This Jupyter notebook analyzes atmospheric data using ERA5 reanalysis files. It calculates tropopause heights using multiple methods, including classical and internship-developed methods: stability, hygropause, and hybrid approaches. The analysis helps visualize global tropopause patterns, vertical profiles, and method comparisons, giving researchers insights into the structure and variability of the tropopause.
- netCDF4: Core library for reading and handling ERA5 NetCDF files.
- xarray: For working with multidimensional ERA5 data arrays.
- numpy: Numerical operations and array manipulations.
- matplotlib.pyplot: 2D visualizations of tropopause data.
- matplotlib.colors / matplotlib.patches / matplotlib.ticker: Custom colormaps, legends, and axis formatting.
- cartopy.crs / cartopy.feature: Geospatial projections and map features (coastlines, borders).
- scipy.ndimage: Gaussian filtering to smooth data.
The notebook implements several tropopause detection methods:
- Thermal (WMO) method: Cold-point/lapse-rate tropopause from ERA5 temperature profiles.
- Dynamic (2 PVU) method: 2-PVU surface as the dynamical tropopause.
- Stability-based method: Uses atmospheric static stability to locate tropopause.
- Hygropause-based method: Identifies tropopause using humidity/moisture profiles.
- Hybrid methods: Combines multiple criteria (stability + hygropause) for robust detection.
Each method calculates tropopause height across all ERA5 grid points and produces visualizations for single time steps and monthly means.
The main Jupyter notebook, tropopause-detection-mapping.ipynb, includes:
- Data loading from ERA5 NetCDF files.
- Tropopause calculation using all detection methods.
- Visualization of global tropopause maps, vertical profiles, and method comparisons.
- Notes on challenges, failures, and lessons learned during method development.
- Output generation for both single snapshots and monthly mean analyses.
bash: pip install netCDF4 xarray numpy matplotlib cartopy metpy scipy
- Pressure-level data: temperature, geopotential, humidity
- Complete dataset: 2-PVU tropopause (optional for comparison)
See the Copernicus Climate Data Store (CDS) for instructions.
Place the downloaded NetCDF files in the data/ directory and update paths in the notebook if necessary.
Open tropopause-detection-mapping.ipynb and execute all cells. The notebook will:
- Load ERA5 data via netCDF4 and
xarray. - Compute tropopause heights using all methods.
- Generate global maps, vertical profiles, and comparison plots.
- Optionally calculate monthly means for selected periods.
These visualizations allow a side-by-side comparison of classical and new methods, highlighting differences and improvements from stability, hygropause, and hybrid approaches.
- Single-location tropopause detection along latitude or longitude.
- Comparison of tropopause heights for different methods.
- Classical and new methods produce consistent results in mid-latitudes.
- Tropical tropopause is higher than polar tropopause across all methods.
- Hybrid methods reduce local inconsistencies and smooth extreme values.
- Monthly mean analyses provide long-term trend insights for selected periods.
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For questions or help with this project, you can contact me at: mohamedabdioui0@gmail.com
Best regards,
Mohammed El Abdioui