This project analyzes global climate data to identify countries experiencing the most drastic changes in climate patterns—such as temperature, rainfall and many more.
This project uses climate data provided by the National Centers for Environmental Information (NCEI), NOAA.
Data Source:
NCEI Climate Data Online (CDO) API
Cite as:
Menne, Matthew J., Imke Durre, Bryant Korzeniewski, Shelley McNeill, Kristy Thomas, Xungang Yin, Steven Anthony, Ron Ray, Russell S. Vose, Byron E.Gleason, and Tamara G. Houston (2012): Global Historical Climatology Network - Daily (GHCN-Daily), Version 3. [indicate subset used]. NOAA National Climatic Data Center. doi:10.7289/V5D21VHZ [access date].
Publications citing this dataset should also cite:
Matthew J. Menne, Imke Durre, Russell S. Vose, Byron E. Gleason, and Tamara G. Houston, 2012: An Overview of the Global Historical Climatology Network-Daily Database. J. Atmos. Oceanic Technol., 29, 897-910. doi:10.1175/JTECH-D-11-00103.1
Thank you to the NOAA NCEI team for making this valuable data publicly available!
- All notebooks and scripts assume a
data/folder exists in your project root. - If you’re using Google Colab, upload the
data/folder before running any notebooks. - The data is already provided in this repository for convenience.
(If you want to collect new data, see below.)
- Run the notebooks in order:
The workflow assumes you execute notebooks serially, from00_...ipynbto10_...ipynb. - Each notebook builds on the outputs of the previous one.
- Data is collected from:
https://www.ncdc.noaa.gov/cdo-web/search - API Key Required:
To collect new data, you must obtain an API key from the NOAA website. - Place your API key in a
.envfile as described in the code. - Run the scripts in
src/data/to fetch new data.
- For detailed information about climate parameters, columns, and their meanings, please refer to the NOAA documentation. For further details about the parameters, see http://www.ncei.noaa.gov/pub/data/cdo/documentation/GSOY_documentation.doc .
- Riskiest countries:
Countries identified as "riskiest" are those that have suffered the most drastic changes in climate patterns—either increases or decreases in temperature, rainfall, and other factors—over the two decades analyzed. - Antarctica and similar regions:
Even a slight increase in temperature in regions like Antarctica is significant. We calculated percentage change to fairly compare all countries and selected the riskiest accordingly. - Disclaimer:
These calculations are not official and may be subject to error. Even small modifications in methodology or data can alter the results.
- Most of the logic and analysis was designed by me.
- Some code generation and automation was assisted by Google Gemini.
data/— Raw and processed climate data (required for all notebooks).models/— Saved models (if applicable).reports/— Generated figures and reports.src/— Source code for data collection and analysis.notebooks/— Jupyter/Colab notebooks (run in order).
This project is intended for educational and research purposes.
Feel free to fork, adapt, or suggest improvements!