WindGrib

A Python library for downloading, reading, and processing meteorological data in GRIB format.

🌍 Overview

WindGrib focuses on efficient wind data extraction and targeted variable downloading from meteorological models. Key features include:

• Subset-Based Downloads: Download only specific variables using GRIB index files
• Automatic Latest Data: Retrieves the most recent available forecast data
• Smart Caching: Intelligent caching with incremental download completion
• Multi-Model Support: GFS Wave, ECMWF IFS, and custom model definitions
• AWS S3 Focused: Specifically designed for meteorological data hosted on Amazon S3
• High Performance: Asyncio-based downloads and parallel GRIB decoding for maximum speed

🚀 Installation

pip install windgrib

📈 Basic Usage

from windgrib import Grib
import numpy as np

# Download GFS wind data
gb = Grib(time='2026/01/3', model='gfswave')
gb.download()

# Access wind data
wind_data = gb['wind'].ds

# Calculate wind speed
wind_speed = np.sqrt(wind_data.u**2 + wind_data.v**2)
print(f"Average speed: {wind_speed.mean().values:.2f} m/s")

📚 Documentation

For complete documentation, examples, and advanced usage:

📖 View Full Documentation

• Usage Examples - Practical examples with working code
• Technical Guide - Implementation details for developers
• Custom Models - Guide for extending WindGrib
• Benchmark Results - Performance comparison with Herbie

🤝 Contributing

Contributions are welcome! See our contribution guide for more information.

📜 License

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

© 2025 WindGrib. All rights reserved.