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Tropopause Detection and Analysis in the Mid Latitude Region

Mohammed El Abdioui

TL;DR

The study addresses tropopause detection in mid-latitudes under complex terrain and extreme events by comparing classical methods (WMO thermal and 2 PVU dynamical) with stability, hygropause, and hybrid approaches using ERA5 data. A Python-based automated framework validates detections against Water Vapor 6.2 μm imagery, with the hybrid method delivering the best overall agreement (mean SSIM = $0.5327$) and robust performance across varied conditions, notably during the October 2024 DANA event. The work demonstrates the value of multi-criteria tropopause identification for improved forecasting in regions like Southeast Morocco, enabling better interpretation of upper-level dynamics and moisture boundaries. These methodological advancements and validated tools offer a practical foundation for enhanced early warning systems and operational meteorology in North Africa and adjacent regions.

Abstract

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 μm 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.

Tropopause Detection and Analysis in the Mid Latitude Region

TL;DR

The study addresses tropopause detection in mid-latitudes under complex terrain and extreme events by comparing classical methods (WMO thermal and 2 PVU dynamical) with stability, hygropause, and hybrid approaches using ERA5 data. A Python-based automated framework validates detections against Water Vapor 6.2 μm imagery, with the hybrid method delivering the best overall agreement (mean SSIM = ) and robust performance across varied conditions, notably during the October 2024 DANA event. The work demonstrates the value of multi-criteria tropopause identification for improved forecasting in regions like Southeast Morocco, enabling better interpretation of upper-level dynamics and moisture boundaries. These methodological advancements and validated tools offer a practical foundation for enhanced early warning systems and operational meteorology in North Africa and adjacent regions.

Abstract

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 μm 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.

Paper Structure

This paper contains 58 sections, 10 equations, 29 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methods and Data
  3. Classical Tropopause Detection Methods
  4. Thermal (WMO Lapse-Rate Definition)
  5. Dynamical (2 PVU Criterion)
  6. New Tropopause Detection Methods
  7. Stability-Based Approach
  8. Humidity / Hygropause Criterion
  9. Hybrid Approach (Stability + Humidity)
  10. Data and Tools
  11. ERA5 Reanalysis
  12. Model Context
  13. Python-Based Automated Comparison Framework
  14. Reference Satellite Data
  15. Results and Discussion
  16. ...and 43 more sections

Figures (29)

  • Figure 1: Flood impacts in Valencia, Spain, during the October 2024 DANA event.
  • Figure 2: Monthly mean tropopause height (January 2010) using the dynamical (2 PVU) method. The thermal (WMO) tropopause is included as reference.
  • Figure 3: Monthly mean tropopause height (January 2010) using the stability method. The thermal (WMO) tropopause is included as reference.
  • Figure 4: Monthly mean tropopause height (January 2010) using the hygropause method. The thermal (WMO) tropopause is included as reference.
  • Figure 5: Monthly mean tropopause height (January 2010) using the hybrid method. The thermal (WMO) tropopause is included as reference.
  • ...and 24 more figures