Articles | Volume 24, issue 5
https://doi.org/10.5194/nhess-24-1657-2024
https://doi.org/10.5194/nhess-24-1657-2024
Research article
 | 
14 May 2024
Research article |  | 14 May 2024

Application of the teaching–learning-based optimization algorithm to an analytical model of thunderstorm outflows to analyze the variability of the downburst kinematic and geometric parameters

Andi Xhelaj and Massimiliano Burlando

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Cited articles

Abd-Elaal, E. S., Mills, J. E., and Ma, X.: A coupled parametric-CFD study for determining ages of downbursts through investigation of different field parameters, J. Wind Eng. Ind. Aerod., 123, 30–42, 2013. 
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Amato, F., Guignard, F., Robert, S., and Kanevski, M.: A novel framework for spatio-temporal prediction of environmental data using deep learning, Sci. Rep.-UK, 10, 22243, https://doi.org/10.1038/s41598-020-79148-7, 2020. 
Bjerknes, J. and Solberg, H.: Life cycle of cyclones and polar front theory of atmospheric circulation, Geophysiks Publikationer, 3, 3–18, 1922. 
Bogensperger, A. and Fabel, Y.: A practical approach to cluster validation in the energy sector, Energy Inform, 4, 18, https://doi.org/10.1186/s42162-021-00177-1, 2021. 
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The study provides an in-depth analysis of a severe downburst event in Sânnicolau Mare, Romania, utilizing an analytical model and optimization algorithm. The goal is to explore a multitude of generating solutions and to identify potential alternatives to the optimal solution. Advanced data analysis techniques help to discern three main distinct storm scenarios. For this particular event, the best overall solution from the optimization algorithm shows promise in reconstructing the downburst.
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