climate-analysis 0.1.11

The Climate Analysis Package is a powerful tool designed for analyzing climate data with a focus on generating time-series visualizations and map-based insights.

Climate Analysis Package

Overview

The Climate Analysis Package is a powerful tool designed for analyzing climate data with a focus on generating time-series visualizations and map-based insights. This package supports processing CMIP6 data, calculating regional means, and visualizing climate anomalies, making it ideal for climate scientists, researchers, and data analysts.

Example pngs:

• Near surface temperature changes w.r.t. 1981-2010 reference

• SSP585 w.r.t. 1981-2010 at the end of the century 2071-2099

Features

• Time-Series Analysis: Generate detailed time-series plots of temperature or other variables over time for specified experiments.
• Map Visualizations: Create spatial maps of climate variables, regridded to a specified resolution.
• Customizable: Set your own region of interest, experiments, and climatological baselines.
• Automated Processing: Handles CMIP6 datasets, including preprocessing and anomaly computation.

Installation

• via pypi :

pip install climate-analysis

• via github:

1. Clone the repository:

   git clone https://github.com/bijanf/climate_analysis.git
   

2. Navigate to the package directory:

   cd climate_analysis_package
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Install the package:

   python setup.py install
   

Usage

The package provides two main functionalities: time-series plots and map-based visualizations.

Time-Series Plots

Generate time-series plots to visualize climate variable anomalies over time.

Example

generate-time-series \
    --catalog_url https://storage.googleapis.com/cmip6/pangeo-cmip6.json \
    --experiments historical ssp126 ssp245 ssp370 ssp585 \
    --lat_range 30 60 \
    --lon_range 30 90 \
    --climatology_start 1981 \
    --climatology_end 2010 \
    --output time_series_plot.png \
    --variable tas

Output

• Plot: Saved as time_series_output.png.
• Model List: A text file time_series_output_models.txt containing all models used for the plot.
• df_all_processed.csv: A csv file of dataframe of time-series.

Map Visualizations

Generate map visualizations of regridded climate variable differences between scenarios.

Example

generate-maps \
    --catalog_url https://storage.googleapis.com/cmip6/pangeo-cmip6.json \
    --experiments ssp585 \
    --lat_range 35 57 \
    --lon_range 45 87 \
    --output climate_map.png

Output

• Map: Saved as map_output.png.

Contributing

We welcome contributions to the Climate Analysis Package! To contribute:

1. Fork the Repository:

   • Visit the GitHub repository and fork it to your account.

2. Clone Your Fork:

   git clone https://github.com/bijanf/climate_analysis.git
   

3. Create a New Branch:

   git checkout -b feature/your-feature-name
   

4. Make Changes:

   • Edit the code, add new features, or fix bugs.
   • Follow the existing code style and conventions.

5. Test Your Changes:

   • Ensure all tests pass by running NOT implemented yet :

     pytest
     

   • Add new tests if needed.

6. Commit Your Changes:

   git add .
   git commit -m "Add your descriptive commit message"
   

7. Push Your Changes:

   git push origin feature/your-feature-name
   

8. Create a Pull Request:

   • Go to the original repository and create a pull request from your branch.

Guidelines

• Code Style: Follow PEP 8 standards for Python code.
• Documentation: Update the README.md or docstrings as needed.
• Tests: Ensure new features or changes are covered by tests.

Key Parameters

• catalog_url: URL to the CMIP6 data catalog (e.g., https://storage.googleapis.com/cmip6/pangeo-cmip6.json).
• experiments: List of experiments to include (e.g., historical, ssp585).
• lat_range and lon_range: Latitude and longitude bounds for the region of interest.
• variable: Climate variable to analyze (e.g., tas, pr).
• climatology_start and climatology_end: Years for the climatology baseline.
• target_resolution: Spatial resolution for regridding (in degrees).

Keywords

• Climate Analysis
• CMIP6
• Regional Climate
• Climate Modeling
• Climate Visualization
• Time-Series Analysis
• Map Visualization

License

This package is licensed under the MIT License. See the LICENSE file for details.

Author

Bijan Fallah
Climate Scientist, Berlin
Linkedin