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Preprints
https://doi.org/10.5194/egusphere-2023-1683
https://doi.org/10.5194/egusphere-2023-1683
20 Sep 2023
 | 20 Sep 2023

Analysis of the phase space of the downburst that occurred on 25 June 2021 in Sânnicolau Mare (Romania)

Andi Xhelaj and Massimiliano Burlando

Abstract. Downbursts winds, characterized by strong, localized downdrafts and subsequent horizontal straight-line winds, presents significant risk to civil structures. The transient nature and limited spatial extent present measurements challenges, necessitating analytical models for accurate understanding and predicting their action on structures. This study analyzes the Sânnicolau Mare downburst event in Romania, from June 25, 2021, using a bi-dimensional analytical model coupled with the Teaching Learning Optimization Algorithm (TLBO). The intent is to understand the distinct solutions generated by the optimization algorithm and assess their physical validity. Supporting this examination is a damage survey and wind speed data recorded during the downburst event. Employed techniques include agglomerative hierarchical clustering with the K-means algorithm (AHK-MC) and principal component analysis (PCA) to categorize and interpret the solutions. Three main clusters emerge, each displaying different storm characteristics. Comparing the simulated maximum velocity with hail damage trajectories indicates that the optimal solution offers the best overlap, affirming its effectiveness in reconstructing downburst wind fields. However, these findings are specific to the Sânnicolau Mare event, underlining the need for a similar examination of multiple downburst events for broader validity.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Journal article(s) based on this preprint

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
Nat. Hazards Earth Syst. Sci., 24, 1657–1679, https://doi.org/10.5194/nhess-24-1657-2024,https://doi.org/10.5194/nhess-24-1657-2024, 2024
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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The study provides an in-depth analysis of a severe downburst event in Sânnicolau Mare, Romania,...
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