Skip to main content
SpringerLink
Log in

StormSeeker: A Machine-Learning-Based Mediterranean Storm Tracer

  • Conference paper
  • First Online:
Internet and Distributed Computing Systems (IDCS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11874))

Included in the following conference series:

Abstract

The Mediterranean area is subject to a range of destructive weather events, including middle-latitudes storms, Mediterranean sub-tropical hurricane-like storms (“medicanes”), and small-scale but violent local storms. Although predicting large-scale atmosphere disturbances is a common activity in numerical weather prediction, the tasks of recognizing, identifying, and tracing trajectories of such extreme weather events within weather model outputs remains challenging. We present here a new approach to this problem, called StormSeeker, that uses machine learning techniques to recognize, classify, and trace the trajectories of severe storms in atmospheric model data. We report encouraging results detecting weather hazards in a heavy middle-latitude storm that struck the Ligurian coast in October 2018, causing disastrous damages to public infrastructure and private property.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Ascione, I., Giunta, G., Mariani, P., Montella, R., Riccio, A.: A grid computing based virtual laboratory for environmental simulations. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006. LNCS, vol. 4128, pp. 1085–1094. Springer, Heidelberg (2006). https://doi.org/10.1007/11823285_114

    Chapter  Google Scholar 

  2. Bengtsson, L., Hodges, K.I., Roeckner, E.: Storm tracks and climate change. J. Clim. 19(15), 3518–3543 (2006)

    Article  Google Scholar 

  3. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  4. Bosler, P., Roesler, E., Taylor, M., Mundt, M.: Stride search: a general algorithm for storm detection in high resolution climate data. Geosci. Model Dev. Discuss. 9, 1383–1398 (2016)

    Article  Google Scholar 

  5. Brandini, C., Perna, M., Taddei, S., Boninsegni, G., Cipriana, L.E.: Monitoring, risk forecasting and coastal planning in the region of Tuscany. In: Abstract Booklet, Convegno Gestione e Difesa delle Coste. Accademia Nazionale dei Lincei (2019). http://bit.ly/2NJiJ3e

  6. Cassola, F., Ferrari, F., Mazzino, A.: Numerical simulations of Mediterranean heavy precipitation events with the WRF model: a verification exercise using different approaches. Atmos. Res. 164, 210–225 (2015)

    Article  Google Scholar 

  7. Ciaramella, A., et al.: Interactive data analysis and clustering of genomic data. Neural Netw. 21(2–3), 368–378 (2008)

    Article  Google Scholar 

  8. Ciaramella, A., Gianfico, M., Giunta, G.: Compressive sampling and adaptive dictionary learning for the packet loss recovery in audio multimedia streaming. Multimed. Tools Appl. 75(24), 17375–17392 (2016)

    Article  Google Scholar 

  9. Ciaramella, A., Longo, G., Staiano, A., Tagliaferri, R.: NEC: a hierarchical agglomerative clustering based on fisher and negentropy information. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds.) NAIS/WIRN -2005. LNCS, vol. 3931, pp. 49–56. Springer, Heidelberg (2006). https://doi.org/10.1007/11731177_8

    Chapter  Google Scholar 

  10. Ciaramella, A., Staiano, A.: On the role of clustering and visualization techniques in gene microarray data. Algorithms 12(6), 123 (2019)

    Article  MathSciNet  Google Scholar 

  11. Claud, C., Alhammoud, B., Funatsu, B.M., Chaboureau, J.P.: Mediterranean hurricanes: large-scale environment and convective and precipitating areas from satellite microwave observations. Natural Hazards Earth Syst. Sci. 10(10), 2199 (2010)

    Article  Google Scholar 

  12. Demaria, M., Aberson, S.D., Ooyama, K.V., Lord, S.J.: A nested spectral model for hurricane track forecasting. Mon. Weather Rev. 120(8), 1628–1643 (1992)

    Article  Google Scholar 

  13. Demaria, M., Jones, R.W.: Optimization of a hurricane track forecast model with the adjoint model equations. Mon. Weather Rev. 121(6), 1730–1745 (1993)

    Article  Google Scholar 

  14. Di Luccio, D., Benassai, G., Budillon, G., Mucerino, L., Montella, R., Pugliese Carratelli, E.: Wave run-up prediction and observation in a micro-tidal beach. Natural Hazards Earth Syst. Sci. 18(11), 2841–2857 (2018)

    Article  Google Scholar 

  15. Di Luccio, D., et al.: Monitoring and modelling coastal vulnerability and mitigation proposal for an archaeological site (Kaulonia, Southern Italy). Sustainability 10(6), 2017 (2018)

    Article  Google Scholar 

  16. Emanuel, K.: Genesis and maintenance of Mediterranean hurricanes. Adv. Geosci. 2, 217–220 (2005)

    Article  Google Scholar 

  17. Gaertner, M.Á., et al.: Simulation of medicanes over the Mediterranean Sea in a regional climate model ensemble: impact of ocean-atmosphere coupling and increased resolution. Clim. Dyn. 51(3), 1041–1057 (2018)

    Article  Google Scholar 

  18. Gascón, E., Laviola, S., Merino, A., Miglietta, M.: Analysis of a localized flash-flood event over the central Mediterranean. Atmos. Res. 182, 256–268 (2016)

    Article  Google Scholar 

  19. Giorgi, F., Lionello, P.: Climate change projections for the Mediterranean region. Global Planet. Change 63(2–3), 90–104 (2008)

    Article  Google Scholar 

  20. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org

    MATH  Google Scholar 

  21. Kim, S., Kim, H., Lee, J., Yoon, S., Kahou, S.E., Kashinath, K., Prabhat: deep-hurricane-tracker: tracking and forecasting extreme climate events. In: IEEE Winter Conference on Applications of Computer Vision, pp. 1761–1769. IEEE (2019)

    Google Scholar 

  22. Krichak, S., Alpert, P.: Signatures of the NAO in the atmospheric circulation during wet winter months over the Mediterranean region. Theoret. Appl. Climatol. 82(1–2), 27–39 (2005)

    Article  Google Scholar 

  23. Kurihana, T., et al.: Cloud characterization with deep learning. In: 9th International Workshop on Climate Informatics (2019)

    Google Scholar 

  24. Lionello, P., Dalan, F., Elvini, E.: Cyclones in the Mediterranean region: the present and the doubled CO2 climate scenarios. Clim. Res. 22(2), 147–159 (2002)

    Article  Google Scholar 

  25. Lionello, P., et al.: Cyclones in the Mediterranean region: climatology and effects on the environment. In: Developments in Earth and Environmental Sciences, vol. 4, pp. 325–372. Elsevier (2006)

    Google Scholar 

  26. Montella, R., Di Luccio, D., Kosta, S.: DagOn*: executing direct acyclic graphs as parallel jobs on anything. In: IEEE/ACM Workshop on Workflows in Support of Large-Scale Science, pp. 64–73. IEEE (2018)

    Google Scholar 

  27. Montella, R., Di Luccio, D., Troiano, P., Riccio, A., Brizius, A., Foster, I.: WaComM: a parallel water quality community model for pollutant transport and dispersion operational predictions. In: 12th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), pp. 717–724. IEEE (2016)

    Google Scholar 

  28. Murray, R.J., Simmonds, I.: A numerical scheme for tracking cyclone centres from digital data. Part II: application to January and July general circulation model simulations. Aust. Meteorol. Mag. 39(3), 167–180 (1991)

    Google Scholar 

  29. Racah, E., Beckham, C., Maharaj, T., Kahou, S.E., Prabhat, Pal, C.: ExtremeWeather: a large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. In: Advances in Neural Information Processing Systems, pp. 3402–3413 (2017)

    Google Scholar 

  30. Romero, R., Emanuel, K.: Medicane risk in a changing climate. J. Geophys. Res.: Atmos. 118(12), 5992–6001 (2013)

    Google Scholar 

  31. Scholz, M., Fraunholz, M., Selbig, J.: Nonlinear principal component analysis: neural network models and applications. In: Gorban, A.N., Kégl, B., Wunsch, D.C., Zinovyev, A.Y. (eds.) Principal manifolds for data visualization and dimension reduction, vol. 58, pp. 44–67. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-73750-6_2

    Chapter  Google Scholar 

  32. Shen, B.W., et al.: Hurricane forecasts with a global mesoscale-resolving model: preliminary results with Hurricane Katrina (2005). Geophys. Res. Lett. 33(13), (2006)

    Google Scholar 

  33. Staiano, A., et al.: Probabilistic principal surfaces for yeast gene microarray data mining. In: 4th IEEE International Conference on Data Mining, pp. 202–208 (2004)

    Google Scholar 

  34. Trigo, I.F., Davies, T.D., Bigg, G.R.: Objective climatology of cyclones in the Mediterranean region. J. Clim. 12(6), 1685–1696 (1999)

    Article  Google Scholar 

  35. Xie, L., Bao, S., Pietrafesa, L.J., Foley, K., Fuentes, M.: A real-time hurricane surface wind forecasting model: formulation and verification. Mon. Weather Rev. 134(5), 1355–1370 (2006)

    Article  Google Scholar 

  36. Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)

    Google Scholar 

Download references

Acknowledgments

This research was supported by project PAUN (ex RIPA PON03PE_00164) and DOE Contract DE-AC02-06CH11357. We are grateful to the University of Napoli “Parthenope” forecast service (http://meteo.uniparthenope.it) for know-how and HPC facilities.

Author information

Authors and Affiliations

  1. Science and Technologies Department, University of Naples “Parthenope”, Naples, Italy

    Raffaele Montella, Diana Di Luccio & Angelo Ciaramella

  2. Computer Science Department, University of Chicago, Chicago, USA

    Ian Foster

  3. Data Science and Learning Division, Argonne National Laboratory, Argonne, USA

    Ian Foster

Authors
  1. Raffaele Montella
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diana Di Luccio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Angelo Ciaramella
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ian Foster
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raffaele Montella .

Editor information

Editors and Affiliations

  1. Department of Science and Technology, Parthenope University of Naples, Napoli, Italy

    Raffaele Montella

  2. Parthenope University of Naples, Napoli, Italy

    Angelo Ciaramella

  3. University of Calabria, Rende, Italy

    Giancarlo Fortino

  4. ICAR, Consiglio Nazionale delle Ricerche, Rende, Cosenza, Italy

    Antonio Guerrieri

  5. Edinburgh Napier University, Edinburgh, UK

    Antonio Liotta

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Montella, R., Di Luccio, D., Ciaramella, A., Foster, I. (2019). StormSeeker: A Machine-Learning-Based Mediterranean Storm Tracer. In: Montella, R., Ciaramella, A., Fortino, G., Guerrieri, A., Liotta, A. (eds) Internet and Distributed Computing Systems . IDCS 2019. Lecture Notes in Computer Science(), vol 11874. Springer, Cham. https://doi.org/10.1007/978-3-030-34914-1_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34914-1_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34913-4

  • Online ISBN: 978-3-030-34914-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation