hw1_patrick_seidel_sentinel5p_no2
Task Summary
For Homework 1, I will work with Sentinel-5P TROPOMI tropospheric NO₂ data accessed through the Copernicus Data Space Ecosystem (CDSE) using the Sentinel Hub Process API. The goal is to develop a clear and reproducible workflow that downloads a scientifically reusable subset of NO₂ data and demonstrates a small example analysis.
Dataset Choice
Sentinel-5P provides global daily measurements of atmospheric pollutants. Tropospheric NO₂ is well-studied in relation to anthropogenic activity, which makes it suitable for a simple but meaningful demonstration such as examining changes during the COVID-19 lockdown period. I will not work with raw L1/L2 data (too large and complex for this assignment) but instead download daily gridded NO₂ mosaics via the Process API. This represents a reasonable trade-off: the data are not overly processed like weekly summaries from the Statistical API, but still usable for later scientific experiments.
Planned Work
- Document search and evaluation of available Sentinel-5P NO₂ data sources within the CDSE.
- Implement a Jupyter notebook that:
- Sets up API access via Sentinel Hub.
- Downloads a test dataset (e.g., several daily mosaics for a European metropolitan region).
- Saves data in GeoTIFF or NetCDF format for later reuse.
- Performs a simple inspection/dummy analysis (e.g., daily averages or a small COVID-19 comparison plot).
- Create a README documenting data access, dataset description, API usage, and scalability considerations.
This issue will later be linked in the pull request when submitting the homework.
hw1_patrick_seidel_sentinel5p_no2
Task Summary
For Homework 1, I will work with Sentinel-5P TROPOMI tropospheric NO₂ data accessed through the Copernicus Data Space Ecosystem (CDSE) using the Sentinel Hub Process API. The goal is to develop a clear and reproducible workflow that downloads a scientifically reusable subset of NO₂ data and demonstrates a small example analysis.
Dataset Choice
Sentinel-5P provides global daily measurements of atmospheric pollutants. Tropospheric NO₂ is well-studied in relation to anthropogenic activity, which makes it suitable for a simple but meaningful demonstration such as examining changes during the COVID-19 lockdown period. I will not work with raw L1/L2 data (too large and complex for this assignment) but instead download daily gridded NO₂ mosaics via the Process API. This represents a reasonable trade-off: the data are not overly processed like weekly summaries from the Statistical API, but still usable for later scientific experiments.
Planned Work
This issue will later be linked in the pull request when submitting the homework.