This study presents an advanced framework for tropopause detection and analysis using ERA5 reanalysis data, with particular application to extreme meteorological events affecting Morocco and Southern Europe. The research implements and compares multiple detection methodologies, including classical approaches (thermal/WMO and dynamical/1.5 PVU criteria) alongside novel hybrid techniques combining stability and humidity parameters originally developed by ECMWF. Through systematic validation against January 2010 monthly means and a detailed case study of the October 2024 DANA (Depression Aislada en Niveles Altos) event, this work demonstrates the superior performance of hybrid approaches in capturing tropopause dynamics. The development of an automated Python-based comparison tool enabled a quantitative evaluation against Water Vapor 6.2 {\mu}m satellite imagery, revealing that the hybrid method achieved the highest structural similarity (mean SSIM: 0.5327) and the most consistent performance across the studied meteorological conditions. The findings highlight the critical role of accurate tropopause detection in improving forecasting capabilities for extreme weather events, particularly in topographically complex regions like Southeast Morocco. This research contributes both methodological advancements in tropopause identification and practical insights for operational meteorology, providing a foundation for enhanced early warning systems and improved understanding of atmospheric processes governing high-impact weather phenomena.