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StormSeeker: A Machine-Learning-Based Mediterranean Storm Tracer

  • Raffaele MontellaEmail author
  • Diana Di Luccio
  • Angelo Ciaramella
  • Ian Foster
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11874)

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.

Keywords

Machine learning Distributed computing Computational environmental data science Extreme weather forecast 

Notes

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.

References

  1. 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_114CrossRefGoogle Scholar
  2. 2.
    Bengtsson, L., Hodges, K.I., Roeckner, E.: Storm tracks and climate change. J. Clim. 19(15), 3518–3543 (2006)CrossRefGoogle Scholar
  3. 3.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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. 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)CrossRefGoogle Scholar
  7. 7.
    Ciaramella, A., et al.: Interactive data analysis and clustering of genomic data. Neural Netw. 21(2–3), 368–378 (2008)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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_8CrossRefGoogle Scholar
  10. 10.
    Ciaramella, A., Staiano, A.: On the role of clustering and visualization techniques in gene microarray data. Algorithms 12(6), 123 (2019)MathSciNetCrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 16.
    Emanuel, K.: Genesis and maintenance of Mediterranean hurricanes. Adv. Geosci. 2, 217–220 (2005)CrossRefGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 19.
    Giorgi, F., Lionello, P.: Climate change projections for the Mediterranean region. Global Planet. Change 63(2–3), 90–104 (2008)CrossRefGoogle Scholar
  20. 20.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.orgzbMATHGoogle Scholar
  21. 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. 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)CrossRefGoogle Scholar
  23. 23.
    Kurihana, T., et al.: Cloud characterization with deep learning. In: 9th International Workshop on Climate Informatics (2019)Google Scholar
  24. 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)CrossRefGoogle Scholar
  25. 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. 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. 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. 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. 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. 30.
    Romero, R., Emanuel, K.: Medicane risk in a changing climate. J. Geophys. Res.: Atmos. 118(12), 5992–6001 (2013)Google Scholar
  31. 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_2CrossRefGoogle Scholar
  32. 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. 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. 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) CrossRefGoogle Scholar
  35. 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)CrossRefGoogle Scholar
  36. 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

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Science and Technologies DepartmentUniversity of Naples “Parthenope”NaplesItaly
  2. 2.Computer Science DepartmentUniversity of ChicagoChicagoUSA
  3. 3.Data Science and Learning DivisionArgonne National LaboratoryArgonneUSA

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