective systems in the United States. Int. J. Climatol. 25(14), 1915–1932 (2010)

    Article  Google Scholar 

  16. Tang, W., Zhou, Q., Liu, X., Zhu, W., Mao, X.: Analysis on verification of national severe convective weather categorical forecasts. Meteorol. Monthly 43(1), 67–76 (2017)

    Google Scholar 

  17. Yang, X., Sun, J., Lu, R., Zhang, X.: Environmental Characteristics of severe convective wind over south China. Meteorol. Monthly 43(7), 769–780 (2017)

    Google Scholar 

  18. Pantillon, F., Lerch, S., Knippertz, P., Corsmeier, U.: Forecasting wind gusts in winter storms using a calibrated convection-permitting ensemble. Q. J. R. Meteorol. Soc. 144(715), 1864–1881 (2018)

    Article  Google Scholar 

  19. Ma, S., Wang, X., Yu, X.: Environmental parameter characteristics of severe wind with extreme thunderstorm. J. Appl. Meteorol. Sci. 30(3), 292–301 (2019)

    Google Scholar 

  20. Fang, C., Wang, X., Sheng, J., Cao, Y.: Temporal and spatial distribution of North China thunder-gust winds and the statistical analysis of physical characteristics. Plateau Meteorol. 36(5), 1368–1385 (2017)

    Google Scholar 

  21. Franch, G., Nerini, D., Pendesini, M., Coviello, L., Jurman, G., Furlanello, C.: Precipitation nowcasting with orographic enhanced stacked generalization: improving deep learning predictions on extreme events. Atmosphere 11(3), 267 (2020)

    Article  Google Scholar 

  22. Lu, Z., Ren, Y., Sun, X., Jia, H.: Recognition of short-time heavy rainfall based on deep learning. J. Tianjin Univ. (Sci. Technol.) 51(2), 111–119 (2018)

    Google Scholar 

  23. Wang, C., Liu, P., Jia, K., Jia, X.: Identification of weather phenomena based on lightweight convolutional neural networks. Comput. Mater. Continua 64(3), 2043–2055 (2020)

    Article  Google Scholar 

  24. Wang, S., Yu, X., Liu, L., Huang, J., Wong, T., Jiang, C.: An approach for radar quantitative precipitation estimation based on spatiotemporal network. Comput. Mater. Continua 65(1), 459–479 (2020)

    Article  Google Scholar 

  25. Yang, X., Sun, J., Zheng, Y.: A 5-yr climatology of severe convective wind events over China. Weather Forecast. 32(4), 1289–1299 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the China Meteorological Administration for the automatic weather station and lightning data, as well the ECMWF for the ERA5 data.

Author information

Authors and Affiliations

  1. Shaanxi Meteorological Observatory, Xi’an, China

    Yu Jing, Nan Wang, Qiang Zhao, Pingyun Li & Qiyuan Hu

Authors
  1. Yu Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pingyun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qiyuan Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jing, Y., Wang, N., Zhao, Q., Li, P., Hu, Q. (2021). Feedforward Deep Neural Network-Based Model for the Forecast of Severe Convective Wind. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1422. Springer, Cham. https://doi.org/10.1007/978-3-030-78615-1_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-78615-1_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78614-4

  • Online ISBN: 978-3-030-78615-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation