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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
Bengtsson, L., Hodges, K.I., Roeckner, E.: Storm tracks and climate change. J. Clim. 19(15), 3518–3543 (2006)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
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)
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)
Ciaramella, A., et al.: Interactive data analysis and clustering of genomic data. Neural Netw. 21(2–3), 368–378 (2008)
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)
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
Ciaramella, A., Staiano, A.: On the role of clustering and visualization techniques in gene microarray data. Algorithms 12(6), 123 (2019)
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)
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)
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)
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)
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)
Emanuel, K.: Genesis and maintenance of Mediterranean hurricanes. Adv. Geosci. 2, 217–220 (2005)
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)
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)
Giorgi, F., Lionello, P.: Climate change projections for the Mediterranean region. Global Planet. Change 63(2–3), 90–104 (2008)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org
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)
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)
Kurihana, T., et al.: Cloud characterization with deep learning. In: 9th International Workshop on Climate Informatics (2019)
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)
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)
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)
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)
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)
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)
Romero, R., Emanuel, K.: Medicane risk in a changing climate. J. Geophys. Res.: Atmos. 118(12), 5992–6001 (2013)
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
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)
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)
Trigo, I.F., Davies, T.D., Bigg, G.R.: Objective climatology of cyclones in the Mediterranean region. J. Clim. 12(6), 1685–1696 (1999)
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)
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)
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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
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)