About the CPFA Project

CPFA (Climate Prediction For All) is an open-source project designed to make climate prediction more accessible to undergraduate students, researchers, and non-experts. Traditional numerical weather prediction systems require complex data preprocessing, difficult environment setup, and substantial domain knowledge to extract meaningful insights. CPFA addresses these challenges by providing a simplified, end-to-end workflow built on top of the Pangu-Weather model.

CPFA enables users to easily perform climate predictions, visualize the results, and evaluate model performance with minimal setup. Its primary goal is to lower the entry barrier for climate data analysis and support learning, experimentation, and rapid prototyping.

Project Goals

CPFA was created to solve several common problems in climate prediction:

  • Complicated workflows involving multiple tools and datasets

  • High computational and domain expertise requirements

  • Lack of beginner-friendly documentation and examples

  • Fragmented processes for prediction, visualization, and model evaluation

By integrating all necessary steps into a unified workflow, CPFA allows users to focus on understanding climate patterns rather than struggling with infrastructure complexities.

Core Features

CPFA offers the following key capabilities:

  • Fast execution of Pangu-Weather-based climate predictions

  • Beginner-friendly data preparation, including guides for ERA5 downloads

  • End-to-end workflow support, from input processing to visualization

  • Model performance evaluation, allowing comparison with ERA5 dataset

  • Windows-friendly setup process, with detailed environment instructions

  • Clear documentation and examples, enabling efficient learning

These features make CPFA a practical educational and research tool for users who want to explore climate data without advanced technical backgrounds.

Workflow Overview

CPFA follows a four-stage workflow that reflects the complete climate prediction process:

  1. Data Collection Users gather ERA5 or other supported climate datasets and prepare input files in the required format.

  2. Climate Prediction (Pangu-Weather) CPFA runs inference using ONNX-based Pangu-Weather models (1-hour, 3-hour, 6-hour, and 24-hour prediction intervals).

  3. Visualization Spatial and temporal results are displayed through maps, graphs, and comparative plots.

  4. Model Evaluation Predictions can be compared against ERA5 ground truth to evaluate accuracy.

This consistent structure provides a smooth learning curve and supports both academic understanding and practical experimentation.

Who Is CPFA For?

CPFA is designed for:

  • Students learning climate science or machine learning

  • Researchers exploring weather prediction models

  • Developers needing a lightweight prediction and visualization framework

  • Educators who want a simple demonstration tool

  • Anyone curious about climate patterns and prediction technologies

The project emphasizes readability, usability, and modular design to make it suitable for multi-disciplinary audiences.

Licensing

CPFA is distributed under the Apache License 2.0, allowing both academic and commercial use while maintaining open-source contributions.

Acknowledgments

CPFA builds upon open datasets such as ERA5 and state-of-the-art prediction models including Pangu-Weather. We thank the open-source community whose tools and research make this project possible.