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Tropopause Detection and Mapping

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.

Libraries Used

  • 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.

Tropopause Detection Methods

The notebook implements several tropopause detection methods:

  1. Thermal (WMO) method: Cold-point/lapse-rate tropopause from ERA5 temperature profiles.
  2. Dynamic (2 PVU) method: 2-PVU surface as the dynamical tropopause.
  3. Stability-based method: Uses atmospheric static stability to locate tropopause.
  4. Hygropause-based method: Identifies tropopause using humidity/moisture profiles.
  5. 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.

Internship Notebook

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.

Setup and Usage

1. Install Python libraries

bash: pip install netCDF4 xarray numpy matplotlib cartopy metpy scipy

2. Download ERA5 Data

  • Pressure-level data: temperature, geopotential, humidity
  • Complete dataset: 2-PVU tropopause (optional for comparison)

See the Copernicus Climate Data Store (CDS) for instructions.

3. Prepare Data

Place the downloaded NetCDF files in the data/ directory and update paths in the notebook if necessary.

4. Run the Notebook

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.

Comparison of Tropopause Detection Methods

These visualizations allow a side-by-side comparison of classical and new methods, highlighting differences and improvements from stability, hygropause, and hybrid approaches.

Vertical Profiles

  • Single-location tropopause detection along latitude or longitude.
  • Comparison of tropopause heights for different methods.

Results Confirmed

  • 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.

References

ECMWF. (2024). Reintroducing analysis humidity in the stratosphere. Retrieved from https://www.ecmwf.int/en/newsletter/183/earth-system-science/reintroducing-analysis-humidity-stratosphere

ECMWF. (2024). IFS Documentation - Cy49r1, Part IV: Physical Processes. Operational implementation from 12 November 2024. Retrieved from https://www.ecmwf.int/sites/default/files/elibrary/112024/81626-ifs-documentation-cy49r1-part-iv-physical-processes.pdf

Elmer, N. J., Berndt, E., Jedlovec, G., & Fuell, K. (2019). Limb Correction of Geostationary Infrared Imagery in Clear and Cloudy Regions to Improve Interpretation of RGB Composites for Real-Time Applications. Journal of Atmospheric and Oceanic Technology, 36(8), 1675–1690. https://doi.org/10.1175/JTECH-D-18-0206.1

Hoskins, B. J., McIntyre, M. E., & Robertson, A. W. (1985). On the use and significance of isentropic potential vorticity maps. Quarterly Journal of the Royal Meteorological Society, 111(470), 877–946. https://doi.org/10.1002/qj.49711147002

ECMWF. (2024). ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Retrieved from https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5

European Centre for Medium-Range Weather Forecasts (ECMWF). (2024). Official website. Retrieved from https://www.ecmwf.int

Santer, B. D., Wehner, M. F., Wigley, T. M. L., Sausen, R., Meehl, G. A., Taylor, K. E., ... & Branson, M. (2004). Contributions of anthropogenic and natural forcing to recent tropopause height changes. Science, 301(5632), 479–483. https://doi.org/10.1126/science.1084123

World Meteorological Organization (WMO). (1957). Definition of the tropopause. WMO Bulletin, 6(4), 136–137.

Hoinka, K. P. (1998). Statistics of the global tropopause pressure. Monthly Weather Review, 126(12), 3303–3325. https://doi.org/10.1175/1520-0493(1998)126<3303:SOTGTP>2.0.CO;2

ECMWF. (2023). Hybrid tropopause detection methods in the IFS. ECMWF Technical Memorandum No. 987.

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612. https://doi.org/10.1109/TIP.2003.819861

Gonzalez, R. C., & Woods, R. E. (2018). Digital Image Processing (4th ed.). Pearson Education. Chapter 5: Image Restoration and Reconstruction.

Lewis, J. P. (1995). Fast normalized cross-correlation. Vision Interface, 10(1), 120–123.

Hore, A., & Ziou, D. (2010). Image quality metrics: PSNR vs. SSIM. In 2010 20th International Conference on Pattern Recognition (pp. 2366–2369). IEEE. https://doi.org/10.1109/ICPR.2010.579

Theodoridis, S., & Koutroumbas, K. (2008). Pattern Recognition (4th ed.). Academic Press. Chapter 7: Feature Generation II.

Szeliski, R. (2010). Computer Vision: Algorithms and Applications. Springer Science & Business Media. Chapter 8: Dense Motion Estimation.

Oppenheim, A. V., & Schafer, R. W. (2009). Discrete-Time Signal Processing (3rd ed.). Pearson Education. Chapter 7: Filter Design Techniques.

Contact

For questions or help with this project, you can contact me at: mohamedabdioui0@gmail.com

Best regards,
Mohammed El Abdioui