nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

StormSeeker: A Machine-Learning-Based Mediterranean Storm Tracer

verfasst von : Raffaele Montella, Diana Di Luccio, Angelo Ciaramella, Ian Foster

Erschienen in: Internet and Distributed Computing Systems

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Rough–Fuzzy Entropy in Neighbourhood Characterization

Nächstes Kapitel Smart Cities and Open WiFis: When Android OS Permissions Cease to Protect Privacy

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_114CrossRef

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

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

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)CrossRef

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

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)CrossRef

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

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)CrossRef

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_8CrossRef

10.

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

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

16.

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

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)CrossRef

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)CrossRef

19.

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

20.

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

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)

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)CrossRef

23.

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

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)CrossRef

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)

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)

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)

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)

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)

30.

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

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_2CrossRef

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)

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)

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) CrossRef

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)CrossRef

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)

Titel: StormSeeker: A Machine-Learning-Based Mediterranean Storm Tracer
verfasst von: Raffaele Montella
Diana Di Luccio
Angelo Ciaramella
Ian Foster
Verlag: Springer International Publishing
Buch: Internet and Distributed Computing Systems
Print ISBN: 978-3-030-34913-4

Electronic ISBN: 978-3-030-34914-1

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-34914-1_42

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner