wass2s 0.1.2

A Python package for seasonal climate forecast.

wass2s: A python-based tool for seasonal climate forecast

wass2s is a comprehensive tool developed to enhance the accuracy and reproducibility of seasonal forecasts in West Africa and the Sahel. This initiative aligns with the World Meteorological Organization's (WMO) guidelines for objective, operational, and scientifically rigorous seasonal forecasting methods.

Overview

The wass2s tool is designed to facilitate the generation of seasonal forecasts using various statistical and machine learning methods including the Exploration of AI methods. It helps forecaster to download data, build models, verify the models, and forecast. A user-friendly jupyter-lab notebook streaming the process of prevision.

🚀 Features

• ✅ Automated Forecasting: Streamlines the seasonal forecasting process, reducing manual interventions.
• 🔄 Reproducibility: Ensures that forecasts can be consistently reproduced and evaluated.
• 📊 Modularity: Highly modular tool. Users can easily customize and extend the tool to meet their specific needs.
• 🤖 Exploration of AI and Machine Learning: Investigates the use of advanced technologies to further improve forecasting accuracy.

📥 Installation

1. Download and Install miniconda

• For Windows, download the executable here

• For Linux (Ubuntu), in the terminal run:

  sudo apt-get update
  sudo apt-get upgrade
  sudo apt-get install wget
  wget -c -r https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh --no-check-certificate
  bash Miniconda3-latest-Linux-x86_64.sh
  

2. Create an environment and activate

• For Windows: download yaml here and run

conda env create -f WAS_S2S_windows.yml
conda activate WASS2S

• For Linux: download yaml here and run

conda env create -f WAS_S2S_linux.yml
conda activate WASS2S

3. Install wass2s

pip install wass2s

4. Download notebooks for simulation git clone https://github.com/hmandela/WASS2S_notebooks.git

⚙️ Usage

Comprehensive usage guidelines, including data preparation, model configuration, and execution steps, are available in the WAS_S2S Training Documentation.

🤝 Contributing

We welcome contributions from the community to enhance the WAS_S2S tool. Please refer to our contribution guidelines for more information.

📜 License

This project is licensed under the GPL-3 License.

Contact

For questions or support, please open a Github issue.

Credits

• scikit-learn: scikit-learn
• EOF analysis: xeofs
• xcast: xcast
• xskillscore: xskillscore
• ... and many more!

🙌 Acknowledgments

I would like to express my sincere gratitude to all the participants of the job-training on the new generation of seasonal forecasts in West Africa and the Sahel.
Your valuable feedback has significantly contributed to the improvement of this tool. I look forward to continuing to receive your insights and, where possible, your contributions.
A seed has been planted within you—now, let’s grow it together.

We also extend our heartfelt thanks to the AICCRA project for supporting this development, and to Dr. Abdou ALI, Head of the Climate-Water-Meteorology Department at AGRHYMET RCC-WAS, for his guidance and support.

📖 For more detailed information, tutorials, and support, please visit the WAS_S2S Training Documentation.