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

Xhelaj, Andi; Burlando, Massimiliano

doi:https://doi.org/10.5194/nhess-24-1657-2024

Articles | Volume 24, issue 5

https://doi.org/10.5194/nhess-24-1657-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-24-1657-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 5

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

Data sets

Thunderstorm Simulation and Optimization A. Xhelaj https://doi.org/10.5281/zenodo.11110453

Model code and software

FactoMineR: An R Package for Multivariate Analysis (https://cran.r-project.org/package=FactoMineR) S. Lê et al. https://doi.org/10.18637/jss.v025.i01

Thunderstorm Simulation and Optimization A. Xhelaj https://doi.org/10.5281/zenodo.11110453

Short summary

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.