Forecasting of convective precipitation through NWP models and algorithm of storms prediction

Šaur, David

dc.title	Forecasting of convective precipitation through NWP models and algorithm of storms prediction	en
dc.contributor.author	Šaur, David
dc.relation.ispartof	Advances in Intelligent Systems and Computing
dc.identifier.issn	2194-5357 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn	978-3-319-57260-4
dc.date.issued	2017
utb.relation.volume	573
dc.citation.spage	125
dc.citation.epage	136
dc.event.title	6th Computer Science On-line Conference, CSOC 2017
dc.event.sdate	2017-04-26
dc.event.edate	2017-04-29
dc.type	conferenceObject
dc.language.iso	en
dc.publisher	Springer Verlag
dc.identifier.doi	10.1007/978-3-319-57261-1_13
dc.relation.uri	https://link.springer.com/chapter/10.1007/978-3-319-57261-1_13
dc.subject	Artificial intelligence	en
dc.subject	Convective precipitation	en
dc.subject	Crisis management	en
dc.subject	Early warning	en
dc.subject	Flash floods	en
dc.subject	Weather forecast	en
dc.description.abstract	This article focuses on contemporary possibilities of forecasting of convective storms which may cause flash floods. The first chapters are presented predictive tools such as numerical weather prediction models (NWP models) and the algorithm of convective storms prediction, which includes a storm prediction based on the principles of mathematical statistics, probability theory and artificial intelligence methods. Discussion section provides outputs from the success rate of these forecasting tools on the historical weather situation for the year 2016. The Algorithm’s output may be useful for early warning of population and notification of crisis management authorities before a potential threat of flash floods in the Zlin Region. © Springer International Publishing AG 2017.	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1007372
utb.identifier.obdid	43876745
utb.identifier.scopus	2-s2.0-85018673353
utb.identifier.wok	000405337000013
utb.source	d-scopus
dc.date.accessioned	2017-09-08T12:14:47Z
dc.date.available	2017-09-08T12:14:47Z
dc.description.sponsorship	IGA/FAI/2017/019, UTB, Univerzita Tomáše Bati ve Zlíně
dc.description.sponsorship	Internal Grant Agency of Tomas Bata University [IGA/FAU2017/019]
utb.contributor.internalauthor	Šaur, David
utb.fulltext.affiliation	David Šaur Tomas Bata University in Zlin, Faculty of Applied Informatics, Nad Stranemi 4511 saur@fai.utb.cz
utb.fulltext.dates	-
utb.fulltext.references	1. Březková, L., Šálek, M., Novák, P., Kyznarová, H., Jonov, M.: New Methods of Flash Flood Forecasting in the Czech Republic (2011) IFIP Advances in Information and Communication Technology, 359 AICT, pp. 550-557. doi:10.1007/978-3-642-22285-6_59 2. Zdenek, S., Dusan, V., Jan, S., Ivan, M., Miroslav, M.: Protection from Flash Floods (2015) Proceedings of the 26th International Business Information Management Association Conference - Innovation Management and Sustainable Economic Competitive Advantage: From Regional Development to Global Growth, IBIMA 2015, pp. 1359-1363. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976391745&partnerID=40&md5=923aa2f309578593d8b5e2cc503d02de 3. Hardy, J., Gourley, J.J., Kirstetter, P.-E., Hong, Y., Kong, F., Flamig, Z.L.: A Method for Probabilistic Flash Flood Forecasting (2016) Journal of Hydrology, 541, pp. 480-494.. doi: 10.1016/j.jhydrol.2016.04.007 4. Šaur D.: The Methodology Uses of Meteorological Radar of the Zlin Region for Crisis Management. Zlin, Czech Republic, 2016. 5. Ravazzani, G., Amengual, A., Ceppi, A., Homar, V., Romero, R., Lombardi, G., Mancini, M.: Potentialities of Ensemble Strategies for Flood Forecasting over the Milano Urban Area (2016) Journal of Hydrology, 539, pp. 237-253. doi: 10.1016/j.jhydrol.2016.05.023 6. Jolivet, S., Chane-Ming, F.: WRF Modelling of Turbulence Triggering Convective Thunderstorms over Singapore (2014) Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 125, pp. 115-122. doi: 10.1007/978-3-662-43489-5_14 7. Novák, P.: The Czech Hydrometeorological Institute's Severe Storm Nowcasting System. doi: 10.1016/j.atmosres.2005.09.014 8. Liechti, K., Panziera, L., Germann, U., Zappa, M.: The Potential of Radar-based Ensemble Forecasts for Flash Flood Early Warning in the Southern Swiss Alps (2013) Hydrology and Earth System Sciences, 17 (10), pp. 3853-3869. doi: 10.5194/hess-17-3853-2013 9. Lakshmanan, V., Crockett, J., Sperow, K., Ba, M., Xin, L.: Tuning AutoNowcaster Automatically (2012) Weather and Forecasting, 27 (6), pp. 1568-1579. doi: 10.1175/WAF-D-11-00141.1 10. Haiden, T., Steinheimer, M.: Improved Nowcasting of Precipitation Based on Convective Analysis Fields (2008) Precipitation: Advances in Measurement, Estimation and Prediction, pp. 389-417. doi: 10.1007/978-3-540-77655-0_15 11. Beheshti, Z., Firouzi, M., Shamsuddin, S.M., Zibarzani, M., Yusop, Z.: A New Rainfall Forecasting Model using the CAPSO Algorithm and an Artificial Neural Network (2016) Neural Computing and Applications, 27 (8), pp. 2551-2565. doi: 10.1007/s00521-015-2024-7 12. Young, C.-C., Liu, W.-C., Chung, C.-E.: Genetic Algorithm and Fuzzy Neural Networks Combined with the Hydrological Modeling System for Forecasting Watershed Runoff Discharge (2015) Neural Computing and Applications, 26 (7), pp. 1631-1643. Cited 2 times. doi: 10.1007/s00521-015-1832-0 13. Chai, S.S., Wong, W.K., Goh, K.L.: Backpropagation vs. Radial Basis Function Neural Model: Rainfall intensity classification for flood prediction using meteorology data (2016) Journal of Computer Science, 12 (4), pp. 191-200. doi: 10.3844/jcssp.2016.191.200 14. Meteorological Explanatory and Terminology Dictionary (EMS). Prague: Czech Meteorological Society (CMES), http://slovnik.cmes.cz 15. Baťka, M.: Projections for the Development Atmosphere by Objective Methods. Prague, Czech Republic, http://kfa.mff.cuni.cz/wp-content/uploads/2015/03/kniha.pdf 16. WeatherOnline, http://www.weatheronline.cz/cgi-bin/expertcharts?LANG=cz&CONT=czcz&MODELL=gfs&VAR=prec. 17. Šaur, D.: Comparison of Success Rate of Numerical Weather Prediction Models with Forecasting System of Convective Precipitation. Proceedings of the 5th Computer Science Online Conference 2016 (CSOC2016), Vol 1, Springer, pp.: 307-319. ISSN 2194-5357, ISBN 978-3-319-33623-7, doi: 10.1007/978-3-319-33625-1. 18. Šaur, D., Ďuricová, L.: Comprehensive System of Intense Convective Precipitation Forecasts for Regional Crisis Management, The Tenth International Conference on Emerging Security Information, System and Technologies, SECURWARE 2016, IARIA, July 24- 28, 2016, pp. 111-116, ISBN: 978-1-64208-493-0. 19. Predictive Analysis, https://www.gaussalgo.cz/prediktivni-analytika 20. Biological Algorithms (5) – Neural Networks: Learning – Backpropagation, https://www.root.cz/clanky/biologicke-algoritmy-5-neuronove-site/ 21. Predictive Analysis, https://www.gaussalgo.cz/prediktivni-analytika 22. An Introducton to Neural Networks: Back-propagation, https://www.ibm.com/developerworks/library/l-neural/ 23. Zacharov P., Diagnostic and Prognostic Precursors of Convection. (2004). Prague: Faculty of Mathematics and Physics UK, KMOP. 61 p https://is.cuni.cz/webapps/zzp/detail/44489/ 24. Calculation of the Pearson Correlation Coefficient, http://portal.matematickabiologie.cz/index.php?pg=aplikovana-analyza-klinickych-a-biologickych-dat--biostatistika-pro-matematickou-biologii--zaklady-korelacni-analyzy--pearsonuv-korelacni-koeficient--vypocet-pearsonova-korelacniho-koeficientu
utb.fulltext.sponsorship	This work was supported by the Internal Grant Agency of Tomas Bata University under the project No. IGA/FAI/2017/019 “Information Support of Crisis Management at the Regional Level”.