Discrete Climate Scenarios

Discrete Climate Scenarios is a Python repository designed to facilitate the download, processing, and bias adjustment of CMIP6 climate scenario data for individual weather station locations. This repository provides tools to streamline the retrieval of climate data and prepare it for further analysis at specific geographic locations.

Features

• Data Download: Automatically fetches CMIP6 climate scenario data from Google Earth Engine for user-specified time periods.
• Data Processing: Filters, cleans, and preprocesses the downloaded climate data to ensure consistency and accuracy.
• Bias Adjustment: Implements scaled distribution mapping for bias adjustment between observed and modeled climate data.
• Station-Specific Analysis: Generates climate scenarios tailored to individual weather station locations, allowing for localized climate analysis and impact assessments.
• Visualizations: Creates figures to assess impact and performance of the bias adjustment.

Installation

To install the Discrete Climate Scenarios package, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/discrete_climate_scenarios.git
   cd discrete_climate_scenarios

2. Install the required dependencies:

   pip install -r requirements.txt

3. Install the package:

   pip install .

Usage

1. Set up your configuration:

   • Create a config.ini file with the following content:

     [settings]
     input_dir = /path/to/your/input_dir
     output_dir = /path/to/your/output_dir
     buffer_radius = 2000
     show = True
     sd_factor = 2
     processes = 30
     download = True
     load_backup = False
     start_region_index = 0
     start_station_index = 0

2. Prepare your input directory structure:

   • The input directory should contain subdirectories for each region. Each region subdirectory should have two types of .xlsx files: precipitation data and temperature data. Each .xlsx file should have three time columns (year, month, day) and one column per weather station.
   • Additionally, each region subdirectory should contain a aws_coords.csv file with the station coordinates, formatted as follows:

     Name,Latitude,Longitude
     Pskem,41.97372000000,70.45579700000
     Chorvoq,41.61610000000,70.03475800000
     Oygaing,42.15526200000,70.86313900000
     Chimyon,41.52359600000,70.02613100000
     

3. Run the main script:

   python main.py

Google Earth Engine

This package utilizes the Google Earth Engine and its Python API to download climate data. Ensure you have set up your Google Earth Engine account and authenticated your environment.

Data Source

The package downloads data from the "NEX-GDDP-CMIP6: NASA Earth Exchange Global Daily Downscaled Climate Projections" dataset. More information can be found here.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

This project is part of the CLIMWATER project funded by the German Ministry for Education and Research (BMBF). It makes use of data from the CMIP6 (Coupled Model Intercomparison Project Phase 6) archive.